ess_optimation_tool.py 20.8 KB
Newer Older
lcn's avatar
lcn committed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475
#!/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)