Multi-Stage Planning of Distribution Networks with Application of Multi-Objective Algorithm Accompanied by DEA Considering Economical, Environmental and Technical Improvements

2016 ◽  
Vol 25 (04) ◽  
pp. 1650025 ◽  
Author(s):  
Mohammad Reza Jannati Oskuee ◽  
Elnaz Babazadeh ◽  
Sajad Najafi-Ravadanegh ◽  
Jafar Pourmahmoud
Keyword(s):  
Decision Maker ◽  
Radial Distribution ◽  
System Performance ◽  
Distribution Network ◽  
Distribution Networks ◽  
Operating Conditions ◽  
Nsga Ii ◽  
Technical Aspects ◽  
Multi Objective ◽  
Network Parameters

The regards to widespread impact of distribution networks and ever increasing demand for electricity, some strategies must be devized in order to well operate the distribution networks. In this paper, to enhance the accountability of the power system and to improve the system performance parameters, simultaneous placement of renewable energy generation (REG) sources (e.g., wind, solar and dispatchable distributed generators (DGs)) and capacitors are investigated in a modified radial distribution network with considering ZIP loads. To enhance all network parameters simultaneously to the best possible condition multi-objective functions are proposed and solved using non-dominated sorting genetic algorithm (NSGA II). The employed objectives contain all economical, environmental and technical aspects of distribution network. One of the most important advantages of the proposed multi-objective formulation is that it obtains non-dominated solutions allowing the system operator (decision maker) to exercise his/her personal preference in selecting each of those solutions based on the operating conditions of the system and the costs. It is clear that the implementation of each non-dominated solution needs related costs according to the technology used and the system performance characteristics. However, there is a paucity of objective methodologies for ranking the obtained non-dominated solutions considering economical, environmental and technical aspects. So, in this paper, data envelopment analysis (DEA) is suggested for this purpose. In other words, in this paper, first NSGA II is applied to the siting and sizing problem, and then the obtained non-dominated solutions are prioritized by DEA. The significant advantage of using DEA is that there is no need to impose the decision maker’s idea into the model and ranking is done based on the efficiencies of the non-dominated solutions. The most efficient solution is the one which has improved network parameters considerably and has lowest costs. So, using DEA gives a realistic view of solutions and the provided results are for all, not for a specific decision maker. To validate the effectiveness of the proposed scheme, the simulations are carried out on a modified test case 33-bus radial distribution network.

scholarly journals Multi-Objective Grey Wolf Optimization for Optimal Allocation of Distributed Generators in Distribution Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 664-670
Keyword(s):  
Radial Distribution ◽  
Power Loss ◽  
Distribution Network ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Real Power ◽  
Multi Objective ◽  
Distributed Generators

The power loss in the radial distribution network is appreciable as compared to transmission network. To reduce the power loss in distribution network which is radial in nature, the solution methodology adopted in this paper is optimal placement of distributed generators (DG). The optimization incorporated is Multi-objective Grey Wolf Optimization (MOGWO). The optimization is accomplished for three different cases. In each case two objective functions are simultaneously optimized to obtain non-dominated solutions using Multi-objective Grey Wolf Optimization. Case (1): To minimize the real power loss and maximize the savings obtained due to DG installation. Case (2): To minimize real power loss and maximum voltage deviation in the network. Case (3): To minimize real power loss and rating of DG installed. MOGWO method maintains an archive which contains pareto-optimal solutions. The archive mimics the behaviour of grey wolves. MOGWO method is verified on radial distribution networks. The effectiveness of the optimization method is proven by comparing the results with other optimization methods available in the literature.

scholarly journals Optimisasi Multi-objektif pada Rekonfigurasi Jaringan Distribusi Tenaga Listrik dengan Integrasi Pembangkit Terdistribusi Menggunakan Metode Sistem Kekebalan Buatan

2020 ◽  
Vol 12 (2) ◽  
pp. 57-71
Author(s):  
Ramadoni Syahputra ◽  
Indah Soesanti
Keyword(s):  
Network Optimization ◽  
Load Balance ◽  
Distribution Network ◽  
Reducing Power ◽  
Distribution Networks ◽  
Operating Conditions ◽  
Network Reconfiguration ◽  
Multi Objective Optimization ◽  
Power Losses ◽  
Multi Objective

