'Market penetration and pay-back period analysis of a solar photovoltaic system under Indian conditions'

The use of pay-back period analysis for economic evaluation of solar photovoltaic (PV) system reinforces the importance of the duration of the system. In a dynamic economic environment, the cost of energy increases at a faster rate than the common inflation rate. A time can be ascertained at which the market entry of the PV system will be profitable, i.e. at which the pay-back time drops below a value considered as the market threshold, provided the parameters describing the dynamic economic system remain unchanged. The market penetration of the PV system has been determined in Indian economic conditions and found to depend mainly on PV array costs and energy income reinvestment rate. The low PV array cost, high-energy income reinvestment rate, high solar cell reference efficiency and high battery efficiency have a substantial effect on the reduction of the energy price and pay-back period with early market penetration by the PV system. Keywords: photovoltaic (PV) system; pay-back period; market penetration; renewable energy economics.renewable energy economics, pay-back period, market penetration, solar photovoltaic (PV) system.

Impacts of electricity pricing on techno-economic performance of photovoltaic-battery centered microgrid

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Relative evaluation of regression tools for urban area electrical energy demand forecasting

Load forecasting is the most fundamental application in Smart-Grid, which provides essential input to Demand Response, Topology Optimization and Abnormally Detection, facilitating the integration of intermittent clean energy sources. In this work, several regression tools are analyzed using larger datasets for urban area electrical load forecasting. The regression tools which are used are Random Forest Regressor, k-Nearest Neighbour Regressor and Linear Regressor. This work explores the use of regression tool for regional electric load forecasting by correlating lower distinctive categorical level (season, day of the week) and weather parameters. The regression analysis has been done on continuous time basis as well as vertical time axis approach. The vertical time approach is considering a sample time period (e.g seasonally and weekly) of data for four years and has been tested for the same time period for the consecutive year. This work has uniqueness in electrical demand forecasting using regression tools through vertical approach and it also considers the impact of meteorological parameters. This vertical approach uses less amount of data compare to continuous time-series as well as neural network techniques. A correlation study, where both the Pearson method and visual inspection, of the vertical approach depicts meaningful relation between pre-processing of data, test methods and results, for the regressors examined through Mean Absolute Percentage Error (MAPE). By examining the structure of various regressors they are compared for the lowest MAPE. Random Forest Regressor provides better short-term load prediction (30 min) and kNN offers relatively better long-term load prediction (24 h).acceptedVersio

Evaluating Anomaly Detection Algorithms through different Grid scenarios using k-Nearest Neighbor, iforest and Local Outlier Factor

Author's accepted manuscript© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage. The available advanced information and communicating platform and computational capability renders smart grid prone to attacks with extreme social, financial and physical effects. The smart network enables energy management of smart appliances contributing support for ancillary services. Cyber threats could affect operation of smart appliances and hence the ancillary services, which might lead to stability and security issues. In this work, an overview is presented of different methods used in anomaly detection, performance evaluation of 3 models, the k-Nearest Neighbor, local outlier factor and isolated forest on recorded smart meter data from urban area and rural regionacceptedVersio

Adaptive energy management strategy for sustainable voltage control of PV-hydro-battery integrated DC microgrid

