Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques

An overview of numerical and mathematical modelling-based distributed generation (DG) system optimisation techniques is presented in this review paper. The objective is to compare different aspects of these two broad classes of DG optimisation techniques, explore their applications, and identify potential research directions from reviewed studies. Introductory descriptions of general electrical power system and DG system are first provided, followed by reviews on renewable resource assessment, load demand analysis, model formulation, and optimisation techniques. In renewable resource assessment model review, uncertain solar and wind energy resources are emphasised whereas applications of forecasting models have been highlighted based on their prediction horizons, computational power requirement, and training data intensity. For DG optimisation framework, (solar, wind and tidal) power generator, energy storage and energy balance models are discussed; in optimisation technique section, both numerical and mathematical modelling optimisation methods are reviewed, analysed and criticised with recommendations for their improvements. In overall, this review provides preliminary guidelines, research gaps and recommendations for developing a better and more user-friendly DG energy planning optimisation tool.

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

A new meta-heuristic optimizer: Pathfinder algorithm

Distributed 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 The authors strongly recommend this review to researchers, scientists and engineers who are working in this field of research work

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

Cuckoo Search (CS) algorithm is nature inspired global optimization algorithm based on the brood parasitic behavior of cuckoos. It has proved to be an efficient algorithm as it has been successfully applied to solve a large number of problems of different areas. CS employs Levy flights to generate step size and to search the solution space effectively. The local search is carried out using switch probability in which certain percentages of solutions are removed. Though CS is an effective algorithm, still its performance can be improved by incorporating the exploration and exploitation during the search process. In this work, three modified versions of CS are proposed to improve the properties of exploration and exploitation. All these versions employ Cauchy operator to generate the step size instead of Levy flights to efficiently explore the search space. Moreover, two new concepts, division of population and division of generations, are also introduced in CS so as to balance the exploration and exploitation. The proposed versions of CS are tested on 24 standard benchmark problems with different dimension sizes and varying population sizes and the effect of probability switch has been studied. Apart from this, the best of the proposed versions is also tested on CEC 2015 benchmark suite. The modified algorithms have been statistically tested in comparison to the state-of-the-art algorithms, namely grey wolf optimization (GWO), differential evolution (DE), firefly algorithm (FA), flower pollination algorithm (FPA) and bat algorithm (BA). The numerical and statistical results prove the superiority of the proposed versions with respect to other popular algorithms available in the literature.

New cuckoo search algorithms with enhanced exploration and exploitation properties

A novel method based on adaptive cuckoo search for optimal network reconfiguration and distributed generation allocation in distribution network

Display Omitted The runner-root algorithm (RRA) is adapted to solve the network reconfiguration problem.Five objectives namely power loss, load balancing among the branches, load balancing among the feeders, number of switching operations and node voltage deviation are considered.The proposed RRA method is applied to the 33-bus and 70-bus test networks for evaluation.The proposed RRA method has better performance in comparison to other methods. This paper presents a runner-root algorithm (RRA) for electric distribution network reconfiguration (NR) problem. The considered NR problem in this paper is to minimize real power loss, load balancing among the branches, load balancing among the feeders as well as number of switching operations and node voltage deviation using max-min method for selection of the final compromised solution. RRA is equipped with two explorative tools, which are random jumps with large steps and re-initialization strategy to escape from local optimal. Moreover, RRA is also equipped with an exploitative tool to search around the current best solution with large and small steps to ensure the obtained result of global optimization. The effectiveness of the applied RRA in both single- and multi-objective has been tested on 33-node and 70-node distribution network systems and the obtained test results have been compared to those from other methods in the literature. The simulation results show that the applied RRA can be an efficient method for network reconfiguration problems with single- and multi-objective.

Multi-objective electric distribution network reconfiguration solution using runner-root algorithm

This paper proposes a distribution network reconfiguration (DNR) methodology based on a backtracking search algorithm (BSA) for minimizing active power loss and minimizing voltage deviation. The BSA is a new evolutionary algorithm for solving of numerical optimization problems. It uses a single control parameter and two crossovers and mutation strategies for powerful exploration of the problem’s search space. The effectiveness of the proposed BSA has been tested on 69-node distribution network system and the obtained test results have been compared to those from other methods in the literature. In addition to BSA, two other algorithms—particle swarm optimization (PSO) and cuckoo search algorithm (CSA)—are implemented for comparisons. The simulation results show that the proposed BSA can be an efficient and promising method for distribution network reconfiguration problems.

Tuan Anh Phung

Papers

A novel method based on adaptive cuckoo search for optimal network reconfiguration and distributed generation allocation in distribution network

Multi-objective electric distribution network reconfiguration solution using runner-root algorithm

A Backtracking Search Algorithm for Distribution Network Reconfiguration Problem