Realistic home energy management system using exogenous grid signals Adnan Ahmad1 , Nadeem Javaid1,∗ , Saeed Ahmad1 , Shah Saud1 , Umar Qasim2 , Zahoor Ali Khan3,4 1 COMSATS Institute of Information Technology, Islamabad 44000, Pakistan1 2 University of Alberta, Edmonton, AB, T6G 2J8, Canada 3 Internetworking Program, FE, Dalhousie University, Halifax, NS B3J 4R2, Canada 4 CIS, Higher Colleges of Technology, Fujairah Campus, 4114, UAE ∗ Correspondence:
[email protected]; www.njavaid.com
Abstract—One of the most promising concepts which make smart grid (SG) superior over the traditional grid is demand side management (DSM). However, most of the recent DSM techniques are centralized by nature and primarily focus on industrial consumers. In contrast, this paper proposes a realistic home energy management system (RHEMS) which incorporate the residential sector into DSM activities and facilitate the integration of renewable energy sources (RESs). Our proposed RHEMS handle the problem of cost minimization as an optimization problem, and household appliances along with RES are scheduled in response to the real-time pricing of the electricity market. First, the constrained optimization problem is mathematically formulated and then solved by using the genetic algorithm. Simulation results show that the proposed scheme significantly reduce the energy expenses of the consumer i.e. by 35.53%. Index Terms—smart grid; demand side management; home energy management; real time pricing; genetic algorithm.
I. I NTRODUCTION Nowadays, energy demand around the globe is rapidly increasing. In past, most of the power generation was being done from fossil fuels. However, the recent penetration of renewable energy sources (RESs) significantly increased power system complexity and dynamics [1]. The existing power system is not capable of maintaining its stability if a large number RESs are integrated into it. One of the current solutions is the transformation of the existing grid into the smart grid (SG) with advanced information and communication technologies (ICTs) [2]. These advanced ICTs not only enable SG to integrate the distributed generation (DG) and RESs but also improve the stability and reliability of power system. The implementation of SG will make the integration of RESs and DG practical, additionally, will also involve the residential and commercial users into demand-side management (DSM) and demand response (DR) activities [3]. DSM is an important feature of SG, the goal of DSM is to manage energy consumption pattern, to efficiently utilize the limited energy resources and to enhance the efficiency of the power system. DSM strategies modify the shape of load pattern by shifting the controllable loads from peak hours to off-peak hours [4]. However, it is totally impractical to ask the consumers to schedule their energy usage optimally, and they will not
compromise on their comfort level. Hence, an automatic home energy management system (HEMS) is required, but it needs a little awareness of consumers to know about the benefits of various scheduling schemes. HEMS is an integral part of the SG, through which DR can be implemented in the residential sector. DR is an effectual technique to reduce the peak-to-average ratio (PAR), to improve power system stability and to facilitate the integration of RESs. The common objectives of different DSM and DR strategies in SG are the reduction of the electricity cost and minimization of energy consumption in peak hours. To achieve these objectives many DSM techniques have been proposed in recent years, such as integer linear programming [5], mixed integer linear programming [6], multi-parametric programming [7], etc. However, these techniques can not tackle a large number of different household appliances having unpredictable, nonlinear and complex energy consumption patterns. Distinct from the above techniques, this paper proposes a genetic algorithm (GA) based realistic home energy management system (RHEMS) which minimize the consumers electricity expenses and reduce the PAR. First, an energy management system (EMS) is designed using exogenous grid signals: real-time pricing (RTP) signal, outdoor temperature, and solar irradiance. Then, GA is applied to get a realistic solution for the formulated objective function. The adequacy of proposed RHEMS is validated via simulations, where unscheduled load scenarios with and without RES and GA-based scheduled load scenario are compared in terms of electricity cost, user comfort level, and PAR. The rest of the paper is organized as follows. Section II and Section III briefly describe the related work and proposed system architecture respectively. Section IV deals with problem formulation. Section V presents proposed algorithm for RHEMS. The simulation results are presented in Section VI, and the paper is concluded in section VII. II. R ELATED WORK Recently, various DSM strategies have been proposed. Their common objectives are the minimization of electricity cost,
