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Optimizing Re-planning Operation for Smart House Applying Solar Radiation Forecasting

Optimizing Re-planning Operation for Smart House Applying Solar Radiation Forecasting Appl. Sci. 2014, 4, 366-379; doi:10.3390/app4030366 OPEN ACCESS applied sciences ISSN 2076-3417 www.mdpi.com/journal/applsci Article Optimizing Re-planning Operation for Smart House Applying Solar Radiation Forecasting 1; 1 2 3 Atsushi Yona *, Tomonobu Senjyu , Toshihisa Funabashi , Paras Mandal and Chul-Hwan Kim Faculty of Engineering, University of the Ryukyus, 1 Senbaru Nishihara-cho Nakagami, Okinawa 903-0213, Japan; E-Mail: b985542@tec.u-ryukyu.ac.jp Department of Electrical Engineering and Computer Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan; E-Mail: funabashi@esi.nagoya-u.ac.jp Department of Electrical and Computer Engineering, University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968, USA; E-Mail: pmandal@utep.edu School of Electrical and Computer Engineering, Sungkyunkwan University, Suwon City 440-746, Korea; E-Mail: chkimskku@yahoo.com * Author to whom correspondence should be addressed; E-Mail: yona@tec.u-ryukyu.ac.jp; Tel./Fax: +81-98-895-8684. Received: 31 January 2014; in revised form: 3 June 2014 / Accepted: 4 August 2014 / Published: 22 August 2014 Abstract: This paper proposes the re-planning operation method using Tabu Search for direct current (DC) smart house with photovoltaic (PV), solar collector (SC), battery and heat pump system. The proposed method is based on solar radiation forecasting using reported weather data, Fuzzy theory and Recurrent Neural Network. Additionally, the re-planning operation method is proposed with consideration of solar radiation forecast error, battery and inverter losses. In this paper, it is assumed that the installation location for DC smart house is Okinawa, which is located in Southwest Japan. The validity of proposed method is confirmed by comparing the simulation results. Keywords: DC smart house; forecast error; re-planning operation; photovoltaic; solar collector Appl. Sci. 2014, 4 367 1. Introduction In the background of fossil fuel depletion and increasing global warming caused by high carbon dioxide density in the atmosphere, power production plants using renewable energy have attracted international attention. In the case of electric power suply in isolated islands of Japan, renewable energy power production plants will be installed in many isolated islands in the near future to replace the fuel-consuming diesel generators. This paper presents an optimal operation method for the DC (direct current) grid system (DC smart house) with PV (photovoltaic) and SC (solar collector), assuming that the installation location for the DC smart house is Okinawa. Okinawa has about 40 isolated islands located in Southwest Japan. These islands mostly depend on diesel generators for power supply due to the long distance to the mainland of Okinawa and their small areas. If it is possible to provide power load and heat load by PV with SC in the DC grid system, the consumers can then enjoy more advantages [1]. However, the solar radiation forecast errors were not considered in our previous work. Though some researchers have proposed a solar radiation forecasting method using weather prediction [2], the electrical loss of inverter and efficiency of battery charge/discharge were not considered. This paper presents a re-planning operation method using Tabu Search for DC smart house considering solar radiation forecast error, battery and inverter losses. Simulation results show that daily running costs are minimized by the proposed method on sunny and rainy days. Once the proposed methods become widely applied, they will be able to contribute to society as a global warming prevention technology by a positive introduction of renewable energy systems. It is well known that photovoltaic (PV) systems are capable of generating electricity in a clean, quiet and reliable way, but the power output fluctuation depends on weather conditions. From the point of view of energy storage, batteries are integrated with a PV system for providing energy to load during night time and sunless period. Nowadays, a new home energy management system (HEMS) with PV, windmill, and electrical storage system has been reported in [3]. Additionally, feed-in-tariffs [4] for residential PV systems are introduced in many countries. Furthermore, solar collector (SC) and heat pump (HP) systems are developed in the heat supply [5]. An advanced efficient utilization method of these renewable systems is important for utilizing solar energy [6]. Solar radiation forecasting [7] is an important tool for determining the amount of storage battery energy, PV power output, and heat thermal energy collection of SC. The power conversion system is usually alternate current (AC) in electric power companies. However, a direct current (DC) grid system [8] for residential housing has been the focus of recent years. In a typical residential house, power load and heat load are the most important factors. Since the DC grid has lower power loss than that of the AC grid system, usually a stand-alone photovoltaic power system is designed and implemented to operate residential DC power appliances such as lamps, heat load, etc. Recently, many related works about energy management for smart house have been reported. Pedrasa et al. introduced enhancements to an energy service decision-support tool that aims to aid households in making more intelligent decisions when operating their major home appliances due to maximizing the net benefits gained by the end users [9]. Kanchev et al. presented an energy management system for a microgrid, including PV with embedded storage units and a gas microturbine [10]. The operational planning is based on the PV power output forecasting. A local Appl. Sci. 2014, 4 368 controlling algorithm was proposed in [11] to control domestic electricity and heat demand. The algorithm of planning is based on the current state of the system and on the external condition on heat demand. Adika and Wang presented the energy management of a single household that is equipped with a grid tied rooftop PV system and a set of devices [12]. The main idea is to encourage customers to participate in energy generation and efficient electricity consumption. Our current work also takes into account the concept of efficient energy managements mentioned in these above, but the approach of operational planning is different. This paper is organized as follows: Section 2 introduces the proposed DC smart house, which includes PV, SC, battery and heat pump system. In Section 3, the optimization method for re-planning operation using Tabu Search is described. Section 4 explains method to determine battery capacity. Section 5 gives a brief explanation of the PV power output forecasting. Section 6 shows the simulation results. Finally, Section 7 concludes. 