A BCS-GDE Multi-objective Optimization Algorithm for Combined Cooling, Heating and Power Model with Decision Strategies

TL;DR

This paper introduces a novel multi-objective optimization algorithm, BCS-GDE, for CCHP systems that effectively balances economic, energy, and environmental goals across various building types.

Contribution

It develops a new multi-objective optimization model for CCHP systems and proposes the BCS-GDE algorithm, which outperforms existing methods in energy scheduling efficiency.

Findings

Reduces economic cost by 72%

Lowers primary energy consumption by 73%

Decreases pollutant emissions by 88%

Abstract

District energy systems can not only reduce energy consumption but also set energy supply dispatching schemes according to demand. In addition to economic cost, energy consumption and pollutant are more worthy of attention when evaluating combined cooling, heating and power (CCHP) models. In this paper, the CCHP model is established with the objective of economic cost, primary energy consumption, and pollutant emissions. The mathematical expression of the CCHP system is proposed, and a multi-objective optimization model with constraints is established. According to different usage requirements, two decision-making strategies are designed, which can adapt to different scenarios. Besides, a generalized differential evolution with the best compromise solution processing mechanism (BCS-GDE) algorithm is proposed to optimize the CCHP model for the first time. The algorithm provides the optimal energy scheduling scheme by optimizing the production capacity of different capacity equipment. The simulation is conducted in three application scenarios: hotels, offices, and residential buildings. The simulation results show that the model established in this paper can reduce economic cost by 72%, primary energy consumption by 73%, and pollutant emission by 88%. Concurrently, the Wilcoxon signed-rank test shows that BCSGDE is significantly better than OMOPSO, NSGA-II, and SPEA2 with greater than 95% confidence.