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Self-consumption of household photovoltaic (PV) storage systems has become profitable for residential owners under the trends of limited feed-in power and decreasing PV feed-in tariffs. For individual PV-storage systems, the challenge mainly lies in managing surplus generation of battery and grid power flow, ideally without relying on error-prone forecasts for both generation and consumption. Considering the large variation in power profiles of different houses in a neighborhood, the strategy is also supposed to be beneficial and applicable for the entire community. In this study, an adaptable battery charging control strategy is designed in order to obtain minimum costs for houses without any meteorological or load forecasts. Based on fuzzy logic control (FLC), battery state-of-charge (SOC) and the variation of SOC (∆SOC) are taken as input variables to dynamically determine output charging power with minimum costs. The proposed FLC-based algorithm benefits from the charging battery as much as possible during the daytime, and meanwhile properly preserves the capacity at midday when there is high possibility of curtailment loss. In addition, due to distinct power profiles in each individual house, input membership functions of FLC are improved by particle swarm optimization (PSO) to achieve better overall performance. A neighborhood with 74 houses in Germany is set up as a scenario for comparison to prior studies. Without forecasts of generation and consumption power, the proposed method leads to minimum costs in 98.6% of houses in the community, and attains the lowest average expenses for a single house each year.
Driven by the need for a sustainable energy transition and a paradigm shift in the energy and mobility sectors, the popularity of electric vehicles is on the rise. Learning curve effects and falling investment costs further accelerate the deployment of electric vehicles with lithium-ion batteries; and as a multi-purpose technology, they are predestined for serving multiple applications. In this work we present an electric vehicle multi-use approach for a German commercial electricity consumer with an electric vehicle fleet. We analyze which behind-the-meter and in front-of-the-meter applications are particularly suitable for electric vehicles from a techno-economic point of view. In addition to providing the mobility service, we investigate the applications self-consumption increase, peak shaving, frequency regulation, and spot market trading. For the implementation of the approach, we introduce a model predictive control framework in which a mixed-integer linear programming algorithm is combined with a semi-empirical degradation model. The approach is analyzed with the investigation of fleet sizes from 1 to 150 vehicles, different application combinations, possible energy shift between the energy partitions, bidirectional charging schemes, and degradation awareness formulations. The results show that the deployment flexibility and application synergies increase with the number of stacked services, leading to additional annual cash flows of up to 2224 EUR per electric vehicle as well as battery lifetime improvements.
Unlocking the Potential of Battery Storage With the Dynamic Stacking of Multiple Applications
(2020)
The ability of a battery energy storage system (BESS) to serve multiple applications makes it a promising technology to enable the sustainable energy transition. However, high investment costs are a considerable barrier to BESS deployment, and few profitable application scenarios exist at present. Here, we show that by tapping into multiple revenue streams using the dynamic stacking of applications, profitable operation is viable under current regulatory conditions. We develop a multi-use optimization framework which distinguishes between behind-the-meter and in-front-of-the-meter applications and considers how power capacity is allotted in addition to energy capacity allocation. The algorithm uses a rolling horizon optimization with an integrated degradation model and is fed with real-world data from a stationary lithium-ion battery in Germany. When combining peak shaving with frequency containment reserve, a net present value per Euro invested of 1.00 is achieved, and 1.24 with the addition of arbitrage trading on the intraday continuous market.
In the context of the increased acceptance and usage of electric vehicles (EVs), vehicle-to-building (V2B) has proven to be a new and promising use case. Although this topic is already being discussed in literature, there is still a lack of experience on how such a system, of allowing bidirectional power flows between an EV and building, will work in a residential environment. The challenge is to optimize the interplay of electrical load, photovoltaic (PV) generation, EV, and optionally a home energy storage system (HES). In total, fourteen different scenarios are explored for a German household. A two-step approach is used, which combines a computationally efficient linear optimizer with a detailed modelling of the non-linear effects on the battery. The change in battery degradation, storage system efficiency, and operating expenses (OPEX) as a result of different, unidirectional and bidirectional, EV charging schemes is examined for both an EV battery and a HES. The simulations show that optimizing unidirectional charging can improve the OPEX by 15%. The addition of V2B leads to a further 11% cost reduction, however, this corresponds with a 12% decrease in EV battery lifetime. Techno-economic analysis reveals that the V2B charging solution with no HES leads to strong self-consumption improvements (EUR 1381 savings over ten years), whereas, this charging scheme would not be justified for a residential prosumer with a HES (only EUR 160 savings).
