ALBERT

Description: Provides a listing of the editors, board members, and current staff for this issue of the publication.

Description: Nowadays, clean renewable energy extraction solutions are becoming a crucial practice in society. Many different sources are being developed including ocean energy and in specific, ocean waves. In deep water conditions, ocean waves can become very power dense, continuous, and forecastable. Wave height, velocity, and frequency are all variable wave characteristics making it challenging to capture wave power economically. The RTI F2 is a promising wave energy conversion device that is currently under research. Its method of power capture is a buoyant vessel oriented normal to oncoming waves. This paper discusses known control methods implemented on the RTI F2, the experimental setup used for control of the device, and wave tank testing done at the University of New Hampshire's Chase Laboratory. Experimental data was obtained across various wave conditions, plate angles, vessel weights, and control strategies. The results of these tests are presented in the subject matter.

Description: Many existing solar irradiance monitoring networks were built particularly for resource assessment purposes; they are often spatially sparse. In order for the networks to handle other increasingly important tasks, such as irradiance forecasting for grid integration, their spatial sparsity must be addressed by adding in new monitoring stations. Optimally expanding these networks using historical information thus becomes an important research topic for engineers. Variability of solar irradiance in space and time can be quantified using statistics such as entropy and covariance. The deployment of the additional monitoring stations should, therefore, utilize these statistics to reduce the variability. More specifically, we aim at maximizing the entropy of the network. A practical difficulty in statistical modeling of solar irradiance is that the data are not ideal. Properties such as stationarity and isotropy are not observed in irradiance random field. We, therefore, focus on hypothesis testing and transformation of the irradiance data, so that the design procedure is statistically justified. We propose the redesign framework in a solar engineering context, using data from 24 irradiance monitoring stations on a tropical island. In the case study, we demonstrate how to find three optimal stations from a pool of 100 potential future monitoring sites.

Description: This paper proposes a novel forecasting model based on a mean trend detector (MTD) and a mathematical morphology-based local predictor (MMLP) to undertake short-term forecast of wind power generation. In the proposed MTD/MMLP model, the nonstationary time series describing wind power generation is first decomposed by the MTD, which employs some new notions and conventional morphological operators. The decomposition yields two componentsthe mean trend, which reveals the tendency of the time series, and the stochastic component, which depicts the fluctuations caused by high frequency of the variability. Subsequently, the $p$ -step forecast is conducted for these two components separately. The mean trend is forecasted on the basis of the least-square support vector machine (LS-SVM) model, while the $p$ -step forecast for the stochastic component is carried out by the MMLP, which involves performing morphological operations employing a novel structuring element (SE) in the phase space. Finally, the forecast of wind power generation is achieved by combining the separate forecasts of two components. In order to evaluate the accuracy and stability of the MTD/MMLP model, simulation studies are carried out using the data obtained from three widely used databases sampled in different periods. The results demonstrate that the MTD/MMLP model provides a more accurate and stable forecast compared to the traditional methods.

Description: The distribution network planning under active network management (ANM) schemes is becoming of interest due to substantial benefits in facilitating the increasing integration of renewable energy sources. This paper presents various potential ANM schemes based on the photovoltaic inverter control (PVIC) considering enhanced utilization of the inverter reactive power capability. Depending on the active power generation of PV arrays, inverter size and desired reactive power settings, several PVIC schemes are proposed. The PVIC schemes are incorporated in the optimal power flow (OPF) and formulated as a nonlinear programming (NLP) problem. In this study, the PVIC schemes are applied to maximize the total wind-distributed generation (DG) penetration on a typical U.K. distribution system. Various case studies are presented and compared to evaluate the performance. The results show that the proposed schemes can significantly increase the wind penetration levels by 45.4% and up to 92.3%.

Description: In this paper, a health monitoring method for photovoltaic (PV) systems based on probabilistic neural network (PNN) is proposed that detects and classifies short- and open-circuit faults in real time. To implement and validate the proposed method in computer programs, a new approach for modeling PV systems is proposed that only requires information from manufacturers datasheet reported under normal-operating cell temperature (NOCT) conditions and standard-operating test conditions (STCs). The proposed model precisely represents characteristics of PV systems at different temperatures, as the temperature dependency of parameters such as ideality factor, series resistance, and thermal voltage is considered in the proposed model. Although this model can be applied to a variety of applications, it is specifically used to test and validate the performance of the proposed fault detection and classification method.

Description: Intermittent generation from wind farms leads to fluctuating power system operating conditions pushing the stability margin to its limits. The traditional way of determining the worst case generation dispatch for a system with several semi-scheduled wind generators yields a conservative solution. This paper proposes a fast estimation of the transient stability margin (TSM) incorporating the uncertainty of wind generation. First, the Kalman filter (KF) is used to provide linear estimation of system angle and then unscented transformation (UT) is used to estimate the distribution of the TSM. The proposed method is compared with the traditional Monte Carlo (MC) method and the effectiveness of the proposed approach is verified using Single Machine Infinite Bus (SMIB) and IEEE 14 generator Australian dynamic system. This method will aid grid operators to perform fast online calculations to estimate TSM distribution of a power system with high levels of intermittent wind generation.

Description: This paper presents a probabilistic-based approach for available transfer capability (ATC) assessment. A composite algorithm is developed to generate ensembles of future wind generation scenarios for the existing and planned wind sites using both measured and model-produced wind data. Then, the ensembles of wind and load are used to calculate their respective probability density functions (pdfs), which are subsequently used to calculate the probabilistic-based ATC for a selected transmission corridor. The method has been tested and validated using historical and operational data provided by the Idaho Power Co. The results show that the method can effectively quantify the uncertainties in the ATC assessment introduced by variable generation resources and load variations. As a result, the grid planners will inform the likelihood for the transmission corridor to exceed its transfer capacity in any targeted future years as well as the duration of such events.

Description: With the increasing size of wind farms, the impact of the wake effect on wind farm energy yields become more and more evident. The arrangement of locations of the wind turbines (WTs) will influence the capital investment and contribute to the wake losses, which incur the reduction of energy production. As a consequence, the optimized placement of the WTs may be done by considering the wake effect as well as the components cost within the wind farm. In this paper, a mathematical model which includes the variation of both wind direction and wake deficit is proposed. The problem is formulated by using levelized production cost (LPC) as the objective function. The optimization procedure is performed by a particle swarm optimization (PSO) algorithm with the purpose of maximizing the energy yields while minimizing the total investment. The simulation results indicate that the proposed method is effective to find the optimized layout, which minimizes the LPC. The optimization procedure is applicable for optimized placement of WTs within wind farms and extendible for different wind conditions and capacity of wind farms.

Description: This paper proposes a novel short-term wind power forecasting approach by mining the bad data of numerical weather prediction (NWP). Today's short-term wind power forecast (WPF) highly depends on the NWP, which contributes the most in the WPF error. This paper first introduces a bad data analyzer to fully study the relationship between the WPF error with several new extracted features from the raw NWP. Second, a hierarchical structure is proposed, which is composed of a K -means clustering-based bad data detection module and a neural network (NN)-based forecasting module. In the NN module, the WPF is fully adjusted based on the output of the bad data analyzer. Simulations are performed comparing with two other different methods. It proves that the proposed approach can improve the short-term wind power forecasting by effectively identifying and adjusting the errors from NWP.

Description: The objective of this study was to develop a reduced-order small-signal model of a microgrid system capable of operating in both the grid-tied and the islanded conditions. The nonlinear equations of the proposed system were derived in the $dq$ reference frame and then linearized around stable operating points to construct a small-signal model. The high-order state matrix was then reduced using the singular perturbation technique. The dynamic equations were divided into two groups based on the small-signal model parameters $varepsilon$ . The slow states, which dominated the systems dynamics, were preserved, whereas the fast states were eliminated. Step responses of the model were compared to the experimental results from a hardware test to assess their accuracy and similarity to the full-order system. The proposed reduced-order model was applied to a modified IEEE-37 bus grid-tied microgrid system to evaluate systems dynamic response in grid-tied mode, islanded mode, and transition from grid-tied to islanded mode.

Description: Many benefits can be achieved through the implementation of a Microgrid controller, such as minimized cost, reduction in peak power, power smoothing, greenhouse gas emission reduction, and increased reliability of service. However, most Microgrid controllers found in the literature and in the industry optimize a single objective, which either exacerbates or does not solve the problems with integrating a high penetration of renewable energy. This paper presents a methodology of formulating a multiobjective optimization (MOO) so that each objective is quantified through valuation functions that can be specific to every Microgrid. The proposed approach attains a Pareto-optimal solution by directly comparing the quantified valuation functions and solving as if it were a single-objective optimization (SOO) problem. Three cases of controllers are presented and compared: 1)a base case system with no controller; 2)an SOO that optimizes the cost of energy; and 3)an MOO that optimizes five identified benefits. Results show that the proposed controller can mitigate the negative impacts of volatile generation to levels below that of the system load.

Description: Variable over voltage, excessive tap counts, and voltage regulator (VR) runaway condition are major operational challenges in distribution network while accommodating generation from photovoltaics (PVs). The conventional approach to achieve voltage control based on offline simulation for voltage set point calculation does not consider forecast errors. In this work, a stochastic optimal voltage control strategy is proposed while considering load and irradiance forecast errors. Stochastic operational risks such as overvoltage and VR runaway are defined through a chance constrained optimization (CCO) problem. This classical formulation to mitigate runaway is further improved by introducing a stochastic index called the Tap Tail Expectation . Operational objectives such as power losses and excessive tap count minimization are considered in the formulation. A sampling approach is proposed to solve the CCO. Along with other voltage control devices, the PV inverter voltage support features are coordinated. The simulation study is performed using a realistic distribution system model and practically measured irradiance to demonstrate the effectiveness of the proposed technique. The proposed approach is a useful operational procedure for distribution system operators. The approach can minimize feeder power losses, avoid voltage violations, and alleviate VR runaway.

