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Power markets are becoming increasingly complex as they move towards (i) integrating higher amounts of variable renewable energy, (ii) shorter trading intervals and lead times, (iii) stronger interdependencies between related markets, and (iv) increasing energy system integration. For designing them appropriately, an enhanced understanding of the dynamics in interrelated short-term physical power and energy markets is required, which can be supported by market simulations. In this paper, we present an agent-based power market simulation model with rule-based bidding strategies that addresses the above-mentioned challenges, and represents market participants individually with a high level of technical detail. By allowing agents to participate in several interrelated markets, such as the energy-only market, a procurement platform for control reserve and a local heat market representing district heating systems, cross-market opportunity costs are well reflected. With this approach, we were able to reproduce EPEX SPOT market outcomes for the German bidding zone with a high level of accuracy (mean absolute percentage error of 8 €/MWh for the years 2016–2019). We were also able to model negative market prices at the energy-only market realistically, and observed that the occurrence of negative prices differs among data inputs used. The simulation model provides a useful tool for investigating different short-term physical power/energy market structures and designs in the future. The modular structure also enables extension to further related markets, such as fuel, CO2, or derivative markets.

Modeling energy storage units realistically is challenging as their decision-making is not governed by a marginal cost pricing strategy but relies on expected electricity prices. Existing electricity market models often use centralized rule-based bidding or global optimization approaches, which may not accurately capture the competitive behavior of market participants. To address this issue, we present a novel method using multi-agent deep reinforcement learning to model individual strategies in electricity market models. We demonstrate the practical applicability of our approach using a detailed model of the German wholesale electricity market with a complete fleet of pumped hydro energy storage units represented as learning agents. We compare the results to widely used modeling approaches and demonstrate that the proposed method performs well and can accurately represent the competitive behavior of market participants. To understand the benefits of using reinforcement learning, we analyze overall profits, aggregated dispatch, and individual behavior of energy storage units. The proposed method can improve the accuracy and realism of electricity market modeling and help policymakers make informed decisions for future market designs and policies.

Sustainable development embraces a broad spectrum of social, economic and ecological aspects. Thus, a sustainable transformation process of energy systems is inevitably multidimensional and needs to go beyond climate impact and cost considerations. An approach for an integrated and interdisciplinary sustainability assessment of energy system transformation pathways is presented here. It first integrates energy system modeling with a multidimensional impact assessment that focuses on life cycle-based environmental and macroeconomic impacts. Then, stakeholders’ preferences with respect to defined sustainability indicators are inquired, which are finally integrated into a comparative scenario evaluation through a multi-criteria decision analysis (MCDA), all in one consistent assessment framework. As an illustrative example, this holistic approach is applied to the sustainability assessment of ten different transformation strategies for Germany. Applying multi-criteria decision analysis reveals that both ambitious (80%) and highly ambitious (95%) carbon reduction scenarios can achieve top sustainability ranks, depending on the underlying energy transformation pathways and respective scores in other sustainability dimensions. Furthermore, this research highlights an increasingly dominant contribution of energy systems’ upstream chains on total environmental impacts, reveals rather small differences in macroeconomic effects between different scenarios and identifies the transition among societal segments and climate impact minimization as the most important stakeholder preferences.

Background Different strategies have been proposed for transforming the energy system in Germany. To evaluate their sustainability, it is necessary to analyze their macroeconomic and distributional effects. An approach to do this analysis in an integrated consistent framework is presented here. Methods Comparing ten energy transition scenarios with emission reduction targets by 2050 of 80% or 95%, respectively, allows evaluating a broad range of energy system transformation strategies with respect to the future technology and energy carrier mix. For this purpose, an energy system model and a macroeconometric model are combined, thus re-modeling the unified scenarios. An important extension of the model was concerned with the integration of synthetic fuels into the energy-economy model. One focus besides the overall macroeconomic assessment is the regional analysis. For this purpose, own assumptions on the regional distribution of the expansion of renewable energies were developed. Results The effects on gross domestic product (GDP) and employment are similar on average from 2030 to 2050 across the scenarios, with most of the more ambitious scenarios showing slightly higher values for the socioeconomic variables. Employment in the construction sector shows the largest effects in most scenarios, while in the energy sector employment is lower in scenarios with high energy imports. At the regional level, the differences between scenarios are larger than at the national level. There is no clear or stable regional pattern of relative loss and profit from the very ambitious transformation, as not only renewable energy expansion varies, and hydrogen strategies enter the scene approaching 2050. Conclusions From the relatively small differences between the scenarios, it can be concluded that, from a macroeconomic perspective, it is not decisive for the overall economy which (supply side) strategy is chosen for the transformation of the energy system. More effort needs to be put into improving assumptions and modeling approaches related to strategies for achieving the final 20% CO 2 reduction, for example the increasing use of hydrogen.

