# Improvement of Energy Performance Index for Domestic Prosumers Based on Newly Proposed Dynamic Tariff and Rule-Based Strategy

Authors: Kuheli Goswami; Arindam Kumar Sil
Journal: International Transactions on Electrical Energy Systems
 (2022)

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## Abstract

A smart and intelligent energy management system (SIEMS) can make the existing system more reliable, robust, and cost-effective to deal with the present power scenario. This also initiates the necessity to develop a new dynamic tariff system and integrate renewable energy sources (RES) along with the conventional grid which in turn reduces carbon emission. Many countries have adopted different strategies for energy conservation based on energy conservation code, the main aim of which is to enforce design and development of household appliances for efficient power consumption. From data provided by utility sectors, it has been found that even in few countries 25% to 28% of the total energy is consumed by domestic sector and 45% of other residential loads also contribute a major part of the total domestic load consumed in any country. As a whole, residences, which consist of light electrical loads and home appliances constitute prosumers. The total energy consumed by prosumers is also intended by Bureau of energy efficiency, to make them fall under these codes (Energy Conservation Building Code) so that they can be treated on a scale of 1 star to 5 stars. This in turn will help in improving the energy performance index (EPI). In this paper, the optimal scheduling model of household appliances and energy storage system (ESS) has been designed based on the availability of renewable energy sources and newly proposed dynamic tariff system. To validate the efficiency of the model, we have comparatively evaluated the performance of the model with different feature selection methods and optimization techniques. A case study has been done and it has been found that the weighted K nearest neighbor method for feature selection and hybrid greedy particle swarm optimization (HGPSO) technique for optimization are performing better than other existing methods. The proposed model reflects the cost-effectiveness and improvement in EPI for prosumers.


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## Body

## 1. Introduction

Energy is the most important of all resources in terms of long-term survival of communities and economic growth. The demand for energy in the form of electricity is increasing day by day. Our greatest concern is to meet the load demand without increasing the stress on the grid. This demand fluctuation depends on habits of mankind, their economic growth, acceptability of advancement in new technology, geographical location, demographic cycle, etc. These factors govern the energy scenario of a country, which is characterized by the difference between the available energy and energy demand, better known as per capita energy consumption of any country. Most case studies reveal that domestic loads are playing a vital role during peak hours in most highly urbanized cities. To face the present power scenario, residential consumers are now becoming prosumers. On the other hand, to meet the best possible contribution by residential prosumers, it is mandatory now to achieve optimized energy consumption. Efficient energy consumption depends on several factors.Most of the researchers worked on the load profiles and power consumptions of home appliances individually for scheduling the domestic load judiciously. This in turn helps to formulate a home energy management system (HEMS). But there are certain problems in the existing HEMS like various practical constraints related to human interaction, unavailable power supply, consumer preference, etc. [1, 2]. Nowadays RES integration has proved itself beneficial in energy management systems (EMS). In contrast, besides RES integration and load scheduling, efficient communication and optimization scheme is of utmost importance. This has been indicated by Malik et al. in [3] and Nguyen et al. in [4]. In this context, several methodologies and future techniques to optimize energy consumption with minimum consumer interaction have been proposed by many researchers [5–7]. Besides all these, Xu et al. [8] and others [9,10], have incorporated several other factors such as consumers’ satisfaction level and cost of energy, which are also required for developing an efficient EMS.With the continuation of RES integration, the ESS and its optimal scheduling have become an integral part of intelligent residential energy management. The optimal sizing and scheduling of ESS play a vital role in cost-effective power supply system. Convex programming method and stochastic mixed integer nonlinear programming were used for optimal ESS sizing and scheduling [11–13]. For optimum use of batteries, it is very important to deal with the characteristics of batteries. Detailed modeling of ESS has been discussed [14,15]. An optimal charging and discharging schedule of ESS will be most effective with dynamic pricing or TOU tariff. A number of research works [16–20] have presented and proved that battery scheduling based on tariff and battery efficiency can improve the overall performance of the energy system.On the other hand, a literature survey revealed that very few have dealt with the proposal of a dynamic tariff system based on RES availability, household appliances scheduling, and ESS scheduling. In view of these shortcomings, in this paper, we have focused on different feature selection techniques to define an efficient model for optimal ESS scheduling and household appliances scheduling, which in turn leads to a proposal of a new three-part dynamic tariff system. To validate the proposed tariff and HGPSO, a newly proposed optimization technique a case study has been done here.To address the energy efficiency in residential sectors, many countries have launched different energy conservation codes and star labeling programs [21]. Summary of relevant research works has been mentioned in Table 1 with their limitations. Here in this article, we have dealt with energy performance index (EPI), which is defined as annual energy consumption in kilowatt-hours per square meter of the building area.Table 1
Summary of related work.
ApproachObjectiveAttributesLimitationsEA [22]Cost reductionOptimization of energy consumption in industrial, commercial, and residential sectorsTime-consuming and complex systemGA [23]Cost reductionEnergy consumption optimizationConsumers’ preference is ignored and system is complexBPSO [24]PAR and cost reductionEfficiency of BPSOComplex and time-consuming systemFP [25]Cost reductionCost-effective model with DGPAR and consumers’ preferences not addressedGA [26]Cost reductionOn the basis of RES availability, energy consumption optimizationPAR reduction not addressedDP [27]Cost and PAR reductionOptimization of energy consumption on the basis of RES availabilityComplex systems and consumers’ preferences not addressedILP [28]PAR and cost reductionLoad analysis using day ahead pricingPAR reduction not addressedHybrid technique (GA and PSO) [29]PAR and cost reductionOptimization of energy consumption considering the use of ESSConsumers’ preferences not addressedGHSA [30]Cost and PAR reductionSingle and multiple homes: AnalysisComplex and time-consuming systemPSO [31]Cost and PAR reductionEfficient use of RESConsumers’ preferences not addressedConvex programming [11]Optimum battery sizing(i) Rapid optimization for smart home(ii) Battery energy storage and charger are optimally sizedApplication of efficient energy consumption and PAR not addressedKalman filter method [32]SOC estimation of LIBSOC and health of LIBApplication of efficient energy consumption not addressedGroup-based and turn-based coordination method [33]Coordinated smart home energy sharingCost reductionFurther improvement is possibleMixed-integer quadratic-programming model predictive control scheme [34]Home energy managementEfficient energy consumptionCost reduction and tariff not addressedImproved dynamic programming [35]Home energy management incorporating RES and batteriesHome energy management systemPAR and peak load reduction not addressedBayesian optimal algorithm [36]Home energy managementEfficient energy consumptionCost reduction not addressedDijkstra algorithm [37]Home energy managementLoad schedulingCost reduction not addressedIn view of these shortcomings, a smart and intelligent energy management system (SIEMS) has been proposed in this paper. The current study will address four important issues. First, reduction in the cost of electricity consumption using an advanced three-part dynamic tariff system. Second, efficient energy consumption by scheduling household appliances. Third, optimized utilization of renewable energy by scheduling ESS. Fourth, improvement in EPI by integrating RES and designing an overall efficient energy consumption model.


