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Carbon markets have been established in many countries and regions with the goal of promoting global carbon neutrality. The position of power producers as the dominant carbon emitters necessitates that they engage in both electricity and carbon markets. However, most studies have considered only short-term electricity markets and unrealistically static carbon markets, and the speculative behavior of power producers in the carbon market remains poorly considered. The present study addresses these issues by proposing an optimized joint decision model based on information gap decision theory to facilitate the participation of power producers in annual and monthly electricity markets, and monthly carbon markets, where uncertainties in the prices of electricity and carbon quotas, and the speculative behavior of power producers in the carbon market are explicitly considered to ensure that the revenues of market participants do not fall below a predetermined minimum acceptable value. The results of simulations based on the rules and actual market data obtained for electricity and carbon markets in a specific province of China demonstrate that the proposed model provides power producers with trading solutions to meet different expected revenue targets, and thereby assists them as much as possible in counteracting the risks to profit associated with fluctuations in electricity and carbon market prices.

As the energy crisis and consumption revolution advance, traditional energy service providers gradually transform into customer-centric integrated energy service providers. However, the customer’s experience with energy depends on the quality. This paper proposes an energy quality model for heat load of the integrated heat and electricity energy systems, from which the derived energy quality factor for heat load participates as one of the objectives of the multi-objective optimization model. The considered heat transfer dynamics accurately describe the heat transmission through the heat network. Finally, the effectiveness of the proposed multi-objective optimal dispatching model is verified by numerical simulations, in which a decision-making model established based on exergy economics enables the selection of a compromise solution from the Pareto front as the basis for dispatching.

To maximize the expected profits and manage the risks of renewable energy system under electricity market environment, scenario-based- stochastic optimization model can be established to generate energy bidding strategies, in which the probabilistic scenarios of risk parameters are usually obtained by using statistical or machine learning methods. This paper proposes a practical multivariate statistical method for risk parameter scenario generation, which is used by a wind energy system faced with uncertain electricity prices and wind power productions, and it considers the correlation between dependent risk parameters by using historical data directly. The probabilities of scenarios containing correlated risk parameters are calculated by using multivariate histograms, in which the asymmetric correlation between different parameters existing in the historical data are preserved. Additionally, in order to make the stochastic optimization problem with large numbers of scenarios tractable, a multivariate scenario reduction method is used to trim down the scenario number. By solving the stochastic optimization problem, optimal day-ahead bidding curves for the wind energy system are generated, and Douglas–Peucker algorithm is used to fit the bidding curves according to market requirements. Case studies based on real world data in electricity markets are performed to prove the effectiveness of the proposed risk parameter scenario generation method and energy bidding strategies. Finally, conclusions and practical suggestions on future research works are provided.

The new energy storage, referring to new types of electrical energy storage other than pumped storage, has excellent value in the power system and can provide corresponding bids in various types of electricity markets. As the scale of new energy storage continues to grow, China has issued several policies to encourage its application and participation in electricity markets. It is urgent to establish market mechanisms well adapted to energy storage participation and study the operation strategy and profitability of energy storage. Based on the development of the electricity market in a provincial region of China, this paper designs mechanisms for independent energy storage to participate in various markets. Then, its current and future operation strategies by division time or capacity for participation in each type of market are analyzed, and the profitability under various scenarios is calculated. Finally, based on the calculation results, the theoretical analysis basis for developing independent energy storage in the province and the policy formulation of participation in the market is provided.