Chance Constraints and Machine Learning integration for uncertainty management in Virtual Power Plants operating in simultaneous energy markets

Abstract

The management of uncertainty is one of the most important issues affecting the optimal operation of Distributed Energy Resources (DERs). Virtual Power Plants (VPPs) aggregate different Energy Nodes (ENs) to enable them to participate in energy markets in an aggregated way. This participation requires very accurate forecast services to trade off between the revenue at bidding time, by making more aggresive bids, and the reduction of penalties at operation time due to deviations from the commitment. In this paper, a stochastic optimization layer is built over a Model Predictive Control (MPC) kernel to define a Stochastic Model Predictive Control (SMPC) scheme by combining Chance-Constrained (CC) and Machine Learning (ML) to handle the uncertainty related to generation and load profiles at optimization time.This technique can be applied to participate simultaneously in different energy markets depending on the configuration or capacity of the VPP, and according to the business model of the system operator. More accurate optimizations allow more profitable operations and more flexibility to redistribute the energy allocation to offer different energy services. The results are satisfactory as this scheme works better than the deterministic approach in terms of penalty reduction for most of the cases.