arXiv subjects

Jalal Kazempour

Publications and source records attributed to Jalal Kazempour.

At least 19 recordsLinked to original sources

arXiv2026

When and How Should a Power Trader Engage in Arbitrage? Predict, then Contextually Optimize

Electricity markets increasingly expose stochastic energy generators to arbitrage opportunities between the day-ahead and balancing markets, driven by widening price spreads. However, opportunistic bidding, deliberately deviating from the production forecast to exploit anticipated price spreads, carries significant risk, and existing frameworks rarely offer explainable, risk-aware decision support. We propose a predict-then-contextual-optimize framework that decomposes the day-ahead bidding decision into three explicit stages to decide, when to engage in arbitrage, in what direction, and to what extent. A probabilistic binary classifier with confidence thresholds determines whether the predicted price spread is sufficiently confident to justify an opportunistic bid. Otherwise, the trader defaults to an arbitrage-free bid equal to the power forecast. A linear decision policy learned for each class via contextual optimization determines the magnitude of the bid deviation from the power forecast. The framework accommodates both standalone renewable generation and hybrid power plants combining renewable generation with other assets, such as an electrolyzer. We evaluate the framework on a real wind farm in the European bidding zones DK1 and DE/LU using a rolling-window procedure and compare it against several benchmark bidding strategies. The results show that the proposed framework increases mean profit relative to an arbitrage-free benchmark, reaching an improvement of about 7% for the hybrid power plant in DK1. The largest gains occur when distributional drift between training and testing windows is low, while the co-located electrolyzer further increases arbitrage value by providing additional operational flexibility.

cs.CE↗

arXiv2026

Refinement of Reliability Grid Codes in the Provision of Ancillary Services

Stochastic resources such as wind farms, electric vehicle aggregators, and demand-side assets are increasingly participating as reserve providers in ancillary service markets. To manage delivery uncertainty, system operators impose minimum reliability thresholds on such providers. Energinet, the Danish transmission system operator (TSO), has pioneered this approach through the P90 requirement, requiring stochastic providers to make accepted reserve capacity bids available with at least 90% probability. Yet this threshold is set by regulatory convention, not optimization: no existing framework treats it as a design variable or characterizes the cost-reliability trade-off it governs. This paper closes that gap. We develop a bilevel optimization framework in which the TSO in the upper level sets the reliability threshold endogenously while providers in the lower levels respond through reliability-constrained bidding, with chance constraints reformulated analytically using a Weibull tail distribution. Applied to the Nordic frequency containment reserve for disturbances (FCR-D) market, the cost-optimal threshold lies below P90 in the studied cases, with cost reductions by up to 14.5% relative to the fixed standard. Dynamic hourly thresholds yield a further reduction of up to 2.4%, suggesting efficiency gains may increase in larger and more diverse reserve markets.

eess.SY↗

arXiv2026

Towards European Hydrogen Market Design: Perspectives from Transmission System Operators

Despite hydrogen being central to Europe's decarbonisation strategy, only a small share of renewable hydrogen projects reached final investment decision. A key barrier is uncertainty about how future hydrogen markets will be designed and operated, particularly under Renewable Fuels of Non-Biological Origin requirements. This study investigates the extent to which future hydrogen market design can be adapted from existing natural gas markets, and the challenges it must address. The analysis was based on a survey targeting European gas transmission system operators, structured around five components: market design principles, trading frameworks, capacity allocation, tariffs, and balancing. The survey produced two outputs: an assessment of mechanism transferability and an identification of challenges for early hydrogen market development. Core market design principles and trading frameworks are broadly transferable from natural gas markets, as entry-exit systems and virtual trading points. Capacity allocation requires targeted adaptation to improve coupling with electricity markets. Tariffs require adaptation through intertemporal cost allocation, distributing infrastructure costs over time to protect early adopters. Balancing regimes should be revisited to reflect hydrogen's physical characteristics and different linepack flexibility usages. Key challenges for early hydrogen markets include: temporal mismatches between variable renewable supply and expected relatively stable industrial demand, limited operational flexibility due to scarce storage and reduced pipeline linepack, fragmented regional hydrogen clusters, and regulatory uncertainty affecting long-term investment decisions. These findings provide empirical input to the hydrogen network code led by the European Network of Hydrogen Network Operators and offer guidance to policymakers designing hydrogen market frameworks.

