Bulgarian Pilot - Energy grid management & predictive maintenance for wind farms
Sustainability Aspects: AI-Driven Optimization of Distributed Renewable Energy
Targeted Vertical: Renewable Energy Systems
Partners:
- Entra Energy: Use Case leader
- University of Cyprus: Technical co-leader. Developer and Integrator
- A1 Bulgaria: Telecom Operator
- Software Company: Developers and Integrators
The Bulgarian use case focuses on energy grid management and predictive maintenance for wind farms. The location of the trial site will be in Sofia Bulgaria and turbines will be located in Sliven region (Southeast Bulgaria)
Motivation and challenges:
The transition to a smart grid, aiming for full renewable energy resources (RES) utilization by 2050, poses environmental benefits and technical hurdles due to renewable power’s unpredictability. Distribution grids face complexity from renewable integration and dynamic loads like electric vehicles and challenging traditional grid management during disturbances. 6G technology promises ubiquitous connectivity and ultra-fast communication, facilitating real-time coordination of distributed energy resources (DERs), like RES and energy storage systems (ESS), enhancing grid stability and efficiency. Storage and demand side management (DMS) offer fast, flexible solutions, but effective coordination is essential. 6G technology, with features like massive MIMO and cell-free networks, enables high-capacity, low-latency communication, fostering dynamic network optimization based on real-time information, as illustrated in the figure below:
Key benefits of 6G-fast-DERs integration are: (1) ultra-fast frequency support (UFFS) down to sub-millisecond communication (as theoretically envisioned for 6G), allowing the fast ESSs to respond instantly to frequency deviations; (2) voltage regulation (VR) by means of coordinated voltage control to stabilize the grid operation during load changes or faults (significant disturbances), (3) resilience: 6G’s facilitate grid resilience and robustness against cyber/physical threats by adopting a zero-trust network-design architecture for all critical grid components which require communication, (4) advanced control strategies: ML and AI algorithms optimize DERs dispatch based on predictive models and decentralized decision-making, ensuring adaptability to changing grid dynamics.
Solutions/Trial scenarios to address the challenges
In this context, the Bulgarian pilot will investigate user-centric systems where fast DERs communicate directly with each other, exploring A1 network infrastructure for flexible and interoperable communication. Moreover, three main challenges of smart grids will be addressed:
(1) enhance reliability and resilience of smart grids using distributed control system (DCS) architectures and 6G communication. DCS allows a decentralized decision-making in smart grids which improves grid resilience and reliability in cases of natural or man-made generated grid disturbances, by allowing the system to quickly adapt and reroute power flow such that to minimize its downtime. For an efficient DSC it is highly important that the 6G communication provides consistent ULL and high reliability communication performance, especially during network congestions or adverse conditions.
(2) improved voltage and frequency control by fine-tuning voltage and frequency levels across the grid (with a focus on high-RES penetration regions of distribution power grids the so-called grid edge). Small DERs integration in remote areas using DCS and 6G communication technology aims to manage variable generation and ultra-fast ESS proactively. This includes AI-based agents’ development to predict and adjust responses to grid disturbances, optimizing DERs and ESS dispatch based on real-time grid conditions, and implementing distributed predictive control strategies to enhance regional coordination of DERs amidst changing grid dynamics.
(3) leverage on edge computing and localization advanced features of 6G networks to support identification of changes in the grid dynamics. Edge computing plays a vital role in enabling real-time processing and decision making in smart grids, prompting the testing of 6G network capabilities to support distributed computing at the network edge. This involves running pre-trained ML models locally on edge network components to minimize data transmission. Additionally, the implementation of smart grid applications necessitates a zero-trust network architecture, requiring default authentication for all devices and users due to the critical nature of data involved.
The main 6G-VERSUS application components (app triplet) are summarized in Table 1.
Table 1: 6G-VERSUS application components for Bulgarian use case
| V-apps | (1) enhance reliability and resilience of the power grids towards 100% RES integration; (2) enhance voltage and frequency control at grid edge (below MV); (3) AI and edge computing for the grid network optimal orchestration of resources. Devices hosted in the field: IoT sensors, PMU measurement units, hardware controller |
|---|---|
| AI-apps | (1) optimal orchestration of network resources; (2) real-time local processing of sensors data for pro-active enabled actions in the DER controllers. |
| N-Apps | (1) fusion and synchronization of data for digital-twining model creation; (2) real-time processing of streams of local measurements in the WAM&C Network applications. |
As sustainability is of vital importance in 6G-VERSUS, the main sustainability challenges are summarized together with the expected outcomes of this use case in Table 2.
Table 2: Main sustainability challenges and expected outcomes for Bulgarian use case.
| Main Sustainability Challenges |
|---|
| 1) Security and privacy concerns for RES (data from critical infrastructures) by implementing intelligence distillation at the edge network: Dedicated network slices and robust security mechanisms ensure the protection of critical workloads and prevent resource sharing with less critical tasks, thereby safeguarding the integrity and reliability of the network. |
| 2) Green-base stations, powered by local RES, integration targets to energy usage optimization in 6G infrastructure, facilitating energy-efficient network operations. |
| 3) Optimization models to adapt to varying network and grid conditions, ensuring minimal energy consumption while delivering network services to smart grids. |
| 4) Exploitation of experimental dynamic sleep mode of the RAN network to optimize device wake-up intervals based on demand to reduce energy consumption. |
| Expected Outcomes |
| 1) Enhanced grid stability and reliability: The distributed wide area controller enhances grid stability by efficiently managing the variability and intermittency of RES. Through dynamic coordination of RES and ESS units, it effectively mitigates voltage fluctuations, frequency deviations, and other grid instabilities, thereby boosting overall system reliability. |
| 2) Optimized utilization of renewable resources: Through advanced optimization algorithms and predictive analytics, the controller maximizes the utilization of renewable resources by forecasting generation patterns and coordinating the dispatch of RES and ESS units, thereby optimizing clean energy integration while minimizing curtailment and waste. |
| 3) Reduced operational costs and environmental impact: The wide area controller utilizes DERs and energy storage to enable efficient power system operation. By employing intelligent energy scheduling and demand-side management, it helps utilities optimize generation, transmission, and distribution, leading to cost savings and environmental benefits. |
| 4) Improved resilience and grid resilience: The distributed wide area controller bolsters the power system's resilience by mitigating threats like extreme weather, equipment failures, and cyber-attacks. Its decentralized control and autonomous decision-making capabilities enable swift response and recovery during emergencies, ensuring minimal impact on critical infrastructure and enhancing overall grid resilience. |
| 5) Facilitation of grid modernization and integration: Distributed wide area controller represents a crucial step towards the modernization and integration of the electric grid. By providing a platform for seamless communication and coordination among diverse grid assets, including RES, ESS, and demand response resources, the controller paves the way for a more flexible, efficient, and sustainable energy system. |