reduction of PAR, curtailment of carbon emissions and improvement of power system efficiency. Some of the most recent related works solve the appliances scheduling problem as an optimization problem is as per the following. Lee et al. [8], presents linear programming (LP) based residential energy management system (REMS) for minimization of electricity cost and PAR. Monotonic optimization based DSM strategy is proposed in [9], an optimal utilization technique for centralized RES has been demonstrated through mathematical modeling. Z. Zhu et al. [10] proposes an integer linear programming (ILP) based appliances scheduling scheme to shift the controllable appliances from peak hours to off-peak hours. In [11], the optimization problem of household appliances is solved via mixed integer linear programming (MILP), different types of appliances are taken in problem formulation with the ultimate goal of electricity bill reduction. In [12], a home automation system based on optimal stopping rule (OSR) is proposed. Simulation results shows, that OSR reduces the electricity bill with minimum appliances waiting time. In [13], the authors demonstrate an efficient HEMS architecture for implementation of DSM in the residential sector. They combine RTP scheme with inclined block rate (IBR) because the use of only RTP signal may shift the peak demand from peak hours to off-peak hours. To eliminate the creation of new peak in the objective function is formulated and solved by using GA. Simulation results illustrate that proposed model is very effective for reduction of PAR and electricity cost. In [14], the authors present a nature-inspired wind driven optimization (WDO) technique to solve multi-objective optimization problems. The WDO is a population-based iterative optimization technique. A population of small air parcels is randomly distributed in search space by assigning random velocities and positions to each particle. In each iteration, the velocity and position of air parcels are updated when it moves toward the optimum pressure point. Simulation results show that WDO outperformed the classical and other heuristic optimization techniques. In [15], the authors discuss that the time of use (ToU) pricing scheme and DR are the two strategies which motivate the consumers to reduce energy consumption during peak hours. A decision support program is demonstrated which forecast the energy demand and find an optimal time for appliance operation. In [16], an evolutionary algorithm (EA) for the solution of cost minimization problem is proposed. Simulation results show that the proposed EA brings the user load curve near to the objective curve, where the objective curve and electricity price have an inverse relationship. P. Yang et al. [17], presents a distributed algorithm (DA) for HEMS and grid optimization. In proposed DA the electricity price is used as an invisible hand to optimize the appliances scheduling and energy consumption. Z. Weng et al. [18], demonstrates a fully automated EMS by using reinforcement learning (RL) techniques. The energy
management and appliance scheduling problem are solved by observe, learn and adapt (OLA) algorithm which adds more intelligence to EMS. III. P ROPOSED SYSTEM ARCHITECTURE In SG, DSM and DR ensure the most stable and reliable grid operation. From the utility point of view, its two main benefits are the management of limited energy resources and reduction of PAR. While from the consumer point of view it minimizes the electricity bill. In this paper, an RHEMS have been proposed, and it is assumed that each prosumer will have a HEMS in future SG. To meet the energy demand with minimum electricity charges smart prosumer utilizes in-house RES along with grid energy. HEMS of the grid friendly prosumer schedule both the appliances and RES, in order to reduce the electricity bill and PAR in the dynamic pricing environment. In this context, the prosumer appliances are divided into two categories: controllable and uncontrollable appliances as shown in Table 1. TABLE I: Load categorization Controllable appliances
Un-controllable appliances
Washing machine Air conditioner Clothes dryer Water heater Dish washer –
Personal computers Security cameras Microwave oven Refrigerator Television Lights
In Fig. 1, the system architecture of our proposed RHEMS is shown, which mainly includes advanced metering infrastructure (AMI), smart meter (SM), smart scheduler (SS), master controller (MC), photovoltaic (PV) system and home appliances. The flow of energy is represented by solid lines while information flow is shown by dotted lines. The SM works as a communication gateway between the smart home and the utility. SM receive exogenous grid signals via AMI, it also measures and transmits energy consumption to the utility. The SS typically installed between SM and MC generates the daily energy usage pattern for all appliances and send it to MC for execution. MC is the core of our proposed RHEMS it controls the operation of each appliance according to the schedule generated by SS. IV. P ROBLEM FORMULATION In this section, the mathematical models and constraints of RES and appliance are presented. Based on these descriptions, the scheduling problem is defined. A. Energy generation model of PV system The house of our smart prosumer is equipped with rooftop PV system. Our proposed RHEMS tries to maximize the benefits from RESs and to minimize the electricity bill, carbon
usage instead of actual price. In ToU pricing scheme the pricing horizon is divided into different blocks, and a fixed price is defined for each block. In our model, here we use RTP in which the price of electricity change on the hourly basis and remain constant in an hour. The electricity price EP (t) in each time slot t is calculated as, X 24 X m 24 X m X EP (t) = Ea,t × Xa (t) + Eb,t t=1 a=1
t=1 b=1
!
×Xb (t) − Epv (t)
(5)
× P (t)
Where, P (t) is RTP, E pv (t) is the estimated renewable energy at time t, and Xa (t) and Xb (t) represent the ON/OF F state of controllable and un-controllable appliances respectively. D. Appliance scheduling problem
Fig. 1: Proposed system architecture
emissions, and PAR. The output energy of PV system Epv (t) is calculated by Eq. (1), E pv (t) = η · A · Iirr 1 − 0.005(Tt − 25) (1) Where, η is energy conversion efficiency of the PV system (%), A is the area of the generator (m2 ), (It ) is the solar irradiance (W/m2 ) and Tt is the outdoor temperature (C) at time t.
