arXiv subjects

Kenneth Bruninx

Publications and source records attributed to Kenneth Bruninx.

10 recordsLinked to original sources

arXiv2026

A Dynamic Capacity Allocation Model for DERs under Non-Firm Connection Agreements

The growing penetration of distributed energy resources (DERs) intensifies congestion in distribution networks by introducing bidirectional power flows and increasing competition for limited network capacity, underscoring the need for effective and efficient congestion management, including flexible grid-access schemes. This paper proposes a bilevel optimization model for the dynamic allocation of connection capacity to DERs under non-firm connection agreements, aligning the objectives of distribution system operator (DSO) and DER owners. The upper-level problem, representing the DSO, determines the allocated connection capacity for all DERs, defined as maximum time-varying power limits, subject to distribution system constraints and the last-in-first-out (LIFO) allocation rule. The lower-level problem, representing DER owners, maximizes the profit of each DER within the allocated power limits. The proposed model is tested on a modified CIGRE medium-voltage (MV) network, demonstrating a balanced trade-off between grid utilization and economic efficiency. Furthermore, the model enhances DER integration, enforces transparent allocation rules, reduces variability in allocation patterns, and achieves up to an 80% reduction in total curtailment costs compared with benchmark methods.

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arXiv2026

Neural Network-Assisted Model Predictive Control for Implicit Balancing

In Europe, balance responsible parties can deliberately take out-of-balance positions to support transmission system operators (TSOs) in maintaining grid stability and earn profit, a practice called implicit balancing. Model predictive control (MPC) is widely adopted as an effective approach for implicit balancing. The balancing market model accuracy in MPC is critical to decision quality. Previous studies modeled this market using either (i) a convex market clearing approximation, ignoring proactive manual actions by TSOs and the market sub-quarter-hour dynamics, or (ii) machine learning methods, which cannot be directly integrated into MPC. To address these shortcomings, we propose a data-driven balancing market model integrated into MPC using an input convex neural network to ensure convexity while capturing uncertainties. To keep the core network computationally efficient, we incorporate attention-based input gating mechanisms to remove irrelevant data. Evaluating on Belgian data shows that the proposed model both improves MPC decisions and reduces computational time.

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arXiv2026

A Portfolio-Level Optimization Framework for Coordinated Market Participation and Operational Scheduling of Hydrogen-Centric Companies

The vision of electrolytic hydrogen as a clean energy vector prompts the emergence of hydrogen-centric companies that must simultaneously engage in electricity, hydrogen, and green certificate markets while operating complex, geographically distributed asset portfolios. This paper proposes a portfolio-level optimization framework tailored for the integrated operational scheduling and market participation of such companies. The model co-optimizes asset scheduling and market decisions across multiple sites, incorporating spatial distribution, technical constraints, and company-level policy requirements. It supports participation in the electricity market, physical and virtual Power Purchase Agreements (PPAs), bundled and unbundled hydrogen markets, and green certificate transactions. The model is applied to three operational scenarios to evaluate the economic and operational impacts of different compliance strategies. Results show that centralized, portfolio-level control unlocks the full flexibility of geographically distributed assets, enabling a 2.42-fold increase in hydrogen production and a 9.4% reduction in daily operational costs, while satisfying all company policy constraints.

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arXiv2026

Coupling Europe's Capacity Markets

European Member States are increasingly introducing national capacity mechanisms (CMs) to manage growing adequacy risks. However, isolated national CMs are inefficient in highly interconnected electricity systems, such as the European system. While progress has been made in facilitating cross-border participation by generation capacity in CMs, existing arrangements are prone to under- or over-investment and do not properly value the contribution of interconnection capacity to Member States' adequacy targets. In this paper, we propose a novel conceptual design for a coupled European capacity market that utilises the logic of flow-based market coupling. In a comparative analysis of different market design scenarios in an illustrative multi-zone case study, using a bespoke long-run equilibrium problem, we show that the proposed flow-based coupling of capacity markets reduces system costs by harnessing available capacity in neighbouring market zones while ensuring deliverability with respect to network constraints in all scarcity situations.

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arXiv2025

Model Predictive Control-Guided Reinforcement Learning for Implicit Balancing

In Europe, profit-seeking balance responsible parties can deviate in real time from their day-ahead nominations to assist transmission system operators in maintaining the supply-demand balance. Model predictive control (MPC) strategies to exploit these implicit balancing strategies capture arbitrage opportunities, but fail to accurately capture the price-formation process in the European imbalance markets and face high computational costs. Model-free reinforcement learning (RL) methods are fast to execute, but require data-intensive training and usually rely on real-time and historical data for decision-making. This paper proposes an MPC-guided RL method that combines the complementary strengths of both MPC and RL. The proposed method can effectively incorporate forecasts into the decision-making process (as in MPC), while maintaining the fast inference capability of RL. The performance of the proposed method is evaluated on the implicit balancing battery control problem using Belgian balancing data from 2023. First, we analyze the performance of the standalone state-of-the-art RL and MPC methods from various angles, to highlight their individual strengths and limitations. Next, we show an arbitrage profit benefit of the proposed MPC-guided RL method of 16.15% and 54.36%, compared to standalone RL and MPC.

