ALBERT

Description: With the increasing proportion of distributed power supplies connected to the power grid, the application of a battery energy storage system (BESS) to a power system leads to new ideas of effectively solving the problem of distributed power grid connections. There is obvious uncertainty involved in distributed power output, and these uncertainties must be considered when optimizing the scheduling of virtual power plants. In this context, scene simulation technology was used to manage the uncertainty of wind power and photovoltaic output, forming a classic scenario. In this study, to reduce the influence of the uncertainty of wind and photovoltaic power output on the stable operation of the system, the time-of-use (TOU) prices and BESS were incorporated into the optimal scheduling problem that is inherent in wind and photovoltaic power. First, this study used the golden section method to simulate the wind and photovoltaic power output; second, the day-ahead wind and photovoltaic power output were used as the random variables; third, a wind and photovoltaic power BESS robust scheduling model that considers the TOU price was constructed. Finally, this paper presents the Institute of Electrical and Electronics Engineers (IEEE) 30 bus system in an example simulation, where the solution set is based on the Pareto principle, and the global optimal solution can be obtained by the robust optimization model. The results show that the cooperation between the TOU price and BESS can counteract wind and photovoltaic power uncertainties, improve system efficiency, and reduce the coal consumption of the system. The example analysis proves that the proposed model is practical and effective. By accounting for the influence of uncertainty of the optimal scheduling model, the actual operating cost can be reduced, and the robustness of the optimization strategy can be improved.

Description: To ensure the stability of park power supply systems and to promote the consumption of wind/photovoltaic generation, this paper proposes a dispatching optimization model for the park power supply system with power-to-gas (P2G) and peak regulation via gas-fired generators. Firstly, the structure of a park power system with P2G was built. Secondly, a dispatching optimization model for the park power supply system was constructed with a peak regulation compensation mechanism. Finally, the effectiveness of the model was verified by a case study. The case results show that with the integration of P2G and the marketized peak regulation compensation mechanism, preferential power energy storage followed by gas storage had the best effect on the park power supply system, which minimized the clean energy curtailment to 11.18% and the total cost by approximately $120.190 and maximized the net profit by approximately $152.005.

Description: Accurately predicting wind power is crucial for the large-scale grid-connected of wind power and the increase of wind power absorption proportion. To improve the forecasting accuracy of wind power, a hybrid forecasting model using data preprocessing strategy and improved extreme learning machine with kernel (KELM) is proposed, which mainly includes the following stages. Firstly, the Pearson correlation coefficient is calculated to determine the correlation degree between multiple factors of wind power to reduce data redundancy. Then, the complementary ensemble empirical mode decomposition (CEEMD) method is adopted to decompose the wind power time series to decrease the non-stationarity, the sample entropy (SE) theory is used to classify and reconstruct the subsequences to reduce the complexity of computation. Finally, the KELM optimized by harmony search (HS) algorithm is utilized to forecast each subsequence, and after integration processing, the forecasting results are obtained. The CEEMD-SE-HS-KELM forecasting model constructed in this paper is used in the short-term wind power forecasting of a Chinese wind farm, and the RMSE and MAE are as 2.16 and 0.39 respectively, which is better than EMD-SE-HS-KELM, HS-KELM, KELM and extreme learning machine (ELM) model. According to the experimental results, the hybrid method has higher forecasting accuracy for short-term wind power forecasting.

Description: Carbon emissions and environmental protection issues have become the pressure from the international community during the current transitional stage of China’s energy transformation. China has set a macro carbon emission target, which will reduce carbon emissions per unit of Gross Domestic Product (GDP) by 40% in 2020 and 60–65% in 2030 than that in 2005. To achieve the emission reduction target, the industrial structure must be adjusted and upgraded. Furthermore, it must start from a high-pollution and high-emission industry. Therefore, it is of practical significance to construct a low-carbon sustainability and green operation benefits of power generation enterprises to save energy and reduce emissions. In this paper, an intuitionistic fuzzy comprehensive analytic hierarchy process based on improved dynamic hesitation degree (D-IFAHP) and an improved extreme learning machine algorithm optimized by RBF kernel function (RELM) are proposed. Firstly, we construct the evaluation indicator system of low-carbon sustainability and green operation benefits of power generation enterprises. Moreover, during the non-dimensional processing, the evaluation index system is determined. Secondly, we apply the evaluation indicator system by an empirical analysis. It is proved that the D-IFAHP evaluation model proposed in this paper has higher accuracy performance. Finally, the RELM is applied to D-IFAHP to construct a combined evaluation model named D-IFAHP-RELM evaluation model. The D-IFAHP evaluation results are used as the input of the training sets of the RELM algorithm, which simplifies the comprehensive evaluation process and can be directly applied to similar projects.

