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egrid-0.0.4
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مشاهده بیشترتوضیحات
Model of an electric distribution network for calculation
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | egrid-0.0.4 |
| نام | egrid |
| نسخه کتابخانه | 0.0.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | pyprg |
| ایمیل نویسنده | pyprg@outlook.com |
| آدرس صفحه اصلی | https://github.com/pyprg/egrid |
| آدرس اینترنتی | https://pypi.org/project/egrid/ |
| مجوز | - |
# egrid
## Purpose
Model of a balanced electric distribution network for experimental power flow
calculation and state estimation. Instances of Model can be the input for
(a variety of) power flow calculation or estimation algorithms. They
provide an easy to use structure for calculating current and power flow
through lines and into consumers (using an additional voltage vector which
is the result of a power-flow-calculation).
Function **make_model(\*args)** creates an instance of Model from arguments
of type
- Slacknode
- Branch (line, series capacitor, transformer winding, transformer,
closed switch)
- Injection (consumer, shunt capacitor, PQ/PV-generator, battery)
- Branchtaps
- Output (indicates that measured flow (I, P or Q) or a part thereof
flows through the referenced terminal (device or node+device))
- PValue (measured active power)
- QValue (measured reactive power)
- IValue (measured electric current)
- Vvalue (measured voltage or setpoint)
- Defk (definition of a scaling factor, for estimation)
- Link (associates a scaling factor to a load)
including tuples, lists and iterables thereof (for a power-flow-calculation
just Slacknode ... Branchtaps are necessary).
Additionally, __make_model__ can consume network descriptions as multiline
strings if package 'graphparser' is installed. This method is intended to
input very small electric networks using a text editor.
Most fields of Model instances provide pandas.DataFrame instances. Electric
values are stored per unit. Branch models are PI-equivalent circuits. Active
and reactive power of injections have a dedicated voltage exponent.
## Details of egrid.model.Model
Fields of egrid.model.Model
---------------------------
nodes: pandas.DataFrame (id of node)
* .idx, int index of node
slacks: pandas.DataFrame
* .id_of_node, str, id of connection node
* .V, complex, given voltage at this slack
* .index_of_node, int, index of connection node
injections: pandas.DataFrame
* .id, str, unique identifier of injection
* .id_of_node, str, unique identifier of connected node
* .P10, float, active power at voltage magnitude 1.0 pu
* .Q10, float, reactive power at voltage magnitude 1.0 pu
* .Exp_v_p, float, voltage dependency exponent of active power
* .Exp_v_q, float, voltage dependency exponent of reactive power
* .index_of_node, int, index of connected node
branchterminals: pandas.DataFrame
* .index_of_branch, int, index of branch
* .id_of_branch, str, unique idendifier of branch
* .id_of_node, str, unique identifier of connected node
* .id_of_other_node, str, unique identifier of node connected
at other side of the branch
* .index_of_node, int, index of connected node
* .index_of_other_node, int, index of node connected at other side
of the branch
* .y_lo, complex, longitudinal branch admittance
* .y_tr_half, complex, half of transversal branch admittance
* .g_lo, float, longitudinal conductance
* .b_lo, float, longitudinal susceptance
* .g_tr_half, float, transversal conductance of branch devided by 2
* .b_tr_half, float, transversal susceptance of branch devided by 2
* .side, str, 'A' | 'B', side of branch, first or second
branchoutputs: pandas.DataFrame
* .id_of_batch, str, unique identifier of measurement batch
* .id_of_node, str, id of node connected to branch terminal
* .id_of_branch, str, unique identifier of branch
* .index_of_node, int, index of node connected to branch terminal
* .index_of_branch, int, index of branch
injectionoutputs: pandas.DataFrame
* .id_of_batch, str, unique identifier of measurement batch
* .id_of_injection, str, unique identifier of injection
* .index_of_injection, str, index of injection
pvalues: pandas.DataFrame
* .id_of_batch, str, unique identifier of measurement batch
* .P, float, active power
* .direction, float, -1: from device into node, 1: from node into device
qvalues: pandas.DataFrame
* .id_of_batch, str, unique identifier of measurement batch
* .Q, float, reactive power
* .direction, float, -1: from device into node, 1: from node into device
ivalues: pandas.DataFrame
* .id_of_batch, str, unique identifier of measurement batch
* .I, float, electric current
vvalues: pandas.DataFrame
* .id_of_node, str, unique identifier of node voltage is given for
* .V, float, magnitude of voltage
* .index_of_node, index of node voltage is given for
branchtaps: pandas.DataFrame
* .id, str, IDs of taps
* .id_of_node, str, ID of associated node
* .id_of_branch, str, ID of associated branch
* .Vstep, float, magnitude of voltage difference per step, pu
* .positionmin, int, smallest tap position
* .positionneutral, int, tap with ratio 1:1
* .positionmax, int, position of greates tap
* .position, int, actual position
shape_of_Y: tuple (int, int)
shape of admittance matrix for power flow calculation
count_of_slacks: int
count_of_slacks
load_scaling_factors: pandas.DataFrame (index: 'step','id')
* .type, 'var'|'const', type of factor decision variable or parameter
* .id_of_source, str, id of scaling factor (previous optimization step)
for initialization
* .value, float, used by initialization if no source factor in previous
optimization step
* .min, float
smallest possible value
* .max, float
greatest possible value
mnodeinj: scipy.sparse.csc_matrix
converts a vector ordered according to injection indices to a vector
ordered according to power flow calculation nodes (adding values of
injections for each node) by calculating 'mnodeinj @ vector'
injection_factor_associations: pandas.DataFrame (index: 'step','injid','part')
* .id, str, unique identifier of scaling factor
messages: pandas.DataFrame
* .message, str, message on reason of error
* .level, int, 0 - information, 1 - warning. 2 - error
## Making a Model
Function **model_from_frames** consumes a dictionary of
pandas.DataFrames. **model_from_frames** aggregates nodes connected without
impedance, creates indices, arranges data per branch-terminal from branch-data,
calculates values of branches from admittances.
