Patrizia Beraldi

The growing interdependence between electricity and gas markets is reshaping the strategic behavior of energy market participants. This paper proposes a stochastic bi-level optimization framework to model the strategic bidding of an Integrated Energy Service Provider (IESP) acting as a price-maker simultaneously in the day-ahead gas and electricity markets. At the upper level, the IESP determines its optimal gas bid and hourly electricity offers while coordinating the dispatch of a diversified portfolio. At the lower level, two distinct but interdependent market-clearing problems are considered: a daily gas market and an hourly electricity market, explicitly coupled through the dependence of gas-fired units’ electricity offers on the cleared gas price. Uncertainty in renewable generation and market conditions is captured through a two-stage stochastic, scenario-based formulation.A tractable single-level reformulation has been obtained by exploiting the Karush-Kuhn-Tucker conditions, strong duality, and McCormick envelopes.The proposed framework is validated on a realistic case study based on real Spanish day-ahead electricity and gas market data. Numerical results show that the IESP’s optimal strategy is to concentrate its market participation in a small number of highly favorable hours, leveraging the flexibility of gas-fired generation to support these sales while inflexible generation covers firm, predictable demand. A comparison against a deterministic expected-value formulation demonstrates that explicitly accounting for uncertainty yields a markedly more profitable and operationally robust bidding strategy.

This paper presents a risk-averse stochastic programming model for the optimal design of a home energy system that integrates renewable energy generation from photovoltaic panels and a battery energy storage system. Prosumer’s loads are classified into base and programmable loads and the possibility of exploiting the flexibility of these latter is considered in the optimal design. Uncertainties associated with weather-related variables, load demand, and electricity tariffs are considered, through the definition of scenarios representing possible joint evolutions of these factors. The model incorporates a risk measure to control the cost variability and the objective function, by the conditional value-at-risk, aims at minimizing the expected costs that the prosumer may incur in a given percentage of worst-case scenarios. The approach is applied to a real case study in the residential sector calibrated on data of the Italian electricity market. Through numerical experiments and sensitivity analysis, optimal system sizing and operational strategies are derived under different risk preferences. Results demonstrate that the risk-averse stochastic programming approach leads to robust decisions, providing a balance between cost-effectiveness and reliability in the management of the home energy system.