{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9853592","patent":{"patent_number":"US-9853592","title":"Method and device for controlling an energy-generating system which can be operated with a renewable energy source","assignee":null,"inventors":[],"filing_date":"2013-12-03T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["G05B","G05B","G06Q"],"num_claims":15,"abstract":"A method and a device for controlling an energy-generating system are operated with a renewable energy source. In the method, a prediction about an energy yield of the energy-generating system is made for a predefined prediction time period, and a predefined area, using a learning system with an input vector and an output vector. The output vector includes operating variables for a multiplicity of successive future times of the time period. The input vector includes variables, influencing the operating variables, for a point in time from a multiplicity of points in time of a predefined observation time period. The input variables include at least three items of information for the observation time period and the predefined area. The energy-generating system is controlled on the basis of the generated prediction such that weather-conditioned fluctuations in the energy yield of the energy-generating system are reduced."},"analysis":{"summary":"The patent titled \"Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source\" introduces a pivotal innovation for stabilizing renewable energy grids. Its core innovation is a sophisticated method and device that employs a learning system to proactively predict and control the energy output of renewable sources, thereby significantly reducing weather-conditioned fluctuations.\n\nThe primary problem this invention solves is the inherent intermittency and unpredictability of renewable energy sources like solar and wind power. These fluctuations make it challenging to maintain grid stability, often necessitating reliance on fossil fuel-based backup power and hindering the widespread adoption of clean energy. Existing solutions often react to real-time changes, which can be inefficient and costly.\n\nThe key technical approach involves a machine learning system that ingests a multi-dimensional 'input vector.' This vector includes various influencing variables, such as detailed weather forecasts, historical energy yield data, and geographical information, collected over a predefined 'observation time period' and 'area.' The learning system then processes this data to generate an 'output vector,' comprising optimal operating variables for a multiplicity of successive future times within a 'prediction time period.' On the basis of this precise, forward-looking prediction, the energy-generating system is intelligently controlled.\n\nThe business value and applications are substantial. This technology enables greater grid stability and reliability, allowing for a higher penetration of renewable energy into national grids without compromising power quality. It reduces operational costs for utilities by minimizing the need for expensive peak-load power plants and optimizing the use of energy storage systems. Furthermore, it provides renewable energy producers with more predictable output, enhancing their ability to participate effectively in energy markets and improving the financial viability of renewable projects.\n\nThe market opportunity for this technology is immense, spanning across utilities, independent power producers, and smart grid developers globally. As the world accelerates its transition to clean energy, solutions that can effectively manage the variability of renewables are in high demand. This innovation offers a robust, AI-driven framework to achieve this, positioning itself as a critical component in the future of sustainable energy infrastructure.","layman_explanation":"### What Problem Does This Solve?\nImagine running a city's power grid. You have traditional power plants that you can turn on and off, but you also want to use a lot of clean energy from solar panels and wind turbines. The problem is, solar panels only work when the sun shines, and wind turbines only spin when the wind blows. This makes the amount of electricity they generate go up and down like a rollercoaster. This unpredictability, known as intermittency, is a huge headache for grid operators. They constantly have to bring in expensive backup power, often from fossil fuels, or risk blackouts. This makes green energy less reliable and more costly to integrate, slowing down our transition to a sustainable future.\n\n### How Does It Work?\nThe patent, \"Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source,\" tackles this challenge with a smart, proactive approach. Think of it like a highly intelligent weather forecaster combined with an energy conductor. This system uses a sophisticated 'learning system' (which is essentially advanced artificial intelligence) to gather vast amounts of information. This includes not just today's weather forecast, but also detailed historical weather patterns for a specific area, how much energy was produced last week at different times, and even geographic details of the power plants.\n\nIt takes all this data and, instead of just reacting to what's happening now, it *predicts* how much energy will be generated in the next few hours, days, or even weeks. Based on this precise forecast, it then tells the energy-generating system what to do. For example, if it predicts that clouds will reduce solar output in two hours, it can preemptively tell a battery storage system to start discharging, or signal another power source to ramp up gradually. This way, the drop in solar power is smoothly offset, and the city's power supply remains stable. It's about anticipating the future and making adjustments ahead of time, rather than scrambling to fix problems as they arise.\n\n### Why Does This Matter?