math.OC↗

arXiv2025

Hedging Hydrogen: Planning and Contracting Under Uncertainty for a Green Hydrogen Producer

Green hydrogen production by water electrolysis using renewable electricity is considered essential for decarbonisation of certain sectors of the global economy, however development of the industry is lagging behind expectations due to the perceived financial risk for individual projects. This risk stems from a number of uncertainties, including future hydrogen demand, variable renewable energy sources, and volatile energy market prices. The interaction of these uncertainties is complex, yet the analysis of hydrogen projects is often carried out using simplified modelling that often omits uncertainty and/or energy hedging practices which are typical for intensive power consumers. In this study, we define a set of planning methods (planning policies) in order to compare the effectiveness of different modelling approaches. We propose a 2-stage market-focused stochastic program to represent a hydrogen producer supplying an industrial customer through a hydrogen offtake contract (a Hydrogen Purchase Agreement, or HPA). The model can be used to obtain equipment sizing decisions, as well as energy hedging decisions using Power Purchase Agreements (PPA's) and power futures. We find that for some HPA contract types, failure to use stochastic modelling can lead to planning decisions that result in 30% higher production costs during scenario stress-testing for the same project. This could lead to some projects being discarded by developers, incorrectly deemed to be unviable due to cost projections being too high. The results also show the importance of HPA contract volumetric obligations in limiting demand uncertainty.

math.OC↗

arXiv2025

Bidding in Ancillary Service Markets: An Analytical Approach Using Extreme Value Theory

To enable the participation of stochastic distributed energy resources in ancillary service markets, the Danish transmission system operator, Energinet, mandates that flexibility providers satisfy a minimum 90% reliability requirement for reserve bids. This paper examines the bidding strategy of an electric vehicle aggregator under this regulation and develops a chance-constrained optimization model. In contrast to conventional sample-based approaches that demand large datasets to capture uncertainty, we propose an analytical reformulation that leverages extreme value theory to characterize the tail behavior of flexibility distributions. A case study with real-world charging data from 1400 residential electric vehicles in Denmark demonstrates that the analytical solution improves out-of-sample reliability, reducing bid violation rates by up to 8% relative to a sample-based benchmark. The method is also computationally more efficient, solving optimization problems up to 4.8 times faster while requiring substantially fewer samples to ensure compliance. Moreover, the proposed approach enables the construction of feasible bids with reliability levels as high as 99.95%, which would otherwise require prohibitively large scenario sets under the sample-based method. Beyond its computational and reliability advantages, the framework also provides actionable insights into how reliability thresholds influence aggregator bidding behavior and market participation. This study establishes a regulation-compliant, tractable, and risk-aware bidding methodology for stochastic flexibility aggregators, enhancing both market efficiency and power system security.

eess.SY↗

arXiv2025

A Scenario-Spatial Decomposition Approach With a Performance Guarantee for the Combined Bidding of Cascaded Hydropower and Renewables

This study develops a scalable co-optimization strategy for the joint bidding of cascaded hydropower, wind, and solar energy units, treated as a unified entity in the day-ahead market. Although hydropower flexibility can manage the stochasticity of renewable energy, the underlying bidding problem is complex due to intricate coupling constraints and nonlinear dynamics. A decomposition in both scenario and spatial dimensions is proposed, enabling the use of distributed optimization. The proposed distributed algorithm is eventually a heuristic due to non-convexities arising from the system's physical dynamics. To ensure a performance guarantee, trustworthy upper and lower bounds on the global optimum are derived, and a mathematical proof is provided to demonstrate their existence and validity. This approach reduces the average runtime by up to 35% compared to alternative distributed methods and by 57% compared to the centralized optimization. Moreover, it consistently delivers solutions, whereas both centralized and alternative distributed approaches fail as the size of the optimization problem grows.

math.OC↗

arXiv2025

Dynamic Dimensioning of Frequency Containment Reserves: The Case of the Nordic Grid

