Weiqing Sun’s research while affiliated with University of Shanghai for Science and Technology and other places

Conducting electricity price prediction research has significance for the operation of the generation and transmission sides, and can guide the planning of electricity consumption. In order to further improve prediction accuracy, this paper constructs new feature based on publicly available market data, and uses feature filtering to find the feature data with the highest correlation with electricity prices in publicly available market data as input features. A model combining feature construction (FC), singular spectrum analysis (SSA), and LSTM is used for electricity price prediction. Compared with traditional LSTM models, this model reduced the MAE by 10.0, MAPE by 16.4%, and RMSE by 19.7 in the test set. This paper also proposes an error correction method for recursive prediction based on the error distribution in training and testing sets to reduce the influence of accumulated errors. The results show that the MAPE decreased by 6.1% in recursive prediction, proving that the model has good performance in prediction. By accurately predicting electricity prices and analyzing possible error ranges, the prediction method proposed in this article can better guide market participants in making decisions.

This paper proposes a pricing and scheduling method for shared mobile energy storage systems (SMSs) in coupled power distribution and transportation networks. Different from existing shared energy storage studies, which mostly focus on stationary resources, the paper investigates the SMS operation considering the negotiation of rental prices as well as mobility and charging/discharging among SMS owners and different users. Specifically, the SMS pricing and scheduling with variable renewable energy are established as a bilevel mixed-integer chance-constrained distributionally robust optimization problem. In the upper-level problem, the SMS owner determines pricing and day-ahead mobility strategy to maximize its payoff. In the lower-level problem, the SMS users, i.e., distribution grid operators, determine the SMS charging/discharging power according to the SMS day-ahead pricing results and intra-day distribution grid operation strategies for accommodating variable renewable energy. The distributionally robust chance constraint is designed to cope with the intra-day operational risk caused by the variability of renewable power generation. To cope with the solution difficulty in the proposed bilevel optimization problem, the chance constraint is reformulated as second-order cone constraints, which are further transformed into a set of linear constraints, and then the reformulated bilevel mixed-integer linear programming problem is decomposed and iteratively solved to avoid enumerating lower-level integer variables. Simulation results show that the utilization rate of SMS batteries is increased and the excess renewable power is fully consumed when SMSs are shared among different distribution grids. The proposed distributionally robust optimization achieves higher revenue for the SMS owner and smaller operating costs of distribution grids than robust optimization under uncertain environments.

To enable the regulation and utilization of electric vehicle (EV) load resources by the power grid in the electricity market environment, a third-party electric vehicle aggregator (EVA) must be introduced. The strategy of EVA participation in the electricity market must be studied. During operation, the EVA faces a double uncertainty in the market, namely, electricity demand and electricity price, and must optimize its market behavior to protect its own interests. To achieve this goal, we propose a robust pricing strategy for the EVA that takes into account the coordination of two-stage market behavior to enhance operational efficiency and risk resistance. A two-stage robust pricing strategy that takes into account uncertainty was established by first considering day-ahead pricing, day-ahead electricity purchases, real-time electricity management, and EV customer demand response for the EVA, and further considering the uncertainty in electricity demand and electricity prices. The two-stage robust pricing model was transformed into a two-stage mixed integer programming by linearization method and solved iteratively by the columns and constraints generation (CCG) algorithm. Simulation verification was carried out, and the results show that the proposed strategy fully considers the influence of price uncertainty factors, effectively avoids market risks, and improves the adaptability and economy of the EVA’s business strategy.

Bulk power grid interconnection and the access of various smart devices make the current grid highly complex. Timely and accurately identifying the power grid operation state is crucial for monitoring the operation stability of the power grid. For this purpose, an evaluation method of the power grid operation state based on random matrix theory and qualitative trend analysis is proposed. This method constructs two evaluation indicators based on the operation data of the power grid, which cannot only find out whether the current state of the power grid is stable but can also find out whether there is a bad operation trend in the current power grid. Compared with the traditional method, this method analyzes the power grid’s operation state from the big data perspective. It does not need to consider the complex network structure and operation mechanism of the actual power grid. Finally, the effectiveness and feasibility of the method are verified by the simulations of the IEEE 118-bus system.

