Electric Water Heater Modelling for Direct Load Control Demand Response

Ahmed, Md. Tofael; Faria, Pedro; Abrishambaf, Omid; Vale, Zita

doi:10.1109/indin.2018.8472102

Cited by 10 publications

(11 citation statements)

References 21 publications

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“…In the direct load control, which is an incentive-based approach and not a price-based one, an aggregator or distributed system operator (DSO) adjusts the consumers' power demand directly. This effective scheme has been widely used in frequency regulation [3] and peak load shaving [4], but it noticeably sacrifices the privacy of consumers and decreases their satisfaction level. On the other hand, the indirect load control is based on incentivizing consumers to contribute to the DR program in exchange for a reward or decreasing their electricity bill.…”

Section: A Indexes and Setsmentioning

confidence: 99%

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Bonus-Based Demand Response Using Stackelberg Game Approach for Residential End-Users Equipped With HVAC System

Tavakkoli

Fattaheian‐Dehkordi

Pourakbari‐Kasmaei

et al. 2021

IEEE Trans. Sustain. Energy

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Section: A Indexes and Setsmentioning

confidence: 99%

“…The first term in (4) shows the difference between the generated wind power and consumers' demand including the non-HVAC and HVAC loads. The second term stands for the multiplication of change in the HVAC power and the amount of bonus for that time.…”

Section: A Leader Modelmentioning

confidence: 99%

Bonus-Based Demand Response Using Stackelberg Game Approach for Residential End-Users Equipped With HVAC System

Tavakkoli

Fattaheian‐Dehkordi

Pourakbari‐Kasmaei

et al. 2021

IEEE Trans. Sustain. Energy

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“…Literature also focus on single EWH optimization [12], [13], [14], [15], where the hot water temperature inside the tank is a known parameter in all the references mentioned.…”

Section: A Electric Water Heaters Literature Reviewmentioning

confidence: 99%

“…Ahmed [13] studies the DR possibilities of a residential EWH, the overall consumption and temperature profile and the end-user economic benefit. Real-time electricity pricing with incentive-based DR is considered.…”

Section: A Electric Water Heaters Literature Reviewmentioning

confidence: 99%

A scheduling optimization model of electric water heaters for electricity cost minimization with limited information

Barja-Martinez

Olivella‐Rosell

Lloret-Gallego

et al. 2019

2019 8th International Conference on Modern Power Systems (MPS)

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The present paper defines the equations that govern the optimization model of an electric water heater in order to manage automatically its available flexibility for the user's own benefit. A case study that focuses on the electric water heater flexibility potential is carried out: it is aimed to minimize the total expected electricity cost of a single household that only has an electric water heater as a flexibility source. The temperature inside the hot water tank is an unknown value since the temperature sensor is not sending any information outside. Moreover, real hot water consumption is uncertain and the model takes decisions based on forecasting models. Given the limited information available, the model is developed in a conservative way with the objective of maintaining both, the hot water temperature and the user's comfort. The results show that it is possible to reduce the electricity bill by managing optimally the available flexibility from the electric water heater.

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“…In some states such as Taiwan, USA and Europe which have subtropical climates, the temperature is right in the range of 38.5 degrees Celsius to 40.5 degrees Celsius [2]. Electric Water Heater (EWH) is a household appliance that typically consumes a large portion of electricity, from 7.5% to 40% of total energy consumption in a house [3]. Conventional EWH has a reservoir tank, in which water is stored.…”

Section: Introductionmentioning

confidence: 99%

High Accuracy Electric Water Heater using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Sudiharto

DwiMurdianto²,

Budikarso³

et al. 2022

KINETIK

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Nowadays, water heater is a common household appliance. Water heater can be divided into three types, based on fuel sources: gas, diesel, and electric. Electric water heater is the most common due to its ease of use. The problems that often occur on electric water heater are over-temperature due to user error in setting up the thermostat and inaccurate readings caused by a conventional system control. These problems will cause a surge in power consumption. Over-temperature and conventional control inaccuracies can be overcome using the Artificial Intelligence (AI) control algorithm in the form of an adaptive neuro-fuzzy inference system (ANFIS). The proposed algorithm acts as a control by maintaining the stability of the temperature to obtain more accurate results. An accurate temperature reading can lower power consumption in electric water heater. This study tries to simulate Electric Water Heater temperature control using the ANFIS algorithm until stable readings can be achieved in all temperature settings. Results from disturbance tests in the form of external condition that causes sudden temperature change show that the system can maintain stability with an average error margin of 0.045% and the rate of accuracy of 99.955%.

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Electric Water Heater Modelling for Direct Load Control Demand Response

Cited by 10 publications

References 21 publications

Bonus-Based Demand Response Using Stackelberg Game Approach for Residential End-Users Equipped With HVAC System

Bonus-Based Demand Response Using Stackelberg Game Approach for Residential End-Users Equipped With HVAC System

A scheduling optimization model of electric water heaters for electricity cost minimization with limited information

High Accuracy Electric Water Heater using Adaptive Neuro-Fuzzy Inference System (ANFIS)

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