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import time
import json
import re
import pendulum
import datetime
import pandas as pd
from pot_libs.logger import log
from pot_libs.common.components.responses import success_res
from unify_api.modules.elec_charge.dao.syncretize_energy_es_dao import \
query_search_kwh_p_new15, query_spfv_price_new15
from unify_api.modules.elec_charge.dao.syncretize_energy_dao import \
get_annual_sunshine_hours, get_p, insert_price_policy_data_dao, \
inset_algorithm_power_factor_dao, get_elec_price_dao,\
get_algorithm_power_factor_dao, get_max_demand_by_inlid
from unify_api.modules.elec_charge.components.syncretize_energy_cps import \
PvEvaluateComputeResp, Optimizecurve, ElectrovalenceResp, \
EssEvaluateComputeResp, OptCurve, PvEvaluateTwoResp
from unify_api.modules.elec_charge.utils.pv_evaluate_tool import PvEvaluateTool
from unify_api.modules.elec_charge.utils.ess_evaluate_tool import \
EssEvaluateTool
from unify_api.utils.common_utils import ChineseCalendar
from unify_api.modules.elec_charge.utils.co2_response import get_co2_price
from unify_api.modules.common.dao.common_dao import inline_zdu_all_by_cid
from unify_api.modules.zhiwei_u.service.scope_operations_service import \
dataframe_excl_download
from unify_api.modules.zhiwei_u.dao.order_operations_dao import \
select_cname_by_cid
# 综合能源-光伏-页面
async def pv_evaluate_service(cid, start, end):
try:
start_list = start.split("-")
end_list = end.split("-")
pendulum_start = pendulum.date(int(start_list[0]),
int(start_list[1]), 1)
pendulum_end = pendulum.date(int(end_list[0]), int(end_list[1]), 1)
day_num = pendulum_end.days_in_month
except:
return success_res(code=4008, msg="日期输入错误")
if (pendulum_end - pendulum_start).in_months() < 1:
return success_res(code=4008, msg="日期最少选择2个月")
elif (pendulum_end - pendulum_start).in_months() > 12:
return success_res(code=4008, msg="日期最多选择12个月")
else:
# kwh_datas = await query_search_kwh_p_new15(cid, f"{start}-01",
# f"{end}-{day_num}", "1h")
p_datas = await query_search_kwh_p_new15(cid, f"{start}-01",
f"{end}-{day_num}")
if not p_datas:
return PvEvaluateTwoResp(kwh_slot=[], p_slot=[], p=[], kwh=[],
electrovalence={}, sum_kwh_p="",
sum_kwh_s="", rule="")
p_slots = {"%02d:%02d" % (i, j): [] for i in range(24) for j in
range(0, 60, 15)}
kwh_slots = {"%02d" % i: [] for i in range(24)}
num, flag = 0, "00"
for data in p_datas:
create_time = data["create_time"].strftime("%Y-%m-%d %H:%M:%S")
if data["p"]:
p_slots[create_time[11:16]].append(data["p"])
if data["kwh"]:
if flag == create_time[11:13]:
num += data["kwh"]
else:
kwh_slots[flag].append(num)
num = data["kwh"]
flag = create_time[11:13]
# for data in p_datas:
# if data["p"]:
# p_slots[create_time[11:16]].append(data["p"])
for key, value in kwh_slots.items():
kwh_slots[key] = round(sum(value)/len(value), 2) if value else ""
# 获取峰时段
elec_price = await get_elec_price_dao(cid)
if not elec_price:
log.error(f"该厂还未设置电价, cid:{cid}, table:price_policy")
return success_res(code=4008, msg="该厂还未设置电价")
section_time_range = get_section_time_range(elec_price["quarters"])
elecs = get_section_time_slot(section_time_range)
