Weather Data Analysis for Prediction of Renewable Energy Sources Production

Transcription

1 Weather Data Analysis for Prediction of Renewable Energy Sources Production Vasco Delgado-Gomes 1, José A. Oliveira-Lima 1, João F. Martins 1 and Celson Lima 2 1 CTS - Uninova, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (Portugal), 2 Instituto de Ciências e Geociências, Universidade Federal do Oeste do Pará (Brazil) vmdg@uninova.pt, jose.a.o.lima@gmail.com, jf.martins@fct.unl.pt, celsonlima@ufpa.br Abstract The Building Management Systems (BMS) are responsible to managing all the systems in the building. With the increasing use of Renewable Energy Sources (RES) in buildings, these systems need additional information like weather forecast in order to provide a more efficient energy management. This paper describes the influence of weather data analysis in buildings fitted with RES. Weather forecast information will allow better energy management due to the possibility of RES production forecast, enabling actions like delaying the switch on of less important building appliances and shutting down or lower the air conditioner level according to energy forecast, but always keeping the building users with a minimum level of comfort. I. INTRODUCTION Nowadays, people and governments are concerned with the efficient use of energy. Several objectives to 2020 are set: reduce by 20% the greenhouse emissions; 20% of total energy production has to be from Renewable Energy Sources (RES); and increase energy efficiency by 20%, in reference to 1990 levels [1]. Net Zero Energy Buildings (NZEBs) are able to have an equal balance between the energy consumed from the grid and the energy provided to the grid [2]. In order to achieve this goal several buildings are being fitted with RES to have their own production systems. The use of RES in buildings rise some problems due to characteristic intermittency of these sources, therefore Building Management Systems (BMS) need to cope with these problems. An advanced BMS needs to be able to communicate with the new generation of devices so-called Intelligent Electronic Devices (IEDs) and also manage all electrical systems to achieve the NZEB goal. The BMS must be able to predict the RES production in order to postpone some less important actions to a day time where it predicts that there will be sufficient RES production. Actions like turning on some appliances, such as washing machines or dishwashers at noon when the solar production will be enough. This paper describes a method to integrate RES information in a software infrastructure together with weather information, in order to enable RES production forecast using weather information analysis. All this information will foster a BMS that can take some actions in order to achieve a NZEB. The next section will describe the software infrastructure itself followed by the description of the weather station used to gather the weather data. The description of the experimental setup system, the collected data analysis and the conclusions are also presented. A. Conceptual Vision II. THE SOFTWARE INFRASTRUCTURE This work targets the development of a software infrastructure to help a BMS to predict the energy production in a building fitted with RES to achieve a NZEB. The conceptual vision guiding this work is that networks of energy-related devices can be operated with the help of a (distributed) software infrastructure based on service oriented paradigm and standards. Any instance of this system can use both new and legacy IEDs, which are required to have a minimum level of intelligence in order to be virtualised. In other words, they have to provide a software-enabled communication channel to be used in a communication process. Two networks, namely energy and software networks compose each so-called Energy System. The former is composed by systems and devices, which produce, distribute and consume energy. The later is used to monitor and control the energy network. Two basic issues are addressed: IEDs recognition and communication with them. The strategy to overcome them relies on two main pillars, namely Service-Oriented Architecture (SOA) and Standards. On the one hand, SOA paradigm provides the bottom line in terms of architectural principles and guidelines used to support the implementation of Energy Software Infrastructure, which is to be service-based and must operate within a naturally distributed environment. Architectural principles in SOA aim, essentially, to enhance the efficiency, agility, and productivity of a given system by positioning services as the primary means through which solution logic is represented in support of the realization of strategic goals associated with service-oriented computing [3],[4]. On the other hand, IEDs are modeled, configured, and characterized into the System using the IEC standard. The role of both DPWS and IEC are described in detail in the next section. B. Standard-based Approach The IEC is a worldwide-accepted standard for handling communication within substations. It integrates an information model, the so-called Abstract Communication Service Interface (ACSI), for substation description and the Substation Configuration Language (SCL), used to describe the ACSI information model. ACSI allows describing an energy system and its respective components in a standard manner, independently from the respective individual manufacturers and with high level of detail. Energy System takes advantage of the intrinsic 258

