Systems Engineering Integration Techniques

Human Factors is having a surprisingly active summer at FDA. In May, FDA finalized its guidance on the Human Factors information expected in medical device marketing submissions. Now, it has revised its core Applying Human Factors and Usability Engineering to Medical Devices guidance for the first time since 2016. One thing to be clear about up front. The original 2016 document was already final guidance. The new version, published August 2026, is a targeted revision, not a finalization of a draft from ten years ago. The most significant update is the explicit connection to the Quality Management System Regulation and ISO 13485:2016. The guidance now states that design and development inputs must pull in applicable outputs from risk management activities and usability requirements linked to the intended use of the device. This does not suddenly make Human Factors mandatory. Human Factors has always been embedded in FDA’s expectations for both design control and risk management. The change is that FDA is making it harder to treat Human Factors as just a late-stage validation step before submission. Human Factors outputs are supposed to flow into design inputs, risk controls, design validation, and the design and development file. FDA has also expanded and updated definitions in the document. New terms include normal use, residual risk, serious harm, use environment, and use-related risk analysis. FDA has based several of these on IEC 62366-1:2015+A1:2020 and ISO 14971:2019, which means the terminology is now much closer to what most manufacturers see in international standards. The HFE/UE report outline has been removed from this document. Reporting expectations now live under the separate Content of Human Factors Information in Medical Device Marketing Submissions guidance. The revised Applying Human Factors guidance is now focused on the process of Human Factors work in development, not the final report structure. My main view: FDA has not rewritten the Human Factors engineering process. What has changed is the underlying regulatory structure, QMS terminology, definitions from the standards, and separation of evidence requirements for submissions. This alignment should make it easier for companies to manage Human Factors work as part of their core development and risk management activities, not just as a pre-submission hurdle. #humanfactors #medtech #fda #medicaldevices

The energy transition is in full swing. But what happens when the wind doesn’t blow and the sun doesn’t shine? Germany aims for a nearly climate-neutral electricity supply by 2035. Political initiatives like the Renewable Energy Act (EEG) and the EU Green Deal are accelerating this shift, pushing for greater integration of renewables. To achieve this, integrating renewable energy sources isn’t enough—we need efficient ways to store energy. 🔋⚡ That’s where Battery Energy Storage Systems (BESS) come in. A recent study by the Technical University of Munich found that BESS can compensate for up to 80% of energy production fluctuations. This makes them a game changer for grid stability and energy security. By providing short-term (daily) storage, BESS helps balance grid fluctuations in real-time, ensuring that energy is available exactly when it’s needed. I see it firsthand in conversations with our partners: manufacturers looking for ways to stabilize their energy supply, municipalities trying to make the most of their solar power, or businesses facing rising electricity costs. They all have the same challenge: How can we store energy efficiently and use it exactly when we need it? The answer lies in intelligent battery storage, and we are helping to turn this potential into real-world solutions. Why does this matter? → Storing energy efficiently lowers costs for businesses and households. → When production fluctuates, battery storage ensures energy is still available—whether for a factory in full operation or a hospital that can’t afford downtime. → The more renewable energy we store, the less we rely on fossil fuels. → Battery storage adapts to different needs, from factories to family homes. Looking ahead, Power-to-X (P2X) technologies will play an important role in complementing battery storage. While BESS ensures stability in the short term, P2X can provide long-term energy storage by converting surplus renewable energy into hydrogen, synthetic fuels, or other energy carriers. This enables seasonal storage and supports industries with high energy demands, further strengthening the resilience of our energy system. ❓How do you see the role of energy storage in the transition to a climate-neutral future? Let me know in the comments below or let’s talk at Hannover Messe 2025—because the time for sustainable energy storage is now. #EnergyTransition #BatteryStorage #Sustainability #Innovation

