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# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import copy
import numpy_financial as npf
class PvEvaluateTool(object):
"""光伏测算工具"""
"""
pv_system={
"user_type": "工商业", #建筑类型
"install_space":1200, #屋顶面积m2
"area_conversion_ratio":0.7, # #面积折算系数
"capacity_per_meter": 100, # 单位面积容量
"self_use_ratio":1.0, #自发自用比例
"efficiency":0.8, # 发电效率
"evaluate_year": 25, #评估年限
"first_3year_decay_rate":0.015, #前3年衰减率
"other_year_decay_rate":0.008, #4-25年衰减率
"annual_sunshine_hours": 1347.945 #年有效日照小时数
}
price={
"rmb_per_wp":7.5, #建设单价
"maintenance_per_wp":0.05, # 运维单价
"coal_in_grid": 0.43, #脱硫电价
"self_use_price_discout":1.0, #自发自用电价折扣
"spfv_price": 0.6 #测算时段平均电价
}
total_capacity = 315
df_load负荷曲线
df_pv负荷曲线
"""
def __init__(self, pv_system, price, total_capacity, df_load, df_pv):
self.pv_system = pv_system
self.price = price
self.total_capacity = total_capacity
self.invest_capacity = self.cal_install_capacity()
self.invest_charge = self.cal_invest_charge()
self.maintenance_year_charge = self.cal_maintenance_year_charge()
self.first_year_kwh = self.cal_first_year_kwh()
self.curve = self._merge_curve(df_load, df_pv)
self.evaluate_table = None
self.static_period = 0
self.dynamic_period = 0
self.irr = 0
self.msg = ''
def _merge_curve(self, df_load, df_pv):
pv_kWp = self.invest_capacity / 1000
df_load1 = copy.deepcopy(df_load)
df_pv1 = copy.deepcopy(df_pv)
df_pv1["pv_curve"] = df_pv1["pv_curve"]/1000 * pv_kWp * self.pv_system[
"efficiency"]
rst = pd.merge(df_load1[["quarter_time", "load_curve"]],
df_pv1[["quarter_time", "pv_curve"]], how="left",
on="quarter_time")
return rst
def cal_install_capacity(self):
rst = self.pv_system["install_space"] * self.pv_system[
"area_conversion_ratio"] * self.pv_system["capacity_per_meter"]
if rst > self.total_capacity*1000:
rst = self.total_capacity*1000
return rst
def cal_maintenance_year_charge(self):
rst = self.invest_capacity * self.price["maintenance_per_wp"]
return rst
def cal_invest_charge(self):
rst = self.invest_capacity * self.price["rmb_per_wp"]
return rst
def cal_first_year_kwh(self):
rst = self.invest_capacity * self.pv_system[
"annual_sunshine_hours"] / 1000
return rst
def cal_self_use_benefit(self, kwh):
rst = self.price["spfv_price"] * self.price[
"self_use_price_discout"] * kwh * self.pv_system["self_use_ratio"]
return rst
def cal_on_grid_benefit(self, kwh):
rst = self.price["coal_in_grid"] * kwh * (
1 - self.pv_system["self_use_ratio"])
return rst
def cal_total_benefit(self, kwh):
rst = self.cal_self_use_benefit(kwh) + self.cal_on_grid_benefit(kwh)
return rst
def cal_evaluate_table(self):
table_title = ["年份", "年衰减率", "剩余容量", "年发电量",
"维护成本", "累计总成本", "本年收益", "累计收益"]
decay_rate = ([0] + [self.pv_system["first_3year_decay_rate"]] * 3
+ [self.pv_system["other_year_decay_rate"]] * (
self.pv_system["evaluate_year"] - 4))
bank_interest = [1 / (1 + self.price["bank_interest"])] * \
self.pv_system["evaluate_year"]
charge_output = [- self.invest_charge] + [0] * (
self.pv_system["evaluate_year"] - 1)
data = pd.DataFrame(
{"年份": range(1, self.pv_system["evaluate_year"] + 1),
"年衰减率": decay_rate,
"支出": charge_output, "折现率": bank_interest},
columns=["年份", "年衰减率", "支出", "折现率"])
data["剩余容量"] = 1 - data["年衰减率"]
data["剩余容量"] = data["剩余容量"].cumprod()
data["折现率"] = data["折现率"].cumprod()
data["年发电量"] = data["剩余容量"] * self.first_year_kwh
data["维护成本"] = -self.maintenance_year_charge
data["累计总成本"] = (data["维护成本"] + data["支出"]).cumsum()
data["本年收益"] = self.cal_total_benefit(data["年发电量"]) + data["维护成本"]
data["本年折现收益"] = data["本年收益"] * data["折现率"]
data["累计收益"] = (data["本年收益"] + data["支出"]).cumsum()
data["折现累计收益"] = (data["本年折现收益"] + data["支出"]).cumsum()
static_temp = data["累计收益"][data["累计收益"] > 0]
