Innovative Approaches to Optimize Future Multi-Energy Systems

Dear Colleagues,

Matching the different energy demands (e.g., electricity, heating, cooling) of end users with available energy sources (e.g., renewable energy sources) in multi-energy systems (MESs) requires the integrated optimization of the capacity and operation of various energy conversion and storage units as well as energy networks. Traditionally, this design–operation optimization problem is tackled by mixed-integer linear or non-linear programming (MILP or MINLP) models over a fixed time horizon (e.g., one year) using as input data all the available historical time series or a restricted set of typical time series found by applying machine learning techniques.

However, MESs optimized only on historical data may not be optimal (or even feasible in some cases) in a future context characterized by uncertain boundary conditions, such as fluctuating weather conditions that affect the availability of renewables, variable energy demands of different consumption sectors (e.g., residential, commercial, industrial, etc.), and volatile prices of different energy carriers (e.g., electricity, natural gas, hydrogen). Such a complex challenge calls for the development of innovative optimization approaches that can lead to realistic, robust, and computationally efficient solutions for the optimal design of future MESs. These solutions could be tested against reliable forecasts of different uncertain variables and against extreme scenarios of variables influenced by disruptive socio-economic events (e.g., fluctuations in energy prices due to geopolitical conflicts).

This Special Issue aims to explore pioneering research and innovative methodologies to optimize MESs, a key area for achieving a sustainable, resilient, and renewable-driven energy future. This Special Issue welcomes the submission of papers that present cutting-edge solutions through case studies and/or propose novel optimization and solution approaches for the design of future MESs across various geographical scales, including local, regional, and national levels.

Topics of interest for this Special Issue include, but are not limited to, the following:

• Integrated design–operation optimization of energy conversion units, storage units, and networks;
• Optimal aggregation of different types of energy end users;
• Demand-side management and demand response programs;
• Multi-objective optimization with energy, economic, environmental, and social targets;
• Optimization under uncertainty, including methods such as stochastic programming, robust optimization, chance-constrained stochastic programming, and distributionally robust optimization;
• Multi-stage optimization frameworks that address evolving uncertainties over time;
• Risk assessment criteria with different attitudes towards risk;
• Application of unsupervised and supervised machine learning techniques (e.g., clustering techniques and artificial neural networks, respectively);
• Time series forecasting models for short- and long-term predictions of energy demand, generation, and prices.

Dr. Gabriele Volpato
Dr. Martina Capone
Guest Editors

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• multi-energy systems
• optimization
• uncertainty
• machine learning
• forecasting