Asian Research Journal of Current Science

Unit commitment and economic dispatch are coupled short-term scheduling problems that determine which thermal generating units should operate and how the committed demand should be allocated among them. Their integration produces a mixed discrete-continuous optimisation problem with non-convex cost characteristics, minimum up- and down-time restrictions, startup costs, generation limits, and spinning-reserve requirements. This paper develops a metaheuristic optimisation framework that separates the binary scheduling search from continuous dispatch while preserving their cost coupling. A priority-seeded, repair-assisted binary particle swarm optimisation layer is proposed for commitment decisions, while an embedded lambda-iteration routine determines the least-cost economic dispatch for every feasible commitment pattern. Constraint repair is applied before penalty evaluation to reduce wasted search effort in infeasible regions. The framework is formulated for quadratic thermal fuel costs and time-dependent hot and cold startup costs. A proof-of-concept numerical validation is performed on the standard 10-unit, 24-hour thermal benchmark with a 10% spinning-reserve requirement. To isolate implementation correctness from stochastic search variability, the published reference commitment schedule is evaluated using the proposed dispatch and cost engine. The framework reproduces a daily fuel cost of $559,847.69 and a startup cost of $4,090.00, giving a total operating cost of $563,937.69. This result agrees with the best reported benchmark value of approximately $563,938 and satisfies the hourly power-balance, capacity, and reserve requirements. The results establish a transparent and reproducible basis for implementing the full stochastic solver. Repeated independent metaheuristic runs on larger systems are identified as a necessary next stage before comparative claims regarding search superiority are made.