Dynamic Priority Approach for Unit Commitment Scheduling Solution

This paper introduces a dynamic priority approach (DPA) for solving unit commitment (UC) problems. The DPA is a novel technique introduced in this paper that is a development and simplification of the conventional priority list (PL) using the cumulative summation form of generating unit capacity. The conventional PL technique maximizes the previous unit's capacity before adding the next one until the entire load is accommodated. According to the maximization focus of the unit capacity, the ramp rate is ignored so that the generation cost result sometimes cannot reach the optimal value. In this research, the proposed method is resolved in 2 main stages: the UC and economic dispatch (ED) stages. Prior to the main stage calculation, the technique begins with two initializations: initial available unit determination, considering the unit status in the last scheduling period, and the unit priority criteria factor calculation, considering fuel cost, startup cost, maximum generation limit, and ramp rate as the criteria factors. The UC stage consists of several phases, starting with the determination of unit availability status, prioritization of the available units based on the criteria factors, cumulative calculation of the unit's maximum generation capacity according to the sorted available units, determination of base units based on the cumulative value index that can satisfy the load demand, and the base unit's evaluation to re-evaluate the unit availability. These base units are the committed units that are optimized for load dispatching in the ED stage using particle swarm optimization (PSO). The ramp rate constraint was deliberated at both stages, as one of the priority criterion factors in UC and the boundary constraint in ED, expecting to minimize the overall generation cost. The performance of the proposed method was verified using a ten-unit test system whose results were compared with the previous related studies. The obtained results show that the proposed method provides effective and efficient scheduling results.