Real Time Pricing Based Appliance Scheduling in Home Energy Management Using Optimization Techniques

  • Basit Amin
  • Adia Khalid
  • Muhammad Azeem Sarwar
  • Asad Ghaffar
  • Adnan Satti
  • Nasir Ayub
  • Nadeem Javaid
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 12)

Abstract

In this paper, appliance scheduling scheme is proposed for residential area. Different types of heuristic and meta-heuristic optimization techniques are being used to solve the general problem of electricity demand. In this paper, a unique swarm based optimization technique Elephant Herding Optimization (EHO) is used to manage the electricity demand in order to manage the single home appliances in such a way that reduction of electricity cost is achieved and certain point of user comfort. For this purpose Real Time Pricing (RTP) scheme is used in this paper for electricity cost. To validate the effectiveness of proposed scheme simulations are performed. The results of EHO are compared with the results of Enhanced Differential Evolution (EDE). The simulations show that proposed scheme i.e. EHO provide best optimal results in achieving the minimum electricity cost and user comfort at certain point.

1 Introduction

The usage of electricity is increasing day by day in residential area and commercial area as well. Because of the increasing demand of electricity the utility companies facing different problems such as utility management, energy storage, and climate issue. In order to tackle these problems, changes are must to occur in utility companies. Smart grid provides more electricity to meet increasing demand. It also increases reliability and quality of power supplies. To meet the increasing demand of electricity lots of work is done by different researchers in home energy management systems. This management lead to the demand side management. Demand side management is also know as demand response. Basic goal of demand side management is to encourage the consumer to use less energy in On-Peak hours or to shift the usage of energy from On-Peak hours to Off-Peak hours. To achieve these goals different heuristics techniques are used to schedule the appliances in residential area. The meta-heuristic algorithms such as Genetic Algorithm (GA), Harmony Search Algorithm (HSA), Enhanced Differential Evaluation (EDE) and many other techniques are used.

In this paper, EHO technique is implemented to schedule the home appliances in order to achieve the reduction in electricity cost. Another technique EDE is also implemented in this paper to validate the results of EHO. Which shows that the EHO outperformed the EDE technique in electricity cost reduction.

The main focus of this paper is the reduction of electricity cost by scheduling the home appliances. for this purpose different types of pricing schemes are used by the utility companies to manage the load in on-Peak hours. The consumer can shift their load from on-peak hours to off peak hours using pricing scheme. By using this consumer can minimize their electricity cost with certain level of user comfort. In this paper we consider the RTP pricing scheme to shift the load. In RTP pricing scheme, the utility companies provide the rate of specific hour to consumer and charge by hourly usage of electricity. The consumer can shift the electricity load from high pricing hours to low pricing hours. By using RTP consumer is able to reduce their electricity bill while consuming the energy they require for them.

The Fig. 1, shows the smart grid for residential area. The power is generated through different methods, i.e. wind power, thermal power, and hydro power etc.
Fig. 1.

Smart grid

The rest of the paper is structured as; Sect. 2 includes the study of related work. The Sect. 3 describe the proposed system model then Sect. 4 discuses the simulations and results of the proposed model. At the end the research work or contribution is concluded in Sect. 5.

2 Related Work

In recent years lot of work is done in DSM for residential consumers in smart grid. Some of the recent work is discussed here in this section. There are many types of optimization techniques used for Home Energy Management System (HEMS). The related work discussed 4 types of different techniques: Heuristic Algorithm, Integer Linear Program, Game Approach and Stochastic optimization technique.

Some of the authors prefer heuristic techniques for solving the HEMS optimization problems. In the paper [1], the authors used GA technique and combination of Real Time Price (RTP) and Inclined Block Rate (IBR) pricing scheme. The HEMS is responsible to achieve the effective reduction in cost and reduction in Peak to Average Ratio (PAR). The time taken is 120 slots per day where each slot represents 12 min. The authors in paper [2], present the DSM model for both utility and consumers. The heuristic techniques used are: GA, Ant Colony Optimization (ACO) and Binary Particle Swarm Optimization (BPSO). The combination of TOU and IBR pricing scheme is used to calculate the electricity cost. The results of each heuristic technique is compared with each other in which, GA outperforms the other two techniques to achieve the objective. The four heuristic techniques and one hybrid technique is used in paper [3] to solve the defined problem. The problem addressed by the authors is to reduce the cost and to maximize the user comfort. The heuristic algorithms used are: GA, BPSO, Wind Driven Optimization (WDO), BFAO and the other technique used is hybrid of GA and WDO which is called as Genetic Wind Driven (GWD) algorithm. In [4], model for household appliances operation scheduling is presented. The authors used commercial software CPLEX and heuristic approaches such as Particle Swarm Optimization (PSO) and greedy algorithm to solve the problem. The expected outcome is achieved by proposing the optimized model. The main drawback of these papers is that the user comforts is ignored.