Description: The electromagnetic stability issues of the grid-connected doubly fed induction generator (DFIG) system are usually overlooked. This paper presents a reduced order small-signal model that can be used to analyze the stability of DFIGs dc-link voltage control system, especially under weak ac grid conditions. This model neglects DFIG flux and fast current control dynamics. However, the effects of operating points, grid strengths and control loops interactions on system dynamic performance are taken into account. An eigenvalue comparison shows the proposed model holds dominant oscillation mode featured by the detailed model and is suitable for stability analysis of dc-link voltage control system of DFIG. Influence coefficients reflecting control loops interactions are also presented. Application studies of the proposed model show it is suitable for illustrating the effect of grid strength on dynamic performance of the DFIGs dc-link voltage control system. Meanwhile, phase-locked loop (PLL) and rotor-side converter (RSC) active power control (APC)/reactive power controls (RPC) effect on system stability are also explored.

Description: Photovoltaic (PV) generation is increasingly popular in power systems. The nonlinear dependence associated with a large number of distributed PV sources adds the complexity to construct an accurate probability model and negatively affects confidence levels and reliability, thereby resulting in a more challenging operation of the systems. Most probability models have many restrictions when constructing multiple PV sources with complex dependence. This paper proposes a versatile probability model of PV generation on the basis of pair copula construction. In order to tackle the computational burden required to construct pair copula in high-dimensional cases, a systematic simplification technique is utilized that can significantly reduce the computational effort while preserving satisfactory precision. The proposed method can simplify the modeling procedure and provide a flexible and optimal probability model for the PV generation with complex dependence. The proposed model is tested using a set of historical data from colocated PV sites. It is then applied to the probabilistic load flow (PLF) study of the IEEE 118-bus system. The results demonstrate the effectiveness and accuracy of the proposed model.

Description: It is important to forecast the wind speed for managing operations in wind power plants. However, wind speed prediction is extremely complex and difficult due to the volatility and deviation of the wind. As existing forecasting methods directly model the raw wind speed data, it is difficult for them to provide higher inference accuracy. Differently, this paper presents a sophisticated deep-learning technique for short-term and long-term wind speed forecast, i.e., the predictive deep Boltzmann machine (PDBM) and corresponding learning algorithm. The proposed deep model forecasts wind speed by analyzing the higher level features abstracted from lower level features of the wind speed data. These automatically learnt features are very informative and appropriate for the prediction. The proposed PDBM is a deep stochastic model that can represent the wind speed very well, and is inspired by two aspects. 1)The stochastic model is suitable to capture the probabilistic characteristics of wind speed. 2)Recent developments in neural networks with deep architectures show that deep generative models have competitive capability to approximate nonlinear and nonsmooth functions. The evaluation of the proposed PDBM model is depicted by both hour-ahead and day-ahead prediction experiments based on real wind speed datasets. The prediction accuracy of the PDBM model outperforms existing methods by more than 10%.

Description: This paper presents a new maximum power point tracking (MPPT) method for photovoltaic (PV) systems. The proposed method improves the working of the conventional perturb and observe (P&O) method in changing environmental conditions by using the fractional short-circuit current (FSCC) method. It takes the initial operating point of a PV system by using the short-circuit current method and later shifts to the conventional P&O technique. The advantage of having this two-stage algorithm is rapid tracking under changing environmental conditions. In addition, this scheme offers low-power oscillations around MPP and, therefore, more power harvesting compared with the common P&O method. The proposed MPPT decides intelligently about the moment of measuring short-circuit current and is, therefore, an irradiance sensorless scheme. The proposed method is validated with computer software simulation followed by a dSPACE DS1104-based experimental setup. A buck-boost dc-dc converter is used for simulation and experimental confirmation. Furthermore, the reliability of the proposed method is also calculated. The results show that the proposed MPPT technique works satisfactorily under given environmental scenarios.

Description: In this paper, hardware-in-the-loop (HIL) implementation of solar photovoltaic (PV) array feeding autonomous load, without dump load, is investigated. Two control algorithms based on the sliding mode approach are designed to guarantee a fast and finite-time convergence without adjustment of the system parameters. The dc-dc boost converter and the current controlled-voltage source converter (CC-VSC) are controlled to maximize the power from the PV, to protect the battery energy storage system (BESS) from overcharging, and to regulate the voltage and frequency at the point of common coupling (PCC). An accurate stability analysis of the system is presented and discussed in this work. The effectiveness and the robustness of the developed controllers are validated by simulation and experimental results during the load perturbation and varying climate conditions.

Description: This paper proposes to use discrete Fourier transform (DFT) and discrete wavelet transform (DWT) methods to schedule grid-scale energy storage systems to mitigate wind power forecast error impacts while considering energy storage properties. This is accomplished by decomposing the wind forecast error signal to different time-varying periodic components to schedule sodium sulfur (NaS) batteries, compressed air energy storage (CAES), and conventional generators. The advantage of signal processing techniques is that the resultant decomposed components are appropriate for cycling of each energy storage technology. It is also beneficial for conventional generators, which are more efficient to operate close to rated capacity. The tradeoff between installing more energy storage units and decreasing the wind spillage, back-up energy, and the standard deviation of residual forecast error signal is analyzed. The NaS battery life cycle analysis and CAES contribution on increasing NaS battery lifetime are studied. The impact of considering the frequency bias constant to allow small frequency deviations is also investigated. To showcase the applicability of the proposed approach, a simulation case study based on a real-world 5-min interval wind data from Bonneville Power Administration (BPA) in 2013 is presented.

Description: One role of grid operators is to identify potential problems before they occur and, if necessary, take preemptive actions. As wind generation becomes increasingly widespread, there is the potential for credible, simultaneous fluctuations of output at different locations to result in limit violations. Existing analysis methods that consider forecast errors either inadequately model the control responses available to system operators (e.g., using participation factors) or ignore network constraints, which limits their utility in identifying situations that would require operator action. An alternative method of handling forecast errors, utilizing bilevel programming, is proposed here to identify situations that may result in branch overloads. What distinguishes this method from prior approaches is that it only identifies overloads that can occur despite optimal operator reaction to forecast errors--i.e., when the overload is unavoidable, given current control capabilities. Studies conducted on 37- and 118-bus test systems demonstrate both the utility and feasibility of using this method for online operations.

Description: In this paper, a control strategy for the participation of photovoltaic (PV) systems in frequency regulation is suggested. A number of strings from every inverter of a PV system are kept as reserve by disconnecting them through dc-relays. Hence, as the control algorithm monitors the grid frequency, it reconnects or disconnects strings, according to the occurring frequency deviations (negative and positive, respectively). Contrary to previous approaches, the suggested methodology here avoids the use of storage devices, which implies additional investment costs, and/or the manipulation of the maximum power point tracking (MPPT) algorithm of the inverter, which represents higher control complexity and special considerations depending on each algorithm. Simulation results from frequency phenomena and solar irradiation changes on a two-bus system in MATLAB Simulink are presented to show the favorable behavior and effective performance of the proposed control strategy. The design concept is also experimentally tested under various operating conditions and on different devices; the results also confirm the feasibility and simplicity of the method.

Description: In case of abnormal conditions, distribution systems should be reconfigured to overcome the impacts of outages such as overloads of network components and increased power losses. For this purpose, energy storage systems (ESS) and renewable energy sources (RES) can be applied to improve operating conditions. An optimal contingency assessment model using two-stage stochastic linear programming including wind power generation and a generic ESS is presented. The optimization model is applied to find the best radial topology by determining the best switching sequence to solve contingencies. The proposed model is applied to a 69-node distribution system and the results of all possible contingencies in the network are examined considering three different case studies with several scenarios. In addition, a reconfiguration analysis including all the contingencies is presented for the case studies.

Description: Ocean energy is a promising resource for renewable electricity generation that presents many advantages, such as being more predictable than wind energy, but also some disadvantages such as large and slow amplitude variations in the generated power. This paper presents a hardware-in-the-loop prototype that allows the study of the electric power profile generated by a wave power plant based on the oscillating water column (OWC) principle. In particular, it facilitates the development of new solutions to improve the intermittent profile of the power fed into the grid or the test of the OWC behavior when facing a voltage dip. Also, to obtain a more realistic model behavior, statistical models of real waves have been implemented.

Description: With the significant penetration of wind generation, the variability and uncertainty of wind energy poses new challenges to power system operations. In particular, more rapid reserve is required, which may result in the scarcity of balancing services. With the increasing penetration of renewable generation, it is envisaged that renewable resources will be required to partake in the system balancing tasks. In this paper, a combined flexible dispatch and reserve scheduling policy is proposed by determining a flexible wind dispatch margin. In order to provide a flexible dispatch margin, wind generators underschedule in the hour-ahead energy market, so as to hold some expected output for reserves. Additional wind energy is then available for mitigating forecast errors and other system uncertainties. This paper presents a framework to find the optimal policy to incorporate the flexible wind dispatch margin into the hour-ahead market. A finite-state Markov chain wind power forecast model, based on spatio-temporal analysis, is utilized. The presented framework is used to find the appropriate level of wind dispatch margin. The proposed approach is tested and the wind generation data are used to analyze the effectiveness of the presented model in coping with forecast errors and achieving a more secure system operation.

