Identification and Estimation for Electric Water Heaters in Direct Load Control Programs

Shad, Mostafa; Momeni, Ahmad; Errouissi, Rachid; Diduch, C. P.; Kaye, Mary E.; Chang, Liuchen

doi:10.1109/tsg.2015.2492950

Cited by 50 publications

(27 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A simple method to identify the TCL system parameters would be to measure the internal and external temperature of the device and, by knowing the desired temperature range [T min , T max ], use any of the the standard system identification methods to approximate the dynamics as a first order system, see [26] for the specific case of TCL parameter identification. However, since in our plug-and-play scenario we do not have access to the internal (or external) temperature of the TCL, and the desired temperature range [T min , T max ] is considered unknown, these methods can not be applied.…”

Section: A Methods For Tcl Power Consumption Model Identificationmentioning

confidence: 99%

Multi-Agent Coordination of Thermostatically Controlled Loads by Smart Power Sockets for Electric Demand Side Management

Franceschelli

Pilloni

Gasparri

2021

IEEE Trans. Contr. Syst. Technol.

View full text Add to dashboard Cite

This paper presents a multi-agent control architecture and an online optimization method based on dynamic average consensus to coordinate the power consumption of a large population of Thermostatically Controlled Loads (TCLs). Our objective is to penalize peaks of power demand, smooth the load profile and enable Demand Side Management (DSM). The proposed architecture and methods exploit only local measurements of power consumption via Smart Power Sockets (SPSs) with no access to their internal temperature. No centralized aggregator of information is exploited and agents preserve their privacy by cooperating anonymously only through consensus-based distributed estimation, robust to node/link failure. The interactions among devices are designed to occur through an unstructured peer-to-peer (P2P) network over the internet.The architecture includes novel methods for parameter identification, state estimation and mixed logical modelling of TCLs and SPSs. It is designed from a multi-agent and plug-and-play perspective in which existing household appliances can interact with each other in an urban environment.Finally, a novel low cost testbed is proposed along with numerical tests and an experimental validation. Index TermsMulti-agent systems, demand side management, thermostatically controlled loads, online optimization, dynamic consensus, anonymous agents, distributed predictive control.The research leading to these results has received funding from the Italian Ministry of Research and Education (MIUR) with project "CoNetDomeSys", code RBSI14OF6H, under call SIR 2014.M. Franceschelli is with the

show abstract

Section: A Methods For Tcl Power Consumption Model Identificationmentioning

confidence: 99%

Multi-Agent Coordination of Thermostatically Controlled Loads by Smart Power Sockets for Electric Demand Side Management

Franceschelli

Pilloni

Gasparri

2021

IEEE Trans. Contr. Syst. Technol.

View full text Add to dashboard Cite

show abstract

“…For example, Idaho Power Company pays incentive credits to residential customers who allow their air conditioners to be switched on less frequently during the afternoon in June, July, and August [50]. The authors of [73] developed a general model for a domestic electrical heater and a method to identify the model's parameters based on measuring the power consumption of the heater with or without the water temperature. This paper proposes a DLC method based on the proposed model, which negligibly decreases comfort levels.…”

Section: Incentive-based Programsmentioning

confidence: 99%

Electrical Market Management Considering Power System Constraints in Smart Distribution Grids

Divshali

Choi

2016

Energies

View full text Add to dashboard Cite

Rising demand, climate change, growing fuel costs, outdated power system infrastructures, and new power generation technologies have made renewable distribution generators very attractive in recent years. Because of the increasing penetration level of renewable energy sources in addition to the growth of new electrical demand sectors, such as electrical vehicles, the power system may face serious problems and challenges in the near future. A revolutionary new power grid system, called smart grid, has been developed as a solution to these problems. The smart grid, equipped with modern communication and computation infrastructures, can coordinate different parts of the power system to enhance energy efficiency, reliability, and quality, while decreasing the energy cost. Since conventional distribution networks lack smart infrastructures, much research has been recently done in the distribution part of the smart grid, called smart distribution grid (SDG). This paper surveys contemporary literature in SDG from the perspective of the electricity market in addition to power system considerations. For this purpose, this paper reviews current demand side management methods, supply side management methods, and electrical vehicle charging and discharging techniques in SDG and also discusses their drawbacks. We also present future research directions to tackle new and existing challenges in the SDG.

show abstract

“…Several recent researches have dealt with the low utilization of clean energy in many ways, including demand side management, flywheel energy storage, and micro-grid. The presence of abundant load resources in power demand would enable it to provide ancillary services for the power supply side [2][3][4][5][6][7], for instance, the load following application in clean energy by controlling residential air-conditionings (ACs). As thermostatically controlled loads, ACs have the potential to store energy and account for a large proportion in power demand [1,[8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Air-Conditioning Resource Management and Control Method based on Cloud Platform for Wind Power Consumption

Liang

Jin-ying

2019

Processes

View full text Add to dashboard Cite

Air-conditionings have energy storage functions. Through reasonable aggregation control, the output tracking can be implemented for wind power with stronger fluctuation to enhance its utilization rate. Cloud technology and intelligent appliances enable the appliance vendor to implement information interaction with the air-conditioning through cloud platforms to realize flexible regulation. In this paper, a management and control method of air-conditioning based on cloud platform is established. Based on this structure, the air-conditionings are divided into several aggregation groups according to the similarity of parameters, and each group completes the consumption task collaboratively. The consumption evaluation model of the air-conditioning group is established. On this basis, the allocation problem on consumption task for the aggregated group is constructed to implement the optimal solution under the condition of guaranteeing the degree of completion and user comfort. Each group implements the control for air-conditioning inside the group through the sliding mode control model. The simulation experiment verifies that the algorithm can effectively follow the output of clean energy, while intervening less in the air-conditioning operation.

show abstract

Identification and Estimation for Electric Water Heaters in Direct Load Control Programs

Cited by 50 publications

References 21 publications

Multi-Agent Coordination of Thermostatically Controlled Loads by Smart Power Sockets for Electric Demand Side Management

Multi-Agent Coordination of Thermostatically Controlled Loads by Smart Power Sockets for Electric Demand Side Management

Electrical Market Management Considering Power System Constraints in Smart Distribution Grids

Air-Conditioning Resource Management and Control Method based on Cloud Platform for Wind Power Consumption

Contact Info

Product

Resources

About