A Merchant Transmission Approach for Uniform-Price Electricity Markets

Uniform-price electricity markets as operated in Germany, for instance, rely on a redispatch mechanism after market clearing to ensure the technical feasibility of generation and consumption schedules with regard to grid constraints. This mechanism determines the costs of congestion management and the welfare loss due to the limited transmission capacity. Therefore, the mechanism is suited to incentivize welfare increasing grid expansion. Depending on the distribution of congestion management costs, it can also align stakeholder interests. In this paper, we present an auction mechanism for transmission grid expansion based on the reduction of redispatch expenditures that theoretically leads to a welfare optimal expansion. The mechanism is applied to a case study in Germany. The results show that the developed mechanism supports an improved planning of grid capacity expansion

Smart Data Selection and Reduction for Electric Vehicle Service Analytics

Battery electric vehicles (BEV) are increasingly used in mobility services such as car-sharing. A severe problem with BEV is battery degradation, leading to a reduction of the already very limited range of a BEV. Analytic models are required to determine the impact of service usage to provide guidance on how to drive and charge and also to support service tasks such as predictive maintenance. However, while the increasing number of sensor data in automotive applications allows for more fine-grained model parameterization and better predictive outcomes, in practical settings the amount of storage and transmission bandwidth is limited by technical and economical considerations. By means of a simulation-based analysis, dynamic user behavior is simulated based on real-world driving profiles parameterized by different driver characteristics and ambient conditions. We find that by using a shrinked subset of variables the required storage can be reduced considerably at low costs in terms of only slightly decreased predictive accuracy.

Managing Intermittent Renewable Generation with Battery Storage using a Deep Reinforcement Learning Strategy

Most of Germany’s existing wind and solar plants have been losing their subsidies after 20 years of operation since 2020. Without support schemes, the challenges for the renewable operators are the intermittent generation and the fluctuating power prices. Consequently, lower-than-expected revenues and high revenue variability make it more difficult for the renewable operators to be active on power markets. Therefore, the renewable operators have to be profit effective as well as cope with the high variability of their revenue. This paper proposes a deep reinforcement learning (DRL) based model to adjust the renewable operators’ short-term energy supply using a battery storage strategy. The simulative empirical evaluation shows that the renewable operators can be profitable on the market and improve their revenue stability using the proposed DRL based battery storage strategy

Designing Local Energy Market Applications

Local energy markets and corresponding information systems are a way to integrate and involve residential customers in the energy transition, which can increase acceptance and drive private investment. This study is focused on the generation of design knowledge for these local energy market user applications in general and specifically to ensure long-term user engagement, which is a crucial success factor to maintain long-term effects. To this end, we derive, instantiate and evaluate seven design principles based on a field implementation with user interaction over 13 months using a design science research approach. The design principles and their instantiations are evaluated based on semi-structured interviews with the participants and a consecutive online experiment. The design principles provide fundamental knowledge for the setup of local energy market user applications and are therefore of value for researchers and practitioners alike

MARKET MECHANISMS FOR NEIGHBOURHOOD ELECTRICITY GRIDS: DESIGN AND AGENT-BASED EVALUATION

The increasing number of prosumers who both consume and produce electricity and the ongoing decentralization of the electricity system lead to the need for new market designs to allow for direct trading of electricity between neighbours. Most currently proposed mechanisms ignore the grid restrictions, which might result in an infeasible dispatch and threaten the system stability. We propose a mechanism that considers grid restrictions and finds the optimal dispatch without relying on a centralized entity such as an independent system operator to oversee the system. We compare the results of the proposed mechanism to a nodal pricing approach and evaluate the welfare distribution among market participants as well as the exposure to market power using agent-based simulation. We find that the welfare distribution does not depend on the technical specifications of the proposed bilateral pricing mechanism but on the grid topology and that the possibility to exercise market power is equally high as under nodal pricing

Ensuring Energy Affordability through Digital Technology: A Research Model and Intervention Design

In order to ensure energy affordability, we propose a design-oriented behavioral research study with the aim of helping low-income tenants to develop an efficient energy behavior by increasing their energy self-efficacy. We propose to compare different digital interventions in field tests to understand, in an unfiltered way, what helps low-income tenants to be able to reduce their energy costs. We thereby contribute towards understanding how the vulnerable group of low-income tenants with their limitations and needs regarding their energy consumption behavior can be effectively supported digitally. In addition, we contribute initial measurement instruments for energy worries, energy literacy and energy self-efficacy to evaluate the effects of digital interventions