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PO035: Advanced Model Predictive Control Strategies for the Provision of Energy and Ancillary Services by Wind Farms Coupled with Storage
Simon Camal, Research engineer, MINES Paris - PSL University
For a large-scale integration of renewable energies, renewable power plants are called to give their contribution in the provision of ancillary services. To this aim, an effective control strategy is required to accommodate in real-time the needs of the electricity system. Among the control algorithms proposed in the literature, Model Predictive Control (MPC) has proven to be able to provide very efficient solutions in the domain of smart grids and power systems control. Traditional tracking MPC strategies are the most common, given by a hierarchical control structure where optimal steady state set-points are computed separately and then fed to a tracking MPC algorithm. More recently, a new tendency has arisen for market-based problems, known as economic MPC (EMPC), where the control actions are directly computed to maximize the economic objectives. In this work we consider a hybrid system, given by a wind farm coupled with storage, and we tackle the problem of participating in the day-ahead energy market while providing balancing services to the grid. To solve this problem, we consider a sequential process given by the combination of optimization and control, both receiving renewable power generation forecast as input. First, we propose a deterministic and a stochastic optimization problem formulation for trading energy and balancing services on the markets. Next, we propose a stochastic economic-oriented MPC strategy and compare it to a traditional reference tracking MPC strategy to accommodate the system needs of ancillary services in real-time. The main benefit of such an innovative combination of stochastic and economic MPC control approaches stands in the possibility to integrate the economic objectives directly into the traditional predictive control approach, while considering also the presence of randomness in the system. We propose a multi-objective formulation both for the deterministic and the stochastic optimization modules and for all the control approaches considered, aiming to maximize the hybrid system profit and minimize the storage degradation. To this aim, we consider a non-linear model for an economic quantification of storage degradation. In the end, three multi-objective frameworks are obtained through the combination of the proposed optimization problems and control algorithms. These frameworks are then compared on a real case of study, the data of which are available in the frame of the European H2020 project Smart4RES, and using historical time-series of market quantities and ancillary services activation signals. The obtained results show an increase of at least 12% in the market revenue and a decrease of at least 23% in the storage degradation when using the economic-oriented approaches instead of the traditional strategy for control. Further, empirical results demonstrate how the solution coming from the stochastic economic-oriented approach is more robust to forecast errors compared to a deterministic economic-oriented strategy.