In this section, the appliance scheduling problem is formulated as an optimization problem. Our goal is to minimize the electricity bill of prosumer and in addition to reduce the PAR. The optimization problem is defined as, Objective function: X m m 24 X 24 X X Eb,t × Xb (t) Ea,t × Xa (t) + min t=1 a=1
!t=1 b=1
(6)
×P (t)
B. Energy consumption model Let we assume that the smart prosumer have two sets M and N of appliances i.e. the set of controllable appliances M = {a1 , a2 , a3 , . . . , am } and the set of un-controllable appliances N = {a1 , a2 , a3 , . . . , an } over a scheduling horizon of T = {1, 2, 3, 4, 5, . . . , 24}. The hourly energy consumption of controllable and un-controllable appliances are given as, a
a a a {Et1 , Et2 , . . . , Et24 }
(2)
b b b E b (t) = {Et1 , Et2 , . . . , Et24 }
(3)
E (t) =
a a a denote the energy consumption of Where, Et1 , Et2 , . . . , Et24 b b b represent the controllable appliances and Et1 , Et2 , . . . , Et24 energy consumption of un-controllable appliances at time t. The per day total energy consumption of all appliances are calculated as,
Etotal =
24 X m X t=1 a=1
Ea,t +
24 X m X
Eb,t
(4)
t=1 b=1
C. Energy pricing model A number of electricity tariffs are available to define the energy pricing over a day. Such as ToU pricing, the day ahead pricing (DAP), peak pricing (PP), critical peak pricing (CPP) and RTP [19]. In most of the appliance scheduling schemes, the pricing of electricity is assumed to be DAP or ToU pricing. However, the DAP only tells us about the trend of electricity
Subject to: X m 24 X t=1 a=1
Ea,t × Xa (t) +
m 24 X X t=1 b=1
≤ Egrid (t) + Epv (t), Where, t Ea (t) Eb (t) Xa (t) Xa (t) P (t) E pv (t) Egrid (t)
Eb,t × Xb (t)
∀
(7)
1 ≤ T ≤ 24
Time slots Energy consumption of controllable appliances Energy consumption of un-controllable appliances ON/OFF state of controllable appliance ON/OFF state of un-controllable appliance RTP sinal Estimated renewable energy Available grid energy. V. S CHEDULING ALGORITHM
The appliances scheduling problem as formulated in Section IV is solved by using GA. Although, appliance scheduling problem is solved by different classical optimization techniques such as LP, ILP, MILP, OSR, dynamic programming, but these techniques can not handle large number appliances and face a lot of difficulties in convergence. Moreover, most of the classical techniques do not have the global perspective and often converge at the local optimum solution.
In contrast, evolutionary algorithms, for example, GA give alternative methods to solve complex problems and outperforms the classical techniques in most of the cases. The GA is an iterative optimization technique, rather than working on a single solution, GA deals with different possible solutions in each iteration [20]. Therefore, we use GA to design our smart scheduler (SS). It the beginning of each day, when the prosumer gets the grid signals SS generates an optimized energy consumption pattern for all appliances. GA begins it search with randomly initialized binary coded chromosomes. The chromosomes pattern of GA represent the ON/OF F state of appliances, and the length of chromosomes show the number of appliances.
VI. S IMULATION RESULTS In this section, the simulation results of our proposed GAbased RHEMS are presented. It is assumed that the utility power supply is available around the clock to support the consumers loads. The utility has advanced ICTs to get the forecasted data of weather, outdoor temperature and solar irradiance from the metrological department on the daily basis, and broadcast it to the consumers. The exogenous grid signals used in our proposed RHEMS are RTP signal, forecasted outdoor temperature and solar irradiance as shown in Fig.3, Fig. 4 and Fig. 5 respectively.
30 RTP Pricing
(8)
where, N is the number of household appliances. Once a population is created, the fitness function of each possible solution is evaluated according to the objective function of the optimization problem. In this case, the fitness of each population is evaluated by using Eq. (6). Then, a new population is generated by applying the natural genetic operators such as selection, crossover, and mutation. The working principle of GA is shown in Fig. 2. In each iteration new population is produced through crossover and mutation. In this work, a single point mutation and binary crossover are used. The rate of convergence of GA is directly proportional to crossover rate, and the optimal solution is inversely proportional to the mutation rate. A tournament based selection is used to form a new population from existing one. Elitism is used to remember and transform best solution from one generation to next one.
25
20 Cost (Rs/kWh)
Length of chromosomes= N
15
10
5
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time (hours)
Fig. 3: RTP signal
35 Forecasted Outdoor Temprature 30
Start
25
Gen
