Un Quadro di Ottimizzazione Multi-Fase per la Partecipazione delle Microreti ai Mercati dell'Energia all'Ingrosso e ai Mercati della Flessibilità Locale

The integration of Distributed Energy Resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESS), and electric vehicles (EVs), opens many great avenues within energy management. Simultaneously, all such progress introduces various challenges like instability in the grid, congestion, and energy curtailment. Local Flexibility Markets (LFMs) have emerged as a promising solution for optimizing the interaction between aggregators of DERs and Microgrids (MGs) with the distribution system operators (DSOs) and helping to address challenges such as grid stability, congestion, back power flow issues, and reliability concerns. Nevertheless, the strategies required for microgrids and aggregators to efficiently participate in these markets are not well explored. This thesis addresses this gap by introducing a novel multi-stage optimization framework for the scheduling and dispatch of MGs across multiple energy markets, including the day-ahead (DA), intraday (ID), and LFMs. The proposed framework integrates renewable energy generation profiles, demand patterns, and electricity prices in both the wholesale market and LFM, utilizing both forecasted and historical data to optimally coordinate DERs, flexible loads, and market interactions, thereby achieving operational efficiency and economic benefits. The optimization framework is structured into four sequential stages: DA forecasting, DA scheduling, ID dispatching, and real-time dispatch optimization. Each stage is designed to address specific challenges and opportunities, such as minimizing energy procurement, operational costs, and energy curtailments, and also maximizing revenues from LFM participation, while ensuring compliance with market regulations and resource constraints. However, the main focus of this thesis is the second stage, which aims to optimize the microgrid's participation in DA Markets and LFM, to reduce costs and maximize participation in LFM while efficiently coordinating its resources. To validate the proposed approach for DA scheduling, a case study has been developed utilizing a multi-objective optimization problem. A big commercial shopping mall is selected as our microgrid model which comprises PV systems, BESS, EV charging stations, and various flexible and inflexible loads. This setup highlights the microgrid's ability to function as an active player in energy markets rather than being a mere passive consumer. The optimization problem is solved using Gurobi Optimizer, a powerful mathematical programming solver, implemented through Python, considering multiple constraints and real-world operational requirements. The results demonstrate the significant economic benefits of dual participation in LFMs and DA market compared to traditional reliance on energy retailers for meeting energy demands. It effectively showcased how each resource could be optimally dispatched on an hourly basis to meet the mall's internal energy demands while simultaneously fulfilling the flexibility demands of DSOs in the LFM. This dynamic resource allocation highlights the potential of flexibility in maximizing economic benefits, reducing costs, and ensuring efficient energy utilization across all operational horizons.