Unified Algorithm for Demand-Side Appliance Commitment

energies

Article Uniﬁed Algorithm for Demand-Side Appliance Commitment

Ahmad H. Besheer 1,* , Momen S. Agamy 2, Hassan M. Emara 2 and Ahmed Bahgat 2

1 Environmental Studies and Research Institute, University of Sadat, Sadat City 32897, Egypt 2 Electrical Power and Machines Department, Cairo University, Giza 12613, Egypt; [email protected] (M.S.A.); [email protected] (H.M.E.); [email protected] (A.B.) * Correspondence: [email protected]; Tel.: +002-048-2603208

Received: 8 November 2018; Accepted: 24 November 2018; Published: 30 November 2018

Abstract: Recent energy efficiency and conservation programs have created an unprecedented demand for home energy management systems (HEMS) in the residential sector aimed at reducing electricity consumption and saving on electricity bills. This paper gives a brief review of the basic algorithms found in the literature for HEMS that target optimum scheduling for home appliances participating in demand response (DR) programs. The working principles, as well as the pros and the cons, of these algorithms are explained and analyzed. Then, a unified algorithm to manage the hourly power consumption of home appliances on a daily basis is suggested using two scenarios. The first scenario aims to simultaneously achieve dual utility/customer benefits while avoiding the individual drawbacks of each presented algorithm. The second scenario aims to actively involve DR customers in making the optimum decision regarding their appliances in the face of their dynamic desires. The proposed algorithm is generic in the sense that it has the ability to achieve three different objectives for dual utility/customer benefits. Moreover, the paper takes into consideration a range of constraints, such as load priority, customer preferences, utility request, and electricity dynamic pricing scheme. The essential goal of this algorithm is not only to curtail or control the power consumption of appliances but to also shift it to a better price period based on different tariff rates. The results reflect the effectiveness of the proposed algorithm, which extends the previous findings in the literature by considering a wider range of limitations applied on HEMS.

Keywords: demand response; home energy management systems; appliance scheduling; load curtailment

1. Introduction The world energy consumption, defined as the total energy used by the entire human civilization, is on the rise. It has more than doubled in the last 40 years and is projected to increase by up to 30% by 2030 [1]. Electric power systems are encountering more frequent stress conditions due to this rise in electricity demand. Repeated outages of transmission lines, which are likely to occur during critical peak hours, have been a major cause of electric grid stress. This has led to concerns among governments about significant power disruptions. At the same time, investment in the electrical infrastructure has struggled to keep up with increased demand to avoid system failures. Such events may cause a supply limit situation, where cascading failures and large-area blackouts are possible. These problems have consequently created a strong incentive for adopting energy efficiency initiatives and conservation plans [2]. Moreover, reducing energy use is not only one of the surest ways of lowering carbon emissions, but it also helps in saving money [3].

Energies 2018, 11, 3337; doi:10.3390/en11123337 www.mdpi.com/journal/energies Energies 2018, 11, 3337 2 of 18

Whilst buildings account for around 40% of primary energy usage in most countries, the energy consumption in the residential sector is the most considerable component. For instance, approximately 100 million family homes in the United States account for 36% of the electricity load and determine the seasonal peak system load [4]. Since the 1990s, several policies and codes have been devised to limit the growing pressure on the energy sector [5], and there is a rising trend in energy conservation measures through the development of policies and the deployment of technologies for new and renewable energy sources. However, these measures are positive, they may take a long time whilst changes need to be implemented promptly. One of the possible solutions for this situation could be exploiting and improving energy management algorithms and systems to reduce energy consumption [6]. This will also help in improving the existing conventional power systems by incorporating the capabilities of advanced control [7]. They can, for instance, provide information to help consumers react to price signals by adjusting their consumption patterns and encourage more consumers to participate in the electricity market according to demand response [8,9]. This transition in power grid necessitates monitoring and control of electric system behavior with a wider scale than those traditionally adopted by energy management systems (EMS). New advances in measurement, communication, and control technologies make such transition to a new generation of EMS possible, which in turn enables the concept of smart grid. Demand response (DR) is an integral part of the smart grid network, and its main aim is to deal with unexpected supply limit events, known as DR events, by selectively curtailing system loads and thus recovering balance between energy supply and demand [10]. In this context, home energy management systems (HEMS) have been proposed to enable energy savings and improve energy performance by making better use of energy-consuming assets [11,12]. Naturally, each home would be interested in minimizing their utility bill. Pricing information, such as real-time electricity prices (RTP) [10] in an automated DR controller, will help manage appliances that are flexible to be controlled and eventually take advantage of the times with lower prices with minimum violation in the customers’ lifestyle during a DR event [13,14]. When a peak period is detected, participants in the DR program receive a request signal from the utility company. The request could be based on a previously agreed contract and will specify how much load should be reduced and for how long. The external signal from the utility is received via a smart meter or via a HEMS-dedicated unit, and it can take the form of a demand curtailment request (KW) and duration (hours), which represent the needed remedy for the emerging DR event. Accordingly, participants can turn off nonessential loads to decrease the spike to a level that the electricity company can provide [15]. Significant academic research works have been recently performed on models and solution algorithms for HEMS. In general, two main types of systems exist in the literature. The first one is aimed at managing the loads, while the other one targets management of the available power sources, such as the Photovoltaic, wind, and the electrical grid [16,17]. Some HEMS are aimed at managing both the loads and the sources at the same time. This paper focuses on load management algorithms that are designed to manage home appliances based on different constraints, such as DR events, price signals, customer preferences, and load priority. The objective of this kind of HEMS is basically curtailment and control of load power, with the system aiming to shorten the operation period of a given load rather than cutting it completely. Another objective of this system is to shift loads or schedule loads to lower price periods. Figure1 shows different working philosophies of HEMS based on algorithm types, objectives, and constraints. A discussion of each objective is given in the following sections. Energies 2018, 11, 3337 3 of 18 Energies 2018, 11, x FOR PEER REVIEW 3 of 18

FigureFigure 1 1.. ClassificationClassiﬁcation of of home home energy energy management management systems systems ( (HEMS).HEMS).

