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And this: Galileo-inspired opportunities for critical infrastructures presented at ITSF The company went on to win a seven-figure contract to supply Rubidium IEEE NTS servers to support a country-scale modern smart grid system in Asia read more here. These servers incorporate a modified version of the cyber-security solution developed as part of the Demetra project.

As such, this is a Horizon success story. Media note: This feature can be republished without charge provided the European GNSS Agency GSA is acknowledged as the source at the top or the bottom of the story. You must request permission before you use any of the photographs on the site.

Updated: May 16, Skip to main content. Home Newsroom News. Timing is everything — GNSS and the energy grids of the future Published:. Robust GNSS will be a key enabler of future smart grids. Galileo time Two projects currently developing Galileo-enabled timing receivers, supported by the GSA through its Fundamental Elements funding programme , are GIANO and GEARS.

And this: Galileo-inspired opportunities for critical infrastructures presented at ITSF The company went on to win a seven-figure contract to supply Rubidium IEEE NTS servers to support a country-scale modern smart grid system in Asia read more here. The new EUSPA EO and GNSS Market Report is out, time to know more!

Published: 23 January Challenge accepted. Published: 16 January Both features can reduce implementation costs i. Learn more in Grid-Interactive Multi-Zone Building Control Using Reinforcement Learning With Global-Local Policy Search.

NREL researchers developed an innovative, distributed photovoltaic PV inverter control architecture that maximizes PV penetration while optimizing system performance and seamlessly integrating control, algorithms, and communications systems to support distribution grid operations.

The growth of PV capacity has introduced distribution system challenges. For example, reverse power flows increase the likelihood of voltages violating prescribed limits, while fast variations in PV output tend to cause transients that lead to wear-out of switchgear.

Recent distributed optimization and control approaches that are inspired by—and adapted from—legacy methodologies and practices are not compatible with distribution systems with high PV penetrations and, therefore, do not address emerging efficiency, reliability, and power-quality concerns.

This project developed distributed controllers that were validated via comprehensive and tiered software-only simulation and hardware-in-the-loop simulation. Microcontroller boards were used to create and embed the optimization-centric controllers into next-generation gateways and inverters.

The objective of this project is to advance the foundational science for control and optimization of multi-energy systems by:. Departing from existing approaches based on off-the-shelf black-box tools, the project produced innovative, computationally affordable distributed optimization and control schemes that are grounded on solid analytical foundations and will pave the way to the next -generation real-time control architecture for integrated energy systems.

Decentralized Low-Rank State Estimation for Power Distribution Systems , IEEE Transactions on Smart Grid Economic Dispatch With Distributed Energy Resources: Co-Optimization of Transmission and Distribution Systems , IEEE Control Systems Letters Distributed Minimization of the Power Generation Cost in Prosumer-Based Distribution Networks , American Control Conference Model-Free Primal-Dual Methods for Network Optimization With Application to Real-Time Optimal Power Flow , American Control Conference Joint Chance Constraints in AC Optimal Power Flow: Improving Bounds Through Learning , IEEE Transactions on Smart Grid Online Optimization as a Feedback Controller: Stability and Tracking , IEEE Transactions on Control of Network Systems Online Primal-Dual Methods With Measurement Feedback for Time-Varying Convex Optimization , IEEE Transactions on Signal Processing Real-Time Feedback-Based Optimization of Distribution Grids: A Unified Approach , IEEE Transactions on Control of Network Systems View all NREL publications about distributed controls and optimization.

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Grid Modernization Menu. Distributed Optimization and Control NREL is working to advance foundational science and translate advances in distributed optimization and control into breakthrough approaches for integrating sustainable and distributed infrastructures into our energy systems.

Autonomous Energy Systems. Real-Time Resilience of Energy Systems. Asynchronous Control and Estimation for Highly Distributed Energy Systems. Multi-Agent Reinforcement Learning Under System Constraints. The reasons are: The inadequacy of reinforcement learning approaches to perform in partially observable control spaces Their inability to satisfy system physical and engineering constraints while pursuing operational objectives.

Dynamic Microgrids for Real-Time Resilience. In particular, when a disruption is identified: A new partition within each layer of hierarchy is established via distributed communications and control algorithms while adhering to the priority of critical loads and availability of communication systems Distributed control algorithms are applied to prevent the system from collapse.

Multi-Objective Deep Reinforcement Learning for Grid-Interactive Energy- Efficient Buildings. Distributed Inverter Controllers Seeking Reliability and Economic Optimality of Photovoltaic-Dominant Distribution System.

Optimization of Smart Multi-Energy Districts. The objective of this project is to advance the foundational science for control and optimization of multi-energy systems by: Uncovering and modeling intrinsic couplings among electricity, natural gas, water, and heating systems, with emphasis on distribution-level operational setups Formulating new classes of multi-energy-system optimization problems incorporating the inter-system dependencies and formalizing well-defined optimization and control objectives Addressing the solution of the formulated problems by tapping into contemporary advances in convex relaxations and distributed optimization and control.

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