The authors in [5] propose mechanism for home area load management. The aim was to minimize the peak load and satisfy both the user comfort and requirement of all appliances. Integer Linear Programming (ILP) approach is considered for optimization solution in this research. The TOU is used as pricing scheme to calculate the electricity cost. The drawback of this research is that the electricity cost is ignored because with the preference or comfort maximization the electricity cost will also increase. In [6], the authors used Taguchi Loss Function to formulate the discomfort. The problem was to overcome the trade-off between discomfort and electricity payments by scheduling the appliances. Day ahead pricing scheme is used to schedule the appliances. The consumer schedules the appliances in response to the announced price of electricity by service provider one day ahead. The problem of load scheduling and power trading in system with high penetration of RES in identified in [7] by the authors. With high penetration of RESs, the power may flow from Distributed Generators (DGs) to substations, which negatively impact the stability of system. This reverse flow of power may cause the voltage rise problem. To tackle this problem users consume their generating power locally then injecting the excess power back into grid. The authors model the interaction between users and formulate the problem of selecting the offered price and output generation. For formulation, the authors considered the mixed-integer linear program technique. An approximate dynamic programming approach is also used to schedule the operation of appliances. The main flaw is that the authors of the paper ignored the initial cost in installation and maintenance cost of RES. The initial cost of the RES is too much that it cannot be neglected.

The authors in [8], presents the model for DSM program to tackle the peak load in residential sectors in smart grid with multiple suppliers. The interaction between the strategies of customers and suppliers are taken into account to determine the optimal daily load profile and the electricity price. For this purpose a non-cooperative game theoretic approach is employed to model the DSM problems as two games. One game is used to maximize the supplier profit and the second game aims to maximize the customer payoff. A computationally tractable distributed algorithm is proposed to check the Nash equilibrium between two games. Objectives are achieved by the proposed solution but the drawback in this paper, that the author ignored the user comfort by maximizing the payoff of customer. In [9], the authors presented the demand response modeling scheme. The purpose of the modeling is the minimization of cost and maximization of profit among the users and utility. Authors used Generalized Tit for Tat (GTFT) mechanism to model the energy scheduling problem in DR modeling. The expected output is achieved but the user comfort is compromised with minimization of electric cost. RTP based demand response algorithm is proposed in [10] to achieve the optimal load control. The purpose of the paper is to reduce electric cost and to reduce energy consumption in high pricing hours. A Stackelberg game approach is used where one leader and n followers exists. The proposed approach is able to control load during high electricity price and achieve efficiency in load management. With control electricity cost the user comfort is compromised.

3 Proposed System Model

This section emphasizes on the proposed approach for smart home appliances scheduling. The proposed approach is based on the RTP pricing scheme. The single home is considered in this approach to get the optimal solution. The appliances of smart home are categorized in two different types i.e. Interruptible Appliances and Non-Interruptible Appliances. Interruptible appliances are the appliances which can be shifted to any time slot of the day but non-interruptible appliances are the appliances that cannot be shifted to other time slots and have fixed start time and Length of Operation Time (LOT).

The Fig. 2 shows the system of the Energy Management Controller with categories of appliances of single home in residential area.
Fig. 2.

Energy management controller

Tables 1 and 2 shows the interruptible appliances and non-interruptible appliances of smart home and their power ratings in Kwh.
Table 1.

Parameters of interruptible appliances

Appliances

Power ratings

Air Conditioner 1

1

Air Conditioner 2

1

Air Conditioner 3

1

Electric Radiator 1

1.8

Electric Radiator 2

1.8

Rice Cooker1

0.5

Rice Cooker2

0.5

Rice Cooker3

0.5

Water Heater

1.5

Dish Washer

0.6

Washing Machine

0.38

Humidifier 1

0.05

Humidifier 2

0.05

Cloth Dryer

0.8

Table 2.

Parameters of non-interruptible appliances

Appliances

Power ratings

Electric Kettle 1

1.5

Electric Kettle 2

1.5

In this approach, One hour slot is broken down into 5 further slots i.e. one slot is equal to 12 min. Therefore one day has total 120 slots instead of 24 slots. Because of 120 slots LOT for each appliance has 12 min resolution time.

EDE technique is also used in this research paper to compare the results of proposed technique. EDE technique; Enhancement of the Differential Evaluation (DE). In EDE 5 types of vectors are calculated to find the fitness value. The trial vector with minimum cost is the fitness function of EDE. The cross over rate in EDE is 0.3, 0.6 and 0.9. The fitness function calculated from trial vectors is further used to calculate the electricity cost of the appliances. The steps of algorithm of EDE are;
  1. Step 1:

    Initialize the population.

     
  2. Step 2:

    Mutation.

