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<p>With the rapid growth of Distributed Energy Resources (DERs), managing modern power systems has become increasingly complex. These resources, ranging from solar and wind to microturbines and storage, pose new challenges that traditional grid management tools struggle to handle. To address this, Virtual Power Plants (VPPs) have emerged as a promising solution. Acting as aggregators, VPPs combine various DERs and controllable loads into a single, coordinated entity capable of participating in energy markets. This study focuses on maximizing VPP profits through smart bidding strategies across Day-Ahead (DA), Real-Time (RT), and Balancing Markets, while considering the operational constraints of Solar Power Plants (SPP), Wind Power Plants (WPP), and microturbines (MT). Ten advanced metaheuristic optimization techniques were evaluated and compared with the analytical MILP approach to identify the most profitable bidding strategy. Among these, the Gorilla Troop Optimizer (GTO) and Manta Ray Foraging Optimization (MRFO) achieved the highest profit of $36,602.99 over the 24-hour horizon. The complex optimization task was formulated as a feasibility problem and implemented in MATLAB. The results demonstrate that with the right bidding strategy, even renewable sources with variability can significantly boost VPP revenues.</p>

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Smart Bidding for Virtual Power Plants: Adaptive Energy Scheduling in Multiple Markets Using Metaheuristic Optimization

  • 期刊论文

  • 发表日期：

    2025年10月18日

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  • Choudhary Om Prakash,
  • Altaf Q. H. Badar

摘要

With the rapid growth of Distributed Energy Resources (DERs), managing modern power systems has become increasingly complex. These resources, ranging from solar and wind to microturbines and storage, pose new challenges that traditional grid management tools struggle to handle. To address this, Virtual Power Plants (VPPs) have emerged as a promising solution. Acting as aggregators, VPPs combine various DERs and controllable loads into a single, coordinated entity capable of participating in energy markets. This study focuses on maximizing VPP profits through smart bidding strategies across Day-Ahead (DA), Real-Time (RT), and Balancing Markets, while considering the operational constraints of Solar Power Plants (SPP), Wind Power Plants (WPP), and microturbines (MT). Ten advanced metaheuristic optimization techniques were evaluated and compared with the analytical MILP approach to identify the most profitable bidding strategy. Among these, the Gorilla Troop Optimizer (GTO) and Manta Ray Foraging Optimization (MRFO) achieved the highest profit of $36,602.99 over the 24-hour horizon. The complex optimization task was formulated as a feasibility problem and implemented in MATLAB. The results demonstrate that with the right bidding strategy, even renewable sources with variability can significantly boost VPP revenues.