This paper presents a privacy-preserving operational decision-making approach for autonomous microgrids networked via a power distribution system, where the distribution system operator (DSO) and microgrid master cont...
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This paper presents a privacy-preserving operational decision-making approach for autonomous microgrids networked via a power distribution system, where the distribution system operator (DSO) and microgrid master controllers (MMCs) make independent decisions as different stakeholders. First, the scheme of bileveloptimization (i.e., the Stackelberg leader-followers game) is applied to investigate the sequential interactions between DSO and MMCs. Then, given the coexistence of continuous and binary variables in the lower-level problems, an exact strong-duality-based reformulation and decomposition framework is customized to cope with the nonconvex nature of the bilevel mixed-integer optimization. Meanwhile, a fast-enhanced Benders decomposition algorithm is proposed to realize local privacy-preserving decision-making, where multiple unified Benders cuts are generated once and the strongest one is selected back to Benders mater problem. Mathematically, the proposed algorithm will not change the equilibrium point and corresponding optimal solution after finite iterations. Finally, through numerical experiments on a simplified two-bus test system and a modified IEEE 123-bus system, we demonstrate the effectiveness of privacy preservation, as well as the robustness and scalability of the proposed approach.
Unit commitment, one of the most critical tasks in electric power system operations, faces new challenges as the supply and demand uncertainty increases dramatically due to the integration of variable generation resou...
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Unit commitment, one of the most critical tasks in electric power system operations, faces new challenges as the supply and demand uncertainty increases dramatically due to the integration of variable generation resources such as wind power and price responsive demand. To meet these challenges, we propose a two-stage adaptive robust unit commitment model for the security constrained unit commitment problem in the presence of nodal net injection uncertainty. Compared to the conventional stochastic programming approach, the proposed model is more practical in that it only requires a deterministic uncertainty set, rather than a hard-to-obtain probability distribution on the uncertain data. The unit commitment solutions of the proposed model are robust against all possible realizations of the modeled uncertainty. We develop a practical solution methodology based on a combination of Benders decomposition type algorithm and the outer approximation technique. We present an extensive numerical study on the real-world large scale power system operated by the ISO New England. Computational results demonstrate the economic and operational advantages of our model over the traditional reserve adjustment approach.
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