Publications

Publications

Our research results show the economic potential of new information technology options in the smart grid. From our research results, we derive concrete requirements for a target-oriented design of energy markets, systems and services. Our contribution to the implementation of the energy transition also includes the identification of sensible adjustments to the regulatory framework. Follow the link below, to check out our most recent research contributions.

Publications from 2023
Privacy-preserving peak time forecasting with Learning to Rank XGBoost and extensive feature engineering

Privacy-preserving peak time forecasting with Learning to Rank XGBoost and extensive feature engineering

Authors: Semmelmann, Leo; Resch, Oliver; Henni, Sarah; Weinhardt, Christof

Publication: IET Smart Grid

Abstract:

In modern power systems, predicting the time when peak loads will occur is crucial for improving efficiency and minimising the possibility of network sections becoming overloaded. However, most works in the load forecasting field are not focusing on a dedicated peak time forecast and are not dealing with load data privacy. At the same time, developing methods for forecasting peak electricity usage that protect customers’ data privacy is essential since it could encourage customers to share their energy usage data, leading to more data points for the effective management and planning of power grids. Hence, the authors employ a dedicated Learning to Rank XGBoost algorithm to forecast peak times with only ranks of loads instead of absolute load magnitudes as input data, thereby offering potential privacy-preserving properties. We show that the presented Learning to Rank XGBoost model yields comparable results to a benchmark XGBoost load forecasting model. Additionally, we describe our extensive feature engineering process and a state-of-the-art Bayesian hyperparameter optimisation for selecting model parameters, which leads to a significant improvement of forecasting accuracy. Our method was used in the context of the final round of the international BigDEAL load forecasting challenge 2022, where we consistently achieved high-ranking results in the peak time track and an overall fourth rank in the peak load forecasting track with our general XGBoost model.

Generating synthetic load profiles of residential heat pumps: a k-means clustering approach

Generating synthetic load profiles of residential heat pumps: a k-means clustering approach

Authors: Semmelmann, Leo; Jaquart, Patrick; Weinhardt, Christof

Publication: Energy Informatics

Abstract:

The creation of synthetic heat pump load profiles is essential for energy system modeling and simulations. This paper proposes a methodology to create synthetic heat pump load profiles based on the k-means algorithm and a data set from water-to-water heat pumps from Hamelin, Germany. The quality of the generated load profiles is shown according to load factors, load distribution curves and the Pearson correlation coefficient, and is also applied on two exemplary geographies in Germany. We publish our work open-source and provide a web-based heat pump load profile generator.

The Impact of Local Energy Market Participants’ Decisions on Efficient Energy Usage: Design of a Multi-Agent System

The Impact of Local Energy Market Participants’ Decisions on Efficient Energy Usage: Design of a Multi-Agent System

Authors: Speck, Christina; Bluhm, Saskia; Weinhardt, Christof; Zwickel, Philipp; Hagenmeyer, Veit

Publication: IEEE XPlore

Abstract:

In recent years, many studies have been conducted on local energy markets. While local energy markets are mostly modeled with economically rational participants, many house- holds also consider energy quality, such as energy locality, in their energy choices. Therefore, we propose to include participants’ heterogeneous preferences from energy locality to economic rationality in the design of a multi-agent system to ensure a more efficient use of locally generated energy, which helps to increase the attractiveness of participating in local energy markets. Our results show that our design can fulfill the individual preferences of local energy market participants and prove that energy is used most efficiently with a dominant local preference group.

Development and Empirical Expert Evaluation of Business Models for Energy Communities

Development and Empirical Expert Evaluation of Business Models for Energy Communities

Authors: Bluhm, Saskia; Hein, Karl; Golla, Armin; Henni, Sarah; Weinhardt, Christof

Publication: IEEE XPlore

Abstract:

Energy communities provide an opportunity for driving and democratizing the renewable energy transition. To lay the foundation for a successful implementation in practice, there is a need to evaluate business models tailored to the context of energy communities. Using a comprehensive literature review, we identify five potential business models for energy communities and six success factors to be included as evaluation criteria to benefit the individual, the community and the provider. Within an expert survey and a subsequent validation discussion the business models are rated along these success factors. While the technical requirements still pose a high burden for the implementation of energy communities in practice, the results reveal that collective investments in renewable assets and peer-to-peer markets provide good starting points, which should be combined and extended with further smart energy services in the future.

