scholarly journals Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4466
Author(s):  
Maël Riou ◽  
Florian Dupriez-Robin ◽  
Dominique Grondin ◽  
Christophe Le Loup ◽  
Michel Benne ◽  
...  
Keyword(s):  
Power Systems ◽  
Rural Areas ◽  
Energy System ◽  
Design Stage ◽  
Multi Objective Optimization ◽  
Renewable Energy Resources ◽  
Nsga Ii ◽  
Renewable Integration ◽  
Multi Objective ◽  
Trade Offs

Microgrids operating on renewable energy resources have potential for powering rural areas located far from existing grid infrastructures. These small power systems typically host a hybrid energy system of diverse architecture and size. An effective integration of renewable energies resources requires careful design. Sizing methodologies often lack the consideration for reliability and this aspect is limited to power adequacy. There exists an inherent trade-off between renewable integration, cost, and reliability. To bridge this gap, a sizing methodology has been developed to perform multi-objective optimization, considering the three design objectives mentioned above. This method is based on the non-dominated sorting genetic algorithm (NSGA-II) that returns the set of optimal solutions under all objectives. This method aims to identify the trade-offs between renewable integration, reliability, and cost allowing to choose the adequate architecture and sizing accordingly. As a case study, we consider an autonomous microgrid, currently being installed in a rural area in Mali. The results show that increasing system reliability can be done at the least cost if carried out in the initial design stage.

scholarly journals Simulation-Based Optimization of Hybrid Renewable Energy System for Off-grid Rural Electrification

2021 ◽  
Vol 10 (4) ◽  
pp. 667-686
Author(s):  
Akinola Sunday Oladeji ◽  
Mudathir Funsho Akorede ◽  
Salihu Aliyu ◽  
Abdulrasaq Apalando Mohammed ◽  
Adebayo Wahab Salami
Keyword(s):  
Renewable Energy ◽  
Rural Areas ◽  
Energy System ◽  
Renewable Energy Resources ◽  
Multi Objective ◽  
Hybrid Renewable Energy System ◽  
Renewable Energy System ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
Hybrid Renewable Energy

There is a need to develop an optimization tool that can be applied in the feasibility study of a hybrid renewable energy system to find the optimal capacity of different renewable energy resources and support the decision makers in their performance investigation. A multi-objective function which minimizes the Levelized Cost of Energy (LCOE) and Loss of Load Probability Index (LLPI) but maximizes the novel Energy Match Ratio (EMR) was formulated. Simulation-based optimization method combined with ε-constraint technique was developed to solve the multi-objective optimization problem. In the study, ten-year hourly electrical load demand, using the end-use model, is estimated for the communities. The performance of the developed algorithm was evaluated and validated using Hybrid Optimization Model for Electric Renewables (HOMER®) optimization software. The developed algorithm minimized the LCOE by 6.27% and LLPI by 167% when compared with the values of LCOE ($0.444/kWh) and LLPI (0.000880) obtained from the HOMER® optimization tool. Also, the LCOE with the proposed approach was calculated at $0.417/kWh, which is lower than the $0.444/kWh obtained from HOMER®. From environmental perspective, it is found that while 141,370.66 kg of CO2 is saved in the base year, 183,206.51 kg of CO2 is saved in the ninth year.The study concluded that the approach is computationally efficient and performed better than HOMER® for this particular problem.The proposed approach could be adopted for carrying out feasibility studies and design of HRES for Off-Grid electrification, especially in the rural areas where access to the grid electricity is limited

Multi-objective optimization of a hybrid distributed energy system using NSGA-II algorithm

2018 ◽  
Vol 12 (4) ◽  
pp. 518-528 ◽  
Author(s):  
Hongbo Ren ◽  
Yinlong Lu ◽  
Qiong Wu ◽  
Xiu Yang ◽  
Aolin Zhou
Keyword(s):  
Energy System ◽  
Multi Objective Optimization ◽  
Distributed Energy ◽  
Nsga Ii ◽  
Multi Objective ◽  
Distributed Energy System

scholarly journals Simulation-Based Optimization of Hybrid Renewable Energy System for Off-grid Rural Electrification

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Akinola Sunday Oladeji ◽  
Mudathir Funsho Akorede ◽  
Salihu Aliyu ◽  
Abdulrasaq Apalando Mohammed ◽  
Adebayo Wahab Salami
Keyword(s):  
Renewable Energy ◽  
Rural Areas ◽  
Energy System ◽  
Renewable Energy Resources ◽  
Multi Objective ◽  
Hybrid Renewable Energy System ◽  
Renewable Energy System ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
Hybrid Renewable Energy

