Bachelorarbeit, 2021
48 Seiten, Note: 1,3
1 Introduction
2 Overview and Defining Dimensions
2.1 Location
2.2 Technology
2.3 Battery Size
2.4 RES
2.5 Method
2.6 Revenue Streams
3 Methodology
3.1 Included Studies
3.2 Initial Literature pool
3.3 Design of a Search String and Evaluation of Relevant Databases
3.4 Manual Filtering
3.5 Backward and Forward Search
4 The current landscape of multi-tasking literature: A high level overview
5 Qualitative Analysis of HES and CES
5.1 HES
5.1.1 Operator
5.1.2 Services and Revenue Streams
5.1.3 Method
5.1.4 Forecasting
5.1.5 Battery Degradation
5.2 CES
5.2.1 Operator
5.2.2 Services and Revenue Streams
5.2.3 Method
5.2.4 Forecasting
5.2.5 Battery Degradation
6 Conclusion & Outlook
This thesis examines the concept of "multi-tasking" or "multi-use" of Battery Storage Systems (BSS) to improve their financial profitability, as single-use scenarios often prove unprofitable due to high investment costs. The primary research goal is to review existing literature, create a high-level overview structured by specific dimensions, and perform a qualitative analysis of key residential clusters (Household Energy Storage - HES and Community Energy Storage - CES) to provide a foundation for future research.
5.1.5 Battery Degradation
BSSs are affected by degradation of their capacity independent of the respective BSS technology that is used. Two main factors affect the battery degradation, namely the number of charge and discharge cycles that the BSS performs and the depth of discharge (DOD) (Obaid, Cipcigan, Muhssin, & Sami, 2020). A BSS will reduce the number of cycles that it can perform by discharging below its DOD, which reduces the lifetime of the BSS. These effects on BSS capacity are taken into account by 22 of the reviewed studies.
To cope with the problem of BSS degradation the studies adopt different approaches. One strategy is to assume the life cycle of a BSS to be a certain number of years. This assumed life cycle ranges from ten (Xiang, Sun, Pei, & Xi, 2019) to 13 (Okur et al., 2019), 15 (Pena-Bello et al., 2017), or 25 years (Jung et al., 2020). von Appen, Stetz, Braun, and Schmiegel (2014) take lifetime expectancy into account by designing the effective BSS capacity to be smaller than the nominal capacity. Also, a yearly 1 % efficiency drop of BSS usage is adopted. After reaching the end of its lifecycle the BSS is then replaced. The lifecycle is assumed to be the period until the BSS capacity remains above 80 % of the nominal capacity (Tant et al., 2013) or when 30% of the nominal capacity is depleted (Pena-Bello et al., 2020).
Other studies account for BSS degradation more actively and include this as a factor in their optimization models. Nizami et al. (2019) and Schram, Lampropoulos, and van Sark (2018) use a Rain Flow method to obtain the DOD and the resulting degradation. Castelo-Becerra et al. (2017) minimize the degradation cost by accounting degradation from state of charge (SoC) deviations from a nominal SoC and from charging and discharging rates with both linear and quadratic components.
1 Introduction: Provides motivation for the study, defines the research problem regarding BSS profitability, and outlines the thesis goals.
2 Overview and Defining Dimensions: Establishes a taxonomy for analyzing BSS literature by defining six core dimensions including location, technology, size, RES, method, and revenue streams.
3 Methodology: Details the systematic literature review process, covering the selection of databases, the search string design, and the manual filtering criteria.
4 The current landscape of multi-tasking literature: A high level overview: Presents a descriptive analysis and visual clustering of the gathered research based on the predefined dimensions.
5 Qualitative Analysis of HES and CES: Offers a deep-dive analysis into the specific operation, services, methodologies, forecasting, and degradation modeling for Household and Community Energy Storage.
6 Conclusion & Outlook: Summarizes the key findings regarding the profitability of multi-use BSS and identifies gaps for future research, particularly regarding forecasting and degradation integration.
Battery Storage Systems, BSS, Multi-tasking, Multi-use, Energy Storage, HES, CES, Self-consumption, Demand-side-management, Profitability, Renewable Energy Sources, Optimization, Ancillary Services, Battery Degradation, Forecasting
The thesis focuses on the concept of "multi-tasking" or "multi-use" of Battery Storage Systems. It investigates how using a single BSS for multiple applications can improve financial profitability in residential and community contexts.
The main themes include BSS location, battery technology, storage size, integration with renewable energy sources (RES), mathematical methods used for optimization, and various revenue streams such as arbitrage, ancillary services, and self-consumption.
The goal is to systematically review and categorize existing literature on multi-use BSS, provide a high-level overview of the field, and conduct a detailed qualitative analysis of the most relevant residential storage clusters (HES and CES).
The thesis employs a systematic literature review approach. This includes the definition of a relevant paper pool, strict manual filtering, and the evaluation of studies based on specific structural dimensions to ensure scientific validity.
The main part provides a high-level overview of the current scientific landscape followed by a qualitative assessment of how HES and CES operators choose services, utilize forecasting, and account for battery degradation in their models.
Key terms include multi-tasking BSS, HES, CES, self-consumption, demand-side-management, optimization, and battery degradation modeling.
Studies show that relying on a single revenue stream like self-consumption is often not profitable due to high capital costs. Combining self-consumption with other services like DSM, ancillary services, or energy arbitrage significantly boosts the economic performance of battery installations.
Only a subset of the literature incorporates degradation models. Approaches range from simplified estimations (assuming a fixed number of life years) to complex active optimization models that include parameters like depth of discharge, state of charge, and temperature.
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