On the Role of Risk Aversion and Market Design in Capacity Expansion Planning

On the Role of Risk Aversion and Market Design in Capacity Expansion Planning

Authors: Frauenholz, Christoph; Miskiw, Kim; Kraft, Bartos; Fichtner, Wolf; Weber, Christoph

Publication: The Energy Journal

Abstract: Investment decisions in competitive power markets are based upon thorough profitability assessments. Thereby, investors typically show a high degree of risk aversion, which is the main argument for capacity mechanisms being implemented around the world. In order to investigate the interdependencies between investors’ risk aversion and market design, we extend the agent-based electricity market model PowerACE to account for long-term uncertainties. This allows us to model capacity expansion planning from an agent perspective and with different risk preferences. The enhanced model is then applied in a multi-country case study of the European electricity market. Our results show that assuming risk-averse rather than risk-neutral investors leads to slightly reduced investments in dispatchable capacity, higher wholesale electricity prices, and reduced levels of resource adequacy. These effects are more pronounced in an energy-only market than under a capacity mechanism. Moreover, uncoordinated changes in market design may also lead to negative cross-border effects.

Bottom-up system modeling of battery storage requirements for integrated renewable energy systems

Bottom-up system modeling of battery storage requirements for integrated renewable energy systems

Authors: Henni, Sarah; Schäffer, Michael; Fischer, Peter; Weinhardt, Christof; Staudt, Philipp

Publication: Elsevier 

Abstract: We introduce a bottom-up modeling framework that allows both the decentral and central planning of an integrated energy system with high shares of renewable generation. We take into account the distribution network structure as well as the changing local consumption due to high electrification rates of building heat supply and the transportation sector. This approach allows the analysis of pathways in between a cost-optimal system design and an equitable spatial distribution of renewable generation and battery storage capacities within the system. In addition, we investigate the optimal combination of short- and medium-term battery storage technologies, namely lithium-ion and redox flow batteries. Our results for the case of Baden-Wuerttemberg, a state in southern Germany, show that a central planning of renewable generation and storage capacity requirements results in lower levelized costs of electricity than a decentral design, as expected. However, pathways in between the two planning paradigms can lead to a more equitable inclusion of communities in the energy transition at reasonable cost increases, which might increase acceptance. The results of this study are of significance for policy-makers and local stakeholders, as they must address the conflicts that arise on a local level when expansion targets are planned centrally.

„Die Energieforschungsdaten systematisch zu sammeln und allen zugänglich zu machen beschleunigt die Energiewende“ – Unter Beteiligung des Karlsruher Instituts für Technologie startet im Februar die Plattform nfdi4energy als Teil des Vereins „Nationale Forschungsdateninfrastruktur“ - Campus-Report am 03.01.2023

„Die Energieforschungsdaten systematisch zu sammeln und allen zugänglich zu machen beschleunigt die Energiewende“ – Unter Beteiligung des Karlsruher Instituts für Technologie startet im Februar die Plattform nfdi4energy als Teil des Vereins „Nationale Forschungsdateninfrastruktur“ - Campus-Report am 03.01.2023

Authors: Fuchs, Stefan; Weinhardt, Christof; Henni,Sarah. 

Publication: Campus-Report 

Abstract: In many research disciplines, the collection of data is associated with enormous effort. The idea of ​​not only publishing research results has therefore prevailed in modern science. The so-called raw data should also be available to all researchers. This makes research results easier to check. At the same time data can be questioned again using advanced analysis methods and thus provide completely new insights. At the end of 2020, the National Research Data Infrastructure Association was founded with headquarters in Karlsruhe. Its task: to permanently store raw research data from the central research disciplines and make it accessible to the entire scientific community. With the participation of the Karlsruhe Institute of Technology, the raw data from the energy research that is so important for decarbonization will now also be made publicly accessible in its entirety.