# 峰时段总用电量
sum_kwh_p = 0
for p in elecs["p"]:
mid_kwh = kwh_slots.get(p[:2], 0) or 0
sum_kwh_p += mid_kwh
return PvEvaluateTwoResp(
kwh_slot=[f"{slot}:00" for slot in kwh_slots],
p_slot=[slot for slot in p_slots],
electrovalence=elecs,
sum_kwh_p=round(sum_kwh_p, 2),
p=[round(sum(p)/len(p), 2) if p else "" for p in p_slots.values()],
kwh=list(kwh_slots.values()))
# 综合能源-光伏-测算
async def pv_evaluate_compute_service(download=None, url=None, **body):
try:
start_list = body.get("start").split("-")
end_list = body.get("end").split("-")
pendulum_start = pendulum.datetime(int(start_list[0]),
int(start_list[1]), 1)
pendulum_end = pendulum.datetime(int(end_list[0]), int(end_list[1]), 1)
day_num = pendulum_end.days_in_month
except:
return success_res(code=4008, msg="日期输入错误")
try:
# 面积
total_capacity = float(body.get("install_space")) * \
float(body.get("area_conversion_ratio"))
# 工厂容量 =屋顶面积*折算系数*单位面积容量
invest_capacity = total_capacity * body.get("capacity_per_meter")/1000
if not invest_capacity:
return success_res(code=4008, msg="场地面积/面积折算系数/单位面积容量不能为0")
except:
return success_res(code=4008, msg="工厂容器参数有误")
if (pendulum_end - pendulum_start).in_months() < 1:
return success_res(code=4008, msg="日期最少选择2个月")
elif (pendulum_end - pendulum_start).in_months() > 12:
return success_res(code=4008, msg="日期最多选择12个月")
else:
# 获取年有效日照小时数
hours = await get_annual_sunshine_hours(body.get("cid"))
if not hours:
log.error(f"未找到该城市日照时间 cid:{body.get('cid')}")
return success_res(code=4008, msg="未找到该城市日照时间")
annual_sunshine_hours = hours.get("annual_effective_hours")
# 获取光伏典型出力曲线 负荷曲线df_pv
ps = await get_p(body.get("cid"))
if not ps:
log.error(f"未找到该城市光伏典型出力曲线 cid:{body.get('cid')}")
return success_res(code=4008, msg="未找到该城市光伏典型出力曲线")
p_slots = {"%02d:%02d:00" % (i, j): [] for i in range(24) for j in
range(0, 60, 15)}
df_pv_curve = [p["p"] for p in ps for _ in range(4)]
df_pv = pd.DataFrame(
{"quarter_time": list(p_slots.keys()), "pv_curve": df_pv_curve},
columns=["quarter_time", "pv_curve"])
# 获取电量和负荷15分钟数据
datas = await query_search_kwh_p_new15(body.get("cid"),
f"{body.get('start')}-01",
f"{body.get('end')}-{day_num}")
if not datas:
return success_res(code=4008, msg="未找到数据")
for data in datas:
create_time = data["create_time"].strftime("%Y-%m-%d %H:%M:%S")
if data["p"]:
p_slots[create_time[11:19]].append(data["p"])
for index, value in p_slots.items():
p_slots[index] = sum(value)/len(value) if value else None
# 负荷曲线df_load
df_load = pd.DataFrame(
{"quarter_time": list(p_slots.keys()),
"load_curve": list(p_slots.values())},
columns=["quarter_time", "load_curve"])
# 获取这段时间平均价格
charge_price, kwh_price = await query_spfv_price_new15(body.get("cid"),
f"{body.get('start')}-01",
f"{body.get('end')}-{day_num}")
spfv_price = charge_price/kwh_price if charge_price and kwh_price else 0
pv_system = {
"user_type": "工商业", # 建筑类型
"install_space": body.get("install_space"), # 屋顶面积m2
"area_conversion_ratio": body.get("area_conversion_ratio"), # 面积折算系数
"capacity_per_meter": body.get("capacity_per_meter"), # 单位面积容量
"self_use_ratio": body.get("self_use_ratio"), # 自发自用比例
