Optimized Energy Management System Using Electric Water Heater

Transcription

1 Optimized Energy Management System Using Electric Water Heater Muhammad Waqas Khan 1, Anzar Mahmood 1, Muhammad Hussain Ayaz 1, Muhammad Waseem 1, Sohail Razzaq 2, Nadeem Javaid 3 1 Department of Electrical Engineering, COMSATS Institute of Information Technology, Islamabad 2 Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad 3 Department of Computer Science, COMSATS Institute of Information Technology, Islamabad mwaqasaqeel@gmail.com, anzarmahmood@gmail.com, mhussainayaz@gmail.com, waseemchangwani@gmail.com, sohail.313@googl .com, nadeemjavaid@comsats.edu.pk Abstract Power generation and supply management is becoming an increasingly difficult task with the increase in population. One of the solutions is intelligent control of power consumption in the households where most of power consumption is due to high load appliances like Electric Water Heater (EWH) in winter and fridge in summer. This paper presents an over view of the EWH use for Demand Side Management (DSM). To reduce the consumption of EWH, the US Electrical Power Research Institute (EPRI) model has been focused in context of DSM in smart grid. Moreover, use of Binary Particle Swarm Optimization (BPSO) has been elaborated to implement a direct load control strategy for EWH management. Keywords Electric Water Heater (EWH), EPRI, BPSO I. INTRODUCTION Nowadays electricity plays an important role in almost every sphere of life. With the increase in fuel prices, it is hard to produce electricity with low prices and most of the people cannot afford the increasing price in electricity. On the other hand demand and supply management is becoming an increasingly difficult task with increasing population. This is particularly a great concern for the people living in very remote areas [1]. Renewable Energy Resources (RERs) are very suitable for the above mentioned mini grids. Wind, solar and hydal power generation systems are examples of RERs. The renewables are cost effective and environment friendly, however the main problem associated with such resources is power fluctuation and requirement of energy storing devices i.e. dry cell batteries etc. which are costly. The efficiency of the power grid system plays key role for the environment. Various methods and techniques are used to improve the power system stability and efficiency. Demand Side Management (DSM) can be improved by peak shaving and valley filling in order to provide frequency regulation and voltage stability to the system [2], [3], [4]. As the electricity production costs are high during peak hours, it is imperative to influence the consumption by making end users a part of control system [5]. For DSM, Electric Water Heater (EWH) is one of the most important appliances to consider as in winter 30% of the house load is because of EWH. Furthermore, it is noticed that the behavior of peak load of EWH is same as the overall peak load so it is one of the best candidates to consider for DSM. Fig.1. Aggregated Daily Household Load and the EWH Load From Fig. 1 [6], we can approximate the effect of EWH on the household load. It shows that EWH is responsible for approximately 30% of daily electricity consumption. In this paper, the EWH mathematical modelling along with the Electric Power Research Institute (EPRI) model has been discussed comprehensively. There are a lot of EWH controlling techniques proposed in

2 Water Drawn (Liter) research such as frequency regulation, valley filling and synchronous reserve etc. to control reduction of peak load and energy cost. Binary Particle Swarm Optimization (BPSO) and Direct Load Control (DLC) have been focused for EWH. A mechanism for the calculation of water consumption is also included. The rest of the paper is organized as follows. EWH modeling and BPSO are presented in section II. Determination of hot water usage is discussed in section III. EWH control methods are presented in section IV while conclusions are briefed in section V. II. EWH MODEL AND BPSO EWH has great importance when it comes to household load management. Many researchers proposed methods to control the demand of EWH. EPRI model is used to control the hot water consumption and control. Basic concept of water heater, its modeling and EPRI model along with its modification is presented here. A. EWH Model EWH is considered as energy storage element. It consist of tank, inlet cold water pipe, outlet hot water pipe, shut off valve, thermostat, heating element or electric resisting element, anode rod and dip tube etc. Fig. 2 [7] shows the basic elements of a water heater. Inlet pipe and shut off valve control the cold water supply to the tank. The tank typically contains 20 to 120 gallon hot water. because of heating element and reaches to the T high thermostat turns off. When the thermostat turns on energy flows through the anode and heat the resisting element which rises the temperature of the hot water. For the effective control of the EWH, water heater load and the water temperature in the tank has to be known. For this purpose, different meters are used that give different values at a specific interval of time like 10 to 15 minutes. Through the meter we can calculate the impact of the water heater on the household load. The temperature of the water present in the tank is measured through the thermometer. There are two resisting elements in a water heater with the range of 3KW to 5.5KW, but for the effective control and modeling we use only one resisting element. Fig. 3 shows the survey about the daily water drawn from the water heater [8] Daily Time in Hours Fig. 3. Daily Water Usage On the basis of load and temperature in the tank the temperature of hot water T H can be easily measured. From [9], EWH mathematical model can be explained with the help of following differential equation. CdT H dt = U SA(T a T H (t)) + W d (t)ρc p (T in T H (t)) + K(t)Q (1) Fig. 2. Various Element of EWH. Thermostat is a device used to control the temperature and to keep the temperature within a certain range. A couple of set points are defined by the user like low temperature T low and high temperature T high. If the temperature becomes low from the threshold thermostat turns on and if the temperature rises T H (t) = Temperature of hot water ( F); T a= Air temperature ( F); C= Thermal capacity (W/ F); C=V ρ C p; T in= Incoming cold water temperature ( F);

