Energy Systems Modeling

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

Gas networks can provide flexibility to power systems by leveraging the stored gas in pipelines (linepack) as a form of short-term storage. While this flexibility could be very helpful to power systems, it is important not to “overestimate” its potential. The integration of power and gas systems requires accurately modeling the gas flow physics governed by partial differential equations (PDEs), necessitating careful decisions regarding both “modeling” and “solution” approaches. Our recent article, accepted for publication in the IEEE Transactions on Power Systems, harmonizes existing modeling and solution techniques, demonstrates their derivation from and application to the PDEs, and explores how certain choices can lead to an overestimation of flexibility. IEEE: https://lnkd.in/dUSZHmTC arXiv: https://lnkd.in/dHmpUhtw You may also watch the video recording of Enrica’s PhD defense from minute 27:49 onwards, where she discusses this study: https://lnkd.in/db_EuDEN Enrica Raheli, Yannick Werner

Excited to share our new review article, which synthesizes evidence across disciplines to understand the #psychological and #contextual determinants of household clean #energy adoption. 🏠 Our review focuses on high-impact technologies with major climate change mitigation potential: • Electric vehicles • Residential solar PV systems • Heat pumps • Battery storage systems Despite technical progress and policy support, adoption rates remain lower than expected in most climate scenarios. Our review discusses why and what can be done to accelerate uptake. 🔎 Some takeaways from the review: 1️⃣ Psychological and contextual factors jointly shape adoption decisions. Decisions to adopt EVs, PV systems, heat pumps, and battery storage are influenced by cognitive biases (e.g., loss aversion, temporal discounting), motivational factors (e.g., values, worldviews, identity), and social influences (e.g., norms, peer behavior, symbolic meaning). These operate alongside and interact with structural conditions, such as income, infrastructure, and policy. 2️⃣ Consumer behavior deviates systematically from techno-economic assumptions. Standard energy models often assume rational utility maximization. However, real-world adoption is more complex and influenced by a wealth of factors, including bounded rationality, information misperceptions, and emotional responses—all of which can distort cost–benefit assessments and delay adoption, even when technologies are financially advantageous. 3️⃣ Contextual heterogeneity is critical but often under-addressed. Socio-demographic characteristics, geographic setting, institutional design, and cultural values significantly impact the likelihood of adoption and the effectiveness of interventions. Yet, much of the empirical evidence remains concentrated in high-income contexts. Broader cross-cultural and field-based studies are urgently needed. 4️⃣ Effective interventions must be both targeted and integrated. Standalone approaches—whether economic (e.g., subsidies) or behavioral (e.g., social norm messaging)—often fall short because they address only a subset of the barriers to adoption. We need intervention portfolios that are strategically matched to specific psychological and contextual determinants. Read-only (free) link: https://rdcu.be/epJUS Published version (paywalled): https://lnkd.in/dKSJZABs (I'm happy to share the published version - just message me.) Huge thanks to Anne Günther and Lukas Engel for leading this collaborative effort and to co-authors Matthew Hornsey, Joyashree Roy, Linda Steg, Kim-Pong Tam, Anne van Valkengoed, Kim Wolske, Gabrielle Wong-Parodi, and Ulf Hahnel! Copenhagen Business School University of Basel Environmental Psychology Groningen Centre for Sustainability International Energy Agency (IEA) #climatechange #behaviorchange

Over the past decade distributed energy resources or DERs have continued to proliferate on the power system. These resources such as rooftop solar, small generators, energy storage, etc are located on the distribution system which poses the question of how to include them in transmission system models. While they can be included as offset load, these DERs can impact the transmission system if there is a large amount of them deployed. At ERCOT we incorporate DERs into both our operations and planning models. Recently ERCOT launched its Modeling page to provide information on its grid models. On there we published a whitepaper detailing our DER modeling methodologies. Here is a summary of those modeling methodologies. We have three categories of DERs: Distributed Generation Resources (DGR) and Distributed Energy Storage Resources (DESR), Settlement Only Distributed Generation (SODG), and Unregistered Distributed Generators (UDG). DGRs and DESRs are resources that fully participate in the electric market and thus provide a full dataset for modeling. These resources are modeled as discrete generators in operations, and in steady state/short circuit/dynamic planning models. For dynamic models, a DGR owner submitted dynamic model will be used and if one is not submitted then the DER_A model is used. SODGs have limited participation in the market and thus are required to submit less data to ERCOT than DGRs do. Therefore we have less data on these models. In operations they are still discreetly modeled but not as full generators but rather under the load itself. For planning models they are aggregated by fuel type at the transmission level bus. For dynamic models, a SODG owner submitted dynamic model will be used if the site is greater than 5 MWs and if one is not submitted then the DER_A model is used. UDGs are not included in the operations models. In the planning domain their contribution is embedded in the load forecast. ERCOT also operates Aggregated Distributed Energy Resources (ADERs). ADERs include aggregated devices such as synchronous generators, stationary batteries, HVAC systems, and more. They can provide energy, non-spin, or ECRS. These can be dispatched in real time operations but are not explicitly included in planning models. If you want more information I recommend heading over the the whitepaper and giving it a read. https://lnkd.in/gJmfSmH9

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.

