The unit commitment is an optimization problem that economically schedules generating units over a short-term planning horizon subject to the satisfaction of demand and other system operating constraints. Many optimization methods have been proposed to solve the unit commitment problem. These methods include priority list methods, dynamic programming methods, sequential method and Lawgrangian relaxation methods etc. Lagrangian relaxation methods are now among the most widely used approaches to solving unit commitment.
Because generating units of a utility company are normally located in different areas interconnected via transmission lines, power flows are subject to thermal limit of transmission lines. This may result in rescheduling of some generating units and may incur significant costs. This paper presents a method for solving the unit commitment problem using Dynamic Programming approach. A first attempt to incorporate AC load flow constraints in unit commitment optimization was detailed in with promising although limited computational testing. At present, the computational requirements of that approach would be prohibitive for practical size problems but that might change with the rapid development in computation technology.
The transmission constraints are formulated as linear constraints based on a DC power flow model. I have considered the transmission constrained unit commitment problem using a dynamic programming method. I have proposed a practical method for solving the security-constrained unit commitment problem using Dynamic programming method. This approach has two types of constraints viz., demand constraints and spinning reserve constraints.This method takes full account of these constraints in the optimization phase and also in locating a feasible solution.
In this project I implemented the dynamic programming method of solving the unit commitment problem. The dynamic programming technique, when applicable, represents or decomposes a multi stage decision problem as a sequence of single decision problems. Thus, an n variable problem is represented as a sequence of n single variable problems, which are solved successively. In most of the cases these n sub problems are easier to solve than the original problem. The decomposition of n sub problems is done in such a manner that the optimal solution of the original problem can be obtained from the optimal solution of n one-dimensional problem.
The advantage of dynamic programming is its ability to maintain solution feasibility, unlike priority list method, which is highly heuristic, and mostly yield sub optimal solutions. Dynamic programming builds and evaluates the complete decision tree to optimize the problem at hand.
This project is done in my college, Bangalore Institute of Technology deals with “Unit commitment solution using Dynamic Programming approach” This program written in C++ helps us to determine the optical schedule of the generating units in a power system industry. It is useful in reducing the cost of a power generation by effectively utilizing the generator units. I have devised this program for the generator units used in thermal power plants taking into account all the operating constraints such as minimum up time, minimum down time, start up- cost, start Time, Run up rate, minimum Capacity, Spinning Reserve, Fuel constraints, Crew constraints etc. I have collected the schedule of load in a year and checked my program with these data’s. It gives a valid output with the scheduling of all the generator units, which are to be committed and de-committed. The program takes into account three generator units working for eight hours with different load demands in each hour.
Arijit Kumar Bose
Abhirampur, PO- Makdampur