scholarly journals Solving Multi-Objective Energy Management of a DC Microgrid using Multi-Objective Multiverse Optimization

2021 ◽  
Vol 10 (4) ◽  
pp. 911-922
Author(s):  
Marouane Lagouir ◽  
Abdelmajid Badri ◽  
Yassine Sayouti

This paper deals with the multi-objective optimization dispatch (MOOD) problem in a DC microgrid. The aim is to formulate the MOOD to simultaneously minimize the operating cost, pollutant emission level of (NOx, SO2 and CO2) and the power loss of conversion devices.  Taking into account the equality and inequality constraints of the system. Two approaches have been adopted to solve the MOOD issue. The scalarization approach is first introduced, which combines the weighted sum method with price penalty factor to aggregate objective functions and obtain Pareto optimal solutions. Whilst, the Pareto approach is based on the implementation of evolutionary multi-objective optimization solution. Single and multi-objective versions of multi-verse optimizer algorithm are, respectively, employed in both approaches to handle the MOOD. For each time step, a fuzzy set theory is selected to find the best compromise solution in the Pareto optimal set. The simulation results reveal that the Pareto approach achieves the best performances with a considerable decrease of 28.96 $/day in the daily operating cost, a slight reduction in the power loss of conversion devices from 419.79 kWh to 419.29 kWh, and in less computational time. While, it is noticing a small increment in the pollutant emission level from 11.54 kg/day to 12.21 kg/day, for the daily microgrid operation. This deviation can be fully covered when comparing the cost related to the treatment of these pollutants, which is only 5.55 $/day, to the significant reduction in the operating cost obtained using the Pareto approach.

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Giovani Gaiardo Fossati ◽  
Letícia Fleck Fadel Miguel ◽  
Walter Jesus Paucar Casas

PurposeThis study aims to propose a complete and powerful methodology that allows the optimization of the passive suspension system of vehicles, which simultaneously takes comfort and safety into account and provides a set of optimal solutions through a Pareto-optimal front, in a low computational time.Design/methodology/approachUnlike papers that consider simple vehicle models (quarter vehicle model or half car model) and/or simplified road profiles (harmonic excitation, for example) and/or perform a single-objective optimization and/or execute the dynamic analysis in the time domain, this paper presents an effective and fast methodology for the multi-objective optimization of the suspension system of a full-car model (including the driver seat) traveling on an irregular road profile, whose dynamic response is determined in the frequency domain, considerably reducing computational time.FindingsThe results showed that there was a reduction of 28% in the driver seat vertical acceleration weighted root mean square (RMS) value of the proposed model, which is directly related to comfort, and, simultaneously, an improvement or constancy concerning safety, with low computational cost. Hence, the proposed methodology can be indicated as a successful tool for the optimal design of the suspension systems, considering, simultaneously, comfort and safety.Originality/valueDespite the extensive literature on optimizing vehicle passive suspension systems, papers combining multi-objective optimization presenting a Pareto-optimal front as a set of optimal results, a full-vehicle model (including the driver seat), an irregular road profile and the determination of the dynamic response in the frequency domain are not found.


2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Shungen Luo ◽  
Xiuping Guo

<p style='text-indent:20px;'>The microgrid technology, which can dispatch power independently, is an effective way to increase the efficiency of energy utilization meanwhile develop and utilize the clean and renewable energy. However, the power generation of a single microgrid is unstable, because it is greatly affected by the external environment. Therefore, the development and application of the multi-microgrid system have gradually drawn various countries' attention. In order to minimize the operating cost and gaseous pollutant emission of the multi-microgrid system, which is composed of renewable energies and electric vehicles and so on, this paper builds a 24 hours day-ahead multi-objective complex constrained optimization model, using interval optimization to handle uncertainties of renewable energies. In view of the model characteristics, the metaheuristic strategies about initialization and repair of solution are designed. Furthermore, the fuzzy membership degree and Chebyshev function are used in parallel to decompose the multi-objective optimization problem, thus a multi-objective evolutionary algorithm based on hybrid decomposition (MOEA/HD) is constructed. Finally, the effectiveness of the metaheuristic strategies can be verified by analyzing the simulation results in this paper. Moreover, the results prove that the MOEA/HD is more efficient, which can get a higher-quality Pareto optimal solution set when compared to other algorithms.</p>


2021 ◽  
Vol 10 (2) ◽  
pp. 333-343
Author(s):  
Marouane Lagouir ◽  
Abdelmajid Badri ◽  
Yassine Sayouti

