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Techno-Economic Optimization of Energy Systems

Karel Herregodts; Didier Colle; Sofie Verbrugge

Publisher
Zenodo
DOI
10.5281/zenodo.17435354
Repository
zenodo.org
Language
en

Abstract

The transition towards renewable energy systems presents both opportunities and challenges, particularly in ensuring economic viability under high levels of renewable penetration. Business cases are often complicated by uncertainties such as fluctuating energy prices and variations in consumer behaviour. This research addresses these challenges by advancing techno-economic models and optimisation techniques to support robust energy system sizing, with a focus on solutions that generate value locally while also participating in wider energy markets. Using real-world datasets on consumption, production, and market prices, simulations were conducted to evaluate the performance of photovoltaic (PV) installations, battery storage, and smart electric vehicle (EV) charging. The analysis explored multiple scenarios across a broad range of asset sizes, assessing both cost and emission impacts. Findings indicate that while increased PV capacity improves self-sufficiency and annual value, batteries often reduce annual value unless additional revenue is obtained through ancillary services such as grid balancing. Moreover, the optimisation of EV charging emerged as a key driver of economic performance: smart charging strategies not only enhanced the net present value of charging hubs but also supported the integration of larger PV systems, particularly when combined with office load profiles. The results suggest that mid-size batteries are unlikely to be economically viable in shared energy districts unless coupled with ancillary services, whereas PV systems remain promising for large buildings or sites, albeit with strong sensitivity to cost and price dynamics. Smart EV charging demonstrates substantial cost reduction potential without compromising user comfort, while also increasing the viability of renewable integration. Future work will focus on reducing model complexity for large-scale stochastic systems and designing evolutionary algorithms to further improve asset sizing optimisation.

Full text

Techno-Economic Optimization of Energy Systems IDLAB TECHNO-ECONOMICS Karel Herregodts, Didier Colle, Sofie Verbrugge Shared energy systems in local energy districts (Figure 1, data 2023) •Larger PV capacity increases both self-sufficiency and annual value. •Battery capacity improves self-sufficiency but reduces annual value, particularly with smaller PV systems. Introduction •Business cases for renewable energy systems face challenges in viability at high renewable penetration levels. •Uncertainties arise from factors such as energy prices and consumer behavior. •This research aims to improve techno-economic models and optimization techniques for robust system sizing. The focus is on energy systems that deliver value both locally and in energy markets. Contributions •Assessed the potential for cost and emission reductions through optimized PV and battery sizing combined with smart EV charging. •Quantified the added value of incorporating ancillary services (e.g., grid balancing) into the asset value stack. •Demonstrated how EV charging optimization and other loads influence the optimal PV size. Methodology •Used real-world consumption, production, and energy price data. •Simulated energy system control optimization to minimize total costs, employing physical models (Python and algebraic modeling). •Evaluated multiple scenarios across a broad range of asset sizes to assess sensitivity to key parameters. Comparative analyis: NPV Smart charging hub with office Smart charging standalone hub Conventional charging hub with office Conventional charging standalone hub 287 381 312 737 17 654 117 (€) •Mid-size battery systems are not economically viable in shared energy districts unless additional value is captured from ancillary services. •PV installations can be attractive for large buildings or sites, though their viability at scale depends strongly on costs and energy prices. •Smart EV charging at office hubs reduces costs substantially without compromising user comfort, while enabling larger economically viable PV installations. Interim Results Conclusions Future Research •Develop techniques to reduce model complexity, enabling optimization of large stochastic systems. •Design efficient evolutionary algorithms for asset sizing optimization. Contact [email protected] technoeconomics.idlab.ugent.be/ Universiteit Gent @ugent Ghent University Figure 2: NPV of scenarios as function of PV capacity Figure 1: Self-sufficiency and annual worth of PV - BESS combinations Optimization of smart charging hubs (Figure 2, data 2024) •Smart charging significantly increases net present value compared with conventional charging. •Optimal PV capacity rises when office load is combined with office EV charging load. •Smart charging further supports higher economically viable PV capacity.