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

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

🚨 New cover article in Cell Reports Sustainability! Excited to share this new collaborative paper led by Postdoc Liying Qiu in the Howland lab, in collaboration with Rahman Khorramfar and Saurabh Amin! Open-access article: https://lnkd.in/eJdn6qf2 This study determines the required spatial resolution of meteorological data and how to integrate it with energy system modeling to plan cost-effective decarbonized energy systems. We design minimum-cost decarbonized energy systems in three diverse geographic regions (ISO-NE, CAISO, ERCOT) and we interpret these kilometer-scale energy system optimizations based on the geophysical drivers and constraints for generalizable conclusions and impact. Using downscaled meteorological data at km-scale yields lower cost compared with typical meteorological data at resolutions over 30 km (i.e. standard reanalysis), underscoring the value of high-resolution weather and climate data in planning energy systems. Decarbonized energy systems will heavily rely on variable wind and solar power production and storage to satisfy time-varying energy demand. Yet it is not clear where wind and solar generation should be sited to maximally support power systems with least cost. The supply of wind and solar relies on spatiotemporal variations and correlations within and across the wind and solar resources. Planning decarbonized energy systems, therefore, depends on the spatial resolution of meteorological data and how it is used to represent wind and solar in energy system modeling. Because planning with coarse data may lead to suboptimal outcomes and supercomputing to synthesize high-resolution meteorological data requires significant carbon-emitting electricity and is financially expensive, it is critical to determine what resolution is necessary to guide energy modeling. Further, current renewable energy tax incentives in the United States reward total energy production – this study suggests that wind and solar complementarity and alignment with power demand can lead to a more cost-effective energy system design. The wind and solar siting locations that minimize the energy system cost differ significantly from the locations with the highest wind/solar resource potential on average. Thanks to MIT Climate & Sustainability Consortium (MCSC) and MIT Climate Grand Challenges for the support! Massachusetts Institute of Technology MIT Civil and Environmental Engineering #RenewableEnergy #energy #powersystems #optimization #climate

Caribbean energy transition towards 100% renewables - Part 2 e-fuels imports, grid integration, accelerated transition 1/ New research LUT University Solomon Oyewo, Ph.D https://lnkd.in/dMDsXb-p This study presents the first-of-its-kind comprehensive analysis of 17 illustrative pathways, exploring e-fuel imports, grid interconnections, and accelerated energy transitions for carbon neutrality by 2050. 2/ Novelties of the research: This study offers novel insights into the energy transition of archipelagic nations, enriching the global discourse on grid interconnection, early decarbonisation, and the strategic importance of e-fuel imports for land-constrained regions. 3/ Background: #Caribbean, heavily reliant on fossil fuels, is among the least researched regions globally for renewable energy #100RE systems, despite growing validation of renewables-driven climate mitigation strategies. 4/ This research adds to the comprehensive literature on #100RE studies on islands https://lnkd.in/d5c3xuWQ  within the wider field of overall #100RE systems research https://lnkd.in/d2cjpWuQ 5/ The energy system includes electricity, heat, and gas storage. Batteries for prosumer and utility-scale use, along with V2G, are essential for daily solar PV storage. 6/ Grid utilisation correlates with dominant technology profiles, such as solar PV or wind power. Scenarios with 30% wind show higher grid utilisation, indicating a wind-grid correlation, while those with 12% wind power have lower utilisation, showing a PV-storage correlation. 7/ e-Fuels will be essential for defossilising the hard-to-abate demands. By 2050, sustainable fuels will contribute to replacing all fossil fuels. 8/ Renewables-dominated paths have 7-24% lower cumulative costs as alternatives, with grid integration cutting costs by 1-10%. Accelerated transition paths cost 3-12% more as full defossilisation by 2050. Importing e-fuels lowers system costs by 7-16% & supports local resource use. 9/ The #PtXeconomy will emerge an important framework across the energy sectors, via direct & indirect electrification approaches. https://lnkd.in/dwWWwzvD 10/ Conclusions: PV-battery hybrid solutions emerge as the most economical option and the possibility of this hybrid configuration dominating the future energy system is also echoed in recent literature. Batteries support PV-battery hybrids & grid interconnection enhances flexibility, reduces costs & supports wind with limited PV. Sector coupling & PtX improve efficiency. Electric road transport with V2G adds flexibility & grid-connected renew paths are 1-10% cheaper. The #Caribbean can reduce reliance on fossil fuels by adopting low-cost solar energy, creating a Solar-to-X Economy ideal for tropical islands globally. Paul Bertheau Henning Meschede Philipp Blechinger Daniel Icaza Mark Jacobson Dominik Keiner Neven Duic Poul Alberg Østergaard

✋ 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.

Regional Spatial Energy Planning (RESP) = THE plan on how a region can meet its energy needs & decide what network investments it should go for. Blink & you may have missed Ofgem's consultation that came out on Tuesday on the policy framework for RESPs. Key proposals: ⚙ Central hub and regional spoke model. The hub essentially sets the tone e.g. the RESP methodology and assumptions that should be followed, and the regions develop their own RESPs. ⏹ 3 x building blocks: modelling supply and demand, identifying system need and technical coordination. 🗺 11 regions, 9 in England, 1 in Scotland & 1 in Wales 📑 RESPs to include multiple long-term pathways (25 years) but 1 x short-term pathway for the next 5-10 years. 👩 Each region should have a Strategic Board, made up of local and devolved govt and network company representatives. ⚡ DNOs and GDNOs should align their investment plans for network capacity with the strategic direction set by the RESPs covering their licence areas. The consultation closes on 8 October 2024, with Ofgem aiming to publish a decision on RESP policy framework in Winter 2024.

"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.