CAISO_LMP.py

#   Developed by Greg Miller, grmiller@ucdavis.edu
#   Version 1
#   Last Updated January 16, 2019

#   Purpose: To create a shapefile and csv file of Locational Marginal Pricing Nodes in CAISO and most of WECC http://wwwmobile.caiso.com/Web.Service.Chart/pricecontourmap.html

import csv
import json
import pandas as pd
from pathlib import Path
import requests
import shapefile

#global constants
price_map_url = "http://wwwmobile.caiso.com/Web.Service.Chart/api/v3/ChartService/PriceContourMap1"
"""
json document tree
<PriceContourMapDto>
    <l>
        <PriceContourLayer> <l>3</l>
            <m>
                <PriceCountourMarker> #each LMP node
                    <c xmlns:d6p1="http://schemas.microsoft.com/2003/10/Serialization/Arrays">
                        <d6p1:decimal>40.53944</d6p1:decimal>           #latitude
                        <d6p1:decimal>-111.89667</d6p1:decimal>         #longitude
                    </c>
                    <dc>52.81386</dc>           #Day-ahead energy $
                    <dg>-3.52064</dg>           #Day-ahead congestion $
                    <dk>0</dk>                  #price color code
                    <dl>-2.26571</dl>           #Day-ahead losses $
                    <do>1</do>                  #unique ordering key for each node
                    <dp>47.02751</dp>           #Day-ahead price $
                    <fc>56.60284</fc>           #Fifteen Minute Market energy $
                    <fg>0</fg>                  #FMM congestion $
                    <fk>1</fk>
                    <fl>-3.01127</fl>           #FMM losses $
                    <fo>1</fo>
                    <fp>53.59157</fp>           #FMM price $
                    <n>118THSO_LNODER1</n>      #Node Name
                    <p>LOAD</p>                 #Load Type
                    <qc>73.41486</qc>           #Real-Time Dispatch energy $
                    <qg>-0.0024</qg>            #RTD congestion $
                    <qk>4</qk>                  
                    <ql>-3.94959</ql>           #RTD losses $
                    <qo>1</qo>                  
                    <qp>69.46287</qp>           #RTD price $
                    <t>Node</t>
                </PriceContourMarker>

"""
lmp_csv = Path.cwd() / "LMP_coordinates.csv"

def get_lmp_loc(): # This function has been adapted from WattTime/pyiso/caiso.py commit c99beaf44436753d30e48cc7f8f692d3a524d2c5 r24mille committed on Dec 14, 2017
    """
    CURRENTLY INCONSISTENTLY FAILING
    status 401, response content "Access is restricted to authorized clients."
    FORMERLY CONSISTENT BEHAVIOR:
    Returns a list of dictionaries containing node atlas data.  Each
    dictionary will contain
    {'node_id': <name of the node>
        'latitide': <latitude of the node>,
        'longitude': <longitude of the node>,
        'area': <control area the node belongs to>}
    This list is generated by parsing the JSON returned from the CAISO
    price map URL:
    http://wwwmobile.caiso.com/Web.Service.Chart/api/v3/ChartService/PriceContourMap1
    This json is what is used in the interactive CAISO price
    contour map found at:
    http://wwwmobile.caiso.com/Web.Service.Chart/pricecontourmap.html
    This retuned JSON does not contain geographic data for all CAISO
    pricing nodes
    """
    # get json from the price map url used in the CAISO interactive price
    # map http://wwwmobile.caiso.com/Web.Service.Chart/pricecontourmap.html
    r = requests.get(price_map_url) 
    json_obj = r.json()

    # parse the json to create the node location dictionary
    node_entries = json_obj['l'][2]['m']
    return_dict = [{'node_id': str(entry['n']),
                    'latitude': entry['c'][0],
                    'longitude': entry['c'][1],
                    'area': str(entry['a']),
                    'node_type': str(entry['p'])} for entry in node_entries]
    return return_dict

def main():
    lmp_dict = get_lmp_loc()
    df_lmp = pd.DataFrame.from_dict(lmp_dict)
    with open(lmp_csv,'w+') as f:    
        df_lmp.to_csv(f, header=True, index=False, lineterminator='\n')
    csv_to_shp()
    print("Shapefile created")

def csv_to_shp(): #adapted from https://glenbambrick.com/2016/01/09/csv-to-shapefile-with-pyshp/
    lmp_shp = shapefile.Writer(Path.cwd() / "LMP/caiso_lmp", shapeType=shapefile.POINT) 
    lmp_shp.autoBalance = 1 #This enforces that for every record there must be a corresponding geometry.
    lmp_shp.field("AREA","C") #create field names and data types
    lmp_shp.field("NODE_ID","C")
    lmp_shp.field("TYPE","C")

    with open(lmp_csv) as csvfile:
        reader = csv.reader(csvfile,delimiter=',')
        next(reader,None)
        for row in reader:
            area = row[0]
            latitude = row[1]
            longitude = row[2]
            node_id = row[3]
            node_type = row[4]
            lmp_shp.point(float(longitude),float(latitude)) #set geometry for each record
            lmp_shp.record(area, node_id, node_type) #create matching record for the geometry using the attributes

if __name__== "__main__":
    main()