Demand Response Model for Duck Curve on Pv Dominated System using Support Vector Machines Based Multistage Modelling
C.R. Sarin1, Geetha Mani2, S.Albert Alexander3

1C.R.Sarin ,M.Tech, MBA, (Ph.D), Research Scholar, School of Electrical Engineering, VIT, Vellore, Tamilnadu, India.
2Geetha Mani M.E, Ph.D, Associate Professor, School of Electrical Engineering, VIT, Vellore, Tamilnadu, India.
3S.Albert Alexander M.E,Ph.D.,PDF (USA),SMIEEE, Associate Professor, Department of Electrical and Electronics Engineering, Kongu Engineering College, Perundurai, Tamilnadu, India.
Manuscript received on September 13, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 5272-5283 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2957109119/2019©BEIESP | DOI: 10.35940/ijeat.A2957.109119
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Abstract: Solar Photovoltaic (PV) generation systems have a less Levelized cost of electricity (LCoE). As such, when solar energy is available, the demand response is scheduled in such a way that maximum utilization of solar energy is practised.But the power generation from a solar PV system is highly uncertain and unpredictable due to irregular solar irradiation. Also, the power 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 an analytical curve called “Duck curve”. “Solar Duck curve” is a graphical representation of time scaled imbalances between a SPV generation to peak demand. A steep or rugged part in a duck curve indicates sudden shortcoming of SPV generation with respect to the peak demand. Hence, during this period, the loads are shifted between solar PV sources and the main grid with respect to the insufficiency of solar power from peak demand. The proposed system is a machine learning-based multistage demand response system for meeting demand response of a SPV dominant duck curve. The model has four layers/stages.The primary layer is used to analyse the behaviour of the duck curve with the help of a Support Vector regression algorithm and the second layer is used for determining theoperating parameters based on the economic constraints imposed. The third layer is a demand response model based on the previous layer, and the fourth layer is aadaptive signal-processing model used to improve the stability of the system.The obtained demand response model is updated continuously in an adaptive manner so as to improve the stability of the system.A hardware experimental setup is made with eighteen numbers of 24V/2kW interconnected solar PV real-time system which is used for validating and analysing the method.
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 𝑃𝑆𝑃𝑉𝑛- Solar power generated on the nth bus of the grid 𝑃𝑆𝑃𝑉,𝑃𝑒𝑎𝑘𝑛 – Peak Solar power generated on the nth bus of grid 𝑃𝑀𝐺,𝑃𝑒𝑎𝑘𝑛 – Peak power share contributed on the nth bus of grid by Microgrid 𝑃𝐿,𝑃𝑒𝑎𝑘𝑛 – Peak load share on the nth bus of grid cfoc – Cost function of the Operating cost