'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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Combined Heat and Power Economic Dispatching within Energy Network using Hybrid Metaheuristic Technique

Combined heat and power (CHP) plants have opportunities to work as distributed power generation for providing heat and power demand. Furthermore, CHP plants contribute effectively to overcoming the intermittence of renewable energy sources as well as load dynamics. CHP plants need optimal solution(s) for providing electrical and heat energy demand simultaneously within the smart network environment. CHP or cogeneration plant operations need appropriate techno-economic dispatching of combined heat and power with minimising produced energy cost. The interrelationship between heat and power development in a CHP unit, the valve point loading effect, and forbidden working regions of a thermal power plant make the CHP economic dispatch’s (CHPED) objective function discontinuous. It adds complexity in the CHPED optimisation process. The key objective of the CHPED is operating cost minimisation while meeting the desired power and heat demand. To optimise the dispatch operation, three different algorithms, like Jaya algorithm, Rao 3 algorithm, and hybrid CHPED algorithm (based on first two) are adopted containing different equality and inequality restrictions of generating units. The hybrid CHPED algorithm is developed by the authors, and it can handle all of the constraints. The success of the suggested algorithms is assessed on two test systems; 5-units and 24-unit power plants.publishedVersio

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

Vertical Approach Anomaly Detection Using Local Outlier Factor

Author's accepted manuscriptDetection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage. In addition anomaly detection can be used as a tool to detect unwanted outliers, caused by operational failures and technical faults, for the pre-processing of data for machine learning, to detect concept drift as well as enhancing cyber-security in smart electrical grid operations. It is known that anomalies are defined through their contextual appearance. Hence, anomalies are divided into point, conceptual and contextual anomalies. In this work the contextual anomaly detection is examined, through a novel type of load forecasting known as vertical approach. This chapter explores the use of anomaly detection in the relevant learning systems for machine learning in smart electrical grid operation and management through data from New South Wales region in Australia. The presented vertical time approach uses seasonal data for training and inference, as opposed to continuous time approach that utilizes all data in a continuum from the start of the dataset until the time used for inference. It is observed that Local Outlier Factor identifies different local outliers given different vertical approaches. In addition, the local outlier factor score vary vertically. An anomaly is defined as a deviation from an established normal pattern. Spotting an anomaly depends on the ability to defy what is normal. Anomaly detection systems aim at finding these anomalies. Anomaly detection systems are in high demand, despite the fact that there is no clear validation approach. These systems rely on deep domain expertise.acceptedVersio

Review of the Hydrogen Evolution Reaction—A Basic Approach

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Blockchain-based trust management and authentication of devices in smart grid

The digitalization of the power grid and advancement in intelligent technologies have enabled the service provider to convert the existing electrical grid into a smart grid. The transformation of the grid will help in integrating cleaner energy technologies with energy management to improve power network efficiency. Internet of things (IoT) and various network components need to be deployed to harness the full potential of the smart grid. Also, integrating intermittent renewable energy sources, energy storage, intelligent control of selected power-intensive loads, etc will improve energy efficiency. But deployment of this information and communication technologies will make the grid more vulnerable to cyber attacks from hackers. In this work, blockchain-based self-sovereign identification and authentication technique is presented to avert identity theft and masquerading. The proposed approach can minimize the chances of identity-based security breaches in the smart grid. This paper provides an overview of the model of identification and authentication of IoT devices in Smart Grid based on Blockchain technology. The Blockchain based implementation of identification and authentication of devices is proposed to validate the model in the distributed electrical energy network. The model is able to authenticate the device using Blockchain in a trusted model. The system works according to plan validating the authenticity of transaction in a node in log(n) time, which justifies presented result.publishedVersio

Fuzzy logic controller and game theory based distributed energy resources allocation

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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