Intelligent approach for residential load scheduling

Sadat-Mohammadi, Milad; Nazari-Heris, Morteza; Nazerfard, Ehsan; Abedi, Mehrdad; Asadi, Somayeh; Jebelli, Houtan

doi:10.1049/iet-gtd.2020.0143

Cited by 24 publications

(11 citation statements)

References 51 publications

(50 reference statements)

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“…Unlike the existence of an intelligent-based prediction methods like as 48 to fit the distribution functions and generate scenarios, stochastic type are taken into account in the current paper due to the simplification of the proposed approach. So, to handle the unpredictability inherent in electricity price, WTPs, and PVs, some modelings have been investigated.…”

Section: Methods Of Dealing With System Uncertaintiesmentioning

confidence: 99%

“…Equation ( 46) indicates the hourly stored hydrogen, restricted to the minimum and maximum capacity of the system stated in (47). The equalization of stored hydrogen in the first and final hours is expressed in (48). N o,t,s is defined as the quantity of hydrogen directly used in hydrogen autonomous industries limited by (49).…”

Section: Thermal Storage System Constraintsmentioning

confidence: 99%

See 1 more Smart Citation

Economic‐environmental stochastic scheduling for hydrogen storage‐based smart energy hub coordinated with integrated demand response program

et al. 2021

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Section: Methods Of Dealing With System Uncertaintiesmentioning

confidence: 99%

Section: Thermal Storage System Constraintsmentioning

confidence: 99%

Economic‐environmental stochastic scheduling for hydrogen storage‐based smart energy hub coordinated with integrated demand response program

et al. 2021

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“…The OS methods mainly focus on minimizing the overall cost of energy (financially and environmentally) and reliance on the grid by producing as much as possible the renewable energy resources. For example, the study in [66] implements an intelligent load scheduling model for residential load. This study aims to minimize the user intervention by considering the degradation cost of the battery pack in the vehicle-to-grid mode.…”

Section: B Optimization and Scheduling Methodsmentioning

confidence: 99%

Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

et al. 2021

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Over 33% of final energy consumption is used in buildings which leads to nearly 40% of total direct and indirect CO 2 emissions in the world. While energy consumption is steadily rising globally, managing building energy utilization by on-site renewable energy generation can help responding this demand. This paper proposes a deep learning method based on a discrete wavelet transformation and long short-term memory method (DWT-LSTM) and a scheduling framework for the integrated modelling and management of energy demand and supply for buildings. This method considers several factors including a two-step electricity price, uncertainty in climatic factors, availability of renewable energy resources (wind and solar), energy consumption patterns in buildings, and the non-linear relationships between these parameters. This novel method analyzes and continuously learns from data patterns based on hourly, daily, weekly and monthly intervals. The method enables monitoring and controlling renewable energy generation, the share of energy imports from the grid, employment of saving strategy based on the user priority list, and energy storage management. The main objective of the proposed method is to minimize the reliance on the grid and electricity cost, especially during the peak days. The results demonstrate that the proposed method can forecast building energy demand and energy supply with a high level of accuracy, showing a 3.63-8.57% error range in hourly data prediction for one month ahead. The results also show that the method can supply 304 days (83.2%) of a year without reliance on energy grids, decreasing 87.2% in energy demand on one hand and exporting annually 7777 kWh to the grid on the other hand. In addition, the rescheduling framework decreased the imported electricity cost with the higher electricity tariff by 98 %. The combination of the deep learning forecasting, energy storage, and scheduling algorithm enables reducing annual energy import from the grid from 6709 to 858 kWh (84.3%).

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“…RTP programs have been utilised by large commercial and industrial (C&I) consumers to make optimal control strategies for more than 10 years [2–4]. With the growing deployment of advanced metering infrastructure (AMI) and automated energy consumption scheduling (ECS) devices, RTP is gradually becoming a viable option for residential consumers [5–7]. For instance, a state‐wide residential hourly pricing program has been launched in Illinois.…”

Section: Introductionmentioning

confidence: 99%

Real‐time pricing response attack in smart grid

Liu

Tian

Yuan

et al. 2022

IET Generation Trans & Dist

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Real-time pricing is an attractive tariff for demand response programs, in which consumers could provide load flexibility by adjusting their electricity consumption in response to timevarying electricity prices. This paper reveals a new threat called real-time pricing response attack (RPRA), which manipulates consumers' electricity consumption by tampering with electricity price signals. Simulations are performed in IEEE 24-bus, 39-bus, and 118-bus systems, and the results show that 8% to 28% of the transmission lines could be overloaded by the attack, which could cause serious security problems. Results of sensitivity analysis indicate that the attack effects are influenced by various factors, such as DR models, price elasticity, line capacities etc. To defend against the RPRA attack, we have proposed various detection and mitigation strategies.

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Intelligent approach for residential load scheduling

Cited by 24 publications

References 51 publications

Economic‐environmental stochastic scheduling for hydrogen storage‐based smart energy hub coordinated with integrated demand response program

Economic‐environmental stochastic scheduling for hydrogen storage‐based smart energy hub coordinated with integrated demand response program

Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

Real‐time pricing response attack in smart grid

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