Conference Agenda

Transmission grid congestion management (CM) involves transmission system operators (TSO) adjusting the market-based dispatch of individual generators to address transmission grid constraints cost-effectively.

Growing grid stress from renewable energies and increasing demand from flexible sector coupling technologies calls for innovative CM solutions to handle future grid bottlenecks efficiently. Flexible demand-side applications not only strain electricity grids further but can also provide TSOs with new means for CM. Since directly controlling decentralized demand-side technologies constitutes a complex task considering the information and communication requirement, incentive mechanisms might be a promising solution for leveraging the demand-side flexibility potential for CM.

This work investigates different incentive schemes to stimulate grid-serving demand-side behavior by applying bi-level programming. We benchmark their economic efficiency for resolving grid congestion against the TSO's direct control of demand-side applications. The incentive mechanisms studied assume different design options for a flexibility premium paid to aggregators managing the energy procurement in charge of households, typically owning the flexible demand-side applications. We apply a case study based on a test grid comprising 118 electricity grid nodes, 114 demand-side applications as well as 72 different generators. The formulated bi-level program provides a model-theoretic and techno-economic framework for describing the different decision levels and interactions between the stakeholders involved. These include a TSO, an aggregator, and a market clearing agent.

Initial results of our study indicate that incentive schemes can lead to a significant cost reduction for CM, provided that respective schemes are designed to be time-dynamic.

The increasing electrification of the mobility and heating sectors in future decentralised energy systems necessitates innovative approaches for managing grid congestion. Regional flexibility markets are becoming pivotal for market-based flexibility coordination between grid operators and suppliers. These markets facilitate the required interaction but also introduce challenges related to market design and stakeholder behaviour, which can significantly influence the efficiency of congestion management solutions.

A critical concern in these markets is the potential for strategic gaming behaviours that exploit the locational nature of grid congestion and regional flexibility trading. This study employs bilevel programming techniques to address this issue and analyse the strategic interactions between aggregators and the resultant market dynamics under various market design scenarios.

We introduce a dynamic price cap scheme designed to optimise the trading of demand-side flexibility products. This mechanism aims to promote flexibility provision by aggregators while preventing the exploitation of gaming strategies. Through this approach, we seek to maintain a competitive and efficient market environment that aligns with the overarching goals of enhanced grid reliability and reduced congestion.

This research contributes to understanding how different market design elements can influence the behaviour of key stakeholders and the overall performance of regional flexibility markets in the context of future electricity systems.

In this work, a new holistic MILP model for the day-ahead energy management of Energy Aggregators (EAs) is developed. Synergies between the different types of flexibility and energy trading options enable profit maximizing EAs to provide economic benefits to participating households but require a detailed consideration of technological properties and constraints of the respective types of resources and their operation. Therefore, in addition to other types of energy resources, power-to-heat-systems are integrated and modeled on a high level of detail. This represents an important contribution to previous works on EAs and comprises high potential for more efficient energy management. Moreover, three different trading-levels are considered. The model application to a case study with up to 111 individually modeled prosumer-households in summer and winter scenarios reveals high synergetic potential of EAs resulting from the combined flexibility of the different system components, thus underlining the significance of holistically modeling the EA decision problem. A trade-off regarding the flexible usage of energy storages is identified between household battery storages and electric vehicle batteries as their respective technical and practical restrictions are shown to have different advantages depending on external conditions. In addition to assessing the impact of different types of energy resources, analyses are deployed to develop operational strategies towards a foresighted use of energy storages. The results show that the concept of EAs is suitable for efficiently performing energy management for communities of small-scale prosumers with renewable energy sources and has the potential for being an important element in future energy systems.

Nowadays, the congestion rate in medium voltage distribution networks (MVDNs) is increasing due to higher integration of renewables, highlighting the importance of developing solutions to deal with this issue. Despite of this fact that upgrading the grid can resolve this problem, it is costly and time-consuming. Therefore, more flexible solutions are needed. This paper explores to what extent producing green hydrogen through power-to-hydrogen (P2H) facilities can help alleviate congestion in such grids, considering other flexible sources. To achieve this aim, a bi-level model is proposed: in the upper level, the grid operator seeks to minimize grid costs, while in the lower level, different flexible sources optimize their operational costs, considering the incentives received from the grid operator. In addition, the decentralized district heating system is considered, addressing how the flexibility derived from this system can influence the operation of P2H units as well as the operating costs of the MVDN. The proposed model is implemented in a typical MVDN in the Netherlands, which is equipped with a district heating system. Based on the results, it can be expected that the operation of the electrolyzer will increase by receiving incentives from the grid and by participating in the local heat market to sell their excess heat. Furthermore, the economic value of producing flexibility is more likely to diminish in the presence of more flexible resources in the grid.