# -*- coding: utf-8 -*- """ gen_dgcup2022a.py —— 电工杯 2022 A 题「高比例风电电力系统储能运行及配置分析」确定性真源模型 ==================================================================================== 赛题背景（真实，第十四届全国大学生电工数学建模竞赛 A 题）： “碳中和”目标驱动下，未来电力系统必将是高比例可再生能源电力系统。可再生能源出力强随机 波动性导致系统功率实时平衡困难；储能被认为是保障系统功率实时平衡的有效手段。以高比例 风电电力系统为例，探究“供给侧”低碳化转型对系统运行经济性与可靠性的影响。 待研究系统含火电、风电、储能、负荷： * 火电机组 3 台，装机容量 1050 MW（最大 600/300/150 MW，最小 180/90/45 MW）。 * 煤耗 F = aP^2 + bP + c（kg/h，P 为机组出力/MW）；运行维护成本按 0.5 倍煤耗成本计； 碳捕集成本 = 碳排放量 × 碳捕集单价（0/60/80/100 元/t）。 * 风电仅计运维成本 0.045 元/kWh；弃风损失 0.3 元/kWh。 * 储能：单位功率成本 3000 元/kW，单位能量成本 3000 元/kWh，运维 0.05 元/kWh，寿命 10 年 （平摊到每天 = 总投资 /10/365）。 * 失负荷损失 8 元/kWh。系统单位供电成本 = 系统发电总成本 / 系统总负荷电量。 * 日负荷最大 900 MW；风电渗透率 = 最大风电功率 / 最大负荷功率。 * 七个子问题：Q1 无风最小成本运行；Q2 风电 300MW 替代机组3；Q3 风电 600MW 替代机组2； Q4 上述两场景在 4 种碳价下的单位供电成本；Q5 风电 900MW 替代机组 2、3 的失负荷与最小 储能配置；Q6 风电替代容量递增的可靠性与经济性挑战；Q7 附件2 十五日（负荷/风电均 1200MW） 替代机组 2、3 场景的功率平衡问题与解决方案。 重要声明：原题附录 1（某日风电/负荷归一化序列，96 点/15min）、附件 2（十五日序列）为官方 Excel 附件，本站无法下载。本脚本采用 SEED=2022 的【确定性合成】序列（负荷/风电形状由固定 公式 + 固定随机种子生成，纯标准库、零外部依赖），仅用于跑通建模方法、训练套路，非官方原题 数据。所有数字可由本文件独立复现。论文与图表中均已注明“练习用确定性合成数据”。 输出： - assets/problems/data/dgcup2022a.csv （参数 + 各问关键结果面板） - 返回 D（dict），供范文附录与配图脚本独立复现全部权威数字。 用法：cd tools/ && python3 gen_dgcup2022a.py """ import os import csv import math import random SEED = 2022 # 版本锚定；序列由本种子确定性生成 HERE = os.path.dirname(os.path.abspath(__file__)) OUT_DIR = os.path.normpath(os.path.join(HERE, "..", "assets", "problems", "data")) # ---------------- 系统 / 经济常数 ---------------- # 三台火电机组：最大/最小出力(MW)、煤耗系数 F=aP^2+bP+c(kg/h)、碳排放(kg/kWh) UNITS = { 1: dict(Pmax=600.0, Pmin=180.0, a=0.226, b=30.42, c=786.80, carbon=0.72), 2: dict(Pmax=300.0, Pmin=90.0, a=0.588, b=65.12, c=451.32, carbon=0.75), 3: dict(Pmax=150.0, Pmin=45.0, a=0.785, b=139.6, c=1049.50, carbon=0.79), } COAL_PRICE = 700.0 # 电煤价格 元/t WIND_OM = 0.045 # 风电运维 元/kWh STO_PC = 3000.0 # 储能单位功率成本 元/kW STO_EC = 3000.0 # 储能单位能量成本 元/kWh STO_OM = 0.05 # 储能运维 元/kWh STO_LIFE = 10 # 储能寿命 年 CURT_COST = 0.3 # 弃风损失 元/kWh LOSS_COST = 8.0 # 失负荷损失 元/kWh ETA = 0.9 # 储能充放电效率（往返） CARBON_PRICES = [0, 60, 80, 100] # 单位碳捕集成本 元/t LOAD_MAX = 900.0 # 日负荷最大功率 MW（Q1–Q6） DT = 0.25 # 时间步长 h（15min） N = 96 # 每日点数 # ===================== 序列合成（确定性） ===================== def synth_load(): """某日负荷归一化序列（96 点），晚高峰(20h)归一到 1.0，夜间谷值约 0.5。""" random.seed(SEED) raw = [] for k in range(N): h = k * DT base = 0.45 m1 = 0.13 * math.exp(-((h - 10.0) / 3.0) ** 2) # 上午小峰 m2 = 0.45 * math.exp(-((h - 20.0) / 2.6) ** 2) # 晚高峰 v = base + m1 + m2 v += (random.random() - 0.5) * 0.02 raw.append(v) vmax = max(raw) return [v / vmax for v in raw] def synth_wind(): """某日风电归一化序列（96 点）：凌晨(3h)大风归一到 1.0，傍晚(20h)偏弱约 0.2。""" random.seed(SEED + 7) raw = [] for k in range(N): h = k * DT base = 0.07 night = 0.95 * math.exp(-((h - 3.0) / 3.0) ** 2) # 凌晨大风 eve = 0.13 * math.exp(-((h - 20.0) / 3.0) ** 2) # 傍晚弱风 v = base + night + eve v += (random.random() - 0.5) * 0.05 raw.append(max(0.02, v)) vmax = max(raw) return [v / vmax for v in raw] # ===================== 经济调度（lambda 迭代） ===================== def econ_dispatch(units_idx, T): """在给定可用机组集合上，按最小煤耗将总出力 T 分配到各机组。""" us = [(i, UNITS[i]) for i in units_idx] tmin = sum(u["Pmin"] for i, u in us) tmax = sum(u["Pmax"] for i, u in us) if T <= tmin: return {i: u["Pmin"] for i, u in us} if T >= tmax: return {i: u["Pmax"] for i, u in us} def Ps_of(lam): d = {} for i, u in us: P = (lam - u["b"]) / (2.0 * u["a"]) d[i] = max(u["Pmin"], min(u["Pmax"], P)) return d lo, hi = 0.0, 6000.0 for _ in range(90): lam = (lo + hi) / 2.0 s = sum(Ps_of(lam).values()) if s < T: lo = lam else: hi = lam return Ps_of((lo + hi) / 2.0) # ===================== 单步无储能调度 ===================== def dispatch_no_storage(units_idx, load, wind_avail): """每 15min 一步：以风电优先（运维最低）消纳、火电补足，核算弃风/失负荷。""" tmin = sum(UNITS[i]["Pmin"] for i in units_idx) tmax = sum(UNITS[i]["Pmax"] for i in units_idx) w_lo = max(0.0, load - tmax) w_hi = load - tmin if w_hi < 0: w_hi = 0.0 if wind_avail <= w_lo: w_used = wind_avail thermal = load - wind_avail loss = max(0.0, thermal - tmax) curt = 0.0 else: w_used = min(wind_avail, w_hi) thermal = load - w_used curt = wind_avail - w_used loss = 0.0 Ps = econ_dispatch(units_idx, thermal) return Ps, w_used, curt, loss # ===================== 单步成本 ===================== def step_cost(units_idx, Ps, w_used, curt, loss, carbon_price): coal = om = carbon = 0.0 for i in units_idx: u = UNITS[i] P = Ps[i] F = u["a"] * P * P + u["b"] * P + u["c"] # kg/h fc = F * DT / 1000.0 * COAL_PRICE # 元 omc = 0.5 * fc emis = P * DT * u["carbon"] # 吨（=P*DT*carbon） coal += fc om += omc carbon += emis * carbon_price wind = w_used * DT * 1000.0 * WIND_OM curt_c = curt * DT * 1000.0 * CURT_COST loss_c = loss * DT * 1000.0 * LOSS_COST total = coal + om + carbon + wind + curt_c + loss_c return dict(coal=coal, om=om, carbon=carbon, wind=wind, curt=curt_c, loss=loss_c, total=total) # ===================== 场景总成本核算 ===================== def scenario_cost(units_idx, load_arr, wind_inst, carbon_price): """对一整天（96 