2. Introduction Electricity generating companies and power systems have the problem of deciding how best to meet the varying demand for electricity, which has a daily and weekly cycle. This study essentially deals with the method of operating Electrical generating sets of different type, to get the economic power from them. The terms ‘Unit Commitment’, ‘Economic Dispatch’ and ‘Incremental Cost’ are widely used in this study discipline.
3. The steps for optimization Long Term System Planning Hourly and Monthly decisions -- Unit Commitment Instantaneous Dispatch decisions -- Economic Dispatch Short term system operation -- Load Flow Model
4. Economic Dispatch- Covered Earlier Energy Policy Act defines “Economic Dispatch” to mean “the operation of generation facilities to produce energy at the lowest cost to reliably serve consumers, recognizing any operational limits of generation and transmission facilities.” [EPAct 2005, Sec. 1234 (b)] Actually, For Power System Economic Operation, the installed generating capacity should be greater than the load at any specific moment This topic is covered in earlier presentation
5. Unit Commitment The electrical unit commitment problem is the problem of deciding which electricity generation units should be running in each period so as to satisfy a predictably varying demand for electricity. The problem is interesting because in a typical electrical system there are a variety of units available for generating electricity, and each has its own characteristics.
11. Dynamic Programming-Illustrated The objective is to find minimum cost from A-N. K=1,2…5= Hour At the end of each hour, a decision is taken to enter on which state.
12. Advances in Unit Commitment Problem Genetic Algorithm -The genetic based UC program starts with a random initial population (at T=0) at it computes the fitness of each individual. -Advanced level of GA is used like Fast Messy Genetic Algorithm (FMGA)
13. Various algorithms to UC Problem Ant Colony System ( S. P. Simon, N. P. Padhy and R. S. Anand) - foraging behavior of the real ants for finding its food from its start (nest) to its destination (food) is simulated to obtain the optimum solution.