3 W D (t) = Average water draw (gallon/h); ρ= Density of water (kg/liter); C p= Specific heat of water (W/kg. F); SA= Surface area of the tank (m 2 ); Q= Input energy (W/h); U= Heat loses (W/ F. h.m 2 ); V= Volume of the tank (liter 3 ); K(t) = Thermostat condition (0 or 1); and P rated = Input power for resisting element (kw); Simplifying this equation and taking step response of the system we get the following equation [10]. T H (t) = T H (τ)e ( 1 R C )(t τ) + {BR T in + GR T a + QR } (1 e ( 1 R C )(t τ) ) (2) τ= initial temperature ( F); R= Tank thermal Resistance (m 2 F/W); G= SA/R (W/ F); B= ρ C p W D (W/ F); and R = 1/ (B+G) (W/ F); This model has ability to measure the hot water temperature at any time if all the parameters are known. The value of τ is kept zero for the initial temperature no matter what is the value of the Q and the water draw. B. BPSO Particle swarm optimization (PSO) is introduced by Kennedy. It is one of the efficient optimization techniques. The idea of PSO is based on the food searching behavior of birds [11]. v i (t+1) = w. v i t + (c1. rand1( )). (x Pbest, i(t) x i (t)) + (c2. rand2( )). (x Gbest, i(t) x i (t)) (3) x i (t+1) = x i (t) + v i (t+1) (4) v i is the velocity of particles, w, c1, and c2 are the momentum, personal and social acceleration constants while rand( ) is the random number generator, where the i th position of particle is x i and x Pbest, is the personal best position of the particle. x Gbest is the global best position obtained from all the particles. Movements of particles are affected by the x Pbest and x Gbest. To avoid this problem, different PSO algorithms have been reported in literature. If the particle has better position the x Pbest will be updated otherwise x Gbest. BPSO is introduced for the discrete optimization by Kennedy. Particle population in BPSO will be 0 or 1 and their velocities are given by the following equations [12]. 1 S(v i ) = 1 + e v i (5) Updating of these particles happened between 0 and 1 from equation. if( rand3( ) < s(v i )), then x i = 1; otherwise x i = 0; (6) C. EPRI Model With the increasing demand people used to introduce the hot water consumption models which tell us about the water consumption with respect to time. For instance, Gilbert and Associates develop a model named as EPRI Model [13]. EPRI model was designed to utilize the electricity according to the demand. Water heater has thermal storage capacity that makes them ideal for the DSM. For EPRI model, different data was collected by the 11 companies. Gilbert and Associates collected data form 110 houses. Each household was monitored on the basis of latitude, air temp, water temp etc. Some features of the EPRI model are: A lot of equations made on different times of day about the water heater structure Model is patronized on the basis of statistical analysis Model uses age categories instead of including full family etc. Demand of water was deducted on the basis of three factors: demographic, climatic, description about water heater. When EWH analyzed for EPRI model it contain sixteen variables, however, for the design only seven variables were used because of correlation of variables. Important variables are stated as follows:

