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#!/usr/bin/python
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import copy
import datetime
class EssOptimizationTool(object):
"""储能优化工具
inline_var:{
"inline_capacity":1000, #进线容量,kVA,
#"capacity_price":23.0, #容量电费
"max_demand_price":32.0, #需量电费
"section_s":{"price":0, "ttl_kwh": 0, "time_range": "14:00-17:00;19:00-22:00"},
"section_p":{"price":0.8888, "ttl_kwh": 0, "time_range": "14:00-17:00;19:00-22:00"},
"section_f":{"price":0.5503, "ttl_kwh": 0, "time_range": "8:00-14:00;17:00-19:00;22:00-24:00"},
"section_v":{"price":0.2900, "ttl_kwh": 0, "time_range":"00:00-8:00"}
}#电量数据为完整月份计量中,峰时段最小电量的月份
ess_system:{
"cell_price":10000, #电池单价,元/kWh
"pcs_price":10000, #pcs单价,元/kW
"other_ttl_charge":0, #其他费用总价,元
"pcs_efficiency":90, #转换效率
"bat_efficiency":90, #充放电效率
"decay_rate":5.0, #衰减率
#"evaluate_year": "5-10", #评估年限
#"invest_income_rate":(15, 12, 10, 8, 6), #投资收益率
"DOD":90.0, #放电深度
"year_use_days": 330.0, #一年可利用时间
"charge_C_rate":0.5, #充放电倍率
#"res_value_bat":30, #电池残值
#"loop_time":3000-5000 #循环次数
},
df_curve:pandas.Series
"""
ess_params = {
"evaluate_year": "5-10", # 评估年限
"invest_income_rate": (15, 12, 10, 8, 6), # 投资收益率
"res_value_bat": 30, # 电池残值
"loop_time": 3000 - 5000 # 循环次数
}
def __init__(self, inline_var, ess_system, df_curve):
self.inline_var = inline_var
self.ess_system = self._update_system_params(ess_system)
self.df_curve = df_curve
self.time_sections = self._check_time_section()
self.curve = self._merge_curve(df_curve)
self.capacity = 0
self.delta_price = self.price_gap()
self.flag = self._check_peak_valley_shifting()
self.economic_evaluate = 0
self.opt_curve = 0
self.opt_analysis = 0
def price_gap(self):
if "section_s" in self.inline_var.keys():
delta_price = (self.inline_var["section_s"]["price"] -
self.inline_var["section_v"]["price"])
else:
delta_price = (self.inline_var["section_p"]["price"] -
self.inline_var["section_v"]["price"])
return delta_price
def _update_system_params(self, data_dict):
data = copy.deepcopy(data_dict)
data.update(self.ess_params)
return data
def _merge_curve(self, df_curve):
df = copy.deepcopy(self.time_sections) # index,quarter_time,spfv_flag
df["time_point"] = df["quarter_time"].apply(
lambda x: datetime.datetime.strftime(x, "%H:%M:%S"))
load_curve = copy.deepcopy(df_curve)
load_curve["time_point"] = load_curve["quarter_time"].apply(
lambda x: datetime.datetime.strftime(x, "%H:%M:%S"))
df_new = pd.merge(df[["time_point", "spfv_flag"]],
load_curve[["time_point", "p"]], how="left",
on="time_point")
df_new.rename(columns={"p": "load_curve"}, inplace=True)
return df_new
def time_15min_parse(self, time_range):
time_sections = time_range.split(";")
sections = []
for section in time_sections:
section_range = section.split("-")
start_time = pd.Timestamp(section_range[0])
if section_range[1] == "24:00":
section_range[1] = "00:00"
stop_time = pd.Timestamp(section_range[1]) + pd.Timedelta(
days=1)
else:
stop_time = pd.Timestamp(section_range[1])
current_time = start_time # + pd.Timedelta(seconds=900)
while True:
if current_time < stop_time:
sections.append(current_time)
current_time += pd.Timedelta(seconds=900)
else:
break
return sections
def _check_time_section(self):
param = {"section_s": 3, "section_p": 2, "section_f": 1, "section_v": 0}
hour = []
spfv_flag = []
for key in self.inline_var.keys():
if key not in ["inline_capacity", "capacity_price",
"max_demand_price"]:
time_point = self.time_15min_parse(
self.inline_var[key]["time_range"])
hour.extend(time_point)
num_15mins = len(time_point)
spfv_flag.extend([param[key]] * num_15mins)
time_section = pd.DataFrame(
{"quarter_time": hour, "spfv_flag": spfv_flag})
time_section = time_section.sort_values(by="quarter_time")
