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Abstract-This paper proposes a distributed multi-energy management framework for the coordinated operation of interconnected biogas-solar-wind microgrids. In this framework, each microgrid not only schedules its local hybrid biogas-solar-wind renewables for coupled multi-carrier energy supplies based on the concept of energy hub, but also exchanges energy with interconnected microgrids and via the transactive market. The multi-microgrid scheduling is a challenging optimization problem due to its severe constraints and strong couplings. A multi-microgrid multi-energy coupling matrix is thus formulated to model and exploit the inherent biogas-solar-wind energy couplings among electricity, gas and heat flows. Furthermore, a distributed stochastic optimal scheduling scheme with minimum information exchange overhead is proposed to dynamically optimize energy conversion and storage devices in the multi-microgrid system. The proposed method has been fully tested and benchmarked on different scaled multi-microgrid system over a 24-hour scheduling horizon. Comparative results demonstrated that the proposed approach can reduce the system operating cost and enhance the system energy-efficiency, and also confirm its scalability in solving large-scale multi-microgrid problems.

This paper presents a security-constrained multiperiod economic dispatch model (M-SCED) for systems with renewable energy sources (RES). A two-stage framework is adopted to model initial operation plans and recourse actions before and after the uncertainty realization of RES power. For ensuring superior system economic efficiency, distributionally robust optimization (DRO) is utilized to evaluate the expectations of operation costs affected by RES uncertainty. Practical issues, including boundedness of uncertainty and inaccurate information, are considered in modeling uncertainty in DRO. Within the framework of DRO, robust optimization is integrated to enhance system security. Besides, decision variables after the first period in M-SCED are approximated by segregated linear decision rules to achieve computational tractability without substantially degrading the model accuracy. A Constraint Generation algorithm is proposed to solve this problem with comprehensive case studies illustrating the effectiveness of the proposed M-SCED.

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