if len(static_temp) > 0:
static_index = list(static_temp.index)[0]
static_index = static_index - 1 if static_index > 0 else 0
self.static_period = data.loc[static_index, "年份"] - data.loc[
static_index, "累计收益"] / (data.loc[
static_index + 1, "本年收益"] + 0.001)
else:
self.static_period = -1
dynamic_temp = data["折现累计收益"][data["折现累计收益"] > 0]
if len(dynamic_temp) > 0:
dynamic_index = list(dynamic_temp.index)[0]
dynamic_index = dynamic_index - 1 if dynamic_index > 0 else 0
self.dynamic_period = data.loc[dynamic_index, "年份"] - data.loc[
dynamic_index, "折现累计收益"] / (data.loc[
dynamic_index + 1, "本年折现收益"] + 0.001)
else:
self.dynamic_period = -1
self.irr = npf.irr([- self.invest_charge] + list(data["本年收益"].values))
return data[table_title]
@staticmethod
def fix_decimal_points(v, decimal_num=4):
""" 将浮点型的值固定小数位, 默认保留4位小数位
:param v: 浮点型的值
"""
rlt = v
fmt = "%.df"
fmt = fmt.replace('d', str(decimal_num))
if isinstance(v, float):
s = fmt % v
rlt = float(s)
return rlt
def output(self):
self.input_param_process()
data = self.cal_evaluate_table()
for key in data.columns:
data[key] = data[key].apply(lambda x: self.fix_decimal_points(x))
for key in ["load_curve", "pv_curve"]:
self.curve[key] = self.curve[key].apply(
lambda x: self.fix_decimal_points(x))
self.evaluate_table = data
def input_param_process(self):
if self.pv_system["area_conversion_ratio"] <= 0:
self.pv_system["area_conversion_ratio"] = 0.0
elif self.pv_system["area_conversion_ratio"] >= 1:
self.pv_system["area_conversion_ratio"] = 1
if self.pv_system["efficiency"] <= 0:
self.pv_system["efficiency"] = 0.0
elif self.pv_system["efficiency"] >= 1:
self.pv_system["efficiency"] = 1
if self.pv_system["self_use_ratio"] <= 0:
self.pv_system["self_use_ratio"] = 0.0
elif self.pv_system["self_use_ratio"] >= 1:
self.pv_system["self_use_ratio"] = 1
if self.pv_system["evaluate_year"] <= 10:
self.pv_system["evaluate_year"] = 10
if self.price["self_use_price_discout"] <= 0:
self.price["self_use_price_discout"] = 0.0
elif self.price["self_use_price_discout"] >= 1:
self.price["self_use_price_discout"] = 1
if __name__ == "__main__":
pv_system = {
"user_type": "工商业", # 建筑类型
"install_space": 2000, # 屋顶面积m2
"area_conversion_ratio": 0.8, # #面积折算系数
"capacity_per_meter": 100, # 单位面积容量
"self_use_ratio": 1.0, # 自发自用比例
"efficiency": 0.8, # 发电效率
"evaluate_year": 25, # 评估年限
"first_3year_decay_rate": 0.015, # 前3年衰减率
"other_year_decay_rate": 0.008, # 4-25年衰减率
"annual_sunshine_hours": 989.04 # 年峰值日照小数数
}
price = {
"rmb_per_wp": 4.3, # 建设单价
"maintenance_per_wp": 0.05, # 运维单价
"coal_in_grid": 0.45, # 脱硫电价
"self_use_price_discout": 1.0, # 自发自用电价折扣
"spfv_price": 0.48885056077566996, # 测算时段平均电价
"bank_interest": 0.085
}
total_capacity = 160 # 月
stat_time = pd.to_datetime(
pd.date_range("2019-01-01", "2019-01-02", freq="0.25H")[:-1])
df_load = pd.DataFrame(
{"quarter_time": stat_time, "load_curve": np.random.random(96)},
columns=["quarter_time", "load_curve"])
df_load["quarter_time"] = df_load["quarter_time"].apply(
lambda x: x.strftime("%H:%M:%S"))
df_pv = pd.DataFrame(
{"quarter_time": stat_time, "pv_curve": np.random.random(96)},
columns=["quarter_time", "pv_curve"])
df_pv["quarter_time"] = df_pv["quarter_time"].apply(
lambda x: x.strftime("%H:%M:%S"))
# print(df_pv)
obj = PvEvaluateTool(pv_system, price, total_capacity, df_load, df_pv)
obj.output()
"""光伏测算工具"""
print(
"**********************************************************************************************************")
print("输入参数")
print()
print(obj.invest_charge)
print(obj. invest_charge) # 获取总投资
print(obj.static_period) # 静态回收期
print(obj.invest_capacity) # 获取装机容量
print(obj.first_year_kwh) # 获取首年发电量
print(obj.evaluate_table) # 测算表
print(obj.curve) # 计算优化分析结果