1.1.1.1. HEMS HEMS Algorithms Algorithms Based Based on on Load Load Power Power Curtailment Curtailment PowerPower curtailment curtailment is is the the basic basic approach approach for for home home appliance appliance management, management, and and the the main main idea idea of of thisthis type type of algorithm isis thatthat appliancesappliances areare normallynormally turned turned off off in in the the event event of of a a DR DR event event according according to tothe the demand demand limit limit signal signal from the from utility the company utility company and depending and depending on predefined on predefined customer preferences customer preferences(load priority (load and priority comfort and level). comfort Based level). onthis Based concept, on this Amer concept, et al. Amer [18] proposedet al. [18] anpro algorithmposed an algorithmthat is capable that is of capable reducing of the reducing overall the consumed overall consumed power of a power household of a household by managing by itsmanaging appliances its appliancesbased on a based predefined on a predefined appliance priority.appliance Pipattanasomporn priority. Pipattanasomporn et al. [19] presentedet al. [19] p aresented HEMS algorithm a HEMS algorithmthat manages that andmanages controls and the control consumptions the consumption of certain of household certain household appliances appliances to guarantee to guarantee the total theenergy total consumption energy consumption below a certainbelow a level certain during level a during DR event a DR and event according and according to predefined to predefined customer customerpreferences. preferences. These preferences These preferences not only include not loadonly priority include but load also priority involve but preset also customer involve comfort preset customerlevel. Many comfort research level. works Many have research evolved works around have the evolved same idea around (see, the for same instance, idea References (see, for instance [20,21]),, Referenceswhere the overall[20,21]), power where consumption the overall power of a single consumption or group of housesa single are or keptgroup below of houses the sanctioned are kept belowlevel, withthe sanctioned minimum level comfort, with violation. minimum Kuzlucomfort et violation. al. [20] sets Kuzlu up aet hardware al. [20] set demonstrations up a hardware to demonstrationvalidate the proposed to validate algorithm the proposed by Pipattanasomporn algorithm by Pipattanasomporn et al. [19]. The et experiment al. [19]. The indicated experiment the indicateeffectivenessd the of effectiveness the proposed of HEMS the proposed in monitoring HEMS and in controlling monitoring household and controlling appliances household under appliancesthe suggested under DR the algorithm. suggested Various DR algorithm. practical Various and implementation practical and aspectsimplementation for different aspects HEMS for differentalgorithms HEMS have algorithms also been discussed have also in been the literature.discussed Forin the example, literature communication. For example time, communication delay and its timeenergy delay consumption and its energy were analyzed consumption in Reference were analyzed [20]. A low-power in Reference wireless [20]. A personal low-power area w networkireless p(6LoWPAN)ersonal area communicationnetwork (6LoWPAN) protocol communication that enables IPv6 protocol over that IEEE802.15.4 enables IPv6 standard over wasIEEE802.15.4 adopted standardin Reference was [ 22adopted] instead in ofReference frequently [22 used] instead protocols of frequent such asly ZigBee used protocols and Z-Wave. such The as ZigBee commercial and Zviability-Wave. of The the commercial HEMS hardware viability setup of was the studied HEMS in hardware Reference [setup23], where was astudied low-cost in home Reference automation [23], wheresystem a that low is-cost capable home of controllingautomation and system managing that is both capable the home of controlling appliance andcomfort managing level and both energy the homeconsumption appliance was comfort proposed. level and energy consumption was proposed. IInn ReferenceReference [21[21],], small small changes change ins thein theabovementioned abovementioned approaches approach fores curtailment-based for curtailment- HEMSbased HEMSwere explored were explored Although Although the same the load same types load of References types of [References19,20] were [ utilized19,20] were here, utilized a dynamic here load, a dynamicpriority was load adopted priority instead was adopted of fixed load instead priority. of fixed This enabledload priority. the authors This enableto demonstrated the authors dynamic to demonstrateload management dynamic by changingload management the priorities by changing of loads tothe be priorities curtailed of during loads ato DR be event.curtailed during a DR event.Despite the partial success of HEMS algorithm based on power curtailment in general, this algorithmDespite may the be partial characterized success by of three HEMS main algorithm drawbacks. based The on first power is the curtailment direct and completein general, cutting this algorithmoff of the scheduled may be characterized load with lower by three priority; main the drawbacks. second is the The high first off-peak is the direct demand and that complete results cuttingjust after off a of DR the event scheduled period load due with to consumers lower priority compensating; the second for is the the curtailed high off- load;peak anddemand the thirdthat results just after a DR event period due to consumers compensating for the curtailed load; and the third relates to the possible violation of customer comfort degree under some circumstances of low

Energies 2018, 11, 3337 4 of 18 relates to the possible violation of customer comfort degree under some circumstances of low demand limit signal from the utility company. These drawbacks can be alleviated by adjusting the algorithm objectives to either load power control or load power shifting.

1.2. HEMS Algorithms Based on Load Power Control The main difference in this approach is that it does not totally sacrifice customer comfort by shedding their loads; instead, it tries to partially satisfy customer preferences by controlling the load operational time and, hence, controlling energy consumption. This approach improves the utilization efficiency of appliances and increases the overall capability of the system. Consequently, it increases the lifetime of appliances and reduces potential damage from directly cutting off the main supply. For example, it may increase the air conditioning (AC) temperature set point, giving the AC unit the chance to be OFF for a longer time according to its priority, thereby decreasing its ON period and the energy drawn by the AC unit. Saha et al. [24], tried to relax the customer preferences limit of the AC and therefore reduce the total power consumption of the AC unit instead of curtailing the load. In this study, a smart thermostat was included in the HEMS to allow flexibility in room temperature control. This prevented the room temperature from rising above a certain acceptable range but helped reduce electricity grid stress situations while ensuring customers’ comfort preferences. Parithi [25] used the same load types and the same load priority as in References [19,20,24], but the proposed algorithm had the benefit of controlling the power consumed in different loads instead of cutting them out completely. The drawbacks of approaches based on load power control are threefold. Firstly, this algorithm is implemented with the aid of an additional hardware component, such as a smart thermostat, which may increase the overall cost of large-scale applications. Secondly, this algorithm is only applicable for thermally controlled appliances, such as AC and water heaters (WH). Thirdly, this algorithm does not take into account the full monetary benefit that can be achieved for customers by deferring the load to a low price period.

1.3. HEMS Algorithms Based on Load Power Shifting In this approach, the HEMS algorithms depend on different factors for organizing the daily load pattern. For instance, they shift loads by incorporating utility tariffs based on various pricing schemes, such as time of use, critical peak, and real time, with two ultimate objectives: relieving the stress on the power system network and providing electricity consumers significant monetary savings. Boynuegri et al. [26] developed a load management mechanism based on a DR event model and multirate tariffs to respond to utility peaks and decrease energy bills for consumers by shifting loads to cheap tariff periods. Chavali et al. [27] adopted an algorithm that schedules the appliance operation as per the price signal. This model assumes that all the appliances are operating in specific modes and that the appliances can switch between various modes depending on the energy consumption. Amer and El-zonkoly implemented another HEMS algorithm [18] using an Arduino microcontroller. Besides managing the end-use appliances as per their preset priority to reduce the total consumption under a certain limit, the algorithm also achieves the purpose of clipping the peak-to-average ratio (PAR). Another approach was outlined in Reference [28], where a scheduling method for a home network based on a smart grid was introduced. To avoid high PAR, the authors combined the real-time pricing model (RTP) with the inclining block rate (IPR) model to reduce electricity bills and strength the stability of the electricity grid system. In Reference [29], the authors were concerned more with the impact electric vehicle charging may have on overloading distribution transformers. Thus, they proposed a DR strategy by performing load scheduling and shaping, thereby avoiding problems related to transformer overloading. However, all the HEMS approaches discussed in this section are incapable of achieving simultaneous dual utility/customer benefits. The curtailment-based algorithms can participate in restoring the balance between production and consumption but they do not achieve energy bill savings for consumers. The HEMS algorithms based on load power control avoid direct cut-off of the home Energies 2018, 11, 3337 5 of 18 loads and thereby prevent violation of customer preferences, but they can only be applied on certain appliances. Moreover, they have the tendency to increase the system cost of large-scale applications. The load power shifting HEMS algorithms can effectively respond to the utility request by taking into account monetary savings for customers. However, unfortunately, they do not take into consideration the sequential operation of some daily activities, which may lead to inappropriate operation of home appliances and increase what is called “inconvenience cost” [30]. In this work, three basic energy management algorithms for home appliances that are generally used with customers who actively engage in demand response programs are studied. The operational performance of home appliances in achieving customer and utility benefits under these algorithms are analyzed. The advantages and disadvantages of each algorithm are further evaluated. Hence, a novel algorithm based on blending these three algorithms is proposed. The main idea behind this unified algorithm is to incorporate the identified advantages of each algorithm while avoiding the shortcomings of the different algorithms. The main contribution of this paper is that it examines the current home energy management algorithms found in the literature and proposes a novel algorithm structure that combines three established HEMS algorithms—load power curtailment, load power control, and load power shifting—to achieve dual benefits for customers and the utility company. The primary advantages of this new algorithm are as follows:

• The proposed algorithm extends the results in the literature and addresses different shortcomings in other basic algorithms, such as peak rebound, appliance direct cut off, and appliance shifting to higher price period. • The proposed algorithm equips both the customer and the utility company with the proper capability to play an active role in reducing energy consumption and home electricity costs while easing network supply limit conditions.

The paper is organized as follows. Section1 gives a generic introduction of the three main kinds of HEMS algorithms, its objectives, and various constraints of such algorithms. A classification of home load appliances is performed in Section2, where the mathematical model and the daily profile for specific load appliances used in the simulation are explained. Section3 discusses the basic concept of each surveyed HEMS algorithms and suggests a novel technique called “unified algorithm”. Different HEMS algorithms are validated and analyzed in Section4. Finally, conclusions are drawn in Section5.

