Reproducing solar curtailment with Fourier analysis using Japan dataset

The 6th International Conference on Power and Energy Systems Engineering (CPESE 2019), 20–23​ September 2019, Okinawa, Japan.Curtailment of variable renewable energy increases the Levelized Cost of Energy (LCOE), which is the tool often used to compare its profitability against traditional energy sources. Recently, the Kyushu Region of Japan had to curtail some of its solar production to meet energy balance. As many countries increase their solar energy production, curtailment will be inevitable. It is therefore important to develop methodologies to calculate it. In the case of Japan, curtailment can easily be estimated using hourly data. However, such data is unavailable in other countries. In this study, a methodology to reproduce curtailment using known periodicity and statistical data is presented. Insights were initially generated by simulating future curtailment scenarios of Kyushu to extract the factors that affect curtailment. Fourier analysis was used to identify the periodicity of demand and solar production. The Fourier representation was simplified using the identified factors. Along with statistical data, the demand and solar data were approximated and the curtailment was reproduced. Results show that curtailment can be closely reproduced using the proposed methodology on a yearly and monthly level. Further research is necessary to test the methodology for other conditions like having different climate, varying daily fluctuations, and other human-related fluctuations

A review of community-based solar home system projects in the Philippines

Solar Home Systems (SHS) are easy to deploy in island and in remote communities where grid connection is costly. However, issues related to maintenance of these systems emerge after they are deployed because of the remoteness and inaccessibility of the communities. This study looked into community-based programs in the Philippines and investigated the following: (1) social preparation, (2) role of the community in the project, and (3) sustainability of the program. In this paper, three communities under two government programs offering SHS are presented. These programs are the Solar Power Technology Support (SPOTS) program of the Department of Agrarian Reform (DAR) and the Household Electrification Program (HEP) of the Department of Energy (DOE). A focused group discussion and key informant interviews were conducted in two communities in Bukidnon province and in a community in Kalinga to obtain information from the project beneficiaries and SHS users on the preparation, implementation and maintenance of the projects. The results revealed that emphasis on the economic value of the technology, proper training of the locals on the technical and management aspects of the project, as well as the establishment of a supply chain for replacement parts are crucial factors for the sustainability of the programs

Weather-Driven Scenario Analysis for Decommissioning Coal Power Plants in High PV Penetration Grids

Despite coal being one of the major contributors of CO2, it remains a cheap and stable source of electricity. However, several countries have turned to solar energy in their goal to “green” their energy generation. Solar energy has the potential to displace coal with support from natural gas. In this study, an hourly power flow analysis was conducted to understand the potential, limitations, and implications of using solar energy as a driver for decommissioning coal power plants. To ensure the results’ robustness, the study presents a straightforward weather-driven scenario analysis that utilizes historical weather and electricity demand to generate representative scenarios. This approach was tested in Japan’s southernmost region, since it represents a regional grid with high PV penetration and a fleet of coal plants older than 40 years. The results revealed that solar power could decommission 3.5 GW of the 7 GW coal capacity in Kyushu. It was discovered that beyond 12 GW, solar power could not reduce the minimum coal capacity, but it could still reduce coal generation. By increasing the solar capacity from 10 GW to 20 GW and the LNG quota from 10 TWh to 28 TWh, solar and LNG electricty generation could reduce the emissions by 37%, but the cost will increase by 5.6%. Results also show various ways to reduce emissions, making the balance between cost and CO2 a policy decision. The results emphasized that investing in solar power alone will not be enough, and another source of energy is necessary, especially for summer and winter. The weather-driven approach highlighted the importance of weather in the analysis, as it affected the results to varying degrees. The approach, with minor changes, could easily be replicated in other nations or regions provided that historical hourly temperature, irradiance, and demand data are available

Appliance recognition system for ILM using AGILASx — Dataset of common appliances in the Philippines

This study presents the development of a system which can automatically recognize home appliances based on a dataset of electric consumption profiles. The authors report the creation of AGILASx, a dataset of 50 common home appliances and devices in the Philippines. The dataset is populated with 100 appliance signatures in .XML format acquired using plug-based sensors. Each appliance signature consists of the following electric characteristics: real power (W), apparent power (VA), reactive power (var), RMS current (A), RMS voltage (V) and Power Factor (PF). A machine learning approach was utilized for the recognition experiment following a set of test protocols - intersession and unseen instances. The baseline recognition algorithm used was the k-Nearest Neighbor (k-NN) for both test protocols and accuracy levels were collected over three different acquisition frequencies. Using results of the confusion matrices, best results were observed at acquisition frequency of 10 -1 Hz for intersession (99%) and unseen instance (99%) test protocols. Lastly, to integrate the dataset and the recognition algorithm, a web application was developed adapting a Web-of-Things architecture to present a smart of recognized appliances and their corresponding consumption

