The electrification of the transport sector plays a key role in the global energy transition and it is of great necessity to assess emissions induced by electric vehicles in the long term for effective policy-making. Typical life cycle assessment may not consider the impact of electric vehicle integration in future electricity systems adequately, or the time-dependent characteristics of electricity generation mix and EV charging patterns. The solution requires modeling methods to integrate electric vehicle into energy system models, especially with vehicle-to-grid option. However, relevant methods have not been evaluated, yet. This integration is mathematically ambitious especially for huge and heterogeneous fleets of electric vehicles and brings energy system models to their computational limits. So far, current studies have proposed several aggregation methods for the load from electric vehicle charging, which simplify the original problem but may provoke bias. In our contribution, we propose a novel method of integrating vehicle-to-grid compliant electric vehicles into energy system models and demonstrate its feasibility by comparing it with two recent others from the literature. Taking the performance of the individual modeling method as the benchmark, we improve one of the two methods from the literature with updated parameters and additional constraints. We apply all three aggregation methods in a simple energy system model for comparing and analyzing their performances from multiple aspects, that is, solution accuracy, computational complexity, parameter requirement, and their impact on greenhouse gas emissions. Finally, we discuss the reasons behind the differences and give recommendations for further research.

The transformation of the European electricity system could generate unintended environment-related trade-offs, e.g., between greenhouse gas emissions and metal depletion. The question thus emerges, how to shape policy packages considering climate change, but without neglecting other environmental and resource-related impacts. In this context, this study analyzes the impacts of different settings of potential policy targets using a multi-criteria analysis in the frame of a coupled energy system and life cycle assessment model. The focus is on the interrelationship between climate change and metal depletion in the future European decarbonized electricity system in 2050, also taking into account total system expenditures of transforming the energy system. The study shows, firstly, that highly ambitious climate policy targets will not allow for any specific resource policy targets. Secondly, smoothing the trade-off is only possible to the extent of one of the policy targets, whereas, thirdly, the potential of recycling as a techno-economic option is limited.

The growing market share of electric vehicles (EV) has increased the interest in charging strategies and their effects on the electricity system as well as their climatic soundness. However, the benefits of different charging strategies including Vehicle-to-Grid (V2G) on a large regional scale, e.g. in Europe, have not been analyzed sufficiently. This study examines the impact of different charging strategies on greenhouse gas (GHG) emissions from electricity generation and EV batteries in Europe in 2050. To consider indirect emissions and potentially additional battery degradation due to V2G, a model coupling concept is applied to link Life Cycle Assessment (LCA) with the electricity system model, PERSEUS-EU. Overall, EV could reduce the GHG emissions by 36% by simply replacing conventional cars. Controlled unidirectional charging and V2G add another 4 or 11 percentage points on the European level. However, for these gains an efficient implementation of V2G is required.

The integration of electric vehicles (EVs) into the electricity systems comprises both threats and chances. A successful control strategy of EV charging processes is beneficial for both EVs and electricity grid. This paper proposes a scenario-based two-stage stochastic linear programming model for scheduling EV charging processes for different grid requirements in real time using a rolling window approach. The model considers the uncertainties in EV availability (i.e. arrival time and departure time) and electricity demand upon arrival (i.e. initial and target state of charge of the battery). Monte Carlo simulation shows how different input parameters may affect the results. Inhomogeneous Markov Chains are used for EV usage pattern simulation and for scenario generation. For reducing computing time, the amount of scenarios is again reduced by scenario reduction technique. The proposed model is applicable for various grid purposes. We demonstrate the applicability of our model by three example cases: Load flattening (only EV charging load), load leveling (together with conventional household load) and demand response (for wind energy integration or ancillary service).

The exploration and evaluation of strategies for decarbonizing the energy system is the subject of a series of national and international studies conducted by governmental, industrial and independent stakeholders. These studies play an important role in the energy policy debate on understanding and assessing different transformation paths of the energy system, technology options and their implications. They support strategic decisions on the type and scale of investments in the energy system under uncertain future conditions. However, in recent years the increasing complexity of these studies lead to a decreasing transparency even though their transparency and traceability is important for society, politics, research, and industry. In this article, three energy scenarios at different regional scales are reviewed according to their compliance with our pre-defined criteria of transparency. They are analysed in detail with regard to their objectives, methods, data used, results obtained and traceability. Our comparison shows that the results are often presented sufficiently in order to inform decision makers. However, the underlying model-based methods lack information on data exchange between the models, the transparent description of model couplings and a discussion on the rationality of method selection and the strengths and weaknesses of the applied approaches. Based on our findings, we present some general advice for energy scenario developers on how to ensure transparency and traceability in future energy scenario studies.

Since renewable energy is well known as a clean source of power, the incentive for providers and customers to join the renewable energy boom is coming in different ways. Under the forcing policy, the wind generation will be guaranteed to be purchased in the wholesale market if wind generators bid at their marginal price. However, a forcing policy may lead to infeasibility if wind spillage is not allowed. The numerical results in this paper show that integrating more wind generation may lead to market inefficiency or more greenhouse gas emissions, e.g. CO2. To test this hypothesis a deterministic unit commitment was solved by AMPL by modeling power markets and calculating operating costs. With the consideration of the start-up costs and ramping emissions, the cost of emissions is compensated by the generators.

Financial transmission rights (FTRs) are financial instruments that aid participants in hedging against uncertainty in congestion cost. FTRs are funded by congestion rent from the physical market. ISOs ensure financial revenue adequacy by running a simultaneous feasibility test (SFT) to guarantee that available funds cover FTR payments. However, the ability of SFT to guarantee this revenue adequacy is contingent upon a static system topology. Consequently, topology changes in real time could cause revenue inadequacy. Past literature focuses on addressing this issue by policy modifications to real-time topology control. This paper attempts to resolve the revenue inadequacy issue by proposing modifications directly to the SFT.