HEM algorithm based smart controller for home power management system

doi:10.1016/j.enbuild.2016.09.026

Energy and Buildings

Volume 131, 1 November 2016, Pages 184-192

https://doi.org/10.1016/j.enbuild.2016.09.026 Get rights and content

Highlights

•
Home Energy Management (HEM) algorithm for source priority is proposed.
•
The algorithm is based on choosing the power source without affecting user comfort and economy of the smart home.
•
By maximizing the use of PV power, the utility power consumption is reduced, hence reducing the cost of electricity and the problem of Peak demand.

Abstract

The home power management system is proposed with the objective to reduce the electricity cost and also to avoid the problem of high peak demand. Recently more methods have been discussed in the area of Home energy management, but prioritizing the operation of power units from customer point of view has its own benefits depending on the comfort level. The Proposed home consists of a smart electrical appliance, photovoltaic system with battery, smart communication network and a robust controller. This controller schedules the power units in response to electricity price at the Time of Use (ToU). The available power units comprising of solar power, battery power, grid supply and the utilization of home appliance are categorized and monitored regularly. The Primary power units, preferably solar are chosen automatically as per the priority of the customer. When the primary power unit (solar) is not able to supply power, due to its intermittent nature of generation, the controller shifts to next power units accordingly. The simulation results show that the proposed system based on Home Energy Management (HEM) algorithm reduces the electricity cost, peak demand problem and enhances the efficiency of energy use. The Time of Use (ToU) is considered for reducing the peak demand. The smart controller is operated based on HEM algorithm and selects the power units accordingly. Also, there is a necessity of Energy conversion from DC (solar) to AC (grid/appliance), there is feasibility of power quality disturbance. This quality of power is improved by using Selective Harmonic elimination (SHE) method. The proposed system is developed and simulated in MATLAB/SimPowerSystem (SPS).

Introduction

The electricity consumption is increasing day by day, due to the technological development and also due to upcoming innovative smart home appliances. Thus the demand gets raised leading to power generation by means of fossil fuels. In order to reduce the demand, it is necessary to save energy by enhancing the use of Renewable energy. The most part of electrical energy consumption occurs in residential areas. Therefore, the proper use of electricity in the residential area plays a vital role in saving electricity.

The home electricity demand varies throughout the day. When most of the residential, uses electricity at the same time (peak hour), the demand of electricity at the residential area increases. To avoid this high peak demand, real time pricing (RTP), time of use (ToU), and critical peak pricing (CPP) [1] are introduced. The price of electricity will be low at the time of low demand and high when the demand is high. The smart home is defined as the home with smart sensors, power units with Renewable energy, smart controller with smart electrical appliances [2], [3]. Much more research focuses towards the aim of reducing the electricity cost. A novel method is introduced that monitors the energy pricing and creates a corresponding time schedule [4], [5], [6], [7], [8], [9], [10]. A smart energy management is also introduced which is based on price variations [11], [12], [13], [14], [15]. Since solar power needs energy conversion through inverters, it is necessary to maintain the quality of power without polluting the grid and appliances. The home management system has no methodology to produce solutions to reduce electricity cost and the peak demand problem by controlling electrical appliances along with power quality improvement. This paper focuses on the power quality improvement by reducing the THD level by using the method of SHC [17].

Section snippets

Model of smart distribution system

The proposed model of smart distribution consists of smart home with smart electrical appliances, power units and a smart controller. This modelling is performed in MATLAB/Simulink environment. In this proposed system, the execution speed is discretized into uniform time slots T = 1 to 24 i.e., tεT = {1,2,3…. T}, such that the total number of time slots in a day is T = 24/^Δ t where ^Δt represents the length of each time slot.

The total number of time slots in a day is T = 24 (length of each time). The

Harmonic reduction

Most of the power system load is more sensitive to harmonics and also the cost of electricity is affected by the increased use of nonlinear equipment. Today’s harmonic problems have serious consequence than in the past history. Hence it is necessary to look after the harmonics in terms of Total Harmonic Distortion (THD) and mitigate it as per the prescribed level of IEEE 519 or IEC61000.

The harmonics have its effect on power system, consumer load, communication circuits and on revenue billing.

