89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
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import math
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import pandas as pd
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data = pd.read_csv('new_file.csv')
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demand = [0] + data['Q_tot_mpc'].to_list()
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t_out = [0] + data['T_out'].to_list()
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data['Datetime'] = pd.to_datetime(data['Datetime'])
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p_electricity = data['Electricity_Price'].to_list()
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cp = 4182 # J/kgK
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# Heat Pump Sizing
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hp_cap = max(demand) * 0.7 * 1000
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hp_cop_curve_coefficients = [1.039924, 0.0146, 6e-06, -0.05026, 0.000635, -0.000154]
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hp_nominal_efficiency = 2.5
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hp_delta_t = 5
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# TES Sizing
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volume = round(max(demand) * 3.6e6 / (1000 * cp * 15))
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height = 2
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d = math.sqrt((4 * volume) / (math.pi * height))
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ua = 0.28
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variable_names = ["t_sup_hp", "t_tank", "t_ret", "m_ch", "m_dis", "q_hp", "hp_cop", "hp_electricity",
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"electricity_cost"]
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num_hours = len(demand)
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variables = {name: [0] * num_hours for name in variable_names}
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t_sup_hp, t_tank, t_ret, m_ch, m_dis, q_hp, hp_cop, hp_electricity, electricity_cost = [variables[name] for name in variable_names]
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t_tank[0] = 40
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for i in range(len(demand) - 1):
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t_tank[i + 1] = t_tank[i] + ((m_ch[i] * (t_sup_hp[i] - t_tank[i])) + (ua * (t_out[i] - t_tank[i])) / cp - m_dis[i] * (t_tank[i] - t_ret[i])) * (900 / (1000 * volume))
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# hp operation and tank charging
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if t_tank[i + 1] < 40:
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q_hp[i + 1] = hp_cap
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m_ch[i + 1] = q_hp[i + 1] / (cp * hp_delta_t)
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t_sup_hp[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cp)) + t_tank[i + 1]
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elif 40 <= t_tank[i + 1] < 55 and q_hp[i] == 0:
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q_hp[i + 1] = 0
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m_ch[i + 1] = 0
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t_sup_hp[i + 1] = t_tank[i + 1]
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elif 40 <= t_tank[i + 1] < 55 and q_hp[i] > 0:
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q_hp[i + 1] = hp_cap
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m_ch[i + 1] = q_hp[i + 1] / (cp * hp_delta_t)
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t_sup_hp[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cp)) + t_tank[i + 1]
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else:
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q_hp[i + 1], m_ch[i + 1], t_sup_hp[i + 1] = 0, 0, t_tank[i + 1]
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if q_hp[i + 1] > 0:
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t_out_fahrenheit = 1.8 * t_out[i] + 32
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t_tank_fahrenheit = 1.8 * t_tank[i] + 32
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hp_cop[i + 1] = (1 / (hp_cop_curve_coefficients[0] +
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hp_cop_curve_coefficients[1] * t_tank_fahrenheit +
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hp_cop_curve_coefficients[2] * t_tank_fahrenheit ** 2 +
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hp_cop_curve_coefficients[3] * t_out_fahrenheit +
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hp_cop_curve_coefficients[4] * t_out_fahrenheit ** 2 +
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hp_cop_curve_coefficients[5] * t_tank_fahrenheit * t_out_fahrenheit)) * hp_nominal_efficiency
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hp_electricity[i + 1] = q_hp[i + 1] / hp_cop[i + 1]
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electricity_cost[i + 1] = hp_electricity[i + 1] * p_electricity[i + 1]
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else:
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hp_cop[i + 1] = 0
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hp_electricity[i + 1] = 0
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electricity_cost[i + 1] = 0
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# storage discharging
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if demand[i + 1] == 0:
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m_dis[i + 1] = 0
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t_ret[i + 1] = t_tank[i + 1]
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else:
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if demand[i + 1] > 0.5 * max(demand):
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factor = 6
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else:
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factor = 4
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m_dis[i + 1] = (max(demand) * 1000) / (cp * factor)
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t_ret[i + 1] = t_tank[i + 1] - (demand[i + 1] * 1000) / (m_dis[i + 1] * cp)
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output = pd.DataFrame(index=data['Datetime'])
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output["demand"] = [x * 1000 for x in demand][1:]
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output["q_hp"] = q_hp[1:]
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output["hp_cop"] = hp_cop[1:]
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output["hp_electricity_consumption"] = hp_electricity[1:]
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output["m_ch"] = m_ch[1:]
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output["m_dis"] = m_dis[1:]
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output["t_sup_hp"] = t_sup_hp[1:]
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output["t_tank"] = t_tank[1:]
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output["t_return"] = t_ret[1:]
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out_file = output.to_csv("results_rule_based.csv")
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