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Energy storage is a crucial flexibility measure to temporally decouple power generation from power demand and is touted as the missing link in realizing a decarbonized energy system based on renewable energy. Energy storage capacity buildup at all levels of the global energy system is expected to accelerate the decarbonization process. To this end, a coherent mathematical framework to ascertain the carbon footprint of localized energy systems with energy storage is indispensable. This article presents an open-source energy system simulation program — Energy System Network (ESN). A variety of energy system configurations can be simulated with the Python program, which incorporates key energy system components such as generation, grid, storage, and loads. ESN features an integrated bottom-up approach that combines energy system modeling with streamlined life cycle assessment techniques to quantify the carbon footprint of all components in a localized energy system. The lifecycle phases of each component, including production, operation, and end-of-life treatment, can be considered. Carbon footprint values are obtained for two demonstrative case studies with lithium-ion battery applications: energy arbitrage and home energy systems. The metric Levelized Emissions of Energy Supply (LEES) has been used to evaluate the carbon footprint of each application. An unconventional energy arbitrage strategy designed to exploit the grid carbon intensity spreads instead of the energy price spreads manages to achieve a LEES value about 17% lower than the conventional variant. The influence of rooftop solar generation, battery energy storage system, and the energy management strategy on the LEES values for a home energy system is explored. A maximum LEES reduction of over 37% vis-á-vis the base scenario was observed with optimal energy management for the solar generation and the battery system. The open-source availability of ESN can contribute to transparency, comparability, and reproducibility in carbon footprint assessments of localized energy systems with energy storage.
The energy transition in the mobility sector is well underway. The electrification of road transport is resulting in a shift of the energy demand from the oil and gas sector to the electricity grid. Increasingly aggressive targets for low charging times for Electric Vehicles (EVs) are slated to raise the demand for High-Power Charging (HPC). This is likely to lead to bottlenecks and overloading in vulnerable sections of the electricity grid. Battery Assistance (BA) is a promising grid integration measure for High-Power Charging (HPC) to mitigate these problems. As decarbonization is the primary objective of the energy transition, the determination and comparison of the Global Warming Potential (GWP) footprints for HPC stations with BA is crucial. A comprehensive mathematical framework for the modelling and quantification of GWP footprints for HPC has been developed. The Levelized Emissions of Energy Supply (LEES) methodology has been extended and generalized to handle energy from the grid. A new state variable for the Battery Energy Storage System (BESS) — the State of Carbon Intensity (SOCI) has been introduced to calculate the operation phase GWP footprint of the BESS. The energy consumption GWP footprint for the load is also described by a new quantity — the Load Energy Consumption (LEC) emissions. The effect of incorporation of local Photovoltaic Solar (PV) generation in the energy flows is also investigated. An optimized Energy Management System (EMS) strategy with rolling horizon optimization to minimize emissions has been implemented to regulate energy flows in scenarios with BA and local PV generation. The Levelized Emissions of Energy Supply (LEES) values are obtained for all simulated scenarios and compared against a baseline rule-based EMS strategy. In combination with on-site PV generation, BA could achieve a reduction of 24% in the LEES vis-á-vis the baseline strategy. For reference, two scenarios with Grid Reinforcement (GR) for the grid section with and without local PV generation have also been simulated. With Grid Reinforcement (GR), a reduction of over 2% can be achieved with respect to the baseline EMS strategy for BA. Grid Reinforcement (GR) in conjunction with local PV generation can bring about a further reduction of about 6% with respect to the baseline EMS strategy for BA.
Electrical energy storage systems are key to the integration of intermittent renewable energy technologies such as photovoltaic solar systems and wind turbines. As installed battery energy storage system capacities rise, it is crucial that the environmental impacts of these systems are also positive. In this work, a methodology to ascertain the effect and effectiveness of integration of energy storage on the carbon footprint of isolated island grid energy systems and its reduction is presented. Two metrics are introduced — the Levelized Emissions of Energy Supply (LEES), and the reduction in emissions per additional unit of energy storage (R). The proposed methodology is applied to an island grid scenario to ascertain the variation in the LEES value with the peak power and energy storage capacity of the BESS. A simplified LCA of a utility-scale Lithium-ion BESS is also carried out for this purpose. It is found that for the considered scenarios, incorporation of battery systems is always effective in reducing emissions, with a maximum possible reduction of nearly 50% compared to no storage. With the help of the metric R, the proposed methodology is also useful in identifying isolated energy systems which should be prioritized for incorporation of additional energy storage capacity.
