Security constrained unit commitment (SCUC) is the most widely used model in power grid clearing scenarios. A solution framework for the super large SCUC spot market clearing problem is established. Firstly, the time decoupling solution method for SCUC problem is introduced, which is intended to reduce the size of the problem to accelerate the solving speed. Then a Lagrangian relaxation technique is proposed to solve the time decoupled subproblem, which effectively reduces the difficulty of solving key complex constraints without affecting the accuracy of the solution. Numerical experiments show that the proposed framework greatly improves the solution efficiency and is very stable.

To promote the construction of a unified national power market system and form a diversified competitive power market environment, it is necessary to clarify the regional electricity market modes which can be coordinated with the operation of national, regional, provincial markets. Based on the typical transaction patterns under the framework of regional competitive electricity trades, five typical regional competitive electricity trade modes are summarized and analyzed. Besides, the corresponding market operation mechanisms, social welfare, characteristics and advantages and disadvantages of different models are analyzed. Theoretical analysis and modeling calculation results indicate that the "single market, two-tier clearing" mode and the "single market, unified clearing" mode will have the same efficiency only under several certain conditions. From the perspective of social welfare, the "single market, unified clearing" mode is better to be chosen as the goal for regional unified power market construction. Moreover, a social welfare redistribution mechanism that matches the "single market, unified clearing" model is proposed, providing decision-making reference for the orderly promotion of the construction of a unified power market.

The deepening of power market reform puts forward new requirements for risk management control of power grid. By summarizing the risk management and control measures of PJM（Pennsylvania-New Jersey-Maryland） power market, this paper analyzes the physical operation risk management and control measures of PJM system from two aspects: long-term and short-term, along with real-time measures before the occurrence of the risk accident, and emergency interruption and recovery measures after the occurrence of risk accident; this paper analyzes its risk management and control measures based on power market from four aspects: day-ahead reliability unit commitment, ancillary service market optimized jointly with electric energy market, medium and long-term capacity market and transmission right market. Compared with the existing risk control measures in China Southern Power Grid, this paper proposes a series of suggestions, such as conducting multi-period risk assessment, adopting the risk control means combining dispatch with market, constructing a complete market system, and establishing a risk management committee, etc.

Fair and effective distribution and trading of cross-province and cross-regional transmission rights are the basis for optimizing the allocation of power resources on a larger scale. As one of the earliest regions to start the reform of electricity marketization, Europe′s experiences and practices of the long-term cross-border transmission right market have certain reference significance for China′s future construction of the transmission right market. Firstrly, the development status and evolution path of the European long-term cross-border transmission right market are reviewed, and then the European long-term cross-border transmission right mechanism is systematically combed, specifically including product design, transaction organization and key links. Finally, based on the actual situation in China, suggestions for the construction of China′s transmission rights market are proposed.

In order to improve the accuracy of ramp capacity demand analysis of new power system, a method for estimating the ramp capacity demand taking into account the time-variant correlation characteristics of net power load forecast errors is proposed. Firstly, the composition of ramp capacity requirements is clarified, and the relationship between ramp capacity requirements and net power load forecast errors is analyzed. Then, the dynamic Copula function of the net power load forecast errors and the forecast values is established. The time series correlation of the parameters of the Copula function is constructed through the evolution equation. The conditional probability theory is applied to establish the probability distribution model of the ramping demand due to the net load forecast errors, and the confidence interval is used to discretize the estimation result of the ramp capacity demand probability distribution,so as to obtain the corresponding ramp-up capacity requirements. Finally, based on the actual operation data of a power grid in the west of China, three different calculation models are constructed based on whether the time-variant correlation characteristics of forecast errors are considered. The effectiveness and feasibility of the methods proposed in this paper are verified by the results of the example.

With the increasing penetration rate of power generation from a high proportion of renewable resources, energy storage plays an increasingly important role in the process of stabilizing uncertainty and volatility in the new power system. In order to encourage them to better participate in the operation of the power grid, it is necessary to pay more attention to the applicability of the market mechanism to energy storage market entities when allocating energy storage resources in a market-oriented manner. Considering that the existing mechanism fails to precisely reflect the technical characteristics of energy storage and the operating costs when participating in different types of markets, this paper proposes a joint clearing model of electric energy and frequency modulation auxiliary service market based on that energy storage participates in the market in a flexible energy operating state. To describe the energy loss of energy storage in the process of frequency modulation, the new model introduces the frequency modulation energy loss coefficient to further model the energy coupling relationship between these two different markets. Finally, the effectiveness of the model in reflecting the operation characteristics of energy storage is verified by simulation comparison.

