arXiv subjects

Elina Spyrou

Publications and source records attributed to Elina Spyrou.

8 recordsLinked to original sources

arXiv2026

Contracting for Long-Duration Energy Storage in Incomplete Risk Markets

Long-duration energy storage (LDES) is increasingly regarded as essential for reliability in decarbonized power systems. To encourage investment, policymakers introduce contracts, such as cap-and-floor schemes. So far, these schemes have only been evaluated using exogenous revenue or price distributions. This paper develops a two-stage stochastic equilibrium model to evaluate how LDES cap-and-floor design affects investment and market outcomes. This model endogenously captures the interactions among contract design, investment capacity, and cost of capital. Results for a stylized Great Britain case study show that market incompleteness substantially suppresses LDES investment. Centrally administered zero-premium contracts can restore the risk-neutral investment level by reducing downside risk, but doing so requires substantial expected transfers from consumers to investors and produces outcomes that are sensitive to the cap, floor, and sharing parameters. Bilaterally negotiated contracts largely eliminate expected transfers and reduce sensitivity to those parameters, but provide weaker investment incentives. To balance investment incentives, transfers, and social welfare, policymakers should jointly consider contract and institutional design.

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arXiv2026

Decision-calibrated prediction sets for robust power system operations

Robust optimization offers a tractable approach to balance operating costs and reliability in power systems dominated by weather-dependent renewable uncertainty, but its performance depends critically on the uncertainty set. Standard data-driven approaches often calibrate uncertainty sets to attain predictive coverage, which can produce unnecessarily large sets and costly operating decisions. In contrast, we introduce decision-calibrated prediction sets and embed them as uncertainty sets in robust optimization problems; these are conditional multivariate prediction sets where calibration is defined in terms of the reliability of downstream decisions, rather than in terms of the coverage. First, we learn these conditional prediction sets as sub-level sets of norm-based score functions represented by partially input-convex neural networks, capturing contextual information and multivariate dependence while preserving convexity and tractability in downstream robust formulations. Second, inspired by conformal risk control, we calibrate a score-threshold parameter that sets the volume of the uncertainty set, thereby controlling the expected violations of downstream operational constraints. We apply our approach to 15-minute-ahead reserve scheduling with network-constrained deliverability, which we formulate as a robust DC optimal power flow problem with affine recourse. Numerical experiments show that decision-calibrated sets attain prescribed constraint-satisfaction targets within about three percentage points, whereas standard coverage-based calibration systematically exceeds these targets by more than eleven percentage points, leading to larger sets and higher operating costs.

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arXiv2026

Comparing Contract-Based Support Mechanisms for Long-Duration Energy Storage

Long-duration energy storage (LDES) faces significant revenue volatility that impedes investment. This paper evaluates four contract-based support mechanisms using an equilibrium model with risk-averse investors and incomplete risk markets. Applied to a stylized 2035 Great Britain case, we find that all mechanisms can achieve the targeted LDES capacity but differ substantially in cost-effectiveness and risk-aversion sensitivity. Contracts that eliminate revenue volatility achieve the lowest costs but may weaken operational incentives, while contracts that preserve market exposure maintain incentives at higher costs.

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arXiv2026

Constructing Deployment Scenarios for Reserve Deliverability via Adaptive Robust Optimization

Network congestion often hinders the deployment of reserves needed to balance forecast errors during real-time operations. A pertinent idea to tackle this challenge involves adding deployment scenarios of spatial distributions of forecast errors as contingencies to the day-ahead problem. However, current approaches disregard the effect of grid topology and the day-ahead schedule on the induced congestion and, consequently, reserve deliverability. In this work, we formulate a two-stage adaptive robust optimization problem to jointly consider interactions between day-ahead and real-time operations and forecast errors. Using a column-and-constraint algorithm, we iteratively construct deployment scenarios by finding the worst-case forecast error for reserve deliverability. Simulations on the RTS-GMLC system show that adding these scenarios to the day-ahead problem significantly reduces the frequency of congestion-driven reserve undeliverability. Notably, the choice and number of scenarios dynamically adapt to the day-ahead schedule.