Wireless real-time performance monitoring with fuzzy logic protection capability for power inverter

The increase of the demand in using renewable energy provides a significant increase of the use of the power inverter in the country. This study provides an opportunity to improve the reliability and capability of power inverters. The minimum and maximum values of the input parameters of the power inverter were characterized and were used as reference for the implementation of the fuzzy logic membership function. A wireless real-time monitoring system has been developed to monitor the critical parameters of the power inverter. It is composed of an Arduino compatible board, power sensors, voltage and current sensors, Zigbee wireless modules and a graphical user interface. For the power inverter protection, a fuzzy logic approach was implemented on the input side of the device. A fuzzy logic charge control is implemented to sustain the input parameters during the operation. Overall results of the study show an improvement on capability and reliability of the power inverter

Wireless power consumption monitoring and analysis system using Winter\u27s forecasting method

The Philippines is one of the rising economies in Southeast Asia. However, it is facing a power crisis where there is a continuous increases in an already huge demand in electricity given the limited and scare supply from different power sectors. Consumers are called to be aware of their power consumption and make necessary efforts towards the smart and efficient use of it. As a response, the proponents developed an appliance-level system that monitors and analyzes power consumption. The monitoring subsystem was implemented through a portable hardware black box which includes the power meter sensor, an Arduino microcontroller clone board and a ZigBee shield for wireless transmission to a microcomputer. The Raspberry Pi microcomputer served as a temporary local server for the sync node and the gateway for the data to be stored in an online database. From this, the analysis subsystem retrieves the consolidated data to undergo both retrospective and Winter\u27s forecasting technique. All necessary information, figures and graphs will be presented to the user for interpretation through a simple web application. Overall, the study fulfills its vision of giving people the power over their utility bills by being a tool for raising awareness towards responsibly reducing power consumption to a more efficient use

Development of an Android based Game Interfaced with QR Codes for a Gamified Power Management System

Filipinos are under the threat of an energy shortage. While efforts are being done both in the public and private sectors in developing alternative solutions to ease the shortage, it will take several years before this will come to full implementation. Conservation of energy through power management is the simplest and closest to implement at this time. Filipino households need to be motivated to use smart energy conservation. The introduction of Gamification as a method of motivations has found its use in many applications. In this study the possibility of Gamification to encourage the use of smart energy conservation is tested. In this study three game elements were used to develop the model for the game; these elements include incentives, competition and feedback. These three elements were implemented in an android game through a three step process; namely the Game Design where the theoretical framework of the game was created’ Game Implementation where the researchers create the functions for the game using the Java language and Eclipse IDE, and the Game Deployment where the researchers perform a bug test and advertise the project using Social Media and Live Advertising. The use of QR Codes was implemented for the participants to control switching on and off of appliances in the selected rooms

Appliance recognition using Hall Effect sensors and k-nearest neighbors for power management systems

Power management systems employ appliance recognition such that the burden of manually configuring the system for each appliance is lifted from the user. This research then aims to develop an appliance recognition functionality through current readings gathered from a data acquisition (DAQ) device consisting of Hall Effect current sensors, and through a machine learning classification algorithm called k-nearest neighbors. Ten appliances were tested, comprising of 6,500 samples of test data in the four outlets tested. The average accuracy for the trials is 92.73%. In addition, the appliance recognition functionality was embedded to a cloud-based power management system following an Internet of Things (IoT) architecture. In the end, the developed system can gather data from plugged appliances, perform recognition, and carry out various power management functionalities such as monitoring and appliance-level smart-recommendations

Hardware Implementation of an Iterative Parallel Scheduler for Optical Interconnection Networks

This paper proposes an iterative parallel scheduler for optical interconnection networks based on the longest queue first algorithm, presents an optimized hardware imple­mentation in commercial FPGA boards, and experimentally assess its performance

Hardware Implementation of an Iterative Parallel Scheduler for Optical Interconnection Networks

This paper proposes an iterative parallel scheduler for optical interconnection networks based on the longest queue first algorithm, presents an optimized hardware imple­mentation in commercial FPGA boards, and experimentally assess its performance