Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Optimal Coordinated Bidding Strategy of Wind and Solar System with Energy Storage in Day-ahead Market
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Affiliation:

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

Fund Project:

This work was supported by the National Natural Science Foundation of China (No. 51337005).

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    Abstract:

    Although wind and solar power is the major reliable renewable energy sources used in power grids, the fluctuation and unpredictability of these renewable energy sources require the use of ancillary services, thereby increasing the integration cost. This study proposes a wind, solar, and pumped-storage cooperative (WSPC) model that can be applied to large-scale systems connected to dispersed renewable energy sources. This model provides an optimized coordinated bidding strategy in the day-ahead market, along with a method to facilitate revenue distribution among participating members. This model takes advantage of the natural complementary characteristics of wind and solar power while using pumped storage to adjust the total output power. In the coordinated bidding strategy, a proportion of the energies is provided as firm power, which can lower the ancillary service requirement. Moreover, a multi-period firm power-providing mode is adopted to reflect the wind-solar output characteristics of each period accurately. The duration of each period is selected as a variable to accommodate seasonal characteristics. This ensures that the provision of firm power can maintain a high proportion under varied connected ratios of wind-solar, thereby obtaining higher revenue. By using the revenue distribution method, the short-term influencing factors of the cooperative model are considered to provide the economic characteristics of wind farms and photovoltaic stations. In this way, revenue distribution can be fairly realized among the participating members. Finally, the effectiveness and economy of the proposed model are validated based on actual data obtained from the power grid in California, USA.

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History
  • Received:January 21,2020
  • Revised:July 06,2020
  • Adopted:
  • Online: January 28,2022
  • Published: