Artificial Intelligence in Energy Demand Response: A Taxonomy of Input Data Requirements

The ongoing energy transition increases the share of renewable energy sources. To combat inherent intermittency of RES, increasing system flexibility forms a major opportunity. One way to provide flexibility is demand response (DR). Research already reflects several approaches of artificial intelligence (AI) for DR. However, these approaches often lack considerations concerning their applicability, i.e., necessary input data. To help putting these algorithms into practice, the objective of this paper is to analyze, how input data requirements of AI approaches in the field of DR can be systematized from a practice-oriented information systems perspective. Therefore, we develop a taxonomy consisting of eight dimensions encompassing 30 characteristics. Our taxonomy contributes to research by illustrating how future AI approaches in the field of DR should represent their input data requirements. For practitioners, our developed taxonomy adds value as a structuring tool, e.g., to verify applicability with respect to input data requirements

The insurance effect of renewable distributed energy resources against uncertain electricity price developments

To combat climate change, many countries all around the world currently foster the development of renewable energy sources (RES). However, in contrast to traditional energy systems that relied on few central power plants, RES are typically highly decentral and spread all over a country. Against this backdrop, the promotion of a decentralization of the energy system by fostering a regional balance of energy demand and supply with a corresponding increase in energy democracy is seen as a promising approach. However, energy democracy driven by an increasing involvement of consumers requires adequate investments of consumers in their own local RES in order to become active players, usually called prosumers. Risk associated with uncertain long-term electricity price developments is generally seen as a barrier to investments. In contrast, we describe that an investment in distributed energy resources (DERs) may actually serve as a consumer's insurance against price risk. Our results set out that the consideration of risk-aversion may actually positively shift an investment decision in renewable DERs. This is due to the prosumer becoming more self-sufficient and less dependent on uncertain price developments. To analyze such an insurance effect, we create a formal decision model considering the prosumer's risk-aversion and derive the prosumer's optimal investment in renewable DERs. However, our results also indicate that under some circumstances the insurance effect disappears: When a prosumer turns into a predominant producer, the prosumer is again exposed to risk in terms of uncertain revenues. Ultimately, our work highlights the importance of a consideration of the insurance effect when assessing an investment in renewable DERs

Artificial Intelligence in Energy Demand Response : A Taxonomy of Input Data Requirements

The ongoing energy transition increases the share of renewable energy sources. To combat inherent intermittency of RES, increasing system flexibility forms a major opportunity. One way to provide flexibility is demand response (DR). Research already reflects several approaches of artificial intelligence (AI) for DR. However, these approaches often lack considerations concerning their applicability, i.e., necessary input data. To help putting these algorithms into practice, the objective of this paper is to analyze, how input data requirements of AI approaches in the field of DR can be systematized from a practice-oriented information systems perspective. Therefore, we develop a taxonomy consisting of eight dimensions encompassing 30 characteristics. Our taxonomy contributes to research by illustrating how future AI approaches in the field of DR should represent their input data requirements. For practitioners, our developed taxonomy adds value as a structuring tool, e.g., to verify applicability with respect to input data requirements

How Germany achieved a record share of renewables during the COVID-19 pandemic while relying on the European interconnected power network

In 2020, Germany reached a maximum share of 50.5% intermittent renewables in electricity generation. Such a high share results in an increasing need for flexibility measures such as international transmission flexibility, i.e., electricity imports and exports. In fact, during the COVID-19 pandemic, Germany changed from a former electricity net exporter to a net importer. This paper, therefore, analyzes what we can learn from the resulting development of German electricity imports as a flexibility measure from a market, environmental, and network perspective. We analyze data on electricity imports/exports, generation, prices, and interconnection capacities of 38 bidding zones, respectively 11 countries within the ENTSO-E. In particular, we formulate three hypotheses to partition our overarching research question. Our results reveal that from a market perspective, Germany's increased need for transmission flexibility did not generally result in increased prices for German electricity imports. Also, from an environmental perspective, Germany increasingly relied on electricity imports from countries that exhibited a lower share of renewables. Finally, during the COVID-19 pandemic some of Germany's interconnection capacities to its neighboring countries exhibited a higher utilization. In view of our results, German policymakers may reflect on decarbonization policies considering a holistic European perspective

The insurance effect of renewable distributed energy resources against uncertain electricity price developments