Hotspot when Navigating the Energy Transition ! Where is the value in " co-optimizing gas and electricity network planning for decarbonization"??? As energy networks utilities navigate the climate change mitigation policies, Energy system modelers and planners must develop strategies for achieving cost-effective Coordinated planning for electricity and natural gas systems investments that address cross –sector operational constraints, competing demands for net-zero emissions fuels, and shifts in energy consumption patterns. In this context, and In order to rapidly integrate substantial productions from renewable energy sources like - renewable gases and renewable electricity sources- to meet those challenge, it is imperative for electricity and gas network utilities to co-optimize the planning and delivery of network infrastructure, ensuring predictability for customers as they navigate the complex transition to a sustainable energy future. Some Key Components of such effective co-optimization should cover: 1. Effective regulatory frameworks to afford market integration which is vital to create an attractive environment for effective investments. Transparent policies will facilitate the integration of renewable sources while ensuring reliability and affordability for consumers. 2. crucial and pivotal roles of "elec., gas" Transmission System Operators (TSOs) and Distribution System Operators (DSOs) must be coherent and aligned to collaboratively enhance capacity management. This synergy will optimize the flow of energy, accommodate fluctuating renewable generation, and maintain both grids dispatchability and stability. 3. increasing the renewable energy production capacity, makes managing this influx is crucial. therefore, Strategic co-optimized modeling and planning of both energy grids will ensure stable handling of peak loads and diverse energy sources without compromising service reliability. 4. Tariff Structures: Evolving inclusive tariff structures will play a significant role in incentivizing investments in both gas and electricity networks. Fair pricing mechanisms are essential to stimulate growth while promoting sustainable energy practices. 5. Investment Planning: Coordinated investment planning across gas and electricity sectors is critical. Prioritizing infrastructure projects that enhance integration and resilience will pave the way for a more robust energy affordability. 6. The Role of Hydrogen and Power-to-X (PTX): Hydrogen and PTX technologies represent a promising avenue for energy transition by leveraging adoption of such solutions to store excess renewable energy and provide flexibility to energy systems, as well as effectively contribute to decarbonization efforts. Indeed …co-optimizing gas and electricity network infrastructure is a critical and strategic job! #EnergyTransition #Decarbonization  #RenewableEnergy #Hydrogen #MarketRegulation #CapacityManagement #InvestmentPlanning

What are #DigitalTwins? Forget Definitions, focus on Capabilities! For example, real-time data management is one of the core digital twin capabilities. To build effective digital twin solutions, the capability of integrating "Simulation, Optimization, Prediction, and Visualization models" is crucial. Let's explore. 1. Simulation models are the foundation of digital twins, creating precise virtual representations of equipment and processes. Using techniques like finite element analysis (FEA) and computational fluid dynamics (CFD), operators replicate physical conditions and test operational changes without impacting production. Discrete event and agent-based modeling add depth, simulating workflows and interactions among assets and operators. These models provide essential insights for process improvement and set the stage for effective optimization and predictive analysis. 2. Optimization models refine processes and resource use based on simulation outputs. Algorithms such as mixed-integer linear programming (MILP), genetic algorithms (GA), and particle swarm optimization (PSO) adjust process parameters and scheduling to maximize efficiency. In smart manufacturing, these models dynamically adapt to real-time data, streamlining production, enhancing energy use, and improving supply chain coordination. This step ensures that operations align with business targets, minimizing costs and waste. 3. Predictive models forecast potential issues by analyzing historical and real-time data using machine learning algorithms like LSTMs, Random Forests, and anomaly detection techniques. These models support predictive maintenance and quality control by identifying equipment failures or process deviations before they occur. Proactive measures based on predictive insights help stabilize production, reduce downtime, and maintain supply chain resilience, directly lowering operational costs and enhancing efficiency. 4. Visualization models present the data and insights from simulation, optimization, and prediction in an interactive, user-friendly format. Using tools such as D3.js, Plotly, and 3D platforms like Unity 3D, operators gain real-time views of equipment status, process flows, and prescriptive recommendations. Effective visualization improves situational awareness and facilitates prompt decision-making, ensuring data-driven actions can be taken quickly and efficiently. Combining simulation, optimization, prediction, and visualization models forms a comprehensive digital twin strategy for smart manufacturing.