"efficiency": body.get("efficiency"), # 发电效率
"evaluate_year": body.get("evaluate_year"), # 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": annual_sunshine_hours # 年峰值日照小数数
}
price = {
"rmb_per_wp": body.get("rmb_per_w"), # 建设单价
"maintenance_per_wp": body.get('maintenance_per_wp'), # 运维单价
"coal_in_grid": body.get("coal_in_grid"), # 脱硫电价
"self_use_price_discout": 1.0, # 自发自用电价折扣
"spfv_price": spfv_price, # 测算时段平均电价
"bank_interest": 0.085
}
log.info(f"pv_system:{pv_system}, price:{price}, "
f"invest_capacity:{invest_capacity}")
obj = PvEvaluateTool(pv_system, price, invest_capacity, df_load, df_pv)
obj.output()
# 测算表
evaluate_table = (obj.evaluate_table.where(obj.evaluate_table.notnull(), None)).round(2)
# 下载
if download:
company = await select_cname_by_cid(body.get("cid"))
table_name = f"{company['shortname']}_" \
f"{body.get('start')}_{body.get('end')}分布式光伏测算表"
return await dataframe_excl_download(evaluate_table, table_name)
# 碳排放(吨) 碳排放系数 0.67 2020年该指标为305.5g/kWh,及0.3055kg/kWh
c_emissions = obj.first_year_kwh * 0.67 * 0.3055 / 1000
# co2排放(吨) 碳元素(C)分子量为12,二氧化碳(CO2)分子量为44,两者折算比例为3.67
co2_emissions = c_emissions * 3.67
# 年收益 爬虫获取价格
co2_price = await get_co2_price()
year_earnings = co2_emissions * co2_price
# 优化曲线
curve = obj.curve
curve["after_curve"] = curve["load_curve"] - curve["pv_curve"]
curve = curve.round(2).where(curve.notnull(), None)
optimizecurve = Optimizecurve(
slot=curve["quarter_time"].values.tolist(),
before_curve=curve["load_curve"].values.tolist(),
after_curve=curve["after_curve"].values.tolist(),
pv_curve=curve["pv_curve"].values.tolist()
)
# 累计碳减排 co2
all_elec = sum(evaluate_table["年发电量"].values.tolist())
all_c_emissions = all_elec * 0.67 * 0.3055 / 1000 * 3.67
# 植树
tree = all_c_emissions * 1000 / 18.3
compute_table = evaluate_table.to_dict("records")
return PvEvaluateComputeResp(
optimize_curve=optimizecurve,
compute_table=compute_table,
invest_capacity=round(invest_capacity, 2),
first_year_kwh=round(obj.first_year_kwh, 2),
static_period=round(obj.static_period, 2),
total_capacity=round(total_capacity, 2),
invest_charge=round(obj.invest_charge, 2),
c_emissions=round(c_emissions, 2),
co2_emissions=round(co2_emissions, 2),
year_earnings=round(year_earnings, 2),
all_c_emissions=round(all_c_emissions, 2),
tree=round(tree),
download_url=url
)
# 综合能源-储能-页面
async def ess_evaluate_service(cid, start, end, work_day):
try:
start_list = start.split("-")
end_list = end.split("-")
pendulum_start = pendulum.datetime(int(start_list[0]),
int(start_list[1]), 1)
pendulum_end = pendulum.datetime(int(end_list[0]), int(end_list[1]), 1)
day_num = pendulum_end.days_in_month
except:
return success_res(code=4008, msg="日期输入错误")
if (pendulum_end - pendulum_start).in_months() < 1:
return success_res(code=4008, msg="日期最少选择2个月")
elif (pendulum_end - pendulum_start).in_months() > 12:
return success_res(code=4008, msg="日期最多选择12个月")
else:
elec = await get_elec_price_dao(cid)
if not elec:
rule = 1
else:
elec_list = [i for i in re.findall("p*", elec["quarters"]) if i]
rule = 2 if len(elec_list) > 1 else 1
p_datas = await query_search_kwh_p_new15(cid, f"{start}-01",