2. DC Smart House Figure 1 shows the assumed DC smart house model in this paper. In our previous work [1], a heat pump system was not included. The heat pump system in FIgure 1 is connected to hot-water storage tank and that system heated by general electric power system or battery energy. The inverter between power system and DC bus is assumed to be a bi-directional inverter. Table 1 shows the system capacities. The simulation result is based on the assumption that conversion efficiencies of battery  and inverter bat inv are 91%, 90%, respectively. Figure 1. DC Smart house model. SC PV Battery Hot-water storage tank Cooling water Heat pump system Heat load Power load Electric flow Water flow Table 1. System capacities (Size of general-purpose model in Japan). Equipments Capacities Rated power output of PV 3.5 kW Rated output of heat pump system 4.5 kW/1.5 kW Hot-water storage tank 370 L Total Storage battery capacity (lithium-ion type) 25 kWh Inverter capacity (bi-directional type) 6 kW Appl. Sci. 2014, 4 369 2.1. PV Power Output A lot of PV power outputs are determined by some parameters. This research assumes that  , PV n and S have a conversion efficiency of PV = 14.4%, the temperature of the cell t is 25 C, PV PV CR the number of PV panels is 18, and the total area of a PV is 1.3 m , respectively [13]. At a weather condition with solar radiation I [kW/m ] and temperature of cell t , the power output of PV in CR P [kW] is determined by the following equation [14]. PV P =  n S I (1 0:005(t 25)) (1) PV PV PV PV in CR In this research, it is assumed that the temperature of cell t is equal to the air temperature. CR 2.2. Solar Collector This research assumes that  = 60%, n = 3 and S = 1.6 m are heat collection efficiency of SC SC SC SC, the number of SC panels and total area of a SC, respectively. The heat collection of SC Q [J] at a SC weather condition with solar radiation I [W/m ] is determined by following equation [15]. sc Q =   n  S  I (2) SC SC SC SC sc Although  is changed by differences of weather or temperature, this paper assumes that  is SC SC constant. In actual operation, available heat collection will be used for optimal operation. The amount of water in the hot-water storage tank W = 370 L is the parameter of the electric water heater. Variation in temperature about the inlet temperature of the hot-water storage tank is represented by the following equations [16]. dT Q + Q + Q Q =  A (3) SC tl sw loss w dt Q =  A  T (4) h w h Q = (T T ) (5) loss h h In the above equations, T [ C] is the inlet temperature of the hot-water storage tank, Q [J] h h is the amount of heat in the hot-water storage tank, [J/(L C)] is the volumetric specific heat of water (=4.184 J/(L C)), A [L] is capacity of hot-water storage tank, is the heat loss w h coefficient (=0.0060209 W/ C), T [ C] is air temperature and Q [W] is the amount of heat loss loss generated by hot-water storage tank. The flow rate of hot water A [L/s] is equal to the flow rate tl of water A [L/s]. The amount of heat requirement Q [W] and amount of heat generated by sw tl water Q [W] are represented by the following equations. sw Q =  A (T T ) (6) tl tl hw h Q =  A (T T ) (7) sw sw w h Appl. Sci. 2014, 4 370 In the above equations, T [ C], T [ C] and T [ C] are the temperature of hot water, temperature of hw w h cooling water and inlet temperature of hot-water storage tank, respectively. The amount of heat generated by the heat pump system Q [J] is represented by the temperature generated by the heat pump system HP T [ C] as shown in the following equation. Q =  A (T T ) (8) HP tl e h The power consumption of heat pump system P [kWh] is represented by following equation. HP HP P = (9) HP HP a In above equation, H (=3.6 MJ/kWh) is the ratio by value from P to Q , and a HP HP (4.5 kW/1.5 kW) is the rated output of the heat pump system, respectively. HP 3. Optimization Method In simulation of this research, Tabu Search (TS) is used for optimal operation of DC smart house. TS was proposed in 1986 by Glover. More details about TS can be found in [17,18]. Minimization of the operational cost for the DC smart house is the goal of this research. Therefore, the objective function is assumed to be the operational cost in this simulation. The objective function is minimized at each time point, so the obtained solutions are assumed to be the hourly amount of charge/discharge of the battery. The objective function and constraint condition for simulation are as follows. Objective function: C = (P P )C (10) day BUY i SALEi pi i=1 P =  (P + P +  P ) (11) BUY i inv PLi HPi bat DCi P = SALEi (P +  P P P ) inv PV i bat DCi PLi HPi (P  0) DC (12) (P  P P P ) inv PV i bat DCi PLi HPi (P > 0) DC In the above objective function, C [YEN] is the operational cost per day, C is electric power day p rate. This is equal to 11.46 YEN/kWh at nighttime (23:00–07:00), 26.22 YEN/kWh during living hours (07:00–10:00 and 17:00–23:00), and 35.04 YEN/kWh during daytime hours (10:00–17:00) [19]. P [kWh] is purchasing electric power consumption, P [kWh] is selling electric power, BUY SALE P [kWh] is power consumption of load, P [kWh] is power consumption of heat pump system, PL HP P [kWh] is charge/discharge power of battery. According to feed-in-tariffs for PV systems in Japan, DC the selling electric power rate is set to 39 YEN/kWh in this paper. If P is positive, the battery will DC charge electric power. [ Appl. Sci. 2014, 4 371 Constraint condition: 20%    100% (13) =  100 (14) Bmax jP j  6 kW (15) P  0:8  P  P  1:2 (16) BUY i1 BUY i BUY i1 P  0:8  P  P  1:2 (17) SALEi1 SALEi SALEi1 In the above constraint conditions, C [kWh] is the total charge/discharge power of battery , P is B I the inverter capacity (=6 kW). This approach aims to obtain more benefit for electrical power selling and to smooth the fluctuating power output of PV. Additionally, the optimal reference fixes the combination output of PV and battery to be constant from 10:00 to 17:00 in order for the power fluctuation in the grid point to be stable. 