One of the main responsibilities of a Transmission System Operator (TSO) operating an electric grid is to maintain a designated frequency (e.g., 50 Hz in Europe). To achieve this, TSOs have created several products called frequency-supporting ancillary services. The Frequency Containment Reserve (FCR) is one of these ancillary service products. This article focuses on the TSO problem of determining the volume procured for FCR. Specifically, we investigate the potential benefits and impact on grid security when transitioning from a traditionally \textit{static} procurement method to a \textit{dynamic} strategy for FCR volume. We take the Nordic synchronous area in Europe as a case study and use a diffusion model to capture its frequency development. We introduce a controlled mean reversal parameter to assess changes in FCR obligations, in particular for the Nordic FCR-N ancillary service product. We establish closed-form expressions for exceedance probabilities and use historical frequency data as input to calibrate the model. We show that a dynamic dimensioning approach for FCR has the potential to significantly reduce the exceedance probabilities (up to $37\%$) while maintaining the total yearly procured FCR volume equal to that of the current static approach. Alternatively, a dynamic dimensioning approach could significantly increase security at limited extra cost.

eess.SY↗

arXiv2025

The Value of Ancillary Services for Electrolyzers

Although primarily designed for hydrogen production, electrolyzers can support power systems by providing various ancillary services, opening new revenue streams that enhance their economic viability. This paper investigates the participation of an electrolyzer in frequency-supporting reserve markets, analyzing how bid structures and activation intensities affect its value. We develop a mixed-integer linear program to co-optimize electricity procurement and reserve provision, and analytically derive the opportunity cost of reserve provision, which determines the optimal bid price. Using historical price and frequency data from western Denmark, we show that asymmetric, hourly reserve products often entail no opportunity cost and can increase profits by up to 47%. However, energy-intensive reserves may disrupt hydrogen production and risk unmet demand. Our findings reveal that flexible bidding can mitigate these risks while maintaining profitability. We also highlight the benefits of diversifying across reserve products and offer two recommendations: System operators should reconsider reserve bid structures to better accommodate electrolyzers, and electrolyzer owners should not overlook energy-intensive reserve services when hydrogen demand is flexible.

math.OC↗

arXiv2025

Towards Replication-Robust Analytics Markets

Despite recent advancements in machine learning, in practice, relevant datasets are often distributed among market competitors who are reluctant to share. To incentivize data sharing, recent works propose analytics markets, where multiple agents share features and are rewarded for improving the predictions of others. These rewards can be computed by treating features as players in a coalitional game, with solution concepts that yield desirable market properties. However, this setup incites agents to strategically replicate their data and act under multiple false identities to increase their own revenue and diminish that of others, limiting the viability of such markets in practice. In this work, we develop an analytics market robust to such strategic replication for supervised learning problems. We adopt Pearl's do-calculus from causal inference to refine the coalitional game by differentiating between observational and interventional conditional probabilities. As a result, we derive rewards that are replication-robust by design.

econ.GN↗

arXiv2025

Electrolyzers Bidding in Electricity Markets under Green Hydrogen Regulations and Uncertainty

Hydrogen produced through electrolysis offers a pathway to decarbonize hard-to-abate sectors by replacing gray hydrogen derived from natural gas reforming when produced using renewable power. However, grid-connected electrolyzers may inadvertently increase power-system emissions, resulting in hydrogen whose life-cycle intensity is similar to or higher than that of gray hydrogen. To address the high cost barrier of electrolytic hydrogen, both the E.U. and U.S. have introduced subsidy schemes conditional on low associated emissions. One key requirement is temporal matching, under which a subsidy applies only to the hydrogen volume that, ex-post, can be shown to match renewable generation over each one-hour interval. This requirement exposes the electrolyzer to uncertainty in the subsidy-eligible volume and thus the value of the produced hydrogen. This paper develops an uncertainty-aware day-ahead bid curve for a grid-connected electrolyzer. We formulate a linear program that maximizes expected profit across scenarios of renewable production and derive the bid curve from its Karush-Kuhn-Tucker conditions. A case study demonstrates that incorporating renewable uncertainty into the bid curve increases electrolyzer profit by approximately 4%, although it does not improve ex-post temporal matching. This finding highlights a potential distortion in the incentive effects of temporal-matching regulations when uncertainty is taken into account.

math.OC↗

arXiv2025

Electricity-Aware Bid Format for Coordinated Heat and Electricity Market Clearing