\nThis innovation is a game-changer for the energy industry and beyond. Firstly, it makes renewable energy sources much more reliable and easier to integrate into our existing power grids. This means we can deploy more solar and wind farms without worrying as much about grid instability. For utility companies, this translates into significant cost savings because they'll rely less on expensive, fast-acting backup power plants. They can also optimize their existing assets, like energy storage, much more efficiently. For renewable energy producers, having a more predictable output means they can make firmer commitments in energy markets, potentially increasing their revenue and making their projects more attractive to investors. Ultimately, this technology accelerates the global shift towards a cleaner, more sustainable energy future, reducing carbon emissions and enhancing energy security.\n\n### What's Next?\nThe potential applications for this technology are vast. We can expect to see it implemented in large-scale solar and wind farms, microgrids, and even smart cities looking to maximize their renewable energy use. As the learning system continues to gather more data and refine its predictions, its accuracy and effectiveness will only improve. This will likely lead to even higher levels of renewable energy integration and further reductions in grid management costs. It represents a critical step towards fully autonomous, intelligent energy grids that can seamlessly adapt to environmental changes, making green energy truly the backbone of our power supply.","technical_analysis":"The patent \"Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source\" (US-9853592) outlines a sophisticated approach to mitigate the inherent variability of renewable energy sources through predictive control. This technical deep dive focuses on the architectural components, algorithmic underpinnings, and potential implementation details of this innovative system.\n\n**Technical Architecture:**\nAt its core, the invention describes a 'learning system' acting as an intelligent controller for an energy-generating system. This architecture can be conceptualized as a closed-loop control system with an advanced prediction engine. The primary components include:\n1.  **Data Ingestion Layer:** Responsible for collecting diverse data streams. This includes real-time meteorological data (temperature, wind speed, solar irradiance, cloud cover, humidity) from local sensors and regional weather models, historical energy yield data from the specific energy-generating system, and potentially grid demand forecasts or energy market signals. The patent emphasizes an 'observation time period' and 'predefined area' for these inputs.\n2.  **Input Vector Construction:** The raw data is processed and aggregated into a multi-dimensional 'input vector.' This vector is crucial for the learning system's effectiveness, as it encapsulates all variables influencing future energy yield. The patent explicitly states the input vector includes at least three items of information, implying a comprehensive feature set.\n3.  **Learning System Core:** This is the brain of the operation. It's an AI/ML model trained to identify complex, non-linear relationships between the input vector variables and the energy yield. This system continuously learns from new data, adapting its prediction model over time.\n4.  **Prediction Engine:** Leveraging the trained learning system, this engine generates a 'prediction about an energy yield' for a 'predefined prediction time period.' This output is not a single value but an 'output vector' containing 'operating variables for a multiplicity of successive future times.' These variables represent the predicted optimal state or control parameters for the energy system at various points in the future.\n5.  **Control Decision Module:** This module takes the predicted operating variables from the output vector and translates them into actionable commands for the physical energy-generating system. This could involve adjusting turbine pitch, solar panel orientation, charging/discharging rates for energy storage, or even momentary curtailment.\n6.  **Energy-Generating System Interface:** The actual physical interface to control the renewable energy assets (e.g., SCADA systems, programmable logic controllers).\n7.  **Feedback Loop:** Critical for continuous improvement, actual energy yield and system performance data are fed back into the data ingestion layer to retrain and refine the learning system.\n\n**Algorithm Specifics:**\nGiven the nature of time-series prediction and complex environmental factors, the learning system likely employs advanced machine learning algorithms. Potential candidates include:\n*   **Recurrent Neural Networks (RNNs) / Long Short-Term Memory (LSTM) / Gated Recurrent Units (GRUs):** Excellent for sequential data and capturing temporal dependencies in weather patterns and energy generation.\n*   **Convolutional Neural Networks (CNNs):** Could be used for spatial feature extraction, especially if satellite imagery or grid-wide sensor data is part of the input vector.\n*   **Ensemble Methods (e.g., Gradient Boosting Machines like XGBoost or LightGBM, Random Forests):** Known for robustness and high predictive accuracy in complex regression tasks.\n*   **Gaussian Processes:** Offer probabilistic predictions and uncertainty estimates, which can be valuable in critical infrastructure control.\n\nThe training process for these models would involve extensive historical data, cross-validation, and potentially transfer learning techniques if deployed across different geographical areas or types of renewable sources. The objective function during training would be to minimize the error between predicted and actual energy yield, and subsequently, to optimize the control actions to reduce fluctuations.