## 2. Area of Contribution

Identifying the shortcomings in the previous research work, we have proposed a SIEMS, considering a large residential complex in tropical countries. The main contributions of this research work are:(i)
Feature selection for household appliances scheduling and optimal ESS scheduling by Complex Tree, Gaussian support vector machines (Gaussian SVM), weighted K nearest neighbor (Weighted KNN), Bagged Trees, and correlation methods and a comparative study based on these methods.(ii)
Static scheduling of ESS based on the relation between its efficiency, tariff, RES availability, and predicted energy consumption.(iii)
Analysis of the model performance with the effects of a newly proposed three-part dynamic tariff system.(iv)
Performance evaluation of the developed model with a new optimization technique, known as HGPSO.(v)
Improvement of EPI by identifying and bridging the gap through efficient utilization of roof-top solar PV systems.


## 3. System Modelling

The research work has been undertaken for a large residential complex in South EM Bypass, Kolkata area of India. It is spread over an area of 1,01,406 sq. meter. It has 7 towers, with 22 floors on each and a total of 900 apartments. It is one of the eco-friendly residential projects in India. Here 50% of the total area is open. It has 3 BHK apartments of approximately 250 sq. meter and 4 BHK apartments of approximately 450 sq. meter built-up area. It offers various facilities such as children’s park, basketball court, badminton court, indoor game area, parking area, community hall, gym, departmental store, swimming pool, lift, spa and salon, regional activity place, first-aid center, and meeting room. The distribution of total land and energy consumption is shown in Figure1.Figure 1
Distribution of area and energy consumption.
(a)(b)To design an energy consumption model for this large residential complex, we need to categorize the total load into three groups such as residential, commercial, and public lighting. The model of our system has been shown in Figure2. First, we have developed energy consumption model. Secondly, on the basis of demand forecasting and availability of RES forecasting [38], we have scheduled the household appliances and charging-discharging of energy storage system (ESS), respectively. For this, we have gone through various feature selection processes and compared the result for obtaining maximum efficiency. To avail maximum benefits and optimum energy consumption, we have proposed a new dynamic tariff and HGPSO method, respectively.Figure 2
System modelling.Finally, the performance of the developed model has been evaluated in terms of savings in monthly electricity bills, improvement in EPI, reduction in carbon emission, and reduction in PAR.


### 3.1. Energy Consumption Model

Here we have considered a large residential complex that offers almost 900 household apartments and each apartment with A appliances and N is the total number of household appliances likea1, a2, ……, aN. Each of the appliances is operated over time t є T [1 to 24]. The operation time interval for scheduled appliances a1 is [starting time Sa1, Finishing time Fa1].Operation time vector ofa1 appliance,(1)ϕa1=α1Ga1,α1PVa1,α2Ga1,α2PVa1,…,α24Ga1,α24PVa1,αtGa1 is time of using grid energy to satisfy demand of a1 at time t (known/forecasted). αtPVa1 is time of using solar energy to satisfy demand of a1 at time t (known/forecasted).Consumers have to specify the rating and switching ON time of the appliancesβa and fa, respectively.Energy consumed,(2)σa1=∑t=1T∑a1ɛAβa1αGa1+αPVa1.Similarly, the cost of the energy consumed bya1 over 24 hours, can be calculated as,(3)δa1=∑t=1T∑a1ɛAβa1γGtαGa1t+γPVtαPVa1t,where γ is the unit energy price.


### 3.2. Scheduling of Household Appliances

Here, in this section, we are dealing with scheduling of home appliances for active prosumers. Domestic appliances can be categorized as interruptible appliances, non-interruptible appliances, and interruptible appliances with minimum delay. Here, we have designed a mathematical model for the priority ranking of the household appliances based on some important features. This section will be dealing with feature selection and mathematical modeling for household appliances.


