Energy and reserve markets-based energy management system in smart grid including integrated system of flexible resources and distributed generations

By coordinating generation units, storage systems, and responsive loads within a unified framework, virtual power plants (VPPs) can function as both energy producers and storage entities. This operational model enables the constituent components to generate favorable revenue when aggregated as a VPP. Furthermore, applying advanced energy management schemes to grid-connected VPPs can significantly improve both the economic viability and the technical operation of power systems. In line with this objective, this study introduces a comprehensive energy management structure for flexible renewable VPPs functioning within an active distribution network (ADN), while explicitly considering their involvement in day-ahead energy and reserve markets. The proposed approach is formulated as a bi-level optimization problem. At the upper level, the operational strategy of the ADN is determined by minimizing a weighted objective function that incorporates both the expected operational cost and the voltage deviation index, subject to AC optimal power flow constraints. At the lower level, a risk-aware framework is adopted to model the participation of renewable and flexible VPPs in both energy and reserve markets. The objective at this stage is to simultaneously maximize the VPP’s expected profit and its conditional value-at-risk (CVaR) in the respective markets. The associated constraints include the operational characteristics of generation units, storage technologies, and flexible loads, as well as the limits related to reserve provision and flexibility capacity of the VPP. The proposed model incorporates uncertainties associated with load variations, fluctuations in renewable generation, and volatility in electricity prices. To effectively handle these uncertainties while maintaining computational efficiency and accurately capturing flexibility behavior, the Unscented Transform (UT) technique is utilized. Subsequently, the Karush–Kuhn–Tucker (KKT) conditions are employed to reformulate the problem into an equivalent single-level optimization model. In order to determine the optimal solution, a hybrid metaheuristic method combining the Ant-Lion Optimizer (ALO) and the Red Panda Optimizer (RPO) is implemented. The main contributions of this study include: assessing the role and performance of bio-waste generation, hydrogen and compressed air storages within the VPP framework; defining the flexibility boundaries of the VPP; facilitating its concurrent participation in both energy and reserve markets; employing the UT method for uncertainty representation; and proposing a novel hybrid metaheuristic optimization approach. Numerical results confirm that the proposed scheme enhances the economic and technical performance of the ADN by approximately 45.3% and between 41.9% and 64.6%, respectively, relative to conventional power flow approaches, owing to optimal VPP energy management. Regarding the technical performance, energy losses and the voltage deviation index are reduced by approximately 44.9% and 48.9%, respectively. In addition, the VPPs achieve 100% flexibility. Furthermore, integrating flexibility resources, namely the demand response program, hydrogen storage, and compressed air, improves the economic performance of VPPs in the energy market by 1.04% and in the reserve market by 1.96%.