Description: A megawatt (MW)-scale hydro-viscous transmission-based continuously variable speed wind turbine is proposed to guarantee a smooth transition among different operating regions and hence to improve power efficiency and quality. This turbine is achieved by highly integrating a hydro-viscous element into the turbine drive-train to mitigate the upstream wind-loading fluctuations. This element allows the turbine speed to be directly regulated by continuously changing the oil film thickness in this element. Three important operating modes of this turbine system are proposed. The control-oriented drive-train model is also established and validated based on experimental data. A cooperative control strategy over the full operating range is then proposed based on such modes. A series of comparative cosimulations are carried out to evaluate the stability and effectiveness of the proposed turbine system in speed and power regulations. This proposed system holds several advantages such as large power capacity, high efficiency, downsized power converters, and low cost. Such advantages make this turbine system particularly attractive and promising for medium-to-large-scale wind power applications.

Description: In this paper, a novel ensemble method consisting of neural networks, wavelet transform, feature selection, and partial least-squares regression (PLSR) is proposed for the generation forecasting of a wind farm. Based on the conditional mutual information, a feature selection technique is developed to choose a compact set of input features for the forecasting model. In order to overcome the nonstationarity of wind power series and improve the forecasting accuracy, a new wavelet-based ensemble scheme is integrated into the model. The individual forecasters are featured with different mixtures of the mother wavelet and the number of decomposition levels. The individual outputs are combined to form the ensemble forecast output using the PLSR method. To confirm the effectiveness, the proposed method is examined on real-world datasets and compared with other forecasting methods.

Description: An optimal reactive power dispatch strategy is proposed to minimize the total electrical losses of a wind farm (WF), including not only losses in the transmission cables and wind turbine (WT) transformers, but also losses inside wind energy generation systems. The reactive power dispatch inside a WT uses optimal splitting strategy over the stator and the grid side converter (GSC), which aims to minimize the total loss of the wind energy generation system, including the generator, the converters, and the filters. Optimization problems are formulated based on established loss models and WT reactive power limits. A WF is carefully designed and used for case studies. Wake effect is considered when calculating the active power at each WT. The total losses of the WF are calculated by implementing the proposed strategy at different wind speeds and reactive power references. The simulation results show the effectiveness of the proposed strategy.

Description: Multibody wave energy converters are composed of several bodies interconnected by joints. Two different formulations are adopted to describe the dynamics of multibody systems: the differential and algebraic equations (DAEs) formulation, and the ordinary differential equations (ODEs) formulation. While the number of variables required for the description of the dynamics of a multibody system is greater in the DAE formulation than in the ODE formulation, the ODE formulation involves an extra computational effort in order to describe the dynamics of the system with a smaller number of variables. In this paper, pseudo-spectral (PS) methods are applied in order to solve the dynamics of multibody wave energy converters using both DAE and ODE formulations. Apart from providing a solution to the dynamics of multibody systems, pseudo-spectral methods provide an accurate and efficient formulation for the control of multibody wave energy converters. As an application example, this paper focuses on the dynamic modeling of a three-body hinge-barge device, where wave-tank tests are carried out in order to validate the DAE and ODE models against experimental data. Comparison of the ODE and DAE PS methods against a reference model based on the straightforward (Runge-Kutta) integration of the equations of motion shows that pseudo-spectral methods are computationally more stable and require less computational effort for short time steps.

Description: Optimal and simultaneous siting and sizing of distributed generators and capacitor banks in distribution systems have attracted a lot of attention from distribution companies. The placement and capacity of these devices have direct effects on the system’s performance. This paper presents a model for the simultaneous allocation of capacitor banks and distributed generation, which takes into account the stochastic nature of distributed generation. To solve the model presented, we propose an efficient hybrid method based on Tabu search and genetic algorithms. The hybrid method is applied to a well-known system in literature.

Description: In this paper, distributed energy storage (DES) devices, like batteries and ultra-capacitors, are used to alleviate detrimental impacts of high penetration photovoltaic (PV) resources on distribution systems. The impacts are studied at mainly two time resolutions—one minute and one hour. To determine accurately the size of the required DES for the purpose of mitigating the impacts of large-scale distributed PV, sizing procedures based on OpenDSS are proposed. After determining the total size of the required DES, optimization techniques can be used to choose the optimal locations for the DES along the feeder, which is a continuous optimization problem taking into account equality constraints of the AC power flow. The continuity of the problem and the radial network structure make it possible to apply a convex optimization technique called second order cone programming (SOCP) relaxation to obtain the globally optimal solution and avoid the problem of NP-hardness. The exactness of the introduced SOCP relaxation is sensitive to the chosen objective function and additional quadratic equalities. The necessary and sufficient condition of exactness for the SOCP relaxation of the DES optimal allocation and operation in radial distribution systems is studied. The proposed methods are applied to an actual feeder in the southwestern US with high penetration of PV using actual measured data. The simulation results demonstrate the efficacy of the proposed approaches.

Description: This paper proposes an integrated stochastic day-ahead scheduling model to dispatch hourly generation and load resources and deploy flexible ramping for managing the variability of renewable energy system. A comprehensive framework for the natural gas transportation network is considered to address the dispatchability of a fleet of fuel-constrained natural gas-fired units. System uncertainties include the day-ahead load and renewable generation forecast errors. Illustrative examples demonstrate that the real-time natural gas delivery can directly impact the hourly dispatch, flexible ramp deployment, and power system operation cost. Meanwhile, the demand side participation can mitigate the dependency of electricity on natural gas by providing a viable option for flexible ramp when the natural gas system is constrained.

Description: If a large disturbance occurs in a power grid, two auxiliary loops for the inertial control of a wind turbine generator have been used: droop loop and rate of change of frequency (ROCOF) loop. Because their gains are fixed, difficulties arise in determining them suitable for all grid and wind conditions. This paper proposes a dynamic droop-based inertial control scheme of a doubly-fed induction generator (DFIG). The scheme aims to improve the frequency nadir (FN) and ensure stable operation of a DFIG. To achieve the first goal, the scheme uses a droop loop, but it dynamically changes its gain based on the ROCOF to release a large amount of kinetic energy during the initial stage of a disturbance. To do this, a shaping function that relates the droop to the ROCOF is used. To achieve the second goal, different shaping functions, which depend on rotor speeds, are used to give a large contribution in high wind conditions and prevent over-deceleration in low wind conditions during inertial control. The performance of the proposed scheme was investigated under various wind conditions using an EMTP-RV simulator. The results indicate that the scheme improves the FN and ensures stable operation of a DFIG.

Description: In this paper, the two-level hierarchical scheme, which consists of wide area centralized and local controls of the power oscillation damper (POD) installed with the doubly-fed induction generator (DFIG) wind turbine and the power system stabilizer (PSS) has been proposed for robust power oscillation damping. In the wide area level, the centralized POD and PSS has received the input signals from synchronized phasor measurement units (PMUs). The geometric measures of controllability and observability have been applied to select the suitable DFIG and synchronous generator (SG) for stabilizing the target oscillation modes, the proper input signals of the centralized POD and PSS, and the location of PMUs. In the local level, the suitable DFIG and SG have been equipped with POD and PSS, respectively. In the parameters optimization of POD and PSS, the practical issues such as damping performance, controller structure, communication latency, and robustness against system uncertainties have been considered. The controller efficiency and resiliency of the proposed controller have been evaluated in comparison with other controllers by eigenvalue analysis and nonlinear simulation for a wide range of operating conditions, line outage contingencies, severe faults, and communication failure.

Description: This paper describes a method for optimal scheduling of hydropower systems for a profit maximizing, price-taking, and risk neutral producer selling energy, and capacity to separate and sequentially cleared markets. The method is based on a combination of stochastic dynamic programming (SDP) and stochastic dual dynamic programming (SDDP), and treats inflow to reservoirs and prices for energy and capacity as stochastic variables. The proposed method is applied in a case study for a Norwegian watercourse, quantifying the expected changes in schedules, and water values when going from an energy-only market to a joint treatment of energy and reserve capacity markets.

Description: In this paper and its companion, the identification of mathematical models describing the behaviour of wave energy devices (WECs) in the ocean is investigated through the use of numerical wave tank (NWT) experiments. This paper deals with the identification tests used to produce the data for the model identification. NWTs, implemented using computational fluid dynamics (CFD), are shown as an effective platform to perform the identification tests. The design of the NWT experiments, to ensure the production of information-rich data for the model identification, is discussed. A case study is presented to illustrate the design and implementation of NWT experiments for the identification of WEC models.

Description: Optimum torque (OT)-based maximum power point tracking (MPPT) is widely used in high power turbines because of its simplicity. Several recent improvements claim better response speeds by augmenting the basic OT algorithm with a suitably scaled inertial torque. However, the underlying dynamic model for all these improved methods is premised on a rigid shaft, which ignores all torsional behavior. This lacuna is addressed in this paper, where a small-signal system description is developed considering a more accurate flexible shaft model. It is shown that the improvements have three possible forms, the third being proposed in this paper. Close-loop stability with each of these is analytically investigated using this accurate small-signal model. It is also proved that one of the reported approaches causes system instability while attempting even moderate improvement over the OT method. The problems in realization of the other two forms are highlighted and a realizable alternative proposed, which requires no additional sensor. Design of the proposed approach is presented in appropriate detail. It is analytically established that the proposed method ensures superior dynamic response. All analytical conclusions are validated by numerical simulations.

Description: Power generation uncertainty is an important characteristic of variable generation (VG) platforms, such as wind and solar power, which brings additional operational costs to the power systems. To manage this uncertainty, responsibilities should be properly allocated to encourage good behaviors of system participants, especially the VG producers. Currently, the imbalance-cost-based mechanism is most commonly used for uncertainty management. Based on this method, we consider a new mechanism in this paper for capturing the uncertainty, which may achieve a better mechanism performance. The basic idea is to allow producers to purchase generation intervals (GIs) for their potential production output. The analysis presented in this paper indicates that producers can be very responsive to this mechanism. With the proper pricing policies, producers can be encouraged to provide additional information on upcoming uncertainties to the system operators. Additionally, three strategies for pricing GIs are included in this paper. Case studies are used to demonstrate the application of the mechanism as well as its effectiveness.

