Novel Numerical Method for Simultaneous Design and Control Optimization of Seasonal Thermal Energy Storage Systems

TL;DR

This paper introduces a novel optimization framework for the integrated design and control of seasonal thermal energy storage systems, significantly reducing computation time and demonstrating cost-effective, autonomous energy solutions.

Contribution

It presents a comprehensive, numerically efficient optimization method for designing and controlling hybrid renewable energy systems with thermal storage, improving upon existing approaches.

Findings

80.5% reduction in computation time

73% lower energy cost than national average

Feasibility of autonomous energy communities demonstrated

Abstract

The transition to a carbon-neutral energy system requires widespread deployment of renewable energy sources and economically feasible energy storage solutions. This study presents a comprehensive optimization framework that jointly addresses the design and control of a nonlinear energy system supplying both heat and electricity to the Dietenbach district in Freiburg, Germany. The proposed system integrates solar and wind power with battery storage and seasonal thermal energy storage coupled via a heat pump, enhancing self-sufficiency and mitigating seasonal supply-demand mismatches. A multi-node lumped-parameter model captures heat transfer within the pit thermal energy storage, forming the basis of a periodic optimal control problem solved numerically. An averaging method reduces computation time by 80.5% while preserving fidelity for year-long optimization. A case study shows a projected total yearly energy cost of 5.93 EUR/m2 for combined heat and electricity, which is 73% lower than the German average. This study underscores the feasibility of designing economically viable, autonomous energy communities in real-world scenarios and provides an efficient, robust optimization framework for designing system components and operational control strategies.