Abstract

Considering the importance of reducing system operating costs and controlling pollutant emissions by optimizing the operation of the integrated energy system, the energy supply structure of the integrated energy system and the joint multiobjective optimization dispatching structure is analyzed in this paper based on a day-ahead economic optimization dispatching model of the integrated energy system. Afterwards, the multiobjective optimization model of the integrated energy system is studied and multiobjective hierarchical progressive parallel algorithm based on improved NSGA-II is proposed according to the characteristics of the model. The algorithm improves the nondominated layer sorting algorithm, changes the convergence judgment condition while introducing the target reaching method to accelerate convergence, and introduces parallel computing technology according to the characteristics of the algorithm. The case shows that the proposed algorithm not only has advantages on the diversity in searching solutions but also can achieve better results in many aspects such as the iteration time and algorithm convergence which are required in practical engineering projects.

Highlights

  • In [1], an island-type integrated energy system in eastern China was taken as the research object, and the optimization goals were set as economical optimization and minimum power exchange with the tie bus. e operation scheme of the integrated energy system was obtained by solving the optimal scheduling model

  • Distributed energy resources (DER) and microgrids were taken as research objects in [13], and an optimization model for optimal management of MG was proposed based on the combined heat and power (CHP) system considering economic, environmental, and reliability aspects. e exchange market algorithm (EMA) and weighted factors method were used to combine three conflicting targets and treat the multiobjective problem as a single target problem, and the fuzzy satisfaction method was applied to select the best compromise solution

  • Optimal scheduling strategy where fCHPi is the energy consumption curve function of ith microturbines; Pti is the electric power output of ith microturbines with the unit of kW; ctGas is the hourly price of gas with the unit of $/(kW·h) after conversion according to the calorific consumption of value of natural the ith gas boiler gas; FtGBi is at time t with the the energy unit of kW; and t is the time period number with the unit of hour

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Summary

Research Article

Considering the importance of reducing system operating costs and controlling pollutant emissions by optimizing the operation of the integrated energy system, the energy supply structure of the integrated energy system and the joint multiobjective optimization dispatching structure is analyzed in this paper based on a day-ahead economic optimization dispatching model of the integrated energy system. Afterwards, the multiobjective optimization model of the integrated energy system is studied and multiobjective hierarchical progressive parallel algorithm based on improved NSGA-II is proposed according to the characteristics of the model. E case shows that the proposed algorithm has advantages on the diversity in searching solutions and can achieve better results in many aspects such as the iteration time and algorithm convergence which are required in practical engineering projects Afterwards, the multiobjective optimization model of the integrated energy system is studied and multiobjective hierarchical progressive parallel algorithm based on improved NSGA-II is proposed according to the characteristics of the model. e algorithm improves the nondominated layer sorting algorithm, changes the convergence judgment condition while introducing the target reaching method to accelerate convergence, and introduces parallel computing technology according to the characteristics of the algorithm. e case shows that the proposed algorithm has advantages on the diversity in searching solutions and can achieve better results in many aspects such as the iteration time and algorithm convergence which are required in practical engineering projects

Introduction
Environmentally optimal scheduling strategy
TNi O
The second objective function value
The first objective function value
Move i to the right
Repeat the process
Delete without layering
Output result
Peak Valley Flat
COX NOX SOX
Cold water storage tank
Pareto optimal solutions
Number of layers
Average test results
Household air conditioner Space heat load
State of energy
NOX emission value after weighting SOX emission value after weighting
Full Text
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