Multiobjective Optimized Dispatching for Integrated Energy System Based on Hierarchical Progressive Parallel NSGA-II Algorithm

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.

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Multi-objective operation optimization of regional integrated energy system based on NSGA-II algorithm

E3S Web of Conferences ◽

10.1051/e3sconf/202125702022 ◽

2021 ◽

Vol 257 ◽

pp. 02022

Author(s):

Zheming Xu ◽

Changbin Hu ◽

Xiaojun Lu

Keyword(s):

Energy System ◽

Optimal Operation ◽

Air Pollutant ◽

Environmental Benefits ◽

Pollutant Emissions ◽

Pollutant Emission ◽

Nsga Ii ◽

Multi Objective ◽

District Energy ◽

Integrated Energy System

With the deepening of China’s energy market reform and the promotion of integrated energy services, the regional integrated energy system becomes an important development direction of energy supply system. In order to maximize the economic efficiency and reduce the air pollutant emission of the regional integrated energy system, the distributed power generation module and the cooling-heat-power (CCHP) triple-supply module are formed into a model, and the power balance, equipment capacity and environmental factors of the system are constrained with the objective function of minimizing the daily operation cost of the system as well as minimizing the air pollutant emission. Based on the mathematical system framework model and the optimal operation control strategy, the NSGA-II algorithm is used to solve the multi-objective programming model to obtain the Pareto solution set, and the hourly output of the optimal operation of the system equipment with both economic and environmental benefits is obtained. The results show that the daily operating costs and pollutant emissions of the district energy system are significantly reduced compared with those without optimization, which effectively solves the problems of low operating efficiency and serious environmental pollution of the district energy system and achieves the optimal operation with both economic and environmental benefits.

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A Two-Stage Offline-to-Online Multiobjective Optimization Strategy for Ship Integrated Energy System Economical/ Environmental Scheduling Problem

Complexity ◽

10.1155/2021/6686563 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Qing An ◽

Jun Zhang ◽

Xin Li ◽

Xiaobing Mao ◽

Yulong Feng ◽

...

Keyword(s):

Multiobjective Optimization ◽

Real World ◽

Operation Time ◽

Energy System ◽

Scheduling Problem ◽

Storage Space ◽

Optimization Strategy ◽

Two Stage ◽

Integrated Energy System ◽

And Storage

The economical/environmental scheduling problem (EESP) of the ship integrated energy system (SIES) has high computational complexity, which includes more than one optimization objective, various types of constraints, and frequently fluctuated load demand. Therefore, the intelligent scheduling strategies cannot be applied to the ship energy management system (SEMS) online, which has limited computing power and storage space. Aiming at realizing green computing on SEMS, in this paper a typical SIES-EESP optimization model is built, considering the form of decision vectors, the economical/environmental optimization objectives, and various types of real-world constraints of the SIES. Based on the complexity of SIES-EESPs, a two-stage offline-to-online multiobjective optimization strategy for SIES-EESP is proposed, which transfers part of the energy dispatch online computing task to the offline high-performance computer systems. The specific constraints handling methods are designed to reduce both continuous and discrete constraints violations of SIES-EESPs. Then, an establishment method of energy scheduling scheme-base is proposed. By using the big data offline, the economical/environmental scheduling solutions of a typical year can be obtained and stored with more computing resources and operation time on land. Thereafter, a short-term multiobjective offline-to-online optimization approach by SEMS is considered, with the application of multiobjective evolutionary algorithm (MOEA) and typical schemes corresponding to the actual SIES-EESPs. Simulation results show that the proposed strategy can obtain enough feasible Pareto solutions in a shorter time and get well-distributed Pareto sets with better convergence performance, which can well adapt to the features of real-world SIES-EESPs and save plenty of operation time and storage space for the SEMS.

