Conference Paper

An Energy Efficient Residential Load Management System for Multi-Class Appliances in Smart Homes

Conference Paper

An Energy Efficient Residential Load Management System for Multi-Class Appliances in Smart Homes

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Abstract

Demand Side Management (DSM) mechanism is used for the implementation of different strategies to encourage residential users to reduce electricity bill as well as energy demand. There is also a close relationship between the consumer and utility for equally benefiting to both in terms of grid stability and bill reduction. Extensive research is undertaken now a days in order to make practical implementation on the possible use of different DSM strategies to regulate the energy demand and carbon emission reduction in the World. The major objective of this work is to study the DSM-based approaches which could be helpful in achieving significant electricity demand reduction at the electricity distribution network which is directly connected to the commercial and residential sector especially. In this work, we use an optimization algorithm to obtain the optimal solution for residential electricity load management in a typical household setting. There are two major tasks of this algorithms; firstly, electricity bill minimization of residential user in time of use pricing models, secondly, peaks reduction of demand curve (peak shaving) which will eventually minimize the investment cost of utility including, peak power plants, and transmission lines. Three types of smart appliances are considered; without delay, delay of one hour, delay of five hours. To validate the effectiveness of the proposed algorithm, mathematical models of appliances based on their length of operation time is developed.

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... Considering consumer convenience is a crucial rule for consumer involvement and participation in DR programs. The consumer convenience and preferences are formulated as a set of constraints by many scholars as in (Baldauf & Member, 2015;Izmitligil & Özkan, 2016;Rasheed et al., 2015;Sou, Weimer, Sandberg, & Johansson, 2011;Yao, Shen, & Lim, 2017). However, few authors tackled the RLSP while considering consumer convenience as objective functions. ...
... The Rasheed et al., 2015;Shakouri & Kazemi, 2017). Results demonstrated that those models could cut down the electricity bill and save the load profile. ...
... First, few studies addressed the RLSP while aiming to minimize electricity cost and consumer inconvenience, simultaneously, as in (Muhsen et al., 2019;Setlhaolo & Xia, 2014Setlhaolo et al., 2014a;Yahia & Pradhan, 2018). Furthermore, few studies addressed the problem while minimizing electrical peak load and electricity cost, simultaneously, as in (Haider et al., 2016b;Izmitligil & Özkan, 2016;Nan et al., 2017;Rasheed et al., 2015;Shakouri & Kazemi, 2017). However, and to the best of our knowledge, the RLSP while aiming to minimize electricity costs, consumer inconvenience, and electrical peak load, simultaneously, has not been addressed previously in the literature. ...
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... Regarding energy usage related objective functions, Logenthiran et al. [22] considered the minimization problem of the peak load demand of the smart grid. Some other authors considered energy usage related objective functions along with financial related ones, for example, minimization of both electricity costs and power losses [12], minimization of electricity bill and peaks of demand curve (peak shaving) [15,[29][30] and minimization of energy bills and balance load scheduling during all time periods [31]. Whilst many authors guaranteed consumer convenience and preferences in their models by constraints [11][12][13][29][30], few authors solved the RLSP with consumer convenience related objective functions. ...
... Some other authors considered energy usage related objective functions along with financial related ones, for example, minimization of both electricity costs and power losses [12], minimization of electricity bill and peaks of demand curve (peak shaving) [15,[29][30] and minimization of energy bills and balance load scheduling during all time periods [31]. Whilst many authors guaranteed consumer convenience and preferences in their models by constraints [11][12][13][29][30], few authors solved the RLSP with consumer convenience related objective functions. Setlhaolo et al. [17] proposed an MINLP optimization model for the RLSP with the objective function that minimizes electricity costs while considering the trade-off between incentive and inconvenience. ...
... Furthermore, studies that considered minimization of peak load and cost simultaneously have not received sufficient attention. Rasheed et al. [33] applied an optimization algorithm to obtain the optimal solution for the HALSP. They focused on two major goals; firstly, electricity bill minimization, and secondly, peaks reduction of demand curve (peak shaving) which will eventually minimize the investment cost of utility including peak power plants and transmission lines. ...
... To the best of our knowledge, only Setlhaolo et al. [26] and Setlhaolo and Xia [27][28][29] tackled the HALSP with the objective function that minimizes electricity costs and consumer inconvenience, simultaneously. Furthermore, only a few number of publications considered the minimization of both electrical peak load and electricity cost simultaneously [24,[33][34][35][36]. To the best of our knowledge, the HALSP with the objective function that simultaneously minimizes electricity costs, consumer inconvenience, and electrical peak load has not been considered in the previous literature. ...
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... Different pricing schemes based and algorithm based home energy management techniques are presented in [15]. In [16] the author proposed a scheduling scheme of home appliances. Appliances are divided into three classes. ...
... In some studies, ToU has been used for energy efficiency with multi-class appliances in smart homes. One of them, (Rasheed et al. 2015) categorized smart appliances of electricity consumption into classes based on the order of importance, and proposed the consumption schedule options. In (Abdollahi et al. 2013), the author examined the impact of different Time of Use pricing is generally modelled in different ways such as customer psychology based model and the price-elasticity matrix of demand. ...
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... It has a three-phase diode bridge rectifier, one switching device (IGBT), its driver circuit and one resistive dump load which makes the DLC simple, cheap and reliable [2]- [5]. SEMS has other local controllers like temperature controller and water level controller with the Fig.1.The block diagram of proposed pico-hydro system aid of which SEMS intelligently distribute the generated power to the consumer loads according to their priority and availability [6]- [8]. ...
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... In this scheme, GA outperforms the rest of the schemes. The cost minimization and waiting time is directly related to user comfort as discussed in [24]. ...
Chapter
