Abstract:Heat and power have become the most indispensable resources. However, the traditional ways of generating power and heat are inefficient and cause high pollution; a CHP (Combined Heat and Power) unit can solve these problems well. In recent years, more attention has been paid to energy conservation and environmental protection, and Combined Heat and Power Economic Emission Dispatch (CHPEED) has become an important multi-objective optimization problem. In this paper, an Indicator & crowding Distance-based Evolut… Show more
“…For example, the impacts of the different energy price policies on the configuration of CHP and CCHP systems were studied by Tichi et al [21] using the particle swarm optimization (PSO) algorithm. Meanwhile, a great quantity of multi-objective evolutionary algorithms was used to solve CHP models [22] [23] [24] [25] [26]. For CCHP problems, PSO and genetic algorithm (GA) are applied to optimize the models by literature [27] [28] [29].…”
“…For example, the impacts of the different energy price policies on the configuration of CHP and CCHP systems were studied by Tichi et al [21] using the particle swarm optimization (PSO) algorithm. Meanwhile, a great quantity of multi-objective evolutionary algorithms was used to solve CHP models [22] [23] [24] [25] [26]. For CCHP problems, PSO and genetic algorithm (GA) are applied to optimize the models by literature [27] [28] [29].…”
“…To overcome this issue, researchers have recently considered hybrid algorithms where the deficiencies of one algorithm can be compensated by utilizing the structures or operators from other algorithms in hybridization. Hybridization can be applied to two sections: the algorithm's structure (popular in MOEAs/MaOEAs) [2,19,23,24] or the algorithm's operators (popular in single-objective EAs) [17,29,30].…”
Hybrid multi-objective evolutionary algorithms have recently become a hot topic in the domain of metaheuristics. Introducing new algorithms that inherit other algorithms' operators and structures can improve the performance of the algorithm. Here, we proposed a hybrid multi-objective algorithm based on the operators of the genetic algorithm (GA) and teaching learning-based optimization (TLBO) and the structures of reference point-based (from NSGA-III) and R2 indicators methods. The new algorithm (R2-HMTLBO) improves diversity and convergence by using NSGA-III and R2-based TLBO, respectively. Also, to enhance the algorithm performance, an elite archive is proposed. The proposed multi-objective algorithm is evaluated on 19 benchmark test problems and compared to four state-of-the-art algorithms. IGD metric is applied for comparison, and the results reveal that the proposed R2-HMTLBO outperforms MOEA/D, MOMBI-II, and MOEA/IGD-NS significantly in 16/19 tests, 14/19 tests and 13/19 tests, respectively. Furthermore, R2-HMTLBO obtained considerably better results compared to all other algorithms in 4 test problems, although it does not outperform NSGA-III on a number of tests.
CCS CONCEPTS• Theory of computation → Bio-inspired optimization; Evolutionary algorithms.
“…Other examples include [5] and [6] where a "whale optimization method" is deployed to solve the CPHED problem. Other algorithms (solution methodologies) include the squirrel search algorithm [7], Kho-Kho optimization Algorithm [8], indicator and crowding distance-based evolutionary algorithm [9], cuckoo search algorithm [10,11], effective cuckoo search algorithm [12], exchange market algorithm [13], gravitational search algorithm [14], group search optimization algorithm [15], and modified group search optimizer [16]. A comprehensive review article on research works utilizing heuristic methods in solving the CHPDEED mathematical works is given in [17].…”
In this paper, the Combined Heat and Power Dynamic Economic Emissions Dispatch (CHPDEED) problem formulation is considered. This problem is a complicated nonlinear mathematical formulation with multiple, conflicting objective functions. The aim of this mathematical problem is to obtain the optimal quantities of heat and power output for the committed generating units which includes power and heat only units. Heat and load demand are expected to be satisfied throughout the total dispatch interval. In this paper, Valve Point effects are considered in the fuel cost function of the units which lead to a non-convex cost function. Furthermore, an Incentive Based Demand Response Program formulation is also simultaneously considered with the CHPDEED problem further complicating the mathematical problem. The decision variables are thus the optimal power and heat output of the generating units and the optimal power curbed and monetary incentive for the participating demand response consumers. The resulting mathematical formulations are tested on four practical scenarios depicting different system operating conditions and obtained results show the efficacy of the developed mathematical optimization model. Obtained results indicate that, when the Incentive-Based Demand Response (IBDR) program’s operational hours is unrestricted with a residential load profile, the energy curtailed is highest (2680 MWh), the energy produced by the generators is lowest (38,008.53 MWh), power losses are lowest (840.5291 MW) and both fuel costs and emissions are lowest.
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