4. Determination of Battery Capacity The advantages of the installed battery is a smooth power output and the ability to provide the required power supply to the load on a cloudy or rainy day. However, it is better to reduce the capital cost of the battery. Therefore, optimal capacities of the battery and inverter are determined by simulation results in this research. In this simulation, it is assumed that I = 0 kW/m . Additionally, customers will use in hot water for 3 h (three persons) per day. In Figure 2, the vertical axis indicates operational cost per day C , and the horizontal axis indicates the capacity of the battery C . According to inverter capacity P day Bx I described in Figure 2, the case of C = 0 is not satisfied with the constraint condition. Similar results day are found in Figure 2b. Therefore, Figure 2 shows that the optimal combination is P = 6 kW and Iopt C = 25 kWh. Bopt Figure 2. Optimal dimension of storage battery. (a) 10 kWh–100 kWh; (b) 20 kWh–30 kWh. P = 6kW 100 I P =7kW -100 -200 = = P 4kW P 5kW I I -300 -400 -500 10 20 30 40 50 60 70 80 90 100 Running energy capacityCB [kWh] (a) day Day cost [Y C = [ Appl. Sci. 2014, 4 372 Figure 2. Cont. -100 -200 P = I 6kW -300 -400 -500 20 21 22 23 24 25 26 27 28 29 30 Running energy capacityC [kWh] (b) 5. PV Power Output Forecasting The authors proposed the power output forecasting technique of PV system based on hourly solar radiation forecasting one day ahead by using weather reported data, Fuzzy theory and Recurrent Neural Network (RNN) [8], as shown in Figure 3. This chapter shows the summary of the PV power output forecasting method. Since the detailed value of solar radiation prediction is not reported by the Meteorological Agency, the Fuzzy theory is adopted to determine the hourly solar radiation by using weather reported data such as humidity and cloud cover. This technique also includes the correction method for annual solar radiation forecast errors. Additionally, since the power output of the PV system fluctuates depending on weather conditions, the training process of NN tends to be unstable. However, Recurrent NN (RNN) is known to be a good tool for time-series data forecasting. The RNN has l = 24 and m = 1–30 neurons in the input layer and hidden layer, and n = 24 neurons in the output layer. These neurons are connected with linear coupling, and x –x are input data to RNN. Input data 1 l x –x (24 h  30 days) for the training dataset are determined by Fuzzy theory. There are connection 1 l weights between each neuron. The output of hidden layer neurons are converted to nonlinear values by hyperbolic tangent sigmoid-function. RNN has a context layer. This layer contains a copy of the hidden layer with time-delay lines, and added as feedback structure [20]. The context layer reflects both the input and output layer’s information to the structure of RNN. In this research, information of one time-step lag from the output units is used in the context layer. After each output unit provides the information relating to one time-step interval, the training of the network becomes stable. Based on conventional research [21], the authors think that it is convenient to make the forecast model by a trial-and-error approach. As a consequence, the past information is maintained to RNN with the progress of learning. The proposed technique for the application of RNN is trained by power output data based on Fuzzy theory and weather reported data. Because the Fuzzy model determines the solar radiation forecast data, RNN will train the power output smoothly. 6. Simulation This section shows the simulation conditions, simulation results and the comparisons for three cases. Day cost [Y Cday = Appl. Sci. 2014, 4 373 Figure 3. Forecasting method. Insolation forecast with Fuzzy and weather reported data Magnification correction of forecast insolation Power output forecasting for PV Power output forecast using Neural Networks 6.1. Simulation Conditions The authors assume three cases for this simulation, case 1: No forecast errors, case 2: Forecast hourly time, case 3: Re-planning for a sunny day and a rainy day. In addition, customers of the DC smart house will use hot water for 3 h (three persons) per day. Here, we assume that one person will use 100 L hot water from the storage tank between 19:00 and 22:00. At 19:00, if T of the inlet temperature in the hot-water storage tank falls much below 60 C, the water in the hot-water storage tank will be increased by a heat pump system in advance at night. The hourly weather reported data is delivered over 24 h at 4 time points (0:00, 6:00, 12:00, 18:00) on 1 day. In this simulation, the assumption of no forecast errors is case 1. The re-planning operation at 06:00 for initial time on one day is assumed to be case 2. The re-planning operation, case 3, is modified at the time of updating weather reported data for 4 times on 1 day. The validity of the proposed method is confirmed by compared with the case of no forecast errors using weather data in Okinawa, Japan, 2005, on sunny and rainy day simulations [22]. This paper focuses on optimizing re-planning operation applying solar radiation forecasting in one day for an existing smart house. Considering other cases, such as yearly weather data, will be another focus of research in the future. If we consider the statistical yearly weather data, the issue includes the optimizing system capacities of the smart house. 6.2. Simulation Results and Discussions The simulation results on a sunny day of case 1 and 3 are shown in Figures 4 and 5. Figures 4a and 5a show the electric power [kW] on the time axis. In these Figures, P [kW] indicates the charge/discharge DC power of battery determined by TS. If P is positive, the battery will charge electric power. P [kW] DC PV is the power output of PV, P [kWh] is the power consumption of load, P [kWh] is the power PL HP consumption of the heat pump system, P [kWh] is the purchasing electric power consumption, BUY P [kWh] is selling electric power. Figures 4b and 5b show the running energy capacity of battery SALE C [kWh], and state of charge  [%]. B CB In Figures 4c and 5c, the left side vertical axis indicates the inlet temperature of hot-water storage tank T [ C], the right side vertical axis indicates the heat quantity Q [MJ], and the horizontal axis indicates hour. In these Figures, Q [MJ] is heat quantity in tank, Q [MJ] is used heat quantity from tank. As t tu shown in Figures 4 and 5, all simulation results are satisfied with constraint conditions as described in Section 3. Heat quantity Q [MJ] Appl. Sci. 2014, 4 374 Figure 4. Simulation results (sunny day, case 1: no forecast errors). (a) Optimized result; (b) Battery; (c) Temperature of tank and used heat