Working with Multi-Network Data

There are occasions when it will be desirable to support multiple replicates of the same network, where there might be additional dimensions to represent variable network parameters. These dimensions might correspond to time for dynamic networks, or scenarios for stochastic networks.

To distinguish between network data (see The Network Data Dictionary) that correspond to a single network or to multiple networks, PowerModels.jl provides

For example, we can do the following:

network_data = PowerModels.parse_file(case3file)
pm = build_generic_model(network_data, ACPPowerModel, PowerModels.post_opf)

PowerModels.ismultinetwork(pm)

PowerModels.jl would generally not read in network data as multi-networks. To generate multiple networks from the same network data, we use the following method

For example, we can make three replicates by calling

network_data3 = PowerModels.replicate(network_data, 3)

Observe that the structure of network_data3 is different from that of network_data, since it is a multi-network. The user can then modify each replicate of the network to vary in the corresponding parameter of interest. See test/common.jl for examples on setting up valid Multi-Network data.

To build a PowerModel from a multinetwork data dictionary (see Building PowerModels from Network Data Dictionaries), we supply multinetwork=true during the call to build_generic_model and replace post_opf with post_mn_opf,

pm3 = PowerModels.build_generic_model(network_data3, ACPPowerModel, PowerModels.post_mn_opf, multinetwork=true)

PowerModels.ismultinetwork(pm3)

data33 = PowerModels.replicate(data3, 3)

Because this is a common pattern of usage, we provide corresponding calls to run_mn_opf (which behaves analogously to run_opf, but for multinetworks), run_mn_opb, run_mn_pf, run_mn_mc_opf, run_mn_strg_opf and run_mn_mc_strg_opf.