2. Home Appliances Modeling Different classes of home load appliances were utilized in this paper. Usually, home appliances are classified into two categories according to their importance for customers. The first includes home appliances that serve vital functions to customers. These activities are performed in a daily basis and cannot be dispensed with or disturbed. This type of home appliances is normally called “critical” loads. In the sense of HEMS algorithms, these critical loads cannot be curtailed, controlled, or deferred (shifted). Lighting loads, TVs, refrigerators, and laptops are some of the common appliances that fall under this category. The second class includes home appliances that serve less important functions to customers. These activities are also performed in a daily basis but they can be delayed or disturbed. This type of home appliances is normally called “noncritical” loads. In the sense of HEMS algorithms, these noncritical loads can be curtailed, controlled, or deferred (shifted). Water heaters (WH), air conditioning (AC) systems, cloth dryers (CD), and electric vehicles (EV) are the common appliances that fall under this category. In this paper, the noncritical home appliance loads were further classified. The classification is based on categorizing the appliances into three groups. The first is the group of home appliance loads that can be directly curtailed or cut off upon a command signal from the HEMS algorithm. In this paper, the CD was chosen to represent this group. The second is the group of home appliance loads that can be controlled without direct and full cutting off. In this type of noncritical load, the HEMS algorithm controls other auxiliary devices that are attached to these loads, which in turn alters the operational state of these noncritical home appliance loads. In this paper, the AC Energies 2018, 11, 3337 6 of 18 and WH were chosen to represent this group. The last group consists of home appliance loads that can be shifted to operate in a low-price period. For this type of noncritical load, the HEMS algorithm recommends a better period of operation in terms of electricity cost. In this paper, the EV was chosen to represent this group. Four different home appliance loads used to represent the three categories of noncritical loads were prioritized from high to low as per their importance to consumers. Table1 shows the rated power of each utilized home appliance, the priority, and its preferences. In general, loads with high priority, such as water heater (WH), are the last to be curtailed or shifted.

Table 1. Appliances’ rated power and priority.

Appliance Loads Priority Rated Power (KW) Appliances Preferences Water heater 1 4 50 ◦C (122 ◦F) AC unit 2 2.352 18 ◦C (64.4 ◦F) Clothes dryer 3 2.5 1 h of operation Electric vehicle 4 3 1 h of charging

Refrigerator, cooking stove, lighting, and some electronic appliances were chosen to represent the critical home appliance loads. An aggregated random load proﬁle of these appliances is shown in Table2 for a 12 h period starting from 12 p.m. to 12 a.m., with maximum and minimum values being 1 and 2 KW, respectively. These critical loads have not been shown but were always added to the daily load proﬁle of noncritical appliances.

Table 2. Typical load proﬁle for critical household appliances.

Time (p.m.) 12–1 1–2 2–3 3–4 4–5 5–6 6–7 7–8 8–9 9–10 10–11 11–12 Power (KW) 1.6 1.3 1.3 1.1 1 1.2 1 1.3 2 1.6 1.3 1.3

The noncritical load profiles for each selected home appliances, as well as their energy consumption that shape the daily home load curve, were developed according to References [18,31]. The details of these models and different input parameters can also be found in Reference [15]. The mathematical models for the WH, AC, CD, and EV home appliances, which represent the daily power consumption of each appliance according to customer needs and desires, were built in a MATLAB/Simulink environment. The total home load profile was deduced from aggregation of individual home appliance load pattern. Sample simulation results of WH and AC MATLAB models are presented in Figures2 and3. Figure2 shows the daily load pattern for the WH appliance, where it consumes its rated power due to two water withdrawal events by the customer. These withdraw events are shown as a drop in the outlet water temperature; the WH temperature set point is also recorded in this figure. The cyclic power consumption of the AC appliance is shown in Figure3. The pulsating load pattern of the AC appliance was mainly due to its fluctuating nature in room temperature depending on customer preference of the room temperature (AC temperature set point). Energies 2018, 11, 3337 7 of 18 Energies 2018, 11, x FOR PEER REVIEW 7 of 18

(a)

(b)

FigureFigure 2 2.. Simulation results results of of water heater ( (WH)WH) model model.. ( (aa)) WH WH power power consumption consumption and and ( (bb)) WH WH outletoutlet temperature temperature versus set point point..

(a)

(b)

FigureFigure 3.3. SimulationSimulation results results of of air air conditioning conditioning (AC) (AC model.) model (a). AC(a) ACpower power consumption consumption and (band) room (b) rtemperatureoom temperature versus versus set point. set point.

3.3. Concepts Concepts behind behind HEMS HEMS Algorithms Algorithms AA DR DR event event can can be be defined defined as as a a period period during during which which noncritical noncritical demand demand of of consumers consumers needs needs to to bebe curtailed, curtailed, controlled controlled,, or shifted shifted to to relieve system stress. stress. HEMS HEMS algorithms algorithms monitor monitor the the household household energyenergy consumption consumption and and perform perform these these actions actions to to keep keep the the total co consumptionnsumption below below the speci specifiedfied KWKW limitlimit during during DR DR events. events. In this In section,this section the main, the working main working ideas behind ideas three behind different three algorithms different algorithmsfor HEMS—load for HEMS power—load curtailment power curtailment algorithm (LPcur-a),algorithm load(LPcur power-a), load control power algorithm control algorithm (LPcon-a), (LPconand load-a), power and load shifting power algorithm shifting algorith (LPshi-a)—arem (LPshi presented.-a)—are presented. These algorithms These algorithms essentially essentially serve to servecontrol to the control power the consumption power consumption of home appliancesof home appliances below a specified below a threshold specified thresholdsignal described signal describedby utility companiesby utility companies as a DR request as a DR for request a predetermined for a predetermined period while period maintaining while maintaining convenient convenientcustomer preferences customer inpreferences terms of load in terms priority of and load comfort priority level. and The comfort presented level. algorithms The presented pave algorithms pave the way to elucidate a new HEMS algorithm, i.e., the unified algorithm. This algorithm blends the three mentioned algorithms in order to achieve dual customer/utility benefit

Energies 2018, 11, 3337 8 of 18

theEnergies way 2018 to, elucidate11, x FOR PEER a new REVIEW HEMS algorithm, i.e., the unified algorithm. This algorithm blends8 of the 18 three mentioned algorithms in order to achieve dual customer/utility benefit and thus avoid the individualand thus avoid drawbacks the individual of the algorithms. drawbacks The of main the benefitsalgorithms. of the The unified main algorithm benefits of are the as follows:unified Foralgorithm the customers, are as follows: it reduces For their the electricity customers cost, it without reduces violatingtheir electricity customer cost preferences. without violating For the utilitycustomer companies, preferences. it relieves For the theutilit stressy companies on the grid,, it reli whicheves the enhances stress on the the system’s grid, which stability, enhances security, the andsystem reliability.’s stability, All investigated security, and algorithms reliability. start All with investigated gathering information algorithms signals start with from customer,gathering theinformation utility company, signals fromand each customer, appliance. the utility This information company, and is treated each appliance. as input signals This information that drive the is algorithmtreated as input to produce signals the that needed drive the output algorithm signals. to produce These output the needed signals output are delivered signals. These to dedicated output controllerssignals are thatdelivered manipulate to dedicated home appliances,controllers takingthat manipulate into account home dual appliances benefits for, taking utility into companies account anddual customers benefits foras wellutility as customercompanies preferences. and customer Figures 4as represents well as thecustomer input/output preferences. modules Figure with 4 correspondingrepresents the input/output signals of different modules HEMS with algorithms. corresponding signals of different HEMS algorithms.

FigureFigure 4.4. Input/outputInput/output m modulesodules with signals of HEMS algorithms algorithms.. 3.1. HEMS Algorithms Based on Load Power Curtailment 3.1. HEMS Algorithms Based on Load Power Curtailment The framework of this algorithm is based on read, check, decide, and update requests. The framework of this algorithm is based on read, check, decide, and update requests. This This algorithm starts with reading input signals from different sources. The algorithm checks for algorithm starts with reading input signals from different sources. The algorithm checks for comfort comfort level violation and then for compliance with utility request. If the demand limit signal is level violation and then for compliance with utility request. If the demand limit signal is higher than higher than the total power consumption of the appliances, the algorithm allows appliances with the total power consumption of the appliances, the algorithm allows appliances with comfort level comfort level violation to operate according to their priority. Otherwise, the algorithm curtails the violation to operate according to their priority. Otherwise, the algorithm curtails the chosen chosen appliances based on their priority. In general, the algorithm permits violating the customer appliances based on their priority. In general, the algorithm permits violating the customer setting setting from least important to the most important appliance. This is performed to match the demand from least important to the most important appliance. This is performed to match the demand limit. limit. The steps of this algorithm are summarized as follows: The steps of this algorithm are summarized as follows: 1. Read Inputs 1. Read Inputs 2. Check comfort level violation 2. Check comfort level violation 3. Check compliance with utility request 4. Check load priority 5. Decide on suitable appliance status 6. Update 7. Return to step 2