Transformation thrives when people are empowered to make the most of technology. 🚀 My recent visit to the Bosch production facility for automotive and eBike drives in Miskolc, Hungary, showcased this perfectly. I was deeply impressed to see firsthand how their progress in digitalization and the implementation of the Bosch Manufacturing and Logistics Platform (BMLP) is reshaping their manufacturing operations. BMLP is a globally standardized, open IT platform that connects all stages of production and logistics. During an insightful plant tour, I observed a successful example of how the platform leads to significant improvements in efficiency, quality, and data transparency across the plant. What stood out most was seeing the passionate and enthusiastic team at Miskolc leverage this technology in action and achieving great results towards operational excellence. Here are three key areas where BMLP is contributing to the plant’s digital transformation success, powered by our NEXEED IAS: 1️⃣ Enhanced Efficiency & Reduced Downtime: The module Shopfloor Management enables a closed PDCA cycle in production by consequent integration of all relevant information in one system. This leads to quick reaction in case of deviations to minimize downtimes and safeguard the daily performance targets.   2️⃣ Improved Product Quality: Continuous monitoring throughout production stages helps the team identify issues early, ensuring top-tier quality while driving process improvements.   3️⃣ Change Management: Change management plays a crucial role in digital transformation within a plant. As seen in Miskolc, effectively managing change ensures that the workforce is engaged, and equipped to embrace new technologies, driving sustainable success. In Miskolc we have seen solutions using gamification that help to involve all associates, making the transition both engaging and effective.   I was also excited to see AI in action with a live demo of 8D Analysis using GenAI, cutting failure analysis time by half. By automating the root cause analysis process, engineers are now spending less time on administrative tasks and more on proactive problem-solving – a great example of how technology empowers people. Beyond the production lines, the most rewarding part of the visit was engaging with the team. Their passion for digitalization, commitment to upskilling, and their drive for innovation truly brought home the message: technology is only as strong as the people behind it. A special thank you to the entire Miskolc team for the inspiring discussions and warm welcome – along with Volker Schilling, Klaus Maeder, Joerg Klingler, Volker Schiek, Norbert Jung, Stephan Brand, Aemen Bouafif, and everyone who joined us on this great trip. I’m excited to see what’s next on this incredible digitalization journey!

🔋 The 1,000 MW/6,000 MWh electrochemical energy storage project in Inner Mongolia commenced construction in June 2025. This project is one of the largest power-side electrochemical energy storage projects worldwide, using advanced lithium iron phosphate technology and integrating power conversion, boosting systems, and an energy management system. It is designed for multiple functions, including independent participation in grid frequency regulation, peak shaving, electricity market transactions, and capacity compensation. This solution is expected to provide an annual peak shaving capacity of 2.16 billion kWh, significantly reducing wind and solar curtailment, enhancing grid stability, and helping Inner Mongolia reach over 50% new energy installed capacity by 2025. The project highlights the global need for such solutions, with US$1.2 trillion in BESS investments needed to support over 5,900 GW of new wind and solar capacity by 2034. The worldwide BESS capacity is projected to triple by 2035.    🔦 A crucial part of this evolution is the Grid-Forming (GFM) control, which is proving vital for integrating increasing renewable energy capacities and strengthening grid stability. Unlike traditional grid-following (GFL) systems that merely respond to grid conditions, GFM BESS can actively establish and maintain grid stability, bridging the gap between abundant renewable energy and strict grid requirements. This ability is essential, especially in regions like Asia-Pacific, where variable renewable energy can constitute between 46% and 92% of peak demand. As shown in the figure, GFM BESS provides key functionalities, including independent voltage source capabilities, support for high current transients during disturbances, inertia response similar to conventional power plants, and black start functions for full system recovery after outages. Although GFM features add an estimated 15% to overall system costs, mainly due to upgraded inverters, controls, and software, this is increasingly manageable as battery prices continue to fall. #battery #energystorage #gridmodernization #efficiency #powerelectronics #cleanenergy