#### 3.2.1. Feature Selection

We have considered seven features here and used four different methods, which are Complex Tree, Gaussian SVM, Weighted KNN, and Bagged Trees. The performance of these four methods has been compared in Table2 on the basis of model accuracy and prediction speed.Table 2
Feature Selection for household appliances scheduling.
Method usedPredictorsPower rating of appliancesNo. of appliancesEnergy consumptionTotal runtimeNo. of switch onFrequency of operationOperation in peak hoursComplex treeModel accuracy36.10%13.90%36.10%38.90%22.20%44.40%19.40%Prediction speed2900 obs/sec15000 obs/sec13000 obs/sec17000 obs/sec11000 obs/sec5600 obs/sec8000 obs/secGaussian SVMModel accuracy52.80%13.90%80.60%88.90%25%88.90%19.40%Prediction speed160 obs/sec93 obs/sec82 obs/sec160 obs/sec99 obs/sec160 obs/sec96 obs/secWeighted KNNModel accuracy52.80%13.90%94.40%88.90%25%88.90%19.40%Prediction speed11000 obs/sec5300 obs/sec4400 obs/sec12000 obs/sec9500 obs/sec7800 obs/sec12000 obs/secBagged treesModel accuracy52.80%13.90%94.40%86.10%25%88.90%19.40%Prediction speed1400 obs/sec790 obs/sec1200 obs/sec1600 obs/sec1600 obs/sec1400 obs/sec1600 obs/secFinally, we have concluded that Weighted KNN is performing better than other methods with highest accuracy and prediction speed. On the basis of the abovementioned results, we have chosen energy consumption, Total runtime, and Frequency of operation as the three most important features to design the following model.


#### 3.2.2. Modelling

Suppose, the request time of appliance a1 isτra1 and the waiting time of appliance a1 is τwa1.Therefore,(4)τwa1=Sa1−τra1.Total waiting time,(5)τw=∑i=1Nτwai.Average waiting time,(6)τw.avg=τwN.Here, we have consideredθa1t as the ratio of operating time of a1 over 24 hours to fa1 as the frequency of operation of a1 or number of switch ON.(7)θa1∗=run−timenumber of switch ON.Utilization factor,(8)Ueai=124∑θait.Priority ranking,(9)λai=Ueai∗θai∗,∀aiɛA.Based on this ranking, finally, the load has been categorized as non-interruptible load, interruptible load with minimum delay time, and interruptible load. Accordingly, the appliances are ranked on the basis of calculated priority.


### 3.3. Scheduling of Energy Storage System

The ESS need to be optimally scheduled to achieve cost-effective energy consumption. This section will be dealing with the scheduling of ESS and its modeling.


#### 3.3.1. Feature Selection

We have considered nine features here and used four different methods, which are Complex Tree, Gaussian SVM, Weighted KNN, and Bagged Trees. The performance of these four methods has been compared in Table3 on the basis of model accuracy and prediction speed.Table 3
Feature Selection for ESS scheduling.
Method usedPredictorsESS cycleDaily energy consumptionRES availabilityCharging rateRound efficiencyDischarging ratePopulationWeekendElectricity billComplex treeModel accuracy40%66.70%33%66.70%66.70%66.70%46.70%40.00%66.70%Prediction speed590 obs/sec5100 obs/sec7100 obs/sec6400 obs/sec5600 obs/sec6200 obs/sec7000 obs/sec3500 obs/sec7100 obs/secGaussian SVMModel accuracy40%100%33.33%93.30%93.30%86.70%60%40%100%Prediction speed540 obs/sec520 obs/sec1100 obs/sec1100 obs/sec870 obs/sec1100 obs/sec1100 obs/sec990 obs/sec1000 obs/secWeighted KNNModel accuracy40%100%26.70%93.30%93.30%86.70%60%40%100%Prediction speed2700 obs/sec4300 obs/sec5400 obs/sec3600 obs/sec3600 obs/sec5400 obs/sec4300 obs/sec5400 obs/sec5400 obs/secBagged treesModel accuracy40%100%33.33%93.30%93.30%80%60%40%100%Prediction speed640 obs/sec720 obs/sec770 obs/sec750 obs/sec770 obs/sec780 obs/sec750 obs/sec770 obs/sec800 obs/secFinally, we have concluded that Weighted KNN is performing better than other methods. On the basis of the abovementioned result, we have chosen daily energy consumption, charging rate, round efficiency, discharging rate, and electricity bill as the five most important features to design the following model.


#### 3.3.2. Modelling

In this section, we have defined the charging and discharging of a battery on the basis of its round efficiency, SOC, and charging-discharging rate.Here, in this section, we have considered a battery with a capacity of 10 kW.Round efficiency,(10)ηB=ηcharging∗ηdischarging,ηcharging=0.98,ηdischarging=0.95.Here, SOCmin and SOCmax have been considered as 15% and 90%, respectively.Therefore, SOC can increase by 0.18 kW by every hour.As per the assumed charging-discharging rate, it will take 5 hours to get fully charged.During charging,(11)Energy used=Change in SOC∗Battery capacityηcharging.During discharging,(12)Energy discharged=Change in SOC∗Battery capacity∗ηdischarging.A rule-based strategy has been discussed later for an efficient charging/discharging schedule for the battery.


### 3.4. Proposed Dynamic Tariff System

A pricing-based approach is another effective way to reduce peak demand and manage energy consumption judiciously. In this approach, modulation of energy price and updating of information provided to the users are the most important steps to be taken care of. Due to the increase in the price of energy in energy shortage, consumers are bound to reduce their consumption during peak hours and use renewable energy. Here in this paper, we have formulated a new tariff structure, that is, inspired by time of day (TOD), critical peak pricing (CPP), and real time pricing (RTP). The flow chart to design the new tariff structure has been shown in Figure3.(13)Total Cost=a+bx+cxt.Figure 3
Flow-chart for proposed dynamic tariff.Here, “a” comprises of meter rent and monthly variable cost adjustment (MVCA), etc., is a fixed charge independent of maximum demand and energy consumption. “B” depends on maximum demand (kVA) or sanctioned demand (kVA). “C” depends on energy consumption (kWh).(14)γ=γC+γR.Here,γC and γR have been considered as the cost of energy from the main grid and renewable energy (fixed), respectively. Here E indicates the total energy consumption over 24 hours and t indicates hour, which varies from 1 to 24.where PP, OPP, and IP have been considered as unit energy prices in peak, off-peak and intermediate hours, respectively.