InIn thisthis system,system, the the algorithm algorithm first first checks checks whether whether or notor not there there is a DRis a event. DR event In case. In ofcase a DR of event,a DR theevent, total the home total power home consumption power consumption is minimized is minimized to satisfy tothe satisfy DR request. the DR Some request. selected Some appliances selected areappliances turned offare completely turned off duringcompletely the DR during event, the such DR as event the CD, such and as EV, the which CD and will EV be,start which operating will be againstart operat after theing DR again event after finishes. the DR Curtailing event finish the loadses. Curtailing will be done the gradually loads will according be done to the gradually preset priorityaccording of eachto the load preset to satisfy priority the of DR each request, load to i.e., satisfy the EV the will DR be request the first, i.e. to, bethe curtailed EV will andbe the the first water to heaterbe curtailed will be and the the last. water Unfortunately, heater will be this the can last. sacrifice Unfortunately, customer this comfort can sacrifice in order customer to minimize comfort the overallin order home to minimize power consumption the overall home and maypower therefore consumption lead to and peak may rebound therefore just lead after to the peak DR event.rebound just after the DR event.

3.2. HEMS Algorithms Based on Load Power Control

Energies 2018, 11, 3337 9 of 18

3.2. HEMS Algorithms Based on Load Power Control In this kind of HEMS algorithms, the algorithm is not allowed to directly interact with the home appliances and curtail them. Instead, the algorithm will control other supplementary devices, such as the smart thermostat in the case of AC, which will in turn control the status of the AC in such a way that permits serving the higher priority appliance and keeps the total power consumption less than the demand limit signal (utility request compliance). This is achieved by adjusting the thermostat set point (increasing the room temperature set point in the case of AC). It can also be used to decrease the water temperature set point in the case of WH. This way, the operational time of the appliance, and thus its daily energy consumption, will decrease. The main advantage of this approach is that the home comfort level will not be totally scariﬁed but rather kept within customer satisfaction limit. The steps of this algorithm are summarized as follows:

1. Read Inputs 2. Check comfort level violation 3. Check compliance with utility request 4. Change thermostat settings 5. Check load priority 6. Decide on suitable appliance status 7. Update 8. Return to step 2

In this paper, the room temperature set point of the AC was increased by 10 ◦C (18 ◦F), which consequently decreased the ON time of the AC and made it draw less energy over the time. Similarly, for the WH, the algorithm has the ability to decrease the hot water temperature set point by 10 ◦C (18 ◦F). Unfortunately, in some circumstances, such as high or low ambient temperatures, this algorithm may not be able to cope with the demand limit signal when both the AC and WH are still operating, even after adjusting their set points.

3.3. HEMS Algorithms Based on Load Power Shifting Although the framework of this algorithm seems to be similar to the LPcur-a, it has an essential difference in that this algorithm checks periods of the day to search for low energy price period according to a prespecified tariffs scheme, which is called time of use (ToU) pricing scheme. In this scheme, the electricity tariff is not fixed the whole day but rather changes (increases) in peak load periods. This motivates the customer to use their appliances out of peak periods. Once the algorithm determines the high-price periods, it avoids shifting the load in those periods. Hence, the algorithm can achieve the same benefits as the LPcur-a while also saving money for the customers. The steps of this algorithm are summarized as follows:

1. Read Inputs 2. Check comfort level violation 3. Check compliance with utility request 4. Check time of use period 5. Check load priority 6. Decide on suitable appliance status 7. Update 8. Return to step 2

In this paper, the algorithm will reschedule the shiftable loads according to suggested (ToU) pricing scheme, outlined in Table3, to limit power consumption during both the DR event and high-tariff periods. In this case, the shiftable loads are the EV and CD loads. The expensive tariff period Energies 2018, 11, 3337 10 of 18 is assumed to be from 8:00 p.m. to 10:00 p.m. during which the HEMS algorithm will shift the EV and CD loads to cheap tariff periods so as to limit the energy consumption during the high-tariff periods.

Table 3. Suggested time of use (ToU) electricity prices.

Time (h) 12 13 14 15 16 17 18 19 20 21 22 23 24 Prices (EGP/KWh) 0.1 0.1 0.1 0.1 0.1 0.15 0.15 0.15 0.3 0.3 0.1 0.1 0.1

3.4. Unified HEMS Algorithms The unified algorithm comprises the three interlinked algorithms, i.e., the algorithms based on shifting, controlling, and curtailment. The key advantage of this novel algorithm is that it achieves simultaneous dual utility/customer benefits while avoiding individual drawbacks of each algorithm. In this section, the main ideas behind this algorithm are presented in two scenarios.

3.4.1. First Scenario By dividing the loads into shifting (deferrable), controllable and behavior sensitive loads, the idea of the unified algorithm will be as follows: The unified algorithm starts by applying the steps of the algorithm based on load power shifting. The justification is that this algorithm satisfies the dual benefit for the utility company and the customer. If the number of the shifted loads is not enough to comply with the demand limit signal value, the unified algorithm can start applying the steps of the algorithm based on load power control on the controllable types of loads, such as the AC and WH. If the total home consumption is still higher than the demand limit signal, the algorithm manipulates other kinds of loads (behavioral sensitive loads), such as the CD, and cuts them out to satisfy the requirement. This way, the unified algorithm is always targeting the achievement of dual customer/utility benefit without directly cutting off the loads that may jeopardize the customer comfort level. At the same time, the unified algorithm is also giving a higher level of interest to the priority of the load appliance and the customer comfort level by taking into account increase in the efficiency of some home appliances, which will enhance its lifetime as a result of reducing its operational time.

3.4.2. Second Scenario The unified algorithm starts by applying the steps of the algorithm based on load power shifting. This will not only ensure the needed grid support at unbalanced event but will also benefit customers providing them savings in their energy bills. Accordingly, the unified algorithm will determine certain loads to be shifted at low price periods. The algorithm then provides the customers with an additional feature of accepting or refusing its suggestions. The decline of the recommended deferred loads may happen due to high demand from customers on these loads. In such a case, the algorithm based on load power control, discussed in Section4, is applied. Hence, the algorithm will try to change the predefined setting of some loads, such as the AC or WH. If the customer refuses this second suggestion as well, the unified algorithm will apply the steps of the algorithm based on load power curtailment and curtail the load according to their priority and preference settings. This way, the unified algorithm will not directly cut off the loads but instead give the consumers other valuable options, such as monetary saving in monthly energy bills to comply with the utility’s request signal, and does not sacrifice the customer preference settings (load priority and comfort level). Furthermore, the algorithm avoids creating high off-peak demand due to random compensation of the curtailed load. The main benefit of this unified algorithm scenario is that it will actively involve the DR users in taking the optimum decision in face of their dynamic desire. In doing so, the algorithm will create another degree of freedom by increasing the level of customer intervention (interaction) with the already agreed DR agreement. The algorithm therefore offers a kind of dynamic deal between the utility and their customers instead of normally fixed agreement that imposes obligations on the customer. Energies 2018, 11, 3337 11 of 18

In this paper, the first scenario of the unified algorithm was adopted, implemented, and compared with the other three algorithms. The idea of the second scenario is only presented for future development. Energies 2018, 11, x FOR PEER REVIEW 11 of 18 3.5. Simulation Results and Discussion 3.5. Simulation Results and Discussion The four mentioned HEMS algorithms were validated and analyzed using a MATLAB/Simulink The four mentioned HEMS algorithms were validated and analyzed using a environment. The steps of each algorithm were performed to represent one simulation cycle. Each MATLAB/Simulink environment. The steps of each algorithm were performed to represent one cycle represented 10 min in real time; therefore, in 1 h, the MATLAB model for different HEMS simulation cycle. Each cycle represented 10 min in real time; therefore, in 1 h, the MATLAB model algorithms made six cycles. These algorithms were applied on a 12 h time frame of one day, which in for different HEMS algorithms made six cycles. These algorithms were applied on a 12 h time frame turn represented 72 simulation cycles. The inputs for each MATLAB model were the load profile of of one day, which in turn represented 72 simulation cycles. The inputs for each MATLAB model various home appliances, which were generated from different load models presented in section II were the load profile of various home appliances, which were generated from different load models as well as the utility request that comprised DR signal in KW and its duration in hours. For the AC presented in section II as well as the utility request that comprised DR signal in KW and its duration appliance, the room temperature set point was 18 ◦C (64.4 ◦F), and for the WH appliance, the hot water in hours. For the AC appliance, the room temperature set point was 18 °C (64.4 °F), and for the WH temperature set point was 50 ◦C (122 ◦F). The output of each MATLAB models was the optimized load appliance, the hot water temperature set point was 50 °C (122 °F). The output of each MATLAB profile subjected to the defined HEMS algorithm. Figure5 shows the MATLAB model for each HEMS models was the optimized load profile subjected to the defined HEMS algorithm. Figure 5 shows the algorithm along with its input and output signals. MATLAB model for each HEMS algorithm along with its input and output signals.