In most regions, renewables paired with batteries have recently become the fastest and least disruptive solution to meet rising electricity demand while maintaining affordability. Utility-scale solar can be financed and built quickly, with grid-scale batteries completed in just 100 days — while new gas plants often take five years amid soaring turbine prices and limited EPC bandwidth. The pace of deployment reflects this speed advantage. The U.S. added an estimated ~50GW of solar plus storage in 2024 ― nearly 2X the prior year — accounting for 81% of all generating capacity. Globally, the world installed ~450GW of solar PV representing 72% of new capacity additions, as well as 205 GWh of battery storage. Batteries are no longer niche; paired with solar, they already undercut gas peakers in sun-rich markets while providing speed to market. Grid-enhancing technologies (GETs) unlock hidden capacity on existing wires — facilitating new construction and better asset utilization of existing generation on constrained grids. The U.S. Department of Energy’s Innovative Grid Deployment Liftoff Report shows that commercially available GETs — such as advanced conductors, dynamic line rating (DLR), power flow controllers, and distribution automation — can unlock 20-100 GW of peak transmission capacity using existing infrastructure. These upgrades deploy quickly at a fraction of new transmission costs. Similar initiatives are underway globally: India’s Green Energy Corridors and Brazil’s ISA CTEEP are deploying GETs to alleviate congestion and accelerate more clean energy integration. At the same time, demand flexibility offers an invisible but key pillar of affordable, resilient clean energy systems. Virtual Power Plants (VPPs) aggregate flexible industrial demand, smart thermostats, electric vehicle chargers, and behind-the-meter batteries into coordinated fleets that act like a dispatchable power plant. According to the DOE VPP Liftoff Report, we can have 20% of peak loads in the U.S. be dispatchable by 2030, avoiding up to $10 billion per year in transmission and distribution infrastructure costs. Because VPPs are software-enabled, they can scale in months, not years, and their capital is largely private — households and businesses buying devices with advanced features they already want. Firming resources are critical compliments to renewables build outs—for high-capacity factor electricity consumers such as data centers. Natural gas will be a part of this mix over the decades to come and addressing methane emissions must be a global priority. Furthermore, clean firm power sources such as advanced nuclear, enhanced geothermal systems (EGS), long-duration energy storage (LDES), and pumped hydro can play an increasingly important role in balancing grids as variable renewables increase their share. - Jonah Wagner https://lnkd.in/eVN2BBYA

A Comprehensive HVDC Power Electronics System in Simulink: A Milestone in Innovation This project presents an advanced High Voltage Direct Current (HVDC) system modeled in Simulink, integrating diverse power electronics components and renewable energy sources into a unified setup. This unique system is a pioneering effort in simulation and modeling, designed to highlight cutting-edge energy transmission and integration techniques. Below is a detailed breakdown of the system and its components. 1. HVDC System Overview Voltage and Distance: The system operates at 230 kV DC and spans a transmission distance of 100 km, enabling high-efficiency long-distance power transfer. Power Transmission: It is designed to transfer a total of 50 MW of power between two Voltage Source Converter (VSC) stations. Grid Integration: The system is connected to an AC grid operating at 220 kV, 50 Hz, with a transformer rated at 220/110 kV to match the transmission voltage. 2. Photovoltaic (PV) Arrays Capacity: The system integrates two 1 MW PV arrays, contributing clean solar energy to the grid. Control Strategy: Each PV array is equipped with Maximum Power Point Tracking (MPPT) controllers to optimize energy harvesting under varying solar irradiance conditions. 3. Wind Energy Integration Wind Turbine: A wind turbine rated at 10 kW is included to supplement the system’s renewable energy input. Boost Converter with MPPT: A boost converter is employed alongside MPPT algorithms to ensure maximum power extraction from the wind turbine under fluctuating wind speeds. 4. Energy Storage System Z-Source Inverter: The system features a Z-source inverter integrated with storage elements, providing robust and reliable energy storage and transfer. Boost Inverter: A boost inverter is included to enhance the storage system’s performance and support the grid during peak demand or renewable energy fluctuations. 5. Key Features and Advantages Modularity: Each component is modularly designed, enabling easy expansion and testing of additional renewable sources or advanced control strategies. Efficiency: The combination of HVDC, advanced inverters, and MPPT controllers maximizes overall system efficiency. Innovation: This is the first published system of its kind to integrate such diverse components, making it a benchmark in power electronics simulation. Conclusion This comprehensive HVDC power electronics system in Simulink serves as a cutting-edge example of modern energy systems. Its ability to integrate solar, wind, and storage solutions into a unified, high-efficiency setup positions it as a vital step toward sustainable and reliable energy solutions. 💡 If you are interested in contributing to scientific publications, sharing insights, or exploring practical applications of this system, feel free to reach out directly. Let’s work together to advance the field and achieve impactful results.

