scholarly journals Multi-Objective Optimization Dispatch Based Energy Management of A Microgrid Running Under Grid Connected and Standalone Operation Mode

2021 ◽  
Vol 10 (2) ◽  
pp. 333-343
Author(s):  
Marouane Lagouir ◽  
Abdelmajid Badri ◽  
Yassine Sayouti
Keyword(s):  
Optimization Problem ◽  
Operation Mode ◽  
Operating Cost ◽  
Optimization Techniques ◽  
Pollutant Emission ◽  
System Control ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Weighted Sum Method

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.

scholarly journals Multi-objective optimization of multi-microgrid power dispatch under uncertainties using interval optimization

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Shungen Luo ◽  
Xiuping Guo
Keyword(s):  
Optimization Problem ◽  
Operating Cost ◽  
Optimal Solution ◽  
Renewable Energies ◽  
Energy Utilization ◽  
Pollutant Emission ◽  
Interval Optimization ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimal Solution Set

<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>

scholarly journals Multi-objective Optimization Approach to Malfatti's Problem

2021 ◽  
Vol 14 ◽  
pp. 82-90
Author(s):  
Rentsen Enkhbat ◽  
◽  
Gompil Battur ◽  
Keyword(s):  
Optimization Problem ◽  
Circle Packing ◽  
Stationary Points ◽  
Optimization Approach ◽  
Theory Approach ◽  
Weighted Sum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
Malfatti’S Problem

In this work, we consider the multi-objective optimization problem based on the circle packing problem, particularly, extended Malfatti's problem (Enkhbat, 2020) with k disks. Malfatti's problem was examined for the first time from a view point of global optimization theory and algorithm in (Enkhbat, 2016). Also, a game theory approach has been applied to Malfatti's problem in (Enkhbat and Battur, 2021). In this paper, we apply the the multi-objective optimization approach to the problem. Using the weighted sum method, we reduce this problem to optimization problem with nonconvex constraints. For solving numerically the weighted sum optimization problem, we apply KKT conditions and find Pareto stationary points. Also, we estimate upper bounds of the global value of the objective function by Lagrange duality. Numerical results are provided.

scholarly journals An Operational Approach to Multi-Objective Optimization for Volt-VAr Control

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5871
Author(s):  
David Raz ◽  
Yuval Beck
Keyword(s):  
Optimization Problem ◽  
Target Function ◽  
Optimization Techniques ◽  
Energy Resources ◽  
Multi Objective Optimization ◽  
Distributed Energy ◽  
Operational Approach ◽  
Multi Objective ◽  
Operational Interpretation ◽  
Photo Voltaic

Recent research has enabled the integration of traditional Volt-VAr Control (VVC) resources, such as capacitor banks and transformer tap changers, with Distributed Energy Resources (DERs), such as photo-voltaic sources and energy storage, in order to achieve various Volt-VAr Optimization (VVO) targets, such as Conservation Voltage Reduction (CVR), minimizing VAr flow at the transformer, minimizing grid losses, minimizing asset operations and more. When more than one target function can be optimized, the question of multi-objective optimization is raised. In this work, a general formulation of the multi-objective Volt-VAr Optimization problem is proposed. The applicability of various multi-optimization techniques is considered and the operational interpretation of these solutions is discussed. The methods are demonstrated using a simulation on a test feeder.

A robust policy bootstrapping algorithm for multi-objective reinforcement learning in non-stationary environments

2019 ◽  
Vol 28 (4) ◽  
pp. 273-292 ◽  
Author(s):  
Sherif Abdelfattah ◽  
Kathryn Kasmarik ◽  
Jiankun Hu
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Markov Property ◽  
Special Kind ◽  
Optimization Techniques ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Sequential Decision ◽  
Major Drawback ◽  
Multi Objective

