Parametric Cost Function Approximations for Robust Energy Systems Planning

过渡到电网，其中越来越多的能源来自风能和太阳能，这要求电网运营商制定一个规划过程，可以处理比今天遇到的更高水平的不确定性。 目前的工具被批评为临时程序，包括围绕确定性预测进行优化，使电网容易受到意外变化的影响。 提出的“随机模型”更难解决，电网运营商仍然在努力解决他们现有的确定性模型。 现有的行业实践，虽然临时，实际上代表了一个简单的例子，一类强大的政策称为“参数成本函数近似”。“这一点几乎没有受到学术文献的关注。 这项研究建立在当今使用的核心策略基础上，但将现有商业优化求解器的确定性模型功能与机器学习算法的功能相结合。 这项工作将使现有的行业实践正规化，为处理不确定性提供一条可实施的路径，这将为电网运营商提供一种自然处理风能和太阳能能源稳定增长的方法。这项研究提出了一种算法策略，代表了从随机规划领域的根本出发，其通过随机前瞻策略的解决方案来处理不确定性，其中使用一组采样实现（场景）来表示未来。 这项研究引入了一类新的政策称为参数成本函数近似。 这些是参数修改的确定性优化问题，这些优化问题被优化以最小化成本和风险，就像任何机器学习模型被优化以适应数据一样。 该策略将高维统计学习的力量与数学规划和随机梯度方法相结合，形成了反馈学习算法的基础，用于识别最佳目标函数以实现ISO的目标。 将开发算法的离线和在线版本，因此可以在模拟器（离线）中调整策略，然后在生产（在线）中不断调整。

The transition to a grid where an increasing portion of our energy comes from wind and solar energy is requiring that the grid operators develop a planning process that can handle a much higher level of uncertainty than is encountered today. Current tools have been criticized as ad-hoc procedures that consist of optimizing around a deterministic forecast, leaving the grid vulnerable to unexpected variations. Proposed "stochastic models" are much harder to solve, and grid operators still struggle to solve their existing deterministic models. Existing industry practice, while ad-hoc, actually represents a simple example of a powerful class of policies called "parametric cost function approximations." that has received virtually no attention from the academic literature. This research builds on the core strategy in use today, but blends the capabilities of existing commercial optimization solvers for deterministic models with the power of machine learning algorithms. The work will formalize existing industry practice, providing an implementable path to handling uncertainty which will provide a way for grid operators to naturally handle the steady increase in energy from wind and solar.The research proposes an algorithmic strategy that represents a fundamental departure from the field of stochastic programming, which handles uncertainty through the solution of stochastic look-ahead policies where the future is represented using a set of sampled realizations (scenarios). This research introduces a new class of policies called parametric cost function approximations. These are parametrically modified deterministic optimization problems which are optimized to minimize cost and risk, just as any machine learning model is optimized to fit data. This strategy blends the power of high-dimensional statistical learning with math programming and stochastic gradient methods, which form the basis of a feedback learning algorithm for identifying the best objective function to achieve the objectives of the ISO. Both offline and online versions of the algorithm will be developed, so policies can be tuned in a simulator (offline) but then continually adapted in production (online).