Economic Dispatch of Multiple-Chiller Plants Using Wild Goats Algorithm

Jabari, Farkhondeh; Dibavar, Alireza Akbari; Mohammadi‐Ivatloo, Behnam

doi:10.1007/978-3-030-34050-6_8

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Salari et al [6] show that the mixed integer problem can be solved using the general algebraic modeling system (GAMS). In parallel, many nature-inspired heuristic algorithms have been proposed, namely, genetic algorithm (GA) [7,8], simulated annealing (SA) [9,10], particle swarm optimization (PSO) [11,12], evolution strategy (ES) [13], differential evolution (DE) [14], cuckoo search algorithm using differential operator (DCSA) [15], differential search (DS) [16], improved firefly algorithm (IFA) [17], teaching-learning-based optimization (TLBO) [18], improved invasive weed optimization (EIWO) [19], distributed chaotic estimation of distribution algorithm (DCEDA) [20], imperialist competitive algorithm (ICA) [21] and wild goat's algorithm (WGA) [22].…”

Section: Introductionmentioning

confidence: 99%

An Exact Algorithm for the Optimal Chiller Loading Problem and Its Application to the Optimal Chiller Sequencing Problem

2020

View full text Add to dashboard Cite

The optimal management of multiple chiller systems calls for the solution of the so-called optimal chiller loading (OCL) problem. Due to the interplay of continuous and logical constraints, OCL is an NP-hard problem, so that a variety of heuristic algorithms have been proposed in the literature. Herein, an algorithm for its exact solution, named X-OCL, is developed under the assumption that the chillers’ power consumption curves are quadratic. The proposed method hinges on a decomposition of the solution space so that the overall OCL problem is decomposed to a set of equality constrained quadratic programming problems that can be solved in closed form. By applying the new X-OCL solver to well known case studies, we assess and compare the performances of several literature algorithms, highlighting also some errors in the published results. Moreover, X-OCL is used to design a greedy optimal chiller sequencing (OCS) solver, called X-OCS. The X-OCS is tested on two literature benchmarks and on the model of the heating, ventilation and air-conditioning (HVAC) system of a semiconductor plant, over a two-year period. The performances of X-OCS are remarkably close to the theoretical optimal performance.

show abstract