A comprehensive framework for evaluating optimization algorithms for smart energy assets in the built environment

As countries aim to reduce CO 2 emissions, intermittent renewables are becoming more prevalent, driving the adoption of Battery Energy Storage Systems (BESSs). This has spurred research into optimization strategies for BESS control. The strategies are typically evaluated using a single KPI, the optimization objective. Such single-focused evaluation neglects side effects on other performance metrics. To address this omission, we propose a framework for multi-perspective evaluation of BESS optimizations across five stakeholder views: grid operations, climate impact, financial performance from an operational and an investor perspective, and energy consumption. These perspectives are represented by the KPIs maximum grid load, CO 2 emissions, end-user costs, net present value, and energy use. The framework was applied to four optimization strategies for a BESS in an office building, i.e. peak load reduction, CO 2 emission minimization, cost minimization, and income optimization. Results show that optimizing for one KPI improves that KPI, but often negatively affects others. The only positive business case was income optimization via imbalance market trading. In conclusion, the framework enables a multi-perspective evaluation of BESS optimizations, revealing trade-offs for balanced energy management strategies. The case study underscores the value of the framework and the need for an integrated approach in BESS research and deployment. • Trade-offs exist between grid load, CO 2 , energy use, and financial performance. • Optimizing for one KPI often worsens others, requiring balanced BESS strategies. • Case study evaluates four BESS algorithms in an office building setting. • BESS is only financially viable when participating in the imbalance market. • Framework enables integral evaluation of BESS algorithms across four KPIs.