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• Combined with the error reconstruction rate, an adaptive VMD technology is developed and used for data preprocessing. • Two mainstream deep learning methods (LSTM, DBN) are used as sub-series predictors, and the classic PSO algorithm is utilized to determine the number of neurons in the DBN network. • A novel hybrid prediction model based on the PSO-DBN nonlinear combination mechanism is proposed. Wind power forecasting plays a vital role in enhancing the efficiency of power grid operation and increasing the competitiveness of power market. In this paper, a novel hybrid forecasting model is developed by using the decomposition strategy, nonlinear weighted combination, and two deep learning models to overcome the drawbacks of the linear weighted combination and further enhance wind power forecasting accuracy and stability. Firstly, the variational mode decomposition (VMD) technique is employed to decompose the original wind power series to extract local features. Then, the long short-term memory neural networks (LSTM) and deep belief networks based on particle swarm optimization (PSO-DBN) are utilized to construct sub-series prediction models. Finally, the multiple sub-series forecasting models are integrated by a nonlinear weighted combination method based on PSO-DBN to construct a hybrid model for short-term wind power forecasting. To verify the performance of the developed forecasting model, wind speed data of 10 min from a wind farm in Shaanxi Dingbian, China are selected as case studies. The results show that the proposed method in this paper is more effective than other existing methods. [ABSTRACT FROM AUTHOR]

• A two-layer optimization frame to estimate and improve the net profit of BESSs in the whole life cycle. • The semi-empirical degradation model of lithium-ion batteries and economic models of BESSs are embedded into the optimization frame. • The optimization frame applies to different lithium-ion batteries. • The optimal configuration and operation varied from the types of lithium-ion batteries. Battery energy storage systems (BESSs) have been widely employed on the user-side such as buildings, residential communities, and industrial sites due to their scalability, quick response, and design flexibility. However, cell degradation is caused by the charging and discharging of batteries, which reduces the economy of BESSs. For the optimal configuration and operation of BESSs, the battery degradation process should be integrated into the optimal problem by considering the equivalent full cycles over the optimization time horizon. This paper proposes a two-layer optimization frame to estimate and improve the net profit of BESSs in the whole life cycle, the outer layer optimizes the rated capacity and power of BESSs, and the inner layer optimizes the daily operation strategy of BESSs. To this end, the semi-empirical degradation model of lithium-ion batteries and economic models of BESSs are embedded into the optimization frame. Particle swarm optimization (PSO) algorithm and fmincon toolbox of MATLAB are adopted to solve the two-layer frame to maximize the net profit of BESSs. Simulation results of the BESS for a typical industrial user in China demonstrate that the proposed frame can effectively improve the net profit of BESSs. [ABSTRACT FROM AUTHOR]

• Gauss-Hermite integral based multi-point estimation method is proposed to calculate the probabilistic transient stability criterion derived through the PDSR. • To guarantee the calculation accuracy and speed at the same time, the optimal number for the sampling points is determined based on PDSR hyperplanes and improved particle swarm optimization algorithm (IPSO). • Extreme learning machine (ELM) is introduced to describe the uncertainty of practical wind power output. • Calculation accuracy of the proposed method is higher than other commonly-used point-estimation methods without much loss of computational efficiency under different wind power penetration and correlation levels. With the increasing penetration of wind power, probabilistic transient stability assessment becomes critical to deal with the uncertainties and correlations of wind power, thus improving the reliability of power system. This paper proposes an improved multi-point estimation based probabilistic transient stability assessment method. Extreme learning machine (ELM) is introduced to describe the uncertainty of practical wind power output. Gauss-Hermite integral based multi-point estimation method is proposed to calculate transient stability criterion derived through practical dynamic security region (PDSR). Optimal number of the sampling points is determined based on PDSR hyperplanes and an improved particle swarm optimization algorithm (IPSO). Test results on the New England 10-generators 39-buses system and a practical provincial power system in China show that the calculation accuracy of the proposed method is higher than that of other commonly-used point-estimation methods without much loss of computational efficiency under different wind power penetration and correlation levels. [ABSTRACT FROM AUTHOR]