Publications from 2022
Integrating Hydrogen in Single-Price Electricity Systems: The Effects of Spatial Economic Signals

Integrating Hydrogen in Single-price electricity systems: The effects of spatial economic signals

Authors: vom Scheidt, Frederik; Qu, Jingyi; Staudt, Philipp; Mallapragada, Dharik S.; Weinhardt, Christof 

Publication: Elsevier

Abstract: Hydrogen can contribute substantially to the reduction of carbon emissions in industry and transportation. However, the production of hydrogen through electrolysis creates interdependencies between hydrogen supply chains and electricity systems. Therefore, as governments worldwide are planning considerable financial subsidies and new regulation to promote hydrogen infrastructure investments in the next years, energy policy research is needed to guide such policies with holistic analyses. In this study, we link an electrolytic hydrogen supply chain model with an electricity system dispatch model. We use this methodology for a cross-sectoral case study of Germany in 2030. We find that hydrogen infrastructure investments and their effects on the electricity system are strongly influenced by electricity prices. Given current uniform single-prices in Germany, hydrogen production increases congestion costs in the electricity grid by 17%. In contrast, passing spatially resolved electricity price signals leads to electrolyzers being placed at low-cost grid nodes and further away from consumption centers. This causes lower end-use costs for hydrogen. Moreover, congestion management costs decrease substantially, by up to 20% compared to the benchmark case without hydrogen. These savings could be transferred into according subsidies for hydrogen production. Thus, our study demonstrates the benefits of differentiating economic signals for hydrogen production based on spatial criteria.

Evaluating the impact of regulation on the path of electrification in Citizen Energy Communities with prosumer investment

Evaluating the impact of regulation on the path of electrification in Citizen Energy Communities with prosumer investment

Authors: Golla, Armin; Röhrig, Nicole; Staudt, Philipp; Weinhardt, Christof 

Publication: Elsevier

Abstract: 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.

Towards Designing Smart Home Energy Applications for Effective Use

Towards Designing Smart Home Energy Applications for Effective Use

Authors: Bluhm, Saskia; Staudt, Philipp; Weinhardt, Christof 

Publication: Springer

Abstract:

To reduce climate change, considerable behavioral changes are required from private households, who often have a low energy literacy and are therefore unaware of the necessary behavioral change.

We introduce a Design Science Research project with the aim to increase energy literacy. To this end, we contribute a theory-grounded design theory for a Smart Home Energy Application based on effective use.

In comparison to previous approaches for designing Smart Home Energy Applications, the design process is user-centered.

We combine semi-structured interviews with a structured survey and a literature review to derive meta requirements and deduct preliminary design principles mapping them to a prototype.

The intermediate results of this study inform research and practice by providing valuable insights on how users interact with a Smart Home Energy Application. The design principles enable the design of information systems allowing for effective use and contribute to a more sustainable energy behavior of households.

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

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

Authors: Zhou, Yuchen; Henni, Sarah; Staudt, Philipp

Publication: AIS

Abstract:

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.

Evaluation of an interactive visualization tool to increase energy literacy in the building sector

Evaluation of an interactive visualization tool to increase energy literacy in the building sector

Authors:Henni, Sarah; Franz, Paulina;  Staudt, Philipp; Peukert, Christian; Weinhardt, Christof

Publication: Elsevier

Abstract:

The building sector, and especially residential households and office buildings, account for a large share of global emissions. Meanwhile, energy literacy is extremely low amongst residents and citizens in general, leading to insufficient evaluations of energy efficiency measures and technology equipment for buildings. To address this issue, we develop a research model and design an experiment to evaluate the ability of a website with interactive and vivid features to convey information in an engaging way, thus increasing the users’ enjoyment and their intention to (re)-use and recommend the website as well as the usefulness for information retrieval and technology evaluation. We conduct an experiment with two treatments in which the participants interact with an animated and a static website, respectively. While participants’ self-assessed knowledge improvement is statistically higher in the animated treatment, no difference was found in tested knowledge assessment or technology-specific knowledge. We find that the vividness of the website plays an important role for both the utilitarian and hedonic purpose of the website. However, somewhat contrasting to existing theories, interactivity did not increase enjoyment or diagnosticity.

