Optimization of large-scale virtual power plants integrating wind farms
Résumé
This study explores various optimization-based control architectures, built respectively on centralized, decentralized, hierarchical and distributed optimization, to tackle the problem of Virtual Power Plants (VPPs) scheduling on the day-ahead market (DAM). First, we formulate the optimal scheduling of a generic large-scale multi-technology VPP on the DAM as centralized and decentralized mixed-integer programming problems. Second, due to the issues that a centralized architecture may create in terms of privacy, resilience and scalability, we explore three augmented Lagrangian relaxation (ALR)-based decomposition coordination algorithms: Alternating Direction Method of Multipliers (ADMM), Auxiliary Problem Principle (APP) and Analytical Target Cascading (ATC). These control strategies are compared using data from a real-world case study provided by the French aggregator Compagnie Nationale du Rhône. Results show the economic advantage of aggregating Distributed Energy Resources (DERs), instead of operating them independently on the DAM. Moreover, we show the benefits that decomposition provides in terms of scalability, protection of DER users’ privacy and robustness to communication failures. Finally, hierarchical (ATC and ADMM) and distributed (APP) approaches are compared in terms of convergence properties and complexity of their implementation (e.g. parameters tuning) in the industrial environment.
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