### 3.5. Rule-Based Strategy

Here, in this section, a rule-based strategy has been proposed for the efficient scheduling of ESS. We have assumed EP, EOP, and EI as energy consumed in peak hours (5 : 00 pm–11 : 00 pm), off-peak hours (11 : 00 pm–6 : 00 am), and intermediate hours (6 : 00 am–5 : 00 pm), respectively. Similarly, PP, OPP, and IP have been considered unit energy prices in peak, off-peak and intermediate hours, respectively.The scheduling of the battery leads to three conditions for each of two different cases.Case 1: When EP < EOP and EP < EI,(15)Condition1,PPOPP∗EPEOP>ηB∗OPPIP∗EOPEI,Condition2,OPPIP∗EOPEI>ηB∗IPPP∗EIEP,Condition3,IPPP∗EIEP>ηB∗PPOPP∗EPEOP.Case 2: When EP > EOP and EP > EI,(16)Condition1,PPOPP∗EPEOP<ηB∗OPPIP∗EOPEI,Condition2,OPPIP∗EOPEI<ηB∗IPPP∗EIEP,Condition3,IPPP∗EIEP<ηB∗PPOPP∗EPEOP.Therefore, the charging and discharging of ESS must follow certain rules, which are mentioned in Table4. In the above-listed cases, if condition 1 is satisfied, then ESS will be charging in intermediate hours, and discharging in peak hours, for condition 2, ESS will be charging in intermediate hours, discharging in off-peak and peak hours, and so on. These are listed below in Table 4.Table 4
Strategy for ESS scheduling.
Clear sky (when sufficient solar energy is available)Condition (Case 1 and 2)Off-peakIntermediatePeak1N/AChargeDischarge2DischargeChargeDischarge3N/ACharge/DischargeDischargeCloudy sky (when sufficient solar energy is not available)Condition (Case 1 and 2)Off-peakPeakIntermediate1ChargeN/ADischarge2ChargeDischargeDischarge3ChargeCharge/DischargeDischarge


### 3.6. Optimization

The geographical conditions and demography define the demand profile for residential prosumers. Here, we have dealt with different optimization techniques and finally, a hybrid optimization technique based on GA and PSO has been developed, which is a greedy selection technique and can result in the personal best and global best solution. This technique is known as hybrid greedy particle swarm optimization (HGPSO). The multiple knapsack problem (MKP) has been used to balance the demand and supply. This has been followed by HGPSO to reach the optimum point.


#### 3.6.1. Pseudocode

(1)
Input: Define the fitness function (based on scheduling of ESS, scheduling of household appliances and tariff), lb, ub, population size, NP (based on home appliances),T (terminating criteria).(2)
Determine the objective function f(P) and calculate its value(3)
Evaluate the personal best and f(P) best. Assign the value.(4)
Evaluate and identify the best solution as the global best. Assign the value.(5)
Loop the Value of t from 1 to T(a)
Loop the Value ofj from 1 to NP(i)
Evaluate the jth particle’s velocity and new position(ii)
Determine the new position of ith particle(iii)
Calculate the value of the objective function(iv)
Modify and update the present value of population, personal best,j, and global best(b)
End(6)
For selecting P in next iteration combine present P and Personal best to perform (μ + λ)(7)
End(8)
IterateT times for final result


## 3.1. Energy Consumption Model

Here we have considered a large residential complex that offers almost 900 household apartments and each apartment with A appliances and N is the total number of household appliances likea1, a2, ……, aN. Each of the appliances is operated over time t є T [1 to 24]. The operation time interval for scheduled appliances a1 is [starting time Sa1, Finishing time Fa1].Operation time vector ofa1 appliance,(1)ϕa1=α1Ga1,α1PVa1,α2Ga1,α2PVa1,…,α24Ga1,α24PVa1,αtGa1 is time of using grid energy to satisfy demand of a1 at time t (known/forecasted). αtPVa1 is time of using solar energy to satisfy demand of a1 at time t (known/forecasted).Consumers have to specify the rating and switching ON time of the appliancesβa and fa, respectively.Energy consumed,(2)σa1=∑t=1T∑a1ɛAβa1αGa1+αPVa1.Similarly, the cost of the energy consumed bya1 over 24 hours, can be calculated as,(3)δa1=∑t=1T∑a1ɛAβa1γGtαGa1t+γPVtαPVa1t,where γ is the unit energy price.


## 3.2. Scheduling of Household Appliances

Here, in this section, we are dealing with scheduling of home appliances for active prosumers. Domestic appliances can be categorized as interruptible appliances, non-interruptible appliances, and interruptible appliances with minimum delay. Here, we have designed a mathematical model for the priority ranking of the household appliances based on some important features. This section will be dealing with feature selection and mathematical modeling for household appliances.


### 3.2.1. Feature Selection

We have considered seven features here and used four different methods, which are Complex Tree, Gaussian SVM, Weighted KNN, and Bagged Trees. The performance of these four methods has been compared in Table2 on the basis of model accuracy and prediction speed.Table 2
Feature Selection for household appliances scheduling.
Method usedPredictorsPower rating of appliancesNo. of appliancesEnergy consumptionTotal runtimeNo. of switch onFrequency of operationOperation in peak hoursComplex treeModel accuracy36.10%13.90%36.10%38.90%22.20%44.40%19.40%Prediction speed2900 obs/sec15000 obs/sec13000 obs/sec17000 obs/sec11000 obs/sec5600 obs/sec8000 obs/secGaussian SVMModel accuracy52.80%13.90%80.60%88.90%25%88.90%19.40%Prediction speed160 obs/sec93 obs/sec82 obs/sec160 obs/sec99 obs/sec160 obs/sec96 obs/secWeighted KNNModel accuracy52.80%13.90%94.40%88.90%25%88.90%19.40%Prediction speed11000 obs/sec5300 obs/sec4400 obs/sec12000 obs/sec9500 obs/sec7800 obs/sec12000 obs/secBagged treesModel accuracy52.80%13.90%94.40%86.10%25%88.90%19.40%Prediction speed1400 obs/sec790 obs/sec1200 obs/sec1600 obs/sec1600 obs/sec1400 obs/sec1600 obs/secFinally, we have concluded that Weighted KNN is performing better than other methods with highest accuracy and prediction speed. On the basis of the abovementioned results, we have chosen energy consumption, Total runtime, and Frequency of operation as the three most important features to design the following model.


