Surrogate Models for Maximizing Net Present Value of Renewable Energy Sources

魁北克水电公司通过经营柴油发电厂向偏远离网地区的客户供电。成本 和环境问题促使人们考虑使用几种类型的可再生能源。的 可以通过求解优化问题来确定不同能量源的最佳组合 最大化离网电网在其寿命期间产生的净现值（NPV），同时 建筑、燃料、维修和拆卸成本的会计核算。然而，这需要数字 与高计算成本和两个函数中的不连续性相关联的模拟， 变量，不允许计算梯度。IREQ尝试了传统技术， 解决这些挑战以便在实际时间范围内解决基于仿真的优化问题， 但是没有任何成功。 本项目旨在开发和实现一个代理辅助的无导数优化框架 来缓解这些挑战。我们将开发基于核平滑（KS）的代理模型， 径向基函数（RBF）和利用我们的知识的问题和它的结构。这些替代 然后，将模型与一类特殊的无导数优化算法（称为 网格自适应直接搜索（MADS）。我们的优化框架将使用代理模型来探索 设计空间局部和加速局部收敛，并将使用昂贵的模拟最终 每次迭代的决策。 本文的研究成果将为魁北克水电公司提供一个强有力的离网网络分析工具 优化，可以考虑可再生能源，以提供经济上可行的，强大的， 为偏远社区提供环境友好型能源解决方案。

Hydro-Québec supplies electricity to customers in remote off-grid locations by operating diesel plants. Cost and environmental concerns motivate the consideration of using several types of renewable energy sources. The optimal combination of different energy sources can be determined by solving an optimization problem to maximize the net present value (NPV) generated by the off-grid electrical network over its lifetime while accounting for construction, fuel, maintenance and dismantling costs. However, this requires numerical simulations that are associated with high computational cost and discontinuities in both functions and variables, which does not allow the computation of gradients. IREQ has tried conventional techniques to address these challenges in order to solve the simulation-based optimization problem in practical time frames, but has not had any success. This project aims at developing and implementing a surrogate-assisted, derivative-free optimization framework to alleviate these challenges. We will develop surrogate models that are based on Kernel Smoothing (KS) and Radial Basis Functions (RBF) and exploit our knowledge of the problem and its structure. These surrogate models will then be integrated with a particular class of derivative-free optimization algorithms termed Mesh-Adaptive Direct Search (MADS). Our optimization framework will use the surrogate models to explore the design space locally and to accelerate local convergence, and will use the expensive simulations for final decision-making at each iteration. The outcome of this research will provide Hydro-Québec with a powerful tool for off-grid network optimization that can consider renewable energy sources to offer economically viable, robust and environmentally benign energy solutions for isolated communities.