Description: Carbon emissions and environmental protection issues have brought pressure from the international community during Chinese economic development. Recently, Chinese Government announced that carbon emissions per unit of GDP would fall by 60–65% compared with 2005 and non-fossil fuel energy would account for 20% of primary energy consumption by 2030. The Beijing-Tianjin-Hebei region is an important regional energy consumption center in China, and its energy structure is typically coal-based which is similar to the whole country. Therefore, forecasting energy consumption related carbon emissions is of great significance to emissions reduction and upgrading of energy supply in the Beijing-Tianjin-Hebei region. Thus, this study thoroughly analyzed the main energy sources of carbon emissions including coal, petrol, natural gas, and coal power in this region. Secondly, the kernel function of the support vector machine was applied to the extreme learning machine algorithm to optimize the connection weight matrix between the original hidden layer and the output layer. Thirdly, the grey prediction theory was used to predict major energy consumption in the region from 2017 to 2030. Then, the energy consumption and carbon emissions data for 2000–2016 were used as the training and test sets for the SVM-ELM (Support Vector Machine-Extreme Learning Machine) model. The result of SVM-ELM model was compared with the forecasting results of SVM (Support Vector Machine Algorithm) and ELM (Extreme Learning Machine) algorithm. The accuracy of SVM-ELM was shown to be higher. Finally, we used forecasting output of GM (Grey Prediction Theory) (1, 1) as the input of the SVM-ELM model to predict carbon emissions in the region from 2017 to 2030. The results showed that the proportion of energy consumption seriously affects the amount of carbon emissions. We found that the energy consumption of electricity and natural gas will reach 45% by 2030 and carbon emissions in the region can be controlled below 96.9 million tons. Therefore, accelerating the upgradation of industrial structure will be the key task for the government in controlling the amount of carbon emissions in the next step.

Description: With the orderly advancement of ‘China's energy development strategic action plan’, the natural gas industry has achieved unprecedented development. Currently, it is planned that by 2020, China’s natural gas consumption will account for at least 10% of the total primary energy consumption, have an orderly and improved energy structure, and achieved energy-saving and emission-reduction targets. Therefore, the accurate prediction of natural gas consumption becomes significantly important. Firstly, based on the research status of forecasting methods and the factors which affect natural gas consumption, this paper used the particle swarm optimization (PSO) algorithm to obtain the input layer weight, and used the optimized extreme learning machine (ELM) algorithm to obtain the hidden layer threshold; by using PSO-ELM as the base predictor and the AdaBoost algorithm, we have constructed the natural gas consumption integrated learning prediction model. Secondly, from the perspective of different provinces and industries, we deeply analyze the current status of natural gas consumption, and the random forest algorithm is used to extract the core influencing factors of natural gas consumption as the independent variables of the prediction model. Finally, data on China's natural gas consumption from 1995 to 2017 are selected, then the feasibility analysis and comparative analysis with other methods are performed. The results show: 1) Using the random forest algorithm to extract the core influencing factors, economic growth, population, household consumption and import dependence degree are significantly representative. 2) Based on the AdaBoost integrated learning algorithm, transforming the weak predictor with poor prediction effect into a strong predictor with strong prediction effect, compared with PSO-ELM、AdaBoost-ELM and ELM algorithm, with R-Square as 0.9999, Mean Square Error (MSE) as 0.8435，Mean Absolute Error (MAE) as 0.2379, Mean Absolute Percentage Error (MAPE) as 0.0008，effectively validated the significant effect of the AdaBoost-PSO-ELM prediction model. 3) Based on the AdaBoost-PSO-ELM prediction model, predict the natural gas core influencing factors and natural gas consumption in the year of 2018–2030. There is an apparent growth trend in the next 13 years, and the average growth rate of natural gas consumption has reached 7.68%.