time_section.index = range(96)
return time_section
def _check_peak_valley_shifting(self):
flag = False
time_sections = copy.deepcopy(self.time_sections)
len_peak = (time_sections["spfv_flag"] >= 2).sum()
len_smooth = (time_sections["spfv_flag"] == 1).sum()
len_valley = (time_sections["spfv_flag"] == 0).sum()
df = copy.deepcopy(self.curve)
df = df.set_index("time_point")
load_max = df.loc[df["spfv_flag"] >= 2, "load_curve"].max()
if "section_s" in self.inline_var.keys():
peak_kwh = self.inline_var["section_s"]["ttl_kwh"] + \
self.inline_var["section_p"]["ttl_kwh"]
else:
peak_kwh = self.inline_var["section_p"]["ttl_kwh"]
pu_peak_kwh = peak_kwh / len_peak
pu_smooth_kwh = self.inline_var["section_f"]["ttl_kwh"] / len_smooth
pu_valley_kwh = self.inline_var["section_v"]["ttl_kwh"] / len_valley
pu_avg = (peak_kwh + self.inline_var["section_f"]["ttl_kwh"] +
self.inline_var["section_v"]["ttl_kwh"]) / (
len_peak + len_smooth + len_valley)
if self.delta_price > 0.5 and pu_peak_kwh >= pu_avg:
capacity = {}
flag = True
capacity["PCS"] = pu_peak_kwh - pu_avg
capacity["PCS"] = capacity["PCS"] if capacity["PCS"] >= 0 else 0
capacity["battery"] = (capacity["PCS"] / (
self.ess_system["charge_C_rate"] * self.ess_system[
"DOD"] / 100.0 *
self.ess_system["bat_efficiency"] / 100.0 *
self.ess_system["pcs_efficiency"] / 100.0 + 0.00001))
capacity["battery"] = capacity["battery"] if capacity[
"battery"] >= 0 else 0
self.capacity = capacity
return flag
def cal_opt_curve(self):
opt_curve = {}
df = copy.deepcopy(self.curve)
df = df.set_index("time_point")
delta_P = 0
load_max = df.loc[df["spfv_flag"] >= 2, "load_curve"].max()
pload = df.loc[df["spfv_flag"] >= 2, "load_curve"]
while delta_P <= self.capacity["PCS"]:
if (pload[pload >= load_max - delta_P] - (
load_max - delta_P)).sum() * 0.25 < self.capacity[
"battery"] * self.ess_system["DOD"] / 100:
delta_P += 10
else:
break
smooth_max = load_max - delta_P
peak_value = df.loc[df["spfv_flag"] >= 2, ["load_curve"]]
shift_kwh = (peak_value.loc[peak_value[
"load_curve"] >= smooth_max, "load_curve"] - smooth_max).sum()
peak_value.loc[
peak_value["load_curve"] >= smooth_max, "load_curve"] = smooth_max
smooth_value = df.loc[df["spfv_flag"] == 1, ["load_curve"]]
valley_value = df.loc[df["spfv_flag"] == 0, ["load_curve"]]
delta_P = valley_value["load_curve"].min()
shift_valley = 0
while shift_valley <= shift_kwh:
delta_kwh = delta_P - valley_value["load_curve"]
shift_valley = delta_kwh[delta_kwh >= 0].sum()
delta_P += 10
# valley_value.loc[:,:] = (valley_value["load_curve"].sum() + shift_kwh)/((df["spfv_flag"]==0).sum()+0.0001)
valley_value[valley_value <= delta_P] = delta_P
try:
valley_value.loc[valley_value["load_curve"] >= self.inline_var[
"inline_capacity"], "load_curve"] = self.inline_var[
"inline_capacity"]
except Exception as e:
pass
psv_value = pd.concat([valley_value, smooth_value, peak_value],
ignore_index=False)
df["load_bat_curve"] = psv_value
df["bat_curve"] = df["load_curve"] - df["load_bat_curve"]
opt_curve["quarter_time"] = df.index
opt_curve["load_curve"] = df["load_curve"].apply(
lambda x: self.fix_decimal_points(x, decimal_num=2))
opt_curve["bat_curve"] = df["bat_curve"].apply(
lambda x: self.fix_decimal_points(x, decimal_num=2))
opt_curve["load_bat_curve"] = df["load_bat_curve"].apply(
lambda x: self.fix_decimal_points(x, decimal_num=2))
opt_curve = pd.DataFrame(opt_curve)
opt_curve = opt_curve.set_index("quarter_time")
self.opt_curve = opt_curve
def _peak_valley_shifting(self):
rst = {}
if self.flag:
peak_valley_flag = True
peak_valley_kwh = (self.capacity["battery"] * self.ess_system[
"DOD"] / 100.0 *
self.ess_system["pcs_efficiency"] / 100.0 *
self.ess_system["bat_efficiency"] / 100.0 *