Energy transition models can improve accuracy by up to 27% when they include social factors like public acceptance and investment risks. Energy system models have struggled to account for the human elements of transitioning to clean energy. While these models excel at technical and economic calculations, they often miss social dynamics that can make or break real-world implementation. A new study examines which societal factors matter most and how to include them effectively. By analyzing power system transitions across 31 European countries from 1990-2019, researchers found that incorporating societal factors improved model accuracy by up to 27% for predicting the installed capacity of individual technologies. Three factors emerged as particularly important: public acceptance of new energy infrastructure, investment risk considerations, and the tendency of existing infrastructure to create system lock-in. This research hints at new pathways for updating energy transition models. By systematically identifying which social factors matter most, modelers can better simulate how energy systems evolve - helping policymakers design more effective interventions. S/O to Vivien Fisch-Romito, Marc Jaxa-Rozen, Xin Wen , and Evelina Trutnevyte.

➡𝗪𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗺𝗼𝘀𝘁 𝗳𝗼𝗿 𝗱𝗲𝗰𝗮𝗿𝗯𝗼𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻 𝘁𝗼𝗱𝗮𝘆: 𝗺𝗼𝗻𝗲𝘆, 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆, 𝗼𝗿 𝗹𝗼𝗰𝗮𝗹 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲? You can read my last paper, co-authored with Arsène Perrot in the Cambridge Journal of Regions, Economy and Society Here’s what we do in this paper 👇 1️⃣ 𝗪𝗲 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗲 𝘁𝗵𝗲 𝗶𝗱𝗲𝗮 𝗼𝗳 𝘁𝗵𝗲 “𝗿𝗲𝗴𝗶𝗼𝗻𝗮𝗹 𝗰𝗮𝗿𝗯𝗼𝗻 𝘁𝗿𝗮𝗽” Some regions are structurally locked into high CO₂ emissions. This is not about “lack of ambition”. It comes from inherited industrial pathways (steel, chemicals, energy), sunk infrastructure, political routines, and dependence on carbon-intensive activities. In other words: the system itself resists transition. 2️⃣ 𝗪𝗲 𝘀𝗵𝗼𝘄 𝘁𝗵𝗮𝘁 𝘁𝗵𝗲𝗿𝗲 𝗶𝘀 𝗻𝗼 𝘀𝗶𝗻𝗴𝗹𝗲 𝗘𝘂𝗿𝗼𝗽𝗲𝗮𝗻 𝘁𝗿𝗮𝗷𝗲𝗰𝘁𝗼𝗿𝘆 — 𝘁𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝗳𝗼𝘂𝗿 𝗿𝗲𝗴𝗶𝗼𝗻𝗮𝗹 𝗽𝗮𝘁𝗵𝘀: • virtuous loop: relatively low emissions, continuing to decrease 📉 • carbon-intensive trap: very high emissions that barely move 🔒 • high-emission trap: historically high emissions, now slowly coming down ⚙️ • evolution trap: regions with lower historical emissions, but emissions are now rising again 🚧 ➡ This matters because climate policy that ignores territorial diversity will fail. Regions are not starting from the same place. 3️⃣ 𝗪𝗲 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝘁𝗵𝗲 𝗸𝗲𝘆 𝗱𝗿𝗶𝘃𝗲𝗿𝘀 𝗯𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲𝘀𝗲 𝘁𝗿𝗮𝗷𝗲𝗰𝘁𝗼𝗿𝗶𝗲𝘀 • industrial specialisation (who produces what, and for whom) 🏭 • government quality and institutional capacity (can the region actually steer change?) 🏛️ • economic diversification (is there an alternative to the legacy carbon-intensive model?) 🔄 4️⃣ 𝗪𝗲 𝗼𝘂𝘁𝗹𝗶𝗻𝗲 𝘁𝗵𝗿𝗲𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝘁𝗼 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗯𝗿𝗲𝗮𝗸 𝗼𝘂𝘁 𝗼𝗳 𝗮 𝗿𝗲𝗴𝗶𝗼𝗻𝗮𝗹 𝗰𝗮𝗿𝗯𝗼𝗻 𝘁𝗿𝗮𝗽 • exnovation: actively phasing out obsolete, fossil-based infrastructures and the rents attached to them 🧯 • diversification: creating new local economic activities to reduce dependence on carbon-heavy sectors 🌱 • leapfrogging: enabling certain regions to jump straight to advanced low-carbon systems — energy, mobility, infrastructure — without reproducing yesterday’s polluting stages 🚀 🎯 𝗣𝗼𝗹𝗶𝗰𝘆 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 Decarbonisation in Europe will only work if it is place-sensitive. Regions with different industrial legacies, institutional capacity and social contracts need different transition pathways. “One-size-fits-all” climate policy will not deliver 2030 and 2050 targets. 🇪🇺 If you work on territorial policy, just transition, or regional industrial strategy, I’d be happy to exchange 💬 #decarbonisation #regionalpolicy #climatepolicy #justtransition #pathdependence #cohesion #EU 🌍 Andrés Rodríguez-Pose Bogdan Ibanescu SAGES project Vinko Mustra Norbert Petrovici

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