Indicator & crowding distance-based evolutionary algorithm for combined heat and power economic emission dispatch

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 Evolutionary Algorithm (IDBEA) is put forward for handling this non-convex and non-linear problem. With consideration of the valve-point effects and power transmission loss, IDBEA is tested on three standard test systems with different types, including four units, five units and seven units. In the experiment, IDBEA is compared with several evolutionary algorithms, the simulation results demonstrate that IDBEA has strong stability and superiority, while the solutions show better convergence and diversity than several typical algorithms.

A BCS-GDE multi-objective optimization algorithm for combined cooling, heating and power model with decision strategies

“…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].…”

A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator

“…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].…”

Combined Heat and Power Dynamic Economic Emissions Dispatch with Valve Point Effects and Incentive Based Demand Response Programs

“…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].…”