4 Inside water temperature Air temperature Thermostats setting Size of tank Dummy variable for unemployed member Four dummy variables because of changing in season with respect to time. Dummy variable is set to be one if one or more family member is unemployed otherwise it is set to be zero. For winter season it is set to be one and zero for the other seasons. The model contains 16 equations and readings are taken for weekdays and weekends. The simple and generic EPRI model is given as: [13]. Use = {a 0 + a 1 per + a 2 age1 + a 3 age2 + a 4 age3 + a 5 term + a 6 tank + a 7 wtmp + a 8 atm + a 9 athome + a 10 spring + a 11 fall + a 12 summer + a 13 winter} (7) EPRI model was the great improvement towards the DSM by considering the previous work but in EPRI model collected data was insufficient. This collected data was based on electricity user not on the gas water heater etc. other drawbacks of the model include: it did not consider the absence of dishwasher, clothe washer, senior people and person who pay or don t pay bills. By considering these limitations, EPRI was considered for the further implementation. For keeping this problem Lawrence Berkeley National Laboratory (LBNL) expanded this model and it have some specific characteristics as follows: House don t have dishwasher House don t have clothe washer Senior citizen of the house People who don t pay for water etc. If there is no dish washer, clothe washer is considered in the house it will be hard to consider the accurate reading of the house load, same is for the senior citizen and the people who don t pay electric bills. So for the accurate model, it is necessary to consider these variables. By considering these four variables the modified EPRI model is given as: [13]. F expanded ( ) = {F EPRI ( ) (no_dw) (no_cw)} Senior no_pay (8) Use = usage of water (L/hour); Per = person in the house hold; age1 = preschool children (0-5 years); age2 = children go to school (6-13 years); age3 = adult (14 years or above); Therm = thermostats setting ( C); Tank = tank size (L); Wtmp = hot water temperature ( C); Atm = air/ atmospheric temperature ( C); Athome = presence of adult during day; Fall = dummy variable (set to be 1 when fall other wise 0); Spring = dummy variable (set to be 1 when spring other wise 0); Winter = dummy variable (set to be 1 when winter other wise 0); Summer = dummy variable (set to be 1 when summer other wise 0) No_cw = household having no cloth washers (L/hour); No_dw = house hold having no dish washer (L/hour); Senior = senior citizen; and No_pay = people who do not pay for hot water; If there is no dishwasher and cloth washer, we can save a lot of water consumption. According to Procter and Gamble (P&G) we can save 28 liters water if there is no cloth washer and if there is no dishwasher in house we can save 19.3 liter per day. Close digits is for senior citizen the people who don t pay bills and if these household are present then we have to consider for the accurate value of water consumption. III. DETERMINING OF HOT WATER USAGE For the control of the EWH, the temperature and usage of hot water should be considered. We can find out temperature of the hot water from equation (2). For the hot water usage, there are two categories of usage: small water draws such as hand wash and large water draws like shower etc. 1. Small water Draw When small amount of water is drawn from the tank, the heating element turns ON because of temperature drop below the high set point as cold water enters the tank to replace the drawn amount. Small water draw can be calculated by the following equation [14].

5 water used = volume of tank T MAX T adj T MAX T in (9) T Max = Maximum temperature set point; T in = Incoming water temperature; and T adj = Adjusted started temperature 2. Large Water Draw Examples of large water draw are dish washer, shower and cloth washer etc. In this type heating element turns ON for long interval of time but it is difficult to measure the adjusted starting temperature. So for convenience, an assumption has been made about consumption that for the large water draw the temperature falls to half temperate of the tank and from this information curve is set that is used to determine the temperature of water when large water is drawn. When we measure the adjusted started temperature where the element turns ON/OFF, one can easily find out the hot water usage from equation (9). IV. CONTROL METHOD EWH consumes 30% of the total energy of the house in the winter and have long thermal time coefficient. There are two different methods to control power consumption [5]. Consumer side control approach Utility side control approach Utility side control involves the monitoring of system s frequency in order to improve stability. Frequency deviation can be classified in two categories: outside (external control) and inside (integrated control). 1. External Control External control can be defined as the power cut when the frequency goes below a certain point and providing it back when the frequency is maintained. The main advantage of this control is disconnection of all the required load but this approach increases the users discomfort. 2. Integrated control The purpose of integrated control is to aggregate EWH load to compensate the frequency deviation and to provide stability. It can shift power when frequency goes below a certain limit and add power to achieve valley load filling when the frequency goes beyond the certain limit. Integrated control system is limited when there is large frequency deviation. The main advantage is to comfort the consumer and prevent the reducing life time of EWH due to switching. Following equation is used for integrated control [7]. T d = T db + m(f f 0 ) (10) The basic integrated control parameters should be chosen according to set point temperature at base case (T db), nominal frequency (F 0) and coefficient of frequency change or slope factors (m). Slope factor should be designed to keep set point temperature (t d) within the range. 3. DLC DLC is the controlling technique for house appliances from utility side. Because of load shedding irregularities occur between power generation and load so DLC is use to maintain balance in generation and load. Peak load demand and temperature combine with Direct Electric Water Heater (DEWH) and gives energy storage capabilities which make DLC as best technique for the controlling of EWH. There is relay controlled wirelessly on the load side which is used to control DEWH. Smart meters are used to measure the electric load of the household. Fig. 4. DLC Control System From Fig. 4 [6], we can see that DLC system is divided into three parts generation, distribution and demand load. In the DLC system the input of the distributor is the load demand of the system operator and the output is passed through BPSO controller and goes to the smart meter. Using BPSO technique water heater is treated as a particle which moves in the space solution and updates its velocity and position through equation (3, 4) and then these values convert to discrete form and give a fitness function in the following equation [6]. N F = ( Td i Ta i. w 1 i=0 + Pd Pa i. w 2 ) (11)