### 3.2.2. Modelling

Suppose, the request time of appliance a1 isτra1 and the waiting time of appliance a1 is τwa1.Therefore,(4)τwa1=Sa1−τra1.Total waiting time,(5)τw=∑i=1Nτwai.Average waiting time,(6)τw.avg=τwN.Here, we have consideredθa1t as the ratio of operating time of a1 over 24 hours to fa1 as the frequency of operation of a1 or number of switch ON.(7)θa1∗=run−timenumber of switch ON.Utilization factor,(8)Ueai=124∑θait.Priority ranking,(9)λai=Ueai∗θai∗,∀aiɛA.Based on this ranking, finally, the load has been categorized as non-interruptible load, interruptible load with minimum delay time, and interruptible load. Accordingly, the appliances are ranked on the basis of calculated priority.


## 3.2.1. Feature Selection

We have considered seven features here and used four different methods, which are Complex Tree, Gaussian SVM, Weighted KNN, and Bagged Trees. The performance of these four methods has been compared in Table2 on the basis of model accuracy and prediction speed.Table 2
Feature Selection for household appliances scheduling.
Method usedPredictorsPower rating of appliancesNo. of appliancesEnergy consumptionTotal runtimeNo. of switch onFrequency of operationOperation in peak hoursComplex treeModel accuracy36.10%13.90%36.10%38.90%22.20%44.40%19.40%Prediction speed2900 obs/sec15000 obs/sec13000 obs/sec17000 obs/sec11000 obs/sec5600 obs/sec8000 obs/secGaussian SVMModel accuracy52.80%13.90%80.60%88.90%25%88.90%19.40%Prediction speed160 obs/sec93 obs/sec82 obs/sec160 obs/sec99 obs/sec160 obs/sec96 obs/secWeighted KNNModel accuracy52.80%13.90%94.40%88.90%25%88.90%19.40%Prediction speed11000 obs/sec5300 obs/sec4400 obs/sec12000 obs/sec9500 obs/sec7800 obs/sec12000 obs/secBagged treesModel accuracy52.80%13.90%94.40%86.10%25%88.90%19.40%Prediction speed1400 obs/sec790 obs/sec1200 obs/sec1600 obs/sec1600 obs/sec1400 obs/sec1600 obs/secFinally, we have concluded that Weighted KNN is performing better than other methods with highest accuracy and prediction speed. On the basis of the abovementioned results, we have chosen energy consumption, Total runtime, and Frequency of operation as the three most important features to design the following model.


## 3.2.2. Modelling

Suppose, the request time of appliance a1 isτra1 and the waiting time of appliance a1 is τwa1.Therefore,(4)τwa1=Sa1−τra1.Total waiting time,(5)τw=∑i=1Nτwai.Average waiting time,(6)τw.avg=τwN.Here, we have consideredθa1t as the ratio of operating time of a1 over 24 hours to fa1 as the frequency of operation of a1 or number of switch ON.(7)θa1∗=run−timenumber of switch ON.Utilization factor,(8)Ueai=124∑θait.Priority ranking,(9)λai=Ueai∗θai∗,∀aiɛA.Based on this ranking, finally, the load has been categorized as non-interruptible load, interruptible load with minimum delay time, and interruptible load. Accordingly, the appliances are ranked on the basis of calculated priority.


## 3.3. Scheduling of Energy Storage System

The ESS need to be optimally scheduled to achieve cost-effective energy consumption. This section will be dealing with the scheduling of ESS and its modeling.


### 3.3.1. Feature Selection

We have considered nine features here and used four different methods, which are Complex Tree, Gaussian SVM, Weighted KNN, and Bagged Trees. The performance of these four methods has been compared in Table3 on the basis of model accuracy and prediction speed.Table 3
Feature Selection for ESS scheduling.
Method usedPredictorsESS cycleDaily energy consumptionRES availabilityCharging rateRound efficiencyDischarging ratePopulationWeekendElectricity billComplex treeModel accuracy40%66.70%33%66.70%66.70%66.70%46.70%40.00%66.70%Prediction speed590 obs/sec5100 obs/sec7100 obs/sec6400 obs/sec5600 obs/sec6200 obs/sec7000 obs/sec3500 obs/sec7100 obs/secGaussian SVMModel accuracy40%100%33.33%93.30%93.30%86.70%60%40%100%Prediction speed540 obs/sec520 obs/sec1100 obs/sec1100 obs/sec870 obs/sec1100 obs/sec1100 obs/sec990 obs/sec1000 obs/secWeighted KNNModel accuracy40%100%26.70%93.30%93.30%86.70%60%40%100%Prediction speed2700 obs/sec4300 obs/sec5400 obs/sec3600 obs/sec3600 obs/sec5400 obs/sec4300 obs/sec5400 obs/sec5400 obs/secBagged treesModel accuracy40%100%33.33%93.30%93.30%80%60%40%100%Prediction speed640 obs/sec720 obs/sec770 obs/sec750 obs/sec770 obs/sec780 obs/sec750 obs/sec770 obs/sec800 obs/secFinally, we have concluded that Weighted KNN is performing better than other methods. On the basis of the abovementioned result, we have chosen daily energy consumption, charging rate, round efficiency, discharging rate, and electricity bill as the five most important features to design the following model.