Figure 5. MATLAB/Simulink model of HEMS. Figure 5. MATLAB/Simulink model of HEMS. Four simulation experiments were performed to validate each HEMS algorithm. In these experiments,Four simulation each simulation experiments results were mainly performed showed to two validate cases scenario. each HEMS The first algorithm. represented In these daily experiments,home appliance each profilessimulation without results DR mainly event, show anded the two second cases representedscenario. The the first modified represent dailyed daily home homeappliances appliance profiles profiles to comply without with DR DR event event., and As athe step second toward represent achievinged betterthe modified appliance daily scheduling, home appliancesthe unified profiles algorithm to was comply then applied. with DR Furthermore, event. As aa comparison step toward between achieving various better HEMS appliance algorithms scheduling,in terms of totalthe unified daily energy algorithm consumption, was then electricity applied. cost,Furthermore, and degree a of comparison comfort level between was established. various HEMSThese algorithms quantized indexesin terms wereof total also daily calculated energy for consumption, the case of aelectricity no DR event. cost, and degree of comfort level was established. These quantized indexes were also calculated for the case of a no DR event. 3.6. LPcur-a Simulation 3.6. LPcur-a Simulation In this simulation experiment, the tested algorithm was triggered by utility request signals. TheIn demand this simulation limits were experiment, chosen as 8the KW, tested 7 KW, algorithm and 6 KW, was respectively, triggered andby utility lasted request for a duration signals of. Th threee demandhours from limits 5:00 were p.m. chosen to 8:00 as p.m. 8 KW Figure, 7 KW6 illustrates, and 6 KW the, dailyrespectively, individual and and last totaled for home a duration appliances of threeprofiles hours without from 5:00a DR p event.m. to (Figure 8:00 p.6ma). andFigure with 6 illustrates a DR event the (Figure daily 6 individualb–d). The and results total showed home appliancesthat the proposed profiles without algorithm a DR managed event ( toFigure curtail 6a) the and home with appliances a DR event and (Figure keep the6b–d). total The household results showconsumptioned that the below proposed the selected algorithm demand managed limit requests. to curtail It the also home showed appliances that the and algorithm keep the complied total household consumption below the selected demand limit requests. It also showed that the algorithm complied with the customers’ predefined priority of appliances, achieving minimum violation of customer desire and comfort levels. Remarks 1 and 2 reflect the pros and cons of this algorithm, respectively.

Energies 2018, 11, 3337 12 of 18 with the customers’ predeﬁned priority of appliances, achieving minimum violation of customer desire Energiesand comfort 2018, 11 levels., x FOR PEER Remarks REVIEW 1 and 2 reﬂect the pros and cons of this algorithm, respectively. 12 of 18

Figure 6. Home load proﬁle for HEMS based on load power curtailment. Figure 6. Home load profile for HEMS based on load power curtailment.

Remark 1: 1. TheThe algorithm algorithm not onlyonly respectedrespectedthe the input input constraint constraint of of the the utility utility in allin all cases cases (Figure (Fig6ureb–d), 6b– butd), butit also it also changed change itsd strategy its strategy in choosing in choosing the appliance the appliance to be curtailed to be curtailed to adapt to with adapt lower with demand lower demandlimit signals limit imposed signals imposed by the utility. by the utility. 2. WWithith the 88 KWKW demand demand limit limit signal signal (Figure (Figure6b), 6b)the, operational the operational strategy strategy of the appliancesof the appliances shows showsthat the that algorithm the algorithm only permitted only permit theted motor the ofm theotor CD of the to be CD ON, to whilebe ON, making while themaking AC ONthe andAC ONOFF and in orderOFF in to order fulﬁll to the fulfill WH the draw WH eventdraw event and serve and serve the noncritical the noncritical loads loads as well. as well. Once Once the thetemperatures temperatures of theof the WH WH tank tank was was raised raised to its to temperatureits temperature set point,set point, the the algorithm algorithm turned turn theed theWH WH tobe to OFFbe OFF and and allowed allow theed the CD CD appliance appliance to be to fullybe fully ON ON (CD (CD motor motor + heater) + heater) and and gave ga theve theEV EV the the chance chance to be to charged. be charged. This This satisﬁed satisf theiedinitial the initial customer customer desire, desire as shown, as shown in Figure in Figure6a. 3. 6a.With the lower demand limit signal of 7 KW (Figure6c), another appliance operational strategy 3. Wwasith adoptedthe lower by demand the algorithm, limit signal which of led7 KW it to (Figure serve only6c), another the WH appliance and the noncritical operational load strategy while waspartially adopted scarifying by the thealgorithm room temperature, which led comfortit to serve level only and the the WH customer and the desire noncritical by letting load the while AC partiallybe turned scarifying OFF and the by shiftingroom temperature both the CD comfort and the level EV toand work the atcustomer 7:00 p.m. desire instead by ofletting 6:00 p.m.the ACand be at turned 8:00 p.m. OFF instead and by of shifting 7:00 p.m., both respectively. the CD and This the situation EV to work altered at 7:00 with p. them. instead WH becoming of 6:00 pOFF,.m. and and atthe 8:00 algorithm p.m. instead helped of in 7:00retrieving p.m., therespectively. room temperature This situation comfort alter leveled andwith the the initial WH becomcustomering desireOFF, and by lettingthe algorithm the AC help anded the in CD retrieving to be ON. the room temperature comfort level and 4. theWhen initial the customer demand limitdesire signal by lettin valueg the decreased AC and even the CD further to be to ON. 6 KW (Figure6d), the CD and the

4. WhenAC were the permitteddemand limit to be signal cyclically value ON decreased and OFF even when further there was to 6 no KW need (Figure to use 6d the), t WH.he CD and the AC were permitted to be cyclically ON and OFF when there was no need to use the WH.

Remark 2 2:: 1. WithWith some some lower lower value value of of demand demand limit limit signal signals,s, the the algorithm algorithm was was required required to to turn turn off off the AC toto comply comply with with the the demand demand limit limit signal signal.. This This meant meant the the room room temperature reached 35.17 ◦°CC (95.32(95.32 °F)◦F),, and and the the customer customer comfort comfort level level was was sacrificed. sacriﬁced. 2. TheThe systemsystem ledled to to a high a high off-peak off-peak demand demand just after just a DR after event a DR period event as customers period as compensated customers compensatefor the curtailedd for load.the curtailed Hence, aload. new Hence, peak rebounded, a new peak which rebound mayed jeopardize, which may the electricjeopardize grid. the electric grid.

