Smart and optimization-based operation scheduling strategies for maximizing battery profitability and longevity in grid-connected application
2024 (English)In: Energy Conversion and Management: X, E-ISSN 2590-1745, Vol. 21, article id 100519Article in journal (Refereed) Published
Abstract [en]
Lithium-ion battery storage has emerged as a promising solution for various energy systems. However, complex degradation behavior, relatively short lifetime, high capital, and operational costs, and electricity market volatility are critical factors that challenge its practical viability. Thus, to ensure sustained profitability of Lithium-ion batteries in real-life applications, a smart and optimal management strategy considering key influencing factors is imperative for achieving efficient battery utilization. This study proposes two day-ahead battery-behavior-aware operation scheduling strategies to maximize profitability and longevity in residential grid-connected applications with dynamic electricity pricing. Each scenario employs unique approaches to make optimal decisions for optimal battery utilization. The first scenario optimizes short-term profitability by prioritizing revenue gains under three charge/discharge rates (high, moderate, low), considering daily charge and discharge timings as decision variables. Conversely, the second scenario proposes a smart strategy capable of making intelligent decisions on a wide range of variables to simultaneously maximize revenue and minimize degradation costs, ensuring short-term and long-term profitability. Decision variables include the cycle frequency for each specific day, timings as well as durations for charging and discharging per cycle. To ensure effective long-term assessment, both scenarios accurately estimate battery performance, calendric and cyclic capacity degradations, remaining-useful-lifetime, and internal states under real operational conditions until battery reaches its end-of-life criteria. The scenarios are assessed economically using various indicators. Furthermore, the impact of battery price and size on optimization outcomes are examined. The key findings indicate that, among the first set of scenarios, the strategy with low charge/discharge rate extends the battery lifetime most efficiently, estimated at 14.8 years. However, it proved to be the least profitable, resulting in negative profit of −3€/kWh/yr. On the other hand, strategies with high and moderate charge/discharge rates resulted in positive profit of 8.3 €/kWh/year and 9.2 €/kWh/year, despite having shorter battery lifetimes, estimated at 10.1 years and 13.6 years, respectively. Furthermore, from a payback perspective, the strategy with fast charge/discharge capability led to 1.5 years shorter payback period than that of the moderate rate strategy. The findings highlight that the first set of scenarios limits the strategy's flexibility in achieving both sustainability and profitability. In contrast, the second scenario achieves impressive profit (18 €/kWh/yr), shortest payback period (7.5 year), a commendable lifespan (12.5 years), contrasting revenue-focused scenarios, emphasizing the importance of striking optimal balance between revenue gain and degradation costs for charging/discharging actions, ensuring sustained profitability. The findings offer valuable insights for decision-makers, enabling informed strategic choices and effective solutions.
Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 21, article id 100519
Keywords [en]
Day-ahead optimization-based battery operation scheduling, Degradation cost minimization, Price arbitrage within real-time electricity price tariff, Residential-grid connected battery application, Revenue maximization, Sustained profitability optimization, Battery management systems, Charging (batteries), Costs, Decision making, Housing, Investments, Lithium-ion batteries, Power markets, Battery applications, Battery operation, Cost minimization, Day-ahead, Electricity prices, Grid-connected, Operations scheduling, Optimisations, Real- time, Profitability
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-65372DOI: 10.1016/j.ecmx.2023.100519ISI: 001155504000001Scopus ID: 2-s2.0-85181971282OAI: oai:DiVA.org:mdh-65372DiVA, id: diva2:1828646
2024-01-172024-01-172024-06-26Bibliographically approved