Arduino controller

This section proposes the Arduino based communication suitable for managing energy in home automation applications. Arduino is a small microcontroller utilized for controlling the appliance and available power supply. Recently the smart home management system is becoming more popular because it makes the residential homes smarter and easier to control through wireless technologies.

Even though different types of wireless technologies are available, Bluetooth is experimented since it is enough to

HEM algorithm

The HEM algorithm provides load shifting and load shedding with least on customer lifestyle during a demand response event. The HEM algorithm for the status of the appliance and the HEM algorithm for load priority, is discussed in the literature [5]. The HEM algorithm for power supply optimization/Priority is proposed in order to ensure that the power drawn from the utility grid is reduced and hence electricity cost reduced consistently. In addition, utilization of renewable energy source is

Numerical study

The smart home with renewable energy (PV) and smart appliance as listed in Table 2 is considered for the study. The details of PV power generation, power consumptions and their corresponding tariff rate are listed in Table 1.

The Power generation by PV and the load demand for smart home per day is graphically represented in Fig. 4. It can be seen that nearly 10 h a day solar PV is able to meet the demand.

Thus, it is an evidence that, the solar energy generated through PV system can be effectively

Simulation results

Since the inverter is the source of harmonics, it is to be eliminated suitably so that the appliance has stable life. The simulation results are scaled down to t = 0.24, modelled 24 h per day. In order to enhance the life of smart appliance & controller, the residential voltage and current harmonics are analyzed. By means of FFT analysis, the level of current harmonics measured in terms of THD is 25.40%. The Table 6 shows the THD analysis before and after compensation of current harmonics during

Conclusion

In this paper, HEM algorithm for power supply optimization is developed in order to reduce the electricity cost and also to avoid peak demand problem for a smart home with the help ToU pricing and PV system. According to the HEM algorithm, the main controller selects the power units that will meet the power demand of the smart home. The proposed controller is economically satisfied with low power consumption. It is ease to control and manage the appliances. Since HEM can handle dynamic

References (19)

Filipe Fernandes et al.
Dynamic load management in a smart home to participate in demand response events
Energy Build.
(2014)
T. Kim et al.
Scheduling power consumption with price uncertainty
IEEE Trans. Smart Grid
(2011)
P. Du et al.
Appliance commitment for household load scheduling
IEEE Trans. Smart Grid
(2011)
A. Mohsenian-Rad et al.
Optimal residential load control with price prediction in real-time electricity pricing environments
IEEE Trans. Smart Grid
(2010)
D.D.B.M.L.D.V. Ricquebourg et al.
The smart home concept: our immediate future
IEEE International Conference on E-Learning Industrial Electronics
(2006)
M. Pipattanasomporn et al.
An algorithm for intelligent home energy management and demand response analysis
IEEE Trans. Smart Grid
(2012)
F. Schweppe et al.
Algorithms for a spot price responding residential load controller
IEEE Trans. Power Syst.
(1989)
D. O’Neill et al.
Residential demand response using reinforcement learning
First IEEE International Conference on Smart Grid Communications (Smart Grid Comm)
(2010)
A. Conejo et al.
Real-time demand response model
IEEE Trans. Smart Grid
(2010)

There are more references available in the full text version of this article.

Cited by (41)

