Model predictive control (MPC) is a method to formulate the optimal scheduling problem for grid flexibilities in a mathematical manner. The resulting time-constrained optimization problem can be re-solved in each optimization time step using classical optimization methods such as Second Order Cone Programming (SOCP) or Interior Point Methods (IPOPT). When applying MPC in a rolling horizon scheme, the impact of uncertainty in forecasts on the optimal schedule is reduced. While MPC methods promise accurate results for time-constrained grid optimization they are inherently limited by the calculation time needed for large and complex power system models. Learning the optimal control behaviour using function approximation offers the possibility to determine near-optimal control actions with short calculation time. A Neural Predictive Control (NPC) scheme is proposed to learn optimal control policies for linear and nonlinear power systems through imitation. It is demonstrated that this procedure can find near-optimal solutions, while reducing the calculation time by an order of magnitude. The learned controllers are validated using a benchmark smart grid.
翻译:模型预测控制(MPC) 是一种以数学方式为电网灵活度制定最佳时间安排问题的方法。 由此产生的受时间限制的优化优化问题可以用典型的优化方法(如第二顺序Cone编程(SOLCP)或内地点方法(IPOPT))在每次优化时间步骤中重新解决。 当在滚动地平线计划中应用 MPC 时, 预测中不确定性对最佳时间表的影响会减少。 虽然 MPC 方法为时间限制的电网优化带来准确的结果, 但由于大型和复杂的电网系统模型所需的计算时间, 它们必然会受到限制。 使用功能近似值来学习最佳控制行为, 有可能在短的计算时间内确定近最佳的控制动作。 提出了神经预测控制(NPC) 计划, 通过模仿来学习线性和非线性电源系统的最佳控制政策。 事实证明, 这个程序可以找到接近最佳的解决方案, 同时用数量顺序缩短计算时间。 学习过的控制器通过基准智能电网进行验证。