Implementation and assessment of physically based electrical load models: application to direct load control residential programmes

Molina, Á.; Gabaldón, A.; Fuentes, J.A.; Álvarez, C.

doi:10.1049/ip-gtd:20020750

Cited by 101 publications

(42 citation statements)

References 8 publications

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“…Two methods for building the thermal parameter model for ACLs are widely discussed and used in previous research: (1) a modeling method based on equivalent thermal parameters [15,27], and (2) a modeling method based on cooling load calculations [28]. The latter is based on simpler principles but may involve complex calculations and a low degree of preciseness.…”

Section: Thermal Parameter Model For the Aclmentioning

confidence: 99%

“…Hence, we chose to build our model on the basis of equivalent thermal parameters. The models built upon the equivalent thermal parameter method can be further categorized as physically based electrical models [27] and simplified equivalent thermal parameter models [15]. Although the physically based electrical model is more precise, it involves a complex structure and a large number of calculations.…”

Section: Thermal Parameter Model For the Aclmentioning

confidence: 99%

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Stochastic Unit Commitment of Wind-Integrated Power System Considering Air-Conditioning Loads for Demand Response

Han

Zhou

et al. 2017

Applied Sciences

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Abstract:As a result of extensive penetration of wind farms into electricity grids, power systems face enormous challenges in daily operation because of the intermittent characteristics of wind energy. In particular, the load peak-valley gap has been dramatically widened in wind energy-integrated power systems. How to quickly and efficiently meet the peak-load demand has become an issue to practitioners. Previous literature has illustrated that the demand response (DR) is an important mechanism to direct customer usage behaviors and reduce the peak load at critical times. This paper introduces air-conditioning loads (ACLs) as a load shedding measure in the DR project. On the basis of the equivalent thermal parameter model for ACLs and the state-queue control method, a compensation cost calculation method for the ACL to shift peak load is proposed. As a result of the fluctuation and uncertainty of wind energy, a two-stage stochastic unit commitment (UC) model is developed to analyze the ACL users' response in the wind-integrated power system. A simulation study on residential and commercial ACLs has been performed on a 10-generator test system. The results illustrate the feasibility of the proposed stochastic programming strategy and that the system peak load can be effectively reduced through the participation of ACL users in DR projects.

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Section: Thermal Parameter Model For the Aclmentioning

confidence: 99%

Section: Thermal Parameter Model For the Aclmentioning

confidence: 99%

Stochastic Unit Commitment of Wind-Integrated Power System Considering Air-Conditioning Loads for Demand Response

Han

Zhou

et al. 2017

Applied Sciences

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“…Each air conditioner responds to the frequency deviation through temperature set point adjustment according to the control law in Equation (8). Changing the temperature set point will influence states of an air conditioner and its conditioned space, which can be depicted by a dynamical model [39] The Monte Carlo simulation method is applied to the aggregation of air conditioners' response. Each air conditioner in the control group is treated as a sample with its parameters randomized.…”

Section: Aggregation Of Air Conditioners' Responsementioning

confidence: 99%

“…Accounting for the user's thermal comfort, the accurate description of conditioned space is required. First-order, second-order and third-order models were built, respectively, in [37][38][39] to depict thermodynamics of the conditioned space. In [30], the control strategy accounts for the utility of loads, but it focuses on the group's characteristic rather than on each individual's.…”

Section: Introductionmentioning

confidence: 99%

Active Participation of Air Conditioners in Power System Frequency Control Considering Users’ Thermal Comfort

et al. 2015

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Air conditioners have great potential to participate in power system frequency control. This paper proposes a control strategy to facilitate the active participation of air conditioners. For each air conditioner, a decentralized control law is designed to adjust its temperature set point in response to the system frequency deviation. The decentralized control law accounts for the user's thermal comfort that is evaluated by a fuzzy algorithm. The aggregation of air conditioners' response is conducted by using the Monte Carlo simulation method. A structure preserving model is applied to the multi-bus power system, in which air conditioners are aggregated at certain load buses. An inner-outer iteration scheme is adopted to solve power system dynamics. An experiment is conducted on a test air conditioner to examine the performance of the proposed decentralized control law. Simulation results on a test power system verify the effectiveness of the proposed strategy for air conditioners participating in frequency control.

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“…A remote terminal unit (RTU), a communication interface connected to a smart meter, transmits information collected by the smart meter to a metering data management system that processes it and delivers feedback. For example, a smart meter could provide the current energy consumption value and a metering data management system could specify the electricity price due to the total consumption of multiple smart meters [2,3], or a system operator could turn on/off each user's device according to a direct load control program that follows the grid state and the defined load shaping policy [4]. There are various options for the applied wireless communication technology such as long term evolution (LTE), worldwide interoperability for microwave access (WiMAX), IEEE 802.15.4 and IEEE 802.11 [5].…”

Section: Introductionmentioning

confidence: 99%

Ad Hoc LTE Method for Resilient Smart Grid Communications

Markkula

Haapola

2017

Wireless Pers Commun

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LTE network is a good choice for delivering smart grid demand response (DR) traffic. However, LTE connectivity is not pervasively available due to smart meter improper positioning, limited of coverage, or base station software or hardware failures. In this paper, a solution is introduced to overcome issues relating to lack of LTE base station connectivity for user equipment (UE) considered as remote terminal units, i.e. communication interfaces connected to smart meters. The solution is an ad hoc mode for the LTEAdvanced UE. The ad hoc mode is applied to reach a relay node that is the nearest UE with base station connection. DR traffic is delivered between clusters of UEs and a relay node using multi-hop communications. Analytical Markov chain models and a Riverbed Modeler network simulation model are implemented to illustrate the functionalities and the performance when DR traffic is delivered with varying transmission power levels. A detailed physical layer propagation model for device-to-device communications, a static resource allocation in time domain, hybrid automatic repeat request retransmissions, and a capability for a UE to receive uplink transmissions are modeled both analytically and in the simulator. Both the disjoint analysis and simulations show that all packets are successfully transmitted at most with the fourth transmission attempt and the average network delay is low enough to support most of the smart grid DR applications (139.2-546.6 ms).

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Implementation and assessment of physically based electrical load models: application to direct load control residential programmes

Cited by 101 publications

References 8 publications

Stochastic Unit Commitment of Wind-Integrated Power System Considering Air-Conditioning Loads for Demand Response

Stochastic Unit Commitment of Wind-Integrated Power System Considering Air-Conditioning Loads for Demand Response

Active Participation of Air Conditioners in Power System Frequency Control Considering Users’ Thermal Comfort

Ad Hoc LTE Method for Resilient Smart Grid Communications

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