Journal Article10.1109/TSG.2018.2833108
Distributed Consensus Based Algorithm for Economic Dispatch in a Microgrid
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TL;DR: Using multi-parameter perturbation theory and graph theory, the convergence of the algorithm is proved and it is obtained that the algorithm converges to the optimal solution at a rate governed by the second largest eigenvalue of the system matrix.
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Abstract: Economic dispatch problem (EDP) is a fundamental optimization problem of power systems. With the penetration of renewable energy sources in microgrids, this paper proposes a distributed algorithm based on consensus theory to solve the EDP with a quadratic cost function. The method takes advantage of the fact that incremental costs need to be equal for all buses at optimal output power values. Thus, the incremental cost of each bus is selected as a consensus variable and the local mismatch between total demand and generation is assigned as a feedback variable to meet demand constraints. Unlike the existing related works, the feedback gains for the feedback variables are different and time-varying. Using multi-parameter perturbation theory and graph theory, the convergence of the algorithm is proved. Furthermore, the upper bounds of the feedback gains are given theoretically, and we obtain that the algorithm converges to the optimal solution at a rate governed by the second largest eigenvalue of the system matrix. Meanwhile, the algorithm is a fully distributed algorithm without a leader or a virtual command node. The simulation results illustrate the effectiveness of the algorithm even though there is a demand change and generator damage. Some simulation results intuitively illustrate how the convergence speed changes with the feedback gains.
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Citations
Privacy-Preserving Distributed Economic Dispatch for Microgrids Based on State Decomposition With Added Noises
Lei Sun,Derui Ding,Hongli Dong,Xingzhen Bai +3 more
TL;DR: Both state decomposition and Laplace noises are embedded into a distributed optimization algorithm in the framework of consensus to improve the privacy-preserving capability of the EED of microgrids.
14
Quantized Distributed Economic Dispatch for Microgrids: Paillier Encryption–Decryption Scheme
TL;DR: This article is concerned with the secure distributed economic dispatch (DED) problem of microgrids, where a quantized distributed optimization algorithm using the Paillier encryption–decryption scheme is developed to optimally coordinate the power outputs of a collection of distributed generators.
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Bi-Level Optimal Strategy of Islanded Multi-Microgrid Systems Based on Optimal Power Flow and Consensus Algorithm
TL;DR: Aiming at problems of power allocation and economic scheduling for independent multi-microgrid systems, a bi-level optimization method based on optimal power flow and consensus algorithm is proposed and results show that the upper-layer method not only realizes the optimal power allocation of microgrids, but also reduces the power loss of the energy mutual aid among the microgrid.
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A Random-Weight Privacy-Preserving Algorithm With Error Compensation for Microgrid Distributed Energy Management
TL;DR: In this article, the authors proposed a random-weight privacy-preserving algorithm with error compensation, termed as REP-CoDEMS, for CoDEMS where each distributed device generates two random weights each time and communicates with its neighbor conveying values based on the weights, incremental cost estimation and power imbalance estimation information along with a novel error compensation term to eliminate the error induced by the random weights.
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Relaxed hybrid consensus ADMM for distributed convex optimisation with coupling constraints
TL;DR: The authors offer a reformulation of the original H-ADMM in an operator theoretical framework, which exploits the known relationship between ADMM and Douglas–Rachford splitting, and proposes an adaptive penalty parameter selection scheme that consistently improves the practical convergence properties of the algorithm.
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