🚀 𝐍𝐞𝐰 𝐏𝐮𝐛𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧! 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐂𝐑𝐀 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐈𝐨𝐓 𝐋𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞: 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬, 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬, 𝐚𝐧𝐝 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 Proud to share our newest peer-reviewed article in Information (MDPI), co-authored with Miguel Ángel Ortega Velázquez, Iris Cuevas Martinez, and Dr. Antonio J. Jara (myself as ISACA CISM/CISA/AAIA). 𝘛𝘩𝘪𝘴 𝘸𝘰𝘳𝘬 𝘢𝘳𝘳𝘪𝘷𝘦𝘴 𝘢𝘵 𝘢 𝘤𝘳𝘶𝘤𝘪𝘢𝘭 𝘮𝘰𝘮𝘦𝘯𝘵, 𝘢𝘴 𝘵𝘩𝘦 𝘌𝘜 𝘊𝘺𝘣𝘦𝘳 𝘙𝘦𝘴𝘪𝘭𝘪𝘦𝘯𝘤𝘦 𝘈𝘤𝘵 (𝘊𝘙𝘈) 𝘣𝘦𝘤𝘰𝘮𝘦𝘴 𝘵𝘩𝘦 𝘮𝘰𝘴𝘵 𝘪𝘮𝘱𝘢𝘤𝘵𝘧𝘶𝘭 𝘳𝘦𝘨𝘶𝘭𝘢𝘵𝘪𝘰𝘯 𝘧𝘰𝘳 𝘐𝘰𝘛 𝘮𝘢𝘯𝘶𝘧𝘢𝘤𝘵𝘶𝘳𝘦𝘳𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘪𝘯𝘨 𝘺𝘦𝘢𝘳𝘴. 🔥 𝐓𝐨𝐩 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 1️⃣ 𝐀 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐦𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐲 𝐭𝐨 𝐜𝐨𝐧𝐯𝐞𝐫𝐭 𝐥𝐞𝐠𝐚𝐥 𝐂𝐑𝐀 𝐭𝐞𝐱𝐭 𝐢𝐧𝐭𝐨 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐫𝐞𝐚𝐥𝐢𝐭𝐲: We introduce a two-phase framework: • Phase 1: Systematically transform CRA Articles 13–14 and Annexes into atomic, testable engineering requirements. • Phase 2: Apply Analytic Hierarchy Process (AHP) quantitative scoring to produce a defensible readiness metric. 2️⃣ 𝐀 𝐟𝐮𝐥𝐥 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞-𝐛𝐚𝐬𝐞𝐝 𝐂𝐑𝐀 𝐜𝐡𝐞𝐜𝐤𝐥𝐢𝐬𝐭 𝐟𝐨𝐫 𝐈𝐨𝐓 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐬: From secure design to post-market obligations, the paper provides an actionable DevSecOps-aligned checklist. 3️⃣ 𝐀 𝐝𝐞𝐟𝐞𝐧𝐬𝐢𝐛𝐥𝐞 𝐫𝐢𝐬𝐤-𝐛𝐚𝐬𝐞𝐝 𝐰𝐞𝐢𝐠𝐡𝐭𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥 𝐮𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐲 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 (𝐀𝐇𝐏): We derive consistent domain weights, ensuring mathematically validated prioritization of CRA domains. 4️⃣ 𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 through the TRUEDATA project funded by INCIBE - Instituto Nacional de Ciberseguridad: We applied the full model to a large industrial OT cybersecurity project (water infrastructure) with Neoradix Solutions AirTrace Bersey UCAM Universidad Católica San Antonio de Murcia at the pilots with the support of the Confederación Hidrográfica del Segura, O.A., Mancomunidad De Los Canales De Taibilla, and FRANCISCO ARAGÓN. 5️⃣ 𝐂𝐥𝐞𝐚𝐫 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐠𝐮𝐢𝐝𝐚𝐧𝐜𝐞. The paper provides best practices for SBOM automation, PSIRT & CVD setup, Secure-by-design, OTA, monitoring, attestation, documentation and conformity assessment Our aim from Libelium with this paper is to give the industry a practical, structured, and evidence-based way to operationalize compliance and strengthen cybersecurity by design. 𝐓𝐑𝐔𝐄𝐃𝐀𝐓𝐀 𝐝𝐞𝐦𝐨𝐧𝐬𝐭𝐫𝐚𝐭𝐞𝐬 𝐡𝐨𝐰 𝐭𝐡𝐞 𝐦𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐲 𝐚𝐩𝐩𝐥𝐢𝐞𝐬 𝐭𝐨 𝐡𝐢𝐠𝐡-𝐬𝐭𝐚𝐤𝐞𝐬 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 𝐬𝐲𝐬𝐭𝐞𝐦𝐬. 𝐓𝐡𝐞 𝐂𝐑𝐀 𝐢𝐬 𝐧𝐨𝐭 “𝐣𝐮𝐬𝐭 𝐚𝐧𝐨𝐭𝐡𝐞𝐫 𝐫𝐞𝐠𝐮𝐥𝐚𝐭𝐢𝐨𝐧”, 𝐢𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐧𝐞𝐰 𝐛𝐚𝐬𝐞𝐥𝐢𝐧𝐞 𝐟𝐨𝐫 𝐈𝐨𝐓 𝐭𝐫𝐮𝐬𝐭 𝐢𝐧 𝐄𝐮𝐫𝐨𝐩𝐞. 👉 Download here: https://lnkd.in/dQu54qE2 European Union Agency for Cybersecurity (ENISA) Felix A. Barrio (PhD, CISM) Global Cybersecurity Forum SITE سايت Betania Allo Axon Partners Group ISACA ISACA VALENCIA

