With the development of smart distribution network and the proposal of dual carbon target, the importance of demand side management in improving the flexible operation of power system is becoming more and more prominent. In order to solve the problems of excessive load peak valley difference, insufficient utilization of demand side resources, and unreasonable pricing of aggregators, this paper propose an economic optimization scheme for aggregators based on electric vehicle three-stage dispatching. First, the loss aversion analysis is conducted on the willingness of electric vehicle users to participate in dispatching. The contract signing methods between aggregators and electric vehicles are divided into three categories: complete dispatching, rolling reward and punishment mechanism dispatching, and free dispatching. Next, the response model of electric vehicle users based on the improved cloud model is obtained. Then, the aggregators conduct three-phase optimal dispatching for electric vehicles according to the bid winning peak shaving capacity. Phase 1 according to the time of regional differences, the dispatching of reward power set rewards and punishment mechanism, phase 2 to determine the full freedom dispatching of electric vehicles, three kinds of dispatching and dynamic load capacity of electricity, phase 3 according to the phase 1 and 2, the amount of information and user loss aversion, the user response model of final rolling rewards and punishment mechanism, get the aggregators final pricing schemes, Finally, a numerical example is given to verify the feasibility of the proposed method.

The rapid increase in the proportion of new energy sources causes great changes in the structure and stability characteristics of the system, and a new type of oscillation appears with multi-mode and broad-band characteristics. In order to predict, locate and suppress broad-band oscillation, it is necessary to identify the mode parameters of broad-band oscillation signals accurately and quickly. First, Both low noise and high noise signals are simultaneously decomposed by VMD, a criterion based on permutation entropy (PE) is used to distinguish between high and low noise signals. Then, improved variational mode decomposition (IVMD) is used to efficiently denoise high noise signals. Finally, hilbert transform (HT) is used to identify decomposed modes, and short-time fourier transform (STFT) provides time-frequency characteristics to help restore oscillations. The simulation and actual oscillation data verify that both obvious oscillation signals with low noise and ambient signals with high noise are effectively identified, and results reveal that the proposed method outperforms the other similar methods in accuracy and time of use.

With the proposal of the carbon peaking and carbon neutrality goals, the number of electric vehicles is increasing day by day. Besides, the insufficient number of charging piles leads to the increase of the queuing time of electric vehicles, the pricing of aggregators and other issues becoming increasingly prominent. To solve the above problems, the aggregator pricing method of two-stage charging station allocation for electric vehicles is proposed. First, the contract signing mode between aggregators and electric vehicles is divided into three categories: complete dispatching, rolling reward and punishment mechanism dispatching, and free dispatching. Considering the impact of external factors on the energy consumption of electric vehicles, a road network model is established. Then, the improved A-star algorithm is used to solve the shortest path, in which the time factor is introduced into the evaluation function of A-star algorithm. At the same time, the evaluation function is improved according to traffic energy consumption, uninterrupted driving time, traffic light waiting time and other factors. The aggregator obtains the attraction of the charging station to the electric vehicle based on the charging and discharging demand of the electric vehicle and the Coulomb’s law, and then establishes the grid aggregator electric vehicle supply chain, and conducts reasonable pricing and charging station allocation in two stages. Finally, an example shows that this scheme can significantly improve the peak shaving efficiency, improve the profits of aggregators, and reduce the queuing time and traffic energy consumption of electric vehicles in terms of the distribution of electric vehicle charging stations and the pricing of aggregators.

As electric vehicles(EVs) begin to participate in the peak regulating auxiliary service market, it has become a major problem that how can price aggregators maximize the peak shaving capacity provided by EVs and maximize their own interests. This paper proposes a bargaining game pricing method based on the psychology cost of EVs and the risk assessment of aggregators. First of all, according to the impact of users’ participation in peak shaving on the battery life of EVs, the impact of participation in peak shaving on users’ original travel plans and time, and the impact of aggregator pricing on users’ psychology, the comprehensive psychology cost of EV users is obtained. Then, based on the user’s psychology cost and the law of gravitation, the evaluation scheme for the peak shaving capacity of EVs is obtained. On the basis of conditional value at risk(CVaR), the mixed CVaR is obtained by considering the behavior of users who may chase risks. Based on the mixed CVaR, the risk assessment of aggregators’ participation in the peak regulating auxiliary service market is carried out. According to the above information, the aggregators and the EV teams are engaged in a bargaining game based on the peak shaving pricing problem, which is divided into complete information game and incomplete information game. Finally, the feasibility of the proposed method is verified by an example analysis.