A novel demand side management program using water heaters and particle swarm optimization

Sepulveda, Arnaldo; Paull, Liam; Morsi, Walid G.; Li, Howard; Diduch, C. P.; Chang, Liuchen

doi:10.1109/epec.2010.5697187

Cited by 70 publications

(37 citation statements)

References 16 publications

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“…Several modeling studies have previously evaluated the potential of ERWH to provide peak curtailment, load following and ancillary services and found significant potential and benefit for ERWH to perform these grid functions (Mathieu et al 2012;Sepulveda et al 2010;Konodoh et al 2011;Diao et al 2012;Saker et al 2011;Lu et al 2011). However, no extensive field evaluation has verified these model results.…”

Section: 2mentioning

confidence: 83%

“…Specifically, residential ERWHs have been identified as ideal candidates for DR because they contain significant thermal storage, they contribute a significant amount of the residential load, they have relatively high power consumption and a large installed base, and they follow a consistent load pattern that is often coincident with utility peak power periods (Sepulveda et al 2010;Diao et al 2012). Also, an ERWH is essentially a resistor, which is not affected by frequent switching and does not require reactive power support to operate (Diao et al 2012).…”

Section: Background On Demand Response With Water Heatersmentioning

confidence: 99%

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Demand Response Performance of GE Hybrid Heat Pump Water Heater

Widder¹,

Parker²,

Petersen³

et al. 2013

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SummaryIncreasing penetration of heat pump water heaters (HPWHs) in the residential sector will offer an important opportunity for energy savings, with a theoretical energy savings of up to 63% per water heater 1 and up to 11% of residential energy use (EIA 2009). However, significant barriers must be overcome before this technology will reach widespread adoption in the Pacific Northwest region and nationwide. One barrier is that the demand response (DR) performance and characteristics of HPWHs is unknown. Previous research has demonstrated the potential of electric resistance water heaters (ERWHs) to provide significant grid stability and control benefits through demand-side management, or DR, strategies (Diao et al. 2012). However, if ERWHs are to be replaced with HPWHs to improve residential energy efficiency, it is important to understand the DR characteristics of HPWHs and how these characteristics will impact DR programs and overall grid stability now and in the future.This project evaluates and documents the DR performance of an HPWH as compared to an ERWH for two primary types of DR events: peak curtailments and balancing reserves. The experiments were conducted with General Electric (GE) second-generation "Brillion™"-enabled GeoSpring™ hybrid water heaters in the Pacific Northwest National Laboratory (PNNL) Lab Homes 2 , with one GE GeoSpring water heater operating in "Standard" electric resistance mode to represent the baseline and one GE GeoSpring water heater operating in "Heat Pump" mode to provide the comparison to heat pump-only DR. Signals were sent simultaneously to the two water heaters in the side-by-side PNNL Lab Homes under highly controlled, simulated occupancy conditions. It is expected that "Hybrid" DR performance, which would engage both the heat pump and electric elements, could be interpolated from these two experimental extremes.Based on the data collected in these DR experiments, both ERWHs and HPWHs are capable of performing peak curtailment and regulation services. However, their characteristics differ, as can be seen in Table 5.1, which shows the average impact on power use during the DR event, energy use during the DR event, and daily energy use for ERWH and HPWH for peak curtailment, 1-2 hour balancing events when generation and load are mismatched either due to higher load than generated power (INC events) or greater power generation than available load (DEC events). In general, the HPWH has much lower power use than the ERWH (587 Watts [W] versus 4,650 W) and provides approximately 38% of the potential to reduce load for peak curtailment or INC balancing events of the ERWH. The ERWH provides more dynamic response with a high magnitude of power increase or decrease per water heater. However, the HPWH has longer and more frequent operating times, which means the HPWH has a higher likelihood of being able to respond when an INC event or peak curtailment is called for. In addition, the inherent efficiency savings of HPWHs (61.7 ± 1.7%, as measured in the PNNL Lab Homes) will resul...

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Section: 2mentioning

confidence: 83%

Section: Background On Demand Response With Water Heatersmentioning

confidence: 99%

Demand Response Performance of GE Hybrid Heat Pump Water Heater

Widder¹,

Parker²,

Petersen³

et al. 2013

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“…However, while the aggregate models provide a good estimate of DWH power consumption they do not consider individual DWHs independently. Consequently, customer discomfort, which is an extremely important variable in ensuring the success of a DSM program may not be considered (Sepulveda, et al, 2010). The behaviour of individual water heaters and domestic loading should therefore be recognised.…”

Section: Water Heatingmentioning

confidence: 99%

Demand Side Management Potential of Domestic Water Heaters and Space Heaters

Qazi

Flynn

2012

IFAC Proceedings Volumes

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Demand Side Management (DSM) is a viable strategy for facilitating integration of renewable energy into power systems. The demand resource from water and space heating can be used to reduce or delay system demand peaks, and in combination with other flexible loads, reshape the aggregate demand profile and manage system ramping. In this paper, the aggregate power draw profiles for heat pump based water heating and under floor space heating systems for the Irish domestic sector have been synthesized and the effects of daily and seasonal variations, the type of dwelling and control / usage patterns on demand are discussed. The results show a significant seasonal and daily variation of the demand profile for water and space heating, indicating its viability as a DSM resource on the Irish power system, but also the challenges of operation.

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“…Moreover, PSO (particle swarm optimization) is evolutionary programming; Refs. [8][9][10][11][12][13] use this heuristic algorithm to optimize the load of household appliances. Additionally, Ref.…”

mentioning

confidence: 99%

Heuristic Algorithm for Minimizing the Electricity Cost of Smart House

Arikiez¹,

Alotaibi²,

Gdhaidh³

et al. 2017

JEPE

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This framework proposes a heuristic algorithm based on LP (linear programming) for optimizing the electricity cost in large residential buildings, in a smart grid environment. Our heuristic tackles large multi-objective energy allocation problem (large number of appliances and high time resolution). The primary goal is to reduce the electricity bills, and discomfort factor. Also, increase the utilization of domestic renewable energy, and reduce the running time of the optimization algorithm. Our heuristic algorithm uses linear programming relaxation, and two rounding strategies. The first technique, called CR (cumulative rounding), is designed for thermostatic appliances such as air conditioners and electric heaters, and the second approach, called MCR (minimum cost rounding), is designed for other interruptible appliances. The results show that the proposed heuristic algorithm can be used to solve large MILP (mixed integer linear programming) problems and gives a decent suboptimal solution in polynomial time.

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A novel demand side management program using water heaters and particle swarm optimization

Cited by 70 publications

References 16 publications

Demand Response Performance of GE Hybrid Heat Pump Water Heater

Demand Response Performance of GE Hybrid Heat Pump Water Heater

Demand Side Management Potential of Domestic Water Heaters and Space Heaters

Heuristic Algorithm for Minimizing the Electricity Cost of Smart House

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