This page is organized as follow:
Table of Contents
The grid2op module allows to model sequential decision making on a powergrid.
It is modular in the sense that it allows to use different powerflow solver (denoted as “Backend”). It proposes an internal representation of the data that can be feed to powergrids and multiple classes to specify how it’s done.
For example, it is possible to use an “action” to set the production value of some powerplant. But we also know that it’s not possible to do this for every powerplant (for example, asking a windfarm to produce more energy is not possible: the only way would be to increase the speed of the wind). It is possible to implement these kind of restrictions in this “game like” environment.
Today, the main usage of this platform is to serve as a computation engine for the L2RPN competitions.
This platform is still under development. If you notice a bug, let us know with a github issue at Grid2Op
Grid2op do not model any object on the powergrid. It has no internal modeling of the equations of the grids, or what kind of solver you need to adopt.
On the other hand, grid2op aims at representing the grid in a relatively “high level” point of view: it knows which “elements” are connected to which, which is the production of this or that generators or how much power is consumed by this load.
But under no circumstances, grid2op will expose some specific modeling of a powergrid. Such modeling are only made in the Backend.
A somewhat relatively “accurate” view of what grid2op is to answer questions such as “if I had put a sensor at this location - eg right next to a powerplant - what would this sensor would have given ? It then takes care of exporting these data to a “format” to the entities acting on the grid.
The primary goal of grid2op is to model decision making process in power systems. Indeed, we believe that developing new flexibilities on the grid would make the “energy transition” an easier, less costly process.
It allows fast development of new “methods” that are able to “take decisions” on a powergrid and assess how well these controllers perform (see section Controlling the grid for more information about the “controls”) .
Thanks to a careful separation between:
the data used to represent how the powergrid is evolving (represented by the Chronics)
the solver that is able to compute the state of the grid (represented by the Backend)
the controller / agent that takes action on the powergrid (represented by the Agent)
All bound together thanks to the
Grid2op attempts also to make the development of new control methods as easy as possible: it is relatively simple to generate data and train agent on them and to use a fast (but less precise powerflow) while trying to develop new state of the art methods. While still being usable in a “closer to reality” setting where data can come from real grid state that happened in the past and the solver is as accurate as possible. You can switch from one to the other almost effortlessly.
For a more detailed description, one can consult the Reinforcement Learning for Electricity Network Operation paper.
Though grid2op has been primarily developed for the L2RPN competitions series, it is more general. Its modularity can also help developing and benchmarking new powerflow solvers for example.
Modeling all what happens in the powergrid would be an extremely difficult task. Grid2op focusing on controls
that could be done today by a human (happening with a frequency of approximately the minute). It does not
aim at simulation really high frequency control that are often automatic today. That being said, such controls
can be taken into account by grid2op if the
grid2op.Backend allows it.
The main focus of grid2op is to make easy to use of the topology to control the flows of the grid. In real time, it is possible to reconfigure the “topology” of the grid (you can think about it by the action on changing the graph of the power network). Such modifications are highly non linear and can have a really counter intuitive impact and we believe they are under used by industry and are under studied by academics at the moment (feel free to visit the notebooks 0_Introduction.ipynb, 0_SmallExample.ipynb or the IEEE BDA Tutorial Series.ipynb of the official grid2op github repository for more information)
Along with the topology, grid2op allows easily to manipulate (and thus control):
the voltages: by manipulating shunts, or by changing the setpoint value of the generators
the active generation: by the use of the “redispatching” action.
the storage units (batteries or pumped storage) that allows to produce some energy / absorb some energy from the powergrid when needed.
Other “flexibilities” (ways to act on the grid) are coming soon (-:
We wanted to emphasize the particularity of the problem proposed in grid2Op. Indeed, the objective is to act on a graph (observation space = a graph, action space = modification of this graph).
As opposed to many graph related problems addressed in the literature, we do not try to find some properties of a dataset represented as one (or many) graph(s).
