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Techno-economic impacts of battery performance models and control strategies on optimal design of a grid-connected PV system
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-7233-6916
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0003-4589-7045
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. KTH Royal Inst Technol, Sch Chem Sci & Engn, Stockholm, Sweden.ORCID iD: 0000-0003-0300-0762
2021 (English)In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 245, article id 114617Article in journal (Refereed) Published
Abstract [en]

A battery storage has emerged as the most widely-used storage option, due to its flexible and complementary functionality for renewable energy systems such as solar PV and wind power. In order to ensure the efficient operation of batteries in energy systems, a proper battery model is essential in predicting realistic battery performance under various operating conditions. Accurate knowledge of the state of charge, state of power, and battery efficiency is a necessity for the development of advanced grid management applications. This paper investigates the techno-economic impacts of two battery modelling scenarios on the sizing and optimization of a grid-connected PV-battery system. Scenario 1 is based on a common simple battery model and control strategy which represents the battery status without reflecting dynamic behavior. By contrast, Scenario 2 is based on a complex battery model involving estimation of battery current-voltage characteristics under various operating conditions. A rule-based operational strategy linked to a non-dominated sorting genetic algorithm is further employed for the simulation and multi-objective optimization of a grid-connected hybrid PV-battery system. The battery life cycle cost and the self-sufficiency ratio are analyzed and optimized as objective functions, and battery capacity constitutes as a decision variable. The results show that in order to reach the same self-sufficiency ratio, the optimization of a hybrid energy system based on Scenario 1 leads to solutions with a higher life cycle cost and requiring bigger battery capacity, compared to that of Scenario 2. Moreover, under the same design parameters, the system optimization based on Scenario 2 delivers more power to the end-user, which leads to a higher selfsufficiency ratio compared to when the system is simulated based on Scenario 1. This study proves that an efficient battery model with sufficient accuracy is techno-economically more beneficial, and leads to more accurate battery sizing.

Place, publisher, year, edition, pages
2021. Vol. 245, article id 114617
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-55891DOI: 10.1016/j.enconman.2021.114617ISI: 000693258300007Scopus ID: 2-s2.0-85113175641OAI: oai:DiVA.org:mdh-55891DiVA, id: diva2:1594900
Available from: 2021-09-16 Created: 2021-09-16 Last updated: 2023-11-13Bibliographically approved
In thesis
1. Techno-economic viability of battery storage for residential applications
Open this publication in new window or tab >>Techno-economic viability of battery storage for residential applications
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Battery storage has emerged as a promising solution in various energy systems. However, challenges exist regarding the viability of batteries in practical stationary applications. Factors such as the capital and operational costs, relatively short lifetime, and battery degradation are among crucial factors which have significant impact on battery profitability. To make batteries more viable technology, effective battery management is a necessity. However, there are multiple critical factors which need to be addressed for effective battery utilization and management in real-life applications under dynamic operational conditions.

In this thesis, different battery modelling approaches within battery operational management are proposed. Each proposed scenario consists of a set of specific methods for the estimation of battery performance, capacity degradation, remaining useful life, state-of-charge, state-of-health, and state-of- power.Moreover, the study explores strategies for efficient battery utilization to maximize sustained profitability. Accordingly, the study deals with 32 different state-of-charge operating control strategies as well as different charge/discharge rates (low, moderate, high) to evaluate their impact on techno-economic profitability of a battery system in a grid-connected residential application. Moreover, two day-ahead and optimization-based operation scheduling strategies to maximize battery profitability are proposed. Each scenario employs unique approaches to make optimal decisions for optimal battery utilization. The first scenario aims to optimize short-term profitability by prioritizing revenue gains. Conversely, the second scenario proposes a smart strategy capable of making intelligent decisions on a wide range of decision-variables to simultaneously maximize daily revenue and minimize daily degradation costs.

The key findings reveal that overlooking or simplifying assumptions about multiple critical aspects of battery behavior led to an improper battery management system in practical applications under dynamic operational conditions. Selecting a proper state-of-charge control strategy positively affects the profitability in which alteration of the allowable SOC window from (40%–90%) to (10%–60%) increase the battery lifetime from 10.2 years to 14 years leading to 31.6% improvement in net present value. The key findings showcase how a smart battery scheduling strategy that strike optimal balance between revenue and degradation achieves impressive profit (18-20 €/kWh/year), short payback (7.5 years), and extended lifespan (12.5 years), contrasting revenue-focused scenarios, ensuring sustained profitability for battery owners in residential applications. The findings offer valuable insights for decision-makers, enabling informed strategic choices and profitable solutions.

Place, publisher, year, edition, pages
Västerås: Mälardalens universitet, 2024
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 398
National Category
Energy Engineering Energy Systems
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-64725 (URN)978-91-7485-623-1 (ISBN)
Public defence
2024-01-12, Lambda, Mälardalens universitet, Västerås, 09:15 (English)
Opponent
Supervisors
Available from: 2023-11-14 Created: 2023-11-13 Last updated: 2023-12-31Bibliographically approved

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Shabani, MasoumeDahlquist, ErikWallin, FredrikYan, Jinyue

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