Intent Profile Strategy for Virtual Power Plant Participation in Simultaneous Energy Markets With Dynamic Storage Management

Abstract

The emergence of distributed energy resources in the electricity system involves new scenarios in which domestic consumers can be aggregated in virtual power plants to participate in energy markets. In this paper, a reconfigurable hierarchical multi-time scale framework is developed by combining the concepts of dynamic storage virtualization and intent profiling with model predictive control. The combined implementation of these concepts allows the simultaneous weighted participation in different energy markets, not only according to some aggregators’ criteria, but also to several risk factors. In a first stage, the framework optimizes the strategy for bidding in day-ahead market whereas the second one consists of a control stage to mitigate deviations and potential penalties. The smart management of individual storage virtualization enables the participation in the demand-response program, which improves the forecasted economical profit related to the day-ahead participation. The changes in the schedule are performed considering new potential penalties. The framework is reconfigurable at every sample time at control stage. This enables to make dynamic participations depending on node availability or system peaks. The proposed case studies cover day-ahead and demand-response participations, but the framework is open to other multi-service configurations. The results have been assessed with satisfactory conclusions.

Keywords

• Energy
• mathematical programming
• optimization
• predictive control
• smart grid
• virtual battery