\n\n**Integration Patterns:**\nThe device would integrate into existing energy infrastructure primarily through standard industrial communication protocols (e.g., Modbus TCP/IP, DNP3, IEC 61850) for interfacing with SCADA systems and plant controllers. Cloud-based platforms would be ideal for hosting the learning system core, enabling scalable data storage, processing (e.g., Apache Kafka for streaming data, Spark for batch processing), and model deployment (e.g., Kubernetes for containerized services). APIs would facilitate seamless data exchange with weather forecasting services, grid operators, and market platforms.\n\n**Performance Characteristics:**\nThe primary performance characteristic is the *quantifiable reduction in weather-conditioned energy yield fluctuations*. This would be measured by metrics such as standard deviation of output, ramp rates, and frequency of grid imbalances before and after implementation. The system's latency (time from data ingestion to control action) and computational efficiency are also critical, especially for real-time control. Robustness against sensor failures, data anomalies, and extreme weather events would require sophisticated error handling and fault tolerance mechanisms. This technology, as described in Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source, offers a foundation for highly resilient and intelligent renewable energy management.","business_analysis":"The patent \"Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source\" (US-9853592) presents a significant business opportunity by directly addressing one of the most persistent challenges in renewable energy: intermittency and unpredictable output. This innovation is poised to unlock substantial market value across the energy sector.\n\n**Market Opportunity Size:**\nThe global renewable energy market is projected to grow exponentially, with significant investments in solar, wind, and other clean power sources. However, the inherent variability of these sources has always capped their integration limits and increased grid management costs. This patent directly tackles this constraint. The market for smart grid solutions, energy management systems, and predictive analytics in energy is already in the tens of billions of dollars and is expected to grow rapidly. This technology slots perfectly into this expanding market, offering a crucial enabler for higher renewable penetration and grid modernization. Every utility, independent power producer, and large-scale renewable project developer is a potential client, representing a multi-billion dollar addressable market globally.\n\n**Competitive Advantages:**\nThis invention provides a distinct competitive edge by shifting from reactive to proactive energy management. Current solutions often rely on real-time balancing acts or energy storage alone, which can be expensive and less efficient. This patent's learning system offers superior prediction accuracy, allowing for optimized control *before* fluctuations occur. This leads to:\n1.  **Enhanced Reliability:** Makes renewable energy sources more dispatchable, reducing the need for fossil fuel peaker plants.\n2.  **Cost Efficiency:** Lowers operational expenditures for grid operators by minimizing grid imbalances and optimizing energy storage utilization.\n3.  **Increased Renewable Integration:** Enables higher percentages of intermittent renewables to be safely and effectively integrated into the grid.\n4.  **Improved Revenue for Producers:** More predictable output allows renewable asset owners to make firmer commitments in energy markets, potentially leading to higher profits and reduced penalties.\n\n**Revenue Potential and Business Models:**\nRevenue streams could be diverse:\n*   **Software-as-a-Service (SaaS):** Offering the predictive control system as a subscription service to utilities and IPPs.\n*   **Licensing:** Licensing the patented technology to energy management system providers or hardware manufacturers.\n*   **Consulting and Integration Services:** Providing expertise for custom implementation and integration into existing grid infrastructure.\n*   **Performance-Based Contracts:** Charging a fee based on the measurable reduction in fluctuations or increased grid stability achieved.\n\nConsidering the potential cost savings for utilities (e.g., reduced ancillary service costs, optimized asset utilization) and increased revenue for producers, the value proposition is strong, supporting premium pricing models.\n\n**Strategic Positioning:**\nThis technology positions its adopters as leaders in intelligent energy management and sustainable grid development. It's a critical tool for achieving decarbonization goals while maintaining energy security. Companies leveraging this patent can differentiate themselves by offering 'predictably green' energy solutions. Strategic partnerships with grid operators, energy storage providers, and smart city initiatives would be key to market penetration and scaling.\n\n**ROI Projections:**\nWhile specific ROI will vary, the benefits are clear. For a utility, a reduction in grid imbalance events, optimized use of battery storage, and decreased reliance on expensive peaker plants can translate into millions of dollars in annual savings. For a renewable energy developer, increased predictability and market confidence can improve project financing terms and boost returns on investment. The ability to increase renewable energy penetration also yields significant environmental ROI, contributing to carbon reduction targets and enhancing corporate social responsibility profiles. This patent, Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source, is not just a technical improvement, but a powerful economic enabler for the global energy transition.","faqs":[{"answer":"The Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source patent (US-9853592) describes an innovative technology designed to enhance the stability and reliability of renewable energy systems. At its core, it's a sophisticated control system that uses a 'learning system' – essentially an advanced AI – to predict how much energy a renewable source (like a solar farm or wind turbine) will generate in the future.