3.7. LPcon-a Simulation

Energies 2018, 11, 3337 13 of 18

3.7.Energies LPcon-a 2018, Simulation11, x FOR PEER REVIEW 13 of 18

InIn this this simulation simulation experiment, experiment, the the imposed imposed demand demand limit limit signal signal by by the the utility utility was was chosen chosen to to be be 66 KW KW for for a durationa duration of three of three hours hours from 5:00 from p.m. 5:00 to p 8:00.m. p.m.to 8:00 Figure p.m7 .illustrates Figure 7 the illustrates daily individual the daily andindividual total home andappliances total home proﬁles appliances without profiles a DR without event (Figurea DR e7venta) and (Figure with 7a) a DR and event with (Figure a DR 7eventb). In(Figure general, 7b) the. In following general, the comments following can comments be deduced: can be deduced:

1.1. ForFor the the case case of ofno noDR DRevent, event, the AC the managed AC managed to be ON to withbe ON temperature with temperature violation. violation. This situation This alteredsituation in thealtered case in of the a DR case event, of a DR where event the, where AC managed the AC managed to be OFF to for be a OFF certain for perioda certain to period serve theto serve higher the priority higher load priority such load as WH such (see as FigureWH (see7b Figure at 5:00 p.m.).7b at 5:00 p.m.). 2.2. ThisThis waswas achievedachieved by by controlling controlling the the thermostat thermostat that that in in turn turn kept kept the the AC AC off off by by increasing increasing its its set set pointpoint temperature; temperature; the the maximum maximum room room temperature temperature reached reached 30.31 30.31◦ C°C (86.57 (86.57◦ F).°F). 3.3. TheThe WHWH managedmanaged toto bebe ONON forfor thethe OFF OFF period period of of the the AC AC (due (due to to a a water water drawn drawn event event on on this this period)period) and and once once it it was was OFF, OFF the, the AC AC managed managed to be to ON be (FigureON (Figure7b); the 7b) minimum; the minimum water setwater point set changedpoint changed to 40 ◦ Cto (10440 °C◦F). (104 °F). 4.4. TheThe algorithmalgorithm was was able able to to make make the the home home aggregated aggregated load load appliances appliances comply comply with with the the demand demand limitlimit signal signal from from the the utility. utility At. A thet the same same time, time it reduced, it reduce thed dailythe daily operational operational time time of the of AC the and AC henceand hence decreased decrease the totald the home total energy home consumption, energy consumption saving will, saving save moneywill save for themoney customer. for the 5. Thecustomer algorithm. was capable of changing the above situation in point 4 when the room temperature

5. increasedThe algorithm and reached was capable the new of set changing point. In such the abovea case, thesituation WH managed in point to 4 be when OFF while the room the ACtemperature was turned increase ON. d and reached the new set point. In such a case, the WH managed to be OFF while the AC was turned ON. 6. The algorithm contributed to conserving energy consumption, with a total saving of 4.764 KWh, 6. The algorithm contributed to conserving energy consumption, with a total saving of 4.764 KWh, or 9.4% saving, through the day. This may also lead to enhancing the lifetime and longevity of or 9.4% saving, through the day. This may also lead to enhancing the lifetime and longevity of the AC unit. the AC unit. 7. Although this algorithm was able to follow the utility request taking into account other constraints, 7. Although this algorithm was able to follow the utility request taking into account other this was not achievable for the high ambient temperature values as it would sacriﬁce the customer constraints, this was not achievable for the high ambient temperature values as it would comfort level. sacrifice the customer comfort level.

FigureFigure 7. 7Home. Home load load proﬁle profile for for HEMS HEMS based based on on load load power power control. control. ( a()a) No No demand demand limit; limit; ( b(b)) 6 6 KW KW demanddemand limit. limit.

3.8. LPshi-a Simulation In this simulation experiment, the imposed demand limit signal by the utility was chosen to be 6 KW for a duration of three hours from 5:00 p.m. to 8:00 p.m., the room temperature set point was

Energies 2018, 11, 3337 14 of 18

3.8. LPshi-a Simulation

EnergiesIn 2018 this, 11 simulation, x FOR PEER experiment, REVIEW the imposed demand limit signal by the utility was chosen14 to of be18 6 KW for a duration of three hours from 5:00 p.m. to 8:00 p.m., the room temperature set point was set ◦ ◦ ◦ ◦ toset 18 to C18 (64.4 °C (64.4F), and °F), the and hot the water hot temperaturewater temperature was set was to 50 setC to (122 50 F).°C Furthermore,(122 °F). Furthermore, this algorithm this tookalgorithm into consideration took into consideration the time of usethe pricingtime of scheme, use pricing where scheme a high-tariff, where period a high was-tariff assumed period to was be appliedassumed from to be 8:00 applied p.m. tofrom 10:00 8:00 p.m. p.m Figure. to 10:008 illustrates p.m. Figure the daily 8 illustrates individual the anddaily total individual home appliance and total profileshome appliance without profiles a DR event without (Figure a DR8a) e andvent with(Figure a DR 8a) event and with (Figure a DR8b). event As it(Figure can be 8b). inferred As it fromcan be theseinferred figures, from the these algorithm figures, not the only algorithm succeeded not only in maintaining succeeded the in totalmaintaining load of thethe hometotal load appliances of the belowhome the appliances demand below limit signal, the demand but it also limit deferred signal the, but operation it also of defer somered appliances the operation to the low-price of some periodappliances (after to 10:00the low p.m.)-price as period recommended (after 10:00 by thep.m adopted.) as recommended time of use by pricing the adopted scheme time (shown of use in Tablepricing3), hencescheme offering (shown the in customerTable 3), significanthence offer monetarying the customer savings. significant The algorithm monetary performed saving a cyclics. The operationalgorithmbetween perform theed CDa cyclic and AC operation while deferring between the the EV CD to the and low-price AC while period defer toring fulfill the all EV required to the constraints,low-price period such as to utility fulfill request, all required customer constraints comfort,, such and as desire utility levels. request, A comparison customer comfort between, and the resultsdesire levels. of this A algorithm comparison and between the first the algorithm results of (LPcur-a) this algorithm reflects and the superioritythe first algorithm of the former(LPcur- ina) performingreflects the superiority two shifts for of thethe EVformer operation in performing to start working two shifts after for 10:00 the EV p.m. oper insteadation ofto workingstart working after 8:00after p.m. 10:00 as p recommended.m. instead of by working the later after algorithm 8:00 p.m (see. as Figures recommended6 and8). In by doing the later so, the algorithm algorithm (see avoidedFigures 8b creating and 6b). a high In doing off-peak so, periodthe algorithm or shifted avoid it toed periods creating where a high the off grid-peak was period lightly or loaded shifted (after it to 10:00periods p.m.). where the grid was lightly loaded (after 10:00 p.m.).

FigureFigure 8.8. HomeHome loadload proﬁleprofile forfor HEMSHEMS basedbased onon loadload powerpower shifting.shifting. ((aa)) NoNo demanddemand limitlimit ((bb)) 66 KWKW demanddemand limit.limit.

3.9.3.9. UnifiedUnified AlgorithmAlgorithm SimulationSimulation InIn thisthis simulationsimulation experiment,experiment, allall homehome applianceappliance loadsloads were chosenchosen toto bebe ONON inin thethe periodperiod startingstarting fromfrom 5:005:00 p.m.p.m. andand 8:008:00 p.m.p.m. InIn thisthis timetime period,period, 66 KWKW demanddemand limitlimit signalsignal waswas appliedapplied asas ◦ ◦ anan inputinput toto thisthis algorithmalgorithm by the utility, thethe roomroom temperaturetemperature setset pointpoint waswas setset toto 1818 °CC (64.4(64.4 °F)F),, ◦ ◦ andand hot water temperature was was set set to to 50 50 °CC (122 (122 °F)F).. A A high high electricity electricity price price period period from from 8:00 8:00 p.m p.m.. to to10:00 10:00 p.m p.m.. was was also also considered. considered. Figure Figure 99 illustratesillustrates the the daily individual andand totaltotal homehome applianceappliance profilesprofiles withoutwithout aa DRDR eventevent (Figure(Figure9 a)9a) and and with with a a DR DR event event (Figure (Figure9b). 9b). This This proposed proposed algorithm algorithm worksworks inin thethe followingfollowing manner.manner. ItIt firstlyfirstly recommendsrecommends shiftingshifting oneone ofof deferrabledeferrable loads,loads, suchsuch asas thethe EV,EV, toto the the low-price low-price period period after after 10:00 10:00 p.m.p.m. It It calculates calculates the the aggregated aggregated home home appliances appliances power power consumption and compares it with the imposed demand limit signal by the utility. If the aggregated power consumption is less than the demand limit signal, the algorithm will not take any further action. This way, this algorithm coincidently resembles the LPshi-a. On the other hand, if the deferred load does not comply with the demand limit signal, the proposed algorithm will apply the procedures of LPcon-a by changing the AC temperature setting to make the AC OFF. This action is

Energies 2018, 11, 3337 15 of 18 consumption and compares it with the imposed demand limit signal by the utility. If the aggregated power consumption is less than the demand limit signal, the algorithm will not take any further action. This way, this algorithm coincidently resembles the LPshi-a. On the other hand, if the deferred load doesEnergies not 2018 comply, 11, x FOR with PEER the REVIEW demand limit signal, the proposed algorithm will apply the procedures15 of of18 LPcon-a by changing the AC temperature setting to make the AC OFF. This action is taken to ensure thetaken aggregated to ensure power the aggregated consumption power of the consumption home appliances of the comply home with appliances the demand compl limity with signal. the However,demand limit if this signal. action However, is not enough, if this theaction algorithm is not enough will start, the to algorithm apply the stepswill start of LPcur-a to apply and the cut steps off theof LPcur CD in-a order and tocut serve off the both CD the in noncritical order to serve higher both priority the noncritical loads and the higher critical priority loads. loads This way,and thethe proposedcritical loads. algorithm This way, blends the or proposed combines algorithm other algorithms blends or in onlycombines one algorithm other algorithms that is more in only ﬂexible one andalgorithm useful that than is the more previous flexible algorithms. and useful In than our the study, previous the HEMS algorithms. contributed In our to study, energy the saving HEMS of 2.8473contributed KWh, to or energy 5.5% saving, saving through of 2.8473 the KW day.h, or 5.5% saving, through the day.

