A Stochastic Programming Model for Anticipative Planning of Integrated Electricity and Gas Systems with Bidirectional Energy Flows under Fuel and CO2 Price Uncertainty

TL;DR

The paper develops a centralised, two-stage stochastic multi-period MILP for anticipative planning of integrated electricity and gas systems with bidirectional electricity-gas flows via Power-to-Gas. By representing short-term operation at hourly resolution through representative days and explicitly modeling fuel and CO2 price uncertainty with scenarios, the approach co-optimises investments (generation capacity, storage, PtG, networks) and operations (hourly dispatch, unit commitment) over a long horizon. A multi-cut Benders decomposition algorithm enables tractable solution, demonstrated on a Italian case study where the stochastic plan increases renewables and storage deployment and achieves substantial cost savings (VSS ≈ 138 billion euros) compared with the deterministic mean-value plan. The results emphasize the value of detailed operational modeling and price uncertainty in design of decarbonised energy systems, with PtG and storage playing key roles in managing renewable intermittency and supply security.

Abstract

A two-stage multi-period mixed-integer linear stochastic programming model is proposed to assist qualified operators in long-term generation and transmission expansion planning of electricity and gas systems to meet policy objectives. The first-stage decisions concern investments in new plants, new connections in the electricity and gas sectors, and the decommissioning of existing thermal power plants; the second-stage variables represent operational decisions, with uncertainty about future fuel and CO2 prices represented by scenarios. The main features of the model are: (i) the bidirectional conversion between electricity and gas enabled by Power-to-Gas and thermal power plants, (ii) a detailed representation of short-term operation, crucial for addressing challenges associated with integrating large shares of renewables in the energy mix, and (iii) an integrated planning framework to evaluate the operation of flexibility resources, their ability to manage non-programmable generation, and their economic viability. Given the computational complexity of the proposed model, in this paper we also implement a solution algorithm based on Benders decomposition to compute near-optimal solutions. A case study on the decarbonisation of the Italian integrated energy system demonstrates the effectiveness of the model. The numerical results show: (i) the importance of including a detailed system representation for obtaining reliable results, and (ii) the need to consider price uncertainty to design adequate systems and reduce overall costs.

Figures (3)

• Figure 1: Hourly solar (left) and wind (right) power production coefficients in Italian power system zones in representative days 1 (top) and 2 (bottom).
• Figure 2: Electricity production [TWh] by power sources in scenario H-C in years 2023, 2030 and 2040 for the SPM plan and in year 2040 for the MVP plan.
• Figure 3: Annual gas supply [TWh$_{th}$] in green and annual gas consumption [TWh$_{th}$] in red in scenario H-C for the SPM plan (years 2023, 2030, 2040) and the MVP plan (year 2040).