\n\nThis prediction is based on a wide array of data, including detailed weather forecasts, historical energy output, and geographical information specific to the energy-generating system's location. By forecasting energy yield for a future time period, the system can then proactively adjust the operation of the renewable energy source.\n\nThe primary goal of this proactive control is to significantly reduce the unpredictable fluctuations in energy output that are typically caused by changing weather conditions. This makes renewable energy sources more consistent and easier to integrate into the overall power grid, addressing a major challenge in the global transition to clean energy. This innovation transforms inherently variable renewable assets into more reliable components of the energy mix.","question":"What is Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source?"},{"answer":"The Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source operates through a multi-step, intelligent process. First, it gathers extensive data, forming what's called an 'input vector.' This input vector includes crucial information such as real-time and forecasted weather conditions (like wind speed, solar irradiance, and cloud cover) for a specific area and over a defined observation period. It also incorporates historical energy yield data from the system itself and other relevant influencing variables.\n\nThis comprehensive input vector is then fed into a 'learning system,' which is an AI/machine learning model trained to understand the complex relationships between these variables and the energy output of the renewable source. The learning system processes this data to generate a precise 'prediction about an energy yield' for a future 'prediction time period.' This prediction is detailed, providing optimal 'operating variables' for multiple successive future times.\n\nFinally, based on this generated prediction, the device actively controls the energy-generating system. For instance, if a drop in solar output is predicted due to approaching clouds, the system might preemptively adjust the output of other connected generators, or manage an energy storage system to discharge power. This proactive adjustment ensures that weather-conditioned fluctuations in energy yield are significantly reduced, leading to a more stable and predictable power supply from renewable sources.","question":"How does Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source work?"},{"answer":"The Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source patent primarily solves the critical problem of intermittency and unpredictability inherent in renewable energy sources. Solar panels only generate electricity when the sun shines, and wind turbines only produce power when the wind blows. This dependence on transient weather conditions leads to significant fluctuations in energy output.\n\nThese fluctuations pose substantial challenges for maintaining grid stability. Power grids require a constant balance between supply and demand; sudden drops or surges from renewable sources can disrupt this balance, leading to frequency deviations, voltage instability, and potentially blackouts. To compensate, grid operators often rely on expensive, fast-ramping fossil fuel power plants or large-scale energy storage systems, which can increase operational costs and diminish the environmental benefits of renewables.\n\nThis innovation mitigates these issues by enabling proactive, intelligent control. By predicting future energy yield and adjusting operations accordingly, it transforms inherently variable renewable sources into more reliable and dispatchable assets. This reduces the need for costly backups, enhances grid resilience, and accelerates the widespread adoption of clean energy, making the transition to a sustainable future more feasible and economical.","question":"What problem does Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source solve?"},{"answer":"While the specific inventors are not detailed in the provided patent information, the Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source (US-9853592) was filed on 2013-12-03 and published on 2017-12-26. Patents are typically the result of extensive research and development efforts by teams of engineers, scientists, and innovators within companies or academic institutions.\n\nThe development of such a sophisticated system, combining advanced machine learning with real-time energy control, would involve expertise in fields like artificial intelligence, power systems engineering, meteorological forecasting, and data science. These types of innovations are crucial for advancing sustainable technology and are often protected by patents to encourage further investment and development in the clean energy sector.\n\nThe intellectual property behind this technology contributes significantly to the ongoing global efforts to make renewable energy a more reliable and integral part of our energy infrastructure.","question":"Who invented Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source?"},{"answer":"The Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source offers several transformative benefits for the renewable energy sector and beyond:\n\nFirstly, it significantly **enhances grid stability and reliability**. By proactively predicting and mitigating weather-conditioned fluctuations, it ensures a more consistent power supply, reducing the risk of grid imbalances, blackouts, or brownouts. This is crucial for maintaining a robust and dependable energy infrastructure.