Aggregated Modeling and Control of Air Conditioning Loads for Demand Response

Zhang, Wei; Lian, Jianming; Chang, Chin-Yao; Kalsi, Karanjit

doi:10.1109/tpwrs.2013.2266121

Cited by 417 publications

(215 citation statements)

References 20 publications

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“…Most control strategies [9,10,11,12,13] have assumed the availability of a real-time communication infrastructure to schedule individual TCL states. The control of TCLs is usually implemented centrally using discretised ('bin') models, but this population level description is unwieldy for inclusion in optimisation tools.…”

Section: B Related Workmentioning

confidence: 99%

Leaky storage model for optimal multi‐service allocation of thermostatic loads

Trovato

Tindemans

Štrbac

2016

IET Generation, Transmission & Distribution

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Section: B Related Workmentioning

confidence: 99%

Leaky storage model for optimal multi‐service allocation of thermostatic loads

Trovato

Tindemans

Štrbac

2016

IET Generation, Transmission & Distribution

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“…The diversity in the approaches is related not only to the aggregation processes but to the modelling approach selected to characterize elemental loads. With respect to elemental models, the main difference is the physical phenomena being considered: [24][25][26][27] follow a model that only reproduce a simplified and inaccurate heat balance (heat losses/gains due to temperature gradient and electrical power conversion) or discard important inputs (solar radiation), whereas [20] and specially [21] take into account the main changes in sensible and latent heat that affects the behaviour of the load: i.e., solar radiation, internal sources (inhabitants, appliances), the losses/gains from other dwellings, the renewal of indoor air, or bidirectional heat fluxes [28]. The problem is that solar radiation through windows and walls are not a negligible factor in the modelling process of TCL.…”

Section: Introductionmentioning

confidence: 99%

Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response

et al. 2018

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Abstract:The objectives of improving the efficiency, and integration, of renewable sources by are complex in practice and should be linked to an increase of demand-side flexibility. The main challenges to achieving this flexibility are the lack of incentives and an adequate framework. For instance, customers' revenue is usually low, the volatility of prices is high and there is not any practical feedback to customers from smart meters. The possibility of increasing customer revenue could reduce the uncertainty with respect to economic concerns, improving investments in efficiency, enabling technology and thus, engaging more customers in these policies. This objective could be achieved by the participation of customers in several markets. Moreover, Demand Response and Energy Efficiency can share ICT technologies but this participation needs to perform an aggregation of demand. The idea of this paper is to present some methodologies for facilitating the definition and evaluation of energy versus cost curves; and subsequently to estimate potential revenues due to Demand Response. This can be accomplished by models that estimate: demand and energy aggregation; economic opportunities and benefits; impacts on customer convenience; customer feedback and price analysis. By doing so, we would have comprehensive information that can help customers and aggregators to define energy packages and their monetary value with the objective of fostering their market participation.

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“…Bashash et al [18] develops a bilinear partial differential equation model and a sliding mode controller for the real-time management of thermostatic AC loads. Zhang et al [19] builds a highly accurate aggregated model for a heterogeneous population of AC loads and designs a novel aggregated control strategy for the load population under realistic conditions. To provide a daily control scheduling, the dynamic programming algorithm is presented and optimized for groups of interruptible AC loads [20][21][22].To avoid the transient payback load phenomenon, a group-direct load control (DLC) program of air-conditioning systems is proposed with a least enthalpy estimator-based thermal comfort controller [23].…”

Section: Introductionmentioning

confidence: 99%

Strategy of constructing virtual peaking unit by public buildings’ central air conditioning loads for day-ahead power dispatching

Yang

Wang

2017

J. Mod. Power Syst. Clean Energy

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With the gradually widely usage of the air conditioning (AC) loads in developing countries, the urban power grid load has swiftly increased over the past decade. Especially in China, the AC load has accounted for over 30% of the maximum load in many cities during summer. This paper proposes a scheme of constructing a virtual peaking unit (VPU) by public buildings' cool storage central AC (CSCAC) systems and non-CSCAC (NCSCAC) systems for the day-ahead power network dispatching (DAPND). Considering the accumulation effect of different meteorological parameters, a short term load forecasting method of public building's central AC (CAC) baseline load is firstly discussed. Then, a second-order equivalent thermal parameters model is established for the public building's CAC load. Moreover, the novel load reduction control strategies for the public building's CSCAC system and the public building's NCSCAC system are respectively presented. Furthermore, based on the multiple-rank control strategy, the model of the DAPND with the participation of a VPU is set up. The VPU is composed of large-scale regulated public building's CAC loads. To demonstrate the effectiveness of the proposed strategy, results of a sample study on a region in Nanjing which involves 22 public buildings' CAC loads are described in this paper. Simulated results show that, by adopting the proposed DAPND scheme, the power network peak load in the region obviously decreases with a small enough deviation between the regulated load value and the dispatching instruction of the VPU. The total electricity-saving amount accounts for 7.78% of total electricity consumption of the VPU before regulation.

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Aggregated Modeling and Control of Air Conditioning Loads for Demand Response

Cited by 417 publications

References 20 publications

Leaky storage model for optimal multi‐service allocation of thermostatic loads

Leaky storage model for optimal multi‐service allocation of thermostatic loads

Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response

Strategy of constructing virtual peaking unit by public buildings’ central air conditioning loads for day-ahead power dispatching

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