Function make_model generates a model from network device objects defined
in **egrid.builder**.
Example - 3 nodes, 2 lines, 1 consumer:
```
node: 0 1 2
| line | line |
+-----=====-----+-----=====-----+
| | |
\|/ consumer
```
Python code for example, suitable input for function **egrid.make_model**
(Branchtap is for demo only, it is used with transformers,
however, transformers/transformerwindings are modeled using class Branch too.):
```
from egrid.builder import (
Slacknode, PValue, QValue, IValue, Output, Branch, Branchtaps,
Injection, Defk, Link)
example = [
Slacknode(id_of_node='n_0', V=1.+0.j),
PValue(
id_of_batch='pq_line_0',
P=30.),
QValue(
id_of_batch='pq_line_0',
Q=8.),
Output(
id_of_batch='pq_line_0',
id_of_node='n_0',
id_of_device='line_0'),
IValue(
id_of_batch='i_line_0',
I=40.0),
Output(
id_of_batch='i_line_0',
id_of_node='n_0',
id_of_device='line_0'),
Branch(
id='line_0',
id_of_node_A='n_0',
id_of_node_B='n_1',
y_lo=1e3-1e3j,
y_tr=1e-6+1e-6j),
Branchtaps(
id='taps_0',
id_of_node='n_0',
id_of_branch='line_0',
Vstep=.2/33,
positionmin=-16,
positionneutral=0,
positionmax=16,
position=0),
Branch(
id='line_1',
id_of_node_A='n_1',
id_of_node_B='n_2',
y_lo=1e3-1e3j,
y_tr=1e-6+1e-6j),
Output(
id_of_batch='pq_consumer_0',
id_of_device='consumer_0'),
Output(
id_of_batch='i_consumer_0',
id_of_device='consumer_0'),
Injection(
id='consumer_0',
id_of_node='n_2',
P10=30.0,
Q10=10.0,
Exp_v_p=2.0,
Exp_v_q=2.0),
Defk(step=(0, 1, 2), id=('kp', 'kq')),
Link(step=(0, 1, 2), objid='consumer_0', part='pq', id=('kp', 'kq'))]
```
Valid input to **make_model** is a multiline pseudo graphic string e.g.
this one:
```
y_tr=1e-6+1e-6j y_tr=1e-6+1e-6j
slack=True y_lo=1e3-1e3j y_lo=1e3-1e3j
n0(---------- line_0 ----------)n1(---------- line_1 ----------)n2
| |
n1->> load0_1_ _load1 <<-n2->> load1_1_
| P10=30.0 P10=20.7 P10=4.3
| Q10=5 Q10=5.7 Q10=2
|
| y_lo=1e3-1e3j
| y_tr=1e-6+1e-6j
n1(---------- line_2 ----------)n3
|
_load2 <<-n3->> load2_1_
P10=20.7 P10=20
Q10=5.7 Q10=5.7
```
نیازمندی
| مقدار | نام |
|---|---|
| ==0.8.2 | graphparser |
| ==2.6.4a0 | networkx |
| ==1.22.3 | numpy |
| ==1.4.0 | pandas |
| ==1.8.0 | scipy |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl egrid-0.0.4:
pip install egrid-0.0.4.whl
نصب پکیج tar.gz egrid-0.0.4:
pip install egrid-0.0.4.tar.gz