Engineering Design Methods

🔍 Solar Heating System Modeling | Sustainability Note In renewable & energy landscape, agility matters. When it comes to designing or scaling solar heating systems, it's not just about estimating peak output, it's about understanding the impact of change. What happens if we tweak the inclination? Reduce the number of panels? Vary the sunlight hours? 💡 This is where mathematical modeling (Steady State and Dynamics) proves invaluable. Using dynamic models, we're able to simulate hundreds of sensitivity cases in minutes, adjusting factors like panel angle, solar irradiance, and operational hours to evaluate performance before physical implementation. Instead of static spreadsheets or trial-and-error decisions, we rely on data-backed simulations to: 1- Quantify power generation under different design scenarios 2- Optimize for cost, output, and footprint 3- Support investment decisions with confidence Whether it’s a 20-panel rooftop or a utility-scale field, modeling gives us the power to plan smarter and move faster. 🌞 Energy output in kW/m² isn’t just a number. It’s a decision driver. #Sustainability #AspenTech #AspenCustomModelr #SolarEnergy #DigitalEngineering #EnergyTransition #MathematicalModeling #SensitivityAnalysis #CleanTech #Simulation #ProcessOptimization #Sustainability #AspenTech #OptimizeXP #UAE #Emerson

Optimizing Energy Networks for a Sustainable Future My recent advancement in energy systems modeling—a high-performance Energy Network Optimization Model, built in #Julia using #JuMP and #HiGHS. This model integrates fossil generation, renewable sources, and battery storage to provide cost-effective, environmentally compliant, and highly reliable energy dispatch strategies. Key Highlights: High-Performance Optimization with Julia & JuMP: - Implemented using JuMP, a powerful algebraic modeling language for optimization. - Solved using HiGHS, an industry-leading solver known for its speed and efficiency in handling large-scale linear programming problems. - Julia’s computational speed and efficient memory handling make this model scalable for real-time market applications. Cost Minimization & Operational Efficiency: - The objective function minimizes total operational costs, balancing generation, start-up, and battery operation expenses for optimal market performance. Renewable Energy Integration & Curtailment Management: - The model maximizes clean energy penetration while effectively managing renewable curtailment to mitigate intermittency. Advanced Battery Storage Dynamics: - Explicit constraints model charging, discharging, and storage efficiency losses, enhancing grid flexibility. Emission Compliance: - Enforces emission cap constraints, ensuring regulatory compliance and supporting sustainability targets. Reliability Through Operational Constraints: - Incorporates demand balance, unit commitment, ramp rate limits, and spinning reserve requirements to maintain grid stability and resilience against unexpected demand fluctuations. Market Advantages: The model leverages mixed integer programming (MIP) for global optimality, ensuring transparent, scalable, and real-time deployable decision-making. Julia + JuMP dramatically improves computational efficiency, making it ideal for real-world energy markets, utility operators, and policymakers seeking cost savings and carbon reductions. Full project access, including source code, CI/CD pipelines, and detailed documentation, is available on my GitHub upon request: https://lnkd.in/eDC7VVHS Looking forward to engaging with industry experts on how this model can be adapted, extended, and applied in real-world energy systems. Let’s push the boundaries of smart, sustainable energy optimization! #EnergyOptimization #JuliaLang #JuMP #CleanEnergy #Sustainability #LinearProgramming #EnergyMarkets #SmartGrid #Innovation

AI-based modelling is becoming a practical tool for managing distributed energy networks. The report "Ask the Energy System: AI Assisted Energy Modelling" shows how a combination of machine learning, agent-based models and open data supports real-world low-voltage network planning. Key findings: • The growth of decentralised resources (DER, EVs, batteries) increases pressure on local networks, while current tools often lack the required resolution • Agent-based modelling helps reproduce interactions between local network elements and assess the impact of new connections on capacity and stability • Machine learning models forecast load and generation in 5-minute intervals with higher accuracy than classical statistical methods • LLM integration improves handling of incomplete or inconsistent data and enables interactive scenario analysis • Use of open time-series repositories and weather APIs improves reproducibility and independent validation of results • Open-source architectures enhance compatibility, transparency and reduce the cost of integrating new data sources and forecasting modules • Main application areas include network capacity assessment, EV charging planning and energy-storage siting The report concludes that building flexible and resilient energy systems depends on compatible and verifiable tools that combine data, models and engineering context within a single analytical environment. What limits wider use of AI in energy modelling? #EnergySystems #AIinEnergy #DataModelling #EnergyTransition #MachineLearning #SmartGrid #OpenSource #GridForecasting #EnergyAnalytics

We’re excited to share our latest research on a topic at the intersection of electrical engineering and building systems to support building to grid integration: 🔌💨 Coupling Induction Machines with HVAC Systems for Integrated Simulation and Control. In this work, we developed a Computationally Efficient and Accurate Induction Machine (CEAIM) model and integrated it with HVAC components like pumps, heat pumps, and chillers. This allows us to study how electrical behavior directly affects thermal and fluid performance—a key step in simulating grid-interactive, energy-efficient buildings. ✅ Validated with experimental data ✅ Up to 1,000× faster than existing induction machine models ✅ Achieved R² values between 0.98 and 1 for power, speed, and torque predictions The CEAIM model is implemented in #Modelica, enabling scalable, equation-based modeling. Our case study shows how this integrated approach can improve accuracy and reduce computational load—especially important for smart grid and load-shedding analyses. Led by SBS Lab Ph.D. student Viswanathan Ganesh, this is a joint effort of Penn State University, Berkeley Lab, Oak Ridge National Laboratory and National Renewable Energy Laboratory (Zhanwei He, Jianjun Hu and Sen Huang). Thanks to the Gordon D. Kissinger Graduate Research Fellowship for Viswanathan Ganesh, this paper is published as Open Access paper: https://lnkd.in/eXyWDEVC #HVAC #BuildingSimulation #SmartGrid #EnergyEfficiency #ElectricalEngineering #IntegratedModeling