self.ess_system["year_use_days"] / 12.0) # 度/月
else:
peak_valley_flag = False
peak_valley_kwh = 0.0
rst["peak_valley_flag"] = peak_valley_flag
rst["peak_valley_kwh"] = self.fix_decimal_points(peak_valley_kwh,
decimal_num=2)
return rst
def _max_demand_control(self):
rst = {}
if self.flag:
diff = (self.opt_curve["load_curve"].max() - self.opt_curve[
"load_bat_curve"].max())
try:
if self.inline_var["inline_capacity"]:
percent_flag = (diff / self.inline_var[
"inline_capacity"] > 0.01)
else:
percent_flag = (diff > 10)
if percent_flag:
max_demand_flag = True
max_demand_benifit = diff * self.inline_var[
"max_demand_price"]
else:
max_demand_flag = False
max_demand_benifit = 0.0
except Exception as e:
max_demand_flag = False
max_demand_benifit = 0.0
else:
max_demand_flag = False
max_demand_benifit = 0.0
rst["max_demand_flag"] = max_demand_flag
rst["max_demand_benifit"] = self.fix_decimal_points(max_demand_benifit,
decimal_num=2)
return rst
def _economic_operation(self):
rst = {}
if self.flag:
diff = (self.opt_curve["load_curve"].max() - self.opt_curve[
"load_bat_curve"].max())
try:
if self.inline_var["inline_capacity"]:
percent_flag = (diff / self.inline_var[
"inline_capacity"] > 0.01)
else:
percent_flag = (diff > 10)
if percent_flag:
economic_operation_flag = True
reduce_peak = diff
reduce_load_factor = self.opt_curve[
"load_bat_curve"].max() / (
self.inline_var[
"inline_capacity"] + 0.001)
else:
economic_operation_flag = False
reduce_peak = 0.0
reduce_load_factor = 0.0
except Exception as e:
economic_operation_flag = False
reduce_peak = 0.0
reduce_load_factor = 0.0
else:
economic_operation_flag = False
reduce_peak = 0.0
reduce_load_factor = 0.0
rst["economic_operation_flag"] = economic_operation_flag
rst["reduce_peak"] = self.fix_decimal_points(reduce_peak, decimal_num=2)
rst["reduce_load_factor"] = self.fix_decimal_points(reduce_load_factor,
decimal_num=4)
return rst
def optimize_analysis(self):
opt_analysis = {}
opt_analysis["peak_valley"] = self._peak_valley_shifting()
opt_analysis["max_demand"] = self._max_demand_control()
opt_analysis["economic_operation"] = self._economic_operation()
return opt_analysis
@staticmethod
def present_value_annuity(rate, years, ttl_invest):
percent = rate / 100.0
f = (1.0 - 1.0 / (1.0 + percent) ** years) / (percent + 0.0)
rst = ttl_invest / f
return np.round(rst, 2)
def invest_income_table(self, ttl_invest):
years = self.ess_system["evaluate_year"].split("-")
rate = np.array(self.ess_system["invest_income_rate"])
df_dict = {}
for year in range(int(years[0]), int(years[1]) + 1):
df_dict[str(year)] = self.present_value_annuity(rate, year,
ttl_invest)
df = pd.DataFrame(df_dict, index=[str(i) + "%" for i in rate])
return df
def cal_economic_evaluate(self):
economic_value = {}
invest_bat = self.ess_system["cell_price"] * self.capacity["battery"]
invest_pcs = self.ess_system["pcs_price"] * self.capacity["PCS"]
economic_value["ttl_invest"] = invest_bat + invest_pcs + \
self.ess_system["other_ttl_charge"]
economic_value["year_use_days"] = self.ess_system["year_use_days"]
economic_value["peak_valley_year_income"] = (
self.opt_analysis["peak_valley"]["peak_valley_kwh"] *
(self.inline_var["section_p"]["price"] -
self.inline_var["section_v"]["price"]) * 12.0)
economic_value["max_demand_year_income"] = \
self.opt_analysis["max_demand"]["max_demand_benifit"] * 12.0
economic_value["ttl_income"] = economic_value[
"peak_valley_year_income"] + \
economic_value["max_demand_year_income"]
economic_value["static_invest_years"] = economic_value["ttl_invest"] / (
economic_value["ttl_income"] + 0.0001)
economic_value["invest_income_table"] = self.invest_income_table(
economic_value["ttl_invest"])
return economic_value
