Abstract
This paper presents an algorithm to solve the unit commitment problem using the intelligence technique based on improved Particle Swarm Optimization (IPSO) for establishing the optimal scheduling of the generating units in the electric power system with the lowest production cost during a specified time and subjected to all the constraints. The minimum production cost is calculated based on using the Lambda Iteration method. A conventional method was also used for solving the unit commitment problem using the Dynamic Programming method (DP). The two methods were tested on the 14-bus IEEE test system and the results of both methods were compared with each other and with other references. The comparison showed the effectiveness of the proposed method over other methods.
Highlights
The usage of electrical utility is varied during the day, week, month, and year
Unit commitment can be realized as the problem of finding the optimal unit scheduling with the lowest production cost, which can be determined by economic load dispatch (ED)
This paper proposes solving the unit commitment problem using two methods, a conventional one (DP) and an intelligent one (IPSO)
Summary
If enough generation is saved online throughout all time, some of the generating units operate at their minimum limit from generation during the off-peak period. It must find an approach to turn off the unnecessary units at the off-peak period on the condition that other units meet load demand [1]. The optimal strategy to perform these requirements is unit commitment (UC). Unit commitment can be realized as the problem of finding the optimal unit scheduling with the lowest production cost, which can be determined by economic load dispatch (ED). Unit scheduling means determining the status of units (ON or OFF).
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