Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm

The deployment of utility-scale energy storage systems (ESSs) can be a significant avenue for improving the performance of distribution networks. An optimally placed ESS can reduce power losses and line loading, mitigate peak network demand, improve voltage profile, and in some cases contribute to the network fault level diagnosis. This paper proposes a strategy for optimal placement of distributed ESSs in distribution networks to minimize voltage deviation, line loading, and power losses. The optimal placement of distributed ESSs is investigated in a medium voltage IEEE-33 bus distribution system, which is influenced by a high penetration of renewable (solar and wind) distributed generation, for two scenarios: (1) with a uniform ESS size and (2) with non-uniform ESS sizes. System models for the proposed implementations are developed, analyzed, and tested using DIgSILENT PowerFactory. The artificial bee colony optimization approach is employed to optimize the objective function parameters through a Python script automating simulation events in PowerFactory. The optimization results, obtained from the artificial bee colony approach, are also compared with the use of a particle swarm optimization algorithm. The simulation results suggest that the proposed ESS placement approach can successfully achieve the objectives of voltage profile improvement, line loading minimization, and power loss reduction, and thereby significantly improve distribution network performance

Assessment of renewable distributed generation in green building rating system for public hospital

This paper presents an optimization solution for renewable Distributed Generation (DG), as imposed in the Green Building Rating System (GBRS) for a public hospital. Solar photovoltaic DG unit (PV-DG) is identified as a type of DG used in this paper. The proposed optimization via PV-DG coordination will improve the sustainable energy performance of the green building by power loss reduction within accepted lower losses region using Artificial Bee Colony (ABC) algorithm. The setup input data from one of Malaysian public hospitals’ power distribution system is been adopted and simulation results via MATLAB programming show that the optimization of DG forming into bigger-scale imposed system provides a better outcome in minimization of total power losses within appropriate voltage profile as compared to current PV-DG imposed in GBRS. The objective function representing total power losses which also supported by related literature give a measure that forming sufficient and optimal PV-DG assessment criteria is highly important, thus, current PV-DG assessment in GBRS is proposed to be reviewed into new parameter setting for public hospital due to its’ high energy demand and distinctive electrical load profile

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

MITIGATE THE REAL POWER LOSSES IN RADIAL DISTRIBUTED NETWORK USING DG BY ABC ALGORITHM

Recently, integration of Distributed generation (DG) in distribution system has increased to high penetration levels. The impact of DG on various aspects of distribution system operation, such as reliability and energy loss depend highly on DG location in distribution feeder .Optimal DG placement plays an important role . This project presents a new methodology using Artificial Bee Colony  algorithm (ABC) to find the optimal size and optimum location for the placement of DG in the radial distribution networks for active power compensation by reduction in real power losses .The proposed technique is tested on standard IEEE-33 bus test system

Optimization of the Thyristor Controlled Phase Shifting Transformer using PSO Algorithm

The increase of power system demand leads to the change in voltage profile, reliability requirement and system robustness against disturbance. The voltage profile can be improved by providing a source of reactive power through the addition of new power plants, capacitor banks, or implementation of Flexible AC Transmission System (FACTS) devices such as Static VAR Compensator (SVC), Unified Power Flow Control (UPFC), Thyristor Controlled Series Capacitor (TCSC), Thyristor Controlled Phase Shifting Transformer (TCPST), and many others. Determination of optimal location and sizing of device injection is paramount to produce the best improvement of voltage profile and power losses reduction. In this paper, optimization of the combined advantages of TCPST and TCSC has been investigated using Particle Swarm Optimization (PSO) algorithm, being applied to the 30-bus system IEEE standard. The effectiveness of the placement and sizing of TCPST-TCSC combination has been compared to the implementation of capacitor banks. The result showed that the combination of TCPST-TCSC resulted in more effective improvement of system power losses condition than the implementation of capacitor banks.  The power losses reduction of 46.47% and 42.03% have been obtained using of TCPST-TCSC combination and capacitor banks respectively. The TCPST-TCSC and Capacitor Bank implementations by using PSO algorithm have also been compared with the implementation of Static VAR Compensator (SVC) using Artificial Bee Colony (ABC) Algorithm. The implementation of the TCSC-TCPST compensation with PSO algorithm have gave a better result than using the capacitor bank with PSO algorithm and SVC with the ABC algorithm

Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems

This paper presents a hybrid approach based on the Genetic Algorithm (GA) and Moth Swarm Algorithm (MSA), namely Genetic Moth Swarm Algorithm (GMSA), for determining the optimal location and sizing of renewable distributed generation (DG) sources on radial distribution networks (RDN). Minimizing the electrical power loss within the framework of system operation and under security constraints is the main objective of this study. In the proposed technique, the global search ability has been regulated by the incorporation of GA operations with adaptive mutation operator on the reconnaissance phase using genetic pathfinder moths. In addition, the selection of artificial light sources has been expanded over the swarm. The representation of individuals within the three phases of MSA has been modified in terms of quality and ratio. Elite individuals have been used to play different roles in order to reduce the design space and thus increase the exploitation ability. The developed GMSA has been applied on different scales of standard RDN of the (33 and 69-bus) power systems. Firstly, the most adequate buses for installing DGs are suggested using Voltage Stability Index (VSI). Then the proposed GMSA is applied to reduce real power generation, power loss, and total system cost, in addition, to improve the minimum bus voltage and the annual net saving by selecting the DGs size and their locations. Furthermore, GMSA is compared with other literature methods under several power system constraints and conditions, in single and multi-objective optimization space. The computational results prove the effectiveness and superiority of the GMSA with respect to power loss reduction and voltage profile enhancement using a minimum size of renewable DG units

Smart management strategies of utility-scale energy storage systems in power networks

Power systems are presently experiencing a period of rapid change driven by various interrelated issues, e.g., integration of renewables, demand management, power congestion, power quality requirements, and frequency regulation. Although the deployment of Energy Storage Systems (ESSs) has been shown to provide effective solutions to many of these issues, misplacement or non-optimal sizing of these systems can adversely affect network performance. This present research has revealed some novel working strategies for optimal allocation and sizing of utility-scale ESSs to address some important issues of power networks at both distribution and transmission levels. The optimization strategies employed for ESS placement and sizing successfully improved the following aspects of power systems: performance and power quality of the distribution networks investigated, the frequency response of the transmission networks studied, and facilitation of the integration of renewable generation (wind and solar). This present research provides effective solutions to some real power industry problems including minimizationof voltage deviation, power losses, peak demand, flickering, and frequency deviation as well as rate of change of frequency (ROCOF). Detailed simulation results suggest that ESS allocation using both uniform and non-uniform ESS sizing approaches is useful for improving distribution network performance as well as power quality. Regarding performance parameters, voltage profile improvement, real and reactive power losses, and line loading are considered, while voltage deviation and flickers are taken into account as power quality parameters. Further, the study shows that the PQ injection-based ESS placement strategy performs better than the P injection-based approach (in relation to performance improvement), providing more reactive power compensations. The simulation results also demonstrate that obtaining the power size of a battery ESS (MVA) is a sensible approach for frequency support. Hence, an appropriate sizing of grid-scale ESSs including tuning of parameters Kp and Tip (active part of the PQ controller) assist in improving the frequency response by providing necessary active power. Overall, the proposed ESS allocation and sizing approaches can underpin a transition plan from the current power grid to a future one

Optimal Placement and Sizing of Wind Generators in AC Grids Considering Reactive Power Capability and Wind Speed Curves

This paper presents an optimization model for the optimal placement and sizing of wind turbines, considering their reactive power capacity, wind speed, and demand curves. The optimization model is nonlinear and is focused on minimizing power losses in AC distribution networks. Also, paired wind turbine and power conversion systems are treated via chargeability factor η at the peak hour. This factor represents the percentage of usage of the power conversion system in the nominal wind speed conditions, and allows to support reactive power dynamically during all periods of the day as a function of the distribution system requirements. In addition, an artificial neural network is used for short-term forecasting to deal with uncertainties in wind power generation. We assume that the number of wind power distributed generators could be from zero to three generators integrated into the system, considering unit power factors and reactive power injections to follow up the effect of reactive power compensation in the daily operation. The General Algebraic Modeling System (GAMS) is employed to solve the proposed optimization model