Load forecasting for energy communities: a novel LSTM-XGBoost hybrid model based on smart meter data

Load forecasting for energy communities: a novel LSTM-XGBoost hybrid model based on smart meter data

Authors:Semmelmann, Leo; Henni, Sarah; Weinhardt, Christof

Publication: Springer

Abstract:

Accurate day-ahead load forecasting is an important task in smart energy communities, as it enables improved energy management and operation of flexibilities. Smart meter data from individual households within the communities can be used to improve such forecasts. In this study, we introduce a novel hybrid bi-directional LSTM-XGBoost model for energy community load forecasting that separately forecasts the general load pattern and peak loads, which are later combined to a holistic forecasting model. The hybrid model outperforms traditional energy community load forecasting based on standard load profiles as well as LSTM-based forecasts. Furthermore, we show that the accuracy of energy community day-ahead forecasts can be significantly improved by using smart meter data as additional input features.

Battery Storage in Low-Carbon Energy Systems : Deployment and Data-Driven Operation Strategies

Battery Storage in Low-Carbon Energy Systems : Deployment and Data-Driven Operation Strategies

Authors: Henni, Sarah

Publication: Link

Abstract:

 

Industrial peak shaving with battery storage using a probabilistic forecasting approach: Economic evaluation of risk attitude

Industrial peak shaving with battery storage using a probabilistic forecasting approach: Economic evaluation of risk attitude

Authors: Henni, Sarah; Becker, Jonas; Staudt, Philipp; vom Scheidt, Frederik; Weinhardt, Christof

Publication: Elsevier

Abstract: Industrial peak shaving is a regularly discussed application of battery storage. We introduce the notion of risk attitude in the context of joint industrial peak shaving and frequency containment reserve provision with battery storage. To this end, we combine a probabilistic quantile forecast with a rolling-horizon battery control mechanism. Probabilistic forecasts incorporate prediction uncertainty by generating a distribution of future load. An industrial consumer has an incentive to plan conservatively when reserving battery capacities for peak shaving, as a single missed peak can drive up annual electricity costs steeply in the presence of peak-load charges. However, this limits the potential use of battery storage capacity for other financially attractive applications. We find that extremely risk averse planning behavior can lead to a decrease of up to 10% in economic performance of a battery investment. This loss might be tolerated in exchange for the significantly reduced risk of missing a critical peak. Moreover, moderately risk averse planning behavior does not lead to financial losses in most cases and can even improves economic performance by up to 3% in certain of the evaluated cases.

Publications from 2021
Merchant Transmission in Single-Price Electricity Markets with Cost-Based Redispatch

Merchant Transmission in Single-Price Electricity Markets with Cost-Based Redispatch

Authors: Staudt, Philipp; Oren, S. S. 

Publication: Elsevier 

Abstract: Transmission expansion is a complex problem in energy market design and research has not yet provided a market-based solution that is superior to a (partly) regulated approach. Furthermore, markets with a single market clearing price lack regional incentives for system friendly generation or transmission capacity expansion. In this paper, we propose a market design for transmission expansion that can be implemented in single-price markets with cost-based redispatch and we describe its properties. We show that our market solution is incentive compatible, satisfies the ’beneficiary pays’ requirement and leads to a welfare optimal grid expansion otherwise achieved by an integrated optimization approach of a benevolent grid operator. We apply the mechanism to the German electricity system in 2018, 2019 and 2030 as an example and show that transmission capacity expansion is greatly reduced using the mechanism instead of a no-congestion regulation. We also test the robustness of the approach to erroneous generation capacity expectations and find that the impact on economic results is limited. Finally, we extend our approach to include congestion reducing generation capacity investment and discuss the strategic effects on a 6-node reference grid. 

A Sharing Economy for Residential Communities with PV-Coupled Battery Storage: Benefits, Pricing and Participant Matching

A Sharing Economy For Residential Communities With PV-Coupled Battery Storage: Benefits, Pricing And Participant Matching ​

Authors: Henni, Sarah; Staudt, Philipp; Weinhardt, Christof 

Publication: Elsevier

Abstract: The transition of the energy sector towards more decentral, renewable, and digital structures and 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.