Utilizing industry 4.0-related technologies and modern techniques for manufacturing customized products – Smart yogurt filling system
2024, Journal of Engineering Research (Kuwait)
The fulfillment of a clean environment and sustainable development goals depends on production processes. Therefore, the recent changes in industrial output caused by digitalization are quite interesting from the perspective of sustainable development. Opportunities to increase productivity, decrease waste, and transition to a more effective manufacturing system are presented by this transformation. The current study aims to examine how Industry 4.0 (IR 4.0) can be advantageous for the environmental aspect of sustainable development, paying special attention to resource efficiency, efficient energy use, and transparency. IR 4.0 enabler technologies increase productivity, minimize waste, and progress toward a more efficient production system, showing the importance of evolving technologies in industrialization and mass customization. In order to observe the effects of new technologies in IR 4.0 on sustainability aspects, a case study of King Saud University's Smart Yogurt Filling System was examined using recent technologies such as the Internet of Things (IoT), reverse engineering, and additive manufacturing. The test and trial process reveals how these technologies might enhance sustainability in IR 4.0. Following the current study, the use of IR 4.0 enabler technologies demonstrated resource efficiency, transparency, and customer satisfaction as well as improving customer satisfaction, saving time and effort, increasing ergonomics, minimizing human interference, and enabling the delivery of customized products.
MILP-based model predictive control for home energy management systems: A real case study in Algarve, Portugal
2023, Energy and Buildings
This paper addresses the development of an innovative home energy management system (HEMS). The presented HEMS relies on a mixed-integer linear programming (MILP)-based model predictive control. The system takes advantage of the powerful formulation capabilities of a MILP-based mathematical programming problem with the capabilities of model predictive control to optimize, at each sample instant the HEMS operation using a receding-horizon formulation. The system is designed for a residence located in Algarve, Portugal. The results of the presented system are compared with the real experimental results obtained by a commercial PV-battery management system. Additionally, an analysis of the system's performance is conducted, in terms of operation planning for 2021 market prices compared to 2022 prices, where there was a significant rise of buying price due to the energy world context. In all simulations performed, it is verified that the MILP-based model predictive control presents better results, with statistical relevance.
Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system
2021, Energy
Citation Excerpt :
Gazafroudi analyzed the participation of a smart home in various price-based demand response strategies, and revealed that the participation of the smart home in TOU not only reduces the operation cost but also leads to smart home profitability [21]. Rajalingam proposed an HEMS algorithm based on TOU, where the use of PV power was maximized to reduce the electricity cost and address the problem of peak demand [22]. Elma developed a load forecasting model in RTP-based HEMS for better matching of energy consumption with renewable energy generation, and as a result, obtained considerable improvement in the cost-savings of residential producers [23].
To address the primary energy shortage problem, Japan has implemented a series of policies and measures for residential energy conservation and emission reduction. Among them, the home energy management system (HEMS) as a hub connecting users and power companies to realize energy visualization has been widely studied. The research object of this study is a two-story detached residence integrated with HEMS in the “Jono Zero Carbon Smart Community” in Japan. To predict the energy consumed on the next day based on historical data, a short-term household load forecasting model based on the particle swarm optimization regression vector machine algorithm was developed. Then a dynamic pricing model was developed to guide the users’ electricity consumption behavior and adjust the grid load. According to the prediction results obtained by the load forecasting model, the annual electricity charges of users under the three pricing schemes of multistep electricity pricing (MEP), time-of-use pricing (TOU), and real-time pricing (RTP) were calculated and compared. The result indicated that the annual electricity cost generated by RTP was less than those generated by MTP and TOU. In addition, after adjusting the users’ peak load and combining it with the fluctuating future electricity prices, RTP presented evident economic advantage over MTP and TOU in terms of the annual electricity cost of the users. The study results can provide policy suggestions for the future Japanese government’s promotion of RTP strategy, while acting as a reference for further developing the characteristics of HEMS and optimizing the relation between the supply and demand sides.
Review on home energy management system
2021, Materials Today: Proceedings
Citation Excerpt :
This system consists of Smart appliances, power unit, communication network and controller (Mixed Integer Linear Programming optimization techniques). S. Rajalingam, et al [21], the paper mainly concentrates on minimizing the energy cost for the individual home and also additionally considers the power quality improvement by using Selective Harmonics Elimination method (SHE). Xiaohua Wu, et al [22], the paper proposes a smart home energy management system with PEV (Plug-in Electric Vehicle – Storage).
