Comparison of modern heuristic algorithms for loss reduction in power distribution network equipped with renewable energy resources

This paper presents a comparison between four modern heuristic algorithms for optimal loss reduction of power distribution network equipped with renewable energy resources. These algorithms are Gravitational Search Algorithm (GSA), Bat Algorithm (BA), Imperialist Competitive Algorithm (ICA) and Flower Pollination Algorithm (FPA). Placing Renewable Distributed Generators (RDGs) such as wind turbine (WT) and photovoltaic panels (PV) in the electrical grid might share in reducing the power loss. In this research, the proposed heuristic algorithms are utilized to find the optimal location and size of RDGs on the distribution network for the purpose of reducing power loss. A probabilistic optimal load flow technique is implemented to model the behavior of RDGs based on different penetration levels. The proposed algorithms are applied to 69-bus system. The acquired results based on the heuristic algorithms are listed to clarify the effectiveness of the proposed algorithms in reducing the power losses of the studied system. Keywords: Heuristic algorithms, Power loss reduction, Probabilistic optimal power flow, Renewable energy resource

Optimal energy management applying load elasticity integrating renewable resources

Abstract Urban growth aimed at developing smart cities confronts several obstacles, such as difficulties and costs in constructing stations and meeting consumer demands. These are possible to overcome by integrating Renewable Energy Resources (RESs) with the help of demand side management (DSM) for managing generation and loading profiles to minimize electricity bills while accounting for reduction in carbon emissions and the peak to average ratio (PAR) of the load. This study aims to achieve a multi-objective goal of optimizing energy management in smart cities which is accomplished by optimally allocating RESs combined with DSM for creating a flexible load profile under RESs and load uncertainty. A comprehensive study is applied to IEEE 69-bus with different scenarios using Sea-Horse Optimization (SHO) for optimal citing and sizing of the RESs while serving the objectives of minimizing total power losses and reducing PAR. SHO performance is evaluated and compared to other techniques such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Whale Optimization (WO), and Zebra Optimization (ZO) algorithms. The results show that combining elastic load shifting with optimal sizing and allocation using SHO achieves a global optimum solution for the highest power loss reduction while using a significantly smaller sized RESs than the counterpart

Comparative study of PID controller designs for AVR using different optimization techniques

This paper presents the optimal PID tuning study to improve the dynamic performance of an automatic voltage regulation (AVR) system. The system under study consists of a synchronous generator whose reference voltage changes in a step function and tries to overcome the transient behavior of its terminal voltage smoothly. To optimally control the performance, different optimization techniques are applied to tune the controller gains to obtain the minimum steady state error (main objective) and better dynamic characteristics (rise time, settling time, max overshoot, etc.). Then the AVR system responses with a PID controller based on different optimization techniques are compared to find out which is the best technique

The impact of smart transformer on different radialdistribution systems

The work is intended to extend the application of a smart transformer on a radialdistribution system. In this paper, an updated algorithm on the backward/forward powerflow is introduced. The so-called direct approach of power flow is employed and analyzed.In addition, the paper focused on integrating a smart transformer to the network and solvingthe updating network also using the direct approach load flow. The solution of the smarttransformer using the direct approach power flow method is quite straightforward. Thismodel is applied to radial distribution systems which are the IEEE 33- and IEEE 69-bussystems as a case study. Also, the paper optimizes the best allocation of the smart transformerto reduce the power losses of the grid

Multi-Regional Optimal Power Flow Using Marine Predators Algorithm Considering Load and Generation Variability

This paper introduces the application of a newly developed heuristic nature-inspired optimization technique, viz, tuned Marine Predator Algorithm (MPA), to solve the optimal power flow (OPF) problem of multi-regional systems. The paper proposes MPA parameters' tuning to enhance the algorithm performance. The paper takes into account the variability of different types of renewable energy resources (RERs) and loads. Two modeling approaches are presented: holistic (multi-regions are modeled as one large network) and inter-bounded (modeling the regional interfaces). The MPA is applied to the IEEE-48 bus connected system, and the results are compared with another well-established heuristic algorithm, namely the Genetic Algorithm (GA). The results demonstrate the validation, applicability and effectiveness of using the MPA for solving multi-region OPF problem considering renewable energy sources and load variability