The fixed service charge pricing model adopted by traditional electric vehicle aggregators (EVAs) is difficult to effectively guide the demand side resources to respond to the power market price signal. At the same time, real-time pricing strategy can flexibly reflect the situation of market supply and demand, shift the charging load of electric vehicles (EVs), reduce the negative impact of disorderly charging on the stable operation of power systems, and fully tap the economic potential of EVA participating in the power market. Based on the historical behavior data of EVs, this paper considers various market factors such as peak-valley time-of-use tariff, demand-side response mode and deviation balance of spot market to formulate the objective function of EVA comprehensive revenue maximization and establishes a quarter-hourly vehicle-to-grid (V2G) dynamic time-sharing pricing model based on deep deterministic policy gradient (DDPG) reinforcement learning algorithm. The EVA yield difference between peak-valley time-of-use tariff and hourly pricing strategy under the same algorithm is compared through the case studies. The results show that the scheme with higher pricing frequency can guide the charging behavior of users more effectively, tap the economic potential of power market to a greater extent, and calm the load fluctuation of power grid.

The rapid development of renewable energy power has improved global energy and environmental problems. However, with the high volatility of renewable energy, it is an important challenge to guarantee the consumption of renewable energy and the reliable operation of high percentage renewable energy power systems. To solve this problem, this paper proposes a tracking absorption strategy for renewable energy based on the interaction between the supply side and the demand side, which adjusts the charging process of electric vehicles (EVs) through electric vehicle aggregator (EVA) to realize the tracking absorption of renewable energy abandoned electricity. In view of this process, we analyze the interaction among power grid, EVA and renewable energy generation (REG) as well as their market characteristics. The master-slave game model of EVA and REG was constructed considering the charging behavior characteristics of EVs and the output characteristics of REGs. Then the model solving strategy based on soft actor-critic (SAC) algorithm is proposed, and the REG pricing strategy and EVA scheduling strategy are calculated to optimize the mutual benefits. The case analysis shows that, under the same scale of electric vehicles, the proposed method can promote about 93.89% of the power abandonment consumption of wind power system, 96.00% of the photovoltaic system, and 97.41% of the wind-solar system. This strategy reduces the electricity purchase cost of EVA, promotes the interaction among renewable energy, vehicles and power grid, and improves the utilization efficiency of renewable energy.

The further liberalization of China's electricity market encourages demand-side entities to participate in electricity market transactions. Electric vehicles (EVs) are developing rapidly and have high regulating potential, and are the main force for demand-side participation in the auxiliary service market. Aiming at the problems of dispatching accuracy and economy in EV participation in auxiliary service market, this paper analyzes the bidding strategy and dispatching scheme of EV-storage participation in auxiliary service market, and proposes EV-storage optimal allocation strategy with the goal of economic optimum. In the process of optimal allocation, based on the market rules of third-party subject participation in auxiliary services, the bidding strategy of EV-storage coordinated EV participation in auxiliary services market considering daily load scale changes is designed, while the conditional value at risk (CVaR) method is used to determine the short-term coordinated energy storage capacity and efficiency storage capacity considering the uncertainty of EV response situation; then, based on the annual EV load scale change, the benefit calculation function is constructed by considering various factors such as auxiliary service market revenue, spread revenue, investment cost and market opportunity cost of EV-storage participation in the auxiliary market. Finally, taking EV aggregation participation in the valley-filling ancillary service market as an example, it is verified that the strategy proposed in this paper can effectively improve the responsiveness of EV participation in the ancillary service market and increase the revenue of electric vehicle aggregator (EVA).

With the continuous improvement of new energy penetration in the power system, the price of the spot market of power frequently fluctuates greatly, which damages the income of a large number of thermal power enterprises. In order to lock in the profit, thermal power enterprises should turn the main target of profit to the medium and long-term power market. With the continuous advancement of the reform in China's power system, major changes have taken place in the medium and long-term power transactions, including the transaction target, organization method, clearing method and so on, so it is urgent to explore the quotation strategy of thermal power enterprises under the medium and long term market changes. Based on the theory of game equilibrium, this paper establishes non-cooperative game and cooperative game models between thermal power companies. Considering that the traditional reinforcement learning method is difficult to solve the multi-agent incomplete information game model, this paper uses the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to solve the above model. Finally, the validity of the proposed model is proved by a numerical example. The results show that, compared with other reinforcement learning algorithms, when solving the multi-agent incomplete information game model, the quotation obtained by MADDPG is more accurate, the revenue is increased by 5.2%, and the convergence time is reduced by 50%.In addition, this paper finds that in the medium and long-term power market, thermal power companies are more inclined to use physical retention methods to make profits. The greater the market power of thermal power companies, the greater the probability of physical retention. When low-cost thermal power companies retain more power, they will increase market clearing electricity prices and harm market efficiency. Regulators should focus on the market behavior of such thermal power companies.

Considerable research efforts have been expended in modeling load-sharing systems. The existing models, however, have various limitations, such as being limited to the exponential time-to-failure distribution, constant component performances, or performances without constraints. In this article, we make contributions by modeling a dynamic load-sharing system (DLSS), where the performance of each component is dynamic according to prespecified load-sharing principles and is limited by its capacity constraint. Moreover, the capacity constraint of a component can reduce due to degradations. In the proposed model, increasing failure rates are also involved since the surviving components must share the load of the failed component and continue working with increasing stresses. When the desired performance for a component exceeds the limitation, the entire system fails. An extended Markov process (EMP) method is proposed for evaluating the reliability of the considered DLSS with nonrepairable components. The proposed analytical method is flexible in handling arbitrary component time-to-failure distributions and in handling diverse load allocation mechanisms. Numerical studies of a power transmission system and a water transmission system are provided to validate the proposed method and its advantages. Effects of several model parameters are also investigated through case studies.

Under the perspective of carbon neutrality, the green electricity absorption target constrained by the quota system policy plays a crucial role in reducing the carbon emission of the power industry. However, the current green certificate policy has not achieved good results. On the premise of reducing the additional market burden as much as possible, the policy parameters should take into account the influence of market behavior to formulate better policy parameters in line with China's carbon emission peak goal. This paper constructs a combined hierarchical reinforcement learning with off-policy correction and multi-agent deep deterministic policy gradient algorithm (HIRO-MADDPG). It realizes the benefit analysis of the existing policy parameters joint with the solution of the optimal policy parameters. The algorithm solves the problem that benefit analysis and parameter formulation cannot be jointly trained and improves the precision. The results indicate: 1) HIRO-MADDPG algorithm can reach the highest policy benefits on the premise of maintaining market fairness; 2) under the new optimal policy parameters, the income per kilowatt hour of thermal power generator(TPG) and renewable power generator(RPG) can be maintained at 10% under the condition of abolishing subsidies; 3) with the help of the new policy parameters, China's power sector will reach the peak of carbon emissions from coal-fired power plants in 2026 ahead of schedule, and reduce carbon emissions by a further 11% by 2030.