     
  3. Step 3:

    Cross over.

     
  4. Step 4:

    Calculate Trails vectors with their fitness function.

     
  5. Step 5:

    Selection: Pick trial vector with minimum cost.

     
  6. Step 6:

    Find the greatest individual in the population.

     
  7. Step 7:

    End.

     
EHO is swarm based heuristic optimization technique to solve the optimization problem. In EHO; two operators are considers as herding behavior of elephant these operators are clan updating operator and separating operator. The clan is considered as category of appliances where as each elephant is representing the appliances of smart home. The clan updating operator is used to update the position of the elephant in each clan. The updating position of each elephant in clan is determined by its leader Matriarch. The algorithm for clan updating operator taken from paper [13] is shown in Algorithm 1;
The separating operator used to separate the worst and fittest value in clan at each generation. The separating operator is used to separate the worst elephant from clan. The algorithm for separating operator process is shown in Algorithm 2;
The herding behavior of the elephant mapped into the HEMS is shown in Table 3. The clan is considered as category of appliances where as each elephant is represented as appliance.
Table 3.

Mapping of EHO in HEMS

EHO elements

Mapping in HEMS

Clan (group of elephants)

Category of appliances, i.e. interruptible and non-interruptible

Each elephant

Appliances in single home

Clan updating operator

Optimal solution

4 Simulation Results

This section discusses the simulation and results of the proposed techniques. RTP pricing scheme is used to calculate the electricity cost of the scheduled appliances. EDE technique is also implemented to validate the results of EHO. This section shows the comparison of these two techniques and unscheduled.

Electricity cost in unscheduled case is 32 cent. In EDE sufficient Electricity cost reduction is achieved using trail vectors having minimum Fitness value i.e. 29 cents. In case of EHO the electricity cost is reduced to 24 cents from 32 cents. EHO performs better in terms of the electricity bill minimization. Electricity prices of every hour are pre defined in RTP pricing scheme. Consumer adjust their load according to the minimum price holding hour which reduce the electricity cost overall. Maximum load is shifted in those hours where electricity cost is low. EHO schedule electric cost is reduced because EHO schedule balance the load. Using clan updating operator the best optimal solution is achieved for scheduling the appliances. The Figs. 3 and 4 shows the comparison of electricity cost of Unscheduled, EHO, and EDE which shows that EHO out perform EDE technique in electricity cost reduction.
Fig. 3.

Electricity cost comparison using EHO and EDE

Fig. 4.

Electricity cost comparison

Figure 5 shows the comparison of PAR of unscheduled load, EHO, and EDE. The comparison figure shows that PAR is minimized by EDE as compared to the EHO and unscheduled. PAR is increased in unscheduled load because the maximum appliances operate in those hours where price of electricity is high using RTP pricing signal which results in creating the peak in those hours. In EHO the PAR is reduced almost 60% and in EDE it reduces to 80% with compare to unscheduled load. In PAR reduction EDE perform better then EHO and Unscheduled.
Fig. 5.

Peak to average ratio

Fig. 6.

Total load of appliances

Figure 6 shows that the maximum energy consumption values of EHO and EDE. EHO and EDE technique utilize the energy in efficient way. EDE load is managed by shifting the load in to Off-Peak hours that reduce the energy consumption of specific hour. By using RTP pricing scheme most of the appliances operate in start hours. The price for electricity in start hours are less than other hours of the day. Load is slightly increased in start hours in EHO but price for electricity does not increase overall because the price for electricity is low in these hours.

The Fig. 7 shows that the waiting time of EHO and EDE. The waiting time of EHO is slightly less than the waiting time of EDE. As the min focus of this paper is reduction of electricity cost. The waiting time of the EHO is compromised at certain level. As there is trade-off between electricity cost and user comfort, if electricity cost is minimized the user comfort cannot be minimized but can be kept at certain level. The difference between waiting time of both of the techniques less. The waiting time of the EHO in whole day is 28 mins where as the waiting time of EDE 32.
Fig. 7.

Waiting time

5 Conclusion

In this paper, consumption of electricity in HEMS is managed by scheduling of appliances. The main objective of the paper is to reduce the electricity cost which is achieved. The main objective is achieved by implementing EHO technique to schedule the appliances. The RTP pricing scheme is used in this paper. The EDE technique is also implemented in this paper to compare the results of EHO. The simulation results show that the EHO outperform the EDE in order to achieve the minimum electricity cost. User comfort is also achieved at certain level as there is a trade-off between electricity cost and user comfort. But PAR in EDE is reduced more than EHO.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Basit Amin
    • 1
  • Adia Khalid
    • 1
  • Muhammad Azeem Sarwar
    • 1
  • Asad Ghaffar
    • 1
  • Adnan Satti
    • 1
  • Nasir Ayub
    • 1
  • Nadeem Javaid
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

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