To combat climate change, many countries all around the world currently foster the development of renewable energy sources (RES). However, in contrast to traditional energy systems that relied on few central power plants, RES are typically highly decentral and spread all over a country. Against this backdrop, the promotion of a decentralization of the energy system by fostering a regional balance of energy demand and supply with a corresponding increase in energy democracy is seen as a promising approach. However, energy democracy driven by an increasing involvement of consumers requires adequate investments of consumers in their own local RES in order to become active players, usually called prosumers. Risk associated with uncertain long-term electricity price developments is generally seen as a barrier to investments. In contrast, we describe that an investment in distributed energy resources (DERs) may actually serve as a consumer's insurance against price risk. Our results set out that the consideration of risk-aversion may actually positively shift an investment decision in renewable DERs. This is due to the prosumer becoming more self-sufficient and less dependent on uncertain price developments. To analyze such an insurance effect, we create a formal decision model considering the prosumer's risk-aversion and derive the prosumer's optimal investment in renewable DERs. However, our results also indicate that under some circumstances the insurance effect disappears: When a prosumer turns into a predominant producer, the prosumer is again exposed to risk in terms of uncertain revenues. Ultimately, our work highlights the importance of a consideration of the insurance effect when assessing an investment in renewable DERs

Negative electricity prices as a signal for lacking flexibility? On the effects of demand flexibility on electricity prices

peer reviewedPurpose: The purpose of this paper is to examine how active consumers, i.e. consumers that can inter-temporally shift their load, can influence electricity prices. As demonstrated in this paper, inter-temporal load shifting can induce negative electricity prices, a recurring phenomenon on power exchanges. Design/methodology/approach: The paper presents a novel electricity-market model assuming a nodal-pricing, energy-only spot market with active consumers. This study formulates an economic equilibrium problem as a linear program and uses an established six-node case study to compare equilibrium prices of a model with inflexible demand to a model with flexible demand of active consumers. Findings: This study illustrates that temporal coupling of hourly market clearing through load shifting of active consumers can cause negative electricity prices that are not observed in a model with ceteris paribus inflexible demand. In such situations, where compared to the case of inflexible demand more flexibility is available in the system, negative electricity prices signal lower total system costs. These negative prices result from the use of demand flexibility, which, however, cannot be fully exploited due to limited transmission capacities, respectively, loop-flow restrictions. Originality/value: Literature indicates that negative electricity prices result from lacking flexibility. The results illustrate that active consumers and their additional flexibility can lead to negative electricity prices in temporally coupled markets, which in general contributes to increased system efficiency as well as increased use of renewable energy sources. These findings extend existing research in both the area of energy flexibility and causes for negative electricity prices. Therefore, policymakers should be aware of such (temporal coupling) effects and, e.g. continue to allow negative electricity prices in the future that can serve as investment signals for active consumers.9. Industry, innovation and infrastructur

How did the German and other European electricity systems react to the COVID-19 pandemic?

The first wave of the COVID-19 pandemic led to decreases in electricity demand and a rising share of Renewable Energy Sources in various countries. In Germany, the average proportion of net electricity generation via Renewable Energy Sources rose above 55% in the first half of 2020, as compared to 47% for the same period in 2019. Given these altered circumstances, in this paper we analyze how the German and other European electricity systems behaved during the COVID-19 pandemic. We use data visualization and descriptive statistics to evaluate common figures for electricity systems and markets, comparing developments during the COVID-19 pandemic with those of previous years. Our evaluation reveals noticeable changes in electricity consumption, generation, prices, and imports/exports. However, concerning grid stability and ancillary services, we do not observe any irregularities. Discussing the role of various flexibility options during the COVID-19 pandemic, a relatively higher grid capacity resulting from a decreased electricity consumption, in particular, may have contributed to grid stability

The challenges and opportunities of energy-flexible factories: a holistic case study of the model region Augsburg in Germany

Economic solutions for the integration of volatile renewable electricity generation are decisive for a socially supported energy transition. So-called energy-flexible factories can adapt their electricity consumption process efficiently to power generation. These adaptions can support the system balance and counteract local network bottlenecks. Within part of the model region Augsburg, a research and demonstration area of a federal research project, the potential, obstacles, effects, and opportunities of the energy-flexible factory were considered holistically. Exemplary flexibilization measures of industrial companies were identified and modeled. Simulations were performed to analyze these measures in supply scenarios with advanced expansion of fluctuating renewable electricity generation. The simulations demonstrate that industrial energy flexibility can make a positive contribution to regional energy balancing, thus enabling the integration of more volatile renewable electricity generation. Based on these fundamentals, profiles for regional market mechanisms for energy flexibility were investigated and elaborated. The associated environmental additional expenses of the companies for the implementation of the flexibility measures were identified in a life-cycle assessment, with the result that the negative effects are mitigated by the increased share of renewable energy. Therefore, from a technical perspective, energy-flexible factories can make a significant contribution to a sustainable energy system without greater environmental impact. In terms of a holistic approach, a network of actors from science, industry, associations, and civil society organizations was established and actively collaborated in a transdisciplinary work process. Using design-thinking methods, profiles of stakeholders in the region, as well as their mutual interactions and interests, were created. This resulted in requirements for the development of suitable business models and reduced regulatory barriers