Alexander Schuller

Charging Strategies for Battery Electric Vehicles: Economic Benchmark and V2G Potential

This paper benchmarks the economic benefits of a smart charging and a vehicle-to-grid (V2G) operating strategy of an electric vehicle (EV) against a zero-intelligence charging strategy in a simulation-based analysis. Smart charging optimizes the timing of EV charging, while V2G additionally allows for selling electricity to the energy market. The strategies internalize the wear on energy storage equipment. The simulation builds on over 11 400 empirical driving profiles from a German mobility panel and applies technical specifications of three currently available EVs. This allows for a realistic analysis of the applicability of smart charging and V2G as well as the extent to which EVs are capable to fulfill current driving needs. Results show that smart charging strategies will reduce charging cost on average by more than 32% in all analyzed cases. V2G could provide additional revenue for EV owners with shorter trip patterns while reducing electricity cost for others.

Economic benchmark of charging strategies for battery electric vehicles

This paper investigates a smart charging strategy and a Vehicle-to-Grid (V2G) strategy and benchmarks the economic benefits for the electric vehicle (EV) owner against a zero-intelligence charging strategy in a simulation-based analysis. Smart charging optimizes the timing of charging the EV and V2G additionally allows for reselling electricity to the energy market.

The impact of charging strategies for electric vehicles on power distribution networks

This work investigates four different generic charging strategies for battery electric vehicles (BEVs) with respect to their economic performance and their impact on the local power distribution network of a residential area in southern Germany. The charging strategies are Simple Charging (uncontrolled), Smart Charging (cost minimal), Vehicle to Grid Charging (V2G) and Heuristic V2G Charging. The simulation setting includes a high share of local renewable generation as well as typical residential load patterns to which different penetration levels of BEVs are added for the evaluation. Prices are determined on a regional energy market with agents representing the participating households (including PV generation and BEVs) as well as the traditional supply for the local power distribution network via the point of common coupling (PCC). Results show that Smart and V2G Charging lead to cost reductions for electric mobility of 40 % or 75% respectively per week and household. At the same time additional stress is put on the distribution network which shows a need for further coordination of BEV charging.

Benchmarking electric vehicle charging control strategies

Electric vehicles (EVs) are expected to become an important part of individual mobility. In order to reduce CO2 emissions and release the full potential for sustainable mobility, EVs need to be charged with energy from renewable energy sources (RES). We employ a deterministic linear optimization approach with different coordination objectives for each simulation scenario. The objectives are to minimize the individual average charging costs, maximize the average use of wind power or minimize the average load factor for the charging times of each EV customer. Customers have real life driving profiles from the German mobility panel and are distinguished in employees and retired with their respective driving behavior. We find that the wind power share used for charging can be nearly doubled for both groups under the respective strategy. Average costs are increased in comparison to the cost oriented strategy but are considerably lower as in the uncoordinated charging case.

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.

Assessing the Economic Potential of Electric Vehicles to Provide Ancillary Services: The Case of Germany

The Vehicle-to-Grid (V2G) concept is a promising possibility for the integration of electric vehicles (EVs) into the power grid. This article presents an economic evaluation of EVs participating in the ancillary service market (primary, secondary and tertiary regulation) for the case of Germany, based on a price data set from 2011 and 2012. We examine the economic potential of nine general options to participate in the regulation market based on real-life EV specifications, connection powers and regulation energy prices. Results show that in the most profitable case a maximum average yearly profit of 730.31 € per vehicle is possible for negative regulation with payment direction TSO to provider in the secondary regulation market. Furthermore a sensitivity analysis is performed for all of the analyzed participation scenarios in order to identify crucial parameters for a possible V2G implementation. Major parameters for the successful implementation of V2G are the provided power per vehicle, the time an EV is available to the grid and the variable energy storage costs it incurs.

Charging Coordination Paradigms of Electric Vehicles

The Smart Grid enables bidirectional communication between distributed actors and resources in the power system. In particular Plug-In Electric Vehicles (PEVs) are a new type of load that has a considerable (time) flexibility in its demand. In order to integrate and harvest this flexibility within a DSM (Demand Side Management) program, the charging of PEVs needs to be coordinated. The coordination must occur with respect to a given objective. In addition, the coordination of demand requirements can be performed within different communication and control architectures. The main architectural concepts that can be distinguished are decentralised and centralised control architectures. These categories refer to the level on which the charging decision is made, given an objective and constraints that need to be met given a certain user scenario. This work reviews in detail the recent work with respect to different charging coordination paradigms and distinguishes between the following main objectives: grid integration and technical implications, explicit integration and direct utilization and balancing of renewable energy sources and finally, economic driven decisions. The discussion performed in this chapter shows that in the category with a predominantly technical focus, V2G (Vehicle-to-Grid) and grid load (regional and system-wide) impacts are the main research areas. Work looking into the integration ability of renewable energy sources enabled by PEV demand flexibility is in particular focused on the reduction of imbalances stemming from fluctuating generators, e.g. wind power, on a system and also on regional scales under consideration of grid constraints. Short term storage applications are also discussed, but the coordination of PEV demand flexibility by dynamic price incentives is not covered very extensively. Work from the economic domain focuses on the assessment of regulation market participation and day-ahead wholesale market oriented charging. These approaches in turn do not intensively investigate the effect of cost minimizing charging strategies with respect to the utilization of fluctuating renewable energy sources. By consistently discussing recent work from various areas looking into the versatile facets of charging coordination paradigms of electric vehicles, this work provides an anchor for further investigations that help to harvest the demand side flexibility of Electric Vehicles.

Simulations in the Smart Grid Field Study MeRegio Simulationen im MeRegio Smart Grid Feldtest

Abstract The aim of the research project MeRegio is to meet the claim for more efficient decentralized energy systems by integrating advanced information and communication technologies (ICT) into all stages of the energy supply chain. Several marketplaces — in particular for power and for ancillary services — which are coupled to the technical energy infrastructure through a powerful and lawful ICT infrastructure should serve as a basis for an efficient and transparent coordination of energy supply, energy demand, and services. The developed concepts will be both validated by simulations and tested within a model region.

Crowd Sensing of Road Conditions and Its Monetary Implications on Vehicle Navigation

This paper quantifies the monetary impact of using road roughness data for path planning. Using a crowd-based data source, a vehicle cost model we performed a sensitivity analysis to investigate the monetary implications on vehicle owners. The results are presented as a collection of trade-off matrices showing potential yearly cost savings for different car types, road roughness levels. Moreover, the dependency between fuel price, overall cost savings is presented. Although the cost savings depend on vehicle type, on the fuel costs, our results show that the main factor is the amount of road segments with high roughness index. In particular, car owners can benefit from rerouting to a smoother road profile only in regions with road roughness at least IRI ~ 4 m/km.

Pricing of demand flexibility: Exploring the impact of Electric Vehicle customer diversity

Electric Vehicles (EVs) are a large, but also quite flexible load in the power system. Car parks constitute major future load clusters that need to coordinate charging requests from EVs according to local grid and supply conditions. For effective grid integration, it is necessary to understand how to influence the charging behavior of EV customers. A deadline differentiated pricing approach is employed to create incentives for EV customers to offer their load flexibility to the car park operator. We explore the effect of different utility diversity models and flexibility levels of EV customers in a car park scenario under consideration of local photovoltaic power generation. Our results indicate that a homogeneous customer utility model overestimates the car park operator profits by more than 17% as compared to a realistic heterogeneous model. Furthermore, we observe that the car park type, and thus the customer parking time also drives the attained profits.