Nowadays energy demand is increasing speedily because of a rise in increase and way of the individuals. This leads to more issues in electricity distribution. In order to avoid such issues, a home energy management system (HEMS) has been introduced. HEMS represent the locations where energy is wasted in the homes and utilization of energy in a better way. A HEMS considers both energy generation and utilization and also provides two-way communications between the distribution centre and consumer. This paper displays a writing survey of the home vitality the board framework (HEMS). This review paper basically deliberates upon research papers related to the HEMS for different conditions and cases depending upon different climate conditions, different appliances, different controllers with algorithms, different home occupants and their living styles.
Hybrid simulation system for testing artificial intelligence algorithms used in smart homes
2020, Simulation Modelling Practice and Theory
Citation Excerpt :
Then a simulation was developed in which users can use this house with virtual reality glasses [18]. In a study about more efficient use of solar energy at home and electricity from the city grid, an algorithm called HEM was developed and this algorithm was tested by simulations in a MATLAB/Simulink environment [19]. In a similar study, the total amount of electricity that the house draws from the city network and the electrical energy consumed separately by the components in the house can be determined.
Smart home automation systems allow the user to control and track lighting, security, ventilation and temperature systems more easily. While these systems provided easy control of the house depending on the commands only given by the user in the early period, today with artificial intelligence algorithms developed for energy saving, home security, control etc., these systems have begun to perform many operations autonomously. Many researchers and R&D companies work in this field and develop new algorithms every day to create new products. Artificial intelligence algorithms must be continuously tested and improved under different conditions in order to function properly and perform the desired function. Performing the algorithm tests in real environments involves many problems and reduces the efficiency in terms of studies. In similar situations in smart homes and different disciplines, algorithm development and improvement studies are performed in simulated environments. However, it may not be possible to include all the variables of real life in simulation environments. Considering all these situations, in this study, a hybrid smart home simulation that runs real and virtual smart homes together is developed to test the artificial intelligence algorithms developed for use in smart homes. A real smart home system is first developed and installed in a room for hybrid simulation. Then, a simulated house is developed with the desired number of rooms, desired number of smart home components with different tasks for this house, virtual individuals who use this house, and weekly life scenarios for these individuals. The room in which the real smart home system works is converted into a room in the virtual house by changing the simulation codes. Likewise, a real person who uses the real smart home becomes an inhabitant living in a virtual home. In addition, virtual inhabitants are created to use the real smart home system. Thus, the real and the virtual smart home and real and virtual inhabitants live together in a hybrid simulation. Finally, the hybrid simulation is operated smoothly under different conditions for two months with different artificial intelligence algorithms.
Time and device based priority induced comfort management in smart home within the consumer budget limitation
2018, Sustainable Cities and Society
Citation Excerpt :
The priority management is a major concern in the electricity load management problem as it increases user satisfaction level. In Rajalingam and Malathi (2016), the authors prioritized the operation of power units from a consumer perspective. For instance, the solar power source unit is assigned a high priority as compared to other installed power units.
In demand side management (DSM), scheduling of appliances based on consumer-defined priorities is an important task performed by a home energy management controller (HEMC). This paper presents a DSM technique that is capable of controlling loads within a smart home considering time-varying appliances’ priorities. An evolutionary accretive comfort algorithm (EACA) is developed based on four postulations that allow time-varying priorities to be quantified in time and device-based features. Based on the input data considering the appliances’ power ratings, its time of use, and absolute comfort derived from priorities, the EACA is able to generate an optimal energy consumption pattern which would give maximum satisfaction at a predetermined user budget. A cost per unit comfort index (χ) which relates the consumer expenditure to the achievable comfort is also demonstrated. To test the applicability of the proposed EACA, three budget scenarios of $1.5/day, $2.0/day and $2.5/day are performed. The results of each of the scenarios using EACA are compared to five base cases in which the appliances are randomly used. The simulation results revealed that the proposed EACA obtained an optimal absolute comfort with reduced cost per unit comfort values for all three scenarios within the budget limits.

View all citing articles on Scopus

View full text

HEM algorithm based smart controller for home power management system

Highlights

Abstract

Introduction

Section snippets

Model of smart distribution system

Harmonic reduction

Arduino controller

HEM algorithm

Numerical study

Simulation results

Conclusion

Energy Build.

Scheduling power consumption with price uncertainty

IEEE Trans. Smart Grid

Appliance commitment for household load scheduling

IEEE Trans. Smart Grid

Optimal residential load control with price prediction in real-time electricity pricing environments

IEEE Trans. Smart Grid

The smart home concept: our immediate future

IEEE International Conference on E-Learning Industrial Electronics

An algorithm for intelligent home energy management and demand response analysis

IEEE Trans. Smart Grid

Algorithms for a spot price responding residential load controller

IEEE Trans. Power Syst.

Residential demand response using reinforcement learning

First IEEE International Conference on Smart Grid Communications (Smart Grid Comm)

Real-time demand response model

IEEE Trans. Smart Grid