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Energy and Price Forecasting

5.1 Forecasting Methodology

5.1.2 Data Acquisition

This section describes the process of data acquisition from different sources.

Three sources are used in this work:

ˆ Spanish energy market

ˆ Climatic data

ˆ DHC Network

Figure 5.6 depicts a graphical interpretation of the data acquisition

Figure 5.6: Data acquisition schema Spanish Energy Market Data acquisition

Energy market information is acquired from the Spanish transport system op-erator, Red Electrica Espa˜nola. This information is necessary to understand the market and evaluate the energy mix at every hour and the OMIE energy market’s price. This information is available on the Red Electrica Espa˜nola website and provides the demand and generation data of the Spanish energy mix in 10-minute steps [137]. Energy price information can also be found at the same source in hourly values and from all the different markets. This information is acquired through recurrent queries to their DB and is stored in SAMPOL’s SQL DB, which has been created as part of this thesis. The information is stored with the same periodicity as acquired but it is always used as hourly values.

The generation of solar and wind energy and market energy price is based on the historical data acquired with this method. Moreover, the transport system operator has been providing a demand forecast of up till 1 week ahead

and a solar and wind power forecast of up till 3 days ahead since the summer of 2015. These forecasted values are used in the thesis. The installed power for solar and wind technologies that is used to calculate the explanatory variable is provided by the Ministry of Industry [124]. Detailed informa-tion of the installainforma-tions is provided, such as locainforma-tion, technology, power, and whether the installation is connected to the grid. This information is pro-cessed and clustered according to the provincial division in Spain. In this way, 50 aggregated installed powers are obtained. Climatic Data Acquisition

Climatic data is required to forecast demand, solar, and wind power. This information is acquired from an NWP and acquired from the web service in hourly steps [138]. The NWP is based on an algorithm that analyses satellite images and predicts the climatic data [139]. The NWP data covers 3 days ahead. To have a homogeneous database for Spain, one station per province is used; those stations are usually located in each province’s main city, which results in 50 stations being selected for Spain. Stations are marked with black dots in Figure 5.7.

The variable grid that takes the provinces as units may lead to errors because the NWP value is given for a point and is used in the whole area regardless of the station and the power plant’s location. The asymmetry in the location of weather stations and the different shapes and sizes of the provinces avoid the possibility of spatially averaging the NWP data used in [127]. Furthermore, only the aggregation of the weighted data in the whole country is carried out in this study. That there is only one station per province and that station’s data is used for the whole province may lead to errors because the station’s value is used for the whole province without considering its exact location. The shadowing effect within wind farms that arises due to the location of the wind turbines in the land and the wind direction may decrease the local wind power generation, thus affecting the aggregated generation. From this service, the following information is retrieved:

ˆ Cloudiness

ˆ Wind speed

ˆ Ambient temperature

For the extraterrestrial hourly radiation calculation, a value of slope (α) is estimated. This value is fixed for all the solar power plants and during the year. The most common slope value for Spain is fixed at the average latitude value of 40. The optimum yearly slope value for an installation is the same as the latitude where it is located [140]. The acquisition and processing of the information and calculations are conducted by an automatic tool designed in Matlab®.

Figure 5.7: Weather Stations Localizations DHC Network Data Acquisition

Due the inherent variability of the load, it is necessary to acquire informa-tion on energy demand. Each customer in the network is equipped with an energy meter (Kamstrup Multical 601) that is supported by a data logger that retrieves historical information every 2 minutes and communicates with the SCADA in the generation plant. The data acquired with this method are flow and return temperature, mass flow, instant power, and aggregated energy consumed. On one hand, the energy meters were installed from the

beginning on the client’s side and record the aggregated energy consumed.

On the other hand, the data-logger solution has been installed as part of this PhD thesis. The equipment is depicted in Figure 5.8. The communication of the data-logger with the SCADA is performed through GSM, which al-lows communication even in remote points. Once the information is received, it is stored in the DB for further use and analysis. Figure 5.9 depicts the principles of thermal data acquisition and processing.




Figure 5.8: Datalogger + Kamstrup Multicall 601

Heat Exchanger SCADA

Shut-off Valve FTP Server

Osaka PID

DataLogger 2 x AO GPRS


2 x AI PT100



Energy Meter Kanstrup 601 Opening

Set Point

Figure 5.9: Substation data acquisition principle