### 3.3.2. Modelling

In this section, we have defined the charging and discharging of a battery on the basis of its round efficiency, SOC, and charging-discharging rate.Here, in this section, we have considered a battery with a capacity of 10 kW.Round efficiency,(10)ηB=ηcharging∗ηdischarging,ηcharging=0.98,ηdischarging=0.95.Here, SOCmin and SOCmax have been considered as 15% and 90%, respectively.Therefore, SOC can increase by 0.18 kW by every hour.As per the assumed charging-discharging rate, it will take 5 hours to get fully charged.During charging,(11)Energy used=Change in SOC∗Battery capacityηcharging.During discharging,(12)Energy discharged=Change in SOC∗Battery capacity∗ηdischarging.A rule-based strategy has been discussed later for an efficient charging/discharging schedule for the battery.


## 3.3.1. Feature Selection

We have considered nine features here and used four different methods, which are Complex Tree, Gaussian SVM, Weighted KNN, and Bagged Trees. The performance of these four methods has been compared in Table3 on the basis of model accuracy and prediction speed.Table 3
Feature Selection for ESS scheduling.
Method usedPredictorsESS cycleDaily energy consumptionRES availabilityCharging rateRound efficiencyDischarging ratePopulationWeekendElectricity billComplex treeModel accuracy40%66.70%33%66.70%66.70%66.70%46.70%40.00%66.70%Prediction speed590 obs/sec5100 obs/sec7100 obs/sec6400 obs/sec5600 obs/sec6200 obs/sec7000 obs/sec3500 obs/sec7100 obs/secGaussian SVMModel accuracy40%100%33.33%93.30%93.30%86.70%60%40%100%Prediction speed540 obs/sec520 obs/sec1100 obs/sec1100 obs/sec870 obs/sec1100 obs/sec1100 obs/sec990 obs/sec1000 obs/secWeighted KNNModel accuracy40%100%26.70%93.30%93.30%86.70%60%40%100%Prediction speed2700 obs/sec4300 obs/sec5400 obs/sec3600 obs/sec3600 obs/sec5400 obs/sec4300 obs/sec5400 obs/sec5400 obs/secBagged treesModel accuracy40%100%33.33%93.30%93.30%80%60%40%100%Prediction speed640 obs/sec720 obs/sec770 obs/sec750 obs/sec770 obs/sec780 obs/sec750 obs/sec770 obs/sec800 obs/secFinally, we have concluded that Weighted KNN is performing better than other methods. On the basis of the abovementioned result, we have chosen daily energy consumption, charging rate, round efficiency, discharging rate, and electricity bill as the five most important features to design the following model.


## 3.3.2. Modelling

In this section, we have defined the charging and discharging of a battery on the basis of its round efficiency, SOC, and charging-discharging rate.Here, in this section, we have considered a battery with a capacity of 10 kW.Round efficiency,(10)ηB=ηcharging∗ηdischarging,ηcharging=0.98,ηdischarging=0.95.Here, SOCmin and SOCmax have been considered as 15% and 90%, respectively.Therefore, SOC can increase by 0.18 kW by every hour.As per the assumed charging-discharging rate, it will take 5 hours to get fully charged.During charging,(11)Energy used=Change in SOC∗Battery capacityηcharging.During discharging,(12)Energy discharged=Change in SOC∗Battery capacity∗ηdischarging.A rule-based strategy has been discussed later for an efficient charging/discharging schedule for the battery.


## 3.4. Proposed Dynamic Tariff System

A pricing-based approach is another effective way to reduce peak demand and manage energy consumption judiciously. In this approach, modulation of energy price and updating of information provided to the users are the most important steps to be taken care of. Due to the increase in the price of energy in energy shortage, consumers are bound to reduce their consumption during peak hours and use renewable energy. Here in this paper, we have formulated a new tariff structure, that is, inspired by time of day (TOD), critical peak pricing (CPP), and real time pricing (RTP). The flow chart to design the new tariff structure has been shown in Figure3.(13)Total Cost=a+bx+cxt.Figure 3
Flow-chart for proposed dynamic tariff.Here, “a” comprises of meter rent and monthly variable cost adjustment (MVCA), etc., is a fixed charge independent of maximum demand and energy consumption. “B” depends on maximum demand (kVA) or sanctioned demand (kVA). “C” depends on energy consumption (kWh).(14)γ=γC+γR.Here,γC and γR have been considered as the cost of energy from the main grid and renewable energy (fixed), respectively. Here E indicates the total energy consumption over 24 hours and t indicates hour, which varies from 1 to 24.where PP, OPP, and IP have been considered as unit energy prices in peak, off-peak and intermediate hours, respectively.


