arXiv subjects

Iacopo Savelli

Publications and source records attributed to Iacopo Savelli.

12 recordsLinked to original sources

arXiv2026

Lifetime Profit-Maximising Co-optimisation of Multi-Service Stacking for Battery Storage

Grid-scale battery energy storage can generate revenue by stacking services across electricity and frequency response markets, yet identifying the lifetime profit-maximising stacking strategy remains challenging. Decisions across services are coupled through shared battery system capacity, constrained by system operator energy management rules, and further shaped by product-specific technical requirements that govern system operation, degradation, and lifetime profitability. This paper presents an ageing-aware receding-horizon framework for co-optimising multi-service stacking that explicitly captures product-specific characteristics and state-of-energy compliance rules. The framework is applied to the Great Britain market, where storage operators can stack electricity trading with multiple dynamic frequency response services procured through the newly introduced 'Enduring Auction Capability' platform under energy management requirements imposed by the National Energy System Operator. Using real market data, we demonstrate that degradation modelling, discount rate, and battery ageing jointly govern both lifetime value and optimal stacking strategy. Accounting for ageing increases lifetime revenue by up to 32% relative to a degradation-agnostic benchmark, while higher-fidelity ageing modelling can provide a further revenue improvement of up to 16% over simpler formulations. Lower discount rates favour strategies that balance calendar and cycling ageing, while higher discount rates favour aggressive operation. Across all strategies, services responding to positive frequency deviations are consistently preferred over those responding to negative deviations. Lifetime profit is maximised by adapting the optimal service mix as the battery ages.

math.OC↗

arXiv2025

Assessing Long-Term Electricity Market Design for Ambitious Decarbonization Targets using Multi-Agent Reinforcement Learning

Electricity systems are key to transforming today's society into a carbon-free economy. Long-term electricity market mechanisms, including auctions, support schemes, and other policy instruments, are critical in shaping the electricity generation mix. In light of the need for more advanced tools to support policymakers and other stakeholders in designing, testing, and evaluating long-term markets, this work presents a multi-agent reinforcement learning model capable of capturing the key features of decarbonizing energy systems. Profit-maximizing generation companies make investment decisions in the wholesale electricity market, responding to system needs, competitive dynamics, and policy signals. The model employs independent proximal policy optimization, which was selected for suitability to the decentralized and competitive environment. Nevertheless, given the inherent challenges of independent learning in multi-agent settings, an extensive hyperparameter search ensures that decentralized training yields market outcomes consistent with competitive behavior. The model is applied to a stylized version of the Italian electricity system and tested under varying levels of competition, market designs, and policy scenarios. Results highlight the critical role of market design for decarbonizing the electricity sector and avoiding price volatility. The proposed framework allows assessing long-term electricity markets in which multiple policy and market mechanisms interact simultaneously, with market participants responding and adapting to decarbonization pathways.

cs.LG↗

arXiv2025

Bilevel Transmission Expansion Planning with Joint Chance-Constrained Dispatch

In transmission expansion planning (TEP), network planners make long-term investment decisions while anticipating market clearing outcomes that are increasingly affected by renewable generation uncertainty. Additionally, market participants' sensitivity to network charges and the requirement for cost recovery by the network planner introduce further complexity. Since the day-ahead market clears before uncertainty realizes, explicitly modelling these uncertainties at the lower-level market clearing becomes important in bilevel TEP problems. In this paper, we introduce a novel bilevel TEP framework with lower-level joint chance-constrained market clearing that manages line flow constraints under wind uncertainty and accounts for the effect of network tariffs on participants' actual marginal costs and utility. To solve this complex problem, we propose a Strengthened Linear Approximation (SLA) technique for handling Wasserstein distributionally robust joint chance constraints with right-hand-side uncertainties (RHS-WDRJCC). The proposed method offers more efficient approximations without additional conservativeness and avoids the numerical issues encountered in existing approaches by introducing valid inequalities. The case study demonstrates that the proposed model achieves the desired out-of-sample constraint satisfaction probability. Moreover, the numerical results highlight the significant computational advantage of SLA, achieving up to a 26x speedup compared to existing methods such as worst-case conditional value-at-risk, while maintaining high solution quality.

math.OC↗

arXiv2025

Scalable Multi-Level Optimization for Sequentially Cleared Energy Markets with a Case Study on Gas and Carbon Aware Unit Commitment

