The installation of solar photovoltaics for charity homes in rural Malaysia: a community-based approach

The adoption of solar panels installation in Malaysia, especially for rural areas are still at the preliminary stage, due to the high cost of the system and the limited access to this technology in the remote region. The availability of this green electricity for public buildings are still limited since the current dissemination of this green energy technology is still funded by the government, resulting in slow progress in the area. In addition, Malaysians also received subsidized power supply which has led to high dependency on this conventional supply from the national electricity provider, Tenaga Nasional Berhad (TNB) which is slowing the progress of solar energy usage. However, since 2014, there has been a gradual increase in electricity tariff that has led to the increasing cost of monthly electricity bills. This has affected many sectors in paying off the utility bill, especially charitable organizations owned by private Non-Governmental Organizations (NGOs). The significant impact may occur to private charity homes in rural areas which usually depended on donations from the public. The worst scenario may arise, for instance the power may be cut-off if the electricity bills cannot be paid on time. Therefore, this research paper will try to determine whether solar panel installation through a community-based approach can help the institutions to gain savings from the monthly electricity bill and provide self-sufficient electricity to charity homes. A case study in the region of Perak Tengah in Malaysia was selected to identify if the community-based approach could help to access this green electricity at the selected charity home. An empirical approach showed that the charity home has the potential to save 70% of monthly electricity bills after installing a 2 kilowatts peak (kWp) solar photovoltaics system under a community service program

Sequential capacitor placements method for voltage improvement in transmission networks

The voltage profile in a transmission line system should be in the tolerance of the lower limit to the upper limit value. Moreover, the power loss during transmission process should be minimized to increase the lifespan of the transmission lines. The poor voltage profile and high power losses are the results of increasing of power demand due to the arising of electrical energy consumptions. These problems can be solved by using the shunt capacitor bank placements. The installation of capacitors required three specifications which are the locations, size and number of capacitors to improve the voltage profile of overall system. However, these three specifications are difficult to be determined. The best locations for capacitor placements will result in minimum number and size. In this study, the specifications of capacitor placements in transmission networks is determined by using power loss indication and the number and the size of capacitor is based on voltage increment algorithms. The proposed methods will be applied on three transmission networks which are 6-bus system, 14-bus system and 30-bus system using Power World Simulator. The results show that the proposed method able to improve the overall voltage profile and reducing the power loss with minimum number of capacitor placement in the transmission networks

Voltage stability prediction on power system network via enhanced hybrid particle swarm artificial neural network

Rapid development of cities with constant increasing load and deregulation in electricity market had forced the transmission lines to operate near their threshold capacity and can easily lead to voltage instability and caused system breakdown. To prevent such catastrophe from happening, accurate readings of voltage stability condition is required so that preventive equipment and operators can execute security procedures to restore system condition to normal. This paper introduced Enhanced Hybrid Particle Swarm Optimization algorithm to estimate the voltage stability condition which utilized Fast Voltage Stability Index (FVSI) to indicate how far or close is the power system network to the collapse point when the reactive load in the system increases because reactive load gives the highest impact to the stability of the system as it varies. Particle Swarm Optimization (PSO) had been combined with the ANN to form the Enhanced Hybrid PSO-ANN (EHPSO-ANN) algorithm that worked accurately as a prediction algorithm. The proposed algorithm reduced serious local minima convergence of ANN but also maintaining the fast convergence speed of PSO. The results show that the hybrid algorithm has greater prediction accuracy than those comparing algorithms. High generalization ability was found in the proposed algorithm

Minimizing power loss considering bulk uncoordinated charging station operation

The widespread usage of electric vehicles (EV) carries a negative impact on the distribution network. Higher penetration of uncoordinated charging station (CS) that used to charge the EV may boost the capacity of demand, especially during off-peak. Next, an increase in power loss may occur since the bulk operation of CS causes a higher power flow at existing networks. Furthermore, the CS varying demand usually challenging to handle, uniquely for the load that has various behaviour. From repetitious researchers, the CS operation often installs at the residency, parking lot and specific station. Then, the charging power requirement is different between EV's, which depend on battery size and characteristic. This research focuses on power loss parameters at the distribution network for uncoordinated CS operation. The analysis will be based on the 48-bus radial distribution system with two residential blocks using MATLAB environment. Then, suitable sizing and placement of multiple passive filters used to minimize power loss with assistance metaheuristic technique and multi-objective approach. From the result, propose method able to improve up to 8.03% compared to existing power loss after implement optimal sizing and placement of passive filter. The proposed method is useful to assist utility owner in reducing power loss issues at a distribution system that cause by extensive uncoordinated CS operation

Economic and emission load dispatch solution via artificial bee colony algorithm

The Economic Load Dispatch (ELD) is considered to minimize the only fuel cost in power system planning and operation. Due to environmental issues, the emission amount released by thermal power generation should be considered in minimizing the total cost generation as well. This make the power dispatch problem become multi-objective problem that called Economic and Emission Load Dispatch (EELD). This paper investigates the performances of Artificial Bee colony (ABC) algorithm for determining optimal solution for EELD problem. In this paper, the EELD problem is formulated as a single objective problem by taking minimization of total cost and emission level as separated objective. The cost characteristic of thermal generator considered the effect of opening and closing of the valve for practical application. The ABC algorithm has been tested on standard test systems which is 10-generating unit test system in order to validate the effectiveness of proposed ABC algorithm. The results obtained have been compared with reported result by other methods in literature. From the result, it found that ABC algorithm is better than other methods for minimizing fuel cost of thermal power generation and obtained comparative results in reducing the emission level