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An Optimization Method for an Integrated Energy System Scheduling Process Based on NSGA-II Improved by Tent Mapping Chaotic Algorithms

Processes ◽

10.3390/pr8040426 ◽

2020 ◽

Vol 8 (4) ◽

pp. 426 ◽

Cited By ~ 1

Author(s):

Shengran Chen ◽

Shengyan Wang

Keyword(s):

Economic Dispatch ◽

Energy System ◽

Optimization Method ◽

Nsga Ii ◽

Tent Map ◽

Chaos Optimization ◽

Basic Model ◽

Proposed Model ◽

Integrated Energy System ◽

Supply Equipment

The integrated energy system is a vital part of distributed energy industries. In addition to this, the optimal economic dispatch model, which takes into account the complementary coordination of multienergy, is an important research topic. Considering the constraints of power balance, energy supply equipment, and energy storage equipment, a basic model of optimal economic dispatch of an integrated energy system is established. On this basis, a multiobjective function solving algorithm of NSGA-II, based on tent map chaos optimization, is proposed. The proposed model and algorithm are applied. The simulation results show that the optimal economic scheduling model of the integrated energy system established in this paper can provide a more economic system operation scheme and reduce the operation cost and risks associated with an integrated energy system. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) multiobjective function solving algorithm, based on tent map chaos optimization, has better performance and efficiency.

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Evaluation of Integrated Energy System in Airports Based on Comprehensive Weighting Method

E3S Web of Conferences ◽

10.1051/e3sconf/202124501022 ◽

2021 ◽

Vol 245 ◽

pp. 01022

Author(s):

Ji Jiayin ◽

Chen Kang

Keyword(s):

Heat Pump ◽

Energy System ◽

Primary Energy ◽

Pollutant Emissions ◽

Energy Storage Devices ◽

Entropy Weight ◽

Analytic Hierarchy ◽

Weighting Method ◽

Distributed Energy System ◽

Integrated Energy System

Airport is a typical integrated energy system in a park with various energy requirements. In this paper, a multi-dimensional quantitative analysis of system performance indicators was conducted by using a comprehensive weighting method based on the analytic hierarchy process (AHP) and anti-entropy weight method. A distributed energy system evaluation matrix model was used to evaluate and compare different integrated energy designs. The results showed that electric boilers would increase the primary energy ratio and primary energy consumption than the ones caused by gas boilers. Also, energy storage devices could significantly decrease pollutant emissions of integrated energy systems but would increase investment costs and reduce the economic indicators of system solutions. In a word, the configuration with ice storage, combined cooling, heating and power (CCHP), gas boiler, ground source heat pump (GSHP), air source heat pump (ASHP), and absorption chiller had the best evaluation indicators.

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NSGA-II and MOPSO based optimization for sizing of hybrid PV/wind/battery energy storage system

International Journal of Power Electronics and Drive Systems (IJPEDS) ◽

10.11591/ijpeds.v10.i1.pp463-478 ◽

2019 ◽

Vol 10 (1) ◽

pp. 463 ◽

Cited By ~ 3

Author(s):

Mohamed Izdin Hlal ◽

Vigna K. Ramachandaramurthya ◽

Sanjeevikumar Padmanaban ◽

Hamid Reza Kaboli ◽

Aref Pouryekta ◽

...

Keyword(s):

Energy Storage ◽

Wind Turbines ◽

Energy System ◽

Weather Data ◽

Optimum Number ◽

Economic Optimization ◽

Nsga Ii ◽

Battery Energy Storage ◽

Hybrid Renewable Energy System ◽

Battery Energy

<span lang="MS">This paper presents a Stand-alone Hybrid Renewable Energy System (SHRES) as an alternative to fossil fuel based generators. The Photovoltaic (PV) panels and wind turbines (WT) are designed for the Malaysian low wind speed conditions with battery Energy Storage (BES) to provide electric power to the load. The appropriate sizing of each component was accomplished using Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) techniques. The optimized hybrid system was examined in MATLAB using two case studies to find the optimum number of PV panels, wind turbines system and BES that minimizes the Loss of Power Supply Probability (LPSP) and Cost of Energy (COE). The hybrid power system was connected to the AC bus to investigate the system performance in supplying a rural settlement. Real weather data at the location of interest was utilized in this paper. The results obtained from the two scenarios were used to compare the suitability of the NSGA-II and MOPSO methods. The NSGA-II method is shown to be more accurate whereas the MOPSO method is faster in executing the optimization. Hence, both these methods can be used for techno-economic optimization of SHRES. </span>

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