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Demand Response Model for Duck Curve on Pv Dominated System Using Support Vector Machines Based Multistage Modelling

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Demand Response Model for Duck Curve on Pv Dominated System Using Support Vector Machines Based Multistage Modelling International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-1, October 2019 Demand Response Model for Duck Curve on Pv Dominated System using Support Vector Machines Based Multistage Modelling C.R.Sarin, Geetha Mani, S.Albert Alexander Abstract: Solar Photovoltaic (PV) generation systems have a cffc - Cost function of the Fixed cost less Levelized cost of electricity (LCoE). As such, when solar cftc - Cost function of the Transmission cost energy is available, the demand response is scheduled in such a x - Load factor way that maximum utilization of solar energy is practised.But the afc- effective fixed cost factor power generation from a solar PV system is highly uncertain and unpredictable due to irregular solar irradiation. Also, the power aoc - effective operating cost factor generation is limited to a time fraction of a day.The impact of these negative traits in a power system is studied with the help of I. INTRODUCTION an analytical curve called “Duck curve”. “Solar Duck curve” is a Swanson's law states that the price of a solar photovoltaic graphical representation of time scaled imbalances between a SPV generation to peak demand. A steep or rugged part in a duck module tends to drop twenty percentages for every curve indicates sudden shortcoming of SPV generation with cumulative shipped volume [1][2]. As per International respect to the peak demand. Hence, during this period, the loads Renewable Energy Agency (IRENA) reports, the Global are shifted between solar PV sources and the main grid with Levelized cost of electricity (GLCE) on utility-scale respect to the insufficiency of solar power from peak demand. perspectives for renewable power generation technologies is The proposed system is a machine learning-based multistage shown in Fig 1. It could be noted that the GLCE of Solar demand response system for meeting demand response of a SPV dominant duck curve. The model has four layers/stages.The PV generation system (SPVGS) has decreased up to 400% primary layer is used to analyse the behaviour of the duck curve between 2010-17 [3][4]. As of now, solar PV systems are with the help of a Support Vector regression algorithm and the having the decidedly less Global Levelized cost of second layer is used for determining theoperating parameters electricity (GLCE) compared to other renewable energy based on the economic constraints imposed. The third layer is a sources [5]. This has accelerated the installation of a very demand response model based on the previous layer, and the high number of solar systems for the electric power fourth layer is aadaptive signal-processing model used to improve the stability of the system.The obtained demand response model is generation. In a grid, if a significant share of power is updated continuously in an adaptive manner so as to improve the generated from the solar PV sources, such grids are labelled stability of the system.A hardware experimental setup is made as solar PV dominant grids [6]. Economic constraints and with eighteen numbers of 24V/2kW interconnected solar PV real- robust demand response are two significant issues to be time system which is used for validating and analysing the considered in the demand response of a solar PV dominant method. grid. Keywords: Demand response, Machine learning, Microgrid, Solar PV. Nomenclature: PSPV, Peak - Total Peak Power share of Solar PV systems PMG- Power share of Main grid PMG,Peak- Peak power share of Maingrid PL - Total load connected to Microgrid PL,Peak - Total peak load connected to Microgrid Xl - Load share between the Main grid and Solar PV Modules 푛 th 푃푆푃푉 - Solar power generated on the n bus of the grid 푛 th 푃푆푃푉,푃푒푎푘 - Peak Solar power generated on the n bus of grid 푛 th 푃푀퐺 ,푃푒푎 푘 - Peak power share contributed on the n bus of grid by Microgrid 푛 th 푃퐿,푃푒푎푘 - Peak load share on the n bus of grid cf - Cost function of the Operating cost oc Fig1 : Comparison of GLCE of various Renewable energy sources (2010-17) Source : IRENA RE cost Revised Manuscript Received on 14September, 2019. database C.R.Sarin ,M.Tech, MBA, (Ph.D), Research Scholar, School of Electrical Engineering,VIT,Vellore, Tamilnadu, India. Geetha Mani M.E, Ph.D, Associate Professor, School of Electrical Engineering, VIT,Vellore, Tamilnadu, India. S.Albert AlexanderM.E,Ph.D.,PDF (USA),SMIEEE, Associate Professor, Department of Electrical and Electronics Engineering, Kongu Engineering College, Perundurai, Tamilnadu, India. Published By: Retrieval Number: A2957109119/2019©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijeat.A2957.109119 5272 & Sciences Publication Demand Response Model for Duck Curve on Pv Dominated