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In the context of a changing energy system towards one dominated by renewable energy sources, the demand for flexible energy generation and consumption will increase. Battery storage systems can provide a significant share of this energy flexibility, especially when combined with an industrial manufacturing plant to shift the industrial electricity demand over time. This paper contributes to a better understanding of the business decision when investing in a battery storage system and when marketing energy flexibility. For this purpose, the work considers the techno-economic and regulatory framework for flexibility measures and examines the optimal investment and dispatch planning for a battery storage system in an industrial company. The studies in this thesis focus on three central aspects. As a first aspect, the various revenue streams for the stored electricity are analysed and how these influence the profitability of a battery storage system. In particular, the provision of frequency containment reserve power, peak load shifting or peak shaving, arbitrage trading on the energy markets and the increase in self-consumption through photovoltaic self-generation are addressed. For this purpose, an optimisation model is formulated as a discrete, linear programme that maps the economic framework of the flexibility markets and integrates the technological constraints of the battery storage system. As a second aspect, uncertainties about market prices, load and generation behaviour are integrated into the optimisation model and the influence on the investment decision is investigated. This is done on the one hand by a two-stage robust optimisation model, which represents the uncertainty about the market success on the intraday market. On the other hand, the significance of the sequence of uncertain market decisions is illuminated through a multi-stage stochastic optimisation model. As a third aspect, the trade-off between the economic and ecological use of a battery storage system is analysed. For this purpose, an ecological, CO₂-minimal dispatch is calculated by deriving national CO₂-emission factors and compared with an economically optimal dispatch. The case studies are analysed based on real industrial load data from small, medium and large enterprises. The thesis discusses the technical and economic framework conditions, with the main focus on Germany. However, a comparison between the countries Germany, Denmark, and Croatia is also presented. The results show that peak shaving and the provision of frequency containment reserve are complementary and make the investment in a battery storage system economically viable. Self-generation through a photovoltaic system can reduce the risk arising from uncertain energy market prices. However, the sequence of uncertain decisions has a significant impact on the design of the battery storage system. Economically feasible operation through arbitrage trading, on the other hand, is not possible due to the small price differences in the markets and limitations due to battery ageing and efficiency. These battery characteristics also influence the use of a battery storage system for CO₂-reduction. Due to the limited number of cycles and relatively high charging losses, battery technology is currently unsuitable for CO₂-minimal storage use. Nevertheless, the economic and ecological potential of battery storage systems strongly depends on individual factors such as local grid charges, the selected battery technology and the individual industrial load profile. Advances in battery technology, such as increased lifetime, and possible new flexibility markets, such as dynamic grid charges, offer new application and marketing opportunities that could increase the economic viability of a battery storage system.

Projekt LowEx-Bestand Analyse, B

Datasets used in the publication "Comparing empirical and model-based approaches for calculating dynamic grid emission factors: an application to CO2-minimizing storage dispatch in Germany" ( https://doi.org/10.1016/j.jclepro.2020.121588 ). The dataset include the following: Match of the ENTSO, EUTL and LCPD database: Matching_Entso_EUTL_LCPD.csv Average emission factor per power plant/generation unit in t/kWh CO2: CO2_EFPerPowerPlant.csv Other emissions: OtherEmissions_EFPerPowerPlant.csv Dynamic emission factors in hourly resolution for the year 2017 in t/kWh: DynEF_....csv Results of the optimized dispatch for two exemplary Companies CO45 and CO46 for a generic storage system and a battery storage system: Results_CO..._....csv It shows the grid load profile in kWh in 15-Minute resolution according to the original load profile: GridLoad_kWh the economic dispatch with a storage system: GridLoadkWh_econ the environemntal dispatch with a storage system following the AEF: GridLoadkWh_AEF the environemntal dispatch with a storage system following the MSR: GridLoadkWh_MSR the environemntal dispatch with a storage system following the MPM: GridLoadkWh_MPM the environemntal dispatch with a storage system following the MPP: GridLoadkWh_MPP Datasets used for the different graphical figures in the publication packed in the file: FiguresData.zip Named after the reference number in the publication: fig...Data....csv &nbsp

Datasets used in the publication "Comparing empirical and model-based approaches for dynamic grid emission factors: an application to CO2-minimizing storage dispatch in Germany". The dataset include the following: Match of the ENTSO, EUTL and LCPD database: Matching_Entso_EUTL_LCPD.csv Average emission factor per power plant/generation unit in t/kWh CO2: CO2_EFPerPowerPlant.csv Other emissions: OtherEmissions_EFPerPowerPlant.csv Dynamic emission factors in hourly resolution for the year 2017 in t/kWh: DynEF_....csv Results of the optimized dispatch for two exemplary Companies CO45 and CO46 for a generic storage system and a battery storage system: Results_CO..._....csv It shows the grid load profile in kWh in 15-Minute resolution according to the original load profile: GridLoad_kWh the economic dispatch with a storage system: GridLoadkWh_econ the environemntal dispatch with a storage system following the AEF: GridLoadkWh_AEF the environemntal dispatch with a storage system following the MSR: GridLoadkWh_MSR the environemntal dispatch with a storage system following the MPM: GridLoadkWh_MPM the environemntal dispatch with a storage system following the MPP: GridLoadkWh_MPP Datasets used for the different graphical figures in the publication: fig...Data....csv &nbsp

As one possibility to increase flexibility, battery storage systems (BSS) will play a key role in the decarbonization of the energy system. The emissions-intensity of grid electricity becomes more important as these BSSs are more widely employed. In this paper, we introduce a novel data basis for the determination of the energy system’s CO$_{2}$ emissions, which is a match between the ENTSO-E database and the EUTL databases. We further postulate four different dynamic emission factors (EF) to determine the hourly CO$_{2}$ emissions caused through a change in electricity demand: the average emission factor (AEF), the marginal power mix (MPM), the marginal system response (MSR) and an energy-model-derived marginal power plant (MPP). For generic and battery storage systems, a linear optimization on two levels optimizes the economic and environmental storage dispatch for a set of 50 small and medium enterprises in Germany. The four different emission factors have different signaling effects. The AEF leads to the lowest CO$_{2}$ reduction and allows for roughly two daily cycles. The other EFs show a higher volatility, which leads to a higher utilization of the storage system from 3.4 to 5.4 daily cycles. The minimum mean value for CO$_{2}$ abatement costs over all 50 companies is 14.13 €/t$_{CO}$$_{2}$.