A Residential Consumer-Centered Load Control Strategy in Real-Time Electricity Pricing Environment

Tiptipakorn, Supun; Lee, Wei-Jen

doi:10.1109/naps.2007.4402357

Cited by 75 publications

(37 citation statements)

References 4 publications

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“…changes in the wholesale prices are not passed down to the customer. Detailed residential energy consumption data can be used in real-time pricing schemes to establish a link between the wholesale and the retail market [22,23,24].…”

Section: Real-time Pricingmentioning

confidence: 99%

Application-oriented modelling of domestic energy demand

Gruber

Jahromizadeh

Prodanović

et al. 2014

International Journal of Electrical Power & Energy Systems

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractDetailed residential energy consumption data can be used to offer advanced services and provide new business opportunities to all participants in the energy supply chain, including utilities, distributors and customers. The increasing interest in the residential consumption data is behind the roll-out of smart meters in large areas and led to intensified research efforts in new data acquisition technologies for the energy sector. This paper introduces a novel model for generation of residential energy consumption profiles based on the energy demand contribution of each household appliance and calculated by using a probabilistic approach. The model takes into consideration a wide range of household appliances and its modular structure provides a high degree of flexibility. Residential consumption data generated by the proposed model are suitable for development of new services and applications such as residential real-time pricing schemes or tools for energy demand prediction. To demonstrate the main features of the model, an individual household consumption was created and the effects of a possible change in the user behaviour and the appliance configuration presented. In order to show the flexibility offered in creation of the aggregated demand, the detailed simulation results of an energy demand management algorithm applied to an aggregated user group are used.

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Section: Real-time Pricingmentioning

confidence: 99%

Application-oriented modelling of domestic energy demand

Gruber

Jahromizadeh

Prodanović

et al. 2014

International Journal of Electrical Power & Energy Systems

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show abstract

“…Equations (10) and (11) calculate and constrain the storage state of charge (SOC), respectively. Using (10) and (11), it is ensured that at time t the storage cannot generate power if it is completely depleted (i.e., C b(t-1) =0), and also cannot store more energy if it is fully charged (i.e., C b(t-1) =C b max ). Residential load is the summation of fixed, adjustable, shiftable and storage loads (12).…”

Section: Hourly Load Model With Specific Characteristicsmentioning

confidence: 99%

“…Furthermore, the role of energy storage devices in reducing building energy costs was investigated. Load control strategies for air conditioner and water heater were investigated in [10]. In [11] incentivebased energy consumption scheduling algorithms was proposed and an automatic residential energy consumption scheduling framework for minimizing the payment was presented.…”

Section: Introductionmentioning

confidence: 99%

Optimal Hourly Scheduling of Community-Aggregated Electricity Consumption

Khodaei¹,

Shahidehpour²,

Choi³

2013

Journal of Electrical Engineering and Technology

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-This paper presents the optimal scheduling of hourly consumption in a residential community (community, neighborhood, etc.) based on real-time electricity price. The residential community encompasses individual residential loads, communal (shared) loads, and local generation. Community-aggregated loads, which include residential and communal loads, are modeled as fixed, adjustable, shiftable, and storage loads. The objective of the optimal load scheduling problem is to minimize the community-aggregated electricity payment considering the convenience of individual residents and hourly community load characteristics. Limitations are included on the hourly utility load (defined as community-aggregated load minus the local generation) that is imported from the utility grid. Lagrangian relaxation (LR) is applied to decouple the utility constraint and provide tractable subproblems. The decomposed subproblems are formulated as mixed-integer programming (MIP) problems. The proposed model would be used by community master controllers to optimize the utility load schedule and minimize the community-aggregated electricity payment. Illustrative optimal load scheduling examples of a single resident as well as an aggregated community including 200 residents are presented to show the efficiency of the proposed method based on real-time electricity price. Keywords

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“…Buildings account for 40% of all electricity consumed in the United States, with half that figure attributed to heating, ventilation and air conditioning (HVAC) [11,12]. There is an extensive literature on adjusting the timing of air conditioner operation in commercial buildings to reduce demand during high-cost times and increase it in low-cost times [13][14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…This strategy uses the building's thermal mass as a form of thermal storage that has the same effect as an electric battery, without encroaching on the comfort of the occupants [17][18][19][20][21][22][23]. Among these algorithms, one of the most advanced, introduced by Corbin, Henze, and May-Ostendorp [24].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance

Izawa¹,

Fripp²

2018

Preprint

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A new model predictive control (MPC) algorithm is used to select optimal air conditioning setpoints for a commercial office building, considering variable electricity prices, weather, occupancy and lighting. This algorithm, Cost-Comfort Particle Swarm Optimization (CCPSO), is the first to combine a realistic, smooth representation of occupants' willingness to pay for thermal comfort with a bottom-up, non-linear model of the building and air conditioning system under control. We find that using a quadratic preference function for temperature can yield solutions that are both more comfortable and lower-cost than previous work that used a "brick wall" preference function with no preference for further cooling within an allowed temperature band and infinite aversion to going outside the allowed band. Using historical pricing data for a summer month in Chicago, CCPSO provided a 3% reduction in costs vs. a "brick-wall" MPC approach with similar comfort and 13% reduction in costs vs. a standard night setback strategy. CCPSO also reduced peak-hours demand by 3% vs. the "brick-wall" strategy and 15% vs. standard night-setback. At the same time, the CCPSO strategy increased off-peak energy consumption by 15% vs. the "brick-wall" strategy. This may be valuable for power systems integrating large amounts of renewable power, which can otherwise become uneconomic due to saturation of demand during off-peak hours. Keywords: HVAC model predictive control, demand response, EnergyPlus, particle swarm optimization (PSO), renewable energy, smart grids MSC: 49M37, 65K05, 90-04, 90B35, 90B50, 90C29, 90C56, 90C90, 91B08, 91B10, 91B26, 91B42, 91B74

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A Residential Consumer-Centered Load Control Strategy in Real-Time Electricity Pricing Environment

Cited by 75 publications

References 4 publications

Application-oriented modelling of domestic energy demand

Application-oriented modelling of domestic energy demand

Optimal Hourly Scheduling of Community-Aggregated Electricity Consumption

Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance

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