## 3.5. Rule-Based Strategy

Here, in this section, a rule-based strategy has been proposed for the efficient scheduling of ESS. We have assumed EP, EOP, and EI as energy consumed in peak hours (5 : 00 pm–11 : 00 pm), off-peak hours (11 : 00 pm–6 : 00 am), and intermediate hours (6 : 00 am–5 : 00 pm), respectively. Similarly, PP, OPP, and IP have been considered unit energy prices in peak, off-peak and intermediate hours, respectively.The scheduling of the battery leads to three conditions for each of two different cases.Case 1: When EP < EOP and EP < EI,(15)Condition1,PPOPP∗EPEOP>ηB∗OPPIP∗EOPEI,Condition2,OPPIP∗EOPEI>ηB∗IPPP∗EIEP,Condition3,IPPP∗EIEP>ηB∗PPOPP∗EPEOP.Case 2: When EP > EOP and EP > EI,(16)Condition1,PPOPP∗EPEOP<ηB∗OPPIP∗EOPEI,Condition2,OPPIP∗EOPEI<ηB∗IPPP∗EIEP,Condition3,IPPP∗EIEP<ηB∗PPOPP∗EPEOP.Therefore, the charging and discharging of ESS must follow certain rules, which are mentioned in Table4. In the above-listed cases, if condition 1 is satisfied, then ESS will be charging in intermediate hours, and discharging in peak hours, for condition 2, ESS will be charging in intermediate hours, discharging in off-peak and peak hours, and so on. These are listed below in Table 4.Table 4
Strategy for ESS scheduling.
Clear sky (when sufficient solar energy is available)Condition (Case 1 and 2)Off-peakIntermediatePeak1N/AChargeDischarge2DischargeChargeDischarge3N/ACharge/DischargeDischargeCloudy sky (when sufficient solar energy is not available)Condition (Case 1 and 2)Off-peakPeakIntermediate1ChargeN/ADischarge2ChargeDischargeDischarge3ChargeCharge/DischargeDischarge


## 3.6. Optimization

The geographical conditions and demography define the demand profile for residential prosumers. Here, we have dealt with different optimization techniques and finally, a hybrid optimization technique based on GA and PSO has been developed, which is a greedy selection technique and can result in the personal best and global best solution. This technique is known as hybrid greedy particle swarm optimization (HGPSO). The multiple knapsack problem (MKP) has been used to balance the demand and supply. This has been followed by HGPSO to reach the optimum point.


### 3.6.1. Pseudocode

(1)
Input: Define the fitness function (based on scheduling of ESS, scheduling of household appliances and tariff), lb, ub, population size, NP (based on home appliances),T (terminating criteria).(2)
Determine the objective function f(P) and calculate its value(3)
Evaluate the personal best and f(P) best. Assign the value.(4)
Evaluate and identify the best solution as the global best. Assign the value.(5)
Loop the Value of t from 1 to T(a)
Loop the Value ofj from 1 to NP(i)
Evaluate the jth particle’s velocity and new position(ii)
Determine the new position of ith particle(iii)
Calculate the value of the objective function(iv)
Modify and update the present value of population, personal best,j, and global best(b)
End(6)
For selecting P in next iteration combine present P and Personal best to perform (μ + λ)(7)
End(8)
IterateT times for final result


## 3.6.1. Pseudocode

(1)
Input: Define the fitness function (based on scheduling of ESS, scheduling of household appliances and tariff), lb, ub, population size, NP (based on home appliances),T (terminating criteria).(2)
Determine the objective function f(P) and calculate its value(3)
Evaluate the personal best and f(P) best. Assign the value.(4)
Evaluate and identify the best solution as the global best. Assign the value.(5)
Loop the Value of t from 1 to T(a)
Loop the Value ofj from 1 to NP(i)
Evaluate the jth particle’s velocity and new position(ii)
Determine the new position of ith particle(iii)
Calculate the value of the objective function(iv)
Modify and update the present value of population, personal best,j, and global best(b)
End(6)
For selecting P in next iteration combine present P and Personal best to perform (μ + λ)(7)
End(8)
IterateT times for final result


## 4. Result and Discussion

The dataset for this case study has been obtained from physical surveys and online surveys. Based on these data sets an efficient MATLAB SIMULINK model has been designed and developed. The efficiency and effectiveness of the model have been evaluated on the basis of proposed optimization technique, dynamic tariff, and ESS scheduling to figure out the improvements over the existing system. The efficiency of the model has also been analyzed in the following sections based on the monthly electricity bill, EPI, PAR, and carbon emission.


### 4.1. Savings in Monthly Electricity Bill

The reduction of daily or monthly electricity expense has been achieved based on two parameters; (i) smart and intelligent scheduling of household appliances, ESS, and (ii) newly proposed, TOU, RTP-based three-part dynamic tariff. Daily load requirements, ratings of appliances, and the best-suited tariff scheme need to be declared by prosumers a day ahead. On the basis of their requirement, the utility company would suggest a demand profile to be followed. The benefits of using this approach have been shown in Figure4 and Table 5.Figure 4
Savings in daily electrical expenses for 3 BHK and 4 BHK apartments.
(a)(b)Table 5
Savings in monthly electricity bill.
ConsumersMonthly electricity expenseSavings in monthly electricity expense (in (%)Without SIEMSWith SIEMS3 BHK consumers4376316427.74 BHK consumers6601472228.5Staff quarters1629135117


### 4.2. Improvement in EPI

The star rating is calculated based on EPI, shown in Table6, which is:Table 6
Residential building star rating plan.
Residential building star rating planPeriod: 14 December 2018 to 31 December 2024Star ratingEnergy performance Index1 (E1 + E2) of dwelling unitCompositeWarm and humidHot and dryTemperate1-Star52 < EPI ≤ 6058 < EPI ≤ 6455 < EPI ≤ 6728 < EPI ≤ 312-Star45 < EPI ≤ 5249 < EPI ≤ 5847 < EPI ≤ 5524 < EPI ≤ 283-Star37 < EPI ≤ 4539 < EPI ≤ 4938 < EPI ≤ 4721 < EPI ≤ 244-Star29 < EPI ≤ 3730 < EPI ≤ 3929 < EPI ≤ 3817 < EPI ≤ 215-StarEPI ≤ 29EPI ≤ 30EPI ≤ 29EPI ≤ 17Indicator = Energy Performance Index (EPI).EPI Calculation = Annual Energy consumption (kWh)/built-up area (sq. meter).EPI Calculation = EPI for air-conditioned spaces (25% area) with 24 deg C as set point (E1) + EPI for other spaces (75% area) with natural ventilation (E2) set points defined by IMAC with Air conditioner switched ON∗ And EPI for other appliances: E3 (constant value).In addition, with the abovementioned the EPI (E3) for appliances is to be considered in the range 7 to 9.On the basis of the above result shown in Figure5, it can be concluded that due to the reduction in energy consumption, an EPI reduction by 6 to 8 units can be achieved. This in turn may improve the star rating of the considered residential complex, which will help to get more subsidies from the Federal Agencies.Figure 5
Reduction in energy consumption.


