Introduction – setting and division of optimization problems and methods for their solution; difficult optimization problem; brute-force and partially search; exact, approximate and heuristic methods; basic steps of optimization problem solutions. Classic methods for solving of optimization problems – convex; linear; nonlinear; integer/discrete and dynamic optimizations; optimization with and without constraints; regression analysis; etc. Advanced methods for solving of optimization problems – basics of the computer intelligence; artificial intelligence; (meta)heuristic; multi-objective optimization (Trade-off, Pareto optimization); stochastic modeling (Monte Karlo simulation); etc. Sensitivity analysis of the optimization problem. Application of optimization methods – optimal power flow; state estimation; volt/var control; network reconfiguration; data modelling; optimal equipment placement; economic dispatching; unit commitment; decision and optimization; etc. Mathematical support – basics of statistics and probability; fuzzy logic.