Optimal Microgrid Topology Design and Siting of Distributed Generation Sources Using a Multi-Objective Substrate Layer Coral Reefs Optimization Algorithm

n this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.This research was partially funded by Ministerio de Economía, Industria y Competitividad, project number TIN2017-85887-C2-1-P and TIN2017-85887-C2-2-P, and by the Comunidad Autónoma de Madrid, project number S2013ICE-2933_02

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

Optimal Number, Location, and Size of Distributed Generators in Distribution Systems by Symbiotic Organism Search Based Method

This paper proposes an approach based on the Symbiotic Organism Search (SOS) for optimal determining sizing, siting, and number of Distributed Generations (DG) in distribution systems. The objective of the problem is to minimize the power loss of the system subject to the equality and inequality constraints such as power balance, bus voltage limits, DG capacity limits, and DG penetration limit. The SOS approach is defined as the symbiotic relationship observed between two organisms in an ecosystem, which does not need the control parameters like other meta-heuristic algorithms in the literature. For the implementation of the proposed method to the problem, an integrated approach of Loss Sensitivity Factor (LSF) is used to determine the optimal location for installation of DG units, and SOS is used to find the optimal size of DG units. The proposed method has been tested on IEEE 33-bus, 69-bus, and 118-bus radial distribution systems. The obtained results from the SOS algorithm have been compared to those of other methods in the literature. The simulated results have demonstrated that the proposed SOS method has a very good performance and effectiveness for the problem of optimal placement of DG units in distribution systems

Power losses reduction by optimal allocation of renewable distributed generation in distribution networks

The electrical energy demand is increasing dramatically in many countries around the world due to population growth. As a result of this significant increase in demand, electricity distribution companies are seeking to promote distributed generation (DG). With the growing integration of decentralized renewable power generation into the distribution network, it becomes an active circuit where power flows and voltages are influenced not only by loads but also by sources. In distribution networks (DN), the optimal allocation of Renewable Distributed Generation (DG) units can significantly improve system performance by reducing power losses and enhancing the voltage profile and stability of the radial distribution network. The main objective of this paper is to apply the marine predator algorithm (MPA) to optimize the siting and sizing of DG units in the DN. The objective function considered is the minimization of active power losses. The proposed algorithm is tested on the IEEE 33-bus and 69-bus DN. The simulation results demonstrate that the MPA algorithm outperforms other optimization algorithms in terms of perform

Coyote multi-objective optimization algorithm for optimal location and sizing of renewable distributed generators

Research on the integration of renewable distributed generators (RDGs) in radial distribution systems (RDS) is increased to satisfy the growing load demand, reducing power losses, enhancing voltage profile, and voltage stability index (VSI) of distribution network. This paper presents the application of a new algorithm called ‘coyote optimization algorithm (COA)’ to obtain the optimal location and size of RDGs in RDS at different power factors. The objectives are minimization of power losses, enhancement of voltage stability index, and reduction total operation cost. A detailed performance analysis is implemented on IEEE 33 bus and IEEE 69 bus to demonstrate the effectiveness of the proposed algorithm. The results are found to be in a very good agreement

Performance Assessment of Pareto and Non-Pareto Approaches for the Optimal Allocation of DG and DSTATCOM in the Distribution System

This paper proposes a Differential Evolution (DE) optimization algorithm and a Pareto-frontier Multi-Objective Differential Evolution (MODE) optimization algorithm for the optimal allocation of Distributed Generation (DG) and Distribution Static Compensator (DSTATCOM) in a radial distribution system. It considers the minimization of active power dissipation, voltage drop and the annual cost as the objectives of this optimization problem. The proposed techniques are tested on an IEEE 33 bus radial distribution system. To compare the performance of the MODE and DE, the weighted sum approach is carried out. This helps to select one solution from the Pareto front of the MODE. Case studies show that the allocation of both DG and DSTATCOM results in a noticeable reduction of system losses, voltage drop and annual cost. Comparative studies also show that the global convergence characteristics of MODE are better than several other optimization algorithms

Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement

This paper presents an implementation of the hybrid Cuckoo search and Grey wolf (CS-GWO) optimization algorithm for solving the problem of distribution network reconfiguration (DNR) and optimal location and sizing of distributed generations (DGs) simultaneously in radial distribution systems (RDSs). This algorithm is being used significantly to minimize the system power loss, voltage deviation at load buses and improve the voltage profile. When solving the high-dimensional datasets optimization problem using the GWO algorithm, it simply falls into an optimum local region. To enhance and strengthen the GWO algorithm searchability, CS algorithm is integrated to update the best three candidate solutions. This hybrid CS-GWO algorithm has a more substantial search capability to simultaneously find optimal candidate solutions for problem. Furthermore, to validate the effectiveness and performances of the proposed hybrid CS-GWO algorithm is being tested and evaluated for standard IEEE 33-bus and 69-bus RDSs by considering different scenarios

A combined analytical method for optimal location and sizing of distributed generation considering voltage stability and power loss in a power distribution system

In this paper, a multi-objective analytical method to evaluate the impacts of optimal location and sizing of distributed generation is presented. This method is based on an analysis of the exact loss formula and continuous power flow in a radial distribution system. Based on two methods of analysis, power loss and weakest voltage buses and lines are calculated and then the optimal size of distributed generation is determined. After that, by considering the minimum power losses and the maximisation of voltage stability, the proposed index determines and ranks positions to decide the optimal distributed generation location in the system. This method allows us to find the best places and size to connect a number of distributed generation units by optimising the objective functions. The simulation results were obtained using a 33-bus radial distribution system to determine the location and size of the distributed generation units. The results show the effectiveness of voltage profile improvement, loading factor improvement and power loss reduction. Further, the problems of a single objective function and the placement of the distributed generation unit using analytical methods are solved by the proposed approach

The role of distributed generation on the performance of electrical radial distribution network

Purpose: This article provides available information on the role of distributed generation (DG) in the performance of a power distribution network.Design/methodology/approach: The study reviewed articles about available methods for reducing technical losses in electrical distribution networks. The second step involved studying various researchers' views on renewable energy in some developing countries for introducing DG into a distribution network. The influence of DG on the economic performance of a distribution network. Finally, the study scouted for available information on the implementation of a demand response (DR) program on the performance of a distribution network in the presence of DG.Findings: Available information reveals that the reliability of DG for reducing the technical losses in a distribution network is higher than relying on alternating current controllers. There are indications of renewable energies in developing countries for introducing DG into a distribution network. According to the articles reviewed, the approach for the optimal location of DG did not include the combination of the voltage stability index and power loss reduction index. It is also worth considering using the power system analysis toolbox (PSAT) for DG sitting. The economic influence of DG on a distribution network's performance has not been evaluated based on the technical loss, generation cost, emission cost and reliability. It is also worth considering the benefits of demand response programs in the presence of DG.Research limitation: The review concentrated mainly on DG's influence in reducing technical loss. Articles relating to the effect of DG on other distribution network technical issues such as voltage stability, harmonics etc. also require attentionPractical implications: Distribution network performance is essential for the operation of electrical gadgets. Therefore, improved distribution network performance will result in the economic development of a country.Originality/Value: This paper provides the platform that stimulates interest in using DG to improve the distribution network performance