### 4.3. PAR Reduction

PAR is the ratio of the maximum aggregated load consumption over a certain time period and the average of the aggregated load. The high value of PAR affects the stability of grid stability and increases cost-effective energy consumption. Reduction in PAR using the proposed approach has been shown in Figure6 and Table 7.Figure 6
Reduction in PAR.Table 7
PAR reduction.
ConsumersPARPAR without SIEMSPAR with SIEMSReduction in %3BHK apartments3.5152.24536%4BHK apartments3.2292.73815%Staff quarters3.473.28%Others2.79572.437813%


### 4.4. Reduction in Carbon Emission

An estimation says that 1 kWh generation produces 0.935 kg CO2. Here, in this case study, approximately each day 2.5 tons of CO2 emission can be reduced using the proposed approach, which can be absorbed by a tree in 100 years. It also means that 1-acre forest can absorb 2.5 tons CO2 per year.


## 4.1. Savings in Monthly Electricity Bill

The reduction of daily or monthly electricity expense has been achieved based on two parameters; (i) smart and intelligent scheduling of household appliances, ESS, and (ii) newly proposed, TOU, RTP-based three-part dynamic tariff. Daily load requirements, ratings of appliances, and the best-suited tariff scheme need to be declared by prosumers a day ahead. On the basis of their requirement, the utility company would suggest a demand profile to be followed. The benefits of using this approach have been shown in Figure4 and Table 5.Figure 4
Savings in daily electrical expenses for 3 BHK and 4 BHK apartments.
(a)(b)Table 5
Savings in monthly electricity bill.
ConsumersMonthly electricity expenseSavings in monthly electricity expense (in (%)Without SIEMSWith SIEMS3 BHK consumers4376316427.74 BHK consumers6601472228.5Staff quarters1629135117


## 4.2. Improvement in EPI

The star rating is calculated based on EPI, shown in Table6, which is:Table 6
Residential building star rating plan.
Residential building star rating planPeriod: 14 December 2018 to 31 December 2024Star ratingEnergy performance Index1 (E1 + E2) of dwelling unitCompositeWarm and humidHot and dryTemperate1-Star52 < EPI ≤ 6058 < EPI ≤ 6455 < EPI ≤ 6728 < EPI ≤ 312-Star45 < EPI ≤ 5249 < EPI ≤ 5847 < EPI ≤ 5524 < EPI ≤ 283-Star37 < EPI ≤ 4539 < EPI ≤ 4938 < EPI ≤ 4721 < EPI ≤ 244-Star29 < EPI ≤ 3730 < EPI ≤ 3929 < EPI ≤ 3817 < EPI ≤ 215-StarEPI ≤ 29EPI ≤ 30EPI ≤ 29EPI ≤ 17Indicator = Energy Performance Index (EPI).EPI Calculation = Annual Energy consumption (kWh)/built-up area (sq. meter).EPI Calculation = EPI for air-conditioned spaces (25% area) with 24 deg C as set point (E1) + EPI for other spaces (75% area) with natural ventilation (E2) set points defined by IMAC with Air conditioner switched ON∗ And EPI for other appliances: E3 (constant value).In addition, with the abovementioned the EPI (E3) for appliances is to be considered in the range 7 to 9.On the basis of the above result shown in Figure5, it can be concluded that due to the reduction in energy consumption, an EPI reduction by 6 to 8 units can be achieved. This in turn may improve the star rating of the considered residential complex, which will help to get more subsidies from the Federal Agencies.Figure 5
Reduction in energy consumption.


## 4.3. PAR Reduction

PAR is the ratio of the maximum aggregated load consumption over a certain time period and the average of the aggregated load. The high value of PAR affects the stability of grid stability and increases cost-effective energy consumption. Reduction in PAR using the proposed approach has been shown in Figure6 and Table 7.Figure 6
Reduction in PAR.Table 7
PAR reduction.
ConsumersPARPAR without SIEMSPAR with SIEMSReduction in %3BHK apartments3.5152.24536%4BHK apartments3.2292.73815%Staff quarters3.473.28%Others2.79572.437813%


## 4.4. Reduction in Carbon Emission

An estimation says that 1 kWh generation produces 0.935 kg CO2. Here, in this case study, approximately each day 2.5 tons of CO2 emission can be reduced using the proposed approach, which can be absorbed by a tree in 100 years. It also means that 1-acre forest can absorb 2.5 tons CO2 per year.


## 5. Conclusion

The increasing ubiquity of artificial intelligence and IoT has actively encouraged the smart control of energy utilization for residential prosumers. This paper has addressed different issues related to energy management by strategic optimal scheduling of household appliances and battery energy storage systems. To enhance the effectiveness of the proposed rule-based strategy, a dynamic three-part tariff has been proposed for the prosumers, so that they can get benefits of an uninterrupted and cost-effective power supply system without compromising their own preferences. To verify the efficiency of the proposed model a case study has been done for a large residential complex. The objective of the case study is two-fold. On one hand, it helps the prosumers of the community to indirectly participate in the utility market and reduce their monthly electricity bill significantly. On the other hand, it helps to improve the EPI level, which in turn promotes the initiatives by utilities and federal agencies to save more energy, make an eco-friendly system and simultaneously maintain a healthy relationship between the utility company and prosumers.This study can be further extended to design a daylight and occupancy sensor-based energy management system and predict the energy consumption with the life cost analysis of the sensors and ESS.



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*Source: 5087908-2022-08-25.xml*