IBM Sales and Distribution Energy & Utilities Thought Leadership White Paper Smarter Energy: optimizing and integrating renewable energy resources Enabling industrial-scale renewable energy generation
2 Smarter Energy: optimizing and integrating renewable energy resources Contents 2 A roadmap for renewables 3 Powering the adoption of renewables with analytics 3 The four phases of renewable energy integration maturity 3 Build 4 Monitor 5 Manage 5 Optimize 6 Conclusion 6 Authors The global energy industry has made great strides in deploying new generation from renewable energy sources. According to the Renewables 2012 Global Status Report from the Renewable Energy Policy Network for the 21st Century, Global new investment in renewable energy increased 17 percent in 2011, to a new record of USD 257 billion. This was more than six times the figure for 2004, and 94 percent more than the total in 2007 These numbers indicate that, while debate over appropriate energy polices continues apace and technology costs fall, the investments energy providers are making in renewables demonstrate steady progress. With centralized renewable energy generation attracting this level of investment and power capacities reaching 10-15 percent of the overall energy mix, integration and management of renewable resources needs to shift from boutique to industrialscale production before they will become a viable, consistent alternative to fossil fuels. A roadmap for renewables The intermittent and variable nature of renewable power hinders scalable production to meet energy demands and renewable growth targets. Harnessing data is becoming more and more critical to increase availability, reduce downtime and optimize performance. Economic pressures also play a role because of reduced subsidies and globally volatile fuel costs. IBM believes these challenges require a new approach to systems and information integration supported by a strong underpinning of advanced analytics. Systems built with this in mind can increase system availability, improve forecasting and reduce the overall operational and maintenance (O&M) expenses associated with integrating large-scale renewable power into the generation portfolio. IBM has identified four phases of maturity along the progression path to large-scale renewable energy integration. Each phase represents a critical step in reaching higher business value for all industry stakeholders: Build: Support project planning, as well as financing and overall project viability, with integrated, effective systems, processes and insights for improved design, siting, construction and power forecasting. Monitor: Integrate fragmented monitoring systems and create common information models to monitor your entire fleet, and bring together systems and information from various original equipment manufacturers (OEMs) to enable better performance and compliance. Manage: Integrate renewable and fossil power generation assets to improve the reliability and performance of all generation assets to manage energy source volatility. Optimize: Plan for scalable integration of intermittent energy using advanced information management and predictive analytics to increase system flexibility, and balance conventional and renewable resources.
IBM Sales and Distribution 3 Optimize Build Renewables information and systems integration Manage Monitor One prime example of where analytics will play an increasingly important role is in forecasting; a crucial factor for a range of the decisions energy providers must make when it comes to renewable energy. According to the Western Wind and Solar Integration Study released in 2010 by the National Renewable Energy Laboratory (NREL) even at a penetration level of 30 percent wind and five percent solar capacity, storage technologies do not make economic sense and that better forecasting is a better investment. For example, analytics can play a crucial role in addressing the uncertainty of power production from solar and wind generators that stems from a lack of real-time predictive capability for both weather conditions and how the generators will respond to granular changes in wind and solar output against consumer demand and market conditions. Advanced weather and power forecasting are important analytics capabilities needed to increase the viability and sustainability of renewable integration projects. Figure 1. The four phases of renewables information and systems integration. We believe that careful planning for each of these phases will dramatically impact and increase the effectiveness of power systems integration and help ensure the economic sustainability of renewable energy. The starting point can be at any of the four phases, but new projects always begin with the build phase. Powering the adoption of renewables with analytics Advanced analytics are critical to all four phases described above because this capability addresses many of the challenges existing power systems design poses for integrating central station renewable power. By providing the insights needed to increase system availability, reduce operational expenditure and improve dispatch, analytics acts as a pivot point for the industrialization of renewable energy. The four phases of renewable energy integration maturity Following is a deeper look at the four phases of maturity in renewable energy integration described above and some real-world examples from around the globe. Build Effective planning, siting and power forecasting is critical to overall project viability. Assets are dynamically growing and require remote fleet monitoring, business key performance indicators (KPIs) and operational best practices, all of which can be leveraged from other industries. All project stakeholders, from the utility to the construction company, play a key role in this phase and it requires close collaboration. Original equipment manufacturers (OEMs) are faced with tough competition and advances in design, manufacturing and maintainability. For instance, today s wind turbines are becoming a commodity, and OEMs are under pressure from the energy
