The impact of electricity pricing schemes on storage adoption in Ontario

Proceedings of the Fourth International Conference on Future Energy Systems

et al. 2013

Reducing peak demand is an important part of ongoing smart grid research efforts. To reduce peak demand, utilities are introducing variable rate electricity prices. Recent efforts have shown how variable rate pricing can incentivize consumers to use energy storage to cut their electricity bill, by storing energy during inexpensive off-peak periods and using it during expensive peak periods. Unfortunately, variable rate pricing provides only a weak incentive for distributed energy storage and does not promote its adoption at scale. In this paper, we present the storage adoption cycle to describe the issues with incentivizing energy storage using variable rates. We then propose a simple way to address the issues: augment variable rate pricing with a surcharge based on a consumer's peak demand.The surcharge encourages consumers to flatten their demand, rather shift as much demand as possible to the lowprice period. We present PeakCharge, which includes a new peak-aware charging algorithm to optimize the use of energy storage in the presence of a peak demand surcharge, and use a closed-loop simulator to quantify its ability to flatten grid demand as the use of energy storage scales. We show that our system i) reduces upfront capital costs since it requires significantly less storage capacity per consumer than prior approaches, ii) increases energy storage's ROI, since the surcharge mitigates free riding and maintains the incentive to use energy storage at scale, and iii) uses aggregate storage capacity within 18% of an optimal centralized system.

mentioning

confidence: 99%

Scaling distributed energy storage for grid peak reduction

Mishra

Proceedings of the Fourth International Conference on Future Energy Systems

et al. 2013

“…For the home in our case study, which has an aggregate power usage close to the average U.S. home, we show that the optimal savings is never more than 20% with realistic energy storage capacities (< 60kWh). Rather than solving the problem with respect to a particular demand distribution, we distill the problem to a linear program that uses our prediction model of future consumption levels Vytelingum et al [34] and Carpenter et al [5] both focus on the economics of storage at scale, which we also discuss. Vytelingum et al show that for sufficiently low adoption rates, the difference between the peak and off-peak prices approaches zero, reducing the financial incentives for installing energy storage.…”

Section: Related Workmentioning

confidence: 99%

GreenCharge: Managing RenewableEnergy in Smart Buildings

Mishra

IEEE J. Select. Areas Commun.

et al. 2013

Abstract-Distributed generation (DG) uses many small onsite energy harvesting deployments at individual buildings to generate electricity. DG has the potential to make generation more efficient by reducing transmission and distribution losses, carbon emissions, and demand peaks. However, since renewables are intermittent and uncontrollable, buildings must still rely, in part, on the electric grid for power. While DG deployments today use net metering to offset costs and balance local supply and demand, scaling net metering for intermittent renewables to a large fraction of buildings is challenging. In this paper, we explore an alternative approach that combines market-based electricity pricing models with on-site renewables and modest energy storage (in the form of batteries) to incentivize DG. We propose a system architecture and optimization algorithm, called GreenCharge, to efficiently manage the renewable energy and storage to reduce a building's electric bill. To determine when to charge and discharge the battery each day, the algorithm leverages prediction models for forecasting both future energy demand and future energy harvesting. We evaluate GreenCharge in simulation using a collection of real-world data sets, and compare with an oracle that has perfect knowledge of future energy demand/harvesting and a system that only leverages a battery to lower costs (without any renewables). We show that GreenCharge's savings for a typical home today are near 20%, which are greater than the savings from using only net metering.

“…For example, if electricity prices are much lower in the evening, consumers might choose to perform energy-intensive tasks, such as doing their laundry or running their dishwasher, at that time, rather than in the middle of the day. In parallel, the benefits of reducing the grid's peak usage have motivated researchers to develop a variety of advanced load scheduling algorithms for buildings and homes that programmatically control when electrical devices (or loads) operate to lower a building's electricity bill, e.g., [2, 3,7,9,11,12,17,18]. Instead of requiring consumers to manually alter their behavior, which many consumers may choose not to do regardless of electricity's price, these scheduling algorithms exploit a limited degree of scheduling freedom available in a subset of loads.…”

Section: Introductionmentioning

confidence: 99%

“…Storing large amounts of power during low price periods and then using this stored power during high price periods is the most effective way to transparently schedule loads and lower a consumer's electricity bill under today's variable rate pricing plans. Prior work explores many different aspects of scheduling energy storage, largely in the form of lead-acid batteries, to exploit this opportunity for arbitrage [3,4,10,11,20,21]. Unfortunately, energy storage, especially at small scales, is expensive.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, energy storage, especially at small scales, is expensive. As prior work shows, either electricity prices must rise, or battery prices must fall, by an order of magnitude before energy storage's return-on-investment (ROI) would near a break-even point [3,11]. One reason for the high ROI is that TOU/RTP pricing plans require consumers to install a large amount of storage capacity to shift as much load as possible from high-price daytime periods to low-price nighttime periods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Incentivizing Advanced Load Scheduling in Smart Homes

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

2013

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.