107 lines
3.3 KiB
Python
107 lines
3.3 KiB
Python
import pandas as pd
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def initialise_SoC(bess):
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"""Initialise the state of charge (SoC) for the BESS."""
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for i in range(0, len(bess["units"])): # initially fully charged
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bess["units"][i]["SoC"] = 1
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return bess
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def initial_site_assignment(c, bess):
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"""Initialise the site assignment for each BESS."""
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for k in range(0, len(bess["units"])):
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# assign each BESS unit to a site
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if k < len(c["site_info"]["sites"]):
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bess["units"][k]["site"] = c["site_info"]["sites"][k]["name"]
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else:
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bess["units"][k]["site"] = "Unassigned"
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return bess
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def discharge_bess(bess, site_name, dt, discharge_power):
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# convert discharge power to discharge energy (kW to kWh)
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discharge_energy = discharge_power * dt / 3600
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"""Discharge the BESS for a specific site."""
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for index, unit in enumerate(bess["units"]):
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if unit["site"] == "Unassigned":
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continue
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if unit["site"] == site_name:
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new_soc = unit["SoC"] - discharge_energy / unit["capacity_kWh"]
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new_soc = 0 if new_soc < 0 else new_soc
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else:
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continue
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# update SoC and current load
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bess["units"][index]["current_load_kW"] = discharge_power
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bess["units"][index]["SoC"] = new_soc
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return bess
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def predict_swap_time(bess_soc_for_cycle):
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"""Predict the swap time for each BESS unit based on its SoC history."""
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swap_times = {}
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min2sec = 60
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threshold = 2 * min2sec # 2 minutes in seconds
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for unit_name, df in bess_soc_for_cycle.items():
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# need to be at least 1 min of operation to start estimation
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if len(df) < threshold:
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swap_times[unit_name] = None
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continue
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# linear extrapolation to estimate swap time
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# calculate the slope of the SoC over time
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m = (df["SoC"].iloc[-1] - df["SoC"].iloc[0]) / (
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df["Timestamp"].iloc[-1] - df["Timestamp"].iloc[0]
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)
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if m == 0:
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swap_times[unit_name] = None
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continue
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# solve for the time when SoC reaches 0
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swap_time = (0 - df["SoC"].iloc[0]) / m + df["Timestamp"].iloc[0]
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# assign to swap_times
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swap_times[unit_name] = swap_time
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return swap_times
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def update_cycle_SoC(bess_data, bess_soc_for_cycle, timestamp):
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init_df = pd.DataFrame(columns=["Timestamp", "SoC"])
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# assign SoC for cycle
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for unit in bess_data["units"]:
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unit_name = unit["name"]
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# reset df if SoC is 0. Start a new cycle
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if unit["SoC"] == 0:
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bess_soc_for_cycle[unit_name] = init_df
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bess_soc_for_cycle[unit_name] = pd.concat(
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[
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bess_soc_for_cycle[unit_name],
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pd.DataFrame(
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[[timestamp, unit["SoC"]]],
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columns=["Timestamp", "SoC"],
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),
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],
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axis=0,
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)
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return bess_soc_for_cycle
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def arrange_swap(bess_data, c):
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for unit in bess_data["units"]:
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if unit["SoC"] < c["bess"]["buffer"]:
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# find for unassigned BESS unit with SOC at 100%
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for candidate in bess_data["units"]:
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if candidate["SoC"] == 1 and candidate["site"] == "Unassigned":
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# assign the candidate to the site
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candidate["site"] = unit["site"]
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break
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