Benefits of meteorological forecasts for the operation of DHW systems using electrical auxiliary heaters discussed for conditions in Brazil

Transcription

1 Benefits of meteorological forecasts for the operation of DHW systems using electrical auxiliary heaters discussed for conditions in Brazil Manfred Kratzenberg 1 and Hans Georg Beyer 2* 1 Departamento de Engenharia Mecânica, Federal University of Santa Catarina, Florianopolis, Brazil, 2 Institutt for Ingeniørvitenskap, Universitetet i Agder, Grimstad, Norway *Corresponding Author, hans-georg.beyer@uia.no Abstract In countries where the auxiliary heating of solar domestic hot water systems (SDHW) is mostly done electrically - as e.g. in Brazil - the influence of these systems on the load characteristics in the distribution systems especially in feeder lines - has not to be neglected. Sharp load peaks may occur during hours of augmented hot water consumption. This is economically critical for both, the grid operator and the consumers, given that the consumption tariffs are time dependent. As - due to storage of the SDHW system the operation of the auxiliary heater has not to be strictly coupled to the time of the hot water consumption a smart control may shift the electricity consumption to less critical periods. This however, requires forecast capabilities for solar irradiance and ambient temperature to avoid the dispelling of solar energy. We discuss the set up of a smart control based on forecast information of the daily irradiance together with options for a forecast procedure based on both, local measurements on the horizontal and the tilted surface and the use of Numerical weather prediction (NWP). Possible benefits of this control scheme are discussed in view of the achievable quality of the forecast information. SDHW using forecast information of the daily irradiance for optimal control 1. Introduction Domestic hot water supply in Brazil is mainly done by continuous-flow water heater, integrated in the shower heads. These electric shower heads are presently installed in 73.1 % of the Brazilian houses and account to 60 % of the peak load in between 6:00 p.m. and 8:00 p.m. [1]. As the consumption within the peak horary has high costs of US$ 377 /kw [2] the suppression of the peak load is highly valuable. The use of Solar Domestic Hot Water system (SDHW) may help in the reduction of the grid s peak load. However, as due to the absence of a general low cost scheme of fossil fuel or gas shower heater the auxiliary heating for the SDHW is in generally electric, the actual amount of peak load limitation requires further investigation. To the users of SDHW the requirements for the system operation are transmitted by the economic figures. Lifetime costs are on one hand governed by the investment costs, on the other hand by the costs for the auxiliary energy. For the case of electrical auxiliary heaters subjected to a time dependent kwh-tariff the exact time of operation of the heater starts to play a role. Thus, there is the aim for the

2 control of the systems to shift the heater operation to low tariff periods with the constraint of maintaining the maximal use of solar energy. In the following section the structure and actual use of SDHW systems in Brazil will be given by the characterization of a typical system, its application context and actual performance figures concerning their repercussion on the peak load characteristics. This will be followed by the discussion on possible upgrading of the systems to smart systems with an improved control system making use of meteorological forecast information. The possible benefits of the smart systems are thereby analysed via simulation. The Numerical Weather Forecast of the solar radiation (NWF) is enhanced by a dedicated post processing. 2. Characterization of SDHW systems as used in Brazil 2.1 A standard single family SDHW A standard compact SDHW system used in Brazil is of the thermosyphon type (see fig. 1). Sized for one household it comprises a collector of 1.7 m² area (collector characteristics e.g.: heat loss coefficients: k = 4.04 W/m²K, zero heat loss ηo = 0.67) and a storage volume of 100l. In the standard configuration there is no auxiliary heating in the storage. The auxiliary heating is left to a (at best electronically controlled) heating resistance in the shower head as accomplished in [2]. Fig.1. A Brazilian compact SDHW sized for single family use. Picture taken during system test at UFSC-EMC-Labsolar. 2.2 Introduction of SDKW to housing projects In a housing project performed in Florianopolis 60 flats were equipped with compact SDHW and its system performance was compared to 30 electric power adjustable shower heads. The 90 systems had been monitored by remote measuring systems. Except for the installation of the SDHW and the shower heads, the houses do not have any dedicated hot water system. It could be shown that the compact SDHW systems are able to reduce the peak load of the local power grid by 60 % [2]. Fig.2. Housing project equipped with SDHW in Florianopolis (Brazil), taken from [2]. Recently continuous-flow water heater which own phase controlled power adjustments are in use in Brazil. The compact SDHW system leads to a reduction in the consumption of reactive power by 29 % of these device [3].