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Now a days, energy is the essential resource and due to increase in power demand, traditional resources are not enough to fulfill the requirement of todays need. The researchers are working on new approaches to enhance and improve the power load demand. The increasing demand of electricity creates peaks on utility. Therefore an improved Home Energy Management System (HEMS) is necessary for the automation of smart home to reduce the cost and peaks on utility. In this paper work, our objective is pro utility and pro-consumer comfort which means, the decrease in Peak to Average Ratio (PAR) in order to reduce the stress on the utility while increasing user comfort. In this Research, we have proposed a new technique called Random Cell Elimination Scheme (RCES) with Demand Side Management (DSM) for a home appliance scheduling. To make the system more effective, we have utilized two pricing systems: Time of Use (ToU) and Real Time Pricing (RTP) in our experiment. The simulation results are compared with two heuristic optimization schemes: Bacterial Foraging (BFA) and Firefly Algorithm (FA). The experimental results shows that the proposed scheme performed 80% better than BFA and FA in reducing PAR and user discomfort.
... However, the proposed model formulation was nonlinear which raised the issue of complexity and computation time. Furthermore, few studies considered minimization of peak load and cost simultaneously (Nan et al. 2018, Rasheed et al. 2015, Haider et al. 2014, İzmitligil and Özkan 2016, and Shakouri and Kazemi 2017. However, the aforementioned work considered minimization of either the inconvenience or the peak load; and did not consider these two objectives simultaneously. ...
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Thesis
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The smart grid plays a vital role in decreasing electricity cost via Demand Side Management (DSM). Smart homes, being a part of the smart grid, contribute greatly for minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the Peak to Average Ratio (PAR) and electricity cost with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time where user waiting time is considered to be minimum for residential consumers with multiple homes. Hence, in contribution 1, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart electricity storage system is also taken into account for more efficient operation of the HEM System. Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is instigated in a smart building which is comprised of thirty smart homes (apartments). Critical Peak Pricing (CPP) and Real-Time Pricing (RTP) signals are examined in terms of electricity cost assessment for both a single smart home and a smart building. In addition, feasible regions are presented for multiple and single smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results prove the effectiveness of our proposed scheme for multiple and single smart homes concerning electricity cost and PAR minimization. Moreover, there subsists a tradeoff between electricity cost and user waiting. With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, DSM is modeled as an optimization problem and the solution is obtained by applying metaheuristic techniques with different pricing schemes. In contribution 2, an optimization technique, the Hybrid Gray Wolf Differential Evolution (HGWDE) is proposed by merging the Enhanced Differential Evolution (EDE) and Gray Wolf Optimization (GWO) schemes using the same RTP and CPP tariffs. Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between User Comfort (UC) and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the PAR is reduced up to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced up to 12.81%, 12.012% and 12.95%, respectively, for 15-min, 30-min and 60-min operational time intervals (OTI). On the other hand, the PAR and electricity bill are reduced up to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff. Microgrid is a community-based power generation and distribution system that interconnects smart homes with renewable energy sources. Microgrid generates power for electricity consumers and operates in both islanded and grid-connected modes more efficiently and economically. In contribution 3, we propose optimization schemes for reducing electricity cost and minimizing PAR with maximum UC in a smart home. We consider a grid-connected microgrid for electricity generation which consists of wind turbine and photovoltaic (PV) panel. First, the problem was mathematically formulated through Multiple Knapsack (MKP) then it is solved by existing heuristic techniques: GWO, binary particle swarm optimization (BPSO), GA and Wind Driven Optimization (WDO). Furthermore, we also propose three hybrid schemes for electricity cost and PAR reduction: (1) hybrid of GA and WDO named as WDGA; (2) hybrid of WDO and GWO named as WDGWO; and (3) WBPSO, which is the hybrid of BPSO and WDO. In addition, a battery bank system has also integrated to make our proposed schemes more cost-efficient and reliable to ensure stable grid operations. Finally, simulations have been performed to verify our proposed schemes. Results show that our proposed schemes efficiently minimize the electricity cost and PAR. Moreover, our proposed techniques: WDGA, WDGWO and WBPSO outperform the existing heuristic techniques. The advancements in smart grid, both consumers and electricity providing companies can benefit from real-time interaction and pricing methods. In contribution 4, a smart power system is considered, where consumers share a common energy source. Each consumer is equipped with a Home Energy Management Controller (HEMC) as scheduler and a smart meter. The HEMC keeps updating the electricity proving utility with the load profile of the home. The smart meter is connected to power grid having an advanced metering infrastructure which is responsible for two way communication. Genetic teaching-learning based optimization, flower pollination teaching learning based optimization, flower pollination BAT and flower pollination genetic algorithm based energy consumption scheduling algorithms are proposed. These algorithms schedule the loads in order to shave the peak formation without compromising UC. The proposed algorithms achieve optimal energy consumption profile for the home appliances equipped with sensors to maximize the consumer benefits in a fair and efficient manner by exchanging control messages. Control messages contain energy consumption of consumer and RTP information. Simulation results show that proposed algorithms reduce the PAR by 34.56% and help the users to reduce their energy expenses by 42.41% without compromising the comfort. The daily discomfort is reduced by 28.18%.
Presentation
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Power Point Presentation of PhD Thesis of Muhammad Babar Rasheed - PDF
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Get smart Power and Energy Magazine
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T. J. Lui, W. Stirling, and H. O. Marcy, " Get smart, " Power and Energy Magazine, IEEE, vol. 8, no. 3, pp. 66–78, 2010.
An integer linear programming based optimization for home demand-side management in smart grid
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Z. Zhu, J. Tang, S. Lambotharan, W. H. Chin, and Z. Fan, "An integer linear programming based optimization for home demand-side management in smart grid," in Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES, pp. 1-5, IEEE, 2012.