quantity. th 6 September, 20 , 2005., in Naha BUY 4 P SALE P PL PV -2 HP DC -4 -6 5 10 15 20 5 10 15 20 Time t [hours] Time t [hours] (a) (b) Q : Heat quantity in tank Q : Used heat Q from tank tu t 5 10 15 20 Time t [hours] (c) It is also confirmed by comparing the Figures 4 and 5, that the result of the proposed re-planning operation in case 3 is similar to case 1. Additionally, the battery charges the electric power at night time at an inexpensive electric rate, and provides the charged power to the heat pump system in a sunless period (19:00). More detail regarding simulation results about rainy days is beyond the scope of this research. However, Table 2 shows the calculation results of operational cost, electric power selling, electric power buying, generated electric power, electric power load, heat load, electric power loss and forecast errors for all cases on a rainy day. The lower line in Table 2 represents the mean absolute value (and the percentage values) for solar radiation forecast errors. The percentage values of solar radiation forecast errors are calculated by dividing the total errors by total solar radiation for 1 day. Table 3 shows the calculation results of operational cost for all cases on a sunny day. These simulation results show that Electric Powers P [kW] [ C] Temperature TT Running energy capacity C [kWh] State of charge [%] B Heat quantity Q [MJ] Appl. Sci. 2014, 4 375 the daily operational cost is minimized by the proposed method on two solar radiation patterns. This also means that the benefit of the installed battery is confirmed by these results. Figure 5. Simulation results (sunny day, case 3: re-planning operation). (a) Optimized result; (b) Battery; (c) Temperature of tank and used heat quantity. th 6 September, 20 , 2005., in Naha BUY SALE P PL PV -2 HP -4 DC 0 0 -6 5 10 15 20 5 10 15 20 Time t [hours] Time t [hours] (a) (b) Q : Heat quantity in tank Q : Used heat Q from tank tu t 0 0 5 10 15 20 Time t [hours] (c) The calculation results of cases 1–3 are similar to the simulation results as described in Figures 4 and 5. It should be noted that weather varies with the seasons. To understand more about simulation results, Table 4 represents the mean generated electric power of PV, absolute values of forecast errors and the percentages every month in 2005. It can be confirmed that case 3 is proposed to be superior to case 2. Whereas the simulation results of operational cost for case 3 are very similar to case 1, the results of electric power loss are higher even than case 2. This reason for this is clear because the objective function in this research is to minimize the operational cost, as described in Section 3. So, if we change the objective function, the simulation results will be different. Otherwise, the difference of the operation time between cases 2 and 3 causes the electric power loss due to the charge/discharge electric power of battery. Therefore, considering further research that is not explored in this article, our future research Electric Powers P [kW] Temperature TT [ C] Running energy capacity C [kWh] State of charge [%] B Appl. Sci. 2014, 4 376 will mainly focus on (1) Applying a new strategy for compensating electric power loss and forecast errors; (2) Optimizing the system capacities of a DC smart house; and (3) Applying new and advanced artificial intelligence techniques for optimal operation. Table 2. Running cost (rainy day). Each case case 1 case 2 case 3 Operational cost [YEN/day] 329.90 392.36 344.28 Electric power selling [kWh/day] 0.68 1.40 0.88 Electric power buying [kWh/day] 30.66 31.05 31.73 Generated electric power [kWh/day] 2.66 2.66 2.66 Electric power load [kWh/day] 24.04 24.04 24.04 Heat load [kWh/day] 4.85 4.65 4.83 Electric power loss [kWh/day] 3.75 3.62 4.64 Forecast errors [kWh/day] — 1.18 0.77 Percentages of forecast errors (44.3%) (29.1%) Table 3. Running cost (sunny day). Each case case 1 case 2 case 3 Operational cost [YEN/day] –291.85 –252.62 –271.61 Electric power selling [kWh/day] 15.48 14.30 15.51 Electric power buying [kWh/day] 26.35 24.75 27.19 Generated electric power [kWh/day] 19.20 19.20 19.20 Electric power load [kWh/day] 24.04 24.04 24.04 Heat load [kWh/day] 0.62 0.62 0.62 Electric power loss [kWh/day] 5.41 4.99 6.22 Forecast errors [kWh/day] — 4.36 2.61 Percentages of forecast errors (22.7%) (13.6%) Appl. Sci. 2014, 4 377 Table 4. Mean generated electric power of PV and forecast errors at Naha in 2005. Month 1 2 3 4 5 6 Mean generated electric power [kWh/day] 8.08 7.33 12.76 16.02 15.26 15.02 Mean forecast errors [kWh/day] (case 2) 2.96 2.97 3.68 3.99 4.45 4.45 Percentages of forecast errors [%] (case 2) 36.60 40.50 28.88 24.93 29.17 29.62 Mean forecast errors [kWh/day] (case 3) 2.00 1.47 1.03 1.59 1.67 1.43 Percentages of forecast errors [%] (case 3) 24.79 20.08 8.06 9.90 10.91 9.50 Month 7 8 9 10 11 12 Mean generated electric power [kWh/day] 21.58 18.51 17.55 15.02 10.85 8.62 Mean forecast errors [kWh/day] (case 2) 4.78 4.82 4.53 3.89 3.50 2.81 Percentages of forecast errors [%] (case 2) 22.16 26.03 25.81 25.90 32.28 32.61 Mean forecast errors [kWh/day] (case 3) 1.19 1.65 2.27 1.77 1.51 1.15 Percentages of forecast errors [%] (case 3) 5.54 8.94 12.94 11.76 13.89 13.39 7. Conclusions This paper proposes the re-planning operation method using Tabu Search for a DC smart house including PV, SC, battery, and a heat pump system. The proposed method is based on the solar radiation forecasting method using weather reported data collection, Fuzzy theory and Recurrent Neural Network. Additionally, the re-planning operation method was used to consider solar radiation forecast error, battery and inverter losses. Simulation results show that daily running costs are minimized by the proposed method on two solar radiation patterns. The validity of the proposed re-planning method is confirmed by comparing the case of no forecast errors. The simulation results show that, if it is possible to provide power load and heat load by PV with SC, the advantage to the consumer will increase. Author Contributions Modeling of the DC smart house and the optimization method were performed by Atsushi Yona and Tomonobu Senjyu. Data analysis was done by Atsushi Yona. Manuscript was written by Atsushi Yona, Tomonobu Senjyu and Paras Mandal. 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Weather Data [CD-ROM]; JMBC (Japan Meteorological Business Support Center): Tokyo, Japan, 2005. 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Sciences Multidisciplinary Digital Publishing Institute

