Development of a Smart Thermostat Controller for Direct Load Control Based Demand Response Applications

Yener, Barış; Erenoğlu, Ayşe Kübra; Şengör, İbrahim; Erdinç, Ozan; Taşçıkaraoğlu, Akın; Catalào, João P. S.

doi:10.1109/sest.2019.8849033

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…However, at the consumer side, there is a need to make it mandatory to automate the accurate appliance switching with respect to dynamic pricing. For this purpose, direct load control models are implemented [10], [11]. On the broader view, the electricity market has gone a rapid transformation with the involvement of smart grids (SG) [12], [13].…”

Section: P Nmentioning

confidence: 99%

A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources

et al. 2021

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In this paper, a two-stage approach is proposed on a joint dispatch of thermal power generation and variable resources including a storage system. Although, the dispatch of alternate energy along with conventional resources has become increasingly important in the new utility environment. However, recent studies based on the uncertainty and worst-case scenario-oriented robust optimization methodology reveal the perplexities associated with renewable energy sources (RES). First, the load demand is predicted through a convolutional neural network (CNN) by taking the ISO-NECA hourly real-time data. Then, the joint dispatch of energy and spinning reserve capacity is performed with the integration of RES and battery storage system (BSS) to satisfy the predicted load demand. In addition, the generation system is penalized with a cost factor against load not served for the amount of energy demand which is not fulfilled due to generation constraints. Meanwhile, due to ramping of thermal units, the available surplus power will be stored in the backup energy storage system considering the state of charge of the storage system. The proposed method is applied on the IEEE-standard 6-Bus system and particle swarm optimization (PSO) algorithm is used to solve the cost minimization objective function. Finally, the proposed system performance has been verified along with the reliability during two worst-case scenarios, i.e., sudden drop in power demand and a short-fall at the generation end. INDEX TERMSCo-Dispatch, Spinning reserve, State of charge, Optimization, Renewable energy resources, Battery storage system, Particle swarm optimization NOMENCLATURE The main notations used in this paper are stated below. Additional symbols are defined in the article where required.

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Section: P Nmentioning

confidence: 99%

A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources

et al. 2021

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“…in return to utility signals like price, penalties, and incentives. Incentive-based Demand Response (IDR) [3,4] forces customers to willingly cooperate in DR programs by offering some incentives in return to their flexibility services. Market-based methods like Real-Time Pricing (RTP), incentive-based DR program and prosumer to prosumer trading, etc.…”

Section: Introductionmentioning

confidence: 99%

The Paradigm Revolution in the Distribution Grid: The Cutting-Edge and Enabling Technologies

Thasnimol

Rajathy

2020

Open Computer Science

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Bi-directional information and energy flow, renewable energy sources, battery energy storage, electric vehicle, self-healing capability, and demand response programs, etc., revolutionized the traditional distribution network into the smart distribution network. Adoption of modern technologies like intelligent meters such as advanced metering infrastructure & Micro-phasor measurement units, data storage, and analysis techniques and incentive-based electricity trading mechanisms can bring this paradigm shift. This study presents an overview of popular technologies that facilitate this transformation, giving focus on some prime technologies such as real-time monitoring based on Micro-phasor measurement units, data storage and analytics, blockchain technology, multi-agent systems, and incentive-based energy trading mechanisms

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Demand response programs: Comparing price signals and direct load control

Miri,

McPherson

2024

Energy

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Development of a Smart Thermostat Controller for Direct Load Control Based Demand Response Applications

Cited by 5 publications

References 21 publications

A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources

A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources

The Paradigm Revolution in the Distribution Grid: The Cutting-Edge and Enabling Technologies

Demand response programs: Comparing price signals and direct load control

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