Controlling a powergrid rather means to find a graph that meets some properties (eg all weights on all edges aka the flows on the powerlines, must be bellow some threshold aka the thermal limits - NB a solver uses some physical laws to compute these “weights” from the amount of power produced / absorbed in different part of the grid where generators and loads are connected).
The simulator is able to emulate a power grid (of any size or characteristics) subject to a set of temporal injections (productions and consumptions) or maintenance / hazards for discretized time-steps (usually there is the equivalent of 5 minutes between two consective steps).
Say a powergrid is represented as a graph with:
the edges being the powerlines (and transformers)
the nodes being the “bus” (a bus is the power system terminology to denotes the “things” (aka nodes) that are connected by the edges)
Grid2op does not explicitly model the “graph” of the grid as a “graph” structure. For performances, it is represented as a vector, as explained in paragraph “Rephrasing the topology control problem”. To be exhaustive, the way to map this graph to this vector is explained in the page “Creating a new backend” (though this page is really detailed and has too much information for most grid2op usage).
Some functions have been coded to retrieve the state, as a “graph” (more precisely a square matrix). These methods are described in the section “But where is the graph ?” of the Observation module.
This graph has some properties:
some buses are labeled “generators” that produces a certain amount of power
some buses are labeled “loads” that consumes a certain amount of power (NB a bus can be both a generator and a load, in this case both the production and the demand should be met at his node)
all edges have some “weights”: some physical laws (eg conservation of energy or more specifically Kirchoff Circuits Laws), that cannot be altered (and are computed by the Backend), induced some flows on the powerline that can be represented as “weights” on this graph
it is dynamic: at different steps, the graph can be different, for example, it is possible to have a “node” with load 1, load 2, line 1 and line 2 and a given step, and to “split” this node in two to have, at another step load 1 and line 2 on a “node” and “load 2” and “line 1” on a different node (and the other way around).
This graph has some constraints:
the total generation (sum of production of all generator) should be exactly equal to the total demand (sum of consumption of all loads) and the power losses (due to the heating of the powerlines for example)
the generators should always be connected to the grid, otherwise this is a blackout
the loads should always be connected to the grid, otherwise this is a blackout
the graph of the grid should be connected (made of one unique connex component): otherwise the condition number 1 above (sum production = sum load + sum losses) will not be met in each of the independant subgraph, most likely.
there exist a solution to the Kirchoff Circuits Laws
For more information on this “graph” and the way to retrieve it in different format, you can consult the page A grid, a graph: grid2op representation of the powergrid of the documentation.
The whole grid2op ecosystem aims at modeling the evolution of a “controller” that is able to make sure the “graph of grid”, at all time meets all the constraints.
More concretely a grid2op environment models “out of the box”:
the mechanism to “implement” a control on the grid (with a dedicated action module) that can be used by any Agent, which takes some decisions to maintain the grid in security
time series of loads and productions: which represents the evolution of the power injected / withdrawn at each bus of the grid, at any time (NB the Agent do not see the future, it means that it cannot have an exact value for each of the loads in the future, but can only observe the current sate)
a mechanism (that can be implemented using different solver) to compute the flows based on the injections (which among of power is produced at each nodes) and the topology (graph of the grid)
the automatic disconnection of powerlines if there are on overflow for too long (known as “time overcurrent (TOC)” see this article for more information overcurrent ) Conceptually this means the environment remember for how long a powergrid is in “overflow” and disconnects it if needed. NB This is an emulation of what happen on the grid, in case you use a Backend that do not have this feature (for example if you use static / steady state powerflow). This emulation might not be necessary (and less “realistic” if you use a time domain simulator)
the disconnection of powerlines if the overflow is too high (known as “instantaneous overcurrent” see the same wikipedia article). This means from one step to another, a given powerline can be disconnected if too much flow goes through it. NB This is an emulation of what happen on the grid, in case you use a Backend that do not have this feature (for example if you use static / steady state powerflow). This emulation might not be necessary (and less “realistic” if you use a time domain simulator)
the maintenance operations: if there is a planned maintenance, the environment is able to disconnect a powerline for a given amount of steps and preventing its reconnection. There are information about such planned event that are given to the controller.
