ALBERT

Description: This paper presents the development of a heuristic-based algorithm for a Home Electric Energy Management System (HEEMS). The novelty of the proposal resides in the fact that solutions of the Pareto front, minimizing both the energy consumption and cost, are obtained by a Genetic Algorithm (GA) considering the renewable energy availability as well as the user activity level (AL) inside the house. The extensive solutions search characteristic of the GAs is seized to avoid the calculation of the full set of Pareto front solutions, i.e., from a reduced set of non-dominated solutions in the Pareto sense, an optimal solution with the best fitness is obtained, reducing considerably the computational time. The HEEMS considers models of the air conditioner, clothes dryer, dishwasher, electric stove, pool pump, and washing machine. Models of the wind turbine and solar PV modules are also included. The wind turbine model is written in terms of the generated active power exclusively dependent on the incoming wind profiles. The solar PV modules model accounts for environmental factors such as ambient temperature changes and irradiance profiles. The effect of the energy storage unit on the energy consumption and costs is evaluated adapting a model of the device considering its charge and discharge ramp rates. The proposed algorithm is implemented in the Matlab® platform and its validation is performed by comparing its results to those obtained by a freeware tool developed for the energy management of smart residential loads. Also, the evaluation of the performance of the proposed HEEMS is carried out by comparing its results to those obtained when the multi-objective optimization problem is solved considering weights assigned to each objective function. Results showed that considerable savings are obtained at reduced computational times. Furthermore, with the calculation of only one solution, the end-user interaction is reduced making the HEEMS even more manageable than previously proposed approaches.

Description: This paper presents the development of a heuristic-based algorithm for a Home Electric Energy Management System (HEEMS). The novelty of the proposal resides in the fact that solutions of the Pareto front, minimizing both the energy consumption and cost, are obtained by a Genetic Algorithm (GA) considering the renewable energy availability as well as the user activity level (AL) inside the house. The extensive solutions search characteristic of the GAs is seized to avoid the calculation of the full set of Pareto front solutions, i.e., from a reduced set of non-dominated solutions in the Pareto sense, an optimal solution with the best fitness is obtained, reducing considerably the computational time. The HEEMS considers models of the air conditioner, clothes dryer, dishwasher, electric stove, pool pump, and washing machine. Models of the wind turbine and solar PV modules are also included. The wind turbine model is written in terms of the generated active power exclusively dependent on the incoming wind profiles. The solar PV modules model accounts for environmental factors such as ambient temperature changes and irradiance profiles. The effect of the energy storage unit on the energy consumption and costs is evaluated adapting a model of the device considering its charge and discharge ramp rates. The proposed algorithm is implemented in the Matlab® platform and its validation is performed by comparing its results to those obtained by a freeware tool developed for the energy management of smart residential loads. Also, the evaluation of the performance of the proposed HEEMS is carried out by comparing its results to those obtained when the multi-objective optimization problem is solved considering weights assigned to each objective function. Results showed that considerable savings are obtained at reduced computational times. Furthermore, with the calculation of only one solution, the end-user interaction is reduced making the HEEMS even more manageable than previously proposed approaches.

Description: The effect of hydrogen pulse addition on digestion performance of sewage sludge was evaluated as a means for studying the increase in efficiency of methane production. Microbial communities were also evaluated to get an insight of the changes caused by the operational modifications of the digester. An energy evaluation of this alternative was performed considering the theoretical process of coupling bioelectrochemical systems (BES) for the treatment of wastewater along with hydrogen production and the subsequent anaerobic digestion. The addition of hydrogen to sewage sludge digestion resulted in an increase of 12% in biogas production over the control (1353 mL CH4 d−1 at an injection flow rate of 1938 mL H2 d−1). The liquid phase of the sludge reactor and the H2 supplemented one did not show significant differences, thus indicating that the application of hydrogen as the co-substrate was not detrimental. High-throughput sequencing analysis showed slight changes in archaeal relative abundance after hydrogen addition, whereas eubacterial community structure and composition revealed noteworthy shifts. The mass and energy balance indicated that the amount of hydrogen obtained from a hypothetical BES can be assimilated in the sludge digester, improving biogas production, but this configuration was not capable of covering all energy needs under the proposed scenario.

Description: Street lighting has a big impact on the energy consumption of Spanish municipalities. To decrease this consumption, the Spanish government has developed two different regulations to improve energy savings and efficiency, and consequently, reduce greenhouse-effect gas emissions. However, after these efforts, they have not obtained the expected results. To improve the effectiveness of these regulations and therefore to optimize energy consumption, a study has been done to analyze the different devices which influence energy consumption with the intention of better understanding their behavior and performance. The devices analyzed were lamps, ballasts, street lamp globes, control systems and dimmable lighting systems. To improve their performance, they have been analyzed from three points of view: changes in technology, use patterns and standards. Thanks to this study, some aspects have been found that could be taken into account if we really wanted to use energy efficiently.

