Charge Scheduling of an Energy Storage System under Time-of-Use Pricing and a Demand Charge

Yoon, Yourim; Kim, Yong-Hyuk

doi:10.1155/2014/937329

Cited by 38 publications

(22 citation statements)

References 32 publications

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“…The author in [16] has pitched the logical solution to beating the on-peak hour charges. A similar approach has been adopted in this thesis as well.…”

Section: Related Workmentioning

confidence: 99%

“…A similar approach has been adopted in this thesis as well. The conditions again differed from [16]. The idea for the battery control algorithm was primarily from interactions with the author of [3].…”

Section: Related Workmentioning

confidence: 99%

“…REMEMBER!! pinsUsed = (5,33,23,15,3,40,38,18,16,12) # Initially setting up these pins on the Raspberry Pi. for pin in pinsUsed: string = "GPIO.setup(" + str(pin) + ", GPIO.OUT)\n" file.write(string) file.write("\n\n") # Initially setting everything low.…”

Section: A Month Clustering Detailsmentioning

confidence: 99%

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Application of Machine Learning Algorithm to Forecast Load and Development of a Battery Control Algorithm to Optimize PV System Performance in Phoenix, Arizona

Hariharan

Karady

Dickinson³

2018

2018 North American Power Symposium (NAPS)

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The students of Arizona State University, under the mentorship of Dr George Karady, have been collaborating with Salt River Project (SRP), a major power utility in the state of Arizona, trying to study and optimize a battery-supported grid-tied rooftop Photovoltaic (PV) system, sold by a commercial vendor. SRP believes this system has the potential to satisfy the needs of its customers, who opt for utilizing solar power to partially satisfy their power needs.An important part of this elaborate project is the development of a new load forecasting algorithm and a better control strategy for the optimized utilization of the storage system. The built-in algorithm of this commercial unit uses simple forecasting and battery control strategies. With the recent improvement in Machine Learning (ML) techniques, development of a more sophisticated model of the problem in hand was possible. This research is aimed at achieving the goal by utilizing the appropriate ML techniques to better model the problem, which will essentially result in a better solution. In this research, a set of six unique features are used to model the load forecasting problem and different ML algorithms are simulated on the developed model. A similar approach is taken to solve the PV prediction problem. Finally, a very effective battery control strategy is built (utilizing the results of the load and PV forecasting), with the aim of ensuring a reduction in the amount of energy consumed from the grid during the "on-peak" hours. Apart from the reduction in the energy consumption, this battery control algorithm decelerates the "cycling aging" or the aging of the battery owing to the charge/dis-charges cycles endured by selectively charging/dis-charging the battery based on need.ii The results of this proposed strategy are verified using a hardware implementation (the PV system was coupled with a custom-built load bank and this setup was used to simulate a house). The results pertaining to the performances of the built-in algorithm and the ML algorithm are compared and the economic analysis is performed. The findings of this research are in the process of being published in a reputed journal.

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“…The author in [16] has pitched the logical solution to beating the on-peak hour charges. A similar approach has been adopted in this thesis as well.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Application of Machine Learning Algorithm to Forecast Load and Development of a Battery Control Algorithm to Optimize PV System Performance in Phoenix, Arizona

Hariharan

Karady

Dickinson³

2018

2018 North American Power Symposium (NAPS)

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“…Energy storage systems are key components in green solutions because of their ability to harness renewable energy and exploit variations in electricity price. The work [7] applies a genetic algorithm to schedule charging of a single energy storage system for an electricity consumer subject to T.O.U. pricing and access to renewable energy.…”

Section: Related Workmentioning

confidence: 99%

Energy storage management in core networks with renewable energy in time-of-use pricing environments

Abji

Tizghadam

Leon-Garcia

2015

2015 IEEE International Conference on Communications (ICC)

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We consider the minimization of electricity cost of a core network where each node has access to solar renewable energy and energy storage in a time-of-use pricing environment. Using an optimization based approach we demonstrate that expenditure on electricity can be reduced by 60% through an effective energy management policy. We also present a distributed, greedy energy management algorithm, which makes hourly electricity purchase and energy storage decisions at each of the nodes in the network and performs close to the optimal case. Finally, we measure the impact of parameters including solar panel size, energy storage capacity and storage charging rate as well as seasonal variations of solar energy on the service provider's expenditure on electricity.

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“…In spare spaces like warehouses, an ESS for stabilizing the fluctuation of the PV generation system or adjusting the peak load of the smart home is installed [19,20]. Although interests in ESSs are rising for the self-reliance of smart home such as zero energy house, most of them are controlled according to time schedule or used in conjunction with PV generation systems [21,22]. In addition, a Home Energy Management System (HEMS) is installed for energy reduction and efficient use of smart home [23].…”

Section: Introductionmentioning

confidence: 99%

Energy Storage System Control Algorithm by Operating Target Power to Improve Energy Sustainability of Smart Home

et al. 2018

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Abstract:As energy issues are emerging around the world, a variety of smart home technologies aimed at realizing zero energy houses are being introduced. Energy storage system (ESS) for smart home energy independence is increasingly gaining interest. However, limitations exist in that most of them are controlled according to time schedules or used in conjunction with photovoltaic (PV) generation systems. In consideration of load usage patterns and PV generation of smart home, this study proposes an ESS control algorithm that uses constant energy of energy network while making maximum use of ESS. Constant energy means that the load consumes a certain amount of power under all conditions, which translates to low variability. The proposed algorithm makes a smart home a load of energy network with low uncertainty and complexity. The simulation results show that the optimal ESS operating target power not only makes the smart home use power constantly from the energy network, but also maximizes utilization of the ESS. In addition, since the smart home is a load that uses constant energy, it has the advantage of being able to operate an efficient energy network from the viewpoint of energy providers.

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Charge Scheduling of an Energy Storage System under Time-of-Use Pricing and a Demand Charge

Cited by 38 publications

References 32 publications

Application of Machine Learning Algorithm to Forecast Load and Development of a Battery Control Algorithm to Optimize PV System Performance in Phoenix, Arizona

Application of Machine Learning Algorithm to Forecast Load and Development of a Battery Control Algorithm to Optimize PV System Performance in Phoenix, Arizona

Energy storage management in core networks with renewable energy in time-of-use pricing environments

Energy Storage System Control Algorithm by Operating Target Power to Improve Energy Sustainability of Smart Home

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