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Markov-Decision-for-Battery-Scheduling

Authors: Hussein Sharadga, Ahmad Dawahdeh

Paper: Scheduling Battery Systems Under Load Uncertianty Using Markov Decision Process

Pricing policy A has full details and notations so you might start with it.

Pricing policies A-C consumes about 15 minutes for Gurobi solver to return the solution.

Pricing policy D is for peak shaving which consumes a lot of time (about 9 hours).

Pricing policy D is for 6 peak thresholds: consumes about 9 hours.

Pricing policy D2 is for 11 peak thresholds: consumes about 3 days (working hours; for example 12 hours day).

Pricing policy A PV - policy C PV is after including the PV energy: 1000 Panels are used to meet some portion of the School demand.

data.pkl is the Solution for Policy D.

data1.pkl is the Solution for Policy D2.

data2.pkl is the Solution for 21 peak thresholds.

#For Solution loading:

import pprint, pickle
pkl_file = open('data.pkl', 'rb')
y_ = pickle.load(pkl_file)
pkl_file.close()

Should you face an issue running the codes, please feel free to drop a LinkedIn message (https://www.linkedin.com/in/hussein-sharadga/).

You can also reach me at [email protected].

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