Coordination between heat and electricity markets is essential to achieve a cost-effective and efficient operation of the energy system. In the current sequential market practice, the heat market is cleared before the electricity market and has no insight into the impacts of heat dispatch on the electricity market. While preserving this sequential practice, this paper introduces an electricity-aware bid format for the coordination of heat and electricity systems. This novel market mechanism defines heat bids conditionally on the day-ahead electricity prices. Prior to clearing heat and electricity markets, the proposed bid selection mechanism selects the valid bids which minimize the heat system operating cost while anticipating heat and electricity market clearing. This mechanism is modeled as a trilevel optimization problem, which we recast as a mixed-integer linear program using a lexicographic function. We use a realistic case study based on the Danish electricity and heat system and show that the proposed bid selection mechanism yields a 4.5% reduction in the total operating cost of heat and electricity systems compared to the existing market-clearing procedure while reducing the financial losses of combined heat and power plants and heat pumps due to invalid bids by up to 20.3 million euros.

eess.SY↗

arXiv2025

Electricity Market Bidding for Renewable Electrolyzer Plants: An Opportunity Cost Approach

Hydrogen produced through electrolysis with renewable power is considered key to decarbonize several hard-to-electrify sectors. This work proposes a novel approach to model the active electricity market participation of co-located renewable energy and electrolyzer plants, based on opportunity-cost bidding. While a renewable energy plant typically has zero marginal cost, selling power to the grid carries a potential opportunity-cost of not producing hydrogen when it is co-located with a hydrogen electrolyzer. We first consider only the electrolyzer, and derive its revenue of consuming electricity based on the non-convex hydrogen production curve. We then consider the available renewable energy production and form a piece-wise linear cost curve representing the opportunity cost of selling (or revenue from consuming) various levels of electricity. This cost curve can be used to model a stand-alone electrolyzer or a co-located hydrogen and renewable energy plant participating in an electricity market. Our case study analyzes the effects of market-bidding electrolyzers on electricity markets and grid operations. We compare two strategies for a co-located electrolyzer-wind plant; one based on the proposed bid curve and one with a more conventional fixed electrolyzer consumption. The results show that electrolyzers that actively participate in the electricity market lower the average cost of electricity and the amount of curtailed renewable energy in the system compared with a fixed consumption case. However, the difference in total system emissions between the two strategies is insignificant. The specific impacts vary based on electrolyzer capacity and hydrogen price, which determines the location of the co-located plant in the electricity market merit order.

math.OC↗

arXiv2025

Day-Ahead Bidding Strategies for Wind Farm Operators under a One-Price Balancing Scheme

We study day-ahead bidding strategies for wind farm operators under a one-price balancing scheme, prevalent in European electricity markets. In this setting, the profit-maximising strategy becomes an all-or-nothing strategy, aiming to take advantage of open positions in the balancing market. However, balancing prices are difficult, if not impossible, to forecast in the day-ahead stage and large open positions can affect the balancing price by changing the direction of the system imbalance. This paper addresses day-ahead bidding as a decision-making problem under uncertainty, with the objective of maximising the expected profit while reducing the imbalance risk related to the strategy. To this end, we develop a stochastic optimisation problem with explicit constraints on the positions in the balancing market, providing risk certificates, and derive an analytical solution to this problem. Moreover, we show how the price-impact of the trading strategy on the balancing market can be included in the ex-post evaluation. Using real data from the Belgian electricity market and an offshore wind farm in the North Sea, we demonstrate that the all-or-nothing strategy negatively impacts the balancing price, resulting in long-term losses for the wind farm. Our risk-constrained strategy, however, can still significantly enhance operational profit compared to traditional point-forecast bidding.

eess.SY↗

arXiv2025

Selling Information in Games with Externalities

A competitive market is modeled as a game of incomplete information. One player observes some payoff-relevant state and can sell (possibly noisy) messages thereof to the other, whose willingness to pay is contingent on their own beliefs. We frame the decision of what information to sell, and at what price, as a product versioning problem. The optimal menu screens buyer types to maximize profit, which is the payment minus the externality induced by selling information to a competitor, that is, the cost of refining a competitor's beliefs. For a class of games with binary actions and states, we derive the following insights: (i) payments are necessary to provide incentives for information sharing amongst competing firms; (ii) the optimal menu benefits both the buyer and the seller; (iii) the seller cannot steer the buyer's actions at the expense of social welfare; (iv) as such, as competition grows fiercer it can be optimal to sell no information at all.