Optimizing Re-planning Operation for Smart House Applying Solar Radiation Forecasting

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Abstract

Appl. Sci. 2014, 4, 366-379; doi:10.3390/app4030366 OPEN ACCESS applied sciences ISSN 2076-3417 www.mdpi.com/journal/applsci Article Optimizing Re-planning Operation for Smart House Applying Solar Radiation Forecasting 1; 1 2 3 Atsushi Yona *, Tomonobu Senjyu , Toshihisa Funabashi , Paras Mandal and Chul-Hwan Kim Faculty of Engineering, University of the Ryukyus, 1 Senbaru Nishihara-cho Nakagami, Okinawa 903-0213, Japan; E-Mail: b985542@tec.u-ryukyu.ac.jp Department of Electrical Engineering and Computer Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan; E-Mail: funabashi@esi.nagoya-u.ac.jp Department of Electrical and Computer Engineering, University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968, USA; E-Mail: pmandal@utep.edu School of Electrical and Computer Engineering, Sungkyunkwan University, Suwon City 440-746, Korea; E-Mail: chkimskku@yahoo.com * Author to whom correspondence should be addressed; E-Mail: yona@tec.u-ryukyu.ac.jp; Tel./Fax: +81-98-895-8684. Received: 31 January 2014; in revised form: 3 June 2014 / Accepted: 4 August 2014 / Published: 22 August 2014 Abstract: This paper proposes the re-planning operation method using Tabu Search for direct current (DC) smart house with photovoltaic (PV), solar collector (SC), battery and heat pump system. The proposed method is based on solar radiation forecasting using reported weather data, Fuzzy theory and Recurrent Neural Network. Additionally, the re-planning operation method is proposed with consideration of solar radiation forecast error, battery and inverter losses. In this paper, it is assumed that the installation location for DC smart house is Okinawa, which is located in Southwest Japan. The validity of proposed method is confirmed by comparing the simulation results. Keywords: DC smart house; forecast error; re-planning operation; photovoltaic; solar collector Appl. Sci. 2014, 4 367 1. Introduction In the background of fossil fuel depletion and increasing global warming caused by high carbon dioxide density in the atmosphere, power production plants using renewable energy have attracted international attention. In the case of electric power suply in isolated islands of Japan, renewable energy power production plants will be installed in many isolated islands in the near future to replace the fuel-consuming diesel generators. This paper presents an optimal operation method for the DC (direct current) grid system (DC smart house) with PV (photovoltaic) and SC (solar collector), assuming that the installation location for the DC smart house is Okinawa. Okinawa has about 40 isolated islands located in Southwest Japan. These islands mostly depend on diesel generators for power supply due to the long distance to the mainland of Okinawa and their small areas. If it is possible to provide power load and heat load by PV with SC in the DC grid system, the consumers can then enjoy more advantages [1]. However, the solar radiation forecast errors were not considered in our previous work. Though some researchers have proposed a solar radiation forecasting method using weather prediction [2], the electrical loss of inverter and efficiency of battery charge/discharge were not considered. This paper presents a re-planning operation method using Tabu Search for DC smart house considering solar radiation forecast error, battery and inverter losses. Simulation results show that daily running costs are minimized by the proposed method on sunny and rainy days. Once the proposed methods become widely applied, they will be able to contribute to society as a global warming prevention technology by a positive introduction of renewable energy systems. It is well known that photovoltaic (PV) systems are capable of generating electricity in a clean, quiet and reliable way, but the power output fluctuation depends on weather conditions. From the point of view of energy storage, batteries are integrated with a PV system for providing energy to load during night time and sunless period. Nowadays, a new home energy management system (HEMS) with PV, windmill, and electrical storage system has been reported in [3]. Additionally, feed-in-tariffs [4] for residential PV systems are introduced in many countries. Furthermore, solar collector (SC) and heat pump (HP) systems are developed in the heat supply [5]. An advanced efficient utilization method of these renewable systems is important for utilizing solar energy [6]. Solar radiation forecasting [7] is an important tool for determining the amount of storage battery energy, PV power output, and heat thermal energy collection of SC. The power conversion system is usually alternate current (AC) in electric power companies. However, a direct current (DC) grid system [8] for residential housing has been the focus of recent years. In a typical residential house, power load and heat load are the most important factors. Since the DC grid has lower power loss than that of the AC grid system, usually a stand-alone photovoltaic power system is designed and implemented to operate residential DC power appliances such as lamps, heat load, etc. Recently, many related works about energy management for smart house have been reported. Pedrasa et al. introduced enhancements to an energy service decision-support tool that aims to aid households in making more intelligent decisions when operating their major home appliances due to maximizing the net benefits gained by the end users [9]. Kanchev et al. presented an energy management system for a microgrid, including PV with embedded storage units and a gas microturbine [10]. The operational planning is based on the PV power output forecasting. A local Appl. Sci. 2014, 4 368 controlling algorithm was proposed in [11] to control domestic electricity and heat demand. The algorithm of planning is based on the current state of the system and on the external condition on heat demand. Adika and Wang presented the energy management of a single household that is equipped with a grid tied rooftop PV system and a set of devices [12]. The main idea is to encourage customers to participate in energy generation and efficient electricity consumption. Our current work also takes into account the concept of efficient energy managements mentioned in these above, but the approach of operational planning is different. This paper is organized as follows: Section 2 introduces the proposed DC smart house, which includes PV, SC, battery and heat pump system. In Section 3, the optimization method for re-planning operation using Tabu Search is described. Section 4 explains method to determine battery capacity. Section 5 gives a brief explanation of the PV power output forecasting. Section 6 shows the simulation results. Finally, Section 7 concludes. 