Decision Support and Strategies for the Electrification of Commercial Fleets

Decision Support And Strategies For The Electrification Of Commercial Fleets​

Authors: Schmidt, Marc; Staudt, Philipp; Weinhardt, Christof

Publication: Elsevier

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. 

Probabilistic forecasting of household loads: Effects of distributed energy technologies on forecast quality

Probabilistic forecasting of household loads: Effects of distributed energy technologies on forecast quality

Authors: vom Scheidt, Frederik; Dong, Xinyuan; Bartos, Andrea; Staudt, Philipp; Weinhardt, Christof

Publication: Association for Computing Machinery

Abstract: Distributed energy technologies introduce new volatility to the edges of low voltage grids and increase the importance of short-term forecasting of electric loads at a granular level. To address this issue, first probabilistic forecasting models for residential loads have been developed in recent years. However, knowledge is lacking about how well these models perform for households with different endowments of distributed energy technologies. Therefore, we first create a new semi-synthetic data set which contains not only conventional residential loads, but net loads of 40 households differentiated regarding heating type (electric space heating, no electric space heating), and rooftop solar installation (solar, no solar). Second, we develop a novel probabilistic forecasting model based on Gated Recurrent Units that uses data from weather forecasts and calendar variables as external features. We apply the developed model, and three benchmarks, to the new data set and find that the GRU model outperforms the other models for households with electric heating, with solar, and with both technologies, but not for households without distributed energy technologies. 

Infrastructural Coupling of the Electricity and Gas Distribution Grid to Reduce Renewable Energy Curtailment

Infrastructural Coupling Of The Electricity And Gas Distribution Grid to Reduce Renewable Energy Curtailment

Authors: Henni, Sarah; Staudt, Philipp; Kandiah, Balendra; Weinhardt, Christof

Publication: Elsevier 

Abstract: Following the European Union’s emission reduction goals, the expansion of intermittent renewable energy sources is being pursued by numerous member states. This poses challenges especially to low-voltage electricity grids that are not designed for the volatile and unpredictable feed-in from renewable generation capacity. In addition to the expansion of renewable capacity, further measures, including the decarbonization of the transport, heating and industrial sectors are needed to achieve the environmental targets. Sector coupling refers to the electrification of end-user energy demand as well as the coupling of different energy infrastructures such as the electricity and gas networks through Power-to-Gas technology. In this paper, we address these issues by developing a methodology that enables distribution system operators to identify future grid constraints in advance and to address them using Power-to-Gas technology using geographical information systems. In further detail, we present a novel approach to identify sections of the distribution network that are likely to be congested in the future in order to locate congestion-induced potential sites for Power-to-Gas plants. We show the applicability of our approach in a case study for a municipality in the German state of Baden-Wurttemberg. We show the economic feasibility of a medium-sized Power-to-Gas plant that couples the gas and electricity distribution networks. Our findings offer insights into the possibility to use the existing gas infrastructure in order to integrate surplus electricity generation, avoid electricity grid congestion and to further decarbonize energy demand.

Publications from 2020
Scaling the Concept of Citizen Communities through a Platform-Based Decision Support System

Scaling The Concept Of Citizen Communities Through A Platform-Based Decision Support System​​ ​

Authors: Golla, Armin; Henni, Sarah; Staudt, Philipp 

Publication: Association for Information Systems

Abstract: The first generation of prototypes for citizen energy communities is completed. While these pilot projects of decentralized energy communities receive much attention in research, their concepts have yet to be implemented on a large scale. We find that potential participants of citizen energy communities lack information and the means to propose and implement joint infrastructure projects like shared electrical storage investment. Furthermore, in current pilots, the main focus is often directed towards electricity generation and consumption. However, for a successful energy transition, the three energy sectors of electricity, heat and mobility need to be considered. In this paper, we introduce a platform-based decision support information system that enables residential consumers and prosumers to create citizen energy communities. We determine the information that is needed to configure a local energy infrastructure and conceptualize a coordination mechanism that merges diverging preferences of participants. We demonstrate the application of the proposed framework on empirical data from the Landau Microgrid Project to provide a proof of concept. The developed platform facilitates the transition of citizen energy communities from a niche phenomenon to a large-scale concept and is therefore an implementable solution from the information system domain towards the mitigation of climate change.

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