scholarly journals An Optimal Allocation Strategy for Multienergy Networks Based on Double-Layer Nondominated Sorting Genetic Algorithms

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Min Mou ◽  
Da Lin ◽  
Yuhao Zhou ◽  
Wenguang Zheng ◽  
Jiongming Ruan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Double Layer ◽  
Optimization Model ◽  
Optimal Allocation ◽  
Environmental Benefits ◽  
Optimization Strategy ◽  
Allocation Strategy ◽  
Photovoltaic Panel ◽  
Wind Power Generator ◽  
Nondominated Sorting

Aiming at the problems of complex structures, variable loads, and fluctuation of power outputs of multienergy networks, this paper proposes an optimal allocation strategy of multienergy networks based on the double-layer nondominated sorting genetic algorithm, which can optimize the allocation of distributed generation (DG) and then improve the system economy. In this strategy, the multiobjective nondominated sorting genetic algorithm is adopted in both layers, and the second-layer optimization is based on the optimization result of the first layer. The first layer is based on the structure and load of the multienergy network. With the purpose of minimizing the active power loss and the node voltage offset, an optimization model of the multienergy network is established, which uses the multiobjective nondominated sorting genetic algorithm to solve the installation location and the capacity of DGs in multienergy networks. In the second layer, according to the optimization results of the first layer and the characteristics of different DGs (wind power generator, photovoltaic panel, microturbine, and storage battery), the optimization model of the multienergy network is established to improve the economy, reliability, and environmental benefits of multienergy networks. It uses the multiobjective nondominated sorting genetic algorithm to solve the allocation capacity of different DGs so as to solve the optimal allocation problem of node capacity in multienergy networks. The double-layer optimization strategy proposed in this paper greatly promotes the development of multienergy networks and provides effective guidance for the optimal allocation and reliable operation of multienergy networks.

scholarly journals Optimal allocation of microgrid using a differential multi-agent multi-objective evolution algorithm

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Liheng Liu ◽  
Miaomiao Niu ◽  
Dongliang Zhang ◽  
Li Liu ◽  
Dietmar Frank
Keyword(s):  
Genetic Algorithm ◽  
Optimal Allocation ◽  
Environmental Benefits ◽  
Nsga Ii ◽  
Multi Objective ◽  
Calculation Results ◽  
Key Issues ◽  
Multi Agent ◽  
Evolution Algorithm ◽  
Simulation Results

Abstract The optimal configuration and allocation of a microgrid are one of the key issues to guarantee the economic and reliable working of a microgrid. This is a multi-objective optimisation problem within which the economic index and the load power shortage rate index should be considered when optimising the configuration. In this article, a differential multi-agent multi-objective evolutionary algorithm (DMAMOEA) was designed to optimise the capacity configuration of a microgrid system, which includes three kinds of equipment: wind turbine, photovoltaic equipment and battery. The final optimisation results were compared with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm. Simulation results showed the effectiveness of the algorithm. At the end of this article, the representative solutions in the calculation results are compared and explained and the environmental benefits are analysed, which show the effectiveness of the implementation of the microgrid system.

scholarly journals Multi-objective optimization on the shock absorber design for the lunar probe using nondominated sorting genetic algorithm II

2017 ◽  
Vol 14 (4) ◽  
pp. 172988141772046 ◽  
Author(s):  
Yuanyuan Liu ◽  
Shunguang Song ◽  
Chunjie Wang
Keyword(s):  
Genetic Algorithm ◽  
Shock Absorber ◽  
Stability Boundary ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Soft Landing ◽  
Simulation Experiments ◽  
Multi Objective ◽  
The Stability ◽  
Nondominated Sorting

In this article, the design on the shock absorber of the lunar probe soft landing can be considered as a single- or multi-objective optimization problem. Here, the optimized objective parameters include the maximum toppling stability, defined as Dmin, and the minimum stroke of primary strut energy absorption, SPmax. However, the two optimized variables are conflict objectives. In order to give an overall consideration about the multi-performances of landing, the multi-objective optimization strategy is proposed and nondominated sorting genetic algorithm II is employed to find the best decision parameters of the shock absorber design. To conduct the optimizations, firstly, the worst landing cases and safety boundaries for both toppling and primary strut energy absorptions are obtained by the computer simulation experiments. Both single- and multi-objective optimizations are then implemented aiming to expand the stability boundary. The results show that the landing stability is effectively improved after optimizations, and also demonstrate that the multi-objective optimization strategy is superior to that of the single-objective optimization.

