[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123970-en":3,"doc-seo-123970-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123970,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Certification of MPC-based zonal controller security properties using accuracy-aware machine learning","Fast growth of renewable energy increases the risk of power congestion, motivating the French Transmission System Operator (RTE) to deploy closed-loop, zone-level controllers for congestion management. The goal is to estimate the probability that a controller preserves equipment safety by avoiding overloaded lines across many renewable production scenarios. Brute-force simulation is theoretically convergent but computationally prohibitive. A faster proxy-based method is introduced with accuracy-aware multivariate Gaussian processes and an adapted central limit theorem to incorporate proxy-induced uncertainty into confidence intervals, achieving significant time gains on a small network.","arXiv :2404 .07275v1 [ stat .AP] 10 Apr 2024  \nCertification of MPC-based zonal controller security properties using accuracy-aware machine learning  \nproxies  \nPierre HOUDOUIN, Manuel RUIZ, Lucas SALUDJIAN, Patrick PANCIATICI  \nFrench Transmission System Operator, RTE, Paris, France  \n{pierre.houdouin, manuel.ruiz, [patrick.panciatici](patrick.panciatici}@rte-france.com)[}](patrick.panciatici}@rte-france.com)[@rte-france.com](patrick.panciatici}@rte-france.com)  \nAbstract—The fast growth of renewable energies increases the power congestion risk. To address this issue, the French Transmission System Operator (RTE) has developed closed-loop controllers to handle congestion. RTE wishes to estimate the probability that the controllers ensure the equipment’s safety to guarantee their proper functioning. The naive approach to estimating this probability relies on simulating many randomly drawn scenarios and then using all the outcomes to build a confidence interval around the probability. Although theory ensures convergence, the computational cost of power system simulations makes such a process intractable.  \nThe present paper aims to propose a faster process using machine-learning-based proxies. The amount of required simulations is significantly reduced thanks to an accuracy-aware proxy built with Multivariate Gaussian Processes. However, using a proxy instead of the simulator adds uncertainty to the outcomes. An adaptation of the Central Limit Theorem is thus proposed to include the uncertainty of the outcomes predicted with the proxy into the confidence interval. As a case study, we designed a simple simulator that was tested on a small network. Results show that the proxy learns to approximate the simulator’s answer accurately, allowing a significant time gain for the machinelearning-based process.  \nIndex Terms—Certification of security properties, Congestion management, Multivariate Gaussian processes, NAZA, Proxies  \nI. INTRODUCTION  \nIntegrating renewable energies on a large scale poses challenges in the operation and management of power systems. It leads to unpredictable and variable flow injections into transmission lines, increasing the risk of power congestion. To address these challenges, the French Transmission System Operator (TSO), RTE, has adopted a decentralized management approach, dividing the entire system into sub-transmission areas (zones) . Real-time constraints within each zone are managed through a local closed-loop controller called NAZA [1], [2], [3] . Designed to handle local problems with local actions, it enables the management of battery devices, topological modifications, and curtailment of renewable production [4] inside the zone. Alongside the massive deployment of these controllers across the network, RTE aims to obtain guarantees regarding their proper functioning. Given a set of renewable power production scenarios, RTE seeks to compute  \nSubmitted to the 23nd Power Systems Computation Conference (PSCC 2024) .  \nthe probability of NAZA ensuring the equipment’s safety, referred to as psafe thereafter. Equipment’s safety is ensured if congestions are avoided during the scenario simulation. If any line in the zone becomes overloaded during the simulation, it is considered a security threat.  \nThe estimation of psafe relies on the law of large numbers. The most basic approach is the brute-force process: scenarios are randomly drawn and simulated until psafe is accurately estimated. Central Limit Theorem (CLT) [5] ensures the convergence of this process. The more iterations are performed, the more accurate the estimation becomes [6]. In our case, only extreme and thus rare-to-observe scenarios will likely pose a security threat. A large number of scenarios must, therefore, be drawn to observe enough threatening situations and obtain a reliable estimation of the probability. Although it theoretically works, the drawback of the brute-force process is that it requires a lot of simulations. As power s","cbCaimP3fi9Tz3FZ","https://ap.wps.com/l/cbCaimP3fi9Tz3FZ","pdf",504805,1,10,"English","en",105,"# Introduction\n## Motivation: congestion risk from renewable integration\n## Zone-level decentralized control (NAZA)\n## Safety probability estimation (psafe)\n## Limits of brute-force simulation\n## Proxy-based acceleration and related work","[{\"question\":\"What does the paper aim to certify about NAZA controllers?\",\"answer\":\"It targets the probability that NAZA ensures equipment safety, defined as congestion avoidance during simulated renewable scenarios.\"},{\"question\":\"Why is the brute-force method for estimating psafe computationally difficult?\",\"answer\":\"Because security threats come from rare, extreme scenarios, requiring a large number of costly power-system simulations to estimate the probability reliably.\"},{\"question\":\"How does the proposed approach speed up psafe estimation?\",\"answer\":\"It replaces many simulator evaluations with an accuracy-aware multivariate Gaussian process proxy, then uses an adapted central limit theorem to reflect proxy prediction uncertainty in the resulting confidence interval.\"}]","Certification of MPC-based zonal controller security properties using accuracy-aware machine learning | 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does the paper aim to certify about NAZA controllers?","Question",{"text":75,"@type":76},"It targets the probability that NAZA ensures equipment safety, defined as congestion avoidance during simulated renewable scenarios.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is the brute-force method for estimating psafe computationally difficult?",{"text":80,"@type":76},"Because security threats come from rare, extreme scenarios, requiring a large number of costly power-system simulations to estimate the probability reliably.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed approach speed up psafe estimation?",{"text":84,"@type":76},"It replaces many simulator evaluations with an accuracy-aware multivariate Gaussian process proxy, then uses an adapted central limit theorem to reflect proxy prediction uncertainty in the resulting confidence 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