Creation of the so-called “smart grid” refers to the rapid balancing of power supplies with power demand using digital interfaces. These smart technologies facilitate communication between power suppliers and consumers to provide power while conserving energy and controlling costs.
Issues facing the power industry include public pressure to reduce carbon footprints while simultaneously providing continuous, low-cost, and reliable power. Business challenges include a complex power demand-response environment. Power providers must navigate changing fuel and energy sources supplied by ever increasing and diversifying generators, balanced against high variability in both production and demand. In addition, aging and inadequately weatherized infrastructure is vulnerable to extreme weather events that also affect power demand. Managing these challenges requires sophisticated, real-time, physics-based, and data-driven decision support systems that can rapidly optimize generation, transmission, and distribution of power.
HGL supports the power industry by addressing complex and rapidly evolving challenges linked to climate change. HGL’s experts are experienced in integrating high-performance computing, machine learning, artificial intelligence, big data, and numerical optimization techniques to address these challenges. Collaboration between sophisticated computational and human decision makers is used to develop customized credible decision support analysis tools and algorithms to operate power systems more efficiently and safely. HGL customizes these tools and algorithms by incorporating its clients’ business insights and production acumen to develop efficient, integrated systems.
HGL knows that optimization is a key component in providing energy managers with the best solutions for cost- and energy-efficient power production and distribution. HGL provides customized computational optimization decision support algorithms that can be integrated with clients’ business insights and production acumen to develop these integrated systems.
HGL’s electricity/power optimization approach includes the following:
- Simulation Tools – These tools test various design options to improve and predict performance of the overall power supply system.
- Optimization Tools – These tools compare multiple alternative inputs and thereby narrow the choices to the very best among the feasible options.
- Reliability Analysis Tools – These tools assess the risk and level of confidence associated with each solution.
- Stakeholder Collaboration – This process ensures that client needs and the “human factor” are at the center of all decisions.
Key benefits of HGL’s electricity/power optimization services include the following:
- Ensuring greater reliability and accuracy of grid dispatch operations, yielding significant cost savings;
- Predicting generators’ behavior (e.g., price, availability) in the power grid for more secure and efficient operation;
- Providing real-time analysis of regional power grids, localized micro grids, and smart grids to produce the most advantageous solutions for maximizing low carbon energy sources and minimizing price; and
- Optimizing outcomes for emerging smart grid technologies by creating operating procedures that maximize outcomes for the mixture of existing power production systems and renewables.
HGL services can be customized for specific energy applications such as the following:
- Maximal leveraging of environmentally derived renewable energy sources,
- Analysis of energy source reliability and sustainability, and
- Analysis of renewable energy: profit, risk, and trade-off.
See the animation below describing how HGL’s PBMO™ toolkit can assist your company in optimizing outcomes for emerging smart grid technologies and create operating procedures that maximize outcomes for the mixture of existing power production systems and renewables.
Successful application of adaptive machine learning approaches for optimizing power dispatching has been documented in the following paper:
Ott, Andrew. “Development of Enhanced Generation/Demand Response Control Algorithm,” Presentation at the Federal Energy Regulatory Commission Meeting, June 23, 2010.