This paper examines Mixed-Integer Multi-Level problems with Sequential Followers (MIMLSF), a specialized optimization model aimed at enhancing upper-level decision-making by incorporating anticipated outcomes from lower-level sequential market-clearing processes. We introduce a novel approach that combines lexicographic optimization with a weighted-sum method to asymptotically approximate the MIMLSF as a single-level problem, capable of managing multi-level problems exceeding three levels. To enhance computational efficiency and scalability, we propose a dedicated Benders decomposition method with multi-level subproblem separability. To demonstrate the practical application of our MIMLSF solution technique, we tackle a unit commitment problem (UC) within an integrated electricity, gas, and carbon market clearing framework in the Northeastern United States, enabling the incorporation of anticipated costs and revenues from gas and carbon markets into UC decisions. This ensures that only profitable gas-fired power plants (GFPPs) are committed, allowing system operators to make informed decisions that prevent GFPP economic losses and reduce total operational costs under stressed electricity and gas systems. The case study not only demonstrates the applicability of the MIMLSF model but also highlights the computational benefits of the dedicated Benders decomposition technique, achieving average reductions of 32.23% in computing time and 94.23% in optimality gaps compared to state-of-the-art methods.

math.OC↗

arXiv2024

The Impact of Grid Storage on Balancing Costs and Carbon Emissions in Great Britain

Grid energy storage can help to balance supply and demand, but its financial viability and operational carbon emissions impact is poorly understood because of the complexity of grid constraints and market outcomes. We analyse the impact of several technologies (Li-ion and flow batteries, pumped hydro, hydrogen) on Great Britain balancing mechanism, the main market for supply-demand balancing and congestion management. We find that, for many locations and technologies, financially optimal operation of storage for balancing can result in higher carbon emissions. For example, the extra emissions associated with a 1 MW 2-hour duration Li-ion battery in winter vary between +230 to -71 kgCO2/h. Although storage enable higher usage of renewables, it can also unlock additional demand leading to greater use of gas. In addition, balancing services alone are presently insufficient for financial viability of storage projects. This work highlights the need for market reform aligning financial incentives with environmental impacts.

eess.SY↗

arXiv2024

An Incentive Regulation Approach for Balancing Stakeholder Interests in Transmission Investment

The merchant-regulatory mechanism represents a promising tool that combines the benefits of merchant investment and regulated investment, thereby providing efficient incentives for merchant Transmission Companies (Transcos) subject to regulatory compliance. However, one of the drawbacks of the H-R-G-V merchant-regulated mechanism is that it allows the Transco to capture the entire surplus increase resulting from investment, without any economic benefits for consumers and generators. To address this issue, we propose an incentive tuning parameter, which is incorporated into the calculation of the incentive fee for the Transco. Accordingly, the regulatory framework can effectively manage the Transco's profit and allow market participants to access economic benefits, thus ensuring a fair distribution of economic advantages among the stakeholders, while the impact on overall social welfare remains relatively modest.

math.OC↗

arXiv2023

An Insurance Paradigm for Improving Power System Resilience via Distributed Investment

Extreme events, exacerbated by climate change, pose significant risks to the energy system and its consumers. However there are natural limits to the degree of protection that can be delivered from a centralised market architecture. Distributed energy resources provide resilience to the energy system, but their value remains inadequately recognized by regulatory frameworks. We propose an insurance framework to align residual outage risk exposure with locational incentives for distributed investment. We demonstrate that leveraging this framework in large-scale electricity systems could improve consumer welfare outcomes in the face of growing risks from extreme events via investment in distributed energy.

econ.GN↗

arXiv2021

On the Design of an Insurance Mechanism for Reliability Differentiation in Electricity Markets

Securing an adequate supply of dispatchable resources is critical for keeping a power system reliable under high penetrations of variable generation. Traditional resource adequacy mechanisms are poorly suited to exploiting the growing flexibility and heterogeneity of load enabled by advancements in distributed resource and control technology. To address these challenges this paper develops a resource adequacy mechanism for the electricity sector utilising insurance risk management frameworks that is adapted to a future with variable generation and flexible demand. The proposed design introduces a central insurance scheme with prudential requirements that align diverse consumer reliability preferences with the financial objectives of an insurer-of-last-resort. We illustrate the benefits of the scheme in (i) differentiating load by usage to enable better management of the system during times of extreme scarcity, (ii) incentivising incremental investment in generation infrastructure that is aligned with consumer reliability preferences and (iii) improving overall reliability outcomes for consumers.

eess.SY↗

arXiv2021

Electricity prices and tariffs to keep everyone happy: a framework for fixed and nodal prices coexistence in distribution grids with optimal tariffs for investment cost recovery