cs.GT↗

arXiv2025

Aggregator of Electric Vehicles Bidding in Nordic FCR-D Markets: A Chance-Constrained Program

The Danish system operator, Energinet, has recently introduced an innovative grid code called the P90 requirement, which allows stochastic flexible resources to bid their flexibility in Nordic ancillary service markets, contingent upon a minimum 90\% probability of successfully realizing the reserve capacity bid. For limited-energy resources, Energinet imposes additional requirements for participation in these markets. Given these requirements, this paper presents a chance-constrained optimization model designed for aggregators of electric vehicles, aiming to optimally place reserve capacity bids in the Nordic Frequency Containment Reserve for Disturbances (FCR-D) market while accounting for uncertainty in future consumption baselines. We analyze both FCR-D up and down markets, reformulating and solving the proposed joint chance-constrained model using two sample-based methods. Using real data from 1400 charging stations in Denmark from March 2022 to March 2023, we demonstrate the out-of-sample profit potential. Our findings indicate that vehicle owners could save between 6\% and 10\% on their annual electricity bills by providing FCR-D services. Additionally, we observed a synergy effect, where having more vehicles in a single portfolio enables larger bids per vehicle compared to a collective bid from multiple portfolios with the same total number of vehicles.

eess.SY↗

arXiv2025

Betting vs. Trading: Learning a Linear Decision Policy for Selling Wind Power and Hydrogen

We develop a bidding strategy for a hybrid power plant combining co-located wind turbines and an electrolyzer, constructing a price-quantity bidding curve for the day-ahead electricity market while optimally scheduling hydrogen production. Without risk management, single imbalance pricing leads to an all-or-nothing trading strategy, which we term 'betting'. To address this, we propose a data-driven, pragmatic approach that leverages contextual information to train linear decision policies for both power bidding and hydrogen scheduling. By introducing explicit risk constraints to limit imbalances, we move from the all-or-nothing approach to a 'trading" strategy', where the plant diversifies its power trading decisions. We evaluate the model under three scenarios: when the plant is either conditionally allowed, always allowed, or not allowed to buy power from the grid, which impacts the green certification of the hydrogen produced. Comparing our data-driven strategy with an oracle model that has perfect foresight, we show that the risk-constrained, data-driven approach delivers satisfactory performance.

eess.SY↗

arXiv2025

Contracting Strategies for Electrolyzers to Secure Grid Connection: The Dutch Case

In response to increasing grid congestion in the Netherlands, non-firm connection and transport agreements (CTAs) and capacity restriction contracts (CRCs) have been introduced, allowing consumer curtailment in exchange for grid tariff discounts or per-MW compensations. This study examines the interaction between an electrolyzer project, facing sizing and contracting decisions, and a network operator, responsible for contract activations and determining grid connection capacity, under the new Dutch regulations. The interaction is modeled using two bilevel optimization problems with alternating leader-follower roles. Results highlight a trade-off between CRC income and non-firm CTA tariff discounts, showing that voluntary congestion management by the network operator increases electrolyzer profitability at CRC prices below 10 euro per MW but reduces it at higher prices. Furthermore, the network operator benefits more from reacting to the electrolyzer owner's CTA decisions than from leading the interaction at CRC prices above 10 euro per MW. Ignoring the other party's optimization problem overestimates profits for both the network operator and the electrolyzer owner, emphasizing the importance of coordinated decision-making.

math.OC↗

arXiv2025

Learning Prosumer Behavior in Energy Communities: Integrating Bilevel Programming and Online Learning

Dynamic pricing through bilevel programming is widely used for demand response but often assumes perfect knowledge of prosumer behavior, which is unrealistic in practical applications. This paper presents a novel framework that integrates bilevel programming with online learning, specifically Thompson sampling, to overcome this limitation. The approach dynamically sets optimal prices while simultaneously learning prosumer behaviors through observed responses, eliminating the need for extensive pre-existing datasets. Applied to an energy community providing capacity limitation services to a distribution system operator, the framework allows the community manager to infer individual prosumer characteristics, including usage patterns for photovoltaic systems, electric vehicles, home batteries, and heat pumps. Numerical simulations with 25 prosumers, each represented by 10 potential signatures, demonstrate rapid learning with low regret, with most prosumer characteristics learned within five days and full convergence achieved in 100 days.

math.OC↗