Description: The paper proposes a gossip algorithm-based architecture for managing power flows on radial distribution grids applying decentralized management of demand. Demand flexibility is associated with a cost-like utility function expressing the inconvenience caused by curtailment. Every node of the distribution grid participates in power flow management by sending locally measured and calculated values to its neighboring nodes following a peer-to-peer architecture. The proposed algorithm is based on the application of gossip algorithms to estimate locally critical physical quantities by reaching a global consensus. More specifically, the solution implements gossip algorithms to achieve consensus in aggregated demand and in minimum cost for curtailment of flexible loads/increase of controllable distributed generators.

Description: The objective of this research was to develop and compare various airfoil precomputational parameterization and analysis techniques for aerostructural optimization of wind turbine blades. The airfoils along the blade were added as optimization design variables through precomputational parameterization methods using thickness-to-chord ratios and blended airfoil family factors. The airfoils’ aerodynamic performance was analyzed with three methods of increasing fidelity: a panel method (XFOIL), Navier–Stokes-based computational fluid dynamics (RANS CFD), and wind tunnel data. The optimizations minimized mass over annual energy production ( $m/AEP$ ) and thereby approximated the minimization of cost of energy. The results were compared to the NREL 5-MW reference turbine and a conventional optimization where the airfoils were fixed. Results showed an average $m/AEP$ reduction of 1.7% over conventional optimization methods. The primary benefit in adding the airfoil shape was through an increase in annual energy production (1.6%) with a similar decrease in turbine mass (1.8%). Using the precomputational airfoil parameterization methods provided significant reductions in the cost of energy with relatively minor additional computational cost.

Description: This paper proposes a co-ordinated four-loop switching controller (SC) for the doubly fed induction generator (DFIG) to improve the transient stability of wind power penetrated power systems. A short-term resilience index is introduced, and it reflects the dynamics of both system frequency and load bus voltage. A four-loop SC is driven by the four outputs of a DFIG, namely, the rotor speed deviation, the reactive power output of stator winding, the reactive power transferred through grid-side converter, and the DC-link voltage, respectively. Referring to a state-dependent switching strategy, the four-loop SC switches between a logic-based bang–bang constant funnel controller (LBCFC) and a vector control theory-based conventional controller (CC) in each control loop. The LBCFC is robust to system nonlinearities, uncertainties, and external disturbances. The control signal of the LBCFC is bang–bang with the upper and lower limits of control variables. Simulation studies are undertaken in a modified IEEE 16-generator 68-bus power system, in which four DFIG-based wind farms are penetrated to provide 9.94% power supply. The performance of the four-loop SC is evaluated in aspects of the integral control of the DFIG and the resilience enhancement of the multimachine power system, respectively.

Description: This paper investigates a control strategy for a wind farm with the direct-driven permanent-magnet synchronous generators (PMSG)-based wind turbines and the fixed speed induction generators (FSIG)-based wind turbines under unbalanced grid voltage condition. By controlling the PMSG-based wind farm to inject negative-sequence current for decreasing voltage unbalance factor (VUF) at point of common coupling (PCC), the double grid frequency oscillations in electromagnetic torque, active, and reactive power output from the FSIG-based wind farm can be suppressed. In this paper, the maximum amplitude of the negative-sequence current provided by the PMSG-based wind farm under different average active power output and different VUF conditions is deduced, and the impacts of its phase angle on the VUF mitigation control effect are further studied. The improved control strategy of injecting negative-sequence current from the PMSG-based wind farm by the modified negative-sequence voltage and current double closed-loop control system is then developed. Finally, the correctness of theoretical analysis and the effectiveness of the proposed control strategy are validated by the experimental results.

Description: In this paper and its companion, the identification of mathematical models describing the behaviour of wave energy devices (WECs) in the ocean is investigated through the use of numerical wave tank experiments. When the wave amplitude and the WEC displacement are not negligible with respect to the WEC dimensions, nonlinear hydrodynamic effects may appear, and the accuracy of linear hydrodynamic models is reduced, leading to the necessity of introducing some nonlinearities in the model structure. This paper proposes, for WEC modelling, the use of discrete-time nonlinear autoregressive with exogenous input (NARX) models, as an alternative to continuous-time models. Techniques of model identification are also explained and applied to a case study.

Description: Wind turbine power curves do not consider specific weather conditions, wind shear, turbulence effects of the location where the turbine is going to be installed, or its age. A true power curve is obtained by analyzing data from an installed wind turbine over a year. Here, a model for a true power curve is proposed, considering a normal distribution for each range of wind speed data. Furthermore, a Monte Carlo-based simulation technique is proposed to reproduce data following the normal-based model. The main use of the model is to simulate data to complete lacking real data or to perform long-term assessments. The model was checked with data from two wind turbines at the Sotavento experimental wind farm in the northwest of Spain.

Description: This paper discusses the control of large-scale grid-connected photovoltaic power plant (GCPPP) operating under unbalanced grid voltages. The positive and negative sequences of the grid currents need to be controlled to regulate the power injected into the grid during unbalanced grid voltages. This paper shows that the use of conventional proportional-integral-based controllers compromises stability and dynamic performance of the inverter. The reason is the delays introduced by the filters needed to extract the sequences of the transformed grid currents. Because of such delays, there is a strong restriction on choosing the parameters for the current and voltage controllers, which forces the GCPPP to perform slowly. This can be improved by using resonant controllers instead, which avoid the need for filtering the transformed grid currents. Additionally, a new overcurrent protection is proposed for the GCPPP when it is providing grid voltage support during voltage sags. Simulation and experimental results are presented to evaluate and compare the performance of the GCPPP when operating with the different controllers.

Description: This paper proposes a novel probabilistic transient stability constrained optimal power flow (P-TSCOPF) model to simultaneously consider uncertainties and transient stability for power system preventive control. While detailed wind generator model with rotor flux magnitude and angle control strategy is used to describe the dynamic behaviors of wind generators, uncertain factors with correlations, such as probabilistic load injections, stochastic fault clearing time, and multiple correlated wind generations, are also included to form a representative P-TSCOPF model. A new GSO-PE approach, consisting of an improved group search optimization (GSO) and $2m + 1$ point estimated (PE) method with Cholesky decomposition, is then designed to effectively solve this challenging P-TSCOPF problem. The proposed P-TSCOPF model and GSO-PE solution approach have been thoroughly tested on a modified New England 39-bus system with correlated uncertain wind generations. Comparative results with Monte Carlo (MC) simulations have confirmed the validity of the P-TSCOPF model and demonstrated the effectiveness of GSO-PE method.

Description: Frequency regulation is critical to the successful operation of remote wind–diesel electrical grids. When the grid is in ‘wind–diesel’ mode, frequency regulation is (classically) the sole duty of the diesel electric generator (DEG). An alternative approach is proposed whereby responsibility for frequency regulation is shared by the DEG and a network of autonomous distributed secondary loads (DSLs) consisting of electric thermal storage (ETS) devices. This allows surplus wind to be distributed to residential consumers (as space heat) without the need for a centralized communication network. Numerical modeling of system dynamics with active DSLs is conducted using a SIMULINK wind–diesel hybrid test bed model. The effects of controller gain, installed capacity, switching time and unit coordination timing on frequency and voltage regulation is explored. It is shown that the DSLs can improve frequency regulation in wind–diesel mode while providing storable thermal energy to distributed consumers.

Description: Appropriate wind speed modeling for generating synthetic wind speed data is crucial in microgrid reliability evaluation studies. This paper proposes a 2-D wind speed statistical model based on historical wind speed data. The first dimension of the proposed wind model focuses on the probability distribution on the time duration of different wind speed scales, while the second dimension focuses on the probability distribution of wind speed in each wind speed scale. Unlike traditional wind speed models, the proposed model can simultaneously deal with the probabilistic characteristics of wind speed and wind time duration. This paper also presents a 2-D wind model-based data sampling method, and incorporates it into the microgrid reliability assessment algorithm. Using historical wind speed data in Tianjin, China, it shows that the proposed 2-D wind speed statistical model helps to fully simulate the volatility of wind energy. Furthermore, the proposed wind speed model is applied on the microgrid reliability evaluation study, which shows that the proposed model can be effectively utilized by planners to conduct reliability evaluation for microgrid.

Description: Wind generation is growing fast worldwide. The stochastic variation of large-scale wind generation may impact the power systems in almost every aspect. Probabilistic analysis method is an effective tool to study power systems with random factors. In this paper, a systematic nonlinear analytical probabilistic method is proposed to evaluate the possible effect of random wind power generation on power system small signal stability. A second-order polynomial is proposed to approximate the nonlinear relationship between the wind generation and the damping of a particular dynamic mode, such as the dominant mode. Gaussian mixture model formulates wind uncertainty in a uniform way. Spectral theorem is adopted to reshape the second-order polynomial into a form without cross-product terms. Cholesky decomposition is used to eliminate correlations among outputs of different wind farms. Thereafter the cumulative distribution function (CDF) of the damping ratio with respect to random wind power is consequently constructed. Numerical simulations are carried out in the IEEE standard test system. The proposed method is verified with higher accuracy than the traditional linearized method. Meanwhile, it is much more time-saving in calculation than Monte Carlo simulation.

