Multi-objective micro-grid planning by NSGA-II in primary distribution system

2011 ◽  
Vol 22 (2) ◽  
pp. 170-187 ◽  
Author(s):  
K. Buayai ◽  
W. Ongsakul ◽  
N. Mithulananthan
Keyword(s):  
Distribution System ◽  
Nsga Ii ◽  
Multi Objective ◽  
Micro Grid ◽  
Grid Planning

scholarly journals Multi-objective Evolutionary Algorithms for Power Distribution System Optimal Reconfiguration

2019 ◽  
Vol 22 (3) ◽  
Author(s):  
Ivo Benitez Cattani
Keyword(s):  
Power Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Multi Objective Optimization ◽  
Power Losses ◽  
Nsga Ii ◽  
Power Distribution System ◽  
Multi Objective ◽  
Three Phase ◽  
Unbalanced Voltage

In this paper two reconfiguration methodologies for three-phase electric power distribution systems based on multi-objective optimization algorithms are developed in order to simultaneously optimize two objective functions, (1) power losses and (2) three-phase unbalanced voltage minimization. The proposed optimization involves only radial topology configurations which is the most common configuration in electric distribution systems. The formulation of the problem considers the radiality as a constraint, increasing the computational complexity. The Prim and Kruskal algorithms are tested to fix infeasible configurations. In distribution systems, the three-phase unbalanced voltage and power losses limit the power supply to the loads and may even cause overheating in distribution lines, transformers and other equipment. An alternative to solve this problem is through a reconfiguration process, by opening and/or closing switches altering the distribution system configuration under operation. Hence, in this work the three-phase unbalanced voltage and power losses in radial distribution systems are addressed as a multi-objective optimization problem, firstly, using a method based on weighted sum; and, secondly, implementing NSGA-II algorithm. An example of distribution system is presented to prove the effectiveness of the proposed method.

scholarly journals Multi-Objective Service Restoration of Radial Distribution System in the Presence of Non-Linear Loads

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 369
Author(s):  
Ishan Srivastava ◽  
Sunil Bhat ◽  
Agarala Ajaysekhar Reddy
Keyword(s):  
Radial Distribution ◽  
Distribution System ◽  
Normal Operation ◽  
Power Network ◽  
Nsga Ii ◽  
Radial Distribution System ◽  
Current Harmonics ◽  
Multi Objective ◽  
Service Restoration ◽  
Non Linear

Most of the power electronic components act as non-linear loads because they draw non- sinusoidal current from the power supply. Due to these non-linear loads, current harmonics are injected in the power network. For normal operation, any power network is equipped with provisions to keep the harmonics level to a minimum value. Whenever a fault occurs in the distribution system, the primary goal is to re-energize the healthy part of the network which got interrupted. It can be done by changing the topology of the network. This method is called as Service Restoration (SR). In this paper, a service restoration strategy is proposed when non-linear loads are present in the radial distribution system. Service restoration problem is formulated as a multi-objective, constrained optimization problem. Three new objectives are included to address the problem of harmonics injection by non-linear loads. Multi-Objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) are used to find the optimal switching sequence for restoration. To test the effectiveness of the proposed methodology, IEEE 33 bus and IEEE 69 bus test systems are taken.

scholarly journals Evolutionary Multi-Objective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2470 ◽  
Author(s):  
Alamaniotis ◽  
Gatsis
Keyword(s):  
Smart Grid ◽  
Optimality Theory ◽  
Power Distribution ◽  
Distribution System ◽  
Electric Energy ◽  
Optimal Solution ◽  
Nsga Ii ◽  
Power Distribution System ◽  
Multi Objective ◽  
Grid Partition

Utilization of digital connectivity tools is the driving force behind the transformation of the power distribution system into a smart grid. This paper places itself in the smart grid domain where consumers exploit digital connectivity to form partitions within the grid. Every partition, which is independent but connected to the grid, has a set of goals associated with the consumption of electric energy. In this work, we consider that each partition aims at morphing the initial anticipated partition consumption in order to concurrently minimize the cost of consumption and ensure the privacy of its consumers. These goals are formulated as two objectives functions, i.e., a single objective for each goal, and subsequently determining a multi-objective problem. The solution to the problem is sought via an evolutionary algorithm, and more specifically, the non-dominated sorting genetic algorithm-II (NSGA-II). NSGA-II is able to locate an optimal solution by utilizing the Pareto optimality theory. The proposed load morphing methodology is tested on a set of real-world smart meter data put together to comprise partitions of various numbers of consumers. Results demonstrate the efficiency of the proposed morphing methodology as a mechanism to attain low cost and privacy for the overall grid partition.

scholarly journals Sensitivity of algorithm parameters and objective function scaling in multi-objective optimisation of water distribution systems

2015 ◽  
Vol 17 (6) ◽  
pp. 891-916 ◽  
Author(s):  
Helena Mala-Jetmarova ◽  
Andrew Barton ◽  
Adil Bagirov
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution System ◽  
Parameter Setting ◽  
Nsga Ii ◽  
Multi Objective ◽  
Optimisation Model

This paper presents an extensive analysis of the sensitivity of multi-objective algorithm parameters and objective function scaling tested on a large number of parameter setting combinations for a water distribution system optimisation problem. The optimisation model comprises two operational objectives minimised concurrently, the pump energy costs and deviations of constituent concentrations as a water quality measure. This optimisation model is applied to a regional non-drinking water distribution system, and solved using the optimisation software GANetXL incorporating the NSGA-II linked with the network analysis software EPANet. The sensitivity analysis employs a set of performance metrics, which were designed to capture the overall quality of the computed Pareto fronts. The performance and sensitivity of NSGA-II parameters using those metrics is evaluated. The results demonstrate that NSGA-II is sensitive to different parameter settings, and unlike in the single-objective problems, a range of parameter setting combinations appears to be required to reach a Pareto front of optimal solutions. Additionally, inadequately scaled objective functions cause the NSGA-II bias towards the second objective. Lastly, the methodology for performance and sensitivity analysis may be used for calibration of algorithm parameters.

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

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