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SDDP

SDDP 18.0 – Integrated Energy System Planning Platform

📅 Date: 2025-05-19 (to be released)
🔗 Download: Windows | Linux

Please visit the release site to explore the highlights of this release.

🚀 A unified experience across all planning horizons

This release marks a major milestone. SDDP 18.0 introduces a unified platform for operation and expansion planning, reliability analysis and maintenance scheduling — offering a seamless experience, from graphical interface to execution and results analysis.

🔷 Graphical interface

The new interface delivers a fully redesigned and unified environment for energy system planning. All modules — operation (SDDP), expansion (OptGen), reliability (CORAL) and maintenance — are now accessible from a single platform, offering a consistent, responsive, and user-friendly experience.

🌐 Unified layout and planning integration

  • Single platform for configuration, execution, and analysis of all SDDP-based models.
  • Fully integrated environment for operation and expansion planning, reliability analysis and maintenance scheduling.
  • No need for external tools or switching contexts — everything is accessible in one place.

🗂️ Flexible data interaction

  • Bulk data edition: tabular views support editing, grouping, and filtering of all input data.
  • Smart filters: dynamic grouping by system, region, technology, or other dimensions.
  • Inline charting: visualize key relationships (e.g., volume × production) directly within data grids.
  • Excel integration: import/export supported for all input data and output results.
  • Mass-editable grids: scrollable, filterable tables provide a comprehensive overview of modeling data.

🗺️ Georeferenced system visualization

  • Interactive visualization of hydraulic cascades, transmission networks, and energy supply chains.
  • Unified topology maps with integrated spatial and operational data.
  • Multiple visualization modes, including:
    • Animated flows on circuits and cascades
    • Scaled node representation (e.g., capacity, cost, stored energy)
    • Contour plots for marginal costs, generation, and energy balance

📊 Built-in dashboards and result analysis

  • Interactive charting through the graphical interface: point-and-click customization of dashboards by selecting variables, time horizons, scenarios, and filters.
  • Automated dashboards with PSRIO: customized post-processing dashboards are generated automatically after each execution, supporting standardized and collaborative workflows.
  • Built-in dashboard: each case includes a ready-to-use dashboard summarizing solution quality, convergence, cost and revenue breakdowns, constraint violations, results by region, marginal costs, execution times, and key information.
  • Comparative dashboards: analyze multiple cases side-by-side, either with the built-in comparison view or fully customized dashboards via PSRIO.

📚 Help system and support

  • New integrated help platform, the PSR Knowledge Hub, with direct access to:
    • Interface guide
    • Methodological documentation
    • Mathematical modeling references
    • Troubleshooting articles

⚡ Energy system modeling

🔗 Energy supply chains

Detailed modeling of energy supply chains, enabling integrated planning of electricity, natural gas, hydrogen, biomass, Power-to-X, X-to-Y, and more.

  • Comprehensive supply chain modeling: represent the full cycle of energy transformation, including production, transport, conversion, storage, and demand.
  • Multiple energy carriers: define energy networks for fuels such as natural gas, hydrogen, methanol, biogas, electricity, and synthetic products.
  • System-wide integration: energy supply chains are directly linked to the power system through converters, fuel-consuming units, and electrification modules.
  • Element types:
    • Producers (e.g., gas wells, steam reformers, anaerobic digesters)
    • Converters (e.g., electrolyzers, methanol synthesis, biogas purifiers)
    • Storage facilities (e.g., hydrogen tanks, gas cylinders, long-term storage)
    • Transport links between nodes with capacity, cost, and losses
    • Demands, elastic or inelastic, at any node in the chain
  • Applications:
    • Natural gas: from gas wells to thermal plants and households via pipelines and storage
    • Hydrogen: production from electrolysis or reforming; distribution and use in vehicles or industry
    • Biomass: conversion to biogas, biomethane, and electricity, with links to transport and heat sectors
    • Power-to-X and X-to-Y: enabling sector coupling between power, mobility, heat, and synthetic fuels

🗼 Power network representation

A detailed transmission model is essential for accurately representing key aspects of modern energy systems — such as renewable integration, curtailment management, and operational flexibility. This enhanced modeling capability supports both operation and expansion planning with a high level of electrical and spatial granularity.

  • Supports a wide range of AC and DC components: lines, 2- and 3-winding transformers, LCC and VSC converters, flexible AC transmission systems, and reactive power compensation
  • Includes advanced attributes such as electrical parameters, flow capacities, fault probabilities, tap settings, and voltage/reactive power control
    Represents corridor constraints, simple and multiple contingency conditions, and flexible network topologies
  • Improved compatibility with other AC grid analysis tools such as PSS®E and others, allowing importing and exporting data

🛠️ Maintenance planning

Enables the explicit and high-resolution modeling of maintenance schedules, allowing users to move beyond simplified capacity derating and achieve more accurate planning results.

