Energy Systems Modeling

🔋 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.

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

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

Impact of temperature dependent coefficient of performance of heat pumps  on heating systems in national and regional energy systems modelling 1/ New study LUT University shows the impact of differentiated modelling of heat pumps in energy systems on the case of #Finland https://lnkd.in/djxpeUDy. Detailing is on an hourly temperature-dependent coefficient of performance (COP), different types, and individual vs district use. 2/ Background: Energy system models often use a simplified representation of heat pumps, typically with a uniform COP. COP, the ratio of heat output to electricity input, is a key efficiency factor for HPs. 3/ Background: COP isn't constant and varies with temperature difference between heat source & sink. Generic COP may not accurately capture the impact of temperature-driven changes on energy demand & system operation. LUT-ESTM is used for hourly resolution and power-to-X details. 4/ Method: The problem this study addresses is the impact of using hourly temperature-dependent COP values & differentiating between HP types (residential, district, industrial) in energy system models, compared to simpler approaches with uniform COP. 5/ Results: We show that while using hourly COP profiles doesn't drastically change the overall cost or primary energy demand in a cold climate like #Finland, it significantly affects the electricity supply and energy storage systems operation. 6/ Results: Using uniform COP could underestimate electricity consumption and overestimate the capacity of HPs, especially in regions with significant temperature variations and high heating demand, even more in cold winter periods. 7/ Results: Findings reveal that using average COP values tends to rely less on wind power & batteries, and more on solar PV & thermal energy storage, compared to using hourly COP. 8/ Results: Industrial HPs can play a substantial role in meeting industrial heat demand, unlike district heating HPs which face more competition in #Finland's energy system. This separation leads to a 1.8% reduction in overall primary energy demand versus using a generic HP. 9/ Discussion: In a broader context, this research emphasises that accurate representation of key technologies like heat pumps, considering their operational nuances, is vital for solid energy transition planning, esp. in regions with strong seasonal variations. 10/ Broader scope: This study on the role of heat pumps in energy systems on the case of #Finland is part of more comprehensive analyses for #Nordic conditions investigating electrification, sector coupling and power-to-X solutions https://lnkd.in/drHgPRKZ 11/ Conclusion: While computational limitations exist in large-scale modelling, this study suggests that incorporating temperature-dependent COP and differentiated HP types is feasible and offers valuable insights without significantly increasing model complexity. Rasul Satymov Jan Rosenow Oliver Ruhnau

✋ The World Bank released the report, "Beyond Borders: Power Grid Interconnections and Regional Electricity Markets for the Sustainable Energy Transition" 👉 This report provides a foundational guide to regional energy integration, with a particular focus on developing and emerging economies 👉Many regions are about to integrate power grids and markets across national boundaries, which can offer economic benefits, enhanced power supply quality and security, and opportunities for scaling up climate change mitigation measures. 👉The report begins with an overview of the different levels of power system integration, followed by an analysis of the primary drivers behind regional energy integration. 👉It identifies five key building blocks essential for achieving deeper integration: interconnection infrastructure, planning and investment coordination, technical and operational coordination, commercial arrangements and market design, and institutional architecture. 👉The report also highlights the key challenges hindering the development of these building blocks, particularly issues related to political cooperation and financing. 👉It concludes by advocating for a collaborative, step-by-step approach, along with institutional capacity building and innovative financing mechanisms, to advance regional energy integration efforts. 👉 Key themes discussed: 1. Power Trade Across Borders 2. Evolution of the Power Grid and Market Integration 3. Drivers of Cross-Border Power Integration 4. Building Blocks of Regional Grid Interconnections and Electricity Markets 5. Challenges of the Power Grid and Market Integration 6. Looking Ahead Full report attached.

"The Role of Regional Energy Networks in a Decarbonised European Energy System." METIS 3 Study S7, commissioned by the European Commission, investigates the impact of regional (NUTS1) versus national (NUTS0) energy modeling on achieving decarbonization goals by 2050. The study considers four investment scenarios: Option 1: Limited to intra-national gas turbines and transmissions. Option 2: Includes cross-border hydrogen and electricity transmissions. Option 3: Adds investments in batteries and electrolysis. Option 4: Allows investments in wind and solar capacities. Key Findings 1. Increased Renewable Capacities Transitioning to NUTS1 enabled additional investments: • Onshore wind: +80 GW. • Offshore wind: +19 GW. • Solar PV: +29 GW (22 GW utility-scale, 7 GW rooftop). 2. Cost Reductions Total system costs decreased progressively across scenarios: • Gas turbine production savings: 358 TWh reduction. • Renewable investments (Option 4) led to lower gas and biomass turbine operation costs. • Option 3 investments in batteries and electrolysis reduced cross-border transmission costs. 3. Flexibility Solutions Flexibility investments enhanced system adaptability: • Electrolysis capacity: +27 GW, concentrated in renewable-rich regions like the UK, Finland, and Germany. • Battery storage: +25 GW. Electrolysis aligned with renewable surpluses, reducing hydrogen transport needs and operating costs. 4. Curtailment and Transmission Renewable curtailment reduced by 129 TWh due to smarter investments in Options 3 and 4. Cross-border electricity flows increased, while hydrogen exports decreased. 5. Regional Optimization Detailed modeling redistributed renewable investments: • Onshore wind capacity increased in Germany (+40 GW) and Finland but decreased in France. • Solar capacity saw minor adjustments, achieving more geographic balance. • Renewable investments followed areas with lower levelized costs of energy (LCOE) and better demand-supply correlation. 6. Hydrogen and Electricity Production Electrolysis production supported local renewable integration, with hydrogen output increasing in regions with higher renewable capacity. Power exports grew for countries like Spain and France, while Northern Europe also became a stronger exporting region. Impact of Regional Modeling Compared to NUTS0, NUTS1 modeling provided: • Higher RES and flexibility investments: • +80 GW onshore wind, +29 GW solar PV, +25 GW batteries, and +27 GW electrolysis. Enhanced system diversity reduced over-dimensioning of RES and improved cost efficiency. Better alignment between renewable production and demand. The study demonstrates the benefits of detailed regional modeling: 1. Enhanced Renewables Integration: Regional flexibility and renewable investments increase efficiency. 2. Cost Savings: Lower production costs and reduced reliance on fossil fuels. 3. Strategic Redistribution: Investments tailored to regional demand and supply dynamics.