A charging and dispatching strategy for optimizing profits from V2G is presented. This optimization strategy builds on temporally resolved electricity market data. A case study shows that this method turns a S$ 1000 annual loss into a S$ 130 profit. Sensitivity analyses indicate potential for further increase of profitability. Employing this strategy in other countries is assumed to yield much greater profits. a r t i c l e i n f o b s t r a c tEmploying electric vehicles as short-term energy storage could improve power system stability and at the same time create a new income source for vehicle owners. In this paper, the economic viability of this concept referred to as Vehicle-to-Grid is investigated. For this purpose, a price-responsive charging and dispatching strategy built upon temporally resolved electricity market data is presented. This concept allows vehicle owners to maximize returns by restricting market participation to profitable time periods. As a case study, this strategy is then applied using the example of Singapore. It is shown that an annual loss of S$ 1000 resulting from a non-price-responsive strategy as employed in previous works can be turned into a S$ 130 profit by applying the price-responsive approach. In addition to this scenario, realistic mobility patterns which restrict the temporal availability of vehicles are considered. In this case, profits in the range of S$ 21eS$ 121 are achievable. Returns in this order of magnitude are not expected to make Vehicle-to-Grid a viable business case, sensitivity analyses, however, show that improved technical parameters could increase profitability. It is further assumed that employing the priceresponsive strategy to other national markets may yield significantly greater returns.

As a result of growing markets for electric vehicles and residential batteries for buffering energy from photovoltaics, the number of grid-integrated lithium-ion batteries has been continuously increasing in the past years. Apart from their primary purpose, these batteries may also be employed to provide services to the power grid in terms of peak shaving or frequency regulation. The profitability of such services for the battery owner, however, remains a controversial issue. Particularly battery degradation resulting from increased energy throughput is discussed as a major impediment for profitable operation. This paper presents a scheduling approach which considers the non-linear dependencies of battery aging from various operation parameters along with real-time prices and price forecasts for computing optimal charging/dispatching schedules. The methodology is applied to price-data obtained from four different electricity markets. The investigation partly confirms existing profitability concerns but further shows that explicit consideration of battery degradation can yield profitable outcomes. Various scenarios using aggregated and locational marginal prices as well as different forecasting horizons and time resolutions are explored to identify favorable operating conditions.

No abstract

The large-scale introduction of plug-in electric vehicles (PEV) may pose challenges to power system operators by causing grid congestion or voltage fluctuations. This work presents a simulation-based approach for investigating the impact of transport electrification on power grids. The framework consists of an agent-based traffic simulation which is coupled with a power system simulation through the IEEE Standard High Level Architecture. As detailed power grid information is often unavailable, the framework further contains a method for synthesizing power networks from tempospatially resolved demand data. Using a high-performance computing infrastructure, the approach allows simulating the traffic and power system on the scale of a megacity faster than real-time. An application to the example of Singapore shows that grid congestion and voltage drops are observed on the low voltage level while the high and medium voltage grid remain unaffected. The presented framework may facilitate infrastructure decisions and support the development of smart charging strategies minimizing power grid impact.

No abstract

This work presents a modular power system planning and power flow simulation framework for the generation and evaluation of power network models (PNM) using spatially resolved demand data. It targets users who want to study large-scale power grids having only limited information on the actual power system. Besides creating cost minimal PNMs, users are able to flexibly configure the framework to produce PNMs individually tailored to their specific use cases. Both greenfield and expansion planning are possible. The framework further comprises a built-in ac power flow simulation designed to simulate power flows in large-scale networks. This allows users to conduct a great variety of simulation studies on entire power systems, which would otherwise not be possible without access to comprehensive power grid data. Apart from the presentation of the methodology, this work comprises a demonstration of the power system planning process at the example of Singapore. The investigation shows that the framework is capable of generating a network that matches the topological and electrical metrics of the Singapore power grid.

With the increasing complexity of real-world energy systems its modeling process becomes even more crucial when large-scale simulations are conducted. Being computational intensive and therefore requiring efficient simulation models a modeling scheme with a well-defined formal syntax definition is developed and together with its meta model proposed in this paper. This Universal Scheme for modeling Energy Systems (USES) is the preferred language for the power system simulation presented here as being part of our Scalable Electro-Mobility Simulation (SEMSim) platform. For investigating the impact of electro-mobility on the city infrastructure the transmission system of Singapore is described as real, data, and formal model, the first two based on USES.

No abstract