3 3. Tasks for system control In view of an improvement of both, the reduction of electricity consumption at the evening peak load horary and the increase of the solar fraction in covering the hot water demand a mode of preheating of the storage and an advanced control schemes have to be developed. Constraints are on one hand the avoidance of energy deficits causing operation forced operation of the auxiliary heater at high tariff hours in the evening. This calls for installation of a heating system in the storage able to rise the storage temperature before the peak hours in the evening. On the other hand it has to be avoidance that an excess of preheating leads to the rejection of solar gain due to excessive auxiliary energy at 4 a.m.. This task calls for the use of information on the expected solar gain and thus of the forecast of the meteorological conditions in the next day. The next sections will describe the status of solar irradiance forecast based on the results of a numerical weather prediction scheme (NWP). The use of the forecast information and solar radiation forecast uncertainties are considered by the control scheme [7]. 4. Irradiance forecast based on NWP results In the context of photovoltaic solar energy systems, the use of NWP results improved by the application of schemes using model output statistics (MOS) today is in an operational status in Europe (see e.g. [4], [5] ). For Brazil the centre for meteorological forecasts CPTEC operates the meso-scale model ETA and at the laboratory LEPTEN accomplishes simulations with the meso-scale model ARPS [10]. The results of 24 hour ARPS simulations using reanalysis samples have been analysed for the Florianopolis region, making use of ground station data including the BSRN [see e.g. [6]) station in Florianopolis [7]. Based on the hourly output of the ARPS model a MOS scheme based on artificial neural network ANN techniques (see and [8] [9]) using a wavelets as neuron activation functions [13] in order to correct systematic (see e.g. [10]) forecast uncertainties [7]. The quality of the original ARPS results is depicted in fig.3 showing the scatter plot of forecasted to measured hourly averages Fig.4 presents the results after of the application of the MOS scheme. The correction scheme leads to a reduction of the RMSE of the hourly solar radiation forecasts from W/m² (49.4 %) to W/m² (44.8 %). The mean bias deviation are 41.2 W/m 2 (11.4 %) for the uncorrected forecasts and -0.4 W/m2 (-0.1%) after correction. For the daily average irradiance (see fig 5) the RMSE before correction was 63.7 W/m² (35.9 %) and the RMSE after correction was 55.6 W/m² (31.3 %). The mean bias deviation of the daily average was reduced from 20.2 W/m² (11.4 %) to 2.2 W/m² (1.3 %). As SDHW is sensitive to the irradiance on a tilted surfaces the forecasted global horizontal irradiances are converted to the irradiance on a tilted surface with 27.5 using standard models [11], [12]. The uncertainties of the MOS-corrected forecasts to the measured irradiances on this surface see (fig. 6) amount to a mean bias deviation of -6.5W/m² (-1,6 %) with an RMSE of W/m² (50.2 %) for the hourly average values. The uncertainties of the daily averages are RMSE = 67.7 W/m² (34.7 %) and MBE = W/m² (- 1.7 %). The performance of the forecasts for the hourly temperatures leads after MOS-correction to the scatter diagram shown in figure 7.

4 Fig. 3. Scatter plot of measured and day ahead forecasts (ARPS) of hourly global horizontal irradiance, Florianopolis. The axe s range from W/m²,. Fig.5. As for fig.4, but for irradiances on the tilted plane. Fig 4. As for fig.3, but after application of a MOS based correction scheme. Fig. 6. Measured and forecasted hourly temperatures, after application of a MOS-correction scheme. 5. A control scheme using forecast information and simulation model of the smart SDHW With the forecasts available, a control scheme for the SDHW is set up [7, 14]. It is based on the simulation of the expected system performance. The simulation is done using a simulation model similar as in TRNSYS of the solar heating system based on the operation characteristics of the collector and the loss characteristics of storage tank. For simplicity homogeneous storage temperature distribution is considered. Using the forecasted values of irradiance and ambient temperature, the expected evolution of the storage temperature is analysed. If the simulation would indicate a storage temperature below 60 C in the evening (18:00), the preheating of the storage is activated during low tariff hours in the early morning. A modelling scheme for this smart SDHW is set up, comprising the control algorithm [7, 14]. As simulation input the measured solar radiation on tilted surface and the measured ambient temperature together with archived forecast of these variables for the considered days are used. For the hot water consumption the load profile the hourly averages measured during the