As power systems transition from controllable fossil fuel plants to variable renewable sources, managing power supply and demand fluctuations becomes increasingly important. Novel approaches are required to balance these fluctuations. The problem of determining the optimal deployment of flexibility options, considering factors such as timing and location, shares similarities with scheduling problems encountered in computer networks. In both cases, the objective is to coordinate various distributed units and manage the flow of either data or power. Among the methods for scheduling and resource allocation in computer networks, stochastic network calculus (SNC) is a promising approach that estimates worst-case guarantees for Quality of Service (QoS) indicators of computer networks, such as delay and backlog. Promising QoS indicators in the power system are given by the amount of stored energy, the serviced demand, and the demand elasticity. In this work, we investigate SNC for its capabilities and limitations to quantify flexibility service guarantees in power systems. We generate and aggregate stochastic envelopes for random processes, which was found useful for modeling flexibility in power systems at multiple time scales. In a case study on the reliability of a solar-powered car charging station, we obtain similar results as from a mixed-integer linear programming problem, which provides confidence that the chosen SNC approach is suitable for modeling power system flexibility.

Background Given the multitude of scenarios on the future of our energy systems, multi-criteria assessments are increasingly called for to analyze and assess desired and undesired effects of possible pathways with regard to their environmental, economic and social sustainability. Existing studies apply elaborate lists of sustainability indicators, yet these indicators are defined and selected by experts and the relative importance of each indicator for the overall sustainability assessments is either determined by experts or is computed using mathematical functions. Target group-specific empirical data regarding citizens’ preferences for sustainability indicators as well as their reasoning behind their choices are not included in existing assessments. Approach and results We argue that citizens’ preferences and values need to be more systematically analyzed. Next to valid and reliable data regarding diverse sets of indicators, reflections and deliberations are needed regarding what different societal actors, including citizens, consider as justified and legitimate interventions in nature and society, and what considerations they include in their own assessments. For this purpose, we present results from a discrete choice experiment. The method originated in marketing and is currently becoming a popular means to systematically analyze individuals’ preference structures for energy technology assessments. As we show in our paper, it can be fruitfully applied to study citizens’ values and weightings with regard to sustainability issues. Additionally, we present findings from six focus groups that unveil the reasons behind citizens’ preferences and choices. Conclusions Our combined empirical methods provide main insights with strong implications for the future development and assessment of energy pathways: while environmental and climate-related effects significantly influenced citizens’ preferences for or against certain energy pathways, total systems and production costs were of far less importance to citizens than the public discourse suggests. Many scenario studies seek to optimize pathways according to total systems costs. In contrast, our findings show that the role of fairness and distributional justice in transition processes featured as a dominant theme for citizens. This adds central dimensions for future multi-criteria assessments that, so far, have been neglected by current energy systems models.

For the grid connection of offshore wind farms today, in many cases a high-voltage direct current (HVDC) connection to the shore is implemented. The scheduled maintenance of the offshore and onshore HVDC stations makes up a significant part of the operational costs of the connected wind farms. The main factor for the maintenance cost is the lost income from the missing energy yield (indirect maintenance costs). In this study, we show an in-depth analysis of the used components, maintenance cycles, maintenance work for the on- and offshore station, and the risks assigned in prolonging the maintenance cycle of the modular multilevel converter (MMC). In addition, we investigate the potential to shift the start date of the maintenance work, based on a forecast of the energy generation. Our findings indicate that an optimized maintenance design with respect to the maintenance behavior of an HVDC energy export system can decrease the maintenance-related energy losses (indirect maintenance costs) for an offshore wind farm to almost one half. It was also shown that direct maintenance costs for the MMC (staff costs) have small effect on the total maintenance costs. This can be explained by the fact that the additional costs for maintenance staff are two orders of magnitude lower than the revenue losses during maintenance.

Flow-based Market Coupling (FBMC) provides welfare gains from cross-border electricity trading by efficiently providing coupling capacity between bidding zones. In the coupled markets of Central Western Europe, common regulations define the FBMC methods, but transmission system operators keep some degrees of freedom in parts of the capacity calculation. Besides, many influencing factors define the flow-based capacity domain, making it difficult to fundamentally model the capacity calculation and to derive reliable forecasts from it. In light of this challenge, the given contribution reports findings from the attempt to model the capacity domain in FBMC by applying Artificial Neural Networks (ANN). As target values, the Maximum Bilateral Exchanges (MAXBEX) have been chosen. Only publicly available data has been used as inputs to make the approach reproducible for any market participant. It is observed that the forecast derived from the ANN yields similar results to a simple carry-forward method for a one-hour forecast, whereas for a longer-term forecast, up to twelve hours ahead, the network outperforms this trivial approach. Nevertheless, the overall low accuracy of the prediction strongly suggests that a more detailed understanding of the structure and evolution of the flow-based capacity domain and its relation to the underlying market and infrastructure characteristics is needed to allow market participants to derive robust forecasts of FMBC parameters.