4 Smarter Energy: optimizing and integrating renewable energy resources providers to have an integrated product lifecycle management (PLM) and a plant lifecycle view across design, siting and construction. As an example, a wind energy company in Denmark needed a better way to manage and analyze location factor data such temperature, precipitation, wind velocity, humidity and atmospheric pressure for advanced wind turbine placement decision support. But their existing data analysis process was becoming increasingly unsatisfactory and their underlying architecture no longer supported their high-performance computing needs. In tandem with the creation of a high-performance computing hardware solution, the company and IBM worked together to implement analytics capabilities designed to help them gain insight from information flows that are characterized by variety, velocity and volume. Critical analyses of massive amounts of data measured in petabytes with hundreds of variable parameters used to take three weeks, are now performed in 15 minutes. As a result, they are now able to manage and analyze weather and location data in ways that were previously not possi- ble to gain insights that will lead to improved decisions for wind turbine placement and operations. Monitor All utilities and renewable energy companies have control systems, but are often lacking an effective means for gaining visibility across the fleet. Subsequently, operators are challenged by fragmented monitoring systems provided by each OEM for equipment such as wind turbine generators. The creation of common information models is becoming more critical for reliable monitoring. systems such as Regional SCADA Systems, Wind Farm SCADA and field controllers, and business support systems such as enterprise resource management (ERP) to make effective business decisions. Another critical need was to define best practices for acquisition, storage, processing and presentation of real-time data. IBM helped the company define, design and implement a system that enables more optimized operations and the use of shared services as the basis for organization-wide information on demand. As a result, they have benefited from improved business effectiveness and cost savings. In addition, their employees now have access to the information they need to make effective and speedy decisions. They also adopted an open, global platform that is scalable to support their vision of doubling their current wind power capacity and capable of adding other renewable assets such as solar- and bio-gas-based power in the future. Maturity Monitor Including KPIs, dashboards and real-time monitoring Optimize Including generation portfolio and asset management optimization, and predictive maintenance Manage Including condition-based maintenance, predictive analytics and integration with business administration systems As an example a leading European utility needed to create a utility-wide implementation for real-time monitoring, control, analysis and management of 4.5GW of renewable assets across different regions. The company also required on demand information access from diverse sources including real-time operation Business value Figure 2. The renewables operations and maintenance progression path towards effective information and systems integration.
IBM Sales and Distribution 5 Manage The effective management of the data coming from various sites and weather forecasting and monitoring systems is essential to improve O&M performance. Advanced analytics in support of predictive maintenance help reduce downtime and increase availability. An increase in the standardization of data and information models is a critical effort to further simplify and centralize overall asset management. Linking intermittent power with conventional power sources, such as coal and gas, and in some cases storage, requires accurate operational data and accurate weather and power forecasting to effectively coordinate dispatch. A good illustration of this issue is the situation for energy providers in China. Due to variability and lack of forecasting, an average of 20 to 50 percent of wind power is curtailed and subsequently not integrated into the national electric grid. As a result, the Chinese government launched a policy in mid-2011 requiring that wind power forecasting become an essential com- of all wind power dispatched into the electrical grid. ponent IBM is working closely with several clients to develop more accurate wind and solar forecasting solutions to help increase the viability of future renewable integration projects. This weather forecasting service employs the numerical weather prediction ( NWP) method, which models complex physical processes that impact weather and produces highly accurate local weather fore- for the wind farm turbines. Calculating factors such as wind casts speed and wind direction, the system forecasts the power output of a wind farm. This method has been found to be highly accurate, and could be used in multiple energy markets with similar integration issues. In fact, first results show that forecasting errors can be reduced to less than 10 percent, leading to less curtailment and improved availability resulting in increased revenue. Optimize Integration of intermittent energy requires increased system flexibility and advanced information management tools that enable further automation of both operations and maintenance. As the importance of predictive maintenance increases, so does the need for advanced analytics supporting topics such as data mining and pattern recognition. According to a study from Electric Power Research Institute (EPRI), predictive maintenance reduces maintenance costs by 47 percent. Optimizing new generation assets, especially in spot markets, has become a critical factor given increased regulations and new considerations such as carbon credits and emissions trading. As a result, the industry must consider alternative business models, such as the merchant model. The experience of the company in charge of managing the power generation and delivery systems in Spain is a good example of the importance of optimization in the renewable energy market because, like all power utility managers and operators, the company must constantly manage a daunting challenge: power must be consumed as it is generated because it cannot be stored easily. Demand, however, fluctuates significantly and is influenced by many variables such as available generating capacity and other costs and restrictions over a given time period. Until recently, the company calculated its unit commitment using a homegrown Fortran solution based on an interactive mathematical methodology. However this methodology did not guarantee the optimum and most viable solution. Furthermore, they needed to comply with mandates of the Kyoto Protocol, and thus required a more robust and flexible application that could accommodate the addition of alternative energy sources and the increasing number of energy systems coming online.
