Interactive fuzzy binary shuffled frog leaping algorithm for multi-objective reliable economic power distribution system expansion planning

This paper intends to consider a multi-objective problem for expansion planning in Power Distribution System (PDS) by focusing on (i) expansion strategy (ii) allocation of Circuit Breaker (CB), (iii) allocation of Distribution Static Compensator (DSTATCOM), (iv) Contingency Load Loss Index (CLLI), and power loss. Accordingly, the encoding parameters decide for expansion, Circuit Breaker (CB) placement, DSTATCOM placement, load of real and reactive powers of expanded bus or node are optimized using Grasshopper Optimization Algorithm (GOA) based on its distance and hence, the proposed algorithm is termed as Distance Oriented Grasshopper Optimization Algorithm (DGOA). The proposed expansion planning model is carried out in IEEE 33 test bus system. Moreover, the adopted scheme is compared with conventional algorithms and the optimal results are obtained.

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Multi-objective Evolutionary Algorithms for Power Distribution System Optimal Reconfiguration

CLEI electronic journal ◽

10.19153/cleiej.22.3.9 ◽

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.

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

Energies ◽

10.3390/en12132470 ◽

2019 ◽

Vol 12 (13) ◽

pp. 2470 ◽

Cited By ~ 1

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.

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