2. DC Smart House Figure 1 shows the assumed DC smart house model in this paper. In our previous work [1], a heat pump system was not included. The heat pump system in FIgure 1 is connected to hot-water storage tank and that system heated by general electric power system or battery energy. The inverter between power system and DC bus is assumed to be a bi-directional inverter. Table 1 shows the system capacities. The simulation result is based on the assumption that conversion efficiencies of battery  and inverter bat inv are 91%, 90%, respectively. Figure 1. DC Smart house model. SC PV Battery Hot-water storage tank Cooling water Heat pump system Heat load Power load Electric flow Water flow Table 1. System capacities (Size of general-purpose model in Japan). Equipments Capacities Rated power output of PV 3.5 kW Rated output of heat pump system 4.5 kW/1.5 kW Hot-water storage tank 370 L Total Storage battery capacity (lithium-ion type) 25 kWh Inverter capacity (bi-directional type) 6 kW Appl. Sci. 2014, 4 369 2.1. PV Power Output A lot of PV power outputs are determined by some parameters. This research assumes that  , PV n and S have a conversion efficiency of PV = 14.4%, the temperature of the cell t is 25 C, PV PV CR the number of PV panels is 18, and the total area of a PV is 1.3 m , respectively [13]. At a weather condition with solar radiation I [kW/m ] and temperature of cell t , the power output of PV in CR P [kW] is determined by the following equation [14]. PV P =  n S I (1 0:005(t 25)) (1) PV PV PV PV in CR In this research, it is assumed that the temperature of cell t is equal to the air temperature. CR 2.2. Solar Collector This research assumes that  = 60%, n = 3 and S = 1.6 m are heat collection efficiency of SC SC SC SC, the number of SC panels and total area of a SC, respectively. The heat collection of SC Q [J] at a SC weather condition with solar radiation I [W/m ] is determined by following equation [15]. sc Q =   n  S  I (2) SC SC SC SC sc Although  is changed by differences of weather or temperature, this paper assumes that  is SC SC constant. In actual operation, available heat collection will be used for optimal operation. The amount of water in the hot-water storage tank W = 370 L is the parameter of the electric water heater. Variation in temperature about the inlet temperature of the hot-water storage tank is represented by the following equations [16]. dT Q + Q + Q Q =  A (3) SC tl sw loss w dt Q =  A  T (4) h w h Q = (T T ) (5) loss h h In the above equations, T [ C] is the inlet temperature of the hot-water storage tank, Q [J] h h is the amount of heat in the hot-water storage tank, [J/(L C)] is the volumetric specific heat of water (=4.184 J/(L C)), A [L] is capacity of hot-water storage tank, is the heat loss w h coefficient (=0.0060209 W/ C), T [ C] is air temperature and Q [W] is the amount of heat loss loss generated by hot-water storage tank. The flow rate of hot water A [L/s] is equal to the flow rate tl of water A [L/s]. The amount of heat requirement Q [W] and amount of heat generated by sw tl water Q [W] are represented by the following equations. sw Q =  A (T T ) (6) tl tl hw h Q =  A (T T ) (7) sw sw w h Appl. Sci. 2014, 4 370 In the above equations, T [ C], T [ C] and T [ C] are the temperature of hot water, temperature of hw w h cooling water and inlet temperature of hot-water storage tank, respectively. The amount of heat generated by the heat pump system Q [J] is represented by the temperature generated by the heat pump system HP T [ C] as shown in the following equation. Q =  A (T T ) (8) HP tl e h The power consumption of heat pump system P [kWh] is represented by following equation. HP HP P = (9) HP HP a In above equation, H (=3.6 MJ/kWh) is the ratio by value from P to Q , and a HP HP (4.5 kW/1.5 kW) is the rated output of the heat pump system, respectively. HP 3. Optimization Method In simulation of this research, Tabu Search (TS) is used for optimal operation of DC smart house. TS was proposed in 1986 by Glover. More details about TS can be found in [17,18]. Minimization of the operational cost for the DC smart house is the goal of this research. Therefore, the objective function is assumed to be the operational cost in this simulation. The objective function is minimized at each time point, so the obtained solutions are assumed to be the hourly amount of charge/discharge of the battery. The objective function and constraint condition for simulation are as follows. Objective function: C = (P P )C (10) day BUY i SALEi pi i=1 P =  (P + P +  P ) (11) BUY i inv PLi HPi bat DCi P = SALEi (P +  P P P ) inv PV i bat DCi PLi HPi (P  0) DC (12) (P  P P P ) inv PV i bat DCi PLi HPi (P > 0) DC In the above objective function, C [YEN] is the operational cost per day, C is electric power day p rate. This is equal to 11.46 YEN/kWh at nighttime (23:00–07:00), 26.22 YEN/kWh during living hours (07:00–10:00 and 17:00–23:00), and 35.04 YEN/kWh during daytime hours (10:00–17:00) [19]. P [kWh] is purchasing electric power consumption, P [kWh] is selling electric power, BUY SALE P [kWh] is power consumption of load, P [kWh] is power consumption of heat pump system, PL HP P [kWh] is charge/discharge power of battery. According to feed-in-tariffs for PV systems in Japan, DC the selling electric power rate is set to 39 YEN/kWh in this paper. If P is positive, the battery will DC charge electric power. [ Appl. Sci. 2014, 4 371 Constraint condition: 20%    100% (13) =  100 (14) Bmax jP j  6 kW (15) P  0:8  P  P  1:2 (16) BUY i1 BUY i BUY i1 P  0:8  P  P  1:2 (17) SALEi1 SALEi SALEi1 In the above constraint conditions, C [kWh] is the total charge/discharge power of battery , P is B I the inverter capacity (=6 kW). This approach aims to obtain more benefit for electrical power selling and to smooth the fluctuating power output of PV. Additionally, the optimal reference fixes the combination output of PV and battery to be constant from 10:00 to 17:00 in order for the power fluctuation in the grid point to be stable. 