About the lab
We develop and evaluate economic mechanisms for coordinating demand and supply in digitalized energy systems
Featured projects (2)
Featured research (64)
The success of incentives for investments in sustainable residential energy technologies depends on individual households actively participating in the energy transition by investing in electrification and by becoming prosumers. This willingness is influenced by the return on investments in electrification and preferences towards environmental sustainability. Returns on investment can be supported by a preferential regulation of Citizen Energy Communities, i.e. a special form of a microgrid regulation. However, the exact effect of such regulation is debated and therefore analyzed in this study. We propose a multi-periodic community development model that determines household investment decisions over a long time horizon, with heterogeneous individual preferences in regards to sustainability and heterogeneous energy consumption profiles. We consider that investment decisions which increase individual utility might be delayed due to inertia in the decision process. Decisions are determined in our model based on individual preferences using a multi-objective evolutionary algorithm embedded in an energy system simulation. In a case study, we investigate the development of a neighborhood in Germany consisting of 30 households in regards to community costs and community emissions with and without Citizen Energy Community regulation as proposed by the European Union. We find that Citizen Energy Community regulation always reduces community costs and emissions, while heterogeneous distributions of economic and ecologic preferences within the community lead to higher gains. Furthermore, we find that decision inertia considerably slows down the transformation process. This shows that policymakers should carefully consider who to target with Citizen Energy Community regulation and that subsidies should be designed such that they counterbalance delayed private investment decisions.
The transition of the energy sector towards more decentral, renewable and digital structures and a higher involvement of local residents as prosumers calls for innovative business models. In this paper, we investigate a sharing economy model that enables a residential community to share solar generation and storage capacity. We simulate 520 sharing communities of five households each with differing load profile configurations and find that they achieve average annual savings of 615€ as compared to individual operation. Using the gathered data on electricity consumption in a sharing community, we discuss a fixed pricing approach to achieve a fair distribution of the profits generated through the sharing economy. We further investigate the impact of prosumers’ and consumers’ load profile patterns on the profitability of the sharing communities. Based on these findings, we explore the potential to match and coordinate suitable communities through a platform-based sharing economy model. Our results enable practitioners to find optimal additions to an energy sharing community and provide new insights for researchers regarding possible pricing schemes in energy communities.
Coordinated operation of Coupled Electric Power and District Heating Networks (CEPDHNs) brings advantages as e.g., the district heating networks can provide flexibility to the electric power network, and the entire system operation can be further decarbonized through heat pumps and electric boilers using electricity from renewable energy sources. Still, today CEPDHNs are often not operated in a coordinated way causing a lack of efficiency. This paper shows, how efficient resource allocation is achieved by determining the power exchange between both networks over an aggregated market. We introduce a welfare-optimizing, market-based operation for a CEPDHN that satisfies operational constraints and considers network losses. The objective is to integrate uniform pricing market-clearing and operational constraints into one approach, in order to obtain high incentive compatibility for the market participants while preventing high uplift costs from redispatch. For this, we use a hybrid market model mainly based on uniform marginal pricing and additionally utilize pay-as-bid pricing for a fraction of the allocated bids and offers. We perform a case study with a real CEPDHN to validate the functionality of the developed approach. The results show that our solution leads to efficient resource allocation while maintaining safe network operation and preventing uplift costs due to redispatch.
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.
About Philipp Staudt
- Philipp Staudt currently works at the Institute of Information Systems and Marketing, Karlsruhe Institute of Technology. Philipp's research interests include the Smart Grid, the digitalization of the energy system, sector coupling, GreenIS and energy markets. He works with agent-based simulations, data analytics and game theory on markets and mechanisms that lead to a better integration of renewable energies into the energy mix.