This suite of tools is adapted to the representation under uncertainty of integrated energy systems – involving multiple generation technologies and taking into account the availability of renewable resources, fuels and transport restrictions in transmission lines and pipelines. The various models use stochastic optimization techniques to solve operational and planning problems.

NetPlan – Model for transmission expansion planning

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NETPLAN is an integrated computational environment for transmission network planning and analysis which includes:

  • data management tools (data editing, external data importation, etc.);
  • study management resources (data coherency checking, database versioning, chronology, sensibilities, etc.);
  • Visualization resources for the network and study results (schematic diagrams, circuit flows, overflow indicators, load marginal cost, contour plots, etc.);
  • Graphical interface which allows executing tools for transmission network expansion planning and analysis.

In the current version two planning tools are available:

  • OPTNET, for analysis and expansion plan-ning of high voltage transmission network (active power);
  • OPTFLOW, for optimal AC power flow and expansion planning of reactive power sources (VaR).

Both models use optimization tools capable of large scale transmission network planning and analysis.

Add-in — TUOS (Comming Soon)

The implementation of a competitive environment in the generation area is conceptually straightforward: agents freely decide to construct generating units and compete for energy sales contracts with utilities and customers. The decision on plant type and size will typically depend on investment and fuel costs, duty cycle, availability rates, etc. However, the plant sitting decision also depends on the transmission cost associated to the energy transport from generation to load centers. For obvious reasons, it is neither feasible nor economical to build independent transmission systems for each generation-load pair.

Under this new environment, the transmission network becomes a service to which all generators and customers have access and it becomes necessary to develop rules which allow the shared use of the transmission system. This transmission service cost is allocated among generators and consumers through the use of Open Access Transmission Tariff (OATT).

Therefore, OATT plays an important role. If well determined, they are responsible for a fair allocation of the transmission costs among the agents and provide efficient economic signals that induce agents to build generation facilities at sites that will lead to the best overall use of the generation-transmission system. The same signals apply to the location of economic activities that increase the system load, such as large industrial consumers.

TUOS has been developed by PSR for transmission regulators and agents worldwide. Therefore it offers several different methodologies for the transmission cost allocation problem. Each allocation scheme has different characteristics of providing economic signals that can be attractive according to the transmission system environment. TUOS includes five different methodologies, classified in categories such as the widely known marginal pricing and the network usage-based allocation.

Add-in — OptNet

Planning of transmission grids, with detailed modeling of Kirchhoff’s laws and N-1 security criteria.


OptNet is a computational tool for determining the least-cost transmission network reinforcements required to ensure the supply of the forecasted load along the study horizon, taking into account the N-1 security criterion (adequate supply under any single circuit outage). An alternative objective function is to minimize the sum of investment costs plus the cost of expected unserved energy due to circuit outages (reliability worth criterion). It is also possible to represent several generation dispatch scenarios for each load scenario (due, for example, to the existence of renewable sources such as hydropower and wind). This allows for a more robust expansion plan and for a better tradeoff between investment costs and supply reliability. The transmission network is represented by a linearized power flow model; different power flow limits can be used for the base case and for the post-contingency situations. The planning can be carried out either sequentially for each time stage (“forward planning”) or by determining the optimal expansion for the final year and going backwards in time to determine the optimal timing for each reinforcement (“horizon year planning”).

System characteristics

  • Execution through a user-friendly graphical interface with resources for visualization of the network;
  • Visualization of results in spreadsheets;
  • Network data plus load and generation dispatch scenarios can be directly imported from the transmission-constrained dispatch model SDDP, developed by PSR;
  • Ranking of candidate reinforcements by cost-benefit indices;
  • Detailed performance analysis of a user-provided expansion plan.

Solution methodology

Advanced optimization techniques are used to solve the transmission planning problem. A major difficulty is that, due to Kirchhoff’s second law, the problem has to be formulated as a nonlinear mixed integer programming model. Initially, the nonlinearities are removed by the use of a new disjunctive formulation developed by PSR. Logical constraints for candidate circuits in parallel and topological constraints are introduced in a pre-processing phase, to reduce computational effort. Finally, there is the option of using incremental reinforcement strategies, where candidate circuits are ranked by cost-benefit indices related to the effectiveness of reducing load curtailment due to overloads in both the base-case and the post-contingency situations.

Some recent applications

The OptNet model has been recently applied in the following studies:

  • Five-year transmission plan for El Salvador (34 contingencies, 200 monthly dispatch scenarios, 47 candidate circuits);
  • Five-year transmission plan for Venezuela (36 contingencies and 125 candidate circuits).

Add-in — OptFlow (Comming Soon)

This computational tool determines the optimal operation of a generation/transmission system, with an integrated representation of both the AC electrical network constraints (bus voltage limits, reactive power limits, etc.) and the hydrothermal system modeling (water balance of plants in cascade, turbining limits, limits on thermal generation, etc.)

OptFlow can be used in short and middle term planning operation studies; in the optimization of reactive sources (e.g., the dimensioning and location of capacitors); and for determining the tariffs of ancillary services such as reactive support and others.

OptFlow problem is formulated as a non-linear optimization model, whose constraints include:

  • Kirchoff laws: nonlinear active and reactive power balance equations for each node of the electrical network;
  • Network operating limits: bus voltage, active and reactive power flow in circuits, transformer taps and others;
  • Hydrothermal generation system constraints.

The objective functions represented by the model vary with the the application:

  • Short-term dispatch problem: minimize the total operating cost, which includes the variable operating cost of thermal plants and the opportunity cost of hydro plants (“water values”), which come from the stochastic dispatch model for middle-term;
  • Operational planning studies: maximize load level for a given set of buses for voltage collapse studies; maximize active power transfer between areas; minimize active power losses through reactive control re-scheduling; minimize load shedding, etc;
  • Reactive support planning studies: minimize the reactive power injection.

The decision variables are:

  • Active power generation of each generation unit;
  • terminal voltage level for each generation unit, synchronous condenser and static VAr compensator;
  • tap control for controllable transformers;
  • switch control for controllable banks, etc.

OptFlow output comprises

  • active/reactive generation in each generation unit
  • active/reactive flow in each circuit
  • bus voltage level
  • transformer tap for each controllable transformer
  • level of losses
  • amount of power transfer between areas
  • marginal costs associated to the active/reactive balance equations in each bus and to line flow limits
  • depending on the objective function, amount of load curtailment, bus final load level, etc

Solution Methodology

The nonlinear optimization problem is solved by an interior point method.