The operation characteristics of energy storage can help the distribution network absorb more renewable energy while improving the safety and economy of the power system. Mobile energy storage systems (MESSs) have a broad application market compared with stationary energy storage systems and electric vehicles due to their flexible mobility and good dispatch ability. However, when urban traffic flows rise, the congested traffic environment will prolong the transit time of MESS, which will ultimately affect the operation state of the power networks and the economic benefits of MESS. This paper proposes a bi-level optimization model for the economic operation of MESS in coupled transportation-power networks, considering road congestion and the operation constraints of the power networks. The upper-level model depicts the daily operation scheme of MESS devised by the distribution network operator (DNO) in order to maximize the total revenue of the system. With fuzzy time windows and fuzzy road congestion indexes, the lower-level model optimizes the route for the transit problem of MESS. Therefore, road congestion that affects the transit time of MESS can be fully incorporated in the optimal operation scheme. Both the IEEE 33-bus distribution network and the 29-node transportation network are used to verify and examine the effectiveness of the proposed model. The simulation results demonstrate that the operation scheme of MESS will avoid the congestion period when considering road congestion. Besides, the transit energy consumption and the impact of the traffic environment on the economic benefits of MESS can be reduced.

In order to help achieve the goal of carbon peak and carbon neutrality, the large-scale development and application of clean renewable energy, like wind generation and solar power, will become an important power source in the future. Large-scale clean renewable energy generation has the uncertain characteristics of intermittency, randomness, and volatility, which brings great challenges to the balance regulation and flexible operation of the power system. In addition, the rapid development of renewable energy has led to strong fluctuations in electricity prices in the power market. To ensure the safe, reliable, and economic operation of the power system, how to improve the power system flexibility in an uncertain environment has become a research hotspot. Considering the uncertainties, this article analyzes and summarizes the research progress related to power system flexibility from the perspective of power system planning, operation, and the electricity market. Aiming at the modeling technology of uncertainty, the related modeling methods including stochastic programming, robust optimization, and distributionally robust optimization are summarized from the perspective of mathematics, and the application of these methods in power system flexibility is discussed.

The uncertainty of user-side resource response will affect the response quality and economic benefit of load aggregator (LA). Therefore, this paper regards the flexible user-side resources as a virtual energy storage (VES), and uses the traditional narrow sense energy storage (NSES) to alleviate the uncertainty of VES. In order to further enhance the competitive advantage of LA in electricity market transactions, the opertion mechanism of LA in day-ahead and real-time market is analyzed, respectively. Besides, truncated normal distribution is used to simulate the response accuracy of VES, and the response model of NSES is constructed at the same time. Then, the hierarchical market access index (HMAI) is introduced to quantify the risk of LA being eliminated in the market competition. Finally, combined with the priority response strategy of VES and HMAI, the capacity allocation model of NSES is established. As the capacity model is nonlinear, Monte Carlo simulation and adaptive particle swarm optimization algorithm are used to solve it. In order to verify the effectiveness of the model, the data from PJM market in the United States is used for testing. Simulation results show that the model established can provide the effective NSES capacity allocation strategy for LA to compensate the uncertainty of VES response, and the economic benefit of LA can be increased by 52.2% at its maximum. Through the reasonable NSES capacity allocation, LA is encouraged to improve its own resource level, thus forming a virtuous circle of market competition.

Real-time price(RTP) is one of the common methods for electricity retailers to adjust load demands and gain revenue from demand response(DR) programs. However, effectiveness and accuracy of the RTP directly affect the DR quantity, which reflects as revenue returns in DR programs. A method is proposed to maximize profit of the electricity retailer by configurating energy storage system(ESS) and coordinate the operation of ESS with RTP to participate in DR programs, which enriches the profitability measures of retailers. Firstly, an improved CNN-LSTM algorithm is utilized to establish the DR dynamic characteristic model with expected consumption as input and RTP as output. Thus, the demand curve during each period can be generated, from which optimal RTP with maximum profit of the retailer can be estimated. Secondly, a bi-level optimization model is established to maximize the profit from the DR program in a whole day. The upper level real-time pricing model takes maximum profit of the retailer as the objective by optimizing the RTP with fixed ESS configuration. The lower level ESS configuration model takes maximum profit of ESS as the objective with RTP fixed by the upper level model, from which the rated power, capacity and daily operation strategy of ESS can be optimized and sent to the upper level model. Finally, the optimal profit gained by the retailer can obtained by iterative calculation between the upper and lower level models. Based on the historical data from PJM market, the case demonstrates that the algorithm proposed can describe the DR dynamic characteristic effectively and accurately. Moreover, by configurating ESS, the retailer’s profit extra increases by 7.19%.