There is a need to develop an optimization tool that can be applied in the feasibility study of a hybrid renewable energy system to find the optimal capacity of different renewable energy resources and support the decision makers in their performance investigation. A multi-objective function which minimizes the Levelized Cost of Energy (LCOE) and Loss of Load Probability Index (LLPI) but maximizes the novel Energy Match Ratio (EMR) was formulated. Simulation-based optimization method combined with ε-constraint technique was developed to solve the multi-objective optimization problem. In the study, ten-year hourly electrical load demand, using the end-use model, is estimated for the communities. The performance of the developed algorithm was evaluated and validated using Hybrid Optimization Model for Electric Renewables (HOMER®) optimization software. The developed algorithm minimized the LCOE by 6.27% and LLPI by 167% when compared with the values of LCOE ($0.444/kWh) and LLPI (0.000880) obtained from the HOMER® optimization tool. Also, the LCOE with the proposed approach was calculated at $0.417/kWh, which is lower than the $0.444/kWh obtained from HOMER®. From environmental perspective, it is found that while 141,370.66 kg of CO2 is saved in the base year, 183,206.51 kg of CO2 is saved in the ninth year.The study concluded that the approach is computationally efficient and performed better than HOMER® for this particular problem.The proposed approach could be adopted for carrying out feasibility studies and design of HRES for Off-Grid electrification, especially in the rural areas where access to the grid electricity is limited

Conceptual Approach in Multi-Objective Optimization of Packed Bed Membrane Reactor for Ethylene Epoxidation Using Real-coded Non-Dominating Sorting Genetic Algorithm NSGA-II

2017 ◽  
Vol 15 (1) ◽  
Author(s):  
Matthew J. Palys ◽  
Stanislav Y. Ivanov ◽  
Ajay K. Ray
Keyword(s):  
Ethylene Oxide ◽  
Membrane Reactor ◽  
Flow Reactor ◽  
Packed Bed ◽  
Design Stage ◽  
Multi Objective Optimization ◽  
Conceptual Approach ◽  
Nsga Ii ◽  
Multi Objective ◽  
Decision Variables

Abstract An isothermal plug flow reactor model with extended Fick diffusion model for transport through the porous membrane is utilized for simulation of ethylene oxide formation in a packed bed membrane reactor (PBMR). The model was verified and validated using published experimental data from an existing lab-scale unit. Sensitivity analysis was performed to determine robustness of the model. A conceptual approach on operation and design stage multi-objective optimization study is discussed. Real-coded NSGA-II is used and effect of its parameters on optimization of reactor performance is also studied. The results of three two-objective operation-stage (with 4 decision variables) and one two-objective design-stage (with 6 decision variables) optimization case studies are presented. Good convergence to a Pareto optimal solution is achieved for all cases. Significant improvement over current experimental operation is observed in terms of increase in conversion of ethylene, selectivity to ethylene oxide and ethylene oxide product flow rate.

scholarly journals Operation Optimization of Regional Integrated Energy System Based on Electric Heating Gas Demand Response

2020 ◽  
Vol 218 ◽  
pp. 01002
Author(s):  
Nan Wang ◽  
Jialin Yang ◽  
Xichao Zhou ◽  
Zhen Li ◽  
Yaling Sun ◽  
...  
Keyword(s):  
Power Consumption ◽  
Wind Power ◽  
Demand Response ◽  
Energy Cost ◽  
Energy System ◽  
Multi Objective Optimization ◽  
Total Energy Consumption ◽  
Multi Objective ◽  
Trade Offs ◽  
Integrated Energy System

Taking into account the energy cost, pollution emission, wind power consumption, and other dispatching objectives in the regionally integrated energy system (RIES), the RIES multi-objective optimization model considering the integrated demand response is established. Firstly, the RIES modeling of equipment including electricity-to-gas, energy storage systems, cogeneration units, etc., and the introduction of a comprehensive demand response that specifically considers load reduction, load transfer, and load replacement in the region, aimed at reducing system load peaks and valleys difference. Then, the objective function to minimize the system energy cost, the abandoned wind power, and the pollutant treatment cost was established respectively, and the multi-objective optimization method was adopted—the Pareto front was solved by fuzzy weighted programming traversal weights, and then the decision was made based on evidence Method to find the optimal scheduling strategy. Finally, based on a typical case study, the results show that the proposed multi-objective optimization algorithm can effectively make trade-offs among multiple scheduling objectives, and RIES considering comprehensive demand response has advantages in terms of total energy consumption, environmental friendliness, and wind power consumption.