This paper presents a novel optimization approach for a day-ahead power management and control of a DC microgrid (MG). The multi-objective optimization dispatch (MOOD) problem involves minimizing the overall operating cost, pollutant emission levels of (NOx, SO2 and CO2) and the power loss cost of the conversion devices. The weighted sum method is selected to convert the multi-objective optimization problem into a single optimization problem. Then, analytic hierarchy process (AHP) method is applied to determine the weight coefficients, according to the preference of each objective function. The system’s performance is evaluated under both grid connected and standalone operation mode, considering power balancing, high level penetration of renewable energy, optimal scheduling of charging/discharging of battery storage system, control of load curtailment and the system technical constraints. Ant lion optimizer (ALO) method is considered for handling MOOD, and the performance of the proposed algorithm is compared with other known heuristic optimization techniques.  The simulation results prove the effectiveness and the capability of the developed approach to deal better with the coordinated control and optimization dispatch problem.They also revealed that economically running the MG system under grid connected mode can reduce the overall cost by around 4.70% compared to when it is in standalone operation mode.


Author(s):  
H Sayyaadi ◽  
H R Aminian

A regenerative gas turbine cycle with two particular tubular recuperative heat exchangers in parallel is considered for multi-objective optimization. It is assumed that tubular recuperative heat exchangers and its corresponding gas cycle are in design stage simultaneously. Three objective functions including the purchased equipment cost of recuperators, the unit cost rate of the generated power, and the exergetic efficiency of the gas cycle are considered simultaneously. Geometric specifications of the recuperator including tube length, tube outside/inside diameters, tube pitch, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles, and main operating parameters of the gas cycle including the compressor pressure ratio, exhaust temperature of the combustion chamber and the air mass flowrate are considered as decision variables. Combination of these objectives anddecision variables with suitable engineering and physical constraints (including NO x and CO emission limitations) comprises a set of mixed integer non-linear problems. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm. This approach is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained, and a final optimal solution is selected in a decision-making process.


Power loss is the most significant parameter in power system analysis and its adequate calculation directly effects the economic and technical evaluation. This paper aims to propose a multi-objective optimization algorithm which optimizes dc source magnitudes and switching angles to yield minimum THD in cascaded multilevel inverters. The optimization algorithm uses metaheuristic approach, namely Harmony Search algorithm. The effectiveness of the multi-objective algorithm has been tested with 11-level Cascaded H-Bridge Inverter with optimized DC voltage sources using MATLAB/Simulink. As the main objective of this research paper is to analyze total power loss, calculations of power loss are simplified using approximation of curves from datasheet values and experimental measurements. The simulation results, obtained using multi-objective optimization method, have been compared with basic SPWM, optimal minimization of THD, and it is confirmed that the multilevel inverter fired using multi- objective optimization technique has reduced power loss and minimum THD for a wide operating range of multilevel inverter.


2020 ◽  
pp. 105-113
Author(s):  
M. Farsi

The main aim of this research is to present an optimization procedure based on the integration of operability framework and multi-objective optimization concepts to find the single optimal solution of processes. In this regard, the Desired Pareto Index is defined as the ratio of desired Pareto front to the Pareto optimal front as a quantitative criterion to analyze the performance of chemical processes. The Desired Pareto Front is defined as a part of the Pareto front that all outputs are improved compared to the conventional operating condition. To prove the efficiency of proposed optimization method, the operating conditions of ethane cracking process is optimized as a base case. The ethylene and methane production rates are selected as the objectives in the formulated multi-objective optimization problem. Based on the simulation results, applying the obtained operating conditions by the proposed optimization procedure on the ethane cracking process improve ethylene production by about 3% compared to the conventional condition.  


Author(s):  
Amit K. Thakur ◽  
Santosh K. Gupta ◽  
Rahul Kumar ◽  
Nilanjana Banerjee ◽  
Pranava Chaudhari

Abstract Slurry polymerization processes using Zeigler–Natta catalysts are most widely used for the production of polyethylene due to their several advantages over other processes. Optimal operating conditions are required to obtain the maximum productivity of the polymer at minimal cost while ensuring operational safety in the slurry phase ethylene polymerization reactors. The main focus of this multi-objective optimization study is to obtain the optimal operating conditions corresponding to the maximization of productivity and yield at a minimal operating cost. The tuned reactor model has been optimized. The single objective optimization (SOO) and multi-objective optimization (MOO) problems are solved using non-dominating sorting genetic algorithm-II (NSGA-II). A complete range of Pareto optimal solutions are obtained to obtain the maximum productivity and polymer yield at different input costs.


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