f"{end}-{day_num}")
if not p_datas:
return PvEvaluateTwoResp(kwh_slot=[], p_slot=[], p=[], kwh=[],
electrovalence={}, sum_kwh_p="",
sum_kwh_s="", rule="")
p_slots = {"%02d:%02d" % (i, j): [] for i in range(24) for j in
range(0, 60, 15)}
kwh_slots = {"%02d" % i: [] for i in range(24)}
num, flag = 0, "00"
# 1全部 2工作日 3非工作日
if work_day == 2:
for data in p_datas:
create_time = data["create_time"].strftime("%Y-%m-%d %H:%M:%S")
if ChineseCalendar(create_time[:10]).is_workday():
if data["p"]:
p_slots[create_time[11:16]].append(data["p"])
if data["kwh"]:
if flag == create_time[11:13]:
num += data["kwh"]
else:
kwh_slots[flag].append(num)
num = data["kwh"]
flag = data["create_time"][11:13]
elif work_day == 3:
for data in p_datas:
create_time = data["create_time"].strftime("%Y-%m-%d %H:%M:%S")
if not ChineseCalendar(create_time[:10]).is_workday():
if data["kwh"]:
if flag == create_time[11:13]:
num += data["kwh"]
else:
kwh_slots[flag].append(num)
num = data["kwh"]
flag = create_time[11:13]
if data["p"]:
p_slots[create_time[11:16]].append(
data["p"])
else:
for data in p_datas:
create_time = data["create_time"].strftime("%Y-%m-%d %H:%M:%S")
if data["p"]:
p_slots[create_time[11:16]].append(data["p"])
if data["kwh"]:
if flag == create_time[11:13]:
num += data["kwh"]
else:
kwh_slots[flag].append(num)
num = data["kwh"]
flag = create_time[11:13]
for key, value in kwh_slots.items():
kwh_slots[key] = round(sum(value) / len(value), 2) if value else ""
# 获取峰时段
elec_price = await get_elec_price_dao(cid)
if not elec_price:
log.error(f"该厂还未设置电价, cid:{cid}, table:price_policy")
return success_res(code=4008, msg="该厂还未设置电价")
section_time_range = get_section_time_range(elec_price["quarters"])
elecs = get_section_time_slot(section_time_range)
# 峰时段总用电量
sum_kwh_p = 0
for p in elecs["p"]:
mid_kwh_p = kwh_slots.get(p[:2], 0) or 0
sum_kwh_p += mid_kwh_p
# 尖时段总用电量
sum_kwh_s = ""
if elec_price.get("price_s") and elecs.get("s"):
sum_kwh_s = 0
for s in elecs["s"]:
mid_kwh_s = kwh_slots.get(s[:2], 0) or 0
sum_kwh_s += mid_kwh_s
sum_kwh_s = round(sum_kwh_s, 2)
return PvEvaluateTwoResp(
rule=rule, p_slot=[slot for slot in p_slots],
kwh_slot=[f"{slot}:00" for slot in kwh_slots],
electrovalence=elecs,
sum_kwh_p=round(sum_kwh_p, 2),
sum_kwh_s=sum_kwh_s,
p=[round(sum(p)/len(p), 2) if p else "" for p in p_slots.values()],
kwh=list(kwh_slots.values()))
# 综合能源-储能-测算
async def ess_evaluate_compute_service(download=None, url=None, **body):
try:
start_list = body.get("start").split("-")
end_list = body.get("end").split("-")
pendulum_start = pendulum.datetime(int(start_list[0]),
int(start_list[1]), 1)
pendulum_end = pendulum.datetime(int(end_list[0]), int(end_list[1]), 1)
day_num = pendulum_end.days_in_month
except:
return success_res(code=4008, msg="日期输入错误")
if (pendulum_end - pendulum_start).in_months() < 1:
return success_res(code=4008, msg="日期最少选择2个月")
elif (pendulum_end - pendulum_start).in_months() > 12:
return success_res(code=4008, msg="日期最多选择12个月")
else:
# 查找电价
elec_prices = await get_elec_price_dao(body.get("cid"))
# elec_price = {key: ";".join(value) for key, value in elec_prices.items() if value}
if not elec_prices:
log.error(f"该厂还未设置电价, cid:{body.get('cid')}, table:price_policy")
return success_res(code=4008, msg="该厂还未设置电价")
section_time_range = get_section_time_range(elec_prices["quarters"])
# 获取工厂容量
inline_zdu_dic = await inline_zdu_all_by_cid(body.get("cid"))
capacity = sum([inline["inline_tc"] if inline.get("inline_tc") else 0
for inline in inline_zdu_dic])
# 需量信息
inlids = [inline["inlid"] for inline in inline_zdu_dic]
max_demand = await get_max_demand_by_inlid(inlids)
max_demand_flag = False
max_demand_pmax = 0
if max_demand:
max_demand_list = [json.loads(demand["has_space"])
for demand in max_demand if demand["has_space"]]
for demand in max_demand_list:
if demand[0] and max_demand_flag is False:
max_demand_flag = True
max_demand_pmax += demand[1]
max_demand_var = {"flag": max_demand_flag, "pmax": max_demand_pmax}
# 获取电量和负荷15分钟数据
datas = await query_search_kwh_p_new15(body.get("cid"),
f"{body.get('start')}-01",
f"{body.get('end')}-{day_num}")
if not datas:
return success_res(code=4008, msg="未找到数据")
p_slots = {"%02d:%02d:00" % (i, j): [] for i in range(24) for j in
range(0, 60, 15)}
for data in datas:
# 1全部 2工作日 3非工作日
if body.get("work_day") == "2":
create_time = data["create_time"].strftime("%Y-%m-%d %H:%M:%S")
if ChineseCalendar(create_time[:10]).is_workday() and data["p"]:
p_slots[create_time[11:19]].append(
data["p"])
elif body.get("work_day") == "3":
if not ChineseCalendar(
create_time[:10]).is_workday() and data["p"]:
p_slots[create_time[11:19]].append(
data["p"])
else:
if data["p"]:
p_slots[create_time[11:19]].append(data["p"])
for index, value in p_slots.items():
p_slots[index] = sum(value)/len(value) if value else None
# 负荷典型用电曲线 df_curve
df_curve = pd.DataFrame(
{"quarter_time": list(p_slots.keys()),
"p": list(p_slots.values())},
columns=["quarter_time", "p"])
df_curve.loc[:, "quarter_time"] = pd.to_datetime(
df_curve.loc[:, "quarter_time"])
if elec_prices.get("price_s") and section_time_range.get("s"):
peak_valley_price = elec_prices["price_s"]-elec_prices["price_v"]
peak_flat_price = elec_prices["price_s"]-elec_prices["price_f"]
else:
peak_valley_price = elec_prices["price_p"]-elec_prices["price_v"]
peak_flat_price = elec_prices["price_p"]-elec_prices["price_f"]
log.info(f"cid:{body.get('cid')}, 峰谷价差:{peak_valley_price}, "
f"峰平价差:{peak_flat_price}")
price = {
"epc_price": body.get("epc_price"), # epc单价,元/Wh
"bank_interest": 0.085, # 折现率
"capacity_price": elec_prices["price_tc"], # 容量电费
"max_demand_price": elec_prices["price_md"], # 需量电费
# 峰谷价差,元/kWh 或者尖谷价差
"peak_valley_price": peak_valley_price,
# 峰平价差,元/kWh 或者尖平价差
"peak_flat_price": peak_flat_price,
"kwh_subsidy": body.get("kwh_subsidy"), # 度电补贴
# "section_s":{"time_range": "14:00-17:00;19:00-22:00"},
"section_f": {"time_range": ";".join(section_time_range["f"])},
"section_p": {"time_range": ";".join(section_time_range["p"])},
"section_v": {"time_range": ";".join(section_time_range["v"])}
}
if section_time_range.get("s"):
price["section_s"] = \
{"time_range": ";".join(section_time_range.get("s"))}
evaluate_year = 5000 // (int(body.get("rule")) *
int(body.get("year_use_days")))
