Wind—it’s powerful, plentiful, and now, predictable. Improvements in technologies for managing big data, like analytics, have enhanced the potential for utility companies to forecast and manage wind better than ever before. This is especially significant for utilities looking for new and innovative ways of utilizing more consistent, clean, economical, and scalable production of the world’s energy supply.
It’s no secret harnessing the power of wind is becoming a main choice for energy. The Canadian Wind Energy Association states that in 2012, more electricity was generated in Ontario using wind than coal; by the end of 2014, Ontario will be the first province in North America to eliminate coal as a source of energy generation, and by 2017, wind will generate more than 4.9 per cent of the world’s energy supply.
Given the inconsistent and variable nature of renewable energy, integrating wind into the grid has not always been an easy task. But combining large amounts of data from weather patterns, economic variances, and grid conditions into one large database and applying analytics helps utilities gain more insight, increasing their predictive capacity into how much power will be available at a given time. This will help utility companies that build, maintain, and operate the world’s electricity systems address some of the industry’s critical challenges, such as limited fossil fuels, environmentally compromising structures, disruptive technologies, and resource restraints.
The global wind market is growing rapidly. With predictive analytics, the cost of producing renewable energy will be reduced and carbon emissions will be limited while the reliability of wind power will improve.
Advancing wind with analytics
Understanding vast and varied data is the first step in optimizing performance and increasing availability of variable energy. Incorporating big data and analytics allows utility providers to forecast weather, wind direction, and wind velocity—all crucial factors that must be considered when integrating this source of power into the grid.
Take the output of a wind farm. Turbines can capture 20 to 40 per cent of energy in the wind and typically operate over a range of wind speeds, making them optimal tools for utilizing wind. To harness its power successfully, turbines leverage electronic sensors that relay field data to a central repository. With advanced analytics, personnel can then accurately model wind flow and boost their understanding of wind patterns. This not only improves turbine placement, but it also helps them control and monitor the quality of wind output and provide insight into how much energy will be available at a given point in time.
New advanced power and weather technologies are also available. This includes IBM’s Hybrid Renewable Energy Forecasting (HyREF), which combines big data analytics, weather modelling capabilities, advanced cloud imaging technology, and sky-facing cameras to track cloud movements while sensors on the turbines monitor wind speed, temperature, and direction. Together, this solution helps accurately predict the availability of wind and solar energy and increase the integration of renewable power generation by 10 per cent. More impressively, it produces precise local weather forecasts within a wind farm in 15-minute increments and as far as one month in advance.
This level of insight will help enable utilities to expand their control over the variable nature of wind and more accurately forecast the amount of energy that can be redirected or stored into the grid. It will also allow organizations to easily integrate wind with other conventional sources of power, such as coal, natural gas, and nuclear.
Integrating renewable in grid
Renewable wind energy generation is creating a high-level of investment for utilities, and this demand requires additional grid intelligence: the ability to recognize loss of energy and reduce its load in the right place and at the right time without impacting customers. Grid stability requires immediate and accurate modelling of the transmission and distribution system with hasty switching and manipulation of certain elements to minimize impact on the overall system.
Grids built in light of this can increase power availability, improve forecasting, and reduce overall operational and maintenance expenditures. To support the forces of wind in the grid, utility companies should consider the following phases of integration, each representing a critical step in reaching higher business value for all industry stakeholders:
Monitor: Most utilities and renewable wind energy companies have control systems, but are often lacking an effective means for gaining visibility across the wind field. As a result, operators are challenged by fragmented monitoring systems produced by original equipment manufacturers for machinery, such as wind turbine generators. To create reliable monitoring, utilities are creating common information models that bring together systems and information from various equipments.
Manage: Managing incoming data from multiple sites, in addition to weather forecasting and monitoring systems, is essential for improving operation and management performances. Advanced analytics in support of predictive maintenance help reduce downtime and increase availability. Optimizing the standardization of data and information models is a critical effort in simplifying and centralizing overall asset management. This helps utilities develop more accurate wind forecasting solutions, increasing the reliability of future sustainable integration projects.
Optimize: Utilities should consider planning for scalable integration of variable energy with advanced information management and predictive analytics. Doing so will increase system flexibility while balancing conventional and renewable resources. Combining advanced information management tools that automate operations and maintenance with optimization technology that regulates power supply will help utilities become more equipped at creating business models to address industry challenges. Demand for power fluctuates significantly. It is influenced by many variables and cannot be easily stored. Optimizing the utilization of power sources across multiple sites to maximize revenue and dispatch available power sources at the lowest possible cost is important for energy operators and consumers alike.
Wind is by no means a fleeting source of energy. Around the world, utilities are looking for the right kind of tools and techniques that efficiently and reliably inject large amounts of wind into the network. But to harness it successfully, the grid must constantly adjust to how much variable sources it can absorb compared to other conventional energy resources. If we want the world to be smarter, we need the future of the grid to rely on wind.
Bruce Orloff is the Canadian smart grid solution leader in IBM’s Energy & Utilities practice.