ENERGY INVESTMENT AND RISK SIMULATOR

A digital twin of the energy system, built for investment and risk decisions

Macrocosm's energy simulator defines every generator, transmission line, and market participant with physical and economic causality. Run counterfactuals, adapt to new information, and train decision-making agents — all within a simulator that captures the full complexity of energy markets.

KEY CAPABILITIES

Intervene anywhere — shut down a plant, build a new battery asset, change a gas price forecast — and trace how consequences propagate through dispatch, prices, and investment decisions. Because every entity has physical meaning, counterfactuals are mechanistic, not pattern-matched.

Run counterfactuals

As real-world data arrives, the world model uses ML-powered Bayesian inference to update its beliefs over uncertain parameters, producing probabilistic forecasts with calibrated uncertainty that tightens as new evidence arrives.

Forecast, observe, update

Test a trading strategy, a hedge, or a policy intervention inside a reactive model of the market — where other participants adapt, grid constraints bind, and second-order effects play out. The same environment serves as a high-fidelity gym where bidding agents learn through interaction, not just historical replay.

Simulate decisions and train agents

Simulate thousands of generators, investors, and regulators making decisions under realistic physical and economic constraints — not as a central optimization problem, but as an emergent process driven by bottom-up behavior capturing the full complexity of an evolving system.

Model the energy transition as an emergent process

KEY CAPABILITIES

Run counterfactuals

Forecast, observe, update

The model uses ML-powered Bayesian inference to update its beliefs over uncertain parameters, producing probabilistic forecasts with calibrated uncertainty that tightens as new, real-world data arrives.

Simulate decisions and train agents

Test a trading strategy, a hedge, or a policy intervention inside a reactive model of the market where other participants adapt, grid constraints bind, and second-order effects play out. The same environment can be used to train bidding agents through interaction, not just historical replay.

Model the energy transition as an emergent process

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