Multi-objective Markov decision processes are a special kind of multi-objective optimization problem that involves sequential decision making while satisfying the Markov property of stochastic processes. Multi-objective reinforcement learning methods address this kind of problem by fusing the reinforcement learning paradigm with multi-objective optimization techniques. One major drawback of these methods is the lack of adaptability to non-stationary dynamics in the environment. This is because they adopt optimization procedures that assume stationarity in order to evolve a coverage set of policies that can solve the problem. This article introduces a developmental optimization approach that can evolve the policy coverage set while exploring the preference space over the defined objectives in an online manner. We propose a novel multi-objective reinforcement learning algorithm that can robustly evolve a convex coverage set of policies in an online manner in non-stationary environments. We compare the proposed algorithm with two state-of-the-art multi-objective reinforcement learning algorithms in stationary and non-stationary environments. Results showed that the proposed algorithm significantly outperforms the existing algorithms in non-stationary environments while achieving comparable results in stationary environments.

Multi-Objective Optimization of Freeway Network Traffic Flow Using Particle Swarm Optimization

2015 ◽  
Vol 713-715 ◽  
pp. 1777-1781
Author(s):  
Kai Ge Wen
Keyword(s):  
Gini Coefficient ◽  
Optimization Problem ◽  
Optimization Approach ◽  
Route Guidance ◽  
Multi Objective Optimization ◽  
Traffic Demand ◽  
Multi Objective ◽  
Network Analyses ◽  
The Gini Coefficient ◽  
Dynamic Route

A multi-objective optimization problem of ramp metering and dynamic route guidance is presented. The problem domain, a freeway integration control application considers the efficiency and equity of system, is formulated as a multi-objective optimization problem. The Gini coefficient is adopted in this study as an indicator of equity. The control strategy’s effect is demonstrated through its application to the simple freeway network. Analyses of simulation results using this approach show the equity of the system have a significant improvement over traditional control, especially for the case of large traffic demand. Using the multi-objective optimization approach, the Gini coefficient of the network has been reduced by 55% compared to traditional method.

scholarly journals An Operational Approach to Multi-Objective Optimization of Volt-VAr Control

2020 ◽  
Author(s):  
David Raz ◽  
Yuval Beck
Keyword(s):  
Optimization Problem ◽  
Target Function ◽  
Optimization Techniques ◽  
Energy Resources ◽  
Multi Objective Optimization ◽  
Distributed Energy ◽  
Operational Approach ◽  
Multi Objective ◽  
Operational Interpretation ◽  
Photo Voltaic

Recent research has enabled the integration of traditional Volt-VAr Control (VVC) resources, such as capacitors banks and transformer tap changers, with Distributed Energy Resources (DERs), such as photo-voltaic sources and energy storage, in order to achieve various Volt-VAr Optimization (VVO) targets, such as Conservation Voltage Reduction (CVR), minimizing VAr flow at the transformer, minimizing grid losses, minimizing asset operations and more. When more than one target function can be optimized, the question of multi-objective optimization is raised. In this work, we propose a general formulation of the multi-objective Volt-VAr optimization problem. We consider the applicability of various multi-optimization techniques and discuss the operational interpretation of these solutions. We demonstrate the methods using simulation on a test feeder.

scholarly journals An Integrated Multi-Criteria and Multi-Objective Optimization Approach for Establishing the Transport Plan of Intercity Trains

2020 ◽  
Vol 12 (2) ◽  
pp. 687 ◽  
Author(s):  
Svetla Stoilova
Keyword(s):  
Objective Function ◽  
Optimal Transport ◽  
Hybrid Methods ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
Transport Plan ◽  
The Impact ◽  
Constraint Method