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arXiv2025

Gaming Strategies in European Imbalance Settlement Mechanisms

Transmission System Operators (TSOs) rely on balancing energy provided by Balancing Service Providers (BSPs) to maintain the supply-demand balance in real time. Balance Responsible Parties (BRPs) can simultaneously deviate from their day-ahead schedules in response to imbalance prices, e.g., by controlling flexible assets such as batteries. According to the European Electricity Balancing Guideline, these imbalance prices should incentivize BRPs performing such implicit or passive balancing to aid the TSO in restoring the energy balance. In this paper, we demonstrate that BRPs are unintentionally offered the opportunity to exploit gaming strategies in European imbalance settlement mechanisms. This is enabled by a disconnect between sub-quarter-hourly dynamics that determine the imbalance prices and the financial settlement on a quarter-hourly basis. We illustrate this behavior in a case study of the imbalance settlement mechanisms in Belgium and the Netherlands. Our results reveal that, in both countries, BRPs can, in theory, exploit the imbalance mechanism by increasing the instantaneous system imbalance during minutes within the quarter-hour that determine the imbalance price while still contributing to restoring the system balance for the rest of the quarter-hour.

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arXiv2024

A New Definition of Demand Response in the Distributed Energy Resource Era

Demand response is a concept that has been around since the very first electric power systems. However, we have seen an explosion of research on demand response and demand-side technologies in the past 30 years, coinciding with the shift towards liberalized/deregulated electricity markets and efforts to decarbonize the power sector. Now we are also seeing a shift towards more distributed/decentralized electric systems; we have entered the era of "distributed energy resources," which require new grid management, operational, and control strategies. Given this paradigm shift, we argue that the concept of demand response needs to be revisited, and more carefully/consistently defined to enable us to better utilize this massive resource for economic, technical, environmental, and societal aims. In this paper, we survey existing demand response definitions, highlight their shortcomings, propose a new definition, and describe how this new definition enables us to more effectively harness the value of demand response in modern power systems. We conclude with a demand response research agenda informed by a discussion of demand response barriers and enablers.

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arXiv2024

Evaluating Offshore Electricity Market Design Considering Endogenous Infrastructure Investments: Zonal or Nodal?

Policy makers are formulating offshore energy infrastructure plans, including wind turbines, electrolyzers, and HVDC transmission lines. An effective market design is crucial to guide cost-efficient investments and dispatch decisions. This paper jointly studies the impact of offshore market design choices on the investment in offshore electrolyzers and HVDC transmission capacity. We present a bilevel model that incorporates investments in offshore energy infrastructure, day-ahead market dispatch, and potential redispatch actions near real-time to ensure transmission constraints are respected. Our findings demonstrate that full nodal pricing, i.e., nodal pricing both onshore and offshore, outperforms the onshore zonal combined with offshore nodal pricing or offshore zonal layouts. While combining onshore zonal with offshore nodal pricing can be considered as a second-best option, it generally diminishes the profitability of offshore wind farms. However, if investment costs of offshore electrolyzers are relatively low, they can serve as catalysts to increase the revenues of the offshore wind farms. This study contributes to the understanding of market designs for highly interconnected offshore power systems, offering insights into the impact of congestion pricing methodologies on investment decisions. Besides, it is useful towards understanding the interaction of offshore loads like electrolyzers with financial support mechanisms for offshore wind farms.

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arXiv2022

Machine Learning for Improved Gas Network Models in Coordinated Energy Systems

The current energy transition promotes the convergence of operation between the power and natural gas systems. In that direction, it becomes paramount to improve the modeling of non-convex natural gas flow dynamics within the coordinated power and gas dispatch. In this work, we propose a neural-network-constrained optimization method which includes a regression model of the Weymouth equation, based on supervised machine learning. The Weymouth equation links gas flow to inlet and outlet pressures for each pipeline via a quadratic equality, which is captured by a neural network. The latter is encoded via a tractable mixed-integer linear program into the set of constraints. In addition, our proposed framework is capable of considering bidirectionality without having recourse to complex and potentially inaccurate convexification approaches. We further enhance our model by introducing a reformulation of the activation function, which improves the computational efficiency. An extensive numerical study based on the real-life Belgian power and gas systems shows that the proposed methodology yields promising results in terms of accuracy and tractability.

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arXiv2019

A Markov Process Approach to Ensemble Control of Smart Buildings

This paper describes a step-by-step procedure that converts a physical model of a building into a Markov Process that characterizes energy consumption of this and other similar buildings. Relative to existing thermo-physics-based building models, the proposed procedure reduces model complexity and depends on fewer parameters, while also maintaining accuracy and feasibility sufficient for system-level analyses. Furthermore, the proposed Markov Process approach makes it possible to leverage real-time data streams available from intelligent data acquisition systems, which are readily available in smart buildings, and merge it with physics-based and statistical models. Construction of the Markov Process naturally leads to a Markov Decision Process formulation, which describes optimal probabilistic control of a collection of similar buildings. The approach is illustrated using validated building data from Belgium.

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