With the application of advanced information and communication technology in building cluster energy system (BCES), energy management based on two-way interaction has become an effective method to improve its operation efficiency. BCES can quickly respond to the mismatch between supply and demand by adjusting flexible load and system operation strategy, which can improve operation reliability and reduce energy cost. This paper proposes an energy management and pricing framework of BCES based on two-Stage optimization method. First, on the basis of profit-seeking modeling of energy service provider (ESP) and building clusters (BCs), a dynamic pricing decision-making framework for energy management in a hierarchical energy market is proposed, which considers both ESP’s energy supply income and BCs’ comprehensive benefit. The dynamic pricing problem is formulated as a discrete finite Markov decision process (MDP), and Q-learning algorithm is adopted to solve the MDP problem. Moreover, an operation optimization model of the BCES based on the obtained optimal price decision is established, and the established model is solved by the alternating direction multiplier method algorithm (ADMM). Through numerical simulation case studies, it is demonstrated that the proposed method can achieve the optimal pricing decision-making closer to the psychological needs of ESP and BCs, and can significantly reduce the cost of BCs and improve the operational efficiency of BCES.

To realize the lower carbon and more efficient operation of energy hubs in the joint electricity and carbon market, a day-ahead bidding strategy is proposed for the energy hub operator (EHO). Considering the uncertainties of prices, demands, and renewable energy sources, this strategy is formulated as a novel two-stage distributionally robust joint chance-constrained optimization problem. A total distance-based ambiguity set is proposed to preserve the mean value of uncertain factors. By introducing this indicator function, this problem is further reformulated as a mixed-integer linear programming (MILP) problem. Simulations are performed based on the electricity and carbon prices in Europe, and the relation between the carbon emission and operational cost is further investigated in the case studies.

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