𝗦𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗮𝗻 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵? 𝗛𝗲𝗿𝗲’𝘀 𝗠𝘆 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 Industrial operations run our daily lives—think metro trains, water systems, power grids, even the checkout at your supermarket. All of this is powered by Operational Technology (OT), which directly impacts physical processes and public safety. But OT systems are under attack more than ever. Many still run on 20-year-old software, are tough to update, and can’t just be “patched” like regular IT systems. Real-world consequences can be huge: from power outages to critical failures in hospitals and transport. So, where do you even begin with OT security? Here’s my take (as discussed with Prabh in his latest podcast): 1. Understand What You Have: Start with an asset inventory. Visibility is everything. You can’t protect what you don’t know exists. 2. Identify Risks: Figure out what could go wrong. Every asset, old or new, has its own risks—especially those running legacy software. 3. Involve Your Operations Team: OT staff are focused on keeping the plant running. Bring them into the conversation from Day 1. Awareness and buy-in are key. 4. Tailor Your Approach: There’s no copy-paste. Every factory, plant, or substation is unique. Build processes that fit your environment, not just what the textbook says. 5. Prioritize the Basics: ✏️ Incident response plans: Who does what when things go wrong? ✏️ Control remote access: Limit those USB sticks, dongles, and remote sessions. ✏️ Access control: Don’t give everyone full admin rights. ✏️ Network segmentation: Create “islands” to limit the spread if something goes wrong. ✏️ Training: Make cybersecurity real for your OT staff. One weak link can break everything. 6. Use the Right Frameworks: IEC 62443 is a great start, covering people, process, and technology. Pair it with industry guidance like NIST 800-82. 7. Continuous Improvement: Cybersecurity isn’t a one-off project. Monitor, learn, and adapt. OT threats evolve—your defenses should too. Why does all this matter? Because OT is critical. Downtime isn’t just about lost money—it can risk lives. And with more cyber threats targeting OT, our collective vigilance matters now more than ever. I’ve built the OT Security Huddle community for this reason: to share, discuss, and solve real OT security problems together. Whether you’re just getting started or deep into your journey, you’re not alone. Watch my full conversation with Prabh Nair for all the details—link below! https://lnkd.in/gjYCnt7j #OTSecurity #Cybersecurity #IEC62443 #CriticalInfrastructure #IndustrialSecurity

Last semester, I taught a graduate-level computer architecture class in which we read many accelerator design papers. After a dozen or so papers, it became clear to us that computer architects are not fully taking advantage of the optimization opportunities for accelerator memory system design. Please consider the following... Unlike CPUs, which strive to run anything well, accelerators typically only run a few specific kernels, allowing their memory system (and memory system interactions) to be significantly specialized and optimized. Below, I've made a design matrix that highlights some new opportunities for accelerator memory system design. When designing your accelerator memory system, ask these two additional design questions: 1) Is my address stream INPUT-DEPENDENT or INPUT-OBLIVIOUS? If input-OBLIVIOUS (as many kernels are), then your memory system design and interactions should be VERY SIMPLE, since you can anticipate addresses as early as needed, including in the compiler, allowing effective use of compiler prefetch. If addresses are input-dependent, then you may need to add speculative prefetch, caching, and cache coherence. 2) Is the data I am accessing DENSE or SPARSE? If dense, then invest in scratchpads and caches; otherwise, attempt to utilize blocking, expose parallelism in the memory system, and build a high-BW memory system. The more interesting design points arise when considering both of these questions in tandem: For example, if you are building a deterministic unpruned DNN inference accelerator (with dense data and input-oblivious addresses), you are locked into "easy design mode", so focus on compiler prefetching into scratchpad memories. (If you are adding a cache to this accelerator, you should ask yourself this question: "Why?" 