System model

The following describes a generalized version of our power market modeling framework. While the core structure and methodology are consistent across regions, specific implementations — including market rules, input data and operational constraints — vary by geography. Detailed region-specific assumptions and inputs are documented separately.

Production cost modelling

The production-cost model is a key component of Modo's forecasting tool and simulates the optimal behavior of a power system. Specifically, it decides how to dispatch generation across various technology types to ensure supply meets demand at the lowest system cost, all while respecting the limits of the transmission network.

Generators

The model includes a full fleet of generators, representing all major fuel and technology types e.g. natural gas (combined cycle, single cycle, reciproacting engines), coal, nuclear, hydro, wind, solar, etc. Each generator is modeled with technical and economic constraints that govern its behavior, such as ramp rates, and efficiency, start costs and running costs.

Outages

Outages are captured using historical patterns to reflect both planned and unplanned events. Each generator is assigned availability profiles based on observed outage rates by technology type and region. This approach ensures that generator availability reflects realistic operating conditions.

Variable Renewable Generation (Wind and Solar)

Wind and solar generation is modeled with region‑specific “weather years,” each tying capacity‑factor profiles to a particular historical year derived from meteorological data and actual generation, capturing spatial and temporal variability.

Key elements include:

• Hourly capacity factors based on multi-year historical weather and generation data.
• Regional granularity, with profiles aligned to renewable resource zones.

This modeling ensures that the variability and intermittency of renewables are realistically reflected in system dispatch and pricing outcomes.

Costs

Generator dispatch is driven by a combination of short-run marginal costs (SRMC) and start-up costs, both of which are modeled explicitly. Both cost components are technology-specific and calibrated using publicly available and proprietary datasets where available.

SRMC represents the cost of producing one additional unit of electricity while the unit is already online, and it forms the basis for economic dispatch within the model.

• SRMC includes the variable operating costs incurred during normal operation:
  • Fuel costs (based on heat rates and fuel prices)
  • Variable operations and maintenance (VOM) costs
  • Carbon costs (where applicable)

Start-up costs influence unit commitment decisions, ensuring that peaking plants or inflexible units are only brought online when economically justified by the system conditions.

• Start-up costs capture the additional cost associated with bringing a generator online from an offline state:
  • Fuel required for start-up
  • Wear-and-tear or fixed O&M charges incurred on start

The model incorporates bid steps for all generators to reflect observed bidding behavior during periods of system scarcity or high load. These steps allow for mark-ups above SRMC, capturing price formation dynamics under tight conditions and better representing real-world market outcomes.

Storage

Grid-scale batteries, are modeled as bi-directional assets with both charging and discharging capabilities. The model tracks state of charge (SoC), round-trip efficiency, and power/energy limits.

Key constraints include:

• Maximum charge/discharge rates
• Energy capacity
• Minimum/maximum state of charge
• Round-trip efficiency losses

Storage is dispatched economically to maximize system cost savings or arbitrage opportunities.

Load

Load is represented at a high temporal and spatial resolution, aligned with historical or forecast demand profiles. It can be broken down by sector where applicable (e.g., residential, commercial, industrial) and may include demand-side flexibility or response behavior if supported by the region's data.

Features:

• Hourly (or finer) demand profiles
• Regional distribution of load
• Flexible load blocks

Transmission

Zonal Modeling

All market models incorporate fundamental zonal transmission constraints and elements, including:

• Transfer capacity limits on zonal interties
• Thermal limits and directional flow constraints
• Wheeling charges or congestion pricing mechanisms aligned with regional market designs

Power Flow Modeling

In certain regions, detailed power flow modeling is employed because grid physics significantly impact pricing and dispatch decisions. For these markets, the model integrates a linearized DC optimal power flow (OPF) formulation, explicitly capturing these operational constraints.

Power flows are calculated by solving for voltage phase angles at each node, with flows along transmission lines governed by their reactance and constrained by thermal transfer limits. The model ensures that all flows respect physical constraints while minimizing total system dispatch cost.

Transmission Interfaces

To represent regional flow constraints and system bottlenecks, the model incorporates transmission interfaces — predefined boundaries or paths across which aggregate flows are tracked and limited that critical network paths defined by the system operator. These interfaces are tailored to the structure and operational conventions of each market.

Each interface includes:

• Thermal transfer limits, typically aligned with system-operator-defined constraints
• Directional flow considerations, where applicable

Interfaces provide an additional layer of constraints to reflect more systemic transmission congestion and regional price separation.

Assumptions and caveats

• Linearized physics: Power flows follow DC approximations — flows are linear functions of phase angle differences and line reactances.
• No losses: Line losses are not explicitly modeled under the DC approximation.
• No contingencies: The model represents a base-case scenario without simulating N-1 security constraints or transmission outages.

Power Balance

At each location and time step, the model enforces a power balance constraint: total supply (generation, storage discharge, and imports) must equal total demand (load and storage charging). This constraint ensures physical feasibility and underpins the calculation of marginal prices across the system.

To maintain system feasibility under all conditions, the model includes a loss-of-load resource, often referred to as a "generator of last resort." This resource has a very high cost and is only dispatched when no other generation or flexibility is available. It acts as a backstop to represent involuntary load shedding and ensures the optimization always has a feasible solution.

The shadow price of the power balance constraint at each location and time represents the marginal cost of meeting one additional unit of demand, forming the basis of modeled energy prices.

Outputs

Zonal Model

• Plant level behaviour
  • The generation output and run hours of each plant at every point in time
  • Generator start-up and shut-down
  • Economic curtailment of renewables
  • Storage behaviour including charge, discharge, state of charge and cycling
• Transmission
  • Flows along all transmission lines
  • Congestion rents
• The price of energy - calculated as the cost of producing an additional unit of electricity at each region in the model (aka the shadow price of the power balance constraint).

Optimal power flow model (if applicable)

• Nodal locational marginal prices (LMP) at every node — representing the marginal cost of serving one additional unit of demand at that location, and considering both generation costs and network congestion.
• Congestion rents, derived from price differences across constrained lines
• Line-level power flows, including directionality and constraint binding status