  • Particularly valuable in contexts where hourly dynamics and short-term constraints matter for both operation and expansion planning—helping avoid distortions caused by average availability assumptions.
  • Also supports system operators and coordination agents in approving, aggregating, or rescheduling maintenance requests under reliability and feasibility criteria.
  • Maximizes the system reserve margin by identifying and protecting the most critical periods using risk-based metrics.
  • Captures key operational constraints, including minimum up/down times, ramp limits, precedence and association between maintenance requests, non-coincident maintenance rules, and forbidden periods or priority preferences.
  • Fully integrated into the broader planning methodology, complementing operation planning with SDDP and expansion planning with OptGen.

💡 Energy efficiency

Modeling of energy efficiency programs as investment decisions that reduce or reshape energy demand across the system.

  • Explicit representation of efficiency programs: each program is linked to a specific demand class and represents an improvement that reduces energy consumption.
  • Flexible modeling structure:
    • Allows modeling demand reductions across different time resolutions, reflecting seasonal or intraday efficiency impacts.
    • Supports varying levels of efficiency adoption over time, enabling the representation of progressive program deployment.
  • Endogenous investment decisions: the level of deployment can be optimized considering cost-effectiveness in both operation and expansion planning.
  • Typical applications include more efficient lighting in public infrastructure, industrial equipment retrofits, and residential upgrades such as improved air conditioning or heating systems.
  • Expansion planning integration: efficiency programs are considered alongside generation and transmission projects as viable investment alternatives.

📈 Expansion planning: efficient frontier analysis

  • Multi-criteria optimization to identify portfolios that minimize cost while managing risk in expansion planning.
  • Allows decision-makers to explore the trade-off between total cost and risk, selecting robust and cost-effective energy mixes.
  • Accounts for uncertainty sources like fuel prices, climate variability, and policy shifts.
  • Compatible with complex system interdependencies, including energy supply chains and technology interactions.
  • Supports the visualization of optimal and sub-optimal portfolios, enhancing understanding of planning alternatives.

⚙️ Modeling improvements

This version introduces several improvements to modeling capabilities, enabling more accurate representations, new investment options, and expanded flexibility for users.

⏱️ Dynamic convergence strategy for hourly MIP

  • Supports a dynamic relative gap strategy to enhance computational efficiency while maintaining solution quality
  • Users can define multiple time intervals with different maximum relative gaps, which the solver adapts to during execution

💧 Representation improvements

  • Hydro unit commitment modeling, enabling more realistic unit-based dispatch decisions
  • Explicit modeling of individual units for hydro, thermal, and renewable plants
  • Detailed connection of generator groups to the transmission network, improving spatial resolution and flow accuracy
  • Combined-cycle plants modeled as unit-based configurations, replacing the traditional operational state abstraction
  • Hydro tables parameterized by net head, enhancing accuracy for production-volume relationships
  • Generation offers with multiple O&M cost tranches, enabling price-based dispatch behavior and better price pattern replication

🧩 Generic modeling extensions

  • Generic variables can now be either continuous or binary, and may include cost terms in the objective function
  • Multi-stage generic constraints and new variables support modeling of green certificates, export limits, energy swaps, and other advanced policies
  • New variables added since version 17.3 include:
    • Hydro joint reserve
    • Circuit flow
    • Fuel reservoir final storage
    • System total demand, generation, and net generation
    • Waterway flow
    • Bus demand
    • Fixed converter commodity (SDDP 18.0 only)
    • Hydro commitment (SDDP 18.0 only)
  • If your use case requires additional variables, feel free to contact us — we’re continuously expanding the modeling possibilities.

📆 Maintenance representation

  • Hourly maintenance modeling for renewables and DC links, allowing precise unavailability scheduling and interaction with reserve requirements

🔁 Transmission network enhancements

  • Multi-contingency SCOPF: support for custom N-k contingency sets
  • Improved loss modeling: better selection of linearizations and marginal cost calculation methods

🎲 Uncertainty representation

  • Markov-based SDDP:
    • Allows the representation of uncertainties in energy transactions via power injections or fuel price scenarios
    • Provides improved modeling of uncertainty in the objective function
    • Users can either apply the built-in Markov clustering tool to generate transition matrices or supply their own scenario data
  • Dynamic Line Rating (DLR):
    • Supports user-defined DLR scenarios, in addition to automated scenario generation via the Time Series Lab

📁 Data compatibility and migration

  • Internal architecture restructured to unify all models under one execution framework
  • Discontinued legacy files and input formats. Migration tools and warnings guide the transition