Description: To make full use of distributed energy resources to meet load demand, this study aggregated wind power plants (WPPs), photovoltaic power generation (PV), small hydropower stations (SHSs), energy storage systems (ESSs), conventional gas turbines (CGTs) and incentive-based demand responses (IBDRs) into a virtual power plant (VPP) with price-based demand response (PBDR). Firstly, a basic scheduling model for the VPP was proposed in this study with the objective of the maximum operation revenue. Secondly, a risk aversion model for the VPP was constructed based on the conditional value at risk (CVaR) method and robust optimization theory considering the operating risk from WPP and PV. Thirdly, a solution methodology was constructed and three cases were considered for comparative analyses. Finally, an independent micro-grid on an industrial park in East China was utilized for an example analysis. The results show the following: (1) the proposed risk aversion scheduling model could cope with the uncertainty risk via a reasonable confidence degree β and robust coefficient Γ. When Γ ≤ 0.85 or Γ ≥ 0.95, a small uncertainty brought great risk, indicating that the risk attitude of the decision maker will affect the scheduling scheme of the VPP, and the decision maker belongs to the risk extreme aversion type. When Γ ∈ (0.85, 0.95), the decision-making scheme was in a stable state, the growth of β lead to the increase of CVaR, but the magnitude was not large. When the prediction error e was higher, the value of CVaR increased more when Γ increased by the same magnitude, which indicates that a lower prediction accuracy will amplify the uncertainty risk. (2) when the capacity ratio of (WPP, PV): ESS was higher than 1.5:1 and the peak-to-valley price gap was higher than 3:1, the values of revenue, VaR, and CVaR changed slower, indicating that both ESS and PBDR can improve the operating revenue, but the capacity scale of ESS and the peak-valley price gap need to be set properly, considering both economic benefits and operating risks. Therefore, the proposed risk aversion model could maximize the utilization of clean energy to obtain higher economic benefits while rationally controlling risks and provide reliable decision support for developing optimal operation plans for the VPP.

Description: To alleviate the shortcomings of large-scale grid connections for clean energy, which require stable thermoelectric units to provide backup services, a stable cooperative alliance among different energy types of power sellers must be established. Consequently, a reasonable method to distribute income is required, due to different contributions of each entity in the alliance. Therefore, this paper constructs a comprehensive correction algorithm for income distribution using an improved Shapely value method. We analyze the operating mode of the power seller, and establish the net income calculation model under both independent and alliance operations. We then establish an alliance operation optimization model that considers the constraints of unit output, as well as the balance between supply and demand, with the goal of maximizing income. Finally, an industrial park in a province of northern China is taken as an example to verify the model’s practicability and effectiveness. The results show that the power sales alliance can effectively promote clean energy consumption. The maximum reduction in thermal power generation and CO2 is 8510 MW and 684.515 tons, respectively. We apply the algorithm to income distribution and find that the thermal power seller’s income increased by ¥1,463,870, which enhances the stability of the alliance. Therefore, our income distributing optimization model guarantees the interests of each participant to the greatest extent, and serves as an important reference for income distribution.

Description: To achieve the commitment of carbon emission reduction in 2030 at the climate conference in Paris, it is an important task for China to decompose the carbon emission target among regions. In this paper, entropy maximization is brought to inter-provincial carbon emissions allocation via the Boltzmann distribution method, which provides guidelines for allocating carbon emissions permits among provinces. The research is mainly divided into three parts: (1) We develop the CO2 influence factor, including per capita GDP, per capita carbon emissions, carbon emission intensity and carbon emissions of per unit industrial added value; the proportion of the second industry; and the urbanization rate, to optimize the Boltzmann distribution model. (2) The probability of carbon emission reduction allocation in each province was calculated by the Boltzmann distribution model, and then the absolute emission reduction target was allocated among different provinces. (3) Comparing the distribution results with the actual carbon emission data in 2015, we then put forward the targeted development strategies for different provinces. Finally, suggestions were provided for CO2 emission permits allocation to optimize the national carbon emissions trading market in China.

Description: Recently, various Chinese provinces have greatly reduced their coal consumption due to new environmental protection policies. Because of these policies, the orderly development of the clean energy heating mode has been effectively promoted. As the problem of air pollution in the northern part of China is particularly prominent, adopting clean heating in winter is an important solution to control air pollution for those regions. However, there is a tricky balance to be struck between the government and the heating companies when it comes to using clean heating during winter. Therefore, it is crucial for the government and heating enterprises to research new strategies. Consequently, this paper carries out a comprehensive study on the multiple factors influencing the game relationship between the government and heating enterprises, and tries to set up a more general model for the theoretical analysis of mechanisms of clean heating promotion, as well as their numerical simulation. The research results show: (1) The initial possibilities available to government and heating enterprises have a significant impact on the final strategy choice for the heating system; (2) due to advantages such as increases in social benefits, subsidies, fines, and clean heating profits, as well as the lessening of traditional heating costs, and regardless of the decrease in traditional heating profits, it is possible for the government to adopt the promotion strategy; and (3) there are more opportunities for heating companies to pursue in order to implement clean heating strategies. In conclusion, this paper proposes valuable suggestions for the government and heating companies concerning clean heating in China.