• Energy Research

The energy transition from a formerly centralized, fossil-fuel based system towards a sustainable system based on a large share of renewable generation calls for a decentralization and regionalization of the electricity system. Local electricity markets (LEMs), on which prosumers and consumers can trade locally produced electricity, meet these requirements and simultaneously enable formerly excluded residential customers to actively take part in the electricity market. However, trading can be complex and time intensive. Therefore, it should be automated. We provide an analysis of intelligent learning strategies for agents of residential electricity customers in LEMs. To this end, we conduct a multi-agent-based simulation of a LEM with a merit order market design based on the current German electricity spot market. LEM agents maximize their individual utility via reinforcement learning. We expand existing approaches of reinforcement learning with generation and storage states as well as time-dependent learning. The evaluation of the strategies is based on the agents' and community electricity storage's revenues, costs, and consumption of local electricity. The results show that for fixed sell prices, time-dependent reinforcement learning of buy bids is the best strategy. It facilitates a market self-consumption of 54 %. For learning buy and sell prices, traditional reinforcement learning with generation states is the dominant strategy.

The energy transition from a formerly centralized, fossil-fuel based system towards a sustainable system based on a large share of renewable generation calls for a decentralization and regionalization of the electricity system. Local electricity markets (LEMs), on which prosumers and consumers can trade locally produced electricity, meet these requirements and simultaneously enable formerly excluded residential customers to actively take part in the electricity market. However, trading can be complex and time intensive. Therefore, it should be automated. We provide an analysis of intelligent learning strategies for agents of residential electricity customers in LEMs. To this end, we conduct a multi-agent-based simulation of a LEM with a merit order market design based on the current German electricity spot market. LEM agents maximize their individual utility via reinforcement learning. We expand existing approaches of reinforcement learning with generation and storage states as well as time-dependent learning. The evaluation of the strategies is based on the agents' and community electricity storage's revenues, costs, and consumption of local electricity. The results show that for fixed sell prices, time-dependent reinforcement learning of buy bids is the best strategy. It facilitates a market self-consumption of 54 %. For learning buy and sell prices, traditional reinforcement learning with generation states is the dominant strategy.

Abstract With an increasing number of electric vehicles (EVs) on the road the need for charging stations increases. Some of these stations are placed at the destinations of drivers rather than along the route of their trip. The aim of this paper is to highlight the importance of user behavior in the context of public destination charging and to provide an extension for existing evaluation methods. We identify losses in demand and potential revenue and show how they are influenced by user behavior. Different scenarios are analyzed to demonstrate the effect of single parameters and to quantify their impact on revenue. To demonstrate the interaction between parameters we present a case study on supermarkets in a medium sized German city. The results show that user behavior is an important factor for the economic evaluation of public destination charging stations and therefore needs to be considered in future evaluation methods. In addition, we show that charging station operators have the possibility to counteract losses by providing more charging stations or increasing the charging power.

Abstract With an increasing number of electric vehicles (EVs) on the road the need for charging stations increases. Some of these stations are placed at the destinations of drivers rather than along the route of their trip. The aim of this paper is to highlight the importance of user behavior in the context of public destination charging and to provide an extension for existing evaluation methods. We identify losses in demand and potential revenue and show how they are influenced by user behavior. Different scenarios are analyzed to demonstrate the effect of single parameters and to quantify their impact on revenue. To demonstrate the interaction between parameters we present a case study on supermarkets in a medium sized German city. The results show that user behavior is an important factor for the economic evaluation of public destination charging stations and therefore needs to be considered in future evaluation methods. In addition, we show that charging station operators have the possibility to counteract losses by providing more charging stations or increasing the charging power.

Abstract Electric vehicles have proven to be a viable mobility alternative that leads to emissions reductions and hence the decarbonization of the transportation sector. Nevertheless, electric vehicle adoption is progressing slowly. Vehicle fleets are a promising starting point for increased market penetration. With this study, we address the issue of fleet electrification by analyzing a data set of 81 empirical mobility patterns of commercial fleets. We conduct a simulation to design a decision support system for fleet managers evaluating which fleets have a good potential for electrification and how fleets can improve the number of successful electric trips by adapting their charging strategy. We consider both heuristics and optimized scheduling. Our results show that a large share of fleets can score a close to optimal charging schedule using a simple charging heuristic. For all other fleets, we provide a decision mechanism to assess the potential of smart charging mechanisms.

Abstract Electric vehicles have proven to be a viable mobility alternative that leads to emissions reductions and hence the decarbonization of the transportation sector. Nevertheless, electric vehicle adoption is progressing slowly. Vehicle fleets are a promising starting point for increased market penetration. With this study, we address the issue of fleet electrification by analyzing a data set of 81 empirical mobility patterns of commercial fleets. We conduct a simulation to design a decision support system for fleet managers evaluating which fleets have a good potential for electrification and how fleets can improve the number of successful electric trips by adapting their charging strategy. We consider both heuristics and optimized scheduling. Our results show that a large share of fleets can score a close to optimal charging schedule using a simple charging heuristic. For all other fleets, we provide a decision mechanism to assess the potential of smart charging mechanisms.

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.

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.