ess_system = {
"capacity": capacity, # 工厂容量,kVA,
"rule": body.get("rule"), # 一充一放或两充两放
"install_capacity": body.get("install_capacity"), # kWh
"bat_efficiency": 0.95, # 电池效率
"pcs_efficiency": 0.95, # pcs转换效率
"DOD": body.get("DOD"), # 放电深度
"decay_rate": body.get("decay_rate"), # 衰减率
# 年运维费用占静态投资额比例
"maintenance_ratio_per_year": body.get("maintenance_ratio_per_year"),
"year_use_days": body.get("year_use_days"), # 一年可利用时间
"evaluate_year": evaluate_year, # 评估年限
"subsidy_year": body.get("subsidy_year"), # 补贴年限
# "invest_income_rate": (15, 12, 10, 8, 6), #投资收益率
"loop_time": 5000 # 循环次数
}
obj = EssEvaluateTool(ess_system, price, max_demand_var, df_curve)
obj.output()
# 测算表
etable = obj.evaluate_table
evaluate_table = etable.where(etable.notnull(), None).round(2)
evaluate_table["固定成本"] = evaluate_table["固定成本"].abs()
# 下载
if download:
company = await select_cname_by_cid(body.get("cid"))
table_name = f"{company['shortname']}_{body.get('start')}" \
f"_{body.get('end')}储能测算表"
return await dataframe_excl_download(evaluate_table, table_name)
curve = (obj.opt_curve.where(obj.opt_curve.notnull(), None)).round(2).reset_index()
opt_curve = OptCurve(
slot=curve["quarter_time"].values.tolist(),
load_curve=curve["load_curve"].values.tolist(),
bat_curve=curve["bat_curve"].values.tolist(),
load_bat_curve=curve["load_bat_curve"].values.tolist(),
)
ess_compute_table = evaluate_table.to_dict("records")
return EssEvaluateComputeResp(
evaluate_table=opt_curve,
ess_compute_table=ess_compute_table,
invest_capacity=round(obj.invest_capacity, 2),
static_period=round(obj.static_period, 2),
pcs_capacity=round(obj.pcs_capacity, 2),
month_average_dc_kwh=round(obj.month_average_dc_kwh, 2),
month_dc_benefit=round(obj.month_dc_benefit, 2),
download_url=url,
)
# 电价设置
async def electrovalence_setting_service(cid, price_md, price_tc, std_cos,
electrovalence):
if std_cos not in (0.8, 0.85, 0.9):
return success_res(code=4008, msg="功率因数错误")
if price_md < 15 or price_md > 50:
return success_res(code=4008, msg="容量单价范围应该在15-50之间")
if price_tc < 15 or price_tc > 50:
return success_res(code=4008, msg="需量单价范围应该在15-50之间")
t = {}
price_s, price_p, price_f, price_v = None, None, None, None
for elec in electrovalence:
if elec["name"] == "s": # 尖
price_s = elec.get("price", None) # s可能没有
if elec["name"] == "p": # 峰
price_p = elec["price"]
if elec["name"] == "f": # 平
price_f = elec["price"]
if elec["name"] == "v": # 谷
price_v = elec["price"]
for slot in elec["slot"]:
if slot[1] == "00:00":
slot[1] = "24:00"
start = slot[0].split(":")
end = slot[1].split(":")
if int(end[0]) < int(start[0]) or \
(int(end[0]) == int(start[0]) and int(end[1]) <= int(start[1])):
return success_res(code=400, msg="结束时间需要大于开始时间")
fina = int(end[0]) + 1 if end[1] != "00" else int(end[0])
for index, num in enumerate(range(int(start[0]), fina)):
num = int(num)
# 开始时间的分钟数不是0
if index == 0:
for j in range(int(start[1]), 60, 15):
now_time = f"%02d:%02d" % (num, j)
if now_time in t.keys():
print(now_time)
print(t)
return success_res(code=400, msg="电价配置信息有误,存在重叠时间")
t[now_time] = elec["name"]
# 结束时间的分钟数不是0
elif index == (fina - 1) and end[1] != "00":