• Optimize the electricity prices based on the DR characteristics of multi-type users. • Construct a flexible power retail portfolio optimization model. • Propose a flexible electricity retail strategy with integrated DR. Since the electricity market is opening up in China, it is necessary for power retailers to participate in the market and find a way to gain benefits. In this context, this paper constructs a two-stage flexible power sales optimization model with multi-type demand response strategies. First, the demand response strategies of different types of users are analyzed. Second, two-stage optimization is conducted: In the first stage, the fuzzy K-means clustering method is used to divide a day into peaks, flats, and valleys, and then, a multi-objective time-of-use pricing model is constructed with the aims of minimizing the peak–valley load ratio and maximizing the response revenue rate of users. A flexible power retailing portfolio optimization model is built considering multiple types of users in the second stage and the model is solved by the chaotic particle swarm optimization algorithm. Finally, a case study with four scenarios is conducted, where the demand response is taken as a changeable factor. The results show that (1) the price-based demand response can shave peaks and fill valleys, whilst also reducing the electricity cost, and optimization for industrial users has the optimal effect, followed by that of commercial users and agricultural users; (2) different types of demand responses have different emphases on peak shaving and valley filling, and a combination can achieve the best effect; (3) with the demand response, an electricity retailer can effectively reduce the deviation assessment cost, and increase the revenue by flexibly allocating electricity sales, without resulting in unsatisfied users. The conclusions verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

• A grey model with ISRU as the initial condition is proposed for electricity consumption forecast. • PSO is applied to determine the introduced coefficients to enhance calculation efficiency. • The model accuracy is validated by comparing with other improved prediction models. • Suggestions for the layout planning of future power system in China is proposed based on prediction. Accurate medium- and long-term power consumption forecast is the premise and foundation of secure scheduling and layout planning of power system, which are conducive to the coordinated development of various power generation channels so that renewable energy generation can be improved. In view of this problem, this paper analyzes power consumption intensity in different regions in China and proposes a modified grey model with novel initial condition to enhance its forecast accuracy. Influence of initial condition in grey model is analyzed synthetically by several commonly used models, and the Inverse Square Root Unit (ISRU) function with adjustable parameters is defined as the novel initial condition to better reveal the characteristics of data growth. Particle Swarm Optimization (PSO) algorithm is adopted to calculate the introduced coefficients. Using data available from National Bureau of Statistics of China from 2007 to 2014, consumption data of 31 provinces (municipalities) from 2015 to 2018 is forecasted to attest the efficiency and adaptability of ISRU-GM(1,1). Forecast results are compared with other improved prediction models as well. Potential power consumption ability in China in the next few years from provincial perspective is studied, which guides the layout planning of power system. [ABSTRACT FROM AUTHOR]

• Structured a wind/photovoltaic/gas microgrid with multi-agent form. • Propose a game model for capacity configuration of microgrids. • Uncertainty of wind power and solar power are taken into account. • The Nash Equilibrium is iterated out, and an optimal scheme is summarized. • Non-cooperation and cooperation game results are analyzed and compared. With the rapid growth of centralized utilization of large-scale renewable energy, to improve the stability of power grid and reduce operating costs, microgrid and renewable distributed generation (DG) has become the key direction of China. For a microgrid integrated with wind turbines, photovoltaic (PV) and micro-gas-turbine, the installed capacities need to be configured rationally to achieve economical and reliable power supply. In this paper, a structure of a microgrid based on multi-agent system (MAS) is established, and a game-theory-based optimization model is presented for the capacity configuration of these agents. The economic interests between the agents and their actions are analyzed by the game model. Besides, the interactions between microgrid and power grid, and the uncertainty of wind power and solar power are taken into account. The Nash Equilibrium of the game is worked out by particle swarm optimization, as the reference for the configuration of the agents. In the end, a wind/solar/gas microgrid model is used for the numerical tests to analyze and compare the effectiveness of the game optimization. The results show that under cooperative game, the investors' profits of wind turbines, PV and micro-gas-turbine increase by 6%, 19%, 88% compared with the non-cooperative game, while the total configured capacities decrease by 10%. [ABSTRACT FROM AUTHOR]