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Communication Dans Un Congrès Année : 2023

Optimization of large-scale virtual power plants integrating wind farms

Résumé

General Summary The large-scale integration of Renewable Energy Sources (RESs), namely wind and solar energy, into power systems is a major lever for the energy transition and decarbonization. However, their volatile production leads to a significant increase of uncertainty in power system operations. A Virtual Power Plant (VPP) aggregates RESs to operate them as a whole. The aggregation permits to smooth out variability and prediction errors as well as enhance the participation in electricity markets and the provision of ancillary services. A major question arises, how to efficiently schedule the available resources to maximize the VPP cumulative profit under the local constraints of each asset. Although there is a broad literature on these matters, the evolution of VPPs into large-scale aggregations integrating several types of Distributed Energy Resources (DERs), like storage, electrical vehicles, residential/industrial flexibility, hydrogen and others, brings new challenges. This study explores various optimization-based control architectures, built respectively on centralized, decentralized and hierarchical optimization. First, we formulate the optimal scheduling of a generic large-scale multi-technology VPP on the day-ahead electricity market 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 hierarchical control approaches: 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 DERs, instead of operating them independently. Moreover, we show the benefits that hierarchical control provides in terms of scalability, protection of DER users’ privacy and robustness to communication failures. Finally, hierarchical approaches are compared in terms of convergence properties and complexity of their implementation (e.g. parameters tuning) in the industrial environment. Method This study explores various optimization-based control architectures. First, we formulate the VPP optimal scheduling as a mixed-integer programming problem. Second, we propose centralized, decentralized and hierarchical control architectures to solve the problem. In the former case, the optimization is carried on in a single controller, which receives measurements from the VPP assets and sends new commands. In the decentralized approach local controllers optimize the operation of each asset (or cluster of assets) independently, without exchanging information with the other controllers. Finally, in the hierarchical architecture, the local computations performed by local controllers associated to the VPP assets (or clusters of assets) are coordinated by a master controller to reach the global objective. For the hierarchical approach we explore three ALR-based architectures: ADMM, APP and ATC. Results The proposed control architectures are evaluated on real-world data from the French aggregator Compagnie Nationale du Rhône and historical time-series of prices from the day-ahead electricity market in France. First, centralized and decentralized architectures are compared in terms of market revenue, storage degradation cost and energy imbalance cost. The former strategy is able to significantly increase the revenue and reduce imbalances through a more intensive use of the energy storage systems. As for hierarchical control architectures, a comparison between ADMM, APP and ATC shows that the APP-based approach is able to achieve superior convergence properties, but requires additional effort in the parameters tuning. Compared to a centralized approach, hierarchical strategies show significant advantages in terms of scalability, privacy and resilience, when dealing with large-scale systems. Conclusions In recent years we have assisted to an increasing penetration of DERs into the network. To fully exploit these assets, while ensure security of the grid, they can be aggregated into a VPP and operated as a whole. Based on data from a real-world VPP integrating storage, wind and solar power, this study shows the economic advantages (e.g. increase in the market revenue) that can be obtained through the coalition of heterogeneous DERs. Moreover, we explore several control architectures that can be used to solve the VPP optimal scheduling on the day-ahead electricity market. This analysis shows that the choice of the optimal control strategy depends on the system characteristics (e.g. VPP size) as well as the VPP operator’s needs of scalability, privacy and resilience. Learning Objectives First, this study aims to clearly show the potential economic advantages of aggregating DERs into a large-scale VPP, compared to a sparse and uncoordinated integration of DERs into the grid. Second, we show a simple decomposition strategy to reformulate the mixed-integer programming problem for VPP optimal scheduling in a form which is suitable to the application of hierarchical optimization. Then, we aim to show how hierarchical control architectures can effectively map the decision-making process of a VPP, where the VPP operator coordinates the local decisions of each asset to reach common objectives. Finally, the convergence performances of the explored hierarchical control strategies are presented and we show that the choice of the VPP control architecture should result from the system characteristics and the operational requirements.
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Dates et versions

hal-04162056 , version 1 (13-07-2023)

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  • HAL Id : hal-04162056 , version 1

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Luca Santosuosso, Simon Camal, Arthur Lett, Guillaume Bontron, Georges Kariniotakis. Optimization of large-scale virtual power plants integrating wind farms. WindEurope Annual Event 2023, WindEurope, Apr 2023, Copenhagen, Denmark. ⟨hal-04162056⟩
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