A Two-Stage Approach for Combined Heat and Power Economic Emission Dispatch: Combining Multi-Objective Optimization with Integrated Decision Making

TL;DR

This paper introduces a novel two-stage method combining multi-objective optimization and integrated decision making to effectively solve the complex combined heat and power economic emission dispatch problem, considering practical system factors.

Contribution

It develops a new two-stage approach integrating a multi-objective evolutionary algorithm with fuzzy clustering and grey relation projection for automatic decision making in CHPEED.

Findings

The approach finds multiple Pareto-optimal solutions effectively.

It successfully identifies best compromise solutions considering conflicting preferences.

Simulation results demonstrate the method's effectiveness and superiority.

Abstract

To address the problem of combined heat and power economic emission dispatch (CHPEED), a two-stage approach is proposed by combining multi-objective optimization (MOO) with integrated decision making (IDM). First, a practical CHPEED model is built by taking into account power transmission losses and the valve-point loading effects. To solve this model, a two-stage methodology is thereafter proposed. The first stage of this approach relies on the use of a powerful multi-objective evolutionary algorithm, called {\theta}-dominance based evolutionary algorithm ({\theta}-DEA), to find multiple Pareto-optimal solutions of the model. Through fuzzy c-means (FCM) clustering, the second stage separates the obtained Pareto-optimal solutions into different clusters and thereupon identifies the best compromise solutions (BCSs) by assessing the relative projections of the solutions belonging to the same cluster using grey relation projection (GRP). The novelty of this work is in the incorporation of an IDM technique FCM-GRP into CHPEED to automatically determine the BCSs that represent decision makers' different, even conflicting, preferences. The simulation results on three test cases with varied complexity levels verify the effectiveness and superiority of the proposed approach.