Figure 9.9. Home load profileprofile for unifiedunified HEMS.HEMS. ((aa)) NoNo demanddemand limit;limit; ((bb)) 66 KWKW demanddemand limit.limit. 4. Discussion of Results 4. Discussion of Results The advantages of the unified algorithm are illustrated in Figure 10 and Tables4 and5. Figure 10 The advantages of the unified algorithm are illustrated in Figure 10 and Tables 4 and 5. Figure shows the room temperature variations during the 12 h period of study for LPcur-a, LPcon-a, and 10 shows the room temperature variations during the 12 h period of study for LPcur-a, LPcon-a, and the unified algorithm. During the proposed demand response period from 5:00 p.m. to 8:00 p.m., the the unified algorithm. During the proposed demand response period from 5:00 p.m. to 8:00 p.m., the unified algorithm and the LPcon-a achieved less fluctuations compared with the high fluctuations unified algorithm and the LPcon-a achieved less fluctuations compared with the high fluctuations that occurred with LPcur-a. The maximum value of the room temperature when applying the unified that occurred with LPcur-a. The maximum value of the room temperature when applying the algorithm was 30.27 ◦C (86.5 ◦F), while it reached 35.17 ◦C (95.32 ◦F) in the case of LPcur-a. This result unified algorithm was 30.27 °C (86.5 °F), while it reached 35.17 °C (95.32 °F) in the case of LPcur-a. reveals the effectiveness of the unified algorithm in complying with utility request while achieving This result reveals the effectiveness of the unified algorithm in complying with utility request while minimum customer comfort level violation and preserving the temperature inside the room within achieving minimum customer comfort level violation and preserving the temperature inside the a satisfactory customer set point. The saving in the energy consumption for the unified algorithm room within a satisfactory customer set point. The saving in the energy consumption for the unified (4.62%) was also higher than the corresponding value for the LPcur-a (3.48%). Table4 shows the energy algorithm (4.62%) was also higher than the corresponding value for the LPcur-a (3.48%). Table 4 cost for 12 h period of study before and after applying the different proposed algorithms. The energy shows the energy cost for 12 h period of study before and after applying the different proposed cost for each algorithm was computed based on ToU pricing in Table3. The energy cost after applying algorithms. The energy cost for each algorithm was computed based on ToU pricing in Table 3. The the LPcur-a algorithm was higher than the energy cost before applying it. This was due to the random energy cost after applying the LPcur-a algorithm was higher than the energy cost before applying it. compensation of the curtailed load just after the demand response event, which did not take into This was due to the random compensation of the curtailed load just after the demand response consideration the period of low price in the ToU pricing scheme. The unified algorithm achieved lower event, which did not take into consideration the period of low price in the ToU pricing scheme. The energy cost compared to the other proposed algorithms. unified algorithm achieved lower energy cost compared to the other proposed algorithms.

Table 4. Energy Cost under HEMS algorithms.

Type of Algorithm/Energy Cost Energy Cost Before (EGP) Energy Cost After (EGP) LPcur-a 8.19 8.69 LPcon-a 8.19 8.03 LPshi-a 8.19 8.06

Energies 2018, 11, x FOR PEER REVIEW 16 of 18 Energies 2018, 11, 3337 16 of 18 Unified Algorithm 8.19 7.90

Figure 10 10.. Room temperature variations with HEMS algorithms.algorithms.

Table 5 shows the energyTable consumption 4. Energy Cost comparison under HEMS between algorithms. the LPcur-a, LPcon-a, and the developedType of Algorithm/Energy unified algorithm. Cost It can be Energy seen that Cost the Before unified (EGP) algorithm outperform Energy Costed After the (EGP)LPcur-a in terms of energy saving. The LPcon-a achieved a higher energy saving among the algorithms as the LPcur-a 8.19 8.69 LPcon-a dependsLPcon-a mainly on alleviating the controllable 8.19 loads (making them totally 8.03 off) to decrease the power consumptionLPshi-a by adjusting the set points 8.19 of AC and WH during the whole 8.06 period of the DR eventUniﬁed, which Algorithm results in more energy savings. 8.19 7.90

TablTablee 5.5. Energy consumption under HEMS algorithms.algorithms.

TypeType of of EnergyEnergy Before Before EnergyEnergy After After EnergyEnergy Saving Saving Energy Energy Saving % Algorithm/EnergyAlgorithm/Energy (KWh)(KWh) (KWh)(KWh) (KWh)(KWh) Saving % LPcur-aLPcur-a 50.71850.718 48.95448.954 1.7641.764 3.483.48 LPcon-aLPcon-a 50.71850.718 45.95445.954 4.7644.764 9.399.39 UniﬁedUnified Algorithm Algorithm 50.71850.718 48.375348.3753 2.34272.3427 4.624.62

5. ConclusionTable5 showss the energy consumption comparison between the LPcur-a, LPcon-a, and the developed unified algorithm. It can be seen that the unified algorithm outperformed the LPcur-a in This paper summarized the literature on HEMS techniques and methodologies, categorized terms of energy saving. The LPcon-a achieved a higher energy saving among the algorithms as the them into three types of algorithms (LPcur-a, LPcon-a, and LPshi-a), and finally proposed a new LPcon-a depends mainly on alleviating the controllable loads (making them totally off) to decrease the unified algorithm that overcomes the drawbacks of these three traditional algorithms. Simulation power consumption by adjusting the set points of AC and WH during the whole period of the DR investigations were performed for the four algorithms using a MATLAB/Simulink environment, event, which results in more energy savings. which facilitated the evaluation of the pros and cons for each algorithm. The results revealed that the 5.proposed Conclusions unified algorithm performed better in terms of customer comfort preservation, compliance with utility DR request, and energy cost savings. The strategy adopted in the proposed unified algorithmThis paperlimited summarized variations in the theliterature room temperature on HEMS around techniques the customer and methodologies,s’ desired set categorizedpoint better themthan the into load three power types curtail of algorithms algorithm. (LPcur-a, The maximum LPcon-a, value and of LPshi-a),the room andtemperature finally proposed when applying a new unifiedthe unified algorithm algorithm that was overcomes 30 °C (86 the °F) drawbacks, compared of to these 35 °C three (95 traditional°F) that was algorithms. reached in Simulationthe case of investigationsLPcur-a, implying were a performedhigher level for of thecustomer four algorithms satisfaction using. The aenergy MATLAB/Simulink cost savings were environment, 3.5%, 2%, whichand 1.5 facilitated% for the the unified evaluation algorithm, of the pros LPcon and-a, cons and for LPshi each-a algorithm. respectively The, results which revealed also proves that the proposedsuperiority unified of the algorithmproposed algorithm performed. better in terms of customer comfort preservation, compliance with utility DR request, and energy cost savings. The strategy adopted in the proposed unified algorithmAuthor Contributions: limited variations conceptualization, in the room A.H.B temperature and M.S.A.; around methodology, the customers’ A.H.B and desired M.S.A; setsoftware, point M.S.A; better thanvalidation, the load M.S.A, power A.B., curtail A.H.B and algorithm. H.M.E.; Theformal maximum analysis, A.H.B value and of the M.S.A.; room investigation, temperature M.S.A, when A.B., applying A.H.B theand unifiedH.M.E.; resources, algorithm A.H.B was and 30 ◦ M.S.A.;C (86 ◦ dataF), compared curation, A.H.B to 35 and◦C M.S.A (95 ◦F).; writing that was—original reached draft in preparation, the case of LPcur-a, implying a higher level of customer satisfaction. The energy cost savings were 3.5%, 2%, and

Energies 2018, 11, 3337 17 of 18

1.5% for the uniﬁed algorithm, LPcon-a, and LPshi-a respectively, which also proves the superiority of the proposed algorithm.

