Decentralized Coordination of Energy Utilization for Residential Households in the Smart Grid. Guo, Y., Pan, M., Fang, Y., & Khargonekar, P. P. IEEE Transactions on Smart Grid, 4(3):1341–1350, September, 2013. Conference Name: IEEE Transactions on Smart Griddoi abstract bibtex In this paper, we investigate the minimization of the total energy cost of multiple residential households in a smart grid neighborhood sharing a load serving entity. Specifically, each household may have renewable generation, energy storage as well as inelastic and elastic energy loads, and the load serving entity attempts to coordinate the energy consumption of these households in order to minimize the total energy cost within this neighborhood. The renewable generation, the energy demand arrival, and the energy cost function are all stochastic processes and evolve according to some, possibly unknown, probabilistic laws. We develop an online control algorithm, called Lyapunov-based cost minimization algorithm (LCMA), which jointly considers the energy management and demand management decisions. LCMA only needs to keep track of the current values of the underlying stochastic processes without requiring any knowledge of their statistics. Moreover, a decentralized algorithm to implement LCMA is also developed, which can preserve the privacy of individual household owners. Numerical results based on real-world trace data show that our control algorithm can effectively reduce the total energy cost in the neighborhood.
@article{guo_decentralized_2013,
title = {Decentralized {Coordination} of {Energy} {Utilization} for {Residential} {Households} in the {Smart} {Grid}},
volume = {4},
issn = {1949-3061},
doi = {10.1109/TSG.2013.2268581},
abstract = {In this paper, we investigate the minimization of the total energy cost of multiple residential households in a smart grid neighborhood sharing a load serving entity. Specifically, each household may have renewable generation, energy storage as well as inelastic and elastic energy loads, and the load serving entity attempts to coordinate the energy consumption of these households in order to minimize the total energy cost within this neighborhood. The renewable generation, the energy demand arrival, and the energy cost function are all stochastic processes and evolve according to some, possibly unknown, probabilistic laws. We develop an online control algorithm, called Lyapunov-based cost minimization algorithm (LCMA), which jointly considers the energy management and demand management decisions. LCMA only needs to keep track of the current values of the underlying stochastic processes without requiring any knowledge of their statistics. Moreover, a decentralized algorithm to implement LCMA is also developed, which can preserve the privacy of individual household owners. Numerical results based on real-world trace data show that our control algorithm can effectively reduce the total energy cost in the neighborhood.},
number = {3},
journal = {IEEE Transactions on Smart Grid},
author = {Guo, Y. and Pan, M. and Fang, Y. and Khargonekar, P. P.},
month = sep,
year = {2013},
note = {Conference Name: IEEE Transactions on Smart Grid},
keywords = {Algorithm design and analysis, Batteries, Demand response, Electricity, LCMA, Lyapunov matrix equations, Lyapunov optimization, Lyapunov-based cost minimization algorithm, Optimization, Pricing, Smart grids, cost reduction, decentralized coordination, demand management decisions, demand side management, elastic energy loads, energy cost function, energy demand arrival, energy management, energy management decisions, energy storage, energy utilization, inelastic and elastic energy loads, inelastic energy loads, load serving entity sharing, multiple residential households, online control algorithm, power system security, power utilisation, privacy preservation, probabilistic laws, probability, renewable generation, residential households, smart grid, smart grid neighborhood, smart power grids, stochastic processes, total energy cost minimization},
pages = {1341--1350},
}
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The renewable generation, the energy demand arrival, and the energy cost function are all stochastic processes and evolve according to some, possibly unknown, probabilistic laws. We develop an online control algorithm, called Lyapunov-based cost minimization algorithm (LCMA), which jointly considers the energy management and demand management decisions. LCMA only needs to keep track of the current values of the underlying stochastic processes without requiring any knowledge of their statistics. Moreover, a decentralized algorithm to implement LCMA is also developed, which can preserve the privacy of individual household owners. 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Specifically, each household may have renewable generation, energy storage as well as inelastic and elastic energy loads, and the load serving entity attempts to coordinate the energy consumption of these households in order to minimize the total energy cost within this neighborhood. The renewable generation, the energy demand arrival, and the energy cost function are all stochastic processes and evolve according to some, possibly unknown, probabilistic laws. We develop an online control algorithm, called Lyapunov-based cost minimization algorithm (LCMA), which jointly considers the energy management and demand management decisions. LCMA only needs to keep track of the current values of the underlying stochastic processes without requiring any knowledge of their statistics. Moreover, a decentralized algorithm to implement LCMA is also developed, which can preserve the privacy of individual household owners. 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