Abstract
This paper deals with a Unit Commitment (UC) problem of a power plant aimed to find the optimal scheduling of the generating units involving cubic cost functions. The problem has non convex generator characteristics, which makes it very hard to handle the corresponding mathematical models. However, Teaching Learning Based Optimization (TLBO) has reached a high efficiency, in terms of solution accuracy and computing time for such non convex problems. Hence, TLBO is applied for scheduling of generators with higher order cost characteristics, and turns out to be computationally solvable. In particular, we represent a model that takes into account the accurate higher order generator cost functions along with ramp limits, and turns to be more general and efficient than those available in the literature. The behavior of the model is analyzed through proposed technique on modified IEEE-24 bus system.
Highlights
The non-storable nature of electrical energy calls for permanent adjustment of production to consumption
The optimal commitment schedule and generation output derived by the proposed algorithm considering the ramp rate and cubic cost equation is shown in Table 2 for 24 hour horizon
A nature inspired Teaching Learning Based Optimization (TLBO) technique has been proposed for solving unit commitment problem with ramp constraints on the thermal generating units involving cubic cost functions
Summary
The non-storable nature of electrical energy calls for permanent adjustment of production to consumption. A recently developed heuristic algorithm named teaching learning based optimization (TLBO) algorithm based on the effect of the influence of a teacher on the output of learners in a class, introduced by Rao et al [18], is utilized for the solution of UC problem. This TLBO algorithm has been implemented in various problem domains of engineering and technology. Unlike other population based techniques, TLBO requires only determination of common controlling parameters like population size and number of generations for its functionality. TLBO based UC (TLBO-UC) with CCF (TLBO-UC-CCF) is carried on 26-unit test system considering ramp rate constraints for a time horizon of 24 hours to prove the scalability of the algorithm
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