Sebastian Gottwalt

Improving Electric Vehicle Charging Coordination Through Area Pricing

Meeting charging demands of large electric vehicle fleets will raise electrical load significantly and may pose challenges for todays power system. Appropriate coordination of electric vehicle charging can reduce these threats. Acknowledging the interdependency between the transportation and the power system created by electric vehicles, we develop a charging coordination model based on German mobility data. We extend the prior work by explicitly accounting for both the temporal and the spatial dimension. We are thus able to analyze the loads from price-based EV fleet charging while at the same time accounting for distribution grid constraints. Furthermore, we propose a heuristic charging strategy based on limited trip and price information. Our results show that the sole use of time-based electricity prices for the coordination of electric vehicle charging produces high load spikes independent of the charging strategies and power levels. These peaks are induced by simultaneous charging activity and may cause stability problems within distribution grids in residential areas. To mitigate these load spikes, we introduce a spatial price component that reflects local capacity utilization. These local prices induce both a temporal and spatial shift of charging activity that mitigates the load spikes.

Modeling and Valuation of Residential Demand Flexibility for Renewable Energy Integration

The share of renewable generation (RG) in the energy mix has seen constant growth in recent years. RG is volatile and not (fully) controllable. Consequently, the alignment of stochastic demand with supply, which is fundamental for ensuring grid stability, becomes more difficult. The utilization of demand side flexibility as well as RG portfolio design are attractive opportunities to avoid excessive investments in conventional power plants and costs for balancing power. This paper provides a comprehensive centralized scheduling model to exploit demand flexibility from residential devices. We analyze the monetary value of households for demand response (DR) by determining the potential of various current and possible available future end consumer devices to reduce generation costs of a flexibility aggregator in a microgrid with a large share of RG. Furthermore, we identify key characteristics affecting the value of demand flexibility and derive recommendations for an aggregator’s RG portfolio structure. Our simulation results indicate that electric vehicles, stationary batteries, and storage heaters are the most promising devices for residential DR. Furthermore, we show that the potential of a device to directly utilize intermittent RG is largely influenced by the composition of the renewable energy source portfolio.

A Revenue Management Approach for Efficient Electric Vehicle Charging Coordination

Ambitious goals of electric vehicle (EV) penetration may conflict with the capabilities of todays power system. Especially simultaneous charging at home may lead to significant load spikes or grid stability issues. Prior research has identified the need for appropriate coordination approaches. This research focuses mostly on coordinating system loads ignoring local grid constraints. The suggested mechanisms either are centralized control approaches ignoring user preferences or based on the electrical energy cost. We propose to complement these approaches by using mechanisms from revenue management for perishable assets. First, we formalize charging coordination as a minimal revenue management problem and then derive an appropriate advance sale mechanism. By accounting for heterogeneous customer segments, this approach can achieve a socially efficient allocation of available charging capacity. Using a local neighborhood scenario, we evaluate the impact of such an approach.

Assessing load flexibility in smart grids: Electric vehicles for renewable energy integration

Demand response can contribute to system stability and foster integration of renewable energy sources. In our work we model static residential electricity demand together with flexible electric vehicles (EVs) as charging loads. We develop a mixed-integer program to assess the ability of an EV fleet operator to coordinate charging in such a way that a maximum amount of renewable energy is used. Such coordinated charging still requires that all projected mobility needs are satisfied. EVs are modeled using empirical driving profiles of full time employees. Our results show that compared to uncoordinated immediate charging, an optimized charging schedule can nearly double the share of renewable energy used and achieve a yearly supply from wind power of up to 67.2%. In addition, we find that coordinated charging decreases load peaks and reduces the amount of conventional generation required as backup capacity.