OPTIMIZATION-BASED SCHEDULING OF HYDROTHERMAL POWER-SYSTEMS WITH PUMPED-STORAGE UNITS

This paper presents an optimization-based method for scheduling hydrot hermal systems based on the Lagrangian relaxation technique. After sys tem-wide constraints are relaxed by Lagrange multipliers, the problem is converted into the scheduling of individual units. This paper conce ntrates on the solution methodology for pumped-storage units. A pumped -storage unit can be operated in generation, pumping or idle states. I t can smooth peak loads and provide reserve, therefore plays an import ant role in reducing total generation costs. There are, however, many constraints limiting the operation of a pumped-storage unit, such as p ond level dynamics and constraints, and discontinuous generation and p umping regions. Moreover, according to the current practice, the dynam ic transitions among operating states (generation, pumping and idle) a re not arbitrary. The most challenging issue in solving pumped-storage subproblems within the Lagrangian relaxation framework is the integra ted consideration of these constraints. The basic idea of our method i s to relax the pond level dynamics and constraints by using another se t of multipliers. The subproblem is then converted into the optimizati on of generation or pumping levels for each operating slate at individ ual hours, and the optimization of operating states across hours. The optimal generation or pumping level for a particular operating state a t each hour can be obtained by optimizing a single variable function w ithout discretizing pond levels. Dynamic programming is then used to o ptimize operating states across hours with only a few number of states and transitions. A subgradient algorithm is used to update the pond l evel Lagrangian multipliers. This method provides an efficient way to solve a class of subproblems involving continuous dynamics and constra ints, discontinuous operating regions, and discrete operating states. Testing results based on Northeast Utilities power system show that th is algorithm is efficient, and near optimal solutions are obtained.