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 CO2 .
In this paper, we investigate the effect of distributed flexibilities on the operation of the transmission grid. The flexibilities considered are heat pumps, electric vehicles, battery energy storage systems and flexible renewable generation. For this purpose, we develop a two-stage approach of first determining an optimal electricity market solution considering the optimal dispatch of each generation element and flexibility. In the second step we determine the required dispatch adjustments due to transmission grid constraints and investigate the effect of integrating battery energy storage systems into the adjustable generators to solve congestions. In our case study, we investigate the central European transmission grid for a scenario based on the Distributed Generation scenario of the Ten-Year Network Development Plan for the year 2030. Integrating distributed flexibilities leads to a strong increase in the security of supply, while the overall effect on the generation adjustment is small. A comparison of the results for an AC and DC formulation shows that both approaches differ significantly in individual cases.
Flow Based Market Coupling is the target model for determining exchange capacities in the internal European Electricity Market. It has been in operation in Central Western Europe since 2015 and is scheduled to be extended to the wider Core region in the near future. Exchange capacities have a significant impact on market prices, exchanges and the energy mix, thus also determining the CO$${}_{2}$$
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footprint of electricity generation in the system. Stakeholders therefore need to develop an understanding for the impact of Flow Based Market Coupling and the parameter choice, like the minimum exchange capacities introduced in 2020, on the market outcome. This article presents a framework to model Flow Based Market Coupling and analyse the impact of different levels of regulatory induced minimum trading capacities as well as the effect of the extension towards the Core region. Electricity prices, exchange positions and the number and nature of binding constraints in the market results under different market coupling scenarios are investigated. The results show that increased level of minimum trading capacities in CWE market coupling decrease the German net export position by up to 7 TW h or 23%, while French exports increase by up to 10 TW h or 9%. The different transfer capacity in the scenarios induce a price difference of up to 13%. Increased exchange capacities allow for more base load generation with the corresponding effects for the CO$${}_{2}$$
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emissions of the system. The nature of coupling constraints is highly dynamic and dependent on the system state, which makes the suitability of static NTC values in energy system scenarios questionable.
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