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<p>This study presents an optimal approach to integrate flexible-renewable virtual power plants (VPPs) into the operation of active distribution networks (ADNs) with multi-criteria objectives for day-ahead energy and reserve markets. The level of interaction with VPPs is determined through a risk model. The proposed framework adopts a bi-level structure. In the upper-level model, the Pareto optimization strategy, based on the weighted sum method, is utilized to minimize the AND’s predicted operating costs and voltage deviation. The network’s optimal AC power flow (AC-OPF) equations serve as constraints for this level. The lower-level model intends to maximize VPPs’ expected profits while incorporating the conditional value-at-risk to address their participation in the aforementioned markets. Additionally, this level is constrained by the reserve and operational characteristics of flexible-renewable VPPs. Stochastic programming is applied to capture uncertainties associated with renewable sources, market prices, and system loads. The problem is transformed into a single-level model applying the Karush–Kuhn–Tucker conditions. A hybrid solver, combining teaching–learning-based optimization and the sine–cosine algorithm, is employed to acquire a reliable and near-optimal solution. Finally, the optimal scheduling of VPPs in ADNs can be utilized to replace conventional power flow studies in determining the network’s operating conditions. The numerical findings indicate that the proposed approach can improve the economic efficiency of resources and responsive loads when applied in a VPP framework.</p>

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Risk-constrained participation of virtual power plants in day-ahead energy and reserve markets based on multi-objective operation of active distribution network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月17日

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  • Morteza Jadidoleslam

摘要

This study presents an optimal approach to integrate flexible-renewable virtual power plants (VPPs) into the operation of active distribution networks (ADNs) with multi-criteria objectives for day-ahead energy and reserve markets. The level of interaction with VPPs is determined through a risk model. The proposed framework adopts a bi-level structure. In the upper-level model, the Pareto optimization strategy, based on the weighted sum method, is utilized to minimize the AND’s predicted operating costs and voltage deviation. The network’s optimal AC power flow (AC-OPF) equations serve as constraints for this level. The lower-level model intends to maximize VPPs’ expected profits while incorporating the conditional value-at-risk to address their participation in the aforementioned markets. Additionally, this level is constrained by the reserve and operational characteristics of flexible-renewable VPPs. Stochastic programming is applied to capture uncertainties associated with renewable sources, market prices, and system loads. The problem is transformed into a single-level model applying the Karush–Kuhn–Tucker conditions. A hybrid solver, combining teaching–learning-based optimization and the sine–cosine algorithm, is employed to acquire a reliable and near-optimal solution. Finally, the optimal scheduling of VPPs in ADNs can be utilized to replace conventional power flow studies in determining the network’s operating conditions. The numerical findings indicate that the proposed approach can improve the economic efficiency of resources and responsive loads when applied in a VPP framework.