Optimal Allocation and Planning of Distributed Power Generation Resources in a Smart Distribution Network Using the Manta Ray Foraging Optimization Algorithm

Masoud Zahedi Vahid,Ebrahim Seifi Najmi,Shady H E Abdel Aleem,Ziad M Ali,Abdollah Ahmadi,Foad H Gandoman

doi:10.3390/en14164856

Abstract

In this study, optimal allocation and planning of power generation resources as distributed generation with scheduling capability (DGSC) is presented in a smart environment with the objective of reducing losses and considering enhancing the voltage profile is performed using the manta ray foraging optimization (MRFO) algorithm. The DGSC refers to resources that can be scheduled and their generation can be determined based on network requirements. The main purpose of this study is to schedule and intelligent distribution of the DGSCs in the smart and conventional distribution network to enhance its operation. First, allocation of the DGSCs is done based on weighted coefficient method and then the scheduling of the DGSCs is implemented in the 69-bus distribution network. In this study, the effect of smart network by providing real load in minimizing daily energy losses is compared with the network includes conventional load (estimated load as three-level load). The simulation results cleared that optimal allocation and planning of the DGSCs can be improved the distribution network operation with reducing the power losses and also enhancing the voltage profile. The obtained results confirmed superiority of the MRFO compared with well-known particle swarm optimization (PSO) in the DGSCs allocation. The results also showed that increasing the number of DGSCs reduces more losses and improves more the network voltage profile. The achieved results demonstrated that the energy loss in smart network is less than the network with conventional load. In other words, any error in forecasting load demand leads to non-optimal operating point and more energy losses.

Highlights

In the power system, a significant part of power losses is related to the distribution sector [1]
Multi-objective allocation of power generation resources as distributed generation with scheduling capability (DGSC) in a smart network is investigated with objective of minimizing the losses and enhancing the voltage profile using the manta ray foraging optimization (MRFO) algorithm
The effect of considering smart loads in planning of DGSCs on the optimal distribution of DGSCs generation is compared to the estimated load in a conventional network with objective of minimizing the loss of energy

Summary

Introduction

A significant part of power losses is related to the distribution sector [1]. Improper allocation and irrational and non-optimal planning of these resources weaken the network performance [8,9]. Optimal DG placement can be applied to all types of DGs and implemented in the design phase with the help of general network information, mainly related to the system worst conditions (e.g., peak load condition). The schedulable DGs such as micro-turbines, gas engines, and fuel cells are sourcing whose output can be controlled by operators. The optimal performance of distributed generation with scheduling capability (DGSC) depends on the network conditions and will change as these conditions change. The network load is one of the parameters that are constantly changing in the network and having more accurate information about it improves the performance of DGs. The MRFO algorithm is inspired by foraging strategies of manta ray, including chained, spinning, and turning. The mathematical model of chained search strategy is defined as follows [31,32]: xid (t + 1)

Methods

Results

Conclusion