The development of the transport plan must take into account various criteria impacting the transport process. The main objective of the study is to propose an integrated approach to determine the transport plan of passenger trains. The methodology consists of five steps. In the first step, the criteria for optimization of the transport plan were defined. In the second step, variants of the transport plan were formulated. In the third step, the weights of the criteria are determined by applying the step-wise weight assessment ratio analysis method (SWARA) multi-criteria method. The multi-objective optimization was conducted in the fourth step. The following multi-objective optimization approaches were used and compared: weighted sum method (WSM), compromise programming method (CP), and the epsilon–constraint method (EC). The study proposes a modified epsilon–constraint method (MEC) by applying normalization of each objective function according to the maximal value of the solution by individual optimization for each objective function, and hybrid methods: hybrid WSM and EC, hybrid WSM and MEC, hybrid CP and EC, and Hybrid CP and MEC. The impact of the variation of passenger flows on the choice of an optimal transport plan was studied in the fifth step. The Laplace’s criterion, Hurwitz’s criterion, and Savage’s criterion were applied to come to a decision. The approbation of the methodology was demonstrated through the case study of Bulgaria’s railway network. Suitable variant of transport plan is proposed.

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
Keyword(s):  
Power Loss ◽  
Operating Cost ◽  
Pollutant Emission ◽  
Computational Time ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Time Step ◽  
Dc Microgrid ◽  
Emission Level ◽  
Multi Objective

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.

Multi-Objective based Optimal Generation Scheduling Considering Wind and Solar Energy Systems

2018 ◽  
Vol 19 (5) ◽  
Author(s):  
Surender Reddy Salkuti ◽  
Vuddanti Sandeep ◽  
B. Chitti Babu ◽  
Chan-Mook Jung
Keyword(s):  
Optimization Problem ◽  
Operating Cost ◽  
Forecast Errors ◽  
Solar Pv ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Reliability Indices ◽  
Multi Objective ◽  
Generation Scheduling ◽  
System Operator

Abstract This paper presents an optimum day-ahead scheduling of thermal and renewable (wind and solar photovoltaic) power generation as a multi-objective optimization (MOO) problem considering the uncertainty. System operating cost (i.e. cost of thermal, wind, solar PV and battery), reliability and emission cost are considered to be optimized simultaneously. The uncertainties due to the generator outages, wind, solar PV power and load forecast errors are incorporated in the proposed optimization problem using the Loss Of Load Probability (LOLP) and Expected Unserved Energy (EUE) reliability indices. In the proposed approach, the amount of spinning reserves (SRs) required are scheduled based on the desired level of system reliability. The proposed multi-objective optimization problem is solved using NSGA-II algorithm. Different case studies are performed considering different objective functions that may be selected by system operator (SO) based on the preference.

scholarly journals COMBINATION OF TAGUCHI METHOD, MOORA AND COPRAS TECHNIQUES IN MULTI-OBJECTIVE OPTIMIZATION OF SURFACE GRINDING PROCESS

2020 ◽  
pp. 1-9
Author(s):  
Do Duc Trung
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Optimization Problem ◽  
Removal Rate ◽  
Optimization Techniques ◽  
Surface Grinding ◽  
Grinding Process ◽  
Multi Objective Optimization ◽  
Multi Objective

This study presentes a combination method of several optimization techniques and Taguchi method to solve the multi-objective optimization problem for surface grinding process of SKD11 steel. The optimization techniques that were used in this study were Multi-Objective Optimization on basis of Ratio Analysis (MOORA) and Complex Proportional Assessment (COPRAS). In surface grinding process, two parameters that were chosen as the evaluation creterias were surface roughness (Ra) and material removal rate (MRR). The orthogonal Taguchi L16 matrix was chosen to design the experimental matrix with two input parameters namely workpiece velocity and depth of cut.  The two optimization techniques that mentioned above were applied to solve the multi-objective optimization problem in the grinding process. Using two above techniques, the optimized results of the cutting parameters were the same. The optimal workpiece velocity and cutting depth were 20 m/min and 0.02 mm. Corresponding to these optimal values of the workpiece velocity and cutting depth, the surface roughness and material removal rate were 1.16 µm and 86.67 mm3/s. These proposed techniques and method can be used to improve the quality and effectiveness of grinding processes by reducing the surface roughness and increasing the material removal rate.

Sign in / Sign up

Export Citation Format

Share Document