Description: Energy consumption rose about 28% over the 2001 to 2011 period in the Spanish residential sector. In this environment, domestic hot water (DHW) represents the second highest energy demand. There are several methodologies to estimate DHW consumption, but each methodology uses different inputs and some of them are based on obsolete data. DHW energy consumption estimation is a key tool to plan modifications that could enhance this consumption and we decided to update the methodologies. We studied DHW consumption with data from 10 apartments in the same building during 18 months. As a result of the study, we updated one chosen methodology, adapting it to the current situation. One of the challenges to improve efficiency of DHW use is that most of people are not aware of how it is consumed in their homes. To help this information to reach consumers, we developed a website to allow users to estimate the final electrical energy needed for DHW. The site uses three estimation methodologies and chooses the best fit based on information given by the users. Finally, the application provides users with recommendations and tips to reduce their DHW consumption while still maintaining the desired comfort level.

Description: Considering that non-renewable energy resources are dwindling, the smart grid turns out to be one of the most promising and compelling systems for the future of energy. Not only does it combine efficient energy consumption with avant-garde technologies related to renewable energies, but it is also capable of providing several beneficial utilities, such as power monitoring and data provision. When smart grid end users turn into prosumers, they become arguably the most important value creators within the smart grid and a decisive agent of change in terms of electricity usage. There is a plethora of research and development areas related to the smart grid that can be exploited for new business opportunities, thus spawning another branch of the so-called “green economy” focused on turning smart energy usage into a profitable business. This paper deals with emerging business models for smart grid prosumers, their strengths and weaknesses and puts forward new prosumer-oriented business models, along with their value propositions.

Description: Energies, Vol. 10, Pages 1330: Experimental Validation of the Simulation Model of a DOAS Equipped with a Desiccant Wheel and a Vapor Compression Refrigeration System Energies doi: 10.3390/en10091330 Authors: Pedro Martínez Carlos Llorca José Pla Pedro Martínez A dedicated outdoor air system (DOAS) can be designed to supply 100% of the outside air and meet the latent load of the room with dry air. The objectives of this study were to develop a model of a DOAS equipped with a desiccant wheel and a vapor-compression refrigeration system, build a prototype, validate the model with experimental data, and gain knowledge about the system operation. The test facility was designed with the desiccant wheel downstream of the cooling coil to take advantage of the operating principles of cooling coils and desiccants. A model of the DOAS was developed in the TRNSYS environment. The root mean standard error (RMSE) was used for model validation by comparing the measured air and refrigerant properties with the corresponding calculated values. The results obtained with the developed model showed that the DOAS was able to maintain an indoor humidity ratio depending on outdoor conditions. Laboratory tests were also used to investigate the effect of changes in the regeneration air temperature and the process airflow rate on the process air humidity ratio at the outlet of the wheel. The results are consistent with the technical literature.

Description: Street lighting installations are an essential service for modern life due to their capability of creating a welcoming feeling at nighttime. Nevertheless, several studies have highlighted that it is possible to improve the quality of the light significantly improving the uniformity of the illuminance. The main difficulty arises when trying to improve some of the installation’s characteristics based only on statistical analysis of the light distribution. This paper presents a new algorithm that is able to obtain the overall illuminance uniformity in order to improve this sort of installations. To develop this algorithm it was necessary to perform a detailed study of all the elements which are part of street lighting installations. Because classification is one of the most important tasks in the application areas of artificial neural networks, we compared the performances of six types of training algorithms in a feed forward neural network for analyzing the overall uniformity in outdoor lighting systems. We found that the best algorithm that minimizes the error is “Levenberg-Marquardt back-propagation”, which approximates the desired output of the training pattern. By means of this kind of algorithm, it is possible to help to lighting professionals optimize the quality of street lighting installations.

Description: Outdoor lighting is an essential service for modern life. However, the high influence of this type of facility on energy consumption makes it necessary to take extra care in the design phase. Therefore, this manuscript describes an algorithm to help light designers to get, in an easy way, the best configuration parameters and to improve energy efficiency, while ensuring a minimum level of overall uniformity. To make this possible, we used a particle swarm optimization (PSO) algorithm. These algorithms are well established, and are simple and effective to solve optimization problems. To take into account the most influential parameters on lighting and energy efficiency, 500 simulations were performed using DIALux software (4.10.0.2, DIAL, Ludenscheid, Germany). Next, the relation between these parameters was studied using to data mining software. Subsequently, we conducted two experiments for setting parameters that enabled the best configuration algorithm in order to improve efficiency in the proposed process optimization.

Description: Solar energy harvesting using Photovoltaic (PV) systems is one of the most popular sources of renewable energy, however the main drawback of PV systems is their low conversion efficiency. An optimal system operation requires an efficient tracking of the Maximum Power Point (MPP), which represents the maximum energy that can be extracted from the PV panel. This paper presents a novel control approach for the Maximum Power Point Tracking (MPPT) based on the differential flatness property of the Boost converter, which is one of the most used converters in PV systems. The underlying idea of the proposed control approach is to use the classical flatness-based trajectory tracking control where a reference voltage will be defined in terms of the maximum power provided by the PV panel. The effectiveness of the proposed controller is assessed through numerical simulations and experimental tests. The results show that the controller based on differential flatness is capable of converging in less than 0.15 s and, compared with other MPPT techniques, such as Incremental Conductance and Perturb and Observe, it improves the response against sudden changes in load or weather conditions, reducing the ringing in the output of the system. Based on the results, it can be inferred that the new flatness-based controller represents an alternative to improve the MPPT in PV systems, especially when they are subject to sudden load or weather changes.