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.

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.

The European energy crisis and the global climate crisis call for a strong reduction of fossil fuel usage in the residential heating sector. Given the rising role of energy communities, we address this challenge by optimizing the design and operation of different energy community systems with a linear program and a genetic algorithm for rolling horizon control, respectively. In particular, we compare status quo systems that are based on natural gas, with purely electricity-based systems, and systems based on electricity as well as the local production, storage, and usage of green hydrogen in the respective community. Applying our method to a case study of a community with 19 households, across various regulatory scenarios, and two different objective scenarios, we find that including hydrogen can achieve considerable CO2 emission reduction, higher self-sufficiency, and lower costs than systems using natural gas for heating. Set-ups without hydrogen, but with larger electric heat pumps, achieve similar emission reductions at lower costs but enable less self-sufficiency in the community.

The increasing number of heat pump installations in Germany will require additional grid expansion and grid reinforcement measures. Grid operators call for measures to block heat pumps during critical, high load phases. However, the exact implementation of possible heat pump blocking periods and their impact on grid expansion costs remains unknown. This paper introduces a framework to evaluate the impact of heat pump blocking mechanisms on grid expansion cost, based on real-world household and heat pump load profiles. We show that previously suggested approaches in the literature, such as blocking all heat pumps in the grid during peak hours, lead to significant increases in distribution grid expansion costs due to catch-up effects. In addition, we introduce an open-source simulation model to assess the impact of heat pump blocking mechanisms on aggregated heat pump loads and peak loads at low computational costs.

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.

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.

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.

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 gen-eral, 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 treat-ment, 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 enjoy-ment or diagnosticity. (c) 2022 Elsevier B.V. All rights reserved.

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.

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.

As levelized costs of electricity for many renewable generation sources are continuing to fall and as feed-in tariffs are consequently being phased out, financial risk hedging for intermittent renewable generators takes a central stage. Battery storage as complementary capacity can support renewable generators regarding a more stable supply of electricity. In this study, we take first steps in modelling battery storage options as service products that are provided by battery storage operators to renewable generation operators. We model the situation theoretically, develop corresponding hedging strategies and apply the models to a fictional solar PV plant. The results show that battery storage options can reduce the risk for intermittent renewable generators and that the options can be financially beneficial for both the battery storage and the renewable capacity operator.

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.