Browsing by Author "Greenwood, David"

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    DREMUS: A data-restricted multi-physics simulation model for lithium-ion battery storage
    (Elsevier, 2020-11-21) Rogall, Martin; Barai, Anup; Brucoli, Maria; Luk, Patrick Chi-Kwong; Bhagat, Rohit; Greenwood, David
    This paper presents a modelling approach to support the techno-economic analysis of Li-Ion battery energy storage systems (BESS) for third party organisations considering the purchase or use of BESS but lacking the detailed knowledge of battery operation and degradation. It takes into account the severe data-limitations and provides the best possible approximation for its long-term electrical, thermal and ageing performance. This is achieved by constructing flexible and scalable ageing models from experimental data based on manufacturer's datasheets, warranties and manuals as key inputs. The precision of the individual models has been determined using experimental data and has been found with <8 % normalised root-mean-square deviation (NRMSD) in all cases to be sufficiently accurate. Through linearization methods, this model is able to compare the long-term performance of BESS and quantify the degradative impact of specific charge/discharge mission profiles, which improves the tangibility of BESS as value generating asset.
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    A reliability-aware chance-constrained battery sizing method for island microgrid
    (Elsevier, 2022-04-14) Huo, Da; Santos, Marcos; Sarantakos, Ilias; Resch, Markus; Wade, Neal; Greenwood, David
    Island Microgrids can coordinate local energy resources, provide post-fault reliability improvements for local customers, and aggregate local power and energy resources to offer services to the wider system. A crucial component of an Island Microgrid is the battery energy storage system, which can manage local imbalances, alleviate constraints, and improve reliability by enabling post-fault islanding. A planning and sizing method is required to quantify and maximize the benefits of battery energy storage while avoiding over-investment and under-utilization. This paper combines comprehensive reliability assessment with chance-constrained convex optimization, via second-order cone programming, to optimally size energy storage within an Island Microgrid. Chance constraints are applied to the battery state-of-charge to avoid sizing the energy storage to accommodate extreme cases of uncertainty, avoiding uneconomic investment. The probability of reaching a state-of-charge constraint also indicates the likelihood that the battery energy storage system will be unable to facilitate island operation in the event of an outage, which affects the Island Microgrid reliability. The method is demonstrated on a real Austrian distribution network as part of the MERLON project. Results illustrate that an optimal trade-off can be identified between system reliability and operating cost when the probability of violating the chance constraints is 4.8%.