for j in range(0, int(end[1]), 15):
now_time = f"%02d:%02d" % (num, j)
if now_time in t.keys():
print(now_time)
print(t)
return success_res(code=400, msg="电价配置信息有误,存在重叠时间")
t[now_time] = elec["name"]
else:
for j in range(0, 60, 15):
now_time = f"%02d:%02d" % (num, j)
if now_time in t.keys():
print(now_time)
print(t)
return success_res(code=400, msg="电价配置信息有误,存在重叠时间")
t[now_time] = elec["name"]
if len(t.keys()) != 24 * 4:
return success_res(code=400, msg="电价配置信息有误,缺少时间段")
quarters = "".join([i[1] for i in sorted(t.items(), key=lambda x: x[0])])
start_month = int(time.time())
effect_time = datetime.date.today() + datetime.timedelta(days=1)
effect_date = datetime.datetime(effect_time.year, effect_time.month,
effect_time.day, 0, 0, 0).timestamp()
# 找出cid 的所有inline_id
inline_zdu_dic = await inline_zdu_all_by_cid(cid)
# 1 设置电度电费 生效时间为明天凌晨
for inline in inline_zdu_dic:
inline_id = inline["inlid"]
# 插入数据
await insert_price_policy_data_dao(cid, inline_id, start_month,
quarters, price_s, price_p, price_f,
price_v, price_md, price_tc,
effect_date)
# 2 设置力调电价
await inset_algorithm_power_factor_dao(inline_id, start_month, std_cos)
return success_res(code=200, msg="设置电价成功")
async def electrovalence_service(cid):
# 查找力调电费
datas = await get_algorithm_power_factor_dao(cid)
std_cos = float(datas.get("std_cos")) if datas.get("std_cos") else None
# 查找电价
elec_price = await get_elec_price_dao(cid)
if not elec_price:
log.error(f"该厂还未设置电价, cid:{cid}, table:price_policy")
return success_res(code=4008, msg="该厂还未设置电价")
section_time_range = get_section_time_range(elec_price["quarters"])
electrovalence = []
for section, value in section_time_range.items():
electrovalence.append({
"name": section,
"price": elec_price.get(f"price_{section}"),
"slot": [v.split("-") for v in value],
})
return ElectrovalenceResp(
price_md=elec_price["price_md"],
price_tc=elec_price["price_tc"],
std_cos=std_cos,
electrovalence=electrovalence
)
def get_section_time_range(quarters):
# vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvffffffffppppssssffffffffppppssssssssppppppppffffffffffffffffffff
# 转化成 {'v': '00:00-08:00', 's': '11:00-12:00;15:00-17:00',
# 'p': '10:00-11:00;14:00-15:00;17:00-19:00',
# 'f': '08:00-10:00;12:00-14:00;19:00-24:00'}
d = {"v": [], "s": [], "p": [], "f": []}
last_i, last_t = "", ""
for index, i in enumerate(quarters):
num = index % 4
q = index // 4
t = "%02d:%02d" % (q, num * 15)
if index == 0:
last_i = i
last_t = t
elif last_i != i:
d[i].append(f"{t}-{t}")
last_i = i
last_t = t
if d[i]:
if (num + 1) * 15 == 60:
q += 1
end = 0
else:
end = (num + 1) * 15
end_t = "%02d:%02d" % (q, end)
q = d[i][-1][:5]
d[i].pop()
d[i].append(f"{q}-{end_t}")
else:
d[i].append(f"{last_t}-{t}")
return d
def get_section_time_slot(elecs):
d = {"v": [], "s": [], "p": [], "f": []}
for name, value in elecs.items():
for slot in value:
start, end = slot.split("-")
start_h, start_m = [int(i) for i in start.split(":")]
end_h, end_m = [int(i) for i in end.split(":")]
if start_m != 0:
start_h += 1
if end_m != 0:
end_h += 1
for i in range(start_h, end_h):
d[name].append("%02d:00" % i)
return d