DC microgrid has relatively more advantages of power quality, not requirement of reactive power, higher operational efficiency compare to AC microgrid. DC microgrid can facilitate effective integration of distributed clean energy resources and efficient solution for providing electricity to remote areas (e.g. North Eastern States of India). Recently, India has commissioned small hydro, solar PV and battery storage integrated DC microgrids (MGs) to meet the locally increasing load demand of northeastern states. The sudden change in solar insolation during the load power dynamics can cause unbalanced power flow in such isolated MGs. Due to the slow response time of small hydro power plant (SHPP) and limited output power of battery storage with fixed C-rate, the unbalanced power flow, during load power dynamics, cannot be compensated. The unbalanced power flow may lead to unsustainable voltage control at the DC bus of MG. To prevent this, MG follows load shedding. But, load shedding reduces the reliability of MG. To achieve the sustainable voltage control of DC MG, a smart adaptive energy management strategy (AEMS) is proposed in this research work. The novel aspect of proposed AEMS is that it operates the SHPP despite its slow response time by estimating the load power dynamics on the iterative basis. The deep charging/deep discharging scenario of battery storage due to mismatch between the total generation with estimated load and the actual load is taken care by the adjustable energy controller of proposed AEMS. To justify the potential contributions of proposed AEMS, it is assessed against various dynamic test load cases. Based on the assessment of obtained results against various test load cases, in this work, a comparative analysis is carried out between the proposed AEMS and the existing control strategies in the literature. The comparative analysis reveals that with the proposed AEMS, voltage sustainability of MG is improved by 22.7% and the utilization factor of SHPP is enhanced by 55.27% with 98.17% reduction in current stress levels of battery storage system. Finally, the proposed AEMS is evaluated in MATLAB/Simulink as well as validated through OPAL-RT real time simulator.publishedVersio

Power dispatching techniques as a finite state machine for a standalone photovoltaic system with a hybrid energy storage

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Placement analysis of combined renewable and conventional distributed energy resources within a radial distribution network

System islanding, relay tripping, and reverse power flow-like issues in the distribution network are all caused by randomly placed distributed energy resources. To minimize such problems, distributed energy resource (DER) optimal placement in the radial distribution network (RDN) is essential to reduce power loss and enhance the voltage profile. When placing DERs, consideration of constraints like size, location, number, type, and power factor (PF) should be considered. For optimal placement, renewable and nonrenewable DERs are considered. The effects of different types and PFs of DER placements have been tested on the IEEE 33 bus RDN to satisfy all limitations. Using various intelligent techniques, distributed energy resource units of optimal type, PF, size, quantity, and position were placed in the IEEE 33 bus RDN. These intelligent strategies for minimizing power loss, enhancing the voltage profile, and increasing the convergence rate are based on an adaptive neuro-fuzzy inference system, a genetic algorithm, and enhanced particle swarm optimization.publishedVersio

Impact of Separator Thickness on Temperature Distribution in Single Cell of Polymer Electrolyte Fuel Cell Operated at Higher Temperature of 90 °C and 100 °C

The New Energy and Industry Technology Development Organization (NEDO) road map (Japan, 2017) has proposed that a polymer electrolyte fuel cell (PEFC) system, which operates at a temperature of 90 °C and 100 °C, be applied for stationary and mobility usage, respectively. This study suggests using a thin polymer electrolyte membrane (PEM) and a thin gas diffusion layer (GDL), at the same time, to achieve better power-generation performance, at a higher temperature than usual. The focus of this paper is to clarify the effect of separator thickness on the distribution of temperature at the reaction surface (Treact), with the relative humidity (RH) of the supply gasses and initial operation temperature (Tini), quantitatively. In this study, separator thickness is investigated in a system using a thin PEM and a thin GDL. Moreover, this study investigates the difference between the maximum temperature and the minimum temperature obtained from the distribution of Treact as well as the relation between the standard deviation of Treact − Tini and total voltage, to clarify the effect of separator thickness. The impact of the flow rates of the supply gases on the distribution of Treact is not large, among the investigated conditions. It is noticed that the temperature distribution is wider when a separator thickness of 2.0 mm is selected. On the other hand, it is observed that the temperature increases along with the gas flow through the gas channel, by approximately 2 °C, when using a separator thickness between 1.5 mm and 1.0 mm. The impact of the RH on the distributions of Treact − Tini is larger at Tini = 100 °C, when a separator thickness of 1.0 mm is selected. It is revealed that the wider temperature distribution provides a reduction in power-generation performance. This study proposes that the thin separators, i.e., with a thickness of 1.5 mm and 1.0 mm, are not suitable for higher temperature operation than usual.publishedVersio