With the increasing grid-connected capacity of wind power generation, the frequency regulation capacity provided by traditional energy sources can no longer meet the needs of system frequency regulation. The current policy allows wind power to participate in system frequency regulation, but the existing frequency regulation auxiliary service mechanism cannot effectively measure the true capacity of wind power participating in frequency regulation, which brings severe challenges to the safe and stable operation of the system. On the basis of the existing frequency regulation auxiliary service mechanism, this paper introduces the wind power frequency regulation credibility index and the comprehensive frequency regulation performance index to adjust the quotations of the main bodies of the frequency regulation market to measure the true capacity of wind power participation in frequency regulation, and at the same time optimizes the compensation detail rules of the existing frequency regulation auxiliary service mechanism. For the above optimization part, the clearing model of wind power participation in the frequency regulation market is established. The results of the example show that the optimized mechanism can effectively measure the true frequency regulation capacity provided by wind power, and promote prioritizing the clearing of high-quality frequency regulation resources. Meanwhile, wind power can improve the frequency regulation credibility index and the comprehensive frequency regulation performance index to obtain higher frequency regulation income.

With the release of the new electricity price policy, the electricity market will be further opened, and the cancellation of catalogue electricity price will accelerate the establishment of time-of-use （TOU） electricity price mechanism. Under this background, how to take effective TOU electricity price measures for power users is an urgent problem to be solved. Therefore, based on the analysis of the electricity consumption characteristics of high-energy consumption industrial users such as iron and steel, calcium carbide in Hunan Province, combined with the costs and benefits of electricity selling companies and industrial users, cost-benefit model for electricity selling companies and a load transfer cost model for industrial users are constructed. Furthermore, a double-layer optimization model for peak and valley TOU electricity prices of electricity selling companies is proposed, with the upper level model optimization goal of maximizing the profits of electricity selling companies, and the optimization objective of the lower model is to minimize the electricity cost of industrial users, so as to determine the peak-valley TOU tariff package. Taking the data of high energy consumption industrial users such as iron and steel, calcium carbide as an example, the simulation calculation results show that the peak-valley TOU tariff package obtained by the bi-level optimization model can ensure the interests of both sides, and effectively guide industrial users to use electricity reasonably and realize peak load shifting.

Under the background of "carbon peaking and carbon neutrality", the further promotion of distributed renewable energy is one of the important ways of low-carbon power system. In order to consume large amount of distributed renewable energy on the user side, a peer-to-peer （P2P） transaction and cloud energy storage joint market is proposed to maximize the market revenue of energy sellers and minimize the consumption cost of energy buyers. Firstly, the relationship between energy buyers and sellers is modeled as a non-cooperative game. Secondly, considering the limitation of the total capacity of cloud energy storage, the Nash equilibrium of energy sellers and the generalized Nash equilibrium of energy buyers are solved. Then, the optimal allocation scheme of cooperative surplus of cloud energy storage is solved based on nucleus. Finally, the community microgrid in Ireland is used as an example to verify the superiority of the proposed peer-to-peer transaction and cloud energy storage joint market. The proposed joint market model can maximize the market returns of both buyers and sellers and realize Pareto improvement in the benefits of market participants. Compared with the participants in the traditional spot market, the power purchase costs of the participants in the P2P transaction in the comparison experiment is reduced by 39.68%. The cloud energy storage market reduces the total cost of the traditional market participants by 27.4%. Therefore, coupling the P2P energy trading with the cloud energy storage market can greatly reduce the users’ power generation and purchase costs, which has great practical application value.

With the gradual marketization of energy trading, the retail price pricing strategy of microgrid service provider in a multi-microgrid system including electric-gas-heat will affect the operation of the system and the interests of all participants. In order to study the pricing strategy of microgrid service providers, this paper firstly describes the internal transaction process of the electric-gas-heat multi-microgrid system and establishes the system model. This pricing problem is then described as a Stackelberg game, and it shows that there is a unique equilibrium point for this game. In order to protect the privacy of each subject, this paper proposes a solution method based on reinforcement learning to solve the Stackelberg game with time coupling. The case study shows that this method can accurately and effectively solve the proposed pricing strategy problem, and the microgrid service providers and all the microgrids have adopted effective strategies to ensure their own interests. At the same time, the method effectively protects the privacy of market participants and exhibits good computing performance.

With the construction requirements of new power systems and the gradual promotion of marketization, an integrated energy operator (IEO) is required to make reasonable pricing strategies to supply energy to energy users (EU) and obtain operational revenue. To reveal the influence law of information asymmetry and its extent on the evolution of IEO pricing behavior strategy, a Stackelberg master-slave game pricing model is built with IEO as the dominant pricing decision-maker and EU load adjustment as the follower. Information gap decision theory (IGDT) and robust game method are introduced to characterize and solve the impact of information asymmetry on IEO-EU pricing game behavior, respectively. Combined with practical examples, this paper analyzes the influence of information asymmetry on IEO pricing revenue and EU revenue, studies the quantitative relationship between information asymmetry and IEO optimal pricing, and proposes an IEO robust pricing strategy considering information asymmetry. The algorithm analysis shows that IEO can take advantage of information to seek overpricing in the case of information asymmetry. Excess profit goes up with the increase of information asymmetry degree. Government regulators should improve the information disclosure and enhance information disclosure requirements of IEOs, as well as enhance the EU′s awareness of energy prices and energy costs.