6 Smarter Energy: optimizing and integrating renewable energy resources Using IBM ILOG, they built a new solution for optimizing unit commitment which gives them the flexibility needed to address their complex variables. With an integrated development environment at its core, the tool allows planners to easily compare power generation scenarios, programs and production costs and then select the optimal demand coverage at any given time. The addition of ILOG Optimization Decision Manager gives planners the ability to compare different scenarios, programs and production costs. As a result, their operations research personnel can now test generation scenarios quickly and easily. In addition, from a development and maintenance viewpoint, there has been a significant reduction in associated costs and process duration. Optimizing the utilization of power sources across multiple sites to maximize revenue is one of the biggest challenges all energy operators face, as is dispatching available power sources at the lowest possible cost. In the case of this client, they applied optimization technology to upgrade the software they use to regulate their power supply. This optimization has helped reduce production costs by one to two percent, realizing savings of between 50,000 and 100,000 per day. Conclusion Many governments in the Organization for Economic Co-operation and Development (OECD) and in developing countries have legislatively mandated specific generation targets for clean energy in an effort to meet environmental and societal goals. In fact, as of the publication of this paper, 39 US states and at least 119 countries have some kind of clean energy policy in place that either mandates or provides financial incentives for the build out of solar, wind, hydro, biomass or alternative fuels production. We believe that energy providers who embrace advanced analytics and the four phases outlined above will be well positioned to improve reliability, increase economic value and meet legislative targets. IBM stands ready to partner with utilities and other stakeholders to help deploy intelligent systems to accelerate and smooth integration of renewable energy into the market place. This is a natural evolution of technology as the renewable energy industry continues its transformation from boutique to industrial-scale power generation that rivals incumbent power sources such as gas, nuclear, coal and oil. Authors Rolf Gibbels, IBM Global Business Development Executive Energy & Utilities, Power Generation Matt Futch, IBM Global Policy Director, Energy & Utilities
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For more information For more information on how to optimize and integrate renewable energy resources, please visit: ibm.com/energy Additionally, IBM Global Financing can help you acquire the software capabilities that your business needs in the most cost-effective and strategic way possible. We ll partner with credit-qualified clients to customize a financing solution to suit your business and development goals, enable effective cash management, and improve your total cost of ownership. Fund your critical IT investment and propel your business forward with IBM Global Financing. For more information, visit: ibm.com/financing Copyright IBM Corporation 2012 IBM Corporation Sales and Distribution Route 100 Somers, NY 10589 Produced in the United States of America November 2012 IBM, the IBM logo, ibm.com, Smarter Energy, and ILOG are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the web at Copyright and trademark information at ibm.com/legal/copytrade.shtml This document is current as of the initial date of publication and may be changed by IBM at any time. THE INFORMATION IN THIS DOCUMENT IS PROVIDED AS IS WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON-INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided. The client is responsible for ensuring compliance with laws and regulations applicable to it. IBM does not provide legal advice or represent or warrant that its services or products will ensure that the client is in compliance with any law or regulation. Statements regarding IBM s future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only. Please Recycle EUW03067-USEN-01