😎) If you are building an accelerator for kNN search over a large dataset (with sparse data and input-dependent addresses), you are locked into "hard design mode", so go crazy and invest in caches, high B/W memory interfaces, parallel memory requests, speculative prefetch, and whatever other (effective) cleverness you can conjure. If you are up for a hardware-software co-design challenge, ask yourself this question: Can I move my accelerator kernel in the direction of a more simple (and likely more efficient) design by making its addresses input-oblivious and/or its data more dense? If you understand your kernel well, the answer may be "yes". Do these design considerations ring true for your designs? What other considerations should accelerator designers ponder? #computerarchitecture #memory #accelerators

Publicly Accessible Energy Storage Systems (ESS) Simulation Price-taker models are suitable for small-scale ESS as their capacity does not influence market prices or system dispatch. This post highlights DOE price-taker valuation tools. 🟦 1) QuESt  QuESt is a free, open-source Python application suite for energy storage simulation and analysis, developed at Sandia National Laboratories. It includes three interconnected applications:  1- QuESt Data Manager,  2-QuESt Valuation, and  3-QuESt BTM, Eligible technologies include BESS (Li-ion, advanced lead-acid, vanadium redox), flywheels, and PV, using a shared model for different BESS and flywheel types based on their parameters. 🟦 2) Renewable Energy Integration and Optimization (REoptTM)  The REopt™ platform, developed by the National Renewable Energy Laboratory (NREL), optimizes energy systems for various applications, recommending the best mix of renewable energy, conventional generation, and energy storage to achieve cost savings, resilience, and performance goals. Eligible technologies include: PV, wind, CHP, electric and thermal energy storage, absorption chillers, and existing heating and cooling systems. 🟦 3) Distributed Energy Resources Customer Adoption Model (DER-CAM)  DER-CAM is a decision support tool from Lawrence Berkeley National Laboratory (LBNL) designed to optimize DER investments for buildings and multienergy microgrids. Eligible technologies include conventional generators, CHP units, wind and solar PV, solar thermal, batteries, electric vehicles, thermal storage, heat pumps, and central heating and cooling systems. 🟦 4) System Advisor Model (SAM) SAM is a techno-economic computer model that evaluates the performance and financial viability of renewable energy projects. It includes performance models for various systems such as PV (with optional battery storage), concentrating solar power, solar water heating, wind, geothermal, and biomass, and a generic model for comparison with conventional systems. Eligible technology types focus on electrochemical ESS, supporting lead-acid, Li-ion, vanadium redox flow, and all iron flow batteries. Users can also model custom battery types by specifying their voltage, current, and capacity. SAM offers detailed modelling of battery cells, power converters, and factors like degradation, voltage variation, and thermal properties. 🟦 5) Energy Storage Evaluation Tool (ESETTM) ESETTM is a suite of modules developed at PNNL that allows utilities, regulators, and researchers to model and evaluate various ESSs. ESETTM features a modular design for ease of use and currently includes five modules for different ESS types, such as BESSs, pumped-storage hydropower, hydrogen energy storage, storage-enabled microgrids, and virtual batteries. Some applications also include distributed generators and photovoltaics (PV). Source: see post image. Link to the modellers: in the comment section This post is for educational purposes only.