Dynamic Optimization Problems and Applications

Stackelberg博弈和激励策略的最新进展是 采用开发一种新的分解方法的解决方案 动态优化问题 该方法涉及阶段性 ”分解，并源于以下观察：”在 动态规划的方法来优化，如果成本去 每个阶段的函数都是已知的，那么N阶段的解 最优控制问题将通过求解（并行）N 解耦静态（单级）优化问题。“拿着这个 作为观察的起点，主要研究人员建议 即使最佳的成本去功能是未知的，人们可以 从一些初始猜测开始，解决相应的N静态 优化问题，使用这些更新初始猜测 解决方案，解决一组新的优化问题，…等等... 直到达到某种收敛。 本研究旨在研究各种 该方案的各个方面，包括其收敛特性， 计算要求和可行性，并将其应用于经济 电力系统中的调度问题。

Recent developments on Stackelberg Games and Incentive Strategies are adopted to develop a novel decomposition approach for the solution of dynamic optimization problems. The approach involves stage-wise decomposition, and stems from the following observation: "In the dynamic programming approach to optimization, if the cost-to-go functions at every stage were known, then the solution of an N-stage optimal control problem would be obtained by solving (in parallel) N decoupled static (single stage) optimization problems." Taking this observation as a starting point, the principal investigators suggest that even if the optimal cost-to-go functions are not known, one can start with some initial guesses, solve the corresponding N static optimization problems, update on the initial guesses using these solutions, solve a new set of optimization problems,...and so on..., until some convergence is achieved. The research is to study various aspects of this scheme, including its convergence properties, computational requirements and feasibility, and apply it to an economic dispatch problem arising in power systems.