\n\nSecondly, it **accelerates the integration of renewable energy**. With more predictable output, utilities can confidently incorporate a higher percentage of intermittent solar and wind power into their grids without compromising stability. This directly supports global decarbonization efforts and the transition to a cleaner energy mix.\n\nThirdly, it leads to **substantial operational cost reductions**. Less reliance on expensive, fast-ramping fossil fuel peaker plants for balancing, and more efficient utilization of energy storage systems, translates into lower costs for grid operators and potentially for consumers. It also optimizes the use of existing renewable assets.\n\nFinally, it **improves the economic viability of renewable projects**. More predictable energy output allows producers to make firmer commitments in energy markets, reducing penalties for under-delivery and potentially increasing revenue. This makes renewable energy investments more attractive and financially secure, driving further growth in the sector. The Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source therefore provides a comprehensive solution for making green energy truly reliable and efficient.","question":"What are the key benefits of Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source?"},{"answer":"The Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source distinguishes itself from prior art through its integrated, proactive, and learning-based approach to energy management. Prior art solutions often fall into categories such as energy storage, demand-side management, or the use of fast-ramping conventional generators, which are either capital-intensive, reactive, or still rely on fossil fuels.\n\nMany existing forecasting tools provide predictions of renewable energy output but typically operate as standalone systems, requiring human intervention or separate control logic to act on those forecasts. This innovation, however, directly links a sophisticated 'learning system' to the control mechanism of the energy-generating system. This means the prediction engine's output (optimal operating variables for future times) is immediately translated into proactive control actions.\n\nThe key differentiator is the combination of a comprehensive, multi-variate input vector for highly accurate prediction with a control strategy explicitly designed to *reduce weather-conditioned fluctuations* before they occur. Unlike reactive systems that respond to imbalances after they've started, this patent enables anticipation and preemptive adjustment. Furthermore, its 'learning system' implies continuous adaptation and improvement over time, making it more robust and effective than static control models. This integrated, intelligent, and predictive capability sets the Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source apart as a next-generation solution for renewable energy management.","question":"How is Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source different from prior art?"},{"answer":"The Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source patent is poised to significantly impact several key industries:\n\n**1. Utilities and Grid Operators:** This is the most direct impact. The technology will enable utilities to manage their grids with greater stability and efficiency, allowing for higher penetration of renewable energy. It will reduce operational costs associated with balancing intermittent generation and minimize the need for expensive ancillary services and fossil fuel backups.\n\n**2. Renewable Energy Developers and Producers:** Solar, wind, and other renewable asset owners will benefit from more predictable energy output. This improves their ability to participate reliably in energy markets, potentially increasing revenue, reducing financial penalties, and making renewable projects more attractive to investors.\n\n**3. Energy Storage Providers:** The predictive control system can optimize the charging and discharging cycles of battery energy storage systems (BESS), maximizing their efficiency, extending their lifespan, and enhancing their value proposition within the grid.\n\n**4. Smart Grid Technology Providers:** Companies developing smart grid hardware and software will find this innovation to be a crucial component for building more intelligent, adaptive, and resilient energy networks. It provides a blueprint for integrating AI into critical energy infrastructure.\n\n**5. Environmental and Sustainability Sectors:** By making renewable energy more reliable and cost-effective, the patent directly contributes to accelerating decarbonization efforts, reducing greenhouse gas emissions, and achieving global climate goals. Its impact will be felt across the entire sustainable energy ecosystem, driving forward the transition to a greener planet.","question":"What industries will Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source impact?"},{"answer":"The patent titled \"Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source\" (US-9853592) was officially filed on **December 3, 2013**. Following the examination process by the patent office, it was subsequently published (or granted) on **December 26, 2017**.\n\nThe period between the filing date and the publication date allows for thorough review, potential amendments, and public disclosure of the invention. This timeline reflects the rigorous process involved in securing intellectual property rights for complex technological innovations. The publication of this patent marks its official entry into the public domain as a granted invention, signaling its recognized novelty and utility in the field of renewable energy control.\n\nThis date is significant as it establishes the priority date for the invention and the beginning of its enforceable patent term, providing legal protection for the methods and devices described within.","question":"When was Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source filed/granted?"