6 , N= Number of heater; Td i =Maximum water temperature; Ta i = Esteemed water temperature; Pd = Desired load; W1 = Load weight factor; W2 = Temperature load factor; The main objective of this equation (11) is to maximize the temperature of the water and minimize the peak load demand within 24 hours and for the consumer satisfaction W1 should be greater than W2. V. CONCLUSIONS In this paper, we have overviewed the use of EWH and its various aspects for DLC purpose. We have also covered the EWH modeling and EPRI model which is used for hot water consumption prediction and its modification. EWH is the best household for the DSM point of view. For determining of the water usage the EPRI model was introduced which has been modified by the introduction of four dummy variables. Use of BPSO provides good results for DLC of EWH. It minimizes the consumer discomfort and increases the lifetime of the equipment and saves electricity. References [1] A. Mahmood, N. Javaid, M. A. Khan and S. Razzaq, "An overview of load management techniques in smart grid," International journal of energy research (Int. J. Energy Res. (2015), Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: /er.3350, [2] J. V. Tonder and I. Lane, "Load model to support demand management," IEEE Transactions on Power Systems, vol. 11, no. 4, pp , [3] M. Ullah, N. Javaid, I. Khan, A. Mahmood and M. U. Farooq, "Residential energy consumption controlling techniques to enable autonomous demand side management in future smart grid communications," in Broadband and Wireless Computing, Communication and Applications (BWCCA), 2013 Eighth International Conference on, [4] B. Gong and I. A. Hiskens, "Two-Stage Model Predictive Control for Voltage," in 44th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2005), seville, [5] R. C. G. Teive and S. H. Vilvert, "DEMAND SIDE MANAGEMENT FOR RESIDENTIAL CONSUMERS BY USING DIRECT CONTROL ON," in Power System Management and Control, London, [6] A. Sepulveda, L. Paull, W. G. Morsi, H. Li, C. P. Diduch and L. Chang, "A Novel Demand Side Management Program using Water Heaters and Particle Swarm Optimization," in IEEE Electrical Power & Energy Conference, Canada, [7] K. I. Elamari, "using electric water heaters (ewhs) for power balancing and frequency control in pv-diesel hybrid mini-grids," Khalid Ibrahim Elamari, 2011, Québec, [8] K. Junji, L. Ning and J. H. Donald, "An Evaluation of the Water Heater Load Potential for Providing Regulation Service," in IEEE PES General Meeting. Accepted for publication in IEEE Trans. on Power Systems, [9] K. Elamari, L. Lopes and R. Tonkoski, "Using Electric Water Heaters (EWHs) for Power Balancing and Frequency Control in PV-Diesel Hybrid Mini-Grids," in world renewable energy congress, Sweden, [10] J. Delport, "Achieving Energy Efficiency for Hot Water Cylinders (Geysers) with the New Dual Element System," in 14th Domestic Use of Energy Conference, Cape Town, [11] J. Kennedy and R. Eberhart, "Particle swarm optimization," in IEEE International Conference on Neural Networks, Perth, Australia, [12] J. Kennedy and C. E. Russell, "A discrete binary version of the particle swarm algorithm," in IEEE International Conference on Systems, Man, and Cybernetics,1997. Computational Cybernetics and Simulation, Florida, [13] J. D. Lutz, X. Liu, J. E. McMahon, C. Dunham, L. J. Shown and Q. T. McGrue, "Modeling patterns of hot water use in households," Energy Analysis Program Energy and Environment Division Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, [14] L. Paull, D. MacKay, H. Li and L. Chang, "AWATER HEATER MODEL FOR INCREASED POWER SYSTEM EFFICIENCY," in Electrical and Computer Engineering, CCECE'09, Canada, 2009.