步）核算：返回成本明细、弃风/失负荷电量、机组出力序列。""" tmin = sum(UNITS[i]["Pmin"] for i in units_idx) tmax = sum(UNITS[i]["Pmax"] for i in units_idx) tot = dict(coal=0.0, om=0.0, carbon=0.0, wind=0.0, curt=0.0, loss=0.0, total=0.0) curt_kwh = 0.0 loss_kwh = 0.0 load_kwh = 0.0 unit_out = {i: [] for i in units_idx} wind_use = [] for load, wn in zip(load_arr, wind_inst): wind_avail = wn Ps, w_used, curt, loss = dispatch_no_storage(units_idx, load, wind_avail) c = step_cost(units_idx, Ps, w_used, curt, loss, carbon_price) for k in tot: tot[k] += c[k] curt_kwh += curt * DT * 1000.0 loss_kwh += loss * DT * 1000.0 load_kwh += load * DT * 1000.0 for i in units_idx: unit_out[i].append(Ps[i]) wind_use.append(w_used) tot["curt_kwh"] = curt_kwh tot["loss_kwh"] = loss_kwh tot["load_kwh"] = load_kwh tot["unit_supply"] = tot["total"] / load_kwh if load_kwh > 0 else 0.0 return tot, unit_out, wind_use, tmin, tmax # ===================== 储能容量求解（Q5） ===================== def storage_size(units_idx, load_arr, wind_arr): """求「避免失负荷」所需最小储能：富余风电可充电、不足时放电；允许弃风（不强制全消纳）。 以火电上下限为边界：S(t) 为可充电功率（富余风电），D(t) 为需放电功率（火电顶满仍缺额）。 通过二分搜索最小能量容量 cap，使逐时仿真中放电足以覆盖 D(t)（多充的弃掉），并返回所需功率。 """ tmin = sum(UNITS[i]["Pmin"] for i in units_idx) tmax = sum(UNITS[i]["Pmax"] for i in units_idx) S = [] # 可充电功率 MW D = [] # 需放电功率 MW for load, wa in zip(load_arr, wind_arr): s = max(0.0, wa - (load - tmin)) # 富余风电（火电在最小出力仍过剩） d = max(0.0, (load - wa) - tmax) # 火电顶满仍缺额 S.append(s) D.append(d) def ok(cap): soc = 0.0 for s, d in zip(S, D): room = max(0.0, cap - soc) ch = min(s, room / (DT * ETA)) # 充电，超出容量则弃风 soc += ch * DT * ETA dis = min(d, soc / DT) # 放电补足缺额 soc -= dis * DT if dis < d - 1e-9: return False return True hi = sum(d * DT for d in D) + 1.0 lo = 0.0 for _ in range(60): mid = (lo + hi) / 2.0 if ok(mid): hi = mid else: lo = mid cap = hi # 在所选容量下回放，记录充放电功率 soc = 0.0 pch = 0.0 pdis = 0.0 for s, d in zip(S, D): room = max(0.0, cap - soc) ch = min(s, room / (DT * ETA)) soc += ch * DT * ETA dis = min(d, soc / DT) soc -= dis * DT pch = max(pch, ch) pdis = max(pdis, dis) power = max(pch, pdis) surplus = sum(s * DT * ETA for s in S) deficit = sum(d * DT for d in D) feasible = deficit <= surplus + 1e-6 return dict(S=S, D=D, capacity_MWh=cap, power_MW=power, surplus_MWh=surplus, deficit_MWh=deficit, feasible=feasible) def storage_daily_cost(power_MW, capacity_MWh, throughput_kwh): """储能日成本：投资平摊 + 运维。""" inv = STO_PC * power_MW * 1000.0 + STO_EC * capacity_MWh * 1000.0 daily_inv = inv / (STO_LIFE * 365.0) daily_om = throughput_kwh * STO_OM return dict(inv=inv, daily_inv=daily_inv, daily_om=daily_om, daily_total=daily_inv + daily_om) # ===================== 十五日序列（Q7） ===================== def synth_15day(): """十五日负荷（峰值 1200MW）与风电（装机 1200MW）确定性序列：复用日形状并加日间扰动。""" random.seed(SEED + 31) ln = synth_load() wn = synth_wind() load = [] wind = [] for day in range(15): lsf = 1.0 + 0.05 * (random.random() - 0.5) wsf = 1.0 + 0.15 * (random.random() - 0.5) for k in range(N): load.append(ln[k] * lsf * (1.0 + 0.02 * (random.random() - 0.5))) wind.append(wn[k] * wsf * (1.0 + 0.05 * (random.random() - 0.5))) lm = max(load) wm = max(wind) load = [v / lm * 1200.0 for v in load] wind = [v / wm * 1200.0 for v in wind] return load, wind # ===================== 主入口 ===================== def gen_dgcup2022a(seed=SEED, out_csv="dgcup2022a.csv"): random.seed(SEED) load_norm = synth_load() wind_norm = synth_wind() load_arr = [v * LOAD_MAX for v in load_norm] # MW，Q1–Q6 日负荷 load_kwh_day = sum(load_arr) * DT * 1000.0 # ---------- Q1：无风电，三机组最小成本运行（4 种碳价） ---------- q1 = {} for cp in CARBON_PRICES: tot, uout, wuse, tmin, tmax = scenario_cost([1, 2, 3], load_arr, [0.0] * N, cp) q1[cp] = dict(total=tot["total"], unit_supply=tot["unit_supply"], coal=tot["coal"], om=tot["om"], carbon=tot["carbon"], curt=tot["curt_kwh"], loss=tot["loss_kwh"]) # 记录三机组日计划（取碳价 0 下的调度，调度与碳价无关） _, uout, _, _, _ = scenario_cost([1, 2, 3], load_arr, [0.0] * N, 0) q1_plan = {i: uout[i] for i in [1, 2, 3]} # ---------- Q2：风电 300MW 替代机组3（可用 1,2） ---------- w2 = [v * 300.0 for v in wind_norm] tot2, uout2, wuse2, tmin2, tmax2 = scenario_cost([1, 2], load_arr, w2, 0) # 风电可降低装机容量（使弃风≈0）：求满足 max(wind_norm*inst)<=load-tmin 的 inst # 约束：wind_norm[k]*inst <= load_arr[k]-tmin2 对所有 k ratios = [(load_arr[k] - tmin2) / wind_norm[k] for k in range(N) if wind_norm[k] > 1e-6 and load_arr[k] > tmin2] inst_no_curt = min(ratios) if ratios else 0.0 q2 = dict(inst=300.0, units=[1, 2], tmin=tmin2, tmax=tmax2, total=tot2["total"], unit_supply=tot2["unit_supply"], coal=tot2["coal"], carbon=tot2["carbon"], curt_kwh=tot2["curt_kwh"], loss_kwh=tot2["loss_kwh"], inst_no_curt=inst_no_curt, reduce_MW=300.0 - inst_no_curt, plan_unit1=uout2[1], plan_unit2=uout2[2], wind_use=wuse2) # ---------- Q3：风电 600MW 替代机组2（可用 1,3） ---------- w3 = [v * 600.0 for v in wind_norm] tot3, uout3, wuse3, tmin3, tmax3 = scenario_cost([1, 3], load_arr, w3, 0) # 为不失负荷可增装：需 wind_avail(peak) >= maxload - tmax3 peak_k = max(range(N), key=lambda k: load_arr[k]) need_wind_peak = LOAD_MAX - tmax3 cur_wind_peak = wind_norm[peak_k] * 600.0 add_MW = max(0.0, need_wind_peak - cur_wind_peak) / wind_norm[peak_k] if wind_norm[peak_k] > 1e-6 else 0.0 q3 = dict(inst=600.0, units=[1, 3], tmin=tmin3, tmax=tmax3, total=tot3["total"], unit_supply=tot3["unit_supply"], coal=tot3["coal"], carbon=tot3["carbon"], curt_kwh=tot3["curt_kwh"], loss_kwh=tot3["loss_kwh"], add_MW=add_MW, plan_unit1=uout3[1], plan_unit3=uout3[3], wind_use=wuse3) # ---------- Q4：Q2、Q3 场景在 4 种碳价下的单位供电成本 ---------- q4 = {} for tag, uidx, winst in [("Q2", [1, 2], 300.0), ("Q3", [1, 3], 600.0)]: wa = [v * winst for v in wind_norm] row = {} for cp in CARBON_PRICES: tot, _, _, _, _ = scenario_cost(uidx, load_arr, wa, cp) row[cp] = tot["unit_supply"] q4[tag] = row # ---------- Q5：风电 900MW 替代机组 2、3（仅机组1） ---------- w5 = [v * 900.0 for v in wind_norm] tot5_0, uout5, wuse5, tmin5, tmax5 = scenario_cost([1], load_arr, w5, 0) sto = storage_size([1], load_arr, w5) # 储能吞吐（充电=富余风电，放电=缺额） throughput_kwh = sum(s * DT * 1000.0 for s in sto["S"]) + sum(d * DT * 1000.0 for d in sto["D"]) sc = storage_daily_cost(sto["power_MW"], sto["capacity_MWh"], throughput_kwh) # 含储能总成本（碳价取 60）：储能消除失负荷，故在无储能成本中扣减失负荷损失后加储能日成本 tot5_60, _, _, _, _ = scenario_cost([1], load_arr, w5, 60) loss5 = tot5_60["loss"] sto_total_60 = tot5_60["total"] - loss5 + sc["daily_total"] unit_supply_5 = sto_total_60 / load_kwh_day q5 = dict(inst=900.0, units=[1], tmin=tmin5, tmax=tmax5, loss_kwh_no_sto=tot5_0["loss_kwh"], curt_kwh_no_sto=tot5_0["curt_kwh"], total_no_sto=tot5_60["total"], unit_supply_no_sto=tot5_60["total"] / load_kwh_day, sto_capacity=sto["capacity_MWh"], sto_power=sto["power_MW"], sto_feasible=sto["feasible"], sto_inv=sc["inv"], sto_daily=sc["daily_total"], carbon_price=60, total_with_sto=sto_total_60, unit_supply=unit_supply_5, S=sto["S"], D=sto["D"], wind_use=wuse5, unit1=uout5[1]) # ---------- Q6：风电替代容量递增（0,300,600,900,1200） ---------- pen_list = [] for inst in [0, 300, 600, 900, 1200]: if inst == 0: uidx = [1, 2, 3] elif inst <= 300: uidx = [1, 2] # 已替代机组3 elif inst <= 600: uidx = [1, 3] # 已替代机组2 else: uidx = [1] # 已替代机组2、3 wa = [v * inst for v in wind_norm] tot, _, _, _, _ = scenario_cost(uidx, load_arr, wa, 60) pen = inst / LOAD_MAX pen_list.append(dict(inst=inst, pen=pen, unit_supply=tot["unit_supply"], curt_kwh=tot["curt_kwh"], loss_kwh=tot["loss_kwh"], total=tot["total"])) q6 = dict(series=pen_list) # ---------- Q7：十五日，负荷/风电均 1200MW，替代机组 2、3 ---------- load15, wind15 = synth_15day() # 仅机组1（tmax 600