Some consumers, particularly households, are unwilling to face volatile electricity prices, and they can perceive as unfair price differentiation in the same local area. For these reasons, nodal prices in distribution networks are rarely employed. However, the increasing availability of renewable resources and emerging price-elastic behaviours pave the way for the effective introduction of marginal nodal pricing schemes in distribution networks. The aim of the proposed framework is to show how traditional non-flexible consumers can coexist with flexible users in a local distribution area. Flexible users will pay nodal prices, whereas non-flexible consumers will be charged a fixed price derived from the underlying nodal prices. Moreover, the developed approach shows how a distribution system operator should manage the local grid by optimally determining the lines to be expanded, and the collected network tariff levied on grid users, while accounting for both congestion rent and investment costs. The proposed model is formulated as a non-linear integer bilevel program, which is then recast as an equivalent single optimization problem, by using integer algebra and complementarity relations. The power flows in the distribution area are modelled by resorting to a second-order cone relaxation, whose solution is exact for radial networks under mild assumptions. The final model results in a mixed-integer quadratically constrained program, which can be solved with off-the-shelf solvers. Numerical test cases based on both 5-bus and 33-bus networks are reported to show the effectiveness of the proposed method.

econ.GN↗

arXiv2019

Ex-ante dynamic network tariffs for transmission cost recovery

This paper proposes a novel tariff scheme and a new optimization framework in order to address the recovery of fixed investment costs in transmission network planning, particularly against rising demand elasticity. At the moment, ex-post network tariffs are utilized in addition to congestion revenues to fully recover network costs, which often leads to over/under fixed cost recovery, thus increasing the investment risk. Furthermore, in the case of agents with elastic market curves, ex-post tariffs can cause several inefficiencies, such as mistrustful bidding to exploit ex-post schemes, imperfect information in applied costs and cleared quantities, and negative surplus for marginal generators and consumers. These problems are exacerbated by the increasing price-elasticity of demand, caused for example by the diffusion of demand response technologies. To address these issues, we design a dynamic ex-ante tariff scheme that explicitly accounts for the effect of tariffs in the longterm network planning problem and in the underlying market clearing process. Using linearization techniques and a novel reformulation of the congestion rent, the long-term network planning problem is reformulated as a single mixed-integer linear problem which returns the combined optimal values of network expansion and associated tariffs, while accounting for price-elastic agents and lumpy investments. The advantages of the proposed approach in terms of cost recovery, market equilibrium and increased social welfare are discussed qualitatively and are validated in numerical case studies.

eess.SY↗

arXiv2018

A Community Microgrid Architecture with an Internal Local Market

This work fits in the context of community microgrids, where members of a community can exchange energy and services among themselves, without going through the usual channels of the public electricity grid. We introduce and analyze a framework to operate a community microgrid, and to share the resulting revenues and costs among its members. A market-oriented pricing of energy exchanges within the community is obtained by implementing an internal local market based on the marginal pricing scheme. The market aims at maximizing the social welfare of the community, thanks to the more efficient allocation of resources, the reduction of the peak power to be paid, and the increased amount of reserve, achieved at an aggregate level. A community microgrid operator, acting as a benevolent planner, redistributes revenues and costs among the members, in such a way that the solution achieved by each member within the community is not worse than the solution it would achieve by acting individually. In this way, each member is incentivized to participate in the community on a voluntary basis. The overall framework is formulated in the form of a bilevel model, where the lower level problem clears the market, while the upper level problem plays the role of the community microgrid operator. Numerical results obtained on a real test case implemented in Belgium show around 54% cost savings on a yearly scale for the community, as compared to the case when its members act individually.

eess.SY↗

arXiv2018

A New Approach to Electricity Market Clearing With Uniform Purchase Price and Curtailable Block Orders

The European market clearing problem is characterized by a set of heterogeneous orders and rules that force the implementation of heuristic and iterative solving methods. In particular, curtailable block orders and the uniform purchase price (UPP) pose serious difficulties. A block is an order that spans over multiple hours, and can be either fully accepted or fully rejected. The UPP prescribes that all consumers pay a common price, i.e., the UPP, in all the zones, while producers receive zonal prices, which can differ from one zone to another. The market clearing problem in the presence of both the UPP and block orders is a major open issue in the European context. The UPP scheme leads to a non-linear optimization problem involving both primal and dual variables, whereas block orders introduce multi-temporal constraints and binary variables into the problem. As a consequence, the market clearing problem in the presence of both blocks and the UPP can be regarded as a non-linear integer programming problem involving both primal and dual variables with complementary and multi-temporal constraints. The aim of this paper is to present a non-iterative and heuristic-free approach for solving the market clearing problem in the presence of both curtailable block orders and the UPP. The solution is exact, with no approximation up to the level of resolution of current market data. By resorting to an equivalent UPP formulation, the proposed approach results in a mixed-integer linear program, which is built starting from a non-linear integer bilevel programming problem. Numerical results using real market data are reported to show the effectiveness of the proposed approach. The model has been implemented in Python, and the code is freely available on a public repository.

q-fin.PR↗