Collaborative voltage regulation by increasing/decreasing the operating power of aggregated air conditioners considering participation priority

Hua, Yongzhu; Xie, Qiangqiang; Hui, Hon Tat; Ding, Yi; Wang, Weiran; Qin, Huibin; Shentu, Xiangrong; Cui, Jiadong

doi:10.1016/j.epsr.2021.107420

Cited by 9 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is observed that if generation of renewable sources becomes more than the demand, the power flow becomes reverse in the distribution system which creates over-voltage problem, on the other hand, if the demand is very high compared to generation, it creates under-voltage problem [2]. Moreover, interrupted output of the renewable sources causes fluctuations in distribution voltage.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Simulation of Thermostatically Controlled Loads for Power System Regulation in Building-to-Grid Integration Framework

Pal,

Harish

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Since last few decades the inclusion of renewable energies in the grid power system is rapidly increasing with the increase in technologies to minimize the use of conventional fossil fuels for minimizing the emission of greenhouse gases. But the uncertain behaviour of the renewable energy sources lead to a challenge of maintaining the stability and reliability of the grid system and to optimize the supply and demand balance. It is identified that more than 40% energy consumption is made by the consumers of buildings. To minimize the power fluctuation, caused by inclusion of renewable systems, the thermostatically controlled loads (TCLs) like ACs, refrigerators, heaters of buildings can be considered as flexible loads for regulation purpose as TCLs share the major part of the total power consumption and also TCLs can be adjusted without compromising the comfort level. In this paper, a practical linear network model of the multi layered construction materials of building is developed with 3R2C electrical circuit model and corresponding state-space equations are formulated, and also a 2ndorder TCL model is proposed as an RC network equivalent where main focus is on the works, which develop specific control strategies by modelling the building energy systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Modelling and Simulation of Thermostatically Controlled Loads for Power System Regulation in Building-to-Grid Integration Framework

Pal,

Harish

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…In [37], air-conditioning usage patterns of three climate zones were analyzed and developed. In [38], a thermal model was developed to calculate the regulating power provided by air conditioners. In [39], an optimal load control strategy for air conditioners was proposed.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Household Appliances Power Consumption

et al. 2022

View full text Add to dashboard Cite

The consumption of household appliances tends to increase. Therefore, the application of energy efficiency measurements is urgently needed to reduce the levels of power consumption. Over the last years, various methods have been used to predict household electricity consumption. As a novelty, this paper proposed a method of predicting the consumption of household appliances by evaluating statistical distributions (Kolmogorov–Smirnov Test and Pearson’s X2 test). To test the veracity of the evaluations, first, a set of random values was simulated for each hour, and their respective averages were calculated. These were compared with the averages of the real values for each hour. With the exception of HVAC during working days, great results were obtained. For the refrigerator, the maximum error was 3.91%, while for the lighting, it was 4.27%. At the point of consumption, the accuracy was even higher, with an error of 1.17% for the dryer while for the washing machine and dishwasher, their minimum errors were less than 1%. The error results confirm that the applied methodology is perfectly acceptable for modeling household appliance consumption and consequently predicting it. However, these consumptions can be only extrapolated to dwellings with similar surface areas and habitats.

show abstract

“…The same issue is faced in [15], where an efficient and scalable MILP/mixed-integer quadratic problem optimization model solved using Gurobi solver, which can handle thousands of appliances, was proposed. In [16], a scheme was proposed with an aggregator managing the operating power of aggregated air conditioners considering participation priority for voltage regulation purposes, while in [17], the authors discuss the optimal participation of Electric Vehicles' aggregators in day-ahead energy and regulation markets, proposing a suitable model for this case. Finally, in [18], the authors proposed a solution for the interaction of aggregators in a common balancing group consisting of an iterative distributed algorithm based on non-cooperative game theory.…”

Section: Introductionmentioning

confidence: 99%

A Methodology for Exploiting Smart Prosumers’ Flexibility in a Bottom-Up Aggregation Process

et al. 2022

View full text Add to dashboard Cite

The electrical power system is evolving in a way that requires new measures for ensuring its secure and reliable operation. Demand-side aggregation represents one of the more interesting ways to provide ancillary services by the coordinated management of a multitude of different distributed resources. In this framework, aggregators play the main role in ensuring the effectiveness of the coordinated action of the distributed resources, usually becoming mediators in the relation between distribution system operators and smart prosumers. The research project DEMAND recently introduced a new concept in demand-side aggregation by proposing a scheme without a central aggregator where prosumers can share and combine their flexibility with a collaboration–competition mechanism in a platform called Virtual Aggregation Environment (VAE). This paper, after a brief introduction to the DEMAND project, presents the algorithm for the day-ahead estimation of prosumers’ flexibility and the cooperative–competitive algorithm for the bottom-up aggregation. The first algorithm evaluates various couples of power variation and desired remuneration to be sent to the VAE for further elaborations and, for showing its potentiality, is applied to two different case studies: a passive user with only controllable loads and prosumers with controllable loads, photovoltaics and a storage system. The aggregation algorithm is instead discussed in detail, and its performance is evaluated for different population sizes.

show abstract

Collaborative voltage regulation by increasing/decreasing the operating power of aggregated air conditioners considering participation priority

Cited by 9 publications

References 34 publications

Modelling and Simulation of Thermostatically Controlled Loads for Power System Regulation in Building-to-Grid Integration Framework

Modelling and Simulation of Thermostatically Controlled Loads for Power System Regulation in Building-to-Grid Integration Framework

Modeling and Simulation of Household Appliances Power Consumption

A Methodology for Exploiting Smart Prosumers’ Flexibility in a Bottom-Up Aggregation Process

Contact Info

Product

Resources

About