Definitions

base case operating point

solution to the n-1 security constrained OPF that a competitor presents at the end of their code1 algorithm.

base case to end of contingency time interval

the time between base case to end of contingency time interval this will be either 5 or 10 minutes.

case

network plus a scenario or base case or contingency.

code 1 algorithm

algorithm that determines the base case solution.

code 2 algorithm.

algorithm that determines each contingency case solution at the conclusion of code1.

competitor

an entrant team.

entrant team

consists of one or more entrants designated as a team.

initial point to base case time interval

the time between the initial point and the base case is either 5 or 10 minutes.

network

describes a transmission system and its generators.

post-contingency response

best possible post-contingency operating point for each contingency defined in the scenario. the post-contingency response must be within the operational range of the base case “starting point”.  a competitor must present a response in individual files for each contingency in a scenario. these individual post-contingency operating points do not need to be proven to be n-1 feasible.

prior operating point

the starting condition for generator ramping and availability for the base case solution. the prior-operating point to the base case determines the ramping limit and fast start availability for generators in the base case operating point.

problem datasets or problem

contains all the parameters necessary to solve an optimization problem in the competition. every individual problem within the competition has a “network” (genus) and a “scenario” (species).

problem scoring

for each problem, a competitor receives a separate score for its base case solution and for each contingency solution. the problem score is the base case score plus the sum of the contingency scores divided by the number of contingencies.

scenario

a scenario and a network complete a problem dataset.  a scenario consists of a prior operating point, maximum load, maximum operating level of renewable generators, and set of contingencies. there may be multiple scenarios for each network. there may be differences in topology, availability, load, renewables, or dispatch/operational situation (e.g. time intervals) between each scenario.

scoring

see the scoring document.

solution

a solution to a dataset consists of a base operating point and an operating point in each contingency

trial problem datasets

are the problems that the competitors must solve during a trial.

Challenge 2 Final Dataset (C2FD) 

will be used for the officially scored round of the GO Competition Challenge 2. Conditions will be like each of the trial events with the new C2FD used for evaluation and scoring. A deadline for the submission of solution software will be established at least one month prior to the final event. Immediately following the deadline, the software from all competitors will be run and scored against the C2FD. The Challenge 2 Final Dataset score for each competitor submission will be displayed on the competition leader board.  Challenge 2 winners will be determined based on the C2FD dataset score subject to the winning criteria specified in the official Competition Rules document.

Challenge 2 Sandbox Dataset

released at the start of Challenge 2 in order to allow competitors to develop solution methods. The dataset will consist of multiple power system network models each containing multiple scenarios. Competitors will be able to download the dataset to test algorithms within their own development environment. Competitors can also submit software to be scored against the Sandbox dataset using the official competition platform using the Sandbox submission.

Challenge 2 Trial Dataset 1 (C2TD1) 

will be accessible, via submissions to the evaluation platform, approximately 6 months after the initiation of Challenge 2 as the first of two dry-run "trial" rounds for the GO Competition. This previously unreleased dataset is expected to be of similar complexity and scope as the P2OD, but will not be released until after the conclusion of the trial events.

Challenge 2 Trial Dataset 2 (C2TD2) 

will be accessible, via submissions to the evaluation platform, approximately 9 months after the initiation of Challenge 2 as the second of two dry-run "trial" rounds for the GO Competition. This previously unreleased dataset is expected to be of similar complexity and scope as the P2OD, but will not be released until after the conclusion of the trial events.

Dataset

A collection of power system network models and scenario data on those models. Challenge 2 will have four distinct datasets: Sandbox, C2TD1, C2TD2, and C2FD.

Dataset Score

is computed by taking the geometric mean of all power system network models in each dataset.

optimal power flow (OPF)

expresses the conditions which give the lowest cost per unit of energy delivered. This is achieved by minimizing an objective (cost) function by changing system controls while considering various constraints that are used to satisfy power balance requirements and limits. An alternating current (AC) OPF is a nonlinear model; nonlinear because the power flow into load impedances is a function of the square of the applied voltages. A direct current (DC) OPF is a linear, but less accurate, power-flow model. Unless otherwise specified, OPF is assumed to be (AC) OPF in the context of the GO Competition.

power system network model 

consists of each hypothetical grid with defined topological structure and characteristics including, but not limited to, locations of generators, loads, transmission lines, transformers, equipment detail, control equipment, and limits.

Power System Network Model Score

(relevant to Divisions 1 and 2 of Challenge 2) power system network model score is calculated by taking the geometric mean across all scenario scores associated with a power system network model.

preventative security-constrained optimal power flow (PSCOPF)

is the form of the SCOPF problem in which none of the contingencies will result in a constraint violation in the absence of corrective action.

scenario

in the context of power systems often means a load snapshot from a coupled sequence of time data. That is not how it is used here. Here a scenario is used to represent an operating instance in time on a power system network model. The scenarios define an instantaneous demand at each bus, renewable resource availability, and other temporary system conditions. This snapshot instance has no correlated relationship to any other instance in the list of scenarios in the dataset. Not only can the instantaneous power demand (load) change between one of the scenarios and another, but so can any of the other parameters, such as line and generator limits and availability. The basic network model, the number of buses, generators, lines, capacitor banks, LTC taps, etc., remains fixed for all the scenarios within a power system network model dataset.

scenario score

(relevant to Divisions 1 and 2 of Challenge 2) is calculated for each scenario of a power system network model. The score consists of the objective function value plus any constraint violation or time penalties. As with the objective function value, a lower score is a better score.

security-constrained optimal power flow problem (SCOPF)

not only minimizes the objective (cost) function for the base (no contingency) case, but provides a solution where all constraints are also satisfied for a set of post-contingency states. It can be formulated in either preventative (PSCOPF) or corrective (CSCOPF) mode. In the preventative mode, none of the contingencies will result in a constraint violation in the absence of corrective action. In the corrective mode, however, remedial actions may be necessary to remove constraint violations.