Lithium-ion cells are subject to degradation due to a multitude of cell-internal aging effects, which can significantly influence the economics of battery energy storage systems (BESS). Since the rate of degradation depends on external stress factors such as the state-of-charge, charge/discharge-rate, and depth of cycle, it can be directly influenced through the operation strategy. In this contribution, we propose a model predictive control (MPC) framework for designing aging aware operation strategies. By simulating the entire BESS lifetime on a digital twin, different aging aware optimization models can be benchmarked and the optimal value for aging cost can be determined. In a case study, the application of generating profit through arbitrage trading on the EPEX SPOT intraday electricity market is investigated. For that, a linearized model for the calendar and cyclic capacity loss of a lithium iron phosphate cell is presented. The results show that using the MPC framework to determine the optimal aging cost can significantly increase the lifetime profitability of a BESS, compared to the prevalent approach of selecting aging cost based on the cost of the battery system. Furthermore, the lifetime profit from energy arbitrage can be increased by an additional 24.9% when using the linearized calendar degradation model and by 29.3% when using both the linearized calendar and cyclic degradation model, compared to an energy throughput based aging cost model. By examining price data from 2019 to 2022, the case study demonstrates that the recent increases in prices and price fluctuations on wholesale electricity markets have led to a substantial increase of the achievable lifetime profit.
The market ramp-up of electromobility is shifting vehicle-to-grid (V2G) issues into the focus of research and industry. Electric vehicles (EVs) have the potential to support the trend towards renewable energies in their role as storage units during idle times. To participate in balancing power and energy markets, EVs are pooled via aggregators. Instead of a random composition, aggregators can smartly compose their pools and add only those vehicles that actually contribute to the pool’s performance, gaining advantages over competitors. The optimization methods presented in this paper form optimized pool combinations based on the power and energy capability profiles of commercial EVs. Genetic algorithms are used to determine the revenues of the possible pools per participating EV. The use cases analyzed are the provision of balancing power on the frequency containment reserve (FCR) market of Central Europe and energy arbitrage trading on the European power exchange intraday continuous and day-ahead auction spot markets. The results show that through smart pool composition, an aggregator can increase revenue per vehicle by up to seven-fold across the markets compared to randomly assembled pools. In the Central European market, for example, the potential V2G revenues on the FCR market (380 €) exceeded those of arbitrage trading (28 € − 203 €) in 2020. In a simulation, we show the increased degradation of the vehicle battery in V2G operation compared to sole use for mobility with a smart charging strategy. However, the additional revenue can make V2G financially worthwhile, depending on costs for measuring equipment, bidirectional charging stations, and aggregator costs.
This work discusses the grid-level suitability for stationary battery energy storage systems based on lithium ion technology in general, focusing on the integration of such systems in the low-voltage grid-level in Europe. The vast majority of recent research regarding stationary battery storage systems focuses on single-electricity-grid voltage levels, whereas the interaction of different electricity grid-levels has been widely neglected. Challenges in distribution grids mostly originate from the quantity of challenges in low-voltage grids, stationary battery energy storage systems that are operated in low-voltage grids may reduce these challenges. Therefore, this work investigates stationary battery energy storage systems installed in low-voltage grids and their effects on superimposed grid-levels. Simulation results show that grid challenges, addressed by battery storage systems in low-voltage grids, have positive multiplicative impacts on upper grid levels, reducing local grid demand and lowering power peak stress. A proposal is presented for a low-voltage grid battery storage system that can be operated to fulfill the aforementioned functionality and yield the benefits via a multi-purpose or multi-tasking battery storage system.