2 ACSI ability of virtualizing IEDs, by decomposing their respective physical properties and functionalities into a data model [5]. IED virtualization using the ACSI data model is further detailed in [6]. Each standard compliant IED carries an XML-based SCL file, where the entire respective ACSI information is stored. The physical features of each device may be enabled and disabled or its information may be requested or changed, through the invocation ACSI services. As shown in Fig. 2, two main ACSI service types are considered: GetDataValues and SetDataValues. While the former is invoked for monitoring operations, when knowledge about the state of a physical feature is required, the later allows the physical control of a given device, replacing the older data attribute value by a new one. GetNonIEC(IS) and PutNonIEC(IS, new value). These additional Services allow the integration and request of noncompliant services. Since the majority of the IEDs do not understand DPWS, they usually need a mediator to make a bridge between DPWS and ACSI. This translation process is also performed by the Connector, who is responsible for offering device s features in the form of Web Services, performing all the necessary mapping between the device s ACSI and DPWS. This process is further described in [5]. III. EXPERIMENTAL SETUP The weather station used to gather data is mounted on the rooftop of Electrical Engineering Department of the Faculty of Science and Technology from the New University of Lisbon in Portugal. This building is also fitted in PV panels and one wind generator. All these components are part of the experimental setup show in Fig. 2. Fig. 1. ACSI Services Invocation. Both require a reference that points to the required Data Attribute (DA) path with which the service is to be invoked. This reference is given by a Functional Constrained Data Attribute (FCDA) which includes, among others, the Logical Device (LD), Logical Node (LN), Data Object (DO), and DA that univocally characterize the physical operation (monitoring or control) to be performed [6]. DPWS, the Web Service standard promoted by OASIS, was chosen to support the operation of the channel and really allow a seamlessly communication among all members of the network, supporting the inter-devices communication [7],[8]. Web Services are the preferred mechanism for SOA implementation [9],[10] and the application of Web Services at device level will improve the operation of the system as well as the development process [11]. The service mapping between ACSI and DPWS allows supporting higher level heterogeneous platforms. Similar to the Specific Communication Service Mapping (SCSM) based on Manufacturing Message Specification (MMS), described in IEC [12], the Energy System uses a SCSM based on DPWS. The aforementioned ACSI Services -GetDataValues(FCDA) and SetDataValues(FCDA, DataAttributeValue) - are identified and mapped into Internal Services, the GetIEC(FCDA) and PutIEC(FCDA, new value)web Services, respectively. Therefore, each Web Service will be able to interact with single or multiple low level device physical features, through the invocation of Internal Services, each of them identified by its ACSI path. Additionally to the services specified by the IEC data model, and in order to provide advanced features to the substation automation system not considered by the standard, the Energy System defines a Communication Service Interface (CSI) [5]. CSI incorporates two Internal Services: Fig. 2. The experimental setup system. The weather station has three types of sensors, namely: Solar Radiation Sensor, Davis Cup Anemometer and a Temperature Sensor. The Solar Radiation sensor gives information about the Watts per square meter while the Davis Cup Anemometer informs the wind speed and also its direction. The wind generator has the capacity to generate 2 kw while the set of PV panels can generate 0.6 kw. Due to the impossibility of gathering information directly from the AC/DC converters, two power meters were connected to be possible to know the production of each renewable source. IV. WEATHER DATA ANALYSIS The weather data was collected during four days, between 10 th and the 14 th of October The sampling period is one minute and the data gathering was made by the implemented BMS. The information in the power meters was collected through the IEC and DPWS standards, while in the weather station the data was collected using the DPWS standard. In Fig. 3 and Fig. 4 it is possible to visualize the active power of the wind generator according to the wind speed. Due to the intermittency of this source, the results are visible, but a little bit noisy. 259

3 Fig. 3. Active power (W) of the wind generator. Fig. 4. Wind Speed (m/s). Fig. 5. Active power (W) of the PV panels. Fig. 6. Solar Radiation (W/m 2 ). In the Fig. 5 and Fig. 6 it is possible to visualize the production of the PV panels according to the solar radiance measured in the weather station. The peeks of production were around noon where the sun is at his highest point. During the analyzed days the weather was sunny with a small breeze, as can be seen in the wind chart where the max wind speed is around 9.5 meters per second. 260

4 V. CONCLUSIONS & FUTURE WORK As previously stated, the increasing number of buildings fitted with RES to achieve a NZEB is increasing. Therefore, BMS must have the capabilities to cope with the intermittency of these types of sources. Through the weather data analysis is possible to forecast the RES production enabling a more efficient energy management in the building. As future work, the implementation of a weather communication protocol is planned, so the interaction between the weather station and the BMS can be done in a standard way like the communication between the BMS and the implemented IEDs. REFERENCES [1] C. Böhringer, A. Löschel, U. Moslener, T.F. Rutherford, EU climate policy up to 2020: An economic impact assessment, Energy Economics, vol. 31, Supplement 2, 2009, pp. S295-S305. [2] A. Marszal, P. Heiselbergand, J. Bourrelle, E. Musalland, K. Vossand, I. Sartori, and A. Napolitano, Zero energy building a review of definitions and calculation methodologies, Energy and Buildings, vol. 43, no. 4, pp , [3] Bloomberg, J. and Schmelzer, R., (2006). Service orient or be doomed, Wiley. [4] Erl, T. (2005). Service-oriented architecture: concepts, technology, and design. Prentice Hall PTR Upper Saddle River, NJ, USA. [5] J. Lima, C. Lima, V. Gomes, J. Martins, J. Barata, L. Ribeiro, and G. Candido, Dpws as specific communication service mapping for iec 61850, in Industrial Informatics (INDIN), th IEEE International Conference on, Caparica, Lisbon, Portugal, July 2011, pp [6] C. Lima, V. Gomes, J. Lima, J. Martins, J. Barata, L. Ribeiro, and G. Candido, A standard-based software infrastructure to support energy efficiency using renewable energy sources, in Industrial Electronics (ISIE), 2011 IEEE International Symposium on, Gdansk, Poland, june 2011, pp [7] G. Cândido, F. Jammes, J. Barata, and A. Colombo, Generic management services for dpws-enabled devices, in Industrial Electronics, IECON th Annual Conference of IEEE, Porto, Potugal, nov. 2009, pp [8] D. Driscoll and A. Mensch, Devices profile for web services version 1.1 specification, [9] L. Ribeiro, J. Barata, et al. (2008). MAS and SOA: Complementary Automation Paradigms. Innovation in Manufacturing Networks. A. Azevedo, Springer Boston. 266/2008: [10] G. Cândido, J. Barata, A. Colombo, and F. Jammes, SOA in reconfigurable supply chains: A research roadmap, Engineering Applications of Artificial Intelligence, vol. 22, no. 6, September, [11] F. Jammes and H. Smit, Service-oriented paradigms in industrial automation, Industrial Informatics, IEEE Transactions on, vol. 1, no. 1, pp , feb [12] I. E. Commission, Iec-tc57-wg10/11/12, communications networks and systems in substations, international standard iec , Geneva,

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