Search

The decarbonization of many heavy power-consuming industries is dependent on the integration of renewable energy sources and energy storage systems in isolated autonomous power systems. The optimal energy management in such schemes becomes harder due to the increased complexity and stability requirements, the rapidly varying operating conditions and uncertainty of renewable sources, the conflicting objectives across different timescales, the limited amount of reliable power sources and energy storage. The state of charge management when energy storage is used for multiple services, such as optimal scheduling and frequency support, is one of the most notorious problems in this context. To address this issue, an optimal energy management system is proposed in this paper. It co-optimizes the primary frequency control layer and the dispatch schedule of conventional generators and energy storage by taking advantage of an algorithm that provides adaptive active power demand uncertainty quantification, theoretical guarantees for frequency stability, and bounds for the reserves for frequency support assigned to the energy storage system. A pattern-based reformulation of the frequency stability constraints is derived enabling the efficient solution of the involved optimization problem, being a test case of an isolated offshore oil and gas platform presented for validation.

With the coming installation of hundreds of GW of offshore wind power, penetration of the inherent power fluctuations into the electricity grid will become significant. Therefore, the use of wind farms as power reserve providers to support the regulation of the grid’s voltage and frequency through delivering a desired power is expected to increase. As a result, wind turbines will not be necessarily delivering the maximum available power anymore – known as curtailed or derated operation – and will have to be able to deal with time-variant power demand. For this purpose, power setpoints from the grid are dispatched at the farm-level and then tracked at the turbine-level under the constraint of available power in the wind (known as active power control). The idea of this work is taking advantage of the additional degree of freedom lying in the power dispatch between turbines when operating in curtailed conditions. As failure of power train system components is frequent, costly and predictable, we seek to introduce power train degradation into the farm control objectives. To this end, a data-driven model of drivetrain fatigue damage as function of wind conditions and derating factor adapted to the farm active power control objective function is developed based on the pre-analysis of single-turbine simulations and degradation calculations, where the increased turbulence intensity due to wind farm wake effect is also considered. The proposed analytical power train degradation model is computationally efficient, can reflect the fatigue damage of individual gears and bearings in the overall power train life function and in contrast with high-fidelity models can be easily adjusted for different drivetrain configurations. A case study on the TotalControl reference wind power plant is demonstrated.

We consider the problem of how to generate uncertainty scenarios to be used in energy storage sizing problems in isolated power systems. More precisely, we consider storage sizing formulations where both loads and generation are stochastic, where no closed form analytical expression is available, and where the presence of multiple discrete random variables makes the sizing problem mixed integer and with combinatorial search spaces. We thus propose and characterize a data-driven scenarios selection strategy that mitigates the computational issues associated with these types of storage sizing problem formulations while guaranteeing statically stable optimal solutions. Specifically, the approach works by first learning the distribution of the uncertainties of the loads and generation starting from field data, and then generating, through the learned distribution, an optimal set of uncertainty scenarios that are subsequently used in a two-stage stochastic programming reformulation of the original sizing problem. The workflow does not impose arbitrary structures to the correlation among the uncertainties, nor does it lump these in a single parameter; thus, it is suitable for systems with any load characteristics. Moreover, the approach ensures to reach a solution that is statistically close to the one that would be computed if the original problem was solvable and not computationally intractable. As a case study, we analyze the problem of designing an energy storage system for a wind powered oil and gas platform to minimize the expected daily system operational costs. Numerical simulations showed that the proposed methodology leads to higher quality solutions compared with other scenario selection strategies. This reveals realistic estimations of the expected benefits, while also highlighting the risk-management limitations, when solving the risk-constrained version of the problem.