Description: In this paper, a distributed local control scheme for dc microgrid is proposed along with the basic droop control. It eliminates the limitations of droop control when the distributed generators are geographically distributed, for which, the line resistances cannot be neglected. Effects of line inductance and constant power loading (CPL) are investigated by analyzing the voltage tracking transfer function for single source system. Stability of two sources single load microgrid with proposed controller is investigated. Simulated responses are presented for two sources single load microgrid (for the sake of simplicity) to depict the proper load sharing and voltage improvement capability of the proposed control method with the consideration of line resistances. However, this can be extended to multiple-source multiple-load configuration connected to the dc bus. A comparison of the result is presented to show the better performance of the proposed control scheme as compared to the conventional droop control and hierarchical secondary control. The interconnected operation of the microgrid is also investigated to show the applicability of the proposed control in the interconnected mode. A centralized controller in each area is used to make the tie-line power flow zero at steady state.

Description: There has been an increasing interest in transformerless inverter for grid-tied photovoltaic (PV) system due to low cost, high efficiency, light weight, etc. Therefore, many transformerless topologies have been proposed and verified with real power injection only. Recently, almost every international regulation has imposed that a definite amount of reactive power should be handled by the grid-tied PV inverter. According to the standard VDE-AR-N 4105, grid-tied PV inverter of power rating below 3.68KVA, should attain power factor (PF) from 0.95 leading to 0.95 lagging. In this paper, a new high efficiency transformerless topology is proposed for grid-tied PV system with reactive power control. The new topology structure and detail operation principle with reactive power flow is described. The high frequency common-mode (CM) model and the control of the proposed topology are analyzed. The inherent circuit structure of the proposed topology does not lead itself to the reverse recovery issues even when inject reactive power which allow utilizing MOSFET switches to boost the overall efficiency. The CM voltage is kept constant at mid-point of dc input voltage, results low leakage current. Finally, to validate the proposed topology, a 1 kW laboratory prototype is built and tested. The experimental results show that the proposed topology can inject reactive power into the utility grid without any additional current distortion and leakage current. The maximum efficiency and European efficiency of the proposed topology are measured and found to be 98.54% and 98.29%, respectively.

Description: Wave energy farms may cause voltage flicker on the local grid to which they will be connected due to the strong fluctuations that their output power may present. IEC standard 61400-21 describes methods for estimating the flicker level for different short-circuit ratios as well as for different numbers of devices composing the farm. This method was initially developed for wind farms but is applicable to wave energy farms as well. However, besides the short-circuit ratio and the number of devices composing the farm, the grid impedance angle has also a strong influence on flicker. Despite this, no method exists in the literature for estimating flicker as a function of this variable. This paper presents the results of a study intended to fill this gap by focusing on developing a simplified method for estimating the flicker level induced by a wave energy farm as a function of the grid impedance angle. The results obtained through this method are compared with those obtained from numerical load flow simulations performed with PowerFactory. These simulations were based on experimental power output time series of a wave energy prototype deployed at sea as part of the European CORES project. The voltage profiles thus generated were then processed by means of a flickermeter compliant with IEC standard 61000-4-15.

Description: A novel monotonic strategy following a consistent charging/discharging direction for each individual battery connected in parallel to form a large-scale battery energy storage system (BESS) is proposed in this paper. The BESS is coordinated with a large wind farm to smooth out the intermittent nature of the farm’s output fed to an electricity grid. The strategy is used to optimize the capacity of each battery reducing the system’s capital cost. The strategy also prolongs the battery’s lifetime and consequently minimizes the system’s operating cost. It is shown that the optimal capacity of the battery decreases as their number increases. Moreover, the optimal capacity of the BESS tends to some positive limit as the number of batteries approaches infinity. A rigorous proof of the mathematical theory underlying the proposed strategy and associated optimization are given in the paper. The effectiveness of the strategy is confirmed with data analysis taken from an actual wind farm. The strategy is generic enough to be applicable to other intermittent generation sources such as solar PV farms.

Description: In islanded microgrids (MGs), the reactive power cannot be shared proportionally among distributed generators (DGs) with conventional droop control, due to the mismatch in feeder impedances. For the purpose of proportional reactive power sharing, a multiagent system (MAS)-based distributed control model for droop-controlled MGs is proposed. The proposed control model consists of two layers, where the bottom layer is the electrical distribution MG, while the top layer is a communication network composed of agents. Moreover, agents on the communication network exchange the information acquired from DGs with neighbors, and calculate set points for DGs they connect to, according to the control laws. Furthermore, a theorem is demonstrated, which yields a systematic method to derive the control laws from a given communication network. Finally, three cases are carried out to test the performance of the control model, in which the uncertainty of intermittent DGs, variations in load demands, as well as impacts of time delays are considered. The simulation results demonstrate the effectiveness of the control model in proportional reactive power sharing, and the plug and play capability of the control model is also verified.

Description: Wind power generation is to play an important role in supplying electric power demand, and will certainly impact the design of future energy and reserve markets. Operators of wind power plants will consequently develop adequate offering strategies, accounting for the market rules and the operational capabilities of the turbines, e.g., to participate in primary reserve markets. We consider two different offering strategies for joint participation of wind power in energy and primary reserve markets, based on the idea of proportional and constant splitting of potentially available power generation from the turbines. These offering strategies aim at maximizing expected revenues from both market floors using probabilistic forecasts for wind power generation, complemented with estimated regulation costs and penalties for failing to provide primary reserve. A set of numerical examples, as well as a case-study based on real-world data, allows illustrating and discussing the properties of these offering strategies. An important conclusion is that, even though technically possible, it may not always make sense for wind power to aim at providing system services in a market environment.

Description: In addition to energy, a concentrating solar power (CSP) plant with thermal energy storage (TES) could also provide ancillary service (AS) in the reserve and regulation markets. On one hand, providing AS contributes to the flexibility of the power systems and increases the revenue of CSP plants. On the other hand, the flexibility of CSP plants to accommodate solar energy, which is of great uncertainty, might be significantly weakened by an inappropriate offering strategy, e.g., offering excessive AS. Insufficient flexibility might cause massive solar energy curtailment and reduce the potential revenue. This paper develops a general model framework on the optimal offering strategy for CSP plants in joint day-ahead energy, reserve and regulation markets, which is robust for solar energy uncertainty and stochastic for market price uncertainty. On this basis, given the optimal day-ahead offering strategy, the offering curves to provide incremental AS capacities in the supplemental AS markets are further derived considering the opportunity cost. A new index, the maximum acceptable curtailment rate, is introduced to formulate the tradeoff of CSP plants between supplying AS to the system and reserving the flexibility for solar energy accommodation. The case study results demonstrate the validity of the proposed model.

Description: This paper presents two methods for online rolling horizon optimal control of an energy storage unit in a grid-connected microgrid, subject to uncertainty in demand and electricity pricing. The proposed methods are based on the concept of rolling horizon control, where battery charge/discharge activities are determined by repeatedly solving a linear optimization problem over a moving control window. The predicted values of the microgrid net electricity demand and electricity prices over the control horizon are assumed to be uncertain. The first formulation of the control is based on the scenario-based stochastic conditional value at risk (CVaR) optimization, where the cost function includes electricity usage cost, battery operation costs, and grid signal smoothing objectives. Multivariate Gaussian distribution is used to model the variations of electricity prices and net demand power around their predicted nominal values. The second formulation of the control reduces the computations by taking a worst-case CVaR stochastic optimization approach. In this case, the uncertainty in demand is still stochastic but the problem constraints are made robust with respect to price variations in a range. Simulation results under different scenarios are presented to demonstrate the effectiveness of the proposed methods.

Description: This paper shows how the power quality issues can be resolved in wind-diesel microgrids by means of a fuel cell/electrolyzer (FC/ELZ) system. In this regard, an autonomous hybrid power system, including a diesel generator and a fixed-speed wind turbine (FSWT) equipped with a FC/ELZ system, has been investigated. The main aim of employing the FC/ELZ system is to reduce fuel consumption and mitigate the aerodynamic effects of wind turbine (i.e., tower shadow, wind shears, yaw error, and turbulence) on power quality in the microgrid. To conduct a comprehensive study, the detailed models of the devices are used. The aerodynamic and mechanical aspects of WT are simulated using AeroDyn and FAST, and the thermodynamic and electrochemical aspects of FC/ELZ are simulated using models validated by experimental data. Furthermore, control of power electronic interfaces for the FC/ELZ system, including a bidirectional dc/ac voltage source converter (VSC), a dc/dc converter to boost the FC output voltage, and a dc/dc converter to reduce input voltage to the ELZ, is presented. The studied system was implemented in a MATLAB/Simulink software environment. The simulation results demonstrate the efficacy of the FC/ELZ system in reducing the flicker level and suppressing the voltage fluctuations induced by yaw error and turbulence.

Description: This paper presents a two-stage stochastic centralized dispatch scheme for AC/DC hybrid smart grids. The developed dispatch scheme coordinates the operations of a variety of distributed energy resources (DERs), such as distributed generators (DGs) and energy storage systems (ESSs). It also ensures the coordinated charging of electric vehicles (EVs) and models the degradation of their batteries that occurs due to vehicle-to-grid (V2G). The energy coordination problem has been formulated as a two-stage day-ahead resource scheduling problem, with the intermittent supply; the variable demand, which includes EVs; and the fluctuating real-time energy price modeled as random variables. The first stage produces day-ahead dispatch decisions for the dispatchable DG units. For a set of possible scenarios over the next 24 h, the second stage determines appropriate corrective decisions with respect to the import/export schedule, storage charging/discharging cycles, and EV charging/discharging patterns. The objective is to minimize the expected total operating cost while satisfying operational and technical constraints. The new two-stage stochastic centralized dispatch model has been tested on a 38-bus AC/DC hybrid distribution system. The simulation results demonstrate the effectiveness of the developed scheme for optimally coordinating the various components of future AC/DC hybrid smart grids. To demonstrate the necessity for uncertainty modeling, the value of the stochastic solution (VSS) and the expected value of perfect information (EVPI) have been applied for comparing the stochastic solution obtained and the deterministic one.