},{"answer":"The commercial applications for the Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source are extensive and impactful across the energy sector:\n\n**1. Large-Scale Renewable Energy Farms:** This technology can be integrated into solar, wind, and hybrid renewable power plants to optimize their output, reduce intermittency, and ensure a more stable power feed to the national grid. This enhances the operational efficiency and profitability of these assets.\n\n**2. Utility-Scale Grid Management:** Utilities can deploy this system to gain superior control over their entire energy portfolio, especially as renewable penetration increases. It aids in balancing supply and demand, managing grid congestion, and reducing reliance on costly ancillary services and fossil fuel peaker plants.\n\n**3. Microgrids and Distributed Energy Resources (DERs):** For microgrids, which often rely heavily on local renewable generation, this patent offers a critical solution for maintaining self-sufficiency and stability. It enables more effective management of distributed assets like rooftop solar, small wind turbines, and community battery storage.\n\n**4. Energy Trading and Market Optimization:** Renewable energy producers can leverage the precise predictions to make more accurate bids in electricity markets, minimizing penalties for forecast errors and maximizing revenue during favorable market conditions. This introduces a new level of financial predictability to renewable energy.\n\n**5. Smart City and Industrial Energy Management:** Smart cities and large industrial complexes aiming for high renewable energy self-consumption can utilize this technology to optimize their local generation, storage, and consumption, driving down energy costs and carbon footprints. The Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source provides a foundational tool for these advanced energy management scenarios.","question":"What are the commercial applications of Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source?"},{"answer":"Looking ahead, several exciting future developments are expected to build upon the foundation laid by the Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source patent:\n\n**1. Enhanced AI and Predictive Capabilities:** As AI and machine learning algorithms continue to advance, the accuracy and granularity of energy yield predictions will improve. This could include incorporating more diverse data sources, such as satellite imagery, drone data, and even real-time atmospheric modeling, to achieve hyper-local and ultra-precise forecasts. Explainable AI (XAI) will also become crucial for building trust and transparency in autonomous control decisions.\n\n**2. Multi-Agent System Integration:** Future developments will likely involve the coordination of multiple such control devices across a wider grid or regional network. Instead of optimizing a single energy-generating system, these systems could communicate and cooperate to optimize energy flow and stability across an entire interconnected grid, creating a truly intelligent and self-healing power infrastructure.\n\n**3. Integration with Real-Time Energy Markets:** The predictive capabilities could be further integrated with real-time energy market dynamics, allowing the system to not only optimize for physical stability but also for economic efficiency, maximizing revenue for producers and minimizing costs for consumers in dynamic pricing environments.\n\n**4. Cyber-Physical Security Enhancements:** As these systems become more autonomous and critical, continuous development in robust cyber-physical security will be paramount to protect against potential cyber threats and ensure uninterrupted, secure operation. This includes resilient control protocols and anomaly detection systems.\n\n**5. Broader Application Scope:** Beyond traditional renewable sources, the principles of this predictive control could be extended to other intermittent or controllable energy assets, such as advanced geothermal systems, wave energy converters, or even large-scale demand-side response mechanisms, further solidifying its role as a cornerstone of future sustainable energy management. The Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source will continue to evolve as a key enabler for the global energy transition.","question":"What are the future developments expected for Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source?"}],"topics":["Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source","US-9853592","renewable energy control","AI energy management","energy yield prediction","global","imperative","transition"],"tech_cluster":null},"seo":{"title":"Renewable Energy Control - Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source - US-9853592","description":"Discover the Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source patent. AI-powered prediction stabilizes renewable energy yield, reducing weather-conditioned fluctuations for enhanced grid reliability.","keywords":["Method and Device for Controlling an Energy-generating System Which Can Be Operated with a Renewable Energy Source","US-9853592","renewable energy control","AI energy management","energy yield prediction","grid stability","sustainable energy","smart grid","weather fluctuations reduction","predictive control","clean energy patent","energy innovation","machine learning grid","renewable power optimization"]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9853592","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-9853592","citation_suggestion":"Patentable. \"Method and device for controlling an energy-generating system which can be operated with a renewable energy source\" (US-9853592). https://patentable.app/patents/US-9853592","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9853592","json":"https://patentable.app/api/llm-context/US-9853592","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-06-06T05:36:05.641Z"}