Decarbonization of the whole energy chain has been recognized as a measure to tackle the global challenge of climate change, and significant progress has already been made on the generation side to integrate renewable energy. However, the demand side is the single largest underlying factor in shaping decarbonization roadmap. Hence, the carbon emission cost should also be shared by the users according to their power consumption. In this paper, a joint electricity-carbon trading framework is designed to reduce the carbon emission through trading and demand response. A delayed carbon emission liability settlement for asynchronous markets is proposed to ameliorate the users' optimal decision from single-point optimization to interval-based optimization. To develop the optimal strategy of trading within the proposed mechanism, an improved proximal policy optimization (PPO) algorithm based on Monte Carlo reward sampling is applied. Simulation studies reveal that, compared with the market without carbon trading and users without delayed settlement, the proposed mechanism has achieved a carbon emission reduction by 40.7% and 12.7% respectively. Simulations also show the algorithm's training efficiency can be significantly improved with the proposed Monte Carlo sampling method.

With the explosion of on-road EVs, private EVs become the most majority of the EV population, and also turns out to have the most flexible dispatching potential for power system. Accurate potential analysis of the flexibility has great significance for charging infrastructure upgrade, power system operation and market mechanism design. A probabilistic evaluation method of household EVs dispatching potential is proposed here considering users' multiple travel needs. Based on a realistic investigation on driving pattern of domestic cars, the probability distributions of multiple trip parameters are fitted with a proposed least-square-estimation based parameter optimization method. Sequentially, the sampling of random trip parameters is conducted considering the plug-in rate and coupling characteristics of trip parameters with a copula based sampling method. Based on evaluating the shifting potential of EV charging load, the influence on the shifted load, brought by the implementation of time-of-use (TOU) price mechanism, is quantified by taking residential energy-consumption behavior into account. Numerical result shows the accuracy and rationality to simulate the charging load considering multiple daily travel needs and the effectiveness of TOU price on EV load shifting based on the dispatching potential evaluation.

Iron and steel industry is the pillar industry of China, providing a solid foundation for China's industrialization. While steel plants are energy intensive mainly consuming coal and electricity, and their power consumption tends to be higher proportional in the near future, due to global confidence of peak and neutrality carbon dioxide emission, electrical substitution, and ongoing energy use right transaction. Thus, these plants will have larger potential to participate in the friendly interaction between power supply and demand. The adjustable potential of purchased power load exists in two aspects: one is the controllable resources of inner energy supply and use system, and the other is the energy schedule regulation accompanied by production process adjustment, which is rarely considered in current energy management research of steel plants. Based on the flexibility analysis of production process control without affecting productivity, the adjustment model of steel-rolling lines, air separation and electric arc furnaces on iron-making line is formulated here, as well as the corresponding energy consumed in these processes. Combined with the optimization of multienergy coupling system and production processes, a joint evaluation model of maximum dispatching potential and duration is proposed and solved by mixed-integer linear programming solver. Simulation result shows the feasibility of the proposed dispatching potential evaluation method and the effectiveness to take the production process adjustment with energy-systemscheduling.

Economic decision-makingof electric power system plays a vital role in the planning, operation, and transaction of the electric power system. While the achievement of behavioral economics tells us that all the decision-making process with human involvement is not that simple. Limited to human's subjectivity on cognition and decision preference, the decision will become bounded rational. Sufficient knowledge on the behavior of power system economic decisions can improve the accuracy and practicability of decision-making modeling, simulation, and forecasting in power system, as well as improve the social elements in developing a cyber-physical-social system of the power system. Among different behavioral economic theories, prospect theory (PT) wins a wide range of research and application. Thus, PT is first introduced here, and then the application of PT is reviewed in the field of power system economic decisions. What follows is the analysis and discussion on three key issues of current theory application, including function model selection, parameter identification, and application framework establishment. Finally, three possible further research prospects are proposed, including the application of developed theory, the combination of multiple behavioral economics, and the consideration of social elements.