This study proposes a multi-objective optimization for power distribution network reconfiguration by integrating distributed generators using an artificial immune system (AIS) method. The most effective and inexpensive technique in reducing power losses in distribution networks is optimizing the network reconfiguration. On the other hand, small to medium scale renewable energy power plant applications are growing rapidly. These power plants are operated on-grid to a distribution network, known as distributed generation (DG). The presence of DG in this distribution network poses new challenges in distribution network operations. In this study, the distribution network optimization was carried out using the AIS method. In optimization, the goal to be achieved is not only one objective but should be multiple objectives. Multi-objective optimization aims to reduce power losses, improve the voltage profile, and maintain a maintained network load balance. The AIS method has the advantage of fast convergence and avoids local minima. To test the superiority of the AIS method, the distribution network optimization with and without DG integration was carried out for the 33-bus and 71-bus models of the IEEE standard distribution networks. The results show that the AIS method can produce better system operating conditions than before the optimization. The parameters for the success of the optimization are minimal active power losses, suitable voltage profiles, and maintained load balance. This optimization has successfully increased the efficiency of the distribution network by an average of 0.61%.

Multi-objective optimization of optimal capacitor allocation in radial distribution systems

2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Sayed Mir Shah Danish ◽  
Mikaeel Ahmadi ◽  
Atsushi Yona ◽  
Tomonobu Senjyu ◽  
Narayanan Krishna ◽  
...  
Keyword(s):  
Radial Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Reactive Power ◽  
Stability Index ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Size ◽  
Multi Objective Optimization ◽  
Multi Objective

AbstractThe optimal size and location of the compensator in the distribution system play a significant role in minimizing the energy loss and the cost of reactive power compensation. This article introduces an efficient heuristic-based approach to assign static shunt capacitors along radial distribution networks using multi-objective optimization method. A new objective function different from literature is adapted to enhance the overall system voltage stability index, minimize power loss, and to achieve maximum net yearly savings. However, the capacitor sizes are assumed as discrete known variables, which are to be placed on the buses such that it reduces the losses of the distribution system to a minimum. Load sensitive factor (LSF) has been used to predict the most effective buses as the best place for installing compensator devices. IEEE 34-bus and 118-bus test distribution systems are utilized to validate and demonstrate the applicability of the proposed method. The simulation results obtained are compared with previous methods reported in the literature and found to be encouraging.

scholarly journals Investigation of Optimal PV Allocation to Minimize System Losses and Improve Voltage Stability for Distribution and Transmission Networks Using MATLAB and DigSilent

2021 ◽  
Vol 9 ◽  
Author(s):  
Mohamed Abdul Rasheed ◽  
Renuga Verayiah
Keyword(s):  
Objective Function ◽  
Radial Distribution ◽  
Power Factor ◽  
Analytical Method ◽  
Distribution Network ◽  
Computation Time ◽  
Transmission Network ◽  
Voltage Profile ◽  
Multi Objective ◽  
Test Systems

Electricity generation from renewable energy sources such as solar energy is an emerging sustainable solution. In the last decade, this sustainable source was not only being used as a source of power generation but also as distributed generation (DG). Many literatures have been published in this field with the objective to minimize losses by optimizing the DG size and location. System losses and voltage profile go hand-in-hand; as a result, when system losses are minimized, eventually the voltage profile improves. With improvement in inverter technologies, PV-DG units do not have to operate at a unity power factor. The majority of proposed algorithms and methods do not consider power factor optimization as a necessary optimization. This article aims to optimize the size, location, and power factor of PV-DG units. The simulations are performed on the IEEE 33 bus radial distribution network and IEEE 14 bus transmission network. The methodologies developed in this article are divided into two sections. The first section aims to optimize the PV-DG size and location. A multi-objective function is developed by using system losses and a voltage deviation index. Genetic algorithm (GA) is used to optimize the multi-objective function. Next, analytical processes are developed for verification. The second section aims to further enhance PV-DG by optimizing the power factor of PV-DG. The simulation is performed for static load in both systems, which are the IEEE 33 bus radial distribution network and IEEE 14 bus transmission network. A mathematical analytical method was developed, and it was found to be sufficient to optimize the power factor of the PV-DG unit. The results obtained show that voltage stability indices help minimize the computation time by determining the optimal locations for DG placement in both networks. In addition, the GA method attained faster convergence than the analytical method and hence is the best optimal sizing for both test systems with minimum computation time. Additionally, the optimization of the power factor for both test systems has demonstrated further improvement in the voltage profile and loss minimization. In conclusion, the proposed methodology has shown promising results for both transmission and distribution networks.

Multi-objective optimization of an industrial slurry phase ethylene polymerization reactor

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Amit K. Thakur ◽  
Santosh K. Gupta ◽  
Rahul Kumar ◽  
Nilanjana Banerjee ◽  
Pranava Chaudhari
Keyword(s):  
Ethylene Polymerization ◽  
Operating Cost ◽  
Operating Conditions ◽  
Multi Objective Optimization ◽  
Reactor Model ◽  
Optimal Operating ◽  
Nsga Ii ◽  
Multi Objective ◽  
Maximum Productivity ◽  
Slurry Phase

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.