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arXiv2025

Quantifying the Impact of Missing Risk Markets for Decarbonized Power Systems with Long Duration Energy Storage

The transition to a fully decarbonised electricity system depends on integrating new technologies that ensure reliability alongside sustainability. However, missing risk markets hinder investment in reliability-enhancing technologies by exposing investors to revenue uncertainty. This study provides the first quantitative assessment of how missing risk markets affect investment decisions in power systems that depend on long-duration energy storage (LDES) for reliability. We develop a two-stage stochastic equilibrium model with risk-averse market participants, which independently sizes power and energy capacity. We apply the method to a case study of a deeply decarbonised power system in Great Britain. The results show that incomplete risk markets reduce social welfare, harm reliability, and discourage investment in LDES and other technologies with volatile revenue streams. Revenue volatility leads to substantial risk premiums and higher financing costs for LDES, creating a barrier to its large-scale deployment. These findings demonstrate the importance of policy mechanisms that hedge revenue risk to lower the cost of capital and accelerate investment in reliability-enhancing, zero-carbon technologies

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arXiv2025

Missing Money and Market-Based Adequacy in Deeply Decarbonized Power Systems with Long-Duration Energy Storage

The ability of deeply decarbonised power systems to ensure adequacy may increasingly depend on long-duration energy storage (LDES). A central challenge is whether capacity markets (CMs), originally designed around thermal generation, can provide efficient investment signals when storage becomes a central participant. While recent studies have advanced methods for accrediting variable renewables and short-duration storage, the effectiveness of these methods in CMs with substantial LDES penetration remains largely unexplored. To address this gap, we extend a two-stage stochastic equilibrium investment model by endogenising continuous, duration-based capacity accreditation for storage and apply it to a Great Britain-based case using 40 years of weather-driven demand and renewable profiles under varying emission limits. Results show that well-calibrated CMs can sustain near-efficient investment and mitigate revenue volatility, but their effectiveness diminishes in deeply decarbonized systems, underscoring both their potential and the regulatory challenges of supporting large-scale LDES.

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arXiv2024

Addressing Imbalance Risk with Reserves and Flexibility Options: An ERCOT-like Case Study

As the role of variable renewables in electricity markets expands, new market products help system operators manage imbalances caused by uncertainty and variability. Whereas work in the last decade has focused on constructing demand curves for central procurement of those products, little attention has been paid to designing their settlement scheme and understanding the connections between the economic value of these products, the schedule of variable resources, and the cost of flexibility. In this article, we compare a new product called Flexibility Options, which addresses these gaps, with a traditional reserve product using a case study similar to the 2019 Texas (ERCOT) system. Our findings suggest that both products are equally effective in managing imbalances, but Flexibility Options have superior risk management properties and keep the system operator revenue adequate.

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arXiv2024

Flexibility Options: A Proposed Product for Managing Imbalance Risk

The presence of variable renewable energy resources with uncertain outputs in day-ahead electricity markets results in additional balancing needs in real-time. Addressing those needs cost-effectively and reliably within a competitive market with unbundled products is challenging as both the demand for and the availability of flexibility depends on day-ahead energy schedules. Existing approaches for reserve procurement usually rely either on oversimplified demand curves that do not consider how system conditions that particular day affect the value of flexibility, or on bilateral trading of hedging instruments that are not co-optimized with day-ahead schedules. This article proposes a new product, `Flexibility Options', to address these two limitations. The demand for this product is endogenously determined in the day-ahead market and it is met cost-effectively by considering real-time supply curves for product providers, which are co-optimized with the energy supply. As we illustrate with numerical examples and mathematical analysis, the product addresses the hedging needs of participants with imbalances cost-effectively, provides a less intermittent revenue stream for participants with flexible outputs, promotes value-driven pricing of flexibility, and ensures that the system operator is revenue-neutral. This article provides a comprehensive design that can be further tested and applied in large-scale systems.

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