Scaling distributed energy storage for grid peak reduction

Mishra, Aditya; Irwin, David; Shenoy, Prashant; Zhu, Ting

doi:10.1145/2487166.2487168

Cited by 56 publications

(27 citation statements)

References 17 publications

(31 reference statements)

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“…The major benefits of the incorporation of DSM and battery storage systems are to shave the peak power demand and to increase renewable self-consumption, resulting in increased grid stability and reduced distribution system losses. There are two potential energy tariff programs: time-of-use (ToU) and real-time price (RTP) which can provide consumers the benefit of reducing their energy bill by deploying this system [8]. In this work, the RTP based directly on the wholesale market price is considered.…”

Section: Introductionmentioning

confidence: 99%

Community power flow control for peak demand reduction and energy cost savings

Pholboon

Sumner

Kounnos

2016

2016 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)

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Abstract-The increase in penetration of renewable energy sources, such as solar or wind, and high peak load demand can cause grid network security issues. The incorporation of demand side management and energy storage devices can provide a solution to these problems. This paper presents a community power flow control (PFC) strategy which reduces peak grid demand, and increases self-consumption of renewable energy which produces energy cost savings in smart communities with grid-connected photovoltaic (PV) systems. The PFC aims to directly control high power consumption appliances and the charge/discharge of a community battery storage using measurement of the instantaneous power demands of the community. Historical data records of the community daily energy consumption and the available renewable energy are taken into account to manage the loads and battery storage. Simulation results show for a community of one hundred houses, with 114 kWp of PV arrays, and a 350kWh battery system that the percentage of the average peak power demand reduction over the year is 32%,whilethePV energy selfconsumption increases by73%. This can produce an annual energy cost saving of up to £1100 when compared to the same community with only PV.Index Terms--Battery energy storage, demand side management, peak demand reduction, power flow control, PV system,

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Section: Introductionmentioning

confidence: 99%

Community power flow control for peak demand reduction and energy cost savings

Pholboon

Sumner

Kounnos

2016

2016 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)

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“…Flat-power pricing is particularly attractive for storage, since it takes much less storage capacity to flatten a home's demand than to shift all of its demand to the lowprice period. We explore this aspect of scheduling storage in recent work [13], although using a peak-based pricing plan based on a home's absolute peak. As we mention, one challenge with peakbased pricing is the difficulty in predicting when the absolute peak will occur.…”

Section: Discussionmentioning

confidence: 99%

“…However, peak-based pricing is not as conducive to algorithmic optimization, since it requires scheduling algorithms to know with high accuracy when the peak will occur each billing period. As part of prior work, we have found that accurately predicting when a peak will occur is challenging [13], hindering load scheduling algorithms that must optimize for the absolute peak. Consumers are typically charged based on their peak usage over an entire month-long billing period, requiring accurate predictions of power usage over a small time window, e.g., fifteen minutes, as far as one month in advance.…”

Section: Advantagesmentioning

confidence: 99%

Incentivizing Advanced Load Scheduling in Smart Homes

Irwin

Shenoy

2013

Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings

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In recent years, researchers have proposed numerous advanced load scheduling algorithms for smart homes with the goal of reducing the grid's peak power usage. In parallel, utilities have introduced variable rate pricing plans to incentivize residential consumers to shift more of their power usage to low-price, off-peak periods, also with the goal of reducing the grid's peak power usage. In this paper, we argue that variable rate pricing plans do not incentivize consumers to adopt advanced load scheduling algorithms. While beneficial to the grid, these algorithms do not significantly lower a consumer's electric bill. To address the problem, we propose flat-power pricing, which directly incentivizes consumers to flatten their own demand profile, rather than shift as much of their power usage as possible to low-cost, off-peak periods. Since most loads have only limited scheduling freedom, load scheduling algorithms often cannot shift much, if any, power usage to low-cost, off-peak periods, which are often many hours in the future. In contrast, flat-power pricing encourages consumers to shift power usage even over short time intervals to flatten demand. We evaluate the benefits of advanced load scheduling algorithms using flat-power pricing, showing that consumers save up to 40% on their electric bill, compared with 11% using an existing time-of-use rate plan.

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“…Vytelingum et al [5] and Mishra et al [22] presented a DSM mechanism which assumed the existence of energy storage on consumers' premises. Ganu et al developed a device called nPlug, which can perform scheduling and voltage sensing to identify peak and off-peak periods on the grid [23].…”

Section: Related Workmentioning

confidence: 99%

Effective consumption scheduling for demand-side management in the smart grid using non-uniform participation rate

Wijaya

Papaioannou

Liu

et al. 2013

2013 Sustainable Internet and ICT for Sustainability (SustainIT)

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Abstract-Periods of peak consumer demand in today's electricity sector are expensive to satisfy and can be the source of power failures. One possible solution is the use of demandside management (DSM) applying dynamic pricing mechanisms. However, instead of reducing peak loads, these mechanisms can lead to peak-shifting due to the herding effect of consumers' loadshifting behavior. To overcome this problem, we explore strategies of assigning (non-uniform) participation rates to consumers. We use a generic method to find a near-optimal distribution setting for participation rates. Our method allows DSM designers to tune the system toward consumer convenience. This means less frequent consumption schedule changes, in the price of system performance. In addition, consumers do not need to reveal their detailed consumption schedules (hence, their privacy is preserved). Using experiments, we show the impact of the herding effect and evaluate the effectiveness of the proposed solution. We thereby demonstrate price fairness for consumers. Finally, we apply our solution to a more realistic environment -one where consumers change their consumption behavior every day.

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Scaling distributed energy storage for grid peak reduction

Cited by 56 publications

References 17 publications

Community power flow control for peak demand reduction and energy cost savings

Community power flow control for peak demand reduction and energy cost savings

Incentivizing Advanced Load Scheduling in Smart Homes

Effective consumption scheduling for demand-side management in the smart grid using non-uniform participation rate

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