4. Determination of Battery Capacity The advantages of the installed battery is a smooth power output and the ability to provide the required power supply to the load on a cloudy or rainy day. However, it is better to reduce the capital cost of the battery. Therefore, optimal capacities of the battery and inverter are determined by simulation results in this research. In this simulation, it is assumed that I = 0 kW/m . Additionally, customers will use in hot water for 3 h (three persons) per day. In Figure 2, the vertical axis indicates operational cost per day C , and the horizontal axis indicates the capacity of the battery C . According to inverter capacity P day Bx I described in Figure 2, the case of C = 0 is not satisfied with the constraint condition. Similar results day are found in Figure 2b. Therefore, Figure 2 shows that the optimal combination is P = 6 kW and Iopt C = 25 kWh. Bopt Figure 2. Optimal dimension of storage battery. (a) 10 kWh–100 kWh; (b) 20 kWh–30 kWh. P = 6kW 100 I P =7kW -100 -200 = = P 4kW P 5kW I I -300 -400 -500 10 20 30 40 50 60 70 80 90 100 Running energy capacityCB [kWh] (a) day Day cost [Y C = [ Appl. Sci. 2014, 4 372 Figure 2. Cont. -100 -200 P = I 6kW -300 -400 -500 20 21 22 23 24 25 26 27 28 29 30 Running energy capacityC [kWh] (b) 5. PV Power Output Forecasting The authors proposed the power output forecasting technique of PV system based on hourly solar radiation forecasting one day ahead by using weather reported data, Fuzzy theory and Recurrent Neural Network (RNN) [8], as shown in Figure 3. This chapter shows the summary of the PV power output forecasting method. Since the detailed value of solar radiation prediction is not reported by the Meteorological Agency, the Fuzzy theory is adopted to determine the hourly solar radiation by using weather reported data such as humidity and cloud cover. This technique also includes the correction method for annual solar radiation forecast errors. Additionally, since the power output of the PV system fluctuates depending on weather conditions, the training process of NN tends to be unstable. However, Recurrent NN (RNN) is known to be a good tool for time-series data forecasting. The RNN has l = 24 and m = 1–30 neurons in the input layer and hidden layer, and n = 24 neurons in the output layer. These neurons are connected with linear coupling, and x –x are input data to RNN. Input data 1 l x –x (24 h  30 days) for the training dataset are determined by Fuzzy theory. There are connection 1 l weights between each neuron. The output of hidden layer neurons are converted to nonlinear values by hyperbolic tangent sigmoid-function. RNN has a context layer. This layer contains a copy of the hidden layer with time-delay lines, and added as feedback structure [20]. The context layer reflects both the input and output layer’s information to the structure of RNN. In this research, information of one time-step lag from the output units is used in the context layer. After each output unit provides the information relating to one time-step interval, the training of the network becomes stable. Based on conventional research [21], the authors think that it is convenient to make the forecast model by a trial-and-error approach. As a consequence, the past information is maintained to RNN with the progress of learning. The proposed technique for the application of RNN is trained by power output data based on Fuzzy theory and weather reported data. Because the Fuzzy model determines the solar radiation forecast data, RNN will train the power output smoothly. 6. Simulation This section shows the simulation conditions, simulation results and the comparisons for three cases. Day cost [Y Cday = Appl. Sci. 2014, 4 373 Figure 3. Forecasting method. Insolation forecast with Fuzzy and weather reported data Magnification correction of forecast insolation Power output forecasting for PV Power output forecast using Neural Networks 6.1. Simulation Conditions The authors assume three cases for this simulation, case 1: No forecast errors, case 2: Forecast hourly time, case 3: Re-planning for a sunny day and a rainy day. In addition, customers of the DC smart house will use hot water for 3 h (three persons) per day. Here, we assume that one person will use 100 L hot water from the storage tank between 19:00 and 22:00. At 19:00, if T of the inlet temperature in the hot-water storage tank falls much below 60 C, the water in the hot-water storage tank will be increased by a heat pump system in advance at night. The hourly weather reported data is delivered over 24 h at 4 time points (0:00, 6:00, 12:00, 18:00) on 1 day. In this simulation, the assumption of no forecast errors is case 1. The re-planning operation at 06:00 for initial time on one day is assumed to be case 2. The re-planning operation, case 3, is modified at the time of updating weather reported data for 4 times on 1 day. The validity of the proposed method is confirmed by compared with the case of no forecast errors using weather data in Okinawa, Japan, 2005, on sunny and rainy day simulations [22]. This paper focuses on optimizing re-planning operation applying solar radiation forecasting in one day for an existing smart house. Considering other cases, such as yearly weather data, will be another focus of research in the future. If we consider the statistical yearly weather data, the issue includes the optimizing system capacities of the smart house. 6.2. Simulation Results and Discussions The simulation results on a sunny day of case 1 and 3 are shown in Figures 4 and 5. Figures 4a and 5a show the electric power [kW] on the time axis. In these Figures, P [kW] indicates the charge/discharge DC power of battery determined by TS. If P is positive, the battery will charge electric power. P [kW] DC PV is the power output of PV, P [kWh] is the power consumption of load, P [kWh] is the power PL HP consumption of the heat pump system, P [kWh] is the purchasing electric power consumption, BUY P [kWh] is selling electric power. Figures 4b and 5b show the running energy capacity of battery SALE C [kWh], and state of charge  [%]. B CB In Figures 4c and 5c, the left side vertical axis indicates the inlet temperature of hot-water storage tank T [ C], the right side vertical axis indicates the heat quantity Q [MJ], and the horizontal axis indicates hour. In these Figures, Q [MJ] is heat quantity in tank, Q [MJ] is used heat quantity from tank. As t tu shown in Figures 4 and 5, all simulation results are satisfied with constraint conditions as described in Section 3. Heat quantity Q [MJ] Appl. Sci. 2014, 4 374 Figure 4. Simulation results (sunny day, case 1: no forecast errors). (a) Optimized result; (b) Battery; (c) Temperature of tank and used heat quantity. th 6 September, 20 , 2005., in Naha BUY 4 P SALE P PL PV -2 HP DC -4 -6 5 10 15 20 5 10 15 20 Time