Author Contributions: Conceptualization, A.H.B and M.S.A.; methodology, A.H.B and M.S.A; software, M.S.A; validation, M.S.A, A.B., A.H.B and H.M.E.; formal analysis, A.H.B and M.S.A.; investigation, M.S.A, A.B., A.H.B and H.M.E.; resources, A.H.B and M.S.A.; data curation, A.H.B and M.S.A.; writing—original draft preparation, A.H.B and M.S.A.; writing—review and editing, M.S.A and A.H.B; visualization, A.H.B, A.B. and H.M.E.; supervision, A.H.B, A.B. and H.M.E. Funding: This research received no external funding. Conﬂicts of Interest: The authors declare no conﬂicts of interest.

References

1. IEA. Energy and Climate Change; World Energy Outlook Special Report; IEA: Paris, France, 2015. [CrossRef] 2. Lund, H.; Andersen, A.N.; Ostergaard, P.A.; Mathiesen, B.V.; Connolly, D. From electricity smart grids to smart energy systems—A market operation based approach and understanding. Energy 2012, 42, 96–102. [CrossRef] 3. Rossell, A.; Soler, J. Towards Efﬁcient Energy Management: Deﬁning HEMS and Smart Grid Objectives. Int. J. Adv. Technol. 2011, 4, 249–263. 4. Manic, M.; Wijayasekara, D.; Amarasinghe, K.; Rodriguez-Andina, J.J. Building Energy Management Systems: The Age of Intelligent and Adaptive Buildings. IEEE Ind. Electron. Mag. 2016, 10.[CrossRef] 5. IEA. Transition to Sustainable Buildings—Strategies and Opportunities to 2050; IEA: Paris, France, 2013; ISBN 978-92-64-20241-2. 6. ISO. Energy Management System ISO 50001; ISO: Geneva, Switzerland, 2014. 7. Wissner, M. The Smart Grid—A saucerful of secrets? Appl. Energy 2011, 88, 2509–2518. [CrossRef] 8. Torstensson, D.; Wallin, F. Exploring the perception for demand response among residential consumers. Energy Procedia 2014, 61.[CrossRef] 9. Goulden, M.; Bedwell, B.; Rennick-egglestone, S.; Rodden, T.; Spence, A. Smart grids, smart users? The role of the user in demand side management. Energy Res. Soc. Sci. 2014, 2.[CrossRef] 10. Medina, J.; Muller, N.; Roytelman, I.; Member, S. Demand Response and Distribution Grid Operations: Opportunities and Challenges. IEEE Trans. Smart Grid 2010, 1, 193–198. [CrossRef] 11. Albadi, M.H.; El-Saadany, E.F. Demand response in electricity markets: An overview. In Proceedings of the 2007 IEEE Power Engineering Society General Meeting, Tampa, FL, USA, 24–28 June 2007. [CrossRef] 12. Moss, K.J. Energy Management in Buildings, 2nd ed.; Taylor Francis: Abingdon, UK, 2010. [CrossRef] 13. Siano, P. Demand response and smart grids—A survey. Renew. Sustain. Energy Rev. 2014, 30, 461–478. [CrossRef] 14. Palensky, P.; Dietrich, D. Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE Trans. Ind. Inform. 2011, 7, 381–388. [CrossRef] 15. Rahimi, F.; Ipakchi, A. Demand response as a market resource under the smart grid paradigm. IEEE Trans. Smart Grid 2010, 1.[CrossRef] 16. Ahmad, A.; Khan, A.; Javaid, N.; Hussain, H.M.; Abdul, W.; Almogren, A.; Alamri, A.; Niaz, I.A. An optimized home energy management system with integrated renewable energy and storage resources. Energies 2017, 10, 549. [CrossRef] 17. Li, M.; Li, G.Y.; Chen, H.R.; Jiang, C.W. QoE-aware smart home energy management considering renewables and electric vehicles. Energies 2018, 11, 2304. [CrossRef] 18. Amer, M.; El-zonkoly, A.M.; Aziz, N.; M’Sirdi, N.K. Smart Home Energy Management System for Peak Average Ratio Reduction. Ann. Univ. Craiova Electr. Eng. Ser. 2014, 180–188. Available online: https://www.researchgate.net/publication/316972677_Smart_Home_Energy_ Management_System_for_Peak_Average_Ratio_Reduction (accessed on 27 November 2018). 19. Pipattanasomporn, M.; Kuzlu, M.; Rahman, S.; Member, S.; Kuzlu, M.; Rahman, S. An Algorithm for Intelligent Home Energy Management and Demand Response Analysis. IEEE Trans. Smart Grid 2012, 3, 2166–2173. [CrossRef] 20. Kuzlu, M.; Pipattanasomporn, M.; Rahman, S. Hardware demonstration of a home energy management system for demand response applications. IEEE Trans. Smart Grid 2012, 3, 1704–1711. [CrossRef] Energies 2018, 11, 3337 18 of 18

21. Rajalingam, S.; Mahamari, R. Dynamic Load Management for Smart Home Using Home Energy Management Algorithm. Int. J. Eng. Sci. Invent. Res. Dev. 2016, II. Available online: http://www.ijesird.com/91011010. PDF (accessed on 27 November 2018). 22. Belcredi, G.; Modernell, P.; Sosa, N.; Steinfeld, L.; Silveira, F. An implementation of a home energy management platform for Smart Grid. In Proceedings of the 2015 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM), Montevideo, Uruguay, 5–7 October 2015. [CrossRef] 23. Gunputh, S.; Murdan, A.P.; Oree, V. Design and Implementation of a Low-Cost Arduino-Based Smart Home System. In Proceedings of the 2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN), Guangzhou, China, 6–8 May 2017. 24. Saha, A.; Kuzlu, M.; Pipattanasomporn, M. Demonstration of a home energy management system with smart thermostat control. In Proceedings of the 2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 24–27 February 2013. [CrossRef] 25. Parithi, I. A Novel Algorithm for Intelligent Home Energy Management System. Int. J. Eng. Res. Technol. 2016.[CrossRef] 26. Boynuegri, A.R.; Yagcitekin, B.; Baysal, M.; Karakas, A.; Uzunoglu, M. Energy management algorithm for smart home with renewable energy sources. In Proceedings of the 4th International Conference on Power Engineering, Energy and Electrical Drives, Istanbul, Turkey, 13–17 May 2013. [CrossRef] 27. Chavali, P.; Yang, P.; Nehorai, A. A distributed algorithm of appliance scheduling for home energy management system. IEEE Trans. Smart Grid 2014, 5, 282–290. [CrossRef] 28. Zhao, Z.; Lee, W.C.; Shin, Y.; Member, S.; Song, K. An Optimal Power Scheduling Method for Demand Response in Home Energy Management System. IEEE Trans. Smart Grid 2013, 4, 1391–1400. [CrossRef] 29. Johal, R.; Ravi; Jain, D.K. Demand Response as a Load Shaping Tool in an Intelligent Grid with Electric Vehicles. In Proceedings of the 2016 IEEE 6th International Conference on Power Systems, ICPS 2016, Delhi, Italy, 4–6 March 2016; Volume 2. [CrossRef] 30. Dzobo, O.; Sun, Y. Optimal power control strategy of a hybrid energy system considering demand response strategy and customer interruption cost. Recent Adv. Electr. Electron. Eng. Available online: http://www.eurekaselect.com/node/160391/article/optimal-power-control-strategy-of-a-hybrid- energy-system-considering-demand-response-strategy-and-customer-interruption-cost (accessed on 29 November 2018). [CrossRef] 31. Shao, S.; Pipattanasomporn, M.; Rahman, S. Development of physical-based demand response-enabled residential load models. IEEE Trans. Power Syst. 2013.[CrossRef]

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