Medium size dual-axis solar tracking system with sunlight intensity comparison method and fuzzy logic implementation

Nowadays, renewable energy such as solar power has become important for electricity generation, and solar power systems have been installed in homes. Furthermore, solar tracking systems are being continuously improved by researchers around the world, who focus on achieving the best design and thus maximizing the efficiency of the solar power system. In this project, a fuzzy logic controller has been integrated and implemented in a medium-scale solar tracking system to achieve the best real-time orientation of a solar PV panel toward the sun. This project utilized dual-axis solar tracking with a fuzzy logic intelligent method. The hardware system consists of an Arduino UNO microcontroller as the main controller and Light Dependent Resistor (LDR) sensors for sensing the maximum incident intensity of solar irradiance. Initially, two power window motors (one for the horizontal axis and the other for the vertical axis) coordinate and alternately rotate to scan the position of the sun. Since the sun changes its position all the time, the LDR sensors detect its position at five-minute intervals through the level of incident solar irradiance intensity measured by them. The fuzzy logic controller helps the microcontroller to give the best inference concerning the direction to which the solar PV panel should rotate and the position in which it should stay. In conclusion, the solar tracking system delivers high efficiency of output power with a low power intake while it operates

Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer

The need for tuning the PI controller is to improve its performance metrics such as rise time, settling time and overshoot. This paper proposed the Grey Wolf Optimizer (GWO) tuning method of a Proportional Integral (PI) controller for fixed speed Wind Turbine. The objective is to overcome the limitations in using the PSO and GA tuning methods for tuning the PI controller, such as quick convergence occurring too soon into a local optimum, and the controller step input response. The GWO, the Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA) tuning methods were implemented in the Matlab 2016b to search the optimal gains of the Proportional and Integral controller through minimization of the objective function. A comparison was made between the results obtained from the GWO tuning method against PSO and GA tuning techniques. The GWO computed the smallest value of the objective function minimized. It exhibited faster convergence and better time response specification compared to other methods. These and more performance indicators show the superiority of the GWO tuning method

Effects of Multiple Combination Weightage using MOPSO for Motion Control Gantry Crane System

This paper presents the implementation of Multi Objective Particle Swarm Optimization in controlling motion control of Gantry Crane System. Three objective functions are considered to be optimized, named (i) steady state error, (ii) overshoot, and (iii) settling time. Six cases with different setting of weight summation are analyzed in order to obtain five parameters (PID and PD) controller. A combination of PID and PD controller is observed and utilized for controlling trolley movement to desired position and reduced the payload oscillation concurrently. Various cases of weight summation values will affect to the controller parameters and system responses. The performances of the system is conducted and presented within Matlab environment

Optimal capacitor placement and sizing via artificial bee colony

Abstract To achieve a more economical distribution system in the future, several methods have been introduced by researchers to accomplish that goal. Among the most commonly used method is to install the capacitors. It operates by supplying reactive power into the system to improve the performance of voltage, thereby reducing power losses. Nevertheless, the location and the size of the capacitor still issues to be resolved by the utilities. Various methods have been introduced to coordinate the capacitor without affect the performance of the distribution system. Basically, the most popular approach used to determine the location of capacitors is based on sensitivity analysis. This approach operates by placing the capacitor at each node in the system and selects the node that gives higher power losses reduction. Meanwhile, the size of capacitor is determined by using the optimization techniques in obtaining optimal values. However, calculation for both location and size in separate analysis could lead the solution trapped in local optimum. Therefore, this paper is investigated a solution to determine the location and size of capacitor simultaneously by using artificial bee colony (ABC). The effectiveness of proposed method is tested on 33-bus and 69-bus test system and compared with other methods. Based from the obtained results, simultaneous approach reduces the power losses by 34.29% and 35.44% for 33-bus and 69-bus test system, respectively. Moreover, the proposed method gives a better voltage improvement compared to the base case

An adjusted weight metric to quantify flexibility available in conventional generators for low carbon power systems

With the increasing shares of intermittent renewable sources in the grid, it becomes increasingly essential to quantify the requirements of the power systems flexibility. In this article, an adjusted weight flexibility metric (AWFM) is developed to quantify the available flexibility within individual generators as well as within the overall system. The developed metric is useful for power system operators who require a fast, simple, and offline metric. This provides a more realistic and accurate quantification of the available technical flexibility without performing time-consuming multi-temporal simulations. Another interesting feature is that it can be used to facilitate scenario comparisons. This is achieved by developing a new framework to assure the consistency of the metric and by proposing a new adjusted weighting mechanism based on correlation analysis and analytic hierarchy process (AHP). A new ranking approach based on flexibility was also proposed to increase the share of the renewable energy sources (RESs). The proposed framework was tested on the IEEE RTS-96 test-system. The results demonstrate the consistency of the AWFM. Moreover, the results show that the proposed metric is adaptive as it automatically adjusts the flexibility index with the addition or removal of generators. The new ranking approach proved its ability to increase the wind share from 28% to 37.2% within the test system. The AWFM can be a valuable contribution to the field of flexibility for its ability to provide systematic formulation for the precise analysis and accurate assessment of inherent technical flexibility for a low carbon power system