TEEM, the TEESlab Modeling suite, is an ensemble of high-resolution energy system simulation and optimization models, which comprises of three main models:
1. the Business Strategy Assessment Model (BSAM),
2. the Agent-based Technology adOption Model (ATOM), and
3. the Dynamic high-Resolution dEmand-sidE Management (DREEM) model.

BSAM is an agent-based electricity wholesale market simulation model which simulates the complex operations within a power pool central dispatch Day Ahead Market. The model simulates electricity generators as entities who progressively learn to bid their capacities in a day-ahead competitive wholesale market, with ultimate goal the maximization of their profits. In parallel, a unit commitment and optimal dispatch algorithm calculates the quantities injected by each generation unit, the system marginal price, the system costs, as well as, derived outputs such as CO2 emissions and profits of each generator. The model can support cost-benefit analysis of future policy and/or technology deployment scenarios. It is very flexible since it simulates the power generators as agents that compete with each other and adapt to policy and/or market changes.

ATOM simulates the dynamics of technology adoption among consumers. The model is supported by a complete framework for parameter estimation based on historical data, and for the quantification of the uncertainty that governs its ability to replicate reality.

DREEM is a fully integrated dynamic high-resolution model resolving key features that are not found together in existing Demand-Side Management (DSM) models. The model serves as an entry point in DSM modeling in the building sector, by expanding the computational capabilities of existing Building Energy System (BES) models to assess the benefits and limitations of demand-flexibility, primarily for consumers, and for other power actors involved.

and two model plugin toolboxes:

The Adaptive PolicymakIng Model (AIM) provides real time visualizations of adaptive policy maps, showing alternative pathways leading to desired policy outcomes. The interactive policy maps facilitate interactive stakeholder consultation for the design of policies which commit to short term objectives and define future contingency actions to prevent policy failure in case of unexpected contextual parameter changes.

The STatistical approximation-based modEl EMulator (STEEM) addresses computational burdens that are typically raised by simulation models’ computational complexity, resulting in time-consuming simulations. Using machine learning techniques, STEEM is trained using inputs and outputs from original models, and then is used to make quick approximations of outputs given new inputs.