GreenCharge: Managing RenewableEnergy in Smart Buildings

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

doi:10.1109/jsac.2013.130711

Cited by 74 publications

(29 citation statements)

References 10 publications

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“…Usually, the optimization target was the minimization of the energy bills of customers [31][32][33]. Power storage devices were considered in extension of the ordinary demand response [20,21], and user inconvenience was also considered [32]. In the modeling of smart appliances, uncertainty was also included [23].…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, the proposed approach models and reflects the uncertainty of customers, and as a result, more reliable total energy reduction can be provided. For the prediction of each customer's response, previous studies usually have built a detailed internal model of each customer [20][21][22][23]. For example, power storage device was modeled [20,21], air-conditioning system was modeled [22], and smart appliances were also modeled [23].…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers

Kang

Lee

2017

Energies

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Abstract:Demand response is nowadays considered as another type of generator, beyond just a simple peak reduction mechanism. A demand response service provider (DRSP) can, through its subcontracts with many energy customers, virtually generate electricity with actual load reduction. However, in this type of virtual generator, the amount of load reduction includes inevitable uncertainty, because it consists of a very large number of independent energy customers. While they may reduce energy today, they might not tomorrow. In this circumstance, a DSRP must choose a proper set of these uncertain customers to achieve the exact preferred amount of load curtailment. In this paper, the customer selection problem for a service provider that consists of uncertain responses of customers is defined and solved. The uncertainty of energy reduction is fully considered in the formulation with data-driven probability distribution modeling and stochastic programming technique. The proposed optimization method that utilizes only the observed load data provides a realistic and applicable solution to a demand response system. The performance of the proposed optimization is verified with real demand response event data in Korea, and the results show increased and stabilized performance from the service provider's perspective.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers

Kang

Lee

2017

Energies

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“…For heat boiler, gas to heat conversion efficiency is 88%. The overall efficiency of the battery system is 80% [39] and the standby loss for battery system is 3% per month [3]. The overall efficiency of heat storage system is 75% [40] and standby loss is 15% a day.…”

Section: A Case Studymentioning

confidence: 99%

“…However, as reported by the U.S. Department of Energy, buildings consume more energy compared with other broad sectors of energy consumption, such as industry and transportation, approximately 40% compared to 30% each respectively [3]. Since domestic buildings are a very significant share of total buildings, households are responsible for a large proportion of CO 2 emissions, through end use of both electricity and gas (or other fossil fuel) [4].…”

Section: Introductionmentioning

confidence: 99%

Rule Based Coordinated Control of Domestic Combined Micro-CHP and Energy Storage System for Optimal Daily Cost

et al. 2017

Applied Sciences

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This paper presents a novel control algorithm for optimising operational costs of a combined domestic micro-CHP (combined heat and power), battery and heat storage system. Using a minute by minute basic time-step, this work proposes a simple and computationally efficient rule based whole-system management, developed from empirical study of realistic simulated domestic electricity and heat loads. The CHP availability is considered in two binary states which, together with leveraging storage effectively, maximises CHP efficiency, and gives the algorithm increased real world feasibility. In addition, a novel application of a dual battery system is proposed to support the micro-CHP with each battery supplying just one of the distinctive morning and evening electrical load peaks, and thus inherently improving overall battery system lifetime. A case study is presented where the algorithm is shown to yield approximately 23% energy cost savings above the base case, almost 3% higher savings than that of the closest previous work, and 96.8% of the theoretical minimum cost. In general, the algorithm is shown to always yield better than 88% of the theoretical minimum cost, a ratio that will be considerably higher when real-world CHP limitations are factored into the theoretical minimum calculation.

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“…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%

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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GreenCharge: Managing RenewableEnergy in Smart Buildings

Cited by 74 publications

References 10 publications

Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers

Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers

Rule Based Coordinated Control of Domestic Combined Micro-CHP and Energy Storage System for Optimal Daily Cost

Incentivizing Advanced Load Scheduling in Smart Homes

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