arXiv subjects

Fabrizio Lacalandra

Publications and source records attributed to Fabrizio Lacalandra.

3 recordsLinked to original sources

arXiv2026

Evaluation of Electricity Market Clearing Mechanisms via Reinforcement Learning: Prices, Remuneration and Competitive Dynamics

The Pay-as-Clear (PaC) mechanism currently used in the European electricity market can generate significant submarginal profits for renewable sources when the clearing price is determined by the marginal offers of gas-fired generation units and the cost of natural gas exceeds certain levels. This exposes consumers to high price volatility related to the cost of natural gas. This report analyzes the recently proposed Segmented Pay-as-Clear (SPaC) mechanism as a market alternative, evaluating its system cost-effectiveness through simulations based on Reinforcement Learning (Q-Learning) to model the strategic behavior of operators. Three market models are compared, the two classic Pay-as-Clear (PaC) and Pay-as-Bid (PaB) along with SPaC, under two scenarios: a simplified one based on the 2030 NECP objectives and one built on the portfolios of ten operators obtained from the GME's 2024 public offers. The results show that the SPaC market clearing mechanism reduces intramarginal profits and price volatility compared to PaC, while maintaining fair participation incentives for all operators, and is more robust than PaB to the exercise of market power in oligopolistic contexts. The developed framework can serve as a support tool for regulators and policymakers in the evaluation of proposals for market design reforms.

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arXiv2021

Data-driven Heuristics for DC optimal transmission switching problem

The goal of Optimal Transmission Switching (OTS) problem for power systems is to identify a topology of the power grid that minimizes the cost of the system operation while satisfying the operational and physical constraints. Among the most popular methods to solve OTS is to construct approximation via integer linear programming formulations, which often come with big-M inequalities. These big-M inequalities increase, considerably, the difficulty of solving the resulting formulations. Moreover, choosing big-M values optimally is as hard as solving OTS itself. In this paper, we devise two data-driven big-M bound strengthening methods which take network structure, power demands and generation costs into account. We illustrate the robustness of our methods to load changes and impressive runtime improvements of mixed-integer solvers achieved by our methods with extensive experiments on benchmark instances. The speedup by one of the proposed methods is almost 13 times with respect to the exact method.

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arXiv2016

Revisiting the use of Robust Optimization for optimal energy offering under price uncertainty

We propose a new Robust Optimization method for the energy offering problem of a price-taker generating company that wants to build offering curves for its generation units, in order to maximize its profit while taking into account the uncertainty of market price. Our investigations have been motivated by a critique to another Robust Optimization method proposed in [Baringo and Conejo, 2011], which entails the solution of a sequence of robust optimization problems imposing full protection and defined over a sequence of nested subintervals of market prices: this method presents a number of issues that may severely limit its application and computational efficiency in practice and that may expose a company to the risk of presenting offering curves resulting into suboptimal or even infeasible accepted offers. To tackle all such issues, our method provides for solving one single robust counterpart, considering an intermediate level of protection between null and full protection, and to make energy offers at zero price, practically eliminating the risk of non-acceptance. Computational results on instances provided by our industrial partners show that our new method is able to grant a great improvement in profit.

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