t [hours] Time t [hours] (a) (b) Q : Heat quantity in tank Q : Used heat Q from tank tu t 5 10 15 20 Time t [hours] (c) It is also confirmed by comparing the Figures 4 and 5, that the result of the proposed re-planning operation in case 3 is similar to case 1. Additionally, the battery charges the electric power at night time at an inexpensive electric rate, and provides the charged power to the heat pump system in a sunless period (19:00). More detail regarding simulation results about rainy days is beyond the scope of this research. However, Table 2 shows the calculation results of operational cost, electric power selling, electric power buying, generated electric power, electric power load, heat load, electric power loss and forecast errors for all cases on a rainy day. The lower line in Table 2 represents the mean absolute value (and the percentage values) for solar radiation forecast errors. The percentage values of solar radiation forecast errors are calculated by dividing the total errors by total solar radiation for 1 day. Table 3 shows the calculation results of operational cost for all cases on a sunny day. These simulation results show that Electric Powers P [kW] [ C] Temperature TT Running energy capacity C [kWh] State of charge [%] B Heat quantity Q [MJ] Appl. Sci. 2014, 4 375 the daily operational cost is minimized by the proposed method on two solar radiation patterns. This also means that the benefit of the installed battery is confirmed by these results. Figure 5. Simulation results (sunny day, case 3: re-planning operation). (a) Optimized result; (b) Battery; (c) Temperature of tank and used heat quantity. th 6 September, 20 , 2005., in Naha BUY SALE P PL PV -2 HP -4 DC 0 0 -6 5 10 15 20 5 10 15 20 Time t [hours] Time t [hours] (a) (b) Q : Heat quantity in tank Q : Used heat Q from tank tu t 0 0 5 10 15 20 Time t [hours] (c) The calculation results of cases 1–3 are similar to the simulation results as described in Figures 4 and 5. It should be noted that weather varies with the seasons. To understand more about simulation results, Table 4 represents the mean generated electric power of PV, absolute values of forecast errors and the percentages every month in 2005. It can be confirmed that case 3 is proposed to be superior to case 2. Whereas the simulation results of operational cost for case 3 are very similar to case 1, the results of electric power loss are higher even than case 2. This reason for this is clear because the objective function in this research is to minimize the operational cost, as described in Section 3. So, if we change the objective function, the simulation results will be different. Otherwise, the difference of the operation time between cases 2 and 3 causes the electric power loss due to the charge/discharge electric power of battery. Therefore, considering further research that is not explored in this article, our future research Electric Powers P [kW] Temperature TT [ C] Running energy capacity C [kWh] State of charge [%] B Appl. Sci. 2014, 4 376 will mainly focus on (1) Applying a new strategy for compensating electric power loss and forecast errors; (2) Optimizing the system capacities of a DC smart house; and (3) Applying new and advanced artificial intelligence techniques for optimal operation. Table 2. Running cost (rainy day). Each case case 1 case 2 case 3 Operational cost [YEN/day] 329.90 392.36 344.28 Electric power selling [kWh/day] 0.68 1.40 0.88 Electric power buying [kWh/day] 30.66 31.05 31.73 Generated electric power [kWh/day] 2.66 2.66 2.66 Electric power load [kWh/day] 24.04 24.04 24.04 Heat load [kWh/day] 4.85 4.65 4.83 Electric power loss [kWh/day] 3.75 3.62 4.64 Forecast errors [kWh/day] — 1.18 0.77 Percentages of forecast errors (44.3%) (29.1%) Table 3. Running cost (sunny day). Each case case 1 case 2 case 3 Operational cost [YEN/day] –291.85 –252.62 –271.61 Electric power selling [kWh/day] 15.48 14.30 15.51 Electric power buying [kWh/day] 26.35 24.75 27.19 Generated electric power [kWh/day] 19.20 19.20 19.20 Electric power load [kWh/day] 24.04 24.04 24.04 Heat load [kWh/day] 0.62 0.62 0.62 Electric power loss [kWh/day] 5.41 4.99 6.22 Forecast errors [kWh/day] — 4.36 2.61 Percentages of forecast errors (22.7%) (13.6%) Appl. Sci. 2014, 4 377 Table 4. Mean generated electric power of PV and forecast errors at Naha in 2005. Month 1 2 3 4 5 6 Mean generated electric power [kWh/day] 8.08 7.33 12.76 16.02 15.26 15.02 Mean forecast errors [kWh/day] (case 2) 2.96 2.97 3.68 3.99 4.45 4.45 Percentages of forecast errors [%] (case 2) 36.60 40.50 28.88 24.93 29.17 29.62 Mean forecast errors [kWh/day] (case 3) 2.00 1.47 1.03 1.59 1.67 1.43 Percentages of forecast errors [%] (case 3) 24.79 20.08 8.06 9.90 10.91 9.50 Month 7 8 9 10 11 12 Mean generated electric power [kWh/day] 21.58 18.51 17.55 15.02 10.85 8.62 Mean forecast errors [kWh/day] (case 2) 4.78 4.82 4.53 3.89 3.50 2.81 Percentages of forecast errors [%] (case 2) 22.16 26.03 25.81 25.90 32.28 32.61 Mean forecast errors [kWh/day] (case 3) 1.19 1.65 2.27 1.77 1.51 1.15 Percentages of forecast errors [%] (case 3) 5.54 8.94 12.94 11.76 13.89 13.39 7. Conclusions This paper proposes the re-planning operation method using Tabu Search for a DC smart house including PV, SC, battery, and a heat pump system. The proposed method is based on the solar radiation forecasting method using weather reported data collection, Fuzzy theory and Recurrent Neural Network. Additionally, the re-planning operation method was used to consider solar radiation forecast error, battery and inverter losses. Simulation results show that daily running costs are minimized by the proposed method on two solar radiation patterns. The validity of the proposed re-planning method is confirmed by comparing the case of no forecast errors. The simulation results show that, if it is possible to provide power load and heat load by PV with SC, the advantage to the consumer will increase. Author Contributions Modeling of the DC smart house and the optimization method were performed by Atsushi Yona and Tomonobu Senjyu. Data analysis was done by Atsushi Yona. Manuscript was written by Atsushi Yona, Tomonobu Senjyu and Paras Mandal. 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Weather Data [CD-ROM]; JMBC (Japan Meteorological Business Support Center): Tokyo, Japan, 2005. 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

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Applied Sciences – Multidisciplinary Digital Publishing Institute

Published: Aug 22, 2014

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