System using Support Vector Machines Based Multistage Modelling 1.1. Economic considerations on grid and user power between the grid and SPVGS becomes more perspectives complicated in this area. The economic aspects of the demand response should be The developed system described in this paper is a demand modelled by considering both user and grid aspects. In a response model focusing on the duck curve. The system has grid-connected solar PV model, if a solar PV generation two objectives. The primary objective of the system is to system is producing an excess of energy after meeting the determine the demand response model for a duck curve local demand, the surplus power is fed back to the grid [7]. where the stability is taken as the primary criteria [19]. If a solar PV generation system (SPVGS) is unable to Instead of performing an instantaneous response, a gradual support local demand, additional power is obtained from the power shifting is performed. The secondary objective of the main grid. In all other circumstances, local SPVGS are system is to regulate the demand response in the perspective disconnected from the main grid [8]. But the unit tariff from of economic constraints [20]. The power-sharing is the grid side is determined by considering dynamic pricing scheduled by incorporating both fixed and dynamic pricing as well as fixed cost [9] [10]. The fixed cost includes the [21]. Support vector machine is a machine learning method installation cost and the depreciation whereas the dynamic which is used as a mathematical model to predict the pricing incorporates the operating costs. behaviour of a duck curve. Support vector regression The grid will be operating 24/7 which will cause a classifies data into groups by creating an imaginary hyper substantial operating cost [12]. However, the user will plane between data points. access the main grid if (and only if) the local SPVGS are This paper has six sections. The primary section provides inadequate of meeting the demand [13] [14]. In order to the technical analysis of grid-connected SPVGSs. It also economize the grid, the total dynamic priceas well as the analyses issues with demand response of grid-connected fixed price for the entire 24/7 operation, should be collected SPVGS. The second section explains the nature of the duck from the user even though the user is utilizing the grid only curve and the constraints for the demand response of a duck for a short time span [15]. This has increased the tariff at curve. The third part explains the experimental setup, grid level which caused accelerated installation of more hardware setup and methodologies used for the analysis. solar PV systems as they have lower tariffs.This will The fourth part focuses on the technical modelling and generate an economic burden on the main grid. However, setting up constraints for demand response. The fifth part the role of the grid cannot be eliminated because the solar includes the algorithm of the developed model which has panels are capable of producing power only for a fraction of three sections. The primary section focus on the behavioural a day. Subsequently, the demand response is to be scheduled study of the duck curve and the second section provides a by considering the best economic options. The developed demand response estimation with economic constraints. The model makes use of machine learning methods in order to tertiary section is the signal conditioning part which will find the best economic models of demand response. reschedule the demand response with respect to the stability constraints. The fourth section is the result and discussions. 1.2. Robust demand response. The power generation from a solar panel entirely depends II. EFFECT OF PV DOMINANCE on many parameters, such as solar energy irradiation, partial If the SPVGS are connected, for a load (PL), the power shading, panel soiling, the angle of inclination, etc. Most of share of the main grid ( ) is shown in equation 1 and Power these parameters are highly dynamic and hence the power share of the SPVGS( ) is shown in equation 2. The variable developed from a solar PV system is highly fluctuating, x is the sharing factor. disturbing, and convulsing. If the variations from SPVGS 푃 =푥 푃 1 are beyond some limits, this will affect the stability of the 푀퐺 푖 퐿 푃푆푃푉 =(푥푖 − 1) 푃퐿 2 power system [16]. If power generation from SPVGS is less than the load demand, the load should be shifted to the main In a Solar PV dominant grid, a major share of the load is grid. As the solar power generation increases, demand energized using SPVGS as expressed in equation 3. response will be shifted from the main grid to SPVGS and 푃푆푃푉 ≥ 푃푀퐺 3 vice versa [17]. In both of the above cases, the power shift At any point in time, the primary criteria for the may be swift, rugged and accelerated. This will affect the calculation are that the total power consumption should be at stability of the system.
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