In order to effectively transmit the cost of primary energy, all industrial and commercial users have now entered the power market. The users who do not meet the requirements for direct market transactions are represented by the power grid agency to purchase electricity and participate in the market, gradually narrowing the scope of the power grid agency purchasing. To meet the current market demand, it is necessary to design the optimal contract for purchasing power through the power gird agency, considering the decision of users to choose power market agency and incentivizing users to authorize power sales companies to participate in the market. According to the principal-agent theory, the optimal contract of the power grid agency is proposed based on the double-layer principal-agent. The upper level is grid-user principal-agent model, which describes the optimal contract design of the power grid under the objective function of maximizing the users′ income. The lower level is user-retailer principal-agent model, which describes the game behavior of users authorizing power sales companies to participate in the market, which is used as the users' incentive constraint of the upper model. Finally, the usability of the proposed model is verified through a numerical example. Based on the analysis of the impact on model parameters and optimization results, suggestions of model parameter calculation and different power grid agency purchase contracts based on users profiling application are proposed.

This paper focuses on the bi-level optimization problem of biddings and clearing of power suppliers under the coupling of electricity, carbon, and green certificate market. Firstly, a bi-level optimization decision model of multi-entities under the coupling of electricity market, carbon market and green certificate market is formulated, in which the constraints of DC power flow, the piecewise linear bidding method and the inverse-demand linear relationship between volume and price of green certificates are considered. The model can help power suppliers optimize their bidding strategies and improve their ability to benefit from competition. Secondly, a bi-level algorithm based on the optimal value function approximation is proposed to solve the problem. The algorithm constructs the optimal value function of the lower model based on the polynomial basis and least square method, and then converts the bi-level optimization model into a single-level optimization model. Different from the traditional Karush-Kuhn-Tucker method, this method does not introduce new integer variables when simplifying the model, and can achieve fast solution of the model. Finally, simulation analysis verifies the rationality of the proposed model and the effectiveness of the proposed algorithm.

Under the background of dual carbon target and power system transformation, the trading strategy of power generation enterprises will change significantly. Based on the implementation of renewable energy quota system, the optimal decision for power generation enterprises to participate in the electricity market is explored by considering both the carbon emission right market and the green license market comprehensively. Firstly, the market structure of market participants' participation in transactions under the condition of multiple policies is set up to clarify the relationship between supply and demand in each market. Then, the profit functions of power generation enterprises are established. Taking the goal of maxing the profits of power generation enterprises, the optimal production and trade model of enterprises is established under the non-cooperative game and co-operative game respectively. Finally, an example is given to verify the transaction model and provide a basis for power generation enterprises to participate in electricity market transactions under the influence of various policies.

In the context of the launch of the carbon market, power producers need to participate in both the carbon and electricity markets, which can affect the equilibrium of electricity market. In order to study the trading decisions of power producers in the carbon and electricity markets and the interactive influence relationship between these two markets, a two-layer equilibrium model for the oligopoly electricity market considering carbon market trading, which considers the gaming behaviors of power producers in the electricity and carbon markets. For the established model, the co-evolutionary algorithm is used to solve the Nash equilibrium. Finally, the validity of the relevant models and algorithms is verified via case studies, and the impacts of the carbon market on the equilibrium of electricity market and the gaming behavior of the power producers market are mainly studied. The results show that, in the current case of using the baseline method for initial carbon allowance allocation, the strategic behaviors of power producers raise both the electricity and carbon prices, where the increase amplitude of electricity prices is much smaller than the increase of carbon prices.

The accurate, objective and effective method for accounting carbon emission is an urgent need to serve the goal of "carbon peak, carbon neutrality" and support scientific carbon emission reduction decisions. At present, indirect carbon emission has been included in several provincial carbon emission trading markets, and it is also an important emission source of carbon emission statistical accounting by all levels of administrative agencies. Therefore, the carbon emission factor of the power grid has a great impact on the overall situation of the national carbon emission reduction work, so it is of great significance to timely improve the carbon emission factor calculation method according to the needs of the new situation. Firstly, this paper discusses the concept and application scope of the carbon emission factor. Then the development of international carbon emission factors is summarized. Based on the current commonly used carbon emission factor calculation method in China, optimization suggestions are put forward from the aspects of accounting cycle and accounting scope, and the power grid carbon emission factor typical algorithm considering the impact of green power consumption is designed. The result of an example shows that the proposed algorithm can more accurately reflect the indirect carbon emission level reflecting users′ electricity consumption behavior, and promote the development of green power consumption more effectively.