A home energy management algorithm in demand response events for household peak load reduction

2017 ◽  
Vol 1 (3) ◽  
pp. 199-202 ◽  
Author(s):  
Maytham AHMED
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Peak Load ◽  
Load Reduction ◽  
Management Algorithm ◽  
Home Energy Management

scholarly journals Demand response program for smart grid through real time pricing and home energy management system

2021 ◽  
Vol 11 (5) ◽  
pp. 4558
Author(s):  
Shibily Joseph ◽  
E. A. Jasmin
Keyword(s):  
Smart Grid ◽  
Real Time ◽  
Energy Management ◽  
Demand Response ◽  
Management System ◽  
Peak Load ◽  
Energy Management System ◽  
Home Energy Management ◽  
Real Time Pricing ◽  
Home Energy Management System

Aim of demand response (DR) programs are to change the usage pattern of electricity in such a way that, beneficial to the consumers as well as to the distributors by applying some methods or technology. This way additional cost to erect new energy sources can be postponed in power grid. Best method to implement demand response (DR) program is by influencing consumer through the implementation of real time pricing scheme. To harness the benefit of DR, automated home energy management system is essential. This paper presents a comprehensive demand response system with real time pricing. The real time price is determined after considering price elasticity of various classes of consumers and their load profiles. A real time clustering algorithm suitable for big data of smart grid is devised for the segmentation of consumers. This paper is novel in its design for real time pricing and modelling and automatic scheduling of appliances for home energy management. Simulation results showed that this new real time pricing method is suitable for DR programs to reduce the peak load of the system as well as reducing the energy expenditure of houses, while ensuring profit for the retailer.

scholarly journals QoE-Aware Smart Home Energy Management Considering Renewables and Electric Vehicles

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2304 ◽  
Author(s):  
Mingfu Li ◽  
Guan-Yi Li ◽  
Hou-Ren Chen ◽  
Cheng-Wei Jiang
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Smart Home ◽  
Control Algorithm ◽  
Renewable Energy Sources ◽  
Peak Load ◽  
Electricity Bill ◽  
Home Energy Management ◽  
Smart Appliance ◽  
User Comfort

To reduce the peak load and electricity bill while preserving the user comfort, a quality of experience (QoE)-aware smart appliance control algorithm for the smart home energy management system (sHEMS) with renewable energy sources (RES) and electric vehicles (EV) was proposed. The proposed algorithm decreases the peak load and electricity bill by deferring starting times of delay-tolerant appliances from peak to off-peak hours, controlling the temperature setting of heating, ventilation, and air conditioning (HVAC), and properly scheduling the discharging and charging periods of an EV. In this paper, the user comfort is evaluated by means of QoE functions. To preserve the user’s QoE, the delay of the starting time of a home appliance and the temperature setting of HVAC are constrained by a QoE threshold. Additionally, to solve the trade-off problem between the peak load/electricity bill reduction and user’s QoE, a fuzzy logic controller for dynamically adjusting the QoE threshold to optimize the user’s QoE was also designed. Simulation results demonstrate that the proposed smart appliance control algorithm with a fuzzy-controlled QoE threshold significantly reduces the peak load and electricity bill while optimally preserving the user’s QoE. Compared with the baseline case, the proposed scheme reduces the electricity bill by 65% under the scenario with RES and EV. Additionally, compared with the method of optimal scheduling of appliances in the literature, the proposed scheme achieves much better peak load reduction performance and user’s QoE.

Peak Load Reduction in a Smart Building Integrating Microgrid and V2B-Based Demand Response Scheme

2019 ◽  
Vol 13 (3) ◽  
pp. 3274-3282 ◽  
Author(s):  
Hanane Dagdougui ◽  
Ahmed Ouammi ◽  
Louis A. Dessaint
Keyword(s):  
Demand Response ◽  
Peak Load ◽  
Load Reduction ◽  
Smart Building ◽  
Response Scheme

Optimal Energy Management of EV Parking Lots Under Peak Load Reduction Based DR Programs Considering Uncertainty

2019 ◽  
Vol 10 (3) ◽  
pp. 1034-1043 ◽  
Author(s):  
Ibrahim Sengor ◽  
Ozan Erdinc ◽  
Baris Yener ◽  
Akin Tascikaraoglu ◽  
Joao P. S. Catalao
Keyword(s):  
Energy Management ◽  
Peak Load ◽  
Load Reduction ◽  
Parking Lots ◽  
Optimal Energy ◽  
Optimal Energy Management

scholarly journals Futuristic Sustainable Energy Management in Smart Environments: A Review of Peak Load Shaving and Demand Response Strategies, Challenges, and Opportunities

2020 ◽  
Vol 12 (14) ◽  
pp. 5561 ◽  
Author(s):  
Bhagya Nathali Silva ◽  
Murad Khan ◽  
Kijun Han
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Energy Demand ◽  
Management Strategies ◽  
Peak Load ◽  
Critical Discussion ◽  
Future Research ◽  
Smart Environments ◽  
Challenges And Opportunities ◽  
Management Approaches

The emergence of the Internet of Things (IoT) notion pioneered the implementation of various smart environments. Smart environments intelligibly accommodate inhabitants’ requirements. With rapid resource shrinkage, energy management has recently become an essential concern for all smart environments. Energy management aims to assure ecosystem sustainability, while benefiting both consumers and utility providers. Although energy management emerged as a solution that addresses challenges that arise with increasing energy demand and resource deterioration, further evolution and expansion are hindered due to technological, economical, and social barriers. This review aggregates energy management approaches in smart environments and extensively reviews a variety of recent literature reports on peak load shaving and demand response. Significant benefits and challenges of these energy management strategies were identified through the literature survey. Finally, a critical discussion summarizing trends and opportunities is given as a thread for future research.

scholarly journals System-Theoretic Process Analysis (STPA) for Hazard Analysis in Complex Systems: The Case of “Demand-Side Management in a Smart Grid”

Systems ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 33 ◽  
Author(s):  
Stylianos Karatzas ◽  
Athanasios Chassiakos
Keyword(s):  
Smart Grid ◽  
Complex Systems ◽  
Energy Management ◽  
Demand Response ◽  
Energy Demand ◽  
Process Analysis ◽  
Hazard Analysis ◽  
Energy Market ◽  
System Modelling ◽  
Home Energy Management

Inelasticity of demand along with the distributed energy sources and energy market democratization pose significant challenges which have considerable negative impacts on overall grid balance. The need for increased capacity and flexibility in the era of energy market digitalization has introduced new requirements in the energy supply network which could not be satisfied without continuous and costly local power network upgrades. Additionally, with the emergence of Smart Homes (SHs) and Home Energy Management (HEM) systems for monitoring and operating household appliances, opportunities have arisen for automated Demand Response (DR). DR is exploited for the modification of the consumer energy demand, in response to the specific conditions within the electricity system (e.g., peak period network congestion). In order to optimally integrate DR in the broader Smart Grid (SG) system, modelling of the system parameters and safety analysis is required. In this paper, the implementation of STPA (System-Theoretic Process Analysis) structured method, as a relatively new hazard analysis technique for complex systems is presented and the feasibility of STPA implementation for loss prevention on a Demand Response system for home energy management, and within the complex SG context, is examined. The applied method delivers a mechanism useful in understanding where gaps in current operational risk structures may exist. The STPA findings in terms of loss scenarios can be used to generate a variety of safeguards to ensure secure operational control and in implementing targeted strategies through standard approaches of risk assessment.

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