Motivated by the benefits of multi-energy integration, this paper establishes a bi-level two-stage framework based on transactive control, in order to achieve optimal energy provision among interconnected multi-energy systems (MESs). At the lower level, each MES autonomously determines the optimal set points of each controllable assets by solving a cost minimization problem, in which rolling horizon optimization is adopted to deal with load and renewable energies stochastic features. A technique is further implemented for optimization model convexification by relaxing storages complementarity constraints, and its mathematical proof verifies the exactness of the relaxation. At the upper level, a coordinator is established to minimize total costs of collaborative interconnected MESs while preventing transformer overloading. This collaborative problem is further decomposed and solved iteratively in a two-stage procedure based on market-clearing mechanism. A distinctive feature of the method is that it is compatible with operational time requirement, while retaining scalability, information privacy and operation authority of each MES. Effectiveness of the proposed framework is verified by simulation cases that conduct detailed analysis of the autonomous-collaborative optimization mechanism.
Air-conditioning loads (ACLs) are among the most promising demand side resources for their thermal storage capacity and fast response potential. This paper adopts the principle of market-based control (MBC) for the ACLs to participate in the ancillary services. The MBC method is suitable for the control of distributed ACLs because it can satisfy diversified requirements, reduce the communication bandwidth and protect users' privacy. The modified bidding and clearing strategies proposed in this paper makes it possible to adjust the switching frequency and strictly satisfy the lockout time constraint for mechanical wear reduction and device protection, without increasing the communication traffic and computational cost of the control center. The performance of the ACL cluster in two typical ancillary services is studied to demonstrate the effect of the proposed method. The case studies also investigate how the control parameters affect the response performance, comfort level and switching frequency.
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