DOI:10.1007/s40565-017-0364-2 |
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Game-theoretic energy management with storage capacityoptimization in the smart grids |
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Net amount: 839 |
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Author:
Bingtuan GAO1
, Xiaofeng LIU1
, Cheng WU1
, Yi TANG1
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Author Affiliation:
1. School of Electrical Engineering, Southeast University,
Nanjing 210096, China
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Foundation: |
This work was supported by the National Science
Foundation of China (No. 51577030), the Excellent Young
Teachers Program of Southeast University (No. 2242015R30024),
and Six Talent Peaks Project of Jiangsu Province (No. 2014-ZBZZ-
001). |
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Abstract: |
With the development of smart grids, a renewable
energy generation system has been introduced into a
smart house. The generation system usually supplies a
storage system with the capability to store the produced
energy for satisfying a user’s future demand. In this paper,
the main objective is to determine the best strategies of
energy consumption and optimal storage capacities for
residential users, which are both closely related to the
energy cost of the users. Energy management with storage
capacity optimization is studied by considering the cost of
renewable energy generation, depreciation cost of storage
and bidirectional energy trading. To minimize the cost to
residential users, the non-cooperative game-theoretic
method is employed to formulate the model that combines
energy consumption and storage capacity optimization.
The distributed algorithm is presented to understand the
Nash equilibrium which can guarantee Pareto optimality in
terms of minimizing the energy cost. Simulation results
show that the proposed game approach can significantly
benefit residential users. Furthermore, it also contributes to
reducing the peak-to-average ratio (PAR) of overall energy
demand. |
Keywords: |
Demand-side management, Non-cooperative
game, Nash equilibrium, Storage capacity optimization,
Energy consumption scheduling |
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Online Time:2018/07/20 |
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