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The orderly release of electricity plan is an important measure for China’s electricity market reform which also lead to excessive pressure for coal power units caused by the low planned electricity quantity. Allowing high-priced units to participate in the market can effectively alleviate the market pressure. However, the participation of high-priced units in electricity market will have an impact on the declaration behavior of other generators. In this context, an evolutionary game model of bidding for two groups of generators considering the market liberalizing degree (MLD) is proposed. Above all, the profit model of two kinds of generators is proposed under two bidding strategies considering the MLD. On this basis, an evolutionary game model of generators’ bidding is built to qualitatively analyze the market equilibrium solution which is affected by the MLD coefficient. Thus, the expected evolutionary stable equilibrium of bidding can be formed spontaneously in electricity market by adjusting the MLD coefficient. The case studies are performed for demonstrating the proposed evolutionary game model of generators’ bidding, and simulation results show that different MLD and the initial state of bidding behavior of generators will influence the final evolutionary stable equilibrium of bidding market, and it also shown that the government can make the electricity market develop towards​ the expected state by adjusting MLD and supervising the bidding behavior of generators.

An energy deviation settlement (EDS) mechanism has been proposed to settle the deviation between consumption and energy contracts in China's forward electricity market. Due to high EDS costs of electricity retailers, a deviation mutual insurance (DMI) mechanism is put forward, representing an option for electricity retailers to reduce the deviation through contract transfer. Given this background, an optimal alliance strategy is proposed to maximize the utility of different types of individual electricity retailers. The alliance income model considers the EDS cost reduction and the cooperation cost analyzed based on transaction cost theory (TCT). An improved income allocation method, which considers distribution indexes selected by game theory and resource dependence theory (RDT), is proposed to ensure the comprehensive and reasonable allocation of alliance income. Considering the expected income and financial risk, an evaluation model using conditional value at risk (CVaR) as the financial risk assessment indicator is presented to measure the stability of alliances. Simulations based on data from a provincial electricity market in China illustrate that the EDS costs of retailers can be reduced by selecting appropriate partners for alliance. In addition, the proposed alliance income allocation method is more reasonable and more targeted than the cooperative game-based methods.

The orderly deregulation of planned electricity generation and consumption is an important measure for electricity market reform in several countries (such as China), and a reasonable proportion of planned electricity in the total energy consumption is conducive to the smooth transition from the regulation mode to the market mode. Under the plan-market double-track mechanism (PMDM) implemented, a modified linear bidding function of generation companies (GCs) is first proposed, and the unified clearing price of unilateral generation market is determined accordingly. Second, considering the robust bidding strategies of generation companies, a bi-level optimal planned electricity allocation model for power exchange (PX) is constructed. In the upper level, the proportion of planned electricity is optimized by PX to minimize the CVaR of Lerner Index (LI), so as to maintain the market power at a low level. In the lower level, based on the robust optimization theory and the prediction of rival bidding, the bidding strategy of a GC is optimized by solving a specified max–min problem. Simulations based on data from a provincial electricity market in China illustrate that the market power can be reduced through a reasonable proportion of planned electricity designed by the PX. In addition, when more GCs tend to avoid a market risk, the proportion of planned electricity can be increased accordingly.

The increasing capacity of the grid-connected solar system has negative influence on the power grid. This paper focuses on the quality evaluation of the solar power. The quality of the solar power is quantified by the penalty function, which considers the total output, the output fluctuation and the forecast error. Firstly, the K-means clustering technique to cluster the sample data of the indices is applied. Then, the significance test using the F-distribution is applied to validate the effectiveness of the proposed model. Finally, the penalty function is proposed. The logarithmic function, the power function, and the exponential function are applied to compare the influences of each index on the penalty function. The Analytic Hierarchy Process (AHP) and the entropy method are used to determine the weight coefficients. The feasibility and application value of the proposed model are validated by the numerical results.