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

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 17, 2025

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 interaction VPPs is determined through a risk model. proposed framework adopts bi-level structure. In upper-level model, Pareto optimization strategy, based on weighted sum method, utilized minimize AND's predicted operating costs voltage deviation. network's AC flow (AC-OPF) equations serve as constraints this level. lower-level model intends maximize VPPs' expected profits while incorporating conditional value-at-risk address their participation in aforementioned Additionally, constrained by operational characteristics VPPs. Stochastic programming applied capture uncertainties associated renewable sources, market prices, system loads. problem transformed single-level applying Karush–Kuhn–Tucker conditions. A hybrid solver, combining teaching–learning-based sine–cosine algorithm, employed acquire reliable near-optimal solution. Finally, scheduling ADNs can be replace conventional studies determining numerical findings indicate that improve economic efficiency resources responsive loads when VPP framework.

Language: Английский

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

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 17, 2025

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 interaction VPPs is determined through a risk model. proposed framework adopts bi-level structure. In upper-level model, Pareto optimization strategy, based on weighted sum method, utilized minimize AND's predicted operating costs voltage deviation. network's AC flow (AC-OPF) equations serve as constraints this level. lower-level model intends maximize VPPs' expected profits while incorporating conditional value-at-risk address their participation in aforementioned Additionally, constrained by operational characteristics VPPs. Stochastic programming applied capture uncertainties associated renewable sources, market prices, system loads. problem transformed single-level applying Karush–Kuhn–Tucker conditions. A hybrid solver, combining teaching–learning-based sine–cosine algorithm, employed acquire reliable near-optimal solution. Finally, scheduling ADNs can be replace conventional studies determining numerical findings indicate that improve economic efficiency resources responsive loads when VPP framework.

Language: Английский

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