MS in Electrical Engineering (M.S.E.E.)
Degree Granting Department
Lingling Fan, Ph.D.
Selcuk Kose, Ph.D.
Zhixin Miao, Ph.D.
Chance Constraints, MILP, Pool Strategy, Electricity Market, Nonlinear Optimization, MPEC, Almost Robust Optimization
The thesis focuses on a mixed integer linear programming (MILP) formulation for a bi-level mathematical program with equilibrium constraints (MPEC) considering chance constraints. The particular MPEC problem relates to a power producer’s bidding strategy: maximize its total benefit through determining bidding price and bidding power output while considering an electricity pool’s operation and guessing the rival producer’s bidding price. The entire decision-making process can be described by a bi-level optimization problem. The contribution of our thesis is the MILP formulation of this problem considering the use of chance constrained mathematical program for handling the uncertainties.
First, the lower-level poor operation problem is replaced by Karush-Kuhn-Tucker (KKT) optimality condition, which is further converted to an MILP formulation except a bilinear item in the objective function. Secondly, duality theory is implemented to replace the bilinear item by linear items. Finally, two types of chance constraints are examined and modeled in MILP formulation. With the MILP formulation, the entire MPEC problem considering randomness in price guessing can be solved using off-shelf MIP solvers, e.g., Gurobi. A few examples and a case study are given to illustrate the formulation and show the case study results.
Scholar Commons Citation
Sadat, Sayed Abdullah, "Optimal Bidding Strategy for a Strategic Power Producer Using Mixed Integer Programming" (2017). USF Tampa Graduate Theses and Dissertations.