Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Optimization of Distributed Solar Photovoltaic Power Generation in Day-ahead Electricity Market Incorporating Irradiance Uncertainty
Author:
Affiliation:

1.Department of Electrical Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India;2.Department of Electrical Engineering, Guru Nanak Dev Engineering College, Ludhiana, India (affiliated to I. K. Gujral Punjab Technical University, Kapurthala, India);3.Department of Electrical Engineering, Delhi Technological University, Delhi, India

Fund Project:

A. Singla extends her gratitude to I. K. Gujral Punjab Technical University, Kapurthala, India, for providing a platform to pursue Ph.D. programme in electrical engineering. Authors are obliged and sincerely thank the authorities of Punjab Energy Development Agency (PEDA), Chandigarh, India, for sharing technical details of photovoltaic power generator which was vital and is incorporated in the present work.

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    Abstract:

    This paper proposes a simple and practical approach to model the uncertainty of solar irradiance and determines the optimized day-ahead (DA) schedule of electricity market. The problem formulation incorporates the power output of distributed solar photovoltaic generator (DSPVG) and forecasted load demands with a specified level of certainty. The proposed approach determines the certainty levels of the random variables (solar irradiance and forecasted load demand) from their probability density function curves. In this process of optimization, the energy storage system (ESS) has also been modeled based on the fact that the energy stored during low locational marginal price (LMP) periods and dispatched during high LMP periods would strengthen the economy of DA schedule. The objective of the formulated non-linear optimization problem is to maximize the social welfare of market participants, which incorporates the assured generation outputs of DSPVG, subject to real and reactive power balance and transmission capability constraints of the system and charging/discharging and energy storage constraints of ESS. The simulation has been performed on the Indian utility 62-bus system. The results are presented with a large number of cases to demonstrate the effectiveness of the proposed approach for the efficient, economic and reliable operation of DA electricity markets.

    表 7 Table 7
    表 2 Table 2
    表 5 Table 5
    图1 切纸机工作过程示意图Fig.1 Schematic diagram of the working process of the paper cutter
    图2 送纸辊电机速度闭环控制系统Fig.2 Closed loop control system of paper feed roller motor speed
    图3 切纸机伺服控制连接图Fig.3 Paper cutter servo control connection diagram
    图4 M&T法测速波形图Fig.4 M&T method speed measurement waveform
    图5 送纸辊控制流程图Fig.5 Control flow chart of paper feed roller
    图6 切纸辊控制流程图Fig.6 Flow chart of paper cutting roller control
    图8 送纸辊速度图Fig.8 The speed of feed roller
    图9 切纸辊速度图Fig.9 The speed of cutting roller
    图10 实际切纸长度图Fig.10 Actual length cut
    图1 Basic electricity market structure and area of concern of this paper.Fig.1
    图2 A typical beta PDF of solar irradiance.Fig.2
    图5 Flowchart for developing DA market dispatching schedule incorporating hourly schedule of SPP and constraints of ESS under two options.Fig.5
    图6 DSPVG power outputs for different certainty levels.Fig.6
    图8 Hourly load curve at bus 15.Fig.8
    图10 Effect of injecting power by DSPVG+ESS on LMPs at bus 15.Fig.10
    图12 Variation of hourly LMPs with multiple DSPVGs.Fig.12
    图13 Hourly LMPs and impact of initial state of ESS on power drawing schedule during 00:00 to 06:00 hours at bus 15 in Case 3A with certainty of 50%. (a) Hourly LMPs. (b) Impact of initial state of ESS on power draw schedule.Fig.13
    图3 Beta PDF of irradiance at 15:00 indicating spmt for certainty level of 40%.Fig.3
    图4 Gaussian and cumulative density functions. (a) A typical Gaussian density function fgt(zt) of zt. (b) Cumulative density function of zt.Fig.4
    图7 Hourly schedule of Psgridkt for different certainty levels in option 1.Fig.7
    图9 Psgridkt injection schedule at bus 15 in Case 3A (option 2).Fig.9
    图11 Variation in Psgridkt of spatially distributed SPV generators. (a) Case 4A. (b) Case 4B.Fig.11
    表 3 Table 3
    表 4 Table 4
    表 8 Table 8
    表 9 Table 9
    表 6 Table 6
    表 1 Table 1
    表 10 Table 10
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History
  • Received:March 13,2019
  • Online: May 19,2021