Multi-Objective Optimization of Combined Heat and Power Industrial Microgrid

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Elham Sheikhi Mehrabadi ◽  
Swamidoss Sathiakumar
Keyword(s):  
Power Systems ◽  
Storage Systems ◽  
Critical Role ◽  
Combined Heat And Power ◽  
Distributed Networks ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Environmental Objectives ◽  
Practical Constraints

Abstract Microgrids play a critical role in the transition from conventional centralized power systems to the smart distributed networks of the future. To achieve the greatest outputs from microgrids, a comprehensive multi-objective optimization plan is necessary. Among various conflicting planning objectives, emissions and cost are primary concerns in microgrid optimization. In this work, two novel procedures, i.e., non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective particle swarm optimization (MOPSO), were developed to minimize emissions and cost in combined heat- and power-based (CHP) industrial microgrids (IMGs) simultaneously, by applying the most practical constraints and considering the variable loads. Two different scenarios, the presence and absence of photovoltaics (PV) and PV storage systems, were analyzed. The results concluded that when considering PVs and PV storage systems, the NSGA-II algorithm provides the most optimized solution in minimizing economic and environmental objectives.

scholarly journals Multi-Objective Lightweight Optimization of Parameterized Suspension Components Based on NSGA-II Algorithm Coupling with Surrogate Model

Machines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 107
Author(s):  
Rongchao Jiang ◽  
Zhenchao Jin ◽  
Dawei Liu ◽  
Dengfeng Wang
Keyword(s):  
Ride Comfort ◽  
Lightweight Design ◽  
Dynamic Performance ◽  
Vehicle Dynamic ◽  
Multi Objective Optimization ◽  
Original Design ◽  
Dynamic Simulations ◽  
Nsga Ii ◽  
Torsion Beam ◽  
Multi Objective

In order to reduce the negative effect of lightweighting of suspension components on vehicle dynamic performance, the control arm and torsion beam widely used in front and rear suspensions were taken as research objects for studying the lightweight design method of suspension components. Mesh morphing technology was employed to define design variables. Meanwhile, the rigid–flexible coupling vehicle model with flexible control arm and torsion beam was built for vehicle dynamic simulations. The total weight of control arm and torsion beam was taken as optimization objective, as well as ride comfort and handling stability performance indexes. In addition, the fatigue life, stiffness, and modal frequency of control arm and torsion beam were taken as the constraints. Then, Kriging model and NSGA-II were adopted to perform the multi-objective optimization of control arm and torsion beam for determining the lightweight scheme. By comparing the optimized and original design, it indicates that the weight of the optimized control arm and torsion beam are reduced 0.505 kg and 1.189 kg, respectively, while structural performance and vehicle performance satisfy the design requirement. The proposed multi-objective optimization method achieves a remarkable mass reduction, and proves to be feasible and effective for lightweight design of suspension components.

scholarly journals Design and characterization of effective solar cells

2021 ◽  
Author(s):  
Varun Ojha ◽  
Giorgio Jansen ◽  
Andrea Patanè ◽  
Antonino La Magna ◽  
Vittorio Romano ◽  
...  
Keyword(s):  
Solar Cells ◽  
Quantum Efficiency ◽  
Cost Benefit Analysis ◽  
Cost Minimization ◽  
Cell Structure ◽  
Cost Benefit ◽  
Structure Design ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

AbstractWe propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximization. We evaluated structures of 15 different cell designs simulated by varying material types and photodiode doping strategies. At first, non-dominated sorting genetic algorithm II (NSGA-II) produced Pareto-optimal-solutions sets for respective cell designs. Then, on investigating quantum efficiencies of all cell designs produced by NSGA-II, we applied a new multi-objective optimization algorithm II (OptIA-II) to discover the Pareto fronts of select (three) best cell designs. Our designed OptIA-II algorithm improved the quantum efficiencies of all select cell designs and reduced their fabrication costs. We observed that the cell design comprising an optimally doped zinc-oxide-based transparent conductive oxide (TCO) layer and rough silver back reflector (BR) offered a quantum efficiency ($$Q_e$$ Q e ) of 0.6031. Overall, this paper provides a full characterization of cell structure designs. It derives relationship between quantum efficiency, $$Q_e$$ Q e of a cell with its TCO layer’s doping methods and TCO and BR layer’s material types. Our solar cells design characterization enables us to perform a cost-benefit analysis of solar cells usage in real-world applications.

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