A Two-Step Stochastic Market-Oriented Approach for Optimal Operation of Commercial VPPs Under Uncertainty

Abstract

Observing the current trends of development in electrical grids, it can be perceived that the installation of distributed generation resources has had rapid growth on the demand side and distribution grid level. These small-scale sources cannot participate in the upper-level electricity markets. However, with the emergence of the virtual power plants (VPP) concept, these small capacities can be integrated and VPPs can participate and compete in the electricity markets. Nevertheless, there are uncertainties in VPP generation caused by the inherent nature of renewable energy sources (RES) and the inability to accurately predict the price, due to high volatility and intermittency. In this article, a bidding strategy to maximize profit in a VPP is presented. A VPP scheme can also be comprised of a microturbine, an energy storage system (ESS), and an aggregator of a demand response program (DRP) for internal loads. The uncertainty parameters, including wind turbine harvested energy and the market clearing price, are modeled in this work. In addition, a stochastic model has been incorporated in order to assess the uncertainty in a day-ahead and real-time electricity market. The whale Optimization Algorithm (WOA) is employed to solve this non-convex and non-smooth optimization problem once the proposed model has been applied to a targeted network as the case study. The results indicate that the deployment of the VPP paradigm can considerably increase the profitability of distributed energy sources while mitigating the risk of participation in the electricity markets.