Distributed model predictive control for joint coordination of demand response and optimal power flow with renewables in smart grid

Demand response is an emerging application of smart grid in exploiting timely interactions between utilities and their customers to improve the reliability and sustainability of power networks. This paper investigates the joint coordination of demand response and AC optimal power flow with curtailme...

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Vydáno v:Applied energy Ročník 290; s. 116701
Hlavní autoři: Shi, Ye, Tuan, Hoang Duong, Savkin, Andrey V., Lin, Chin-Teng, Zhu, Jian Guo, Poor, H. Vincent
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.05.2021
Témata:
algorithms
Demand response
Distributed model predictive control
electrical equipment
energy
Nonconvex optimization
Optimal power flow
power generation
Renewable energy resources
renewable energy sources
Smart grid
statistics
system optimization
Distributed model predictive control
Demand response
Nonconvex optimization
Optimal power flow
Smart grid
Renewable energy resources
ISSN:0306-2619, 1872-9118
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Shrnutí:Demand response is an emerging application of smart grid in exploiting timely interactions between utilities and their customers to improve the reliability and sustainability of power networks. This paper investigates the joint coordination of demand response and AC optimal power flow with curtailment of renewable energy resources to not only save the total amount of power generation costs, renewable energy curtailment costs and price-elastic demand costs but also manage the fluctuation of the overall power load under various types of demand response constraints and grid operational constraints. Its online implementation is very challenging since the future power demand is unpredictable with unknown statistics. Centralized and distributed model predictive control (CMPC and DMPC)-based methods are respectively proposed for the centralized and distributed computation of the online scheduling problem. The CMPC can provide a baseline solution for the DMPC. The DMPC is quite challenging that invokes distributed computation of a nonconvex optimization problem at each time slot. A novel alternating direction method of multipliers (ADMM)-based DMPC algorithm is proposed for this challenging DMPC. It involves an iterative subroutine computation during the update procedure of primal variables that can efficiently handle the difficult nonconvex constraints. Comprehensive experiments have been conducted to test the proposed methods. Simulation results show that the gap in objective values between the DMPC and its baseline counterpart (CMPC) are all within 1%, further verifying the effectiveness of the proposed ADMM-based DMPC algorithm. •Joint coordination of diversified demand response and AC optimal power flow is studied.•Total economical costs and the fluctuation of overall power load are minimized.•High penetration of renewable energy is considered in smart grid.•A novel distributed model predictive control is developed for solution.
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2021.116701