Optimal Energy Scheduling Method under Load Shaping Demand Response Program in a Home Energy Management System

2019 ◽  
Vol 20 (2) ◽  
Author(s):  
Sandeep Kakran ◽  
Saurabh Chanana
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Demand Response ◽  
Management System ◽  
Electricity Consumption ◽  
Electricity Sector ◽  
Energy Management System ◽  
Home Energy Management ◽  
Utility Companies ◽  
Consumption Cost

Abstract With the latest smart technologies in the electricity sector, the consumers of electricity got the opportunity to reduce their electricity consumption cost by participating in the demand response programs offered by the utility companies. In this paper, a model of energy management system is introduced for the energy scheduling at home. Residential automatic smart appliances of general use are selected for energy scheduling. The energy controlling device in the EMS model receives the real time electricity price signals from the utility company and schedule the appliances according to the user requirements in such a way so that the electricity consumption cost could be minimized. The appliances are scheduled under real time pricing combined with inclined block rate pricing scheme so that the peak to average ratio of power could be maintained in the satisfactory range. This helps the utility companies in maintaining the system reliability. For the solution of the scheduling problem, particle swarm optimization algorithm is used due to its effectiveness and easy implementation. Finally, the results have been compared and verified against the results achieved by genetic algorithm.

scholarly journals Analysis of the Effects of the Home Energy Management System from an Open Innovation Perspective

2018 ◽  
Vol 4 (3) ◽  
pp. 31 ◽  
Author(s):  
EungSuk Park ◽  
BoRam Kim ◽  
SooHyun Park ◽  
Daecheol Kim
Keyword(s):  
Power Consumption ◽  
Electric Power ◽  
Real Time ◽  
Energy Management ◽  
Management System ◽  
Electricity Consumption ◽  
Electric Power Consumption ◽  
Energy Management System ◽  
Home Energy Management ◽  
Home Energy Management System

The Home Energy Management System (HEMS) is a system for the efficient electric power consumption of each household. It can provide real-time electricity cost information according to electricity consumption, and households can immediately control their consumption of electricity. In this study, we analyzed the effects of the HEMS on the stability of demand for electric power. To do this, we analyzed the causal relationship between the amounts of electric power generation and consumption, from the system dynamics perspective. From the analysis, we found that in the current structure, the fluctuation of the quantity of demand became large due to the time delay in households recognizing the electric bill and adjusting their electric power consumption. However, when the HEMS was introduced, it could be seen that electric power demand remained stable since consumers could see their electricity bill in real-time and could manage their electricity consumption by themselves.

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.

Operation Scheduling of Household Load, EV and BESS Using Real Time Pricing, Incentive Based DR and Peak Power Limiting Strategy

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Sandeep Kakran ◽  
Saurabh Chanana
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Cost Reduction ◽  
Management System ◽  
Peak Power ◽  
Base Case ◽  
Energy Management System ◽  
Electricity Cost ◽  
Home Energy Management ◽  
Home Energy Management System

Abstract Demand response (DR) programs have become powerful tools of the smart grids, which provide opportunities for the end-use consumers to participate actively in the energy management programs. This paper investigates impact of different DR strategies in a home-energy management system having consumer with regular load, electric vehicle (EV) and battery-energy storage system (BESS) in the home. The EV is considered as a special type of load, which can also work as an electricity generation source by discharging the power in vehicle-to-home mode during high price time. BESS and a small renewable energy source in form of rooftop photovoltaic panels give a significant contribution in the energy management of the system. As the main contribution to the literature, a mixed integer linear programming based model of home energy management system is formulated to minimize the daily cost of electricity consumption under the effect of different DR programs; such as real time price based DR program, incentive based DR program and peak power limiting DR program. Finally, total electricity prices are analysed in the case studies by including different preferences of the household consumer under mentioned DR programs. A total of 26.93 % electricity cost reduction is noticed with respect to base case, without peak limiting DR and 19.93 % electricity cost reduction is noticed with respect to base case, with peak limiting DR.

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