Achieving High Data Availability from Wind and Solar AssetsAs the cumulative deployment of renewable energy grows, digital solutions for renewable asset management and analytics are becoming increasingly popular. Data accessibility and data quality serve as system cornerstones.
By Feng Zhang and Gopi Maniachari, Utopus Insights, USA
This article focuses on the significance of data availability and commonly used solution architectures to achieve high data availability. Inaccurate key performance indicator (KPI) calculations and misleading analytics that recommend inappropriate actions are caused by a lack of availability and poor quality data. It is difficult to manage data availability and collection from the numerous renewable energy installations. One of the key parameters for KPI computations for these renewable assets is the availability of operational data.
To illustrate the significance of data availability, in this study we will use a straightforward example from availability management in the wind industry. The conventional industrial solutions will then be covered in depth. Each solution is described with its technical architecture and how to weigh cost and scalability against performance and dependability. Our objective is to provide a deeper awareness of the importance of data availability and the best ways to deal with it.
Case StudyConsider the following example of a 100MW wind farm with a capacity factor of 35% and an energy price of $ 30 per MWh. The revenue computation based on the data availability is shown in Table 1.
The ‘availability’ presented in the software will be 98% for the 95% ‘available data’, even though the ‘actual’ availability is 95.6%. The unaccounted ‘availability’ loss is 2.4%, and the unaccounted ‘revenue’ is $ 18,144.
Industry Solutions
To effectively manage the data collected from renewable fleets, several digital solutions have been developed. All these solutions rely on fast, dependable data sources. Many data ingestion solutions face the following difficulties:
To effectively manage the data collected from renewable fleets, several digital solutions have been developed. All these solutions rely on fast, dependable data sources. Many data ingestion solutions face the following difficulties:
- Disparate equipment (turbines, solar panels) and systems (SCADA, historians) from various vendors and built at various times
- Multiple data formats (proprietary, XML, IEC) and protocols and interfaces (Open Platform Communications United Architecture -OPC DA/UA,
- Modbus, DNP3, Application programming interface (PIs), no-APIs/flat files)
- Different data definitions (IEC 61850, IEC 61450-25, Reference Designation System for Power Plants (RDS-PP))
- Varied data source resolution (from near real time to 1 minute, hourly and daily)
- Extra-large datasets with sampling rates as low as sub-second and up to 10,000 tags of data points per asset in the set.
Diverse integration solutions have been created for the renewable industry due to different customer site IT infrastructure and integration needs. The next parts highlight the solution architecture, discuss the advantages and disadvantages of the most common options and demonstrate how specially designed technical solutions provide high data availability for each unique requirement.
Solution Architecture
Figure 1 illustrates the reference architecture for the integration solution used by the renewable energy sector, which consists of a variety of services and software:
Figure 1 illustrates the reference architecture for the integration solution used by the renewable energy sector, which consists of a variety of services and software:
1. Internet of Things (IoT) Connection
In this kind of integration solution, a client program will normally be operating at the customer’s site and collecting data using an OPC server. Internet outages at the customer’s site, a lack of network bandwidth, and differing OPC protocols are typical issues with these connections.
In this kind of integration solution, a client program will normally be operating at the customer’s site and collecting data using an OPC server. Internet outages at the customer’s site, a lack of network bandwidth, and differing OPC protocols are typical issues with these connections.
2. Streaming Using Kafka or Microsoft EventHub
The data in this kind of integration solution will be transmitted using well-known streaming software, such as Kafka or Microsoft EventHub, or any other streaming architecture. Data drop and duplicate dates are frequent issues with these types of solutions.
The data in this kind of integration solution will be transmitted using well-known streaming software, such as Kafka or Microsoft EventHub, or any other streaming architecture. Data drop and duplicate dates are frequent issues with these types of solutions.
3. Database Integration
Using continuous data replication mechanisms, such as Amazon Web Services data migration services or any other data capture mechanisms from other vendors, the data will be copied from the customer’s site to the cloud in this integration solution. Database and Internet outages at the customer’s site are regular issues with these kinds of solutions.
Using continuous data replication mechanisms, such as Amazon Web Services data migration services or any other data capture mechanisms from other vendors, the data will be copied from the customer’s site to the cloud in this integration solution. Database and Internet outages at the customer’s site are regular issues with these kinds of solutions.
4. File Transfer Protocol (FTP) or Secure File Transfer Protocol (SFTP) Integration
The data file will be sent from the customer’s location to the cloud as part of this integration solution. FTP/SFTP server- and client-related problems are the most common concerns with these kinds of solutions.
The data file will be sent from the customer’s location to the cloud as part of this integration solution. FTP/SFTP server- and client-related problems are the most common concerns with these kinds of solutions.
5. API Integration
The customer-provided API will be used in this kind of integration solution to pull the data. The downtime of the API server and client issues are frequent concerns for these kinds of solutions.
The customer-provided API will be used in this kind of integration solution to pull the data. The downtime of the API server and client issues are frequent concerns for these kinds of solutions.
Pros and ConsTable 2 provides a summary of the benefits and drawbacks of each of the mentioned integration solutions.
Conclusion
To produce accurate operational KPI calculations for wind farms, data availability is essential. In this article, we have utilised a time-based availability calculation example to illustrate the significant financial impact associated with contractual obligations that can have an impact on a company’s bottom line. There are several alternatives for integration solutions, as shown in Table 2, and each one has its own set of challenges. To ensure high data availability, it is important to consider key factors, including latency, scalability and cost.The best integration solution can be determined with the assistance of data platform experts based on the requirements and integration challenges of an organisation.
To produce accurate operational KPI calculations for wind farms, data availability is essential. In this article, we have utilised a time-based availability calculation example to illustrate the significant financial impact associated with contractual obligations that can have an impact on a company’s bottom line. There are several alternatives for integration solutions, as shown in Table 2, and each one has its own set of challenges. To ensure high data availability, it is important to consider key factors, including latency, scalability and cost.The best integration solution can be determined with the assistance of data platform experts based on the requirements and integration challenges of an organisation.
Biography of the Authors
Dr Feng Zhang is Product Director of Scipher.Vx+ at Utopus Insights. Prior to joining Utopus Insights, Zhang held positions in various aspects of the power and renewables industry, including wind turbine R&D, wind farm operations, data analytics SaaS for wind and solar farm operations, renewables project development, and renewable asset/portfolio transactions. Zhang was previously Executive Committee member, Director of Global Digital Energy Center, Director of Global Engineering, and Product Head of a 1.5MW turbine platform for Envision Energy. He holds a PhD in Dynamics Systems and Controls.
Dr Feng Zhang is Product Director of Scipher.Vx+ at Utopus Insights. Prior to joining Utopus Insights, Zhang held positions in various aspects of the power and renewables industry, including wind turbine R&D, wind farm operations, data analytics SaaS for wind and solar farm operations, renewables project development, and renewable asset/portfolio transactions. Zhang was previously Executive Committee member, Director of Global Digital Energy Center, Director of Global Engineering, and Product Head of a 1.5MW turbine platform for Envision Energy. He holds a PhD in Dynamics Systems and Controls.
Gopi Maniachari works as a solution architect at Utopus Insights. He has over 20 years of IT experience and over five years of experience in the renewables sector. He holds a bachelor’s degree (BTech) in electrical and electronics engineering from the University of Calicut, India. His core technical expertise includes IoT, Big Data, data analytics and databases.