Swarm-Intelligence-Based Optimal Placement and Sizing of Distributed Generation in Distribution Network

2018 ◽  
pp. 29-48 ◽  
Author(s):  
Mahesh Kumar ◽  
Perumal Nallagownden ◽  
Irraivan Elamvazuthi ◽  
Pandian Vasant ◽  
Luqman Hakim Rahman
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Distribution System ◽  
Voltage Stability ◽  
Reactive Power ◽  
Distribution Network ◽  
Stability Index ◽  
Pso Algorithm ◽  
Distribution Networks ◽  
Optimal Placement

In the distribution system, distributed generation (DG) are getting more important because of the electricity demands, fossil fuel depletion and environment concerns. The placement and sizing of DGs have greatly impact on the voltage stability and losses in the distribution network. In this chapter, a particle swarm optimization (PSO) algorithm has been proposed for optimal placement and sizing of DG to improve voltage stability index in the radial distribution system. The two i.e. active power and combination of active and reactive power types of DGs are proposed to realize the effect of DG integration. A specific analysis has been applied on IEEE 33 bus system radial distribution networks using MATLAB 2015a software.

Effective design and development of hybrid ABC-CSO-based capacitor placement with load forecasting based on artificial neural network

2019 ◽  
Vol 39 (5) ◽  
pp. 917-930 ◽  
Author(s):  
Sarika Sharma ◽  
Smarajit Ghosh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Distribution ◽  
Power Loss ◽  
Distribution Network ◽  
Load Forecasting ◽  
Distribution Networks ◽  
Content Type ◽  
Power Loss Reduction ◽  
Artificial Neural

Purpose This paper aims to develop a capacitor position in radial distribution networks with a specific end goal to enhance the voltage profile, diminish the genuine power misfortune and accomplish temperate sparing. The issue of the capacitor situation in electric appropriation systems incorporates augmenting vitality and peak power loss by technique for capacitor establishments. Design/methodology/approach This paper proposes a novel strategy using rough thinking to pick reasonable applicant hubs in a dissemination structure for capacitor situation. Voltages and power loss reduction indices of distribution networks hubs are shown by fuzzy enrollment capacities. Findings A fuzzy expert system containing a course of action of heuristic rules is then used to ascertain the capacitor position appropriateness of each hub in the circulation structure. The sizing of capacitor is solved by using hybrid artificial bee colony–cuckoo search optimization. Practical implications Finally, a short-term load forecasting based on artificial neural network is evaluated for predicting the size of the capacitor for future loads. The proposed capacitor allocation is implemented on 69-node radial distribution network as well as 34-node radial distribution network and the results are evaluated. Originality/value Simulation results show that the proposed method has reduced the overall losses of the system compared with existing approaches.

scholarly journals Multi-objective Optimization of Accommodation Capacity for Distributed Generation Based on Mixed Strategy Nash Equilibrium, Considering Distribution Network Flexibility

2019 ◽  
Vol 9 (20) ◽  
pp. 4395 ◽  
Author(s):  
Weisheng Liu ◽  
Jian Wu ◽  
Fei Wang ◽  
Yixin Huang ◽  
Qiongdan Dai ◽  
...  
Keyword(s):  
Nash Equilibrium ◽  
Distributed Generation ◽  
Mixed Strategy ◽  
Distribution Network ◽  
Distribution Networks ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Mixed Strategy Nash Equilibrium ◽  
Network Flexibility ◽  
Strategy Nash Equilibrium

The increasing penetration of distributed generation (DG) brings about great fluctuation and uncertainty in distribution networks. In order to improve the ability of distribution networks to cope with disturbances caused by uncertainties and to evaluate the maximum accommodation capacity of DG, a multi-objective programming method for evaluation of the accommodation capacity of distribution networks for DG is proposed, considering the flexibility of distribution networks in this paper. Firstly, a multi-objective optimization model for determining the maximum accommodation of DG by considering the flexibility of distribution networks is constructed, aiming at maximizing the daily energy consumption, minimizing the voltage amplitude deviation, and maximizing the line capacity margin. Secondly, the comprehensive learning particle swarm optimization (CLPSO) algorithm is used to solve the multi-objective optimization model. Then, the mixed strategy Nash equilibrium is introduced to obtain the frontier solution with the optimal joint equilibrium value in the Pareto solution set. Finally, the effectiveness of the proposed method is demonstrated with an actual distribution network in China. The simulation results show that the proposed planning method can effectively find the Pareto optimal solution set by considering multiple objectives, and can obtain the optimal equilibrium solution for DG accommodation capacity and distribution network flexibility.

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