@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 fix_output_point(self):
for key in self.capacity.keys():
self.capacity[key] = self.fix_decimal_points(self.capacity[key],
decimal_num=2)
for key in self.economic_evaluate.keys():
if key != "invest_income_table":
self.economic_evaluate[key] = self.fix_decimal_points(
self.economic_evaluate[key], decimal_num=2)
def output(self):
if self.flag:
self.cal_opt_curve()
self.opt_analysis = self.optimize_analysis()
self.economic_evaluate = self.cal_economic_evaluate()
self.fix_output_point()
if __name__ == "__main__":
# file = r"D:\工作资料\算法\load_curve.xlsx"
# df_curve = pd.read_excel(file)
import os
path = "D:/工作资料/算法/优化工具/test"
os.chdir(path)
inline_var = {
"inline_capacity": 40000.0, # 进线容量,kVA,
# "capacity_price": 23.0, #容量电费
"max_demand_price": 32.0, # 需量电费
# "section_s":{"price":0, "ttl_kwh", 0, "time_range": "14:00-17:00;19:00-22:00"},
"section_p": {"price": 0.8286, "ttl_kwh": 122503.30078125,
"time_range": "14:00-17:00;19:00-22:00"},
"section_f": {"price": 0.5022, "ttl_kwh": 225055.927734375,
"time_range": "08:00-14:00;17:00-19:00;22:00-24:00"},
"section_v": {"price": 0.2511, "ttl_kwh": 69800.4599609375,
"time_range": "00:00-8:00"}
} # 电量数据为完整月份计量中,峰时段最小电量的月份
ess_system = {
"cell_price": 2000, # 电池单价,元/kWh
"pcs_price": 300, # pcs单价,元/kW
"other_ttl_charge": 0, # 其他费用总价,元
"pcs_efficiency": 95, # pcs转换效率
"bat_efficiency": 95, # 充放电效率
"decay_rate": 5.0, # 衰减率
# "evaluate_year": "5-10", #评估年限
# "invest_income_rate": (15, 12, 10, 8, 6), #投资收益率
"DOD": 70.0, # 放电深度
"year_use_days": 330.0, # 一年可利用时间
"charge_C_rate": 0.8, # 充放电倍率
# "res_value_bat": 30, #电池残值
# "loop_time": "3000-5000" #循环次数
}
df_load = {}
df_load["quarter_time"] = pd.date_range("2019-01-01", "2019-01-02",
freq="0.25H")[:-1]
df_load["p"] = ([2000.0] * 24 + [2050.0, 2100.0, 2150.0, 2200.0] + [2650.0,
3100.0,
3550.0,
4000.0] + [
4500.0, 5200.0, 6000.0] + [6800.0, 7900.0, 8600.0,
8800.0] + [9200.0, 8500.0,
7700.0, 7200.0] +
[7000.0, 6900.0, 6800.0, 6700.0] + list(
np.arange(6500.0, 7501.0, 125.0)) + list(
np.arange(7500.0, 7001.0, -62.5)) + list(
np.arange(7000.0, 4001.0, -375.0)) + list(
np.arange(4000.0, 2001.0, -83.3)))
df_curve = pd.DataFrame(df_load)
# df_curve = pd.read_csv("load1.csv", index_col=0)
df_curve.loc[:, "quarter_time"] = pd.to_datetime(
df_curve.loc[:, "quarter_time"])
df_curve["p"].plot()
import time
t1 = time.time()
obj = EssOptimizationTool(inline_var, ess_system, df_curve)
obj.output()
print("runtime", time.time() - t1)
try:
import matplotlib.pyplot as plt
obj.opt_curve.plot()
plt.show()
except Exception:
pass
print(
"**********************************************************************************************************")
print("优化分析")
print(
"经济运行 存在空间{economic_operation_flag},可降低峰荷{reduce_peak}kW,最高负载率降低为{reduce_load_factor}".format(
**obj.opt_analysis["economic_operation"]))
print("需量控制 存在空间{max_demand_flag},可降低用电成本{max_demand_benifit}元/月".format(
**obj.opt_analysis["max_demand"]))
print(
"移峰填谷{peak_valley_flag} 可减少峰段电网用电月{peak_valley_kwh}度/月,消纳光伏用电费用需根据用户与光伏电站协商电价确定".format(
**obj.opt_analysis["peak_valley"]))
print(
"***********************************************************************************************************")
print("容量规模")
print("PCS功率 {PCS}kW 电池容量 {battery}kWh".format(**obj.capacity))
print(
"************************************************************************************************************")
print("经济测算")
print("投资额:{ttl_invest}元 折算天数:{year_use_days}天 \n"
"移峰填谷年收益:{peak_valley_year_income}元 需量控制年收益:{max_demand_year_income}元 年总收益:{ttl_income}元 静态投资回报年限:{static_invest_years}年".format(
**obj.economic_evaluate))
print(obj.economic_evaluate["invest_income_table"])
pd.set_option('display.max_rows', None)
# print(obj.opt_curve)