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

arXiv: https://arxiv.org/abs/1808.05704 · HTML: https://mlsanity.com/paper/1808.05704/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2018.

## Built on by (most cited first, 7 of 7)

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- [What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization](https://mlsanity.com/paper/2311.11288/) (2023)
- [Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach](https://mlsanity.com/paper/2212.13472/) (2022)
- [A survey on the development status and application prospects of knowledge graph in smart grids](https://mlsanity.com/paper/2211.00901/) (2022)
- [The State of the Art in transformer fault diagnosis with artificial intelligence and Dissolved Gas Analysis: A Review of the Literature](https://mlsanity.com/paper/2304.11880/) (2023)
- [Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling](https://mlsanity.com/paper/2308.12554/) (2023)
- [Physics-informed Convolutional Neural Network for Microgrid Economic Dispatch](https://mlsanity.com/paper/2404.18362/) (2024)

## Abstract (arXiv)

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 θ-dominance based evolutionary algorithm (θ-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.