Tool requirement and pre-scheduling optimization model of the tool flow system of a digital workshop

2017 ◽  
Vol 231 (5) ◽  
pp. 838-849
Author(s):  
Qi Lei ◽  
Li Zeng ◽  
Yuchuan Song
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Method ◽  
Double Layer ◽  
Heuristic Algorithm ◽  
Optimization Model ◽  
Optimization Problems ◽  
Flow System ◽  
Scheduling Optimization ◽  
Purchase Cost

A new mathematical method and an optimization model are proposed in this study to solve the tool requirement and pre-scheduling optimization problems involved in the tool flow system of digital workshops. This model aims to minimize the system makespan under the constraint of the tool purchase cost. A double-layer genetic algorithm based on the heuristic algorithm is then developed. This algorithm not only combines the advantages but also avoids the weaknesses of the two algorithms. Finally, a case study is conducted to validate the effectiveness and superiority of the proposed algorithm and the tool-machine dual-resource pre-scheduling optimization model.

scholarly journals Allocation Optimization Strategy for High-Precision Control of Picosatellites and Nanosatellites

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Bo Yang ◽  
Jun Miao ◽  
Zichen Fan ◽  
Jun Long ◽  
Xuhui Liu
Keyword(s):  
Energy Consumption ◽  
High Precision ◽  
Attitude Control ◽  
Optimal Allocation ◽  
Control Allocation ◽  
Integer Programming Problem ◽  
Optimization Strategy ◽  
Allocation Strategy ◽  
Precision Control ◽  
Value Decomposition

The high-precision control of picosatellites and nanosatellites has always plagued the astronautics field. Aiming to change the status quo of the actuators not being able to meet the high-precision attitude control of picosatellites and nanosatellites, this article formulates a control allocation strategy for picosatellites and nanosatellites using the solid propellant microthruster array (SPMA). To solve the problem of the diversity and complexity of ignition combinations brought about by the high integration of the SPMA, the energy consumption factor of the optimal allocation is established, and the relationships of the array’s energy consumption factor, the control accuracy, the number, and the ignition combinations of the thruster array are deduced. The optimization objective is introduced by Sherman-Morrison formula and singular value decomposition. Thus, the energy consumption problem is transformed into an integer programming problem, acquiring the control allocation strategy and the optimal thruster energy. Simulation results show that the proposed algorithm can effectively reduce the thrust energy consumption and improve the precision control, demonstrating the feasibility and efficiency of the proposed algorithm for picosatellites and nanosatellites.

scholarly journals Real-Time Optimal Control Strategy for Multienergy Complementary Microgrid System Based on Double-Layer Nondominated Sorting Genetic Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Min Mou ◽  
Yuhao Zhou ◽  
Wenguang Zheng ◽  
Zhongping Zhang ◽  
Da Lin ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Double Layer ◽  
Control Strategy ◽  
Energy Supply ◽  
Operation Mode ◽  
Complex Load ◽  
Load Demand ◽  
Power Load ◽  
Nondominated Sorting

Because of the problems of low operation efficiency and poor energy management of multienergy input and output system with complex load demand and energy supply, this paper uses the double-layer nondominated sorting genetic algorithm to optimize the multienergy complementary microgrid system in real-time, allocating reasonably the output of each energy supply end and reducing the energy consumption of the system on the premise of meeting the demand of cooling, thermal and power load, so as to improve the economy of the whole system. According to the system load demand and operation mode, the first layer of this double-layer operation strategy calculates the power required by each node of the microgrid system to reduce the system loss. The second layer calculates the output of each equipment by using nondominated sorting genetic algorithm with various energy values calculated in the first layer as constraint conditions, considering the operation characteristics of various equipment and aiming at economy and environmental protection. In this paper, a typical model of energy input-output is established. This model combines with the operation control strategy suitable for multienergy complementary microgrid system, considers the operation mode and equipment characteristics of the system, and uses a double-layer nondominated sorting genetic algorithm to optimize the operation of each equipment in the multienergy complementary system in real time, so as to reduce the operation cost of the system.

scholarly journals An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jing Xiao ◽  
Jing-Jing Li ◽  
Xi-Xi Hong ◽  
Min-Mei Huang ◽  
Xiao-Min Hu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Experimental Comparison ◽  
Software Project ◽  
Project Portfolio ◽  
Multiobjective Evolutionary Algorithms ◽  
Reference Distance ◽  
Nondominated Sorting

As it is becoming extremely competitive in software industry, large software companies have to select their project portfolio to gain maximum return with limited resources under many constraints. Project portfolio optimization using multiobjective evolutionary algorithms is promising because they can provide solutions on the Pareto-optimal front that are difficult to be obtained by manual approaches. In this paper, we propose an improved MOEA/D (multiobjective evolutionary algorithm based on decomposition) based on reference distance (MOEA/D_RD) to solve the software project portfolio optimization problems with optimizing 2, 3, and 4 objectives. MOEA/D_RD replaces solutions based on reference distance during evolution process. Experimental comparison and analysis are performed among MOEA/D_RD and several state-of-the-art multiobjective evolutionary algorithms, that is, MOEA/D, nondominated sorting genetic algorithm II (NSGA2), and nondominated sorting genetic algorithm III (NSGA3). The results show that MOEA/D_RD and NSGA2 can solve the software project portfolio optimization problem more effectively. For 4-objective optimization problem, MOEA/D_RD is the most efficient algorithm compared with MOEA/D, NSGA2, and NSGA3 in terms of coverage, distribution, and stability of solutions.

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