Digital Modeling for Energy System Optimization

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

🔋 Modeling Large-Scale Renewable Energy Plants🌍 With the rising share of solar and wind power, ensuring seamless grid integration is becoming more complex. How do we predict plant performance? Optimize design? Ensure grid stability? The answer lies in renewable energy (RE) modeling. 🌱 The Need for RE Plant Modeling Modeling plays a crucial role in: ✅ Planning & Design – Optimizing solar panel/wind turbine placement, inverter configurations ✅ Performance Prediction – Simulating real-world conditions for accurate energy yield forecasts ✅ Grid Stability – Ensuring system resilience with the right protection mechanisms ✅ Seamless Grid Integration – Making RE plants behave like traditional generators ☀️ Solar PV Power Plant Modeling: More Than Just Panels! A solar farm isn’t just about panels; it’s an ecosystem of inverters, transformers, storage, and control systems. But how do we model it? 🔹 Detailed Models – Every inverter, capacitor, and control loop is represented (used in EMT studies) 🔹 Averaged Models – Captures dominant dynamics for balanced simulation accuracy & speed 🔹 Generic Models – Simplified equivalent models for large-scale power system studies 🌬️ Wind Turbine Modeling: Understanding Grid Interaction Unlike solar, wind turbines operate at varying speeds. This requires precise control to extract maximum power and ensure stable grid interaction. There are two main types: 🔹 Type-3 (DFIG-Based) – Power flows from both the stator and rotor, allowing sub/super-synchronous speed operation 🔹 Type-4 (Full Converter) – No gearbox, wide speed range, all power flows through converters Since RE plants are massive, modeling every single inverter/turbine in detail is impractical. This is where equivalent models help. ⚡ How Do We Model Large-Scale RE Plants? To simplify simulations, we aggregate multiple units into a single equivalent plant model. There are three ways to simulate these: 1️⃣ Load-Flow (Steady-State) – For basic power planning 2️⃣ RMS Simulations – Captures dominant dynamic behavior 3️⃣ EMT Simulations – Required for weak grids & inverter-grid interactions But how do we ensure consistency across industry studies? Standardized models come to the rescue! 🏛️ Industry Standard Models: The Backbone of RE Modeling To ensure consistency across studies, global standards have been developed: 🔹 WECC Generic Models – Widely used for grid simulation studies 🔹 NERC & AEMO Guidelines – Setting best practices for inverter-based resources 🔹 EPRI & GE Models – Providing high-fidelity modeling approaches As renewable penetration increases, the importance of accurate modeling cannot be overstated. It’s not just about predicting energy generation—it’s about ensuring a stable, reliable, and resilient grid.

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.

I have spent nearly twenty years building energy system models. Continental-scale at granular spatial scales. Hourly (or finer) temporal resolution. Co-optimising generation, storage, transmission, distributed energy resources (DERs), and demand simultaneously. Thousands of scenarios. I have published in Nature Climate Change, Science and PNAS. My work has over 4,300 academic citations. Here is what I have learned: the tools most organisations still use to plan energy systems are not fit for the decisions ahead. Most capacity expansion models optimise generation only. They bolt on storage as an afterthought. They treat the transmission network as a copper plate or a simplified transport model. They run on annual energy balances, missing the hourly dynamics that determine whether the system actually works. They assume stable, predictable fuel prices. The last four weeks have demonstrated why every one of those assumptions is dangerous. When gas was £30/MWh, a model that ignored fuel price volatility produced a plausible answer. At £67/MWh and rising, with Ras Laffan physically destroyed, with the BoE pricing rate hikes instead of cuts, with the Ofgem cap headed for £2,000+, the same model produces an answer that could lead to billions in misallocated capital. What we actually need: models that co-optimise across the whole system (generation, storage, transmission, DERs, demand) at nodal or zonal resolution with sub-hourly dispatch, weather-synchronised across wind, solar, and demand, with stochastic fuel prices that reflect the world we actually live in. Where you build matters as much as what you build. A wind farm in northern Scotland connected to a constrained transmission corridor produces curtailed energy and consumer costs. The same wind farm sited where the grid has capacity produces revenue and system value. The UK is making decisions right now about grid investment, generation siting, storage deployment, and demand connections that will lock in infrastructure for decades. The grid queue reform, the Clean Power 2030 target, the SSEP, the data centre surge, the Hormuz shock. These are not separate problems. They are one system. The planning tools need to catch up with the reality. #EnergyModelling #EnergyTransition #UKEnergy #PowerSystems #CleanEnergy #RenewableEnergy #GridReform #EnergyPolicy #NetZero #EnergyStorage #CapacityExpansion #SystemPlanning

🔍 How can advanced modeling using #machinelearning support gas sweetening #energyefficiency? The gas #sweetening unit is the heart of any gas plant and refinery’s environmental performance. Published by Iran University of Science and Technology in Nature Magazine, working with over 1,227 days of operational data, this research team applied both Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs) to model and optimize energy consumption. The results were impressive. ⚙️ Modeling accuracy: why does it matter? RSM achieved an R² of 0.930, but the ANN models went further: - R² = 0.981 for Radial Basis Function (RBF) - R² = 0.986 for Multilayer Perceptron (MLP) The MLP model also posted the lowest error (0.002), making it the preferred tool for process optimization. 🌱 What does this mean in practice? By optimizing the input feed to 8.3 MMSCM, with 30.3% DEA amine and 12.7% MDEA, the model predicts fuel consumption can be reduced to 17,380.2 SCM—a saving of 12,710 SCM compared to baseline operation. This is not a theoretical claim: the MLP model’s predictions were validated in Aspen Technology HYSYS simulations, confirming its effectiveness for real-world energy reduction. 📊 Broader impact: Machine learning and deep learning algorithms have enabled refineries to reduce energy usage by up to 15% and boost production efficiency by ~10% (across reviewed studies). These improvements directly translate to lower carbon footprints and operational costs. Source: https://lnkd.in/ecyjvC5M #GasRefining #EnergyEfficiency #MachineLearning #ProcessOptimization

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

Engineers love details. But for Digital Twins, Maximum Detail is a trap. The biggest mistake I see? They try to model everything before they’ve defined the problem. They build a DT that is heavy, expensive, and slow - because they focused on graphical perfection instead of data relevance. Here is the engineering reality: The Purpose dictates the Level of Detail (LoD). If you don't define why you are building the DT, you cannot define what needs to be modeled. Here is how the top players strip away the noise: ❇️ The Automotive Approach (Tesla) They don't just scan the car. They simulate specific data sets: aerodynamics, motor performance, suspension, and body design materials. Goal: Predict performance before design. LoD: Functional data > Visual data. ❇️ The Manufacturing Approach (Siemens) Here, precision matters. The DT is linked to an inspection database including geometry interpretation and kinematic relations. Goal: Eliminate processing errors and improve throughput. LoD: High geometric fidelity to handle tolerances in the assembly line. ❇️ The Energy Approach (General Electric) This is the gold standard for ROI. GE created a "digital twin farm" for wind turbines. Did they model every bolt? No. They focused on the motor temperature and wind strength. The Result: Energy production increased by 20%. Generated $100 million in extra value over the farm's lifespan. The Engineering Takeaway? 🖐️ To implement a DT, you need a process to identify the appropriate complexity. Step 1: Imagine the opportunity (the problem). Step 2: Identify the configuration with the highest value. Step 3: Only then do you determine the LoD. If you are building a DT for facilities management, you don't need the same LoD as an aerospace engineer predicting fatigue failure. Start only modeling what matters! -------- Follow me for #digitaltwins Links in my profile Florian Huemer