Description: This paper attempts to contribute to the development of a new concept focused on a cooperative control scheme for a smart network of residential buildings (SNRB) and to demonstrate advantages of interconnections and coordination among a set of smart residential buildings (SRBs), by taking advantages of fluctuations of stochastic renewable sources and loads, and exploiting the operational flexibilities of thermal loads defined as the required hot water and desired buildings temperature. A comprehensive finite-horizon scheduling optimization problem is formulated to optimally control a SNRB using model predictive control (MPC) method, which integrates both forecasts and newly updated information. The cooperation is reached through a bidirectional communication infrastructure in the SNRB, where the master controller (MC) is available at the network level and in charge of coordinating and managing power in the SNRB. A MPC-based algorithm is used for the future scheduling of power exchanges, charge/discharge state of each energy storage device (ESD), the state of each micro-CHP and the charging state of each plug-in electric vehicle (PEV) available in the network. The MPC strategy is tested through case studies where the influences of both network topology and thermal loads on the operation of each SRB are analyzed via numerical results.

Description: The present evolution of fuel prices together with the reduction of premiums for renewable energies make it of vital importance to improve renewable production management. This paper proposes a model of a single renewable power producer to compete more efficiently against other generators. The single unit is composed of a wind power producer and a hydro-pump storage power producer. The synergies between both renewable producers make relevant the possibility of mitigating wind power uncertainty, and due to this, the imbalances of the wind power producer will be reduced. The reduction of wind imbalances can come from deviating part of the excess of wind generation through a physical connection toward the pumping system or by increasing hydro generation to mitigate the lack of wind generation. To evaluate the problem, stochastic mixed integer linear programming is proposed to address the problem of selling the energy from the single renewable unit through a bilateral contract and in the day-ahead market, as a new contribution to earlier studies. Furthermore, a balancing market is considered to penalize the imbalances. The decision is made to maximize the profit, considering risk-hedging through the Conditional Value at Risk. The model is tested and analyzed for a case study and relevant conclusions are presented.

Description: Cascading trip faults in large-scale wind power centralized integration areas bring new challenges to the secure operation of power systems. In order to deal with the complexity of voltage security regions and the computation difficulty, this paper proposes an autonomous voltage security region (AVSR) for each wind farm and the point of common coupling (PCC) substation, whose voltage can be controlled in a decoupled way. The computation of the AVSR can be completed using a stepwise search method exchanging voltage and power information between the control center and the wind farms. At each wind farm, an AVSR is determined to guarantee the normal operation of each wind turbine generator (WTG), while in the control center, each region is designed in order to guarantee secure operation both under normal conditions and after an N -1 contingency. A real system in Northern China was used to carry out case studies to verify the effectiveness of the AVSRs proposed, and good performance was demonstrated using the Monte Carlo method.

Description: Evaluating the impact related to stochastic wind generation and generic storage on economic dispatch in distribution system operation is an important issue in power systems. This paper presents the analysis of the impacts of high wind power and storage participation on a distribution system over a period of 24 h using grid reconfiguration for electrical distribution system (EDS) radial operation. In order to meet this objective, a stochastic mixed integer linear programming (SMILP) is proposed, where the balance between load and generation has to be satisfied minimizing the expected cost during the operation period. The model also considers distributed generation (DG) represented by wind scenarios and conventional generation, bus loads represented through a typical demand profile, and generic storage. A case study provides results for a weakly meshed distribution network with 70 buses, describing in a comprehensive manner the effects of stochastic wind scenarios and storage location on distribution network parameters, voltage, substation behavior as well as power losses, and the expected cost of the system.

Description: This paper presents a second order sliding mode control strategy to control the generator and the grid sides of a variable speed experimental wind energy conversion system. At the generator side, the rotational speed is controlled to track a profile generated from the power curve of the wind turbine for maximum power extraction. At the grid side, the dc-link voltage is regulated for a proper transfer of power. The control strategy is based on a disturbed single input-single output error model and a second order sliding mode control algorithm. The proposed second order sliding mode control strategy offers interesting characteristics such as robustness to parametric uncertainties in the turbine and the generator as well as external disturbances. The proposed strategy, for speed and dc-link voltage control in wind energy conversion system, is validated on an emulated wind turbine driven by the OPAL-RT real-time simulator (OP5600). Experimental results show that the proposed control strategy is effective in terms of speed and dc-link voltage control. The sliding mode control approach is robust against unknown disturbances, parametric variations, and uncertainties in the system. Furthermore, it produces no chattering in the generated torque, which reduces the mechanical stress on the wind turbine.

Description: Microgrids consisting of diesel generators, storage devices, and renewable sources present an effective approach for an economic energy supply to rural areas. Advanced control methods are needed to improve the energy dispatch, enable a cost-efficient operation and guarantee an uninterrupted power supply. In particular, sudden variations in load demand or additional power supply from renewable sources are often unpredictable and underline the need for enhanced control. This paper presents an advanced control strategy for the optimal microgrid operation using a two-layer model predictive method. The first optimization layer presents an optimal control problem, based on real-time predictions of future power profiles, for the calculation of the optimal energy dispatch. To improve the robustness of the control strategy toward prediction errors, a boundary value problem is solved to adjust the diesel generator power in the second stage. The model predictive control framework is further used to adapt the weights of the forecast algorithm. Simulation studies are carried out by using real-world data to illustrate the performance and economic benefits of the proposed method. Results show the effectiveness of the control strategy in terms of computational feasibility, accuracy, increased robustness, and reduced cost.

Description: This paper presents a solar power forecasting scheme, which uses spatial and temporal time series data along with a photovoltaic (PV) power conversion model. The PV conversion model uses the forecast of three different variables, namely, irradiance on the tilted plane, ambient temperature, and wind speed, in order to estimate the power produced by a PV plant at the grid connection terminals. The forecast values are obtained using a spatio-temporal method that uses the data recorded from a target meteorological station as well as data of its surrounding stations. The proposed forecasting method exploits the sparsity of correlations between time series data in a collection of stations. The performance of both the PV conversion model and the spatio-temporal algorithm is evaluated using high-resolution real data recorded in various locations in Italy. Comparison with other benchmark methods illustrates that the proposed method significantly improves the solar power forecasts, particularly over short-term horizons.

Description: A promising configuration for future smart grids is an AC/DC hybrid topology that enables the integration of AC/DC energy resources and modern loads, thus permitting the consequent formation of AC/DC hybrid microgrids (HMGs). An understanding of AC/DC HMGs and their operational premise during islanding will certainly pave the way toward the realization of a future smart grid that includes a plug-and-play feature. However, the planning and operation of such isolated and hybrid systems are reliant on a powerful and efficient power flow tool. To this end, this paper proposes a novel unified, generic, and flexible power flow algorithm for isolated AC/DC HMGs. The power flow subproblems related to AC and DC subgrids are described mathematically by a set of linear and nonlinear equations and are solved simultaneously using a Newton trust-region method. The proposed algorithm is generic in the sense that it includes consideration of the unique characteristics of islanded AC/DC HMGs: a variety of possible topologies, droop controllability of the distributed resources (DRs), and bidirectionality of the power flow in the interlinking converters (ICs). The new power flow formulation is flexible and permits the easy incorporation of any changes in DR operating modes and IC control strategies. The developed algorithm was tested and applied for analyzing selected operational and control aspects of isolated AC/DC HMGs, including inaccurate power sharing and interlinking converters characterized by differing control strategies. The proposed load flow program can form the basis of and provide direction for further studies of isolated AC/DC HMGs.

Description: With the rapidly increasing penetration of renewable distributed generation (DG), the maximum hosting capacity (MHC) of a distribution system has become a major concern. To effectively evaluate the ability of a distribution system to accommodate DGs, this paper proposes an MHC evaluation method while considering the robust optimal operation of on load tap changers (OLTCs) and static var compensators (SVCs) in the uncertain context of DG power outputs and load consumptions. The proposed method determines the DG hosting capacities corresponding to different conservative levels. Furthermore, this paper discusses how to find out the most critical technical constraint that may limit the MHC by adjusting parameters of the proposed robust formulation. The effectiveness of the proposed method is demonstrated using a modified IEEE 33-bus distribution system.

Description: This paper presents a numerical-based algorithm to solve the probabilistic power flow problem. Parzen window density estimator is used to efficiently estimate probabilistic characteristics of power flow outputs. Correlations between wind generation, load, and plug-in hybrid electric vehicle charging stations are taken into account. The proposed algorithm works properly for random variables with various probability distribution functions and is very useful when limited information is available for each random variable. The algorithm is tested on the IEEE 14-bus and IEEE 118-bus systems considering correlated and uncorrelated conditions. Comparison between the proposed algorithm with 2n, $text{2n} + 1$ point estimation methods as well as Monte Carlo simulation and linear diffusion method are provided. In addition, probability density and cumulative distribution functions are determined using the proposed algorithm, diffusion method, and the combined Cumulants and Gram-Charlier for $text{2n} + 1$ point estimation method. Error indices are introduced to evaluate all random variables in a single benchmark. Simulation results show the effectiveness of the proposed algorithm to provide complete statistical information for probabilistic power flow outputs.

Description: Penetration of solar energy into main grid has gradually increased in recent years due to a growing number of large-scale photovoltaic (PV) farms. The power output of these PV farms may fluctuate due to a wide variability of meteorological conditions, and, thus, we need to compensate for this effect in advance. In this paper, we propose a solar power prediction model based on various satellite images and a support vector machine (SVM) learning scheme. The motion vectors of clouds are forecasted by utilizing satellite images of atmospheric motion vectors (AMVs). We analyze 4 years’ historical satellite images and utilize them to configure a large number of input and output data sets for the SVM learning. We compare the performance of the proposed SVM-based model, the conventional time-series model, and an artificial neural network (ANN) model in terms of prediction accuracy.

