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Energy sharing has the potential to decrease operation costs by exploiting the complementarity of different entities. The further increase of microgrids’ generation capacity in the distribution network could trigger the emergence of MW-level microgrids aggregator (MGA) to participate in the regional energy sharing market (ESM) and sell excess energy. However, the uncertainty of renewable energy generation will cause the failure of energy sharing and an economic loss for the energy sharing market. Therefore, a novel two-sided market with the clearing price and risk management is proposed in this paper. In order to optimize the energy sharing strategy of MGAs, a network-constrained risk-averse two-stage stochastic game model based on DC optimal power flow (OPF) is established. The objective of the MGA is to maximize the revenue in the ESM while the subtransmission network operator (SNO) aims to achieve regional energy self-sufficiency. The sample average approximation (SAA) method is employed to approximate the stochastic Cournot-Nash equilibrium. A distributed market clearing algorithm based on the alternating direction method of multipliers (ADMM) and best response seeking is developed to solve the optimal bidding problem of MGAs and the optimal dispatch problem of the SNO. Furthermore, the existence of the SAA Nash equilibrium is also investigated. Numerical simulations prove that the proposed game model can effectively control the overbidding risk and promote regional energy self-sufficiency. [ABSTRACT FROM AUTHOR]

Steel production is one of the most energy-intensive industries on demand side. Highly distributed energy resource-penetrated multi-energy microgrids (MEMGs) with combined heat and power (CHP) units can supply both electricity and heat while the by-product coal gases during manufacturing can be reused for onsite power supply. However, there is a lack of coordination between steel production and MEMG operation, and the steelmaking process is not fully modelled. Thus, this paper proposes a three-stage coordinated operation method for steel plant-based MEMGs, aiming to minimize the total operating cost. In this method, the steel production is scheduled weekly-ahead to meet the production demand considering carbon emission reduction. Then, the CHP commitment and day-ahead energy transaction are optimized in a day-ahead stage, while the dispatchable device output and intraday energy transaction are determined hourly-ahead based on uncertainty realizations. Accordingly, the steel production is modelled as continuous and discontinuous processes in parallel or series. To tackle the uncertainty of renewable generation, a scenario-based stochastic optimization method is utilized. Moreover, different carbon prices are applied to investigate their effects on steel production. The results show that the proposed method can decrease the operating cost by 14.33% and 1.45% compared with the other two conventional methods. [Display omitted] • Three-stage steel plant MEMG low-carbon operation coordinated with process optimization. • Generalized continuous/discontinuous process modelling for steel production. • Uncertain intervals of renewable generation tackled with SO and SAA in different stage. [ABSTRACT FROM AUTHOR]