• Energy Research

Due to the expansion of small-scale distributed generation, residential consumers are evolving to active participants in energy markets. Concepts like Local Energy Markets (LEM) are designed to harvest flexibility of these prosumers and contribute to a stable power system operation. However, the stochastic nature of the consumption of households increases the difficulty of accurate forecasts and can lead to erroneous bids and penalty payments. State of the art load forecasting methods can reduce this error to a certain extend. Yet, for a systematic assessment of the implications of forecast errors, a method capable of generating forecast time series with defined errors is required. With this method, measures to decrease the implications of forecast errors (e.g., aggregation of participants) can be evaluated. In this paper, we introduce such a method based on nonlinear optimization. After an analysis of typically used error metrics and achieved forecast errors in the literature, the proposed method is evaluated using German household load profiles demonstrating similar statistical properties as found in the literature. Additionally, we show the application of the method to a LEM simulation case revealing that a participation of a household without flexible assets would only be profitable for forecast errors instead of accuracies below 30-40%.

State estimation for distribution systems faces the challenge of dealing with limited real-time measurements and historical data. This work describes a Bayesian state estimation approach tailored for practical implementation in different data availability scenarios, especially when both real-time and historical data are scarce. The approach leverages statistical correlations of the state variables from a twofold origin: (1) from the physical coupling through the grid and (2) from similar behavioral patterns of customers. We show how these correlations can be parameterized, especially when no historical time series data are available, and that accounting for these correlations yields substantial accuracy gains for state estimation and for the recognition of critical system states, i.e., states with voltage or current limit violations. In a case study, the approach is tested in a realistic European-type, medium-voltage grid. The method accurately recognizes critical system states with an aggregated true positive rate of 98%. Compared to widely used approaches that do not consider these correlations, the number of undetected true critical cases can be reduced by a factor of up to 9. Particularly in the case where no historical smart meter time series data is available, the recognition accuracy of critical system states is nearly as high as with full smart meter coverage.

The decarbonization of energy systems has led to a fundamental change in their topology since generation is shifted to locations with favorable renewable conditions. In planning, this change is reflected by applying optimization models to regions within a country to optimize the distribution of generation units and to evaluate the resulting impact on the grid topology. This paper proposes a globally applicable framework to find a suitable regionalization for energy system models with a data-driven approach. Based on a global, spatially resolved database of demand, generation, and renewable profiles, hierarchical clustering with fine-tuning is performed. This regionalization approach is applied by modeling the resulting regions in an optimization model including a synthesized grid. In an exemplary case study, South Africa’s energy system is examined. The results show that the data-driven regionalization is beneficial compared to the common approach of using political regions. Furthermore, the results of a modeled 80% decarbonization until 2045 demonstrate that the integration of renewable energy sources fundamentally changes the role of regions within South Africa’s energy system. Thereby, the electricity exchange between regions is also impacted, leading to a different grid topology. Using clustered regions improves the understanding and analysis of regional transformations in the decarbonization process.

Improving the control of flexible assets in distribution grids, e.g., battery storages, electric vehicle charging points, and heat pumps, can balance power peaks caused by high renewable power generation or load to prevent overloading the grid infrastructure. Renewable energy communities, introduced as part of the recast of the Renewable Energy Directive, provide a regulatory framework for this. As a multi-site energy management method, they can tap flexibility potential. The present work quantifies stimulus for renewable energy communities to incentivize the grid-friendly operation of flexible assets, depending on the shares of participants in rural, suburban, and urban grid topologies. Results indicate that an operation of the community, driven by maximizing the economic benefits of its members, does not clearly reduce the annual peak load at the low-voltage substation, while the operation strategy of a grid-friendly renewable energy community achieves a peak power reduction of 23–55%. When there is not full participation, forecasts of the residual load of non-participants provided by the distribution system operator can be considered in the optimization of the renewable energy community. For all simulation cases, the economic benefit between the two operation strategies differs by less than one percent, resulting in a very low additional incentive required for grid-friendliness in terms of reduced peak power. Thus, grid-friendly renewable energy communities might be a cost-effective way to defer future grid reinforcements.

Local Energy Markets (LEMs) were recently proposed as a measure to coordinate an increasing amount of distributed energy resources on a distribution grid level. A variety of market models for LEMs are currently being discussed; however, a consistent analysis of various proposed grid tariff designs is missing. We address this gap by formulating a linear optimization-based market matching algorithm capable of modeling a variation of grid tariff designs. A comprehensive simulative study is performed for yearly simulations of a rural, semiurban, and urban grids in Germany, focusing on electric vehicles, heat pumps, battery storage, and photovoltaics in residential and commercial buildings. We compare energy-based grid tariffs with constant, topology-dependent and time-variable cost components and power-based tariffs to a benchmark case. The results show that grid tariffs with power fees show a significantly higher potential for the reduction of peak demand and feed-in (30–64%) than energy fee-based tariffs (8–49%). Additionally, we show that energy-based grid tariffs do not value the flexibility of assets such as electric vehicles compared to inflexible loads. A postprocessing of market results valuing the reduction of power peaks is proposed, enabling a compensation for the usage of asset flexibility.

This paper includes an approach to summarize energy systems of countries worldwide in energy system archetypes. These archetypes are generated by a clustering algorithm and modeled in an energy system optimization model by standardized rules. The results show that archetypes provide an opportunity to compare decarbonization pathways of different countries on a global level. Furthermore, they can be used to simplify data collection and elaborate the potential of new technologies in different energy systems. <br>

In fourth-generation district heating systems (DHSs), the supply temperature of modern heat sources such as heat pumps and waste heat can potentially be reduced by mixing in hot water from combustion-based producers, thereby increasing efficiency and facilitating integration into networks with unrenovated buildings. However, this approach introduces the risk of thermo-hydraulic oscillations driven by mixing dynamics, transport delays, and mass flow adjustments by consumers. These oscillations can increase wear and cost and may potentially lead to system failure. This study addresses the asymptotic stability of multi-supplier DHSs by combining theoretical analysis and practical validation. Through linearization and Laplace transformation, we derive the transfer function of a system with two suppliers. Using pole-zero analysis, we show that transport delay can cause instability. We identify a new control law, demonstrating that persisting oscillations depend on network temperatures and low thermal inertia and enabling stabilization through careful temperature selection, thorough choice of the slack supplier, or installation of buffer tanks. We validate our findings using dynamic simulations of a nonlinear delayed system in Modelica, highlighting the applicability of such systems to real-world DHSs. These results provide actionable insights for designing robust DHSs and mitigating challenges in multi-supplier configurations by relying on thoughtful system design rather than complex control strategies.