Battery-Assistance vs. Grid Reinforcement for High-Power EV Charging: An Emissions Perspective
(2021)
An increasingly large number of battery electric vehicles (BEVs) and electric buses are hitting the roads globally each year. These numbers are expected to grow further in light of ever-more favourable costs, and improved battery technology that addresses concerns about range anxiety and charging times. This upward trend in electrification of the automobile sector essentially shifts the mobility energy demand from the oil & gas sector to the electricity sector. Power transmission bottlenecks in the grid, caused on the supply-side by periods of strong renewable generation, may get further aggravated by adding demand-side bottlenecks to the mix as well. The grid, at several locations, will need to be overhauled to be able to supply huge bursts of power intermittently to cover large power demands to support fast-charging simultaneously at multiple locations. This can entail huge monetary investments for the upgradation of grid infrastructure. Battery-assisted high power charging (BA-HPC) is thought of as a convenient solution to this problem. This solution enables demand-side peak- load shaving, and draws energy more uniformly over extended periods of time. While this solution may well be economically profitable in some cases, an investigation of the environmental impact of this solution can yield interesting insights, and aid decision-makers by identifying scenarios in which one solution is more favorable over the other. This is crucial, since the rationale for shifting to electromobility is the curbing of greenhouse gas emissions. In this work, we employ in-house python-based time-series analysis simulation tools to simulate the grid reinforcement and the battery energy storage systems, and present comparative lifetime emissions analyses for both solutions in a variety of configurations.
Lithium-ion batteries are used for both stationary and mobile applications. While in the automotive industry standard profiles are used to compare the performance and efficiency of competing vehicles, a similar comparative metric has not been proposed for stationary battery energy storage systems. Because standard profiles are missing, the comparable evaluation of different applications with respect to efficiency, long-term behavior and profitability is very difficult or not possible at all. This work presents a method to create these standard profiles and the results are available as open data for download. Input profiles including frequency data, industry load profiles and household load profiles are transformed into storage profiles including storage power and state of charge using a holistic simulation framework. Various degrees of freedom for the energy management system as well as for the storage design are implemented and the results are post-processed with a profile analyzer tool in order to identify six key characteristics, these being: full-equivalent cycles, efficiency, depth of cycles, resting periods, number of changes of sign and energy throughput between changes of sign. All applications examined in this paper show unique characteristics which are essential for the design of the storage system. E.g., the numbers for annual full-equivalent cycles vary from 19 to 282 and the efficiency lies between 83% and 93%. With aid of this work in conjunction with the open data results, users can test and compare their own cell types, operation strategies and system topologies with those of the paper. Furthermore, the storage power profiles and state of charge data can be used as a reference for lifetime and profitability studies for stationary storage systems.
SimSES: A holistic simulation framework for modeling and analyzing stationary energy storage systems
(2022)
The increasing feed-in of intermittent renewable energy sources into the electricity grids worldwide is currently leading to technical challenges. Stationary energy storage systems provide a cost-effective and efficient solution in order to facilitate the growing penetration of renewable energy sources. Major technical and economical challenges for energy storage systems are related to lifetime, efficiency, and monetary returns. Holistic simulation tools are needed in order to address these challenges before investing in energy storage systems. One of these tools is SimSES, a holistic simulation framework specialized in evaluating energy storage technologies technically and economically. With a modular approach, SimSES covers various topologies, system components, and storage technologies embedded in an energy storage application. This contribution shows the capabilities and benefits of SimSES by providing in-depth knowledge of the implementations and models. Selected functionalities are demonstrated, with two use cases showing the easy-to-use simulation framework while providing detailed technical analysis for expert users. Hybrid energy storage systems consisting of lithium-ion and redox-flow batteries are investigated in a peak shaving application, while various system topologies are analyzed in a frequency containment reserve application. The results for the peak shaving case study show a benefit in favor of the hybrid system in terms of overall cost and degradation behavior in applications that have a comparatively low energy throughput during lifetime. In terms of system topology, a cascaded converter approach shows significant improvements in efficiency for the frequency containment reserve application.