The rapid proliferation of distributed energy resources—including rooftop solar arrays, wind turbines, battery storage systems, and electric vehicles—is fundamentally reshaping how power systems operate. Virtual power plants (VPPs) have emerged as a proven technology for aggregating and coordinating these scattered resources, but optimizing their operation across competing economic and environmental objectives remains challenging.
A new computational framework addresses this complexity by applying the cheetah optimization algorithm (COA) to balance energy costs, carbon emissions, and distributed resource scheduling within virtual power plants. The methodology enables real-time coordination among prosumers (who both consume and produce power), consumers, and the utility grid while respecting operational constraints.
Key to the approach is a multi-objective optimization formulation that weighs economic performance against environmental impact. Rather than treating these as conflicting goals, the framework identifies operating points where both objectives improve together—maximizing renewable energy utilization while reducing grid demand and associated emissions from fossil fuel generation.
Validation on a 15-bus test distribution system containing multiple virtual power plants with integrated photovoltaic systems, battery storage, and electric vehicles demonstrated substantial improvements. The algorithm effectively coordinated charging and discharging cycles for vehicle batteries, timed renewable energy consumption to match production peaks, and reduced net power flows to the main grid during congested periods.
Comparative testing against conventional optimization methods confirmed that the COA-based approach delivered superior computational performance and robustness across varying operating conditions. The framework scales effectively from individual neighborhood microgrids to larger distribution networks, making it practical for utilities managing portfolios of distributed resources.
As energy markets increasingly reward carbon reduction and demand-side flexibility, this carbon-aware coordination methodology provides utilities and aggregators with a practical tool for operating virtual power plants that are economically viable and environmentally responsible. The approach supports emerging transactive energy markets where distributed participants can trade energy and carbon credits efficiently.
