Experimental testing of a real aggregator system performing rigorous optimal control of electrical and thermal storage

This paper provides an experimentally validated optimal control approach based on a Hamilton–Jacobi–Bellman (HJB) model for optimising aggregated distributed energy resources across multiple energy carriers. The research incorporates nonlinear effects arising from storage degradation, conversion efficiency and self-discharge as well as multiple energy carrier storage. A semi-Lagrangian HJB solver was implemented on low-cost digital controllers, and integrated into a real fully functional cloud-based aggregation platform. The computational cost is kept at a minimum, enabling on-line computations on the low-cost controller, while maintaining a rigorous proof of convergence to the theoretical value function of the nonlinear, non-convex optimal control problem. The controller links into a distributed optimal control platform that is using local as well as cloud-based information and performs all the computation and decision-making locally. The distributed controllers were tested and validated on site with an electrical and a thermal storage device. Experimental results confirm that the framework is practical, accommodates nonlinear effects and inaccurate external forecasts, has a small computational cost, is robust and can deliver significant cost benefits to the stakeholders.