Placement and sizing of photovoltaic and bio-waste unit with hydrogen storage considering economic energy management of intelligent distribution network.

A virtual power plant integrates diverse energy sources and storage systems, functioning as producers within a network. Strategically determining its optimal placement and the size of its components leads to notable improvements in key economic and technical metrics of the network. This research builds a framework for sizing and positioning renewable virtual power plants, incorporating hydrogen storage systems as part of a broader multi-objective energy management strategy for smart grids. The proposed approach operates as a bi-level optimization model. In the upper-level model, it addresses technical, financial, and environmental objectives from the perspective of the distribution system operator. The aim is to minimize the total weighted operating costs, energy losses, and emissions within the distribution network. The model considers constraints such as the AC power flow equations for smart grids, alongside operational and voltage security restrictions. Meanwhile, the lower-level model focuses on the placement and sizing of renewable virtual power plants incorporating hydrogen storage systems. Here, the goal is to reduce planning costs, with constraints tied to the operation and flexibility models of renewable sources and storage systems. To integrate these models effectively, the Karush-Kuhn-Tucker (KKT) conditions are applied to create a unified single-level framework, while a Fuzzy decision-making technique facilitates the derivation of compromise solutions. This approach also accounts for uncertainties related to load demand, renewable energy outputs, and fluctuating energy prices using scenario-based stochastic optimization combined with the Kantorovich method and Roulette Wheel Mechanism. For solution optimization, Red Panda Optimization (RPO) is employed, demonstrating superior convergence speed and precision in comparison to other solvers. The research incorporates innovative elements by addressing optimal placement and sizing of flexible virtual power plants within active distribution grids, while factoring in bio-waste energy management and the role of hydrogen storage capacities in network operation and security. Evaluation through numerical case studies highlights significant enhancements in grid performance. In comparison to conventional load flow methods, the proposed solution optimizes operations by reducing network operating costs, improving voltage security, and mitigating environmental impacts. When adopting a compromise solution framework, the distances of network operation costs, energy losses, and emissions from their minimal achievable values are by 14%, 29%, and 21%, respectively. Moreover, RPO demonstrates remarkable precision with a final solution's standard deviation of approximately 0.97%, effectively identifying optimal results with enhanced convergence efficiency. This study underscores the transformative potential of virtual power plants in improving energy management and distribution grid planning.