Cost Effective Operating Strategy for Unit Commitment and Economic Dispatch of Thermal Power Plants with Cubic Cost Functions Using TLBO Algorithm

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.

Optimization of Unit Commitment Problem with Third Order Polynomials using Grasshopper Optimization Algorithm

“…The proposed GOA methodology is validated for two case studies known in the literature for twenty six and six thermal generating units [18], [19]. The results obtained are analyzed and compared with others presented in the literature, which highlight the efficiency of the GOA approach proposed in this paper.…”

“…where F i (P i (t)) is the fuel cost of generator i at hour t, P i (t) is the output power of i th generator at hour t, U it is the on/off status of i th generator at hour t. The fuel cost function F i (P i (t)) for the generating unit i (in $/h) [18], [19], which is defined by cubic polynomial can be mathematically formulated as,…”

“…Hence, the objective function F T in equation 1is alone considered as the fitness function for the GOA. This case study comprises of modified IEEE-24 bus test system with twenty six generating units [18]. The test system information including cost coefficient, power demand, ramp rate and power generation limits of the units are taken from the literatures [20]- [23].…”

“…The optimum generator schedule and corresponding output found from the proposed method with cubic objective function is given in Table-II for a period of 24 hours. A comparative study is presented in Table-III in relation to others reported in the literature. Table-III depicts the comparison of the total fuel cost, total operating cost and execution time accomplished from the GOA technique with the other existing techniques, such as dynamic programming sequential and truncated combination (DP-STC) [22], piecewise linear iterative (PLI) [22] and teaching learning based optimization (TLBO) algorithm [18]. The total fuel cost got from the GOA technique is 793815.30 $, whereas the fuel cost obtained from the techniques PLI, DP-STC and TLBO are 795698.33 $, 795489.09 $ and 795488.869 $ respectively.…”