Commit 9c6af9eb authored by Alberto Dognini's avatar Alberto Dognini
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Update README.md, with new, simplified files

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The service restoration aims at reconfiguring the electrical network after the occurrence of faults, in which the protection system coordinates the tripping of circuit breakers upstream and downstream of the fault location.
Distribution networks are managed with radial scheme; therefore, the nodes downstream of the faulted zone become de-energized and they have to be re-powered from an alternative source.
The algorithm present in this repository considers, among the de-energized nodes, the one with highest priority as target for the restoration and identify which is the most suitable primary substation to energize it, by closing the normally open bus-tie unit.
The restoration schemes from each substation toward the target node are evaluated with a state estimation approach (which also allows to check the voltage, thermal and radiality constraints); the restoration having the lowest summation of total power losses and utilization of the most consumed electrical lines (weighted with parameters `u1`, `u2`, `u3` and `u4`) is implemented.
The algorithm present in this repository considers, among the de-energized nodes, the one with highest priority as target for the restoration and identifies which is the most suitable primary substation to energize it, by closing the normally open bus-tie unit.
The restoration schemes from each substation toward the target node are evaluated with a state estimation approach (which also allows to check the voltage, thermal and radiality constraints); the algorithm considers total power losses and utilization of the most consumed electrical lines as objectives included in the Multi-Critera Decision Making (MCDM) approach, setted by the users.
Once the selected tie unit successfully closes, the process repeats untill all the de-energized loads are restored or the constraints are violated. The algorithm is able to manages multiple/cascade faults in active distribution grids (with Distributed Energy Resources).
## Contents
This repository contains the following material:
- Python code [`RBO_Service_Restoration_offline.py`](https://git.rwth-aachen.de/acs/public/automation/rbosr/blob/master/RBO_Service_Restoration_offline.py) of rule-based optimization algorithm for service restoration (RBOSR).
- Python code [`DSSE_Functions_FLISR.py`](https://git.rwth-aachen.de/acs/public/automation/rbosr/blob/master/DSSE_Functions_FLISR.py) that includes the distribution system state estimator functions
- Excel file [`Networkdata_MV_FLISR_use_case_1.xlsx`](https://git.rwth-aachen.de/acs/public/automation/rbosr/blob/master/Networkdata_MV_FLISR_use_case_1.xlsx) that includes all the necessary grid data to run the service restoration process.
- Python code [`RBO_Service_Restoration.py`](https://git.rwth-aachen.de/acs/public/automation/rbosr/-/blob/master/RBO_Service_Restoration.py.py) of rule-based optimization algorithm for service restoration (RBOSR).
- Python code [`DSSE_Functions_FLISR_1.py`](https://git.rwth-aachen.de/acs/public/automation/rbosr/-/blob/master/dsse_functions_FLISR_1.py) that includes the distribution system state estimator functions
- Excel file [`networkdata_FLIS.xlsx`](https://git.rwth-aachen.de/acs/public/automation/rbosr/-/blob/master/networkdata_FLISR.xlsx) that includes all the necessary grid data to run the service restoration process.
- PDF file [`Networkdata_MV_FLISR_use_case.pdf`](https://git.rwth-aachen.de/acs/public/automation/rbosr/blob/master/Networkdata_MV_FLISR_use_case.pdf) that summarizes the relevant grid data.
- Backup file [`RBOSR_Database.backup`](https://git.rwth-aachen.de/acs/public/automation/rbosr/blob/master/RBOSR_Database.backup) of the PostgreSQL database.
## Usage
......@@ -24,22 +23,14 @@ Instructions to use the service restoration algorithm:
1. The two Python codes and the excel file must be located in the same folder.
2. Configure the excel sheet, providing information about grid topology, loads, switches and fault location (set the tripped circuit breakers).
3. If changed, set the excel file name in the RBOSR Python code (in line 1726).
4. Set the weighting factors `u1`, `u2`, `u3` and `u4` according to the desired restoration target in the RBOSR Python code (in line 1714). Additional information are found in the paper "Rule-Based Optimization Algorithm for Service Restoration of Active Distribution Grids", authors: A. Dognini, A. Sadu, A. Angioni, F. Ponci, A. Monti, currently under revision.
5. Set the information about the PostgreSQL database in the RBOSR Python code (in line 1679).
Considerations about the PostgreSQL database:
The algorithm has been tested using the PostgreSQL database developed with the software "pgAdmin3".
Download the software from [`this page`](https://www.pgadmin.org/download/) and follow the installation instructions.
Connect to server as described in the [`documentation`](https://www.pgadmin.org/docs/pgadmin3/1.22/) and restore the [`RBOSR_Database.backup`](---) file, to recreate the implemented database.
3. If changed, set the excel file name in the RBOSR Python code (in line 910).
4. Set the comparison factors `uL1`, `uL2`, `uL3`, `u12`, `u13` and `u23` according to the MCDM desired restoration targets in the RBO_Service_Restoration Python code (in line 928). Additional information are found in the papers "How to make a decision: The analytic hierarchyprocess", author: Thomas L. Saaty and "Service Restoration Algorithm for Distribution Grids under High Impact Low Probability Events", authors: A. Dognini, A. Sadu, A. Angioni, F. Ponci, A. Monti, currently under publication process.
5. Run the Python code `RBO_Service_Restoration.py`, which retrieves the faulted grid data, computes the possible existing solutions and publishes the results
Considerations about the RBOSR Python code:
- It generates an Excel sheets (line 1703) with information about the elapsed computation time.
- The code has been developed with Python 3.6
- The code has been developed with Python 3.7
- It needs, among others, the installation of the following libraries:
- `pandas`
- `networkx`
......@@ -60,8 +51,7 @@ https://git.rwth-aachen.de/acs/public/automation/rbosr
For additional information, refer to the following articles:
- A. Dognini, A. Sadu, A. Angioni, F. Ponci, A. Monti, "Rule-Based Optimization Algorithm for Service Restoration of Active Distribution Grids" (_currently under revision_)
- A. Dognini, A. Sadu, A. Angioni, F. Ponci, A. Monti, [`Service Restoration Algorithm for Distribution Grids under High Impact Low Probability Events`] (_under publication process_), 2020 IEEE Innovative Smart Grid Technologies - Europe (ISGT-Europe), Oct 2020.
- C. Muscas, S. Sulis, A. Angioni, F. Ponci, and A. Monti, [`Impact of Different Uncertainty Sources on a Three-Phase State Estimator for Distribution Networks`](https://ieeexplore.ieee.org/document/6775299), IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 9, pp. 2200-2209, Sep. 2014.
......@@ -69,6 +59,8 @@ For additional information, refer to the following articles:
- P. Jamborsalamati, A. Sadu, F. Ponci, A. Monti and M. J. Hossain, [`Improvement of supply restoration in multi-spare-feeder active distribution grids using IEC 61850`](https://ieeexplore.ieee.org/document/8378326), 2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), pp. 1-5, Dec 2017.
- Thonas L. Saaty "How to make a decision: The analytic hierarchy process", European Journal of Operational Research, Volume 48, Issue 1, 5 September 1990, Pages 9-26, https://doi.org/10.1016/0377-2217(90)90057-I
## Copyright
2019, Institute for Automation of Complex Power Systems, EONERC
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