5 monitoring project of 60 compact SDHW in Florianopolis see (section 2.2) are applied. Residence individual variations of this load profile are not considered in this simulation. 6. Performance of smart SDHW To demonstrate the performance of the smart SDHW system, the time traces of the actual irradiance storage temperature for (a) a standard SDHW are given together respective simulated samples for (b) a SDHW system which storage is heated to a fixed temperature of 65 C at 4p.m. and (c) a smart SDHW which uses the forecasted irradiances for the control of the preheating process. Figure 7: Example of a 3 day sequence of simulated performance of a compact SDHW system (auxiliary heating in the shower head- top (a), a system applying obligatory pre heating of the storage in case of low storage temperature in the early morning - middle (b) and a smart SDHW using irradiance forecast to assist the system control - bottom(c) In Figure 7 are shown the time trace of the irradiance (green) and the storage temperature (red). An operation of a heating system at evening peak hours is indicated by black open circles. For the smart SDHW (bottom) the blue line gives the forecasted irradiance. The blue points are now the actual storage temperature. The green points are the result of the first controller simulation run, without preheating, the red points result from the second simulation run with preheating activated in case of expected low storage temperatures in the evening. A positive step in one of the hourly temperature traces indicates the input due to preheating. As can be observed, for this sequence at the first day power consumption at the peak hours would be necessary for the conventional system. With the obligatory preheating this would be avoided. However, this system would call for high amounts of preheating in each morning, as indicated by the pronounced steps in the trace of the storage temperature in the morning hours. The smart system also shows no need for electricity during the peak hours. Compared to the system with obligatory preheating (b), the requirements for preheating as can be observed by the less pronounced steps in the actual storage temperature with the simulation (c).

6 Fig. 8 gives similar example for the benefits of the smart SDHW system, but for the case of higher uncertainties in the forecasted irradiance. Figure 8: As figure 7 but for days with higher uncertainties in the forecasted irradiance series. The smart SDHW system can avoid electricity consumption during the peak horary stem. For this example peak load consumption of the smart SDHW may be avoided due to the fact of a beneficial sequence in the errors of the forecast. Within one year of data, a wide range of sequences of forecast errors are present. Thus a conclusion of the benefit of the smart SDHW can only been taken considering the annual statistics of the system performance. Table 1 gives the results of an annual simulation of a standard and a smart SDHW with same sizing. The results given refer to the average data obtained by the simulation with various consumption patterns. The costs in this table are calculated under adoption of the electricity prices in tarifa sazonal. It can be observed, that the smart system offers some, however small benefits in view of the reduction of consumption during peak hours. The small reduction of the total auxiliary heating costs may be traced back to the increased losses from the storage preheated in the morning. The results are however sensitive to the system sizing and the forecast uncertainties [14]. An increased storage volume or reduced forecast uncertainty [14] can lead to the almost total suppression of the consumption during peak hours. Results for smart SDHW with a 200 liter storage tank are given in table 2.

7 Table 1: Results from an annual simulation of the performance of a compact SDHW system as shown in figure 7a and a smart SDHW (figure 7 c). The results (expressed as average figures of a two years simulation run) refer to the average of the performance of systems with identical consumption profiles. smart SDHW energy [kwh/month] costs [R$] compact SDHW energy [kwh/month] costs [R$] solar gain ,80 - demand for hot water ,70 - energy pre-heating 35,00 8,74 0,00 0,00 shower head consumption 1,40 2,11 4,80 7,40 peak hours shower head total 14,70 5,44 53,80 19,65 total auxiliary energy 64,10 14,18 53,80 19,65 solar fraction 0,46-0,55 - reduction of consumption at peak 96,20-86,80 - hours [%] max. power during peak hours [kw] 0,43-0,60 - Table 2: Results for smart SDHW systems using 200l of storage volume. energy [kwh/month] costs [R$] Solar gain 99,90 - demand for hot water 119,70 - energy for pre-heating 41,70 10,43 shower head consumption peak hours 0,00 0,04 total caonsumption shower head 0,70 0,22 Total auxiliary energy 43,10 10,65 Solar fraction 0,65 - Reduction of consumption during peak hours [%] 99,90 - maximum power during peak 0,070 hours[kw] - 8. Conclusions It is demonstrated, that a smart SDHW, which control use weather forecast information, can be operated with benefits reducing the energy consumption during peak power horary. Due to the necessary preheating of the storage however the total electricity consumption is increased as compared to compact systems which do not own auxiliary heating in its storage. But due to staggered energy tariffs this lead to a reduction of the cost for auxiliary energy. As the power grid is set free form peak load by use of the smart system, this calls for a shearing of its installation and operation costs between consumers and utility company.

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