Most distributed renewable energy sources, such as solar and wind turbine generators, exhibit significant uncertainty and variability in their outputs. In this work, we propose a method for determining the optimal portfolio for VPPs and evaluate it based on the uncertainty parameters and imbalance settlement rules for different markets. A mixed integer nonlinear programming approach combined with Monte-Carlo simulations is used to evaluate the proposed scheme. Numerical tests are performed using the model, and the results for two different portfolio configurations under certain market rules are compared.
DOI: 10.22982/NEXTWP.2022.2.10
Abstract
Most distributed renewable energy sources, such as solar and wind turbine generators, exhibit significant uncertainty and variability in their outputs. This poses a challenge to aggregators participating in the market as virtual power plants (VPPs). Therefore, several independent system operators impose a penalty for deviations between the predicted and metered outputs. This penalty may have a negative effect on the profit of VPPs. Two approaches have been proposed for solving this problem. One is reducing the forecasting errors during the operation stage, and the other is minimizing the output variance while configuring the VPP’s portfolio during the planning stage. In this work, we propose a method for determining the optimal portfolio for VPPs and evaluate it based on the uncertainty parameters and imbalance settlement rules for different markets. A mixed integer nonlinear programming approach combined with Monte-Carlo simulations is used to evaluate the proposed scheme. Numerical tests are performed using the model, and the results for two different portfolio configurations under certain market rules are compared.