hazards / unplanned outages / attacks: another issue on power system is the fact that sometimes, some powerline get disconnected in a non planned manner. For example, a tree can fall on a powerline, the grid might suffer a cyber attack etc. This can also be modeled by grid2op.
prevent the action on some powerlines: whether it is to model the fact in reality it is not possible to always act on the same equipment or because some powerline are out of service (because of an attack, a maintenance or because it needs to be repaired), grid2op can model the impossibility of acting on a given powerline
prevent the action on some substations: for the same reasons, sometimes you cannot act on given part of the network, preventing you to do some topological actions.
voltage control: though it is not the main focus of the current platform, grid2op can model automatons that can take voltage corrective measures (in the near future we think of adding some protection monitoring voltage violation too).
non violation of generator physical constraints: in real life, generator cannot produce too little nor too much (we speak about gen_pmin and gen_pmax) nor their production can vary too much between consecutive steps (this is called gen_max_ramp_down and gen_max_ramp_up)
stops the game if the grid is in a too bad shape. This can happen if a load or a generator has been disconnected, or if some part of the grid is “islanded” (the graph representing the power network is not connex) or if there is no feasible solution to the power system equations
Here are a summary of the main modules:
Implements all the mechanisms described above
In charge of feeding the data (loads, generations, planned maintenance, etc.) to the Environment
Carries out the computation of the powergrid state
The controller, in charge of managing the safety of the grid
The control send by the Agent to the Environment
The information sent by the Environment to the Agent, represents the powergrid state as seen by the Agent
Is present to model the unplanned disconnections of powerline
Computes whether or not an action is “legal” at a given time step
Store the parameters that defines for example, on which case an action is legal, or how long a powerline can stay on overflow etc.
The grid2op environments have multiple shared properties:
highly constrained environments: these environments obey physical laws. You cannot directly choose how much power flow on a given powerline, what you can do it choosing the “graph” of the power network and (under some constraints) the production of each generators. Knowing these information at any time steps, the powergrid state must satisfy the Kirchhoff’s circuit laws .
stochastic environments: in all environment, you don’t know the future, which makes it a “Partially Observable” environments (if you were in a maze, you would not see “from above” but rather see “at the first person”: only seeing in front of you). Environments can be “even more stochastic” if there are hazards or even adversarial: a malicious agent can take attacks targeted to endanger your policy.
with both continuous and discrete observation space: some part of the observation are continuous (for example the amount of flow on a given powerline, or the production of this generator) and some are discrete ( for example the status - connected / disconnected - of a powerline, or how long this powerline has been in overflow etc.)
with both continuous and discrete action space: the preferred type of action is the topology, which is represented as a discrete type of action (eg you can either connect / disconnect a powerline) but there exist also some continuous actions (for example you can adjust in real time the production of a set of generators)
dynamic graph manipulation: power network can be modeled as graphs. In these environments both the observation and the action are focused on graph. The observation contains the complete state of the grid, including the “topology” (you can think of it a its graph) and actions are focused on adapting this graph to make the grid as robust and secure as possible. NB As opposed to most problem in the literature, where you need to find some properties (label of of the edges or the nodes, etc.) in grid2op you need to find a graph that meets some properties: find a graph that meets constraints on its edges and its nodes.
strong emphasis on safety and security: power system are highly critical system (who would want to short circuit a powerplant? Or causing a blackout preventing an hospital to cure the patients?) and as such it is critical that the controls keep the powergrid safe in all circumstances.
Grid2op is a research testbed platform, it shall not be use in “production” for any kind of application.
To get started into the grid2op ecosystem, we made a set of notebooks that are available, without any installation thanks to Binder . Feel free to visit the “getting_started” page for more information and a detailed tour about the issue that grid2op tries to address.
As of writing (december 2020) most of these notebooks focus on the “agent” part of grid2op. We would welcome any contribution to better explain the other aspect of this platform.