Description: This paper presents a novel energy management strategy for a hybridized power source small urban electric vehicle (EV). First, an analysis of load requirements for typical urban driving cycles is presented. Thereafter, the energy and power management issues are addressed for a multisource EV to define an improved management architecture. A dynamically restricted search space strategy coupled with a simulated annealing technique is exploited to accomplish the global optimization of the energy management system (EMS). The control of the dc/dc converter operations based on this EMS is also presented. The multiple sources have been simulated using an overall model implemented in Matlab/Simulink. A reduced-scale prototype has been built to analyze the performance of the energy management strategy. The results obtained show that energy management has been enhanced leading to an increase of the vehicle performance with reduced size embarked sources.

Description: Prediction intervals (PIs) are a promising tool for quantification of uncertainties associated with point forecasts of wind power. However, construction of PIs using parametric methods is questionable, as forecast errors do not follow a standard distribution. This paper proposes a nonparametric method for construction of reliable PIs for neural network (NN) forecasts. A lower upper bound estimation (LUBE) method is adapted for construction of PIs for wind power generation. A new framework is proposed for synthesizing PIs generated using an ensemble of NN models in the LUBE method. This is done to guard against NN performance instability in generating reliable and informative PIs. A validation set is applied for short listing NNs based on the quality of PIs. Then, PIs constructed using filtered NNs are aggregated to obtain combined PIs. Performance of the proposed method is examined using data sets taken from two wind farms in Australia. Simulation results indicate that the quality of combined PIs is significantly superior to the quality of PIs constructed using NN models ranked and filtered by the validation set.

Description: This paper presents a supervisory control unit (SCU) combined with short-term ahead wind speed prediction for proper and effective management of the stored energy in a small capacity flywheel energy storage system (FESS) which is used to mitigate the output power fluctuations of an aggregated wind farm. Wind speed prediction is critical for a wind energy conversion system since it may greatly influence the issues related to effective energy management, dynamic control of wind turbine, and improvement of the overall efficiency of the power generation system. In this study, a wind speed prediction model is developed by artificial neural network (ANN) which has advantages over the conventional prediction schemes including data error tolerance and ease in adaptability. The proposed SCU-based control would help to reduce the size of the energy storage system for minimizing wind power fluctuation taking the advantage of prediction scheme. The model for prediction using ANN is developed in MATLAB/Simulink and interfaced with PSCAD/EMTDC. Effectiveness of the proposed control system is illustrated using real wind speed data in various operating conditions.

Description: Due to its reduced communication overhead and robustness to failures, distributed energy management is of paramount importance in smart grids, especially in microgrids, which feature distributed generation (DG) and distributed storage (DS). Distributed economic dispatch for a microgrid with high renewable energy penetration and demand-side management operating in grid-connected mode is considered in this paper. To address the intrinsically stochastic availability of renewable energy sources (RES), a novel power scheduling approach is introduced. The approach involves the actual renewable energy as well as the energy traded with the main grid, so that the supply–demand balance is maintained. The optimal scheduling strategy minimizes the microgrid net cost, which includes DG and DS costs, utility of dispatchable loads, and worst-case transaction cost stemming from the uncertainty in RES. Leveraging the dual decomposition, the optimization problem formulated is solved in a distributed fashion by the local controllers of DG, DS, and dispatchable loads. Numerical results are reported to corroborate the effectiveness of the novel approach.

Description: The present paper investigates the effect of high penetration of photovoltaic (PV) systems on the small signal stability of a large power system. Reduced system inertia and altered power flow patterns as a result of the addition of the utility scale and residential rooftop PVs that replace a portion of conventional generation resources, may lead to decreased damping of the critical modes of the system. To identify the critical modes of the system and the effect of the high PV penetration on these modes, eigenvalue analysis is carried out on the aforementioned system under various PV penetration levels. To substantiate the results observed from the small signal analysis, transient analysis is carried out on the system under various PV penetration levels. The simulation results effectively identify the impact of high PV penetration on small signal stability of the studied system.

Description: A speed exclusion zone algorithm to prevent the rotor rotational frequency from exciting the side-side tower mode in a variable-speed wind turbine was designed. The algorithm was first tested on a simple wind turbine model developed in Simulink and then implemented and tested on an experimental test rig. Different speed exclusion zone widths were tested to observe how the performance of the wind turbine is affected. Results obtained from both simulations and experiments showed the effectiveness of the speed exclusion zone algorithm in preventing excitation of tower resonance. Results from different speed exclusion zone widths showed that with a wider exclusion zone, the tower mode is excited less at the expense of a larger power loss.

Description: A control strategy for the quasi-Z-source inverter (qZSI) with a battery-based photovoltaic (PV) power conversion system is proposed. A battery-assisted qZSI can buck/boost PV panel voltage by introducing shoot-through states, and make full use of PV power by the energy-stored battery paralleled to the quasi-Z-source capacitor. A dynamic small-signal model of the battery-assisted qZSI is established to design a closed-loop controller for regulating shoot-through duty ratio and managing the battery's energy storage. A modified space vector modulation (SVM) technique for the qZSI is applied to achieve low harmonics, high voltage utilization, and high efficiency. A P-Q decoupled grid-tie power injection is fulfilled with the maximum power capture from PV panels and the unity power factor. The validity of the proposed PV system is proved by experimental results, showing an efficient method for the energy-stored PV power generation.

Description: Doubly-fed induction generator (DFIG)-based wind turbines (WTs) are reported to suffer reliability problems due to the presence of slip-rings, brushes, and the gearbox. These disadvantages encouraged several research groups to investigate the viability of employing single or double-frame brushless cascade DFIGs (BCDFIGs) in grid-connected multimegawatt WTs especially offshore and in solar chimney power plants. In this regard, this paper tackles three main issues. Initially, the expected reduction in gear ratio when BCDFIGs are used instead of DFIGs is examined from a steady state perspective. A reduction would lead to less frequent maintenance and an improved return on investment. Next, a detailed comparison between DFIG-WTs and BCDFIG-WTs under unbalanced grid voltage is presented. Finally, the extent to which a multimegawatt BCDFIG-WT is grid code compliant in terms of fault ride-through capability is studied. This is illustrated by comparing and quantifying the response of a DFIG-WT and a BCDFIG-WT to a severe three-phase voltage dip. Simulation and experimental results indicate promising behavior for BCDFIGs during disturbances.

Description: We analyze the potential benefits of colocating wind and concentrating solar power (CSP) plants in the southwestern U.S. Using a location in western Texas as a case study, we demonstrate that such a deployment strategy can improve the capacity factor of the combined plant and the associated transmission investment. This is because of two synergies between wind and CSP: 1) the negative correlation between real-time wind and solar resource availability and 2) the use of low-cost high-efficiency thermal energy storage in CSP. The economic tradeoff between transmission and system performance is highly sensitive to CSP and transmission costs. We demonstrate that a number of deployment configurations, which include up to 67% CSP, yield a positive net return on investment.

Description: Renewable energy resources, especially wind power, are expected to provide a considerable portion of the world energy requirements in the near future. Large-scale wind power penetration impacts the electricity industry in many aspects and raises a number of technical challenges for the electricity network. A day-ahead network-constrained market clearing formulation is proposed which considers demand side resources. The proposed approach can provide flexible load profile and reduce the need for ramp up/down services by the conventional generators. This method can potentially facilitate a large penetration of wind power by shifting the wind power generation from the off-peak periods to the high-peak hours. The validity of the proposed approach has been verified using the IEEE 30 bus and 57 bus test systems.

Description: The small generation units including cogeneration units and renewables have been widely spread during the last decades. These units raise several questions regarding the power system, e.g., their integration into power system control or their role on the supply-side of smart grids. A multiagent model was established in order to facilitate the investigations of the emerged questions. Several viewpoints are taken into account and built into the model considering technical specifications, proper wind-speed and temperature simulations, constraints of heating service, fuel consumption, legal regulation, outages, and services. A state-based method is elaborated in order to generalize different technologies. The different aspects of operation control are represented by agent strategies. Applying these methods resulted in an easy-to-follow and flexible model. This paper is dealing with the developed multiagent-based model applied to study the distributed generation system from many viewpoints like power system balance, virtual power plant, or smart grid scheduling.

Description: The trend towards multimegawatt (multi-MW) wind turbines and the increasing interest in direct-drive variable-speed wind energy systems have made multilevel converters a promising candidate for large wind energy conversion systems. This paper presents a new hybrid modular multilevel converter for interfacing a full-scale, permanent magnet synchronous generator (PMSG)-based direct-drive variable-speed wind energy conversion system (WECS). The proposed hybrid converter, which is used on the grid side of the system, consists of a three-level modular multilevel converter (MMC) in series connection with three H-bridge modules. The generator-side converter is based on a conventional three-level neutral-point-clamped converter. The proposed hybrid converter, as opposed to the existing full-scale multilevel converter-based wind energy systems, provides structural modularity and a higher dc-bus voltage utilization. This paper formulates and analyzes the internal dynamics of the proposed hybrid converter including the circulating currents and the capacitor voltage ripples. The ac components of the circulating currents, if not properly reduced, increase the amplitude of the capacitor voltage ripples, rating values of the converter components, and losses. Based on the analysis, closed-loop circulating current and capacitor voltage ripple reduction techniques are developed. The reduction of capacitor voltage ripples help reduce the capacitor value. A mathematical model is also developed for the overall WECS. Performance of the overall WECS, under the proposed multilevel converter-based topology and controls, is evaluated based on time domain simulations in the PSCAD/EMTDC environment.

Description: Electric vehicles (EVs) are likely to have a continued presence in the light-vehicle market in the next few decades. As a result, EV charging will put an extra burden on the distribution grid and adjustments need to be made in some cases. On the other hand, EVs have the potential to support the grid as well. This paper presents a single-phase bidirectional charger topology which pairs up a photovoltaic (PV) source with an EV charger resulting in production cost reduction. The presented topology is then used for vehicle-to-grid (V2G) services. The main focus of this paper is on power quality services which only slightly discharge the battery. Among these services, it studies the possibility of local reactive injection of EVs connected to the grid through a single-phase charger to compensate for voltage drops caused by motor startup or inductive loads. It also studies the possibility of active power injection of EVs for short time periods during PV transients in cloudy weather to keep the system stable. It also studies the potential of EVs to help during low voltage ride-through of the PV sources. The studies are performed using Simulink simulations and a real-time implementation in Real Time Digital Simulator (RTDS). The results demonstrate the effectiveness of power quality V2G services with small wear on the EV battery.

Description: The short-term wind power scenarios have a significant impact on the operation cost and power system reliability due to the stochastic generation scheduling of wind-integrated power systems. In order to obtain the scenarios containing the information of forecast error distribution and fluctuation distribution for short-term wind power, a scenario generation method is proposed. This paper characterizes forecast error via empirical distributions of a set of forecast bins and assumes that wind power fluctuations over unit interval follow $t$ location-scale distribution. An inverse transform sampling from a multivariate normal distribution is adopted to generate a large number of wind power scenarios. The covariance matrix of the multivariate normal distribution is estimated to fit the distribution of historical wind power fluctuations. The proposed scenario generation method is applied to the actual aggregate wind power data in the whole regions of Ireland's Power System. The results indicate that the variability of wind power scenarios can be adjusted by estimating the key range parameter in the exponential covariance structure of a multivariate normal distribution.

Description: A fuzzy fault-tolerant control (FFTC) framework is proposed for wind-diesel-hybrid systems (WDHS) with time-varying bounded sensor faults. The algorithm utilizes fuzzy systems based on “Takagi–Sugeno” (TS) fuzzy models to represent nonlinear systems. A fuzzy proportional-integral estimation observer (FPIEO) design is proposed to achieve fault estimation of TS models with abrupt sensor faults. Sufficient conditions are derived for robust stabilization in the sense of Lyapunov asymptotic stability and are formulated in the format of linear matrix inequalities (LMIs) to obtain controller gains and observer gains. The proposed algorithm maximizes the produced power, minimizes the voltage ripple, and is able to maintain the system's stability during the sensor faults. A physical model of the WDHS is presented and transformed into a TS model. Then, an FFTC algorithm is developed and applied to a WDHS to demonstrate the effectiveness of this method.

Description: Modeling wind speed time series (WSTS) is an essential part of network planning studies in order to generate synthetic wind power time series (WPTS). Hence, this paper proposes a methodology to equip planners with accurate simulation of wind speed and power variations as well as complete temporal dependence structure based on the copula theory. Unlike traditional autoregressive and Markov chain methods, the suggested technique is well-prepared to deal with “nonlinear long-memory temporal dependence” and “non-Gaussian empirical probability distributions” of the WSTS. Meanwhile, the proposed statistical modeling framework is compatible with the scenario-based analysis of active networks as well. Furthermore, a case study for optimal sizing of an autonomous wind/photovoltaic/battery system is presented. The purpose of the presented study is to fully examine the accuracy and effectiveness of the copula-based model of wind generation for planning nonmemoryless power systems. Among a list of commercially available system devices, the optimal number and type of units are calculated ensuring both a minimum 20-year round total system cost and a perfect reliability. The genetic algorithm is used in four wind generation scenarios consisting of real and simulated WPTS. Then, considering the corresponding optimal solutions, the autoregressive moving average (ARMA), nonparametric Markov and proposed copula-based simulations are compared against real data.

Description: This paper presents series grid interface topologies for enhancing the fault ride-through (FRT) performance of doubly-fed induction generator (DFIG)-based wind turbines (WTs). Two voltage booster schemes, 1) dynamic voltage restorer (DVR) and 2) resistive type high temperature superconducting fault current limiter (HTS-FCL), are designed and implemented. The test system represents a WT connected to an electric grid with alternatively employing DVR and HTS-FCL. Both schemes provide fast mitigation of voltage dip that maintains the nominal operating conditions for DFIG-WT. To achieve a flexible control solution for balanced and unbalanced fault conditions, the DVR employs positive and negative sequence controllers while the HTS-FCL is designed to perform fast quenching for each phase individually. The potential of the two booster schemes is evaluated and analyzed in positive and negative sequence reference frames. Comprehensive simulation studies are presented to verify the capability of the series grid interface schemes for ensuring the normal operation and smooth wind power evacuation with effective isolation from grid faults. Furthermore, the grid code requirements of reactive current support are evaluated for both schemes at various fault scenarios.

Description: This paper presents a real-time mechanism to tolerate faults occurring in a wind turbine (WT) system. This system is a FAST coded simulator designed by the U.S. National Renewable Energy Laboratory's (NREL's) National Wind Turbine Center. The demonstrated mechanism lies under the taxonomy of active fault-tolerant control (AFTC) systems, namely the projection-based approach. In the proposed approach, we do not use any a priori information about the model of the turbine in real-time. In fact, we use the online measurements generated by WT. Based on the given control objectives, and the observed measurements, an occurring fault is accommodated by reconfiguring the controller such that the turbine generates the rated power even under faulty conditions. Second, no use of an explicit fault-diagnosis module is seen in this approach. Therefore, the fault accommodation delay in the proposed AFTC structure is smaller than the delay as experienced in the traditional structure of AFTC systems.

Description: Conventionally operated full power converter wind plants show high short-term power output variability caused by variable winds, and does not contribute to the power system inertia due to the decoupled generator speed and grid frequency. There is, however, abundant inertial resources in wind plant rotors for both smoothing of output power and for synthetic inertia contribution. Together with added frequency controlling functionality, this could facilitate inclusion of wind power in islanding systems, enabling greater system loads and enhancing power system stability. This paper describes modeling of power smoothing and frequency controlling wind plants and assesses different control strategies as well as the grid frequency performance gains achievable over hydro powered islanding systems and over islanding systems incorporating both hydro power and conventional wind plants. The results show that wind plant power output could be smoothed in the short time frame, and support frequency in both primary and secondary frequency control timescales including droop functionality.

Description: This paper deals principally with the grid connection problem of a kite-based system, named the “Kite Generator System (KGS).” It presents a control scheme of a closed-orbit KGS, which is a wind power system with a relaxation cycle. Such a system consists of a kite with its orientation mechanism and a power transformation system that connects the previous part to the electric grid. Starting from a given closed orbit, the optimal tether's length rate variation (the kite's tether radial velocity) and the optimal orbit's period are found. The trajectory-tracking problem is not considered in this paper; only the kite's tether radial velocity is controlled via the electric machine rotation velocity. The power transformation system transforms the mechanical energy generated by the kite into electrical energy that can be transferred to the grid. A Matlab/simulink model of the KGS is employed to observe its behavior, and to insure the control of its mechanical and electrical variables. In order to improve the KGS's efficiency in case of slow changes of wind speed, a maximum power point tracking (MPPT) algorithm is proposed.

Description: To maximize the revenue from selling energy, photovoltaic systems (PVs) in general operate in the so-called maximum power point tracking mode. However, the increasing penetration of renewable energy sources in power systems has motivated the design of innovative control to provide ancillary services. The focus of this paper is to develop a new control strategy that enables PVs to adjust the active power outputs and provide frequency regulation to power systems. In this strategy, two different modes are designed: 1) the frequency droop control mode for PVs to provide primary frequency support to power systems, and 2) the emergency control mode to prevent system frequency collapse and, therefore, to prevent too much generation tripping after fault. Based on a detailed PV dynamic model, simulation results show the effectiveness of the proposed control strategy in improving the frequency stability.

Description: Standalone microgrids with renewable sources and battery storage play an important role in solving power supply problems in remote areas such as islands. To achieve reliable and economic operations of a standalone microgrid, in addition to the consideration of utilization of renewable resources, the lifetime characteristics of a battery energy storage system also need to be fully investigated. In this paper, in order to realize the economic operation of a recently developed standalone microgrid on Dongfushan Island in China, an optimization model including battery life loss cost, operation and maintenance cost, fuel cost, and environmental cost is established to obtain a set of optimal parameters of operation strategy. Considering the lifetime characteristics of lead–acid batteries, a multiobjective optimization to minimize power generation cost and to maximize the useful life of lead–acid batteries has been achieved via the nondominated sorting genetic algorithm (NSGA-II). The results show that the proposed method can optimize the system operations under different scenarios and help users obtain the optimal operation schemes of the actual microgrid system.

Description: Plug-in electric vehicles in the future will possibly emerge widely in city areas. Fleets of such vehicles in large numbers could be regarded as considerable stochastic loads in view of the electrical grid. Moreover, they are not stabled in unique positions to define their impact on the grid. Municipal parking lots could be considered as important aggregators letting these vehicles interact with the utility grid in certain positions. A bidirectional power interface in a parking lot could link electric vehicles with the utility grid or any storage and dispersed generation. Such vehicles, depending on their need, could transact power with parking lots. Considering parking lots equipped with power interfaces, in more general terms, parking-to-vehicle and vehicle-to-parking are propose here instead of conventional grid-to-vehicle and vehicle-to-grid concepts. Based on statistical data and adopting general regulations on vehicles (dis)charging, a novel stochastic methodology is presented to estimate total daily impact of vehicles aggregated in parking lots on the grid. Different scenarios of plug-in vehicles' penetration are suggested in this paper and finally, the scenarios are simulated on standard grids that include several parking lots. The results show acceptable penetration level margins in terms of bus voltages and grid power loss.