Incentive pricing and economic profitability of load management program

Sheen, J.N.

doi:10.1016/j.energy.2005.08.009

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…Peak demand control methods use water tank or building A GREY-BOX MODEL OF NEXT-DAY BUILDING THERMAL LOAD PREDICTION thermal mass such as walls, floors, and furnishings as cool storage, and shift peak load by storing cooling capacity in a water tank or building mass during off-peak period when electrical rates are relatively low. For HVAC systems with a water tank or the building thermal mass as cool storage, the objectives are to optimize the dynamic zone temperature set-points and water storage capacity to be stored in the tank by predicted profile of the building load respectively as described by Braun [15], Braun et al [16], Dincer [17], Rosen and Dincer [18], Xu and Zagreus [19] and Sheen [20]. In the optimal chiller start control, the operating number is usually optimized with regard to the building load to meet the start-up load, and the optimal starting time or sequence is decided by the dynamic building load characteristics to limit the starting peak.…”

Section: Introductionmentioning

confidence: 99%

A grey-box model of next-day building thermal load prediction for energy-efficient control

Zhou

Wang

et al. 2008

Int. J. Energy Res.

105

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SUMMARYAccurate building thermal load prediction is essential to many building energy control strategies. To get reliable prediction of the hourly building load of the next day, air temperature/relative humidity and solar radiation prediction modules are integrated with a grey-box model. The regressive solar radiation module predicts the solar radiation using the forecasted cloud amount, sky condition and extreme temperatures from on-line weather stations, while the forecasted sky condition is used to correct the cloud amount forecast. The temperature/relative humidity prediction module uses a dynamic grey model (GM), which is specialized in the grey system with incomplete information. Both weather prediction modules are integrated into a building thermal load model for the on-line prediction of the building thermal load in the next day. The validation of both weather prediction modules and the on-line building thermal load prediction model are presented.

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Section: Introductionmentioning

confidence: 99%

A grey-box model of next-day building thermal load prediction for energy-efficient control

Zhou

Wang

et al. 2008

Int. J. Energy Res.

105

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“…As a matter of fact, the PU benefits from reducing its peak demand, saving on costly reserve, restoring the quality of its services and ensuring its reliability. The customer also benefits from reducing its demanded power through discounts on price tariffs and receiving more reliable power [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Pricing Policy for Interruptible Load Contracts in Power Markets: A Case Study for Iran

Ziaee¹,

Rahimi-Kian²,

Parsa-Moghadam³

2012

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The electricity market restructuring has created new opportunities for customers to partner with utilities to alter their demand in response to electric system reliability needs or high prices. The consumer participation in wholesale energy markets tends to increase the electricity system reliability, reduce the electricity price volatility, and lower the average electricity prices paid by the consumers. This paper presents a new demand management program for the electric distribution companies (Dist Co.). It models and solves a profit maximization mathematical program for a Dist Co. subject to some constraints related to the customers' costs of power interruptions, market price of electricity, and maximum amounts of interruptible loads by different consumers. The outputs of the mathematical program are the optimal load curtailments and optimal price tariffs for different customers that maximize the Dist Co's profit function. The particle swarm optimization (PSO) and invasive weed optimization (IWO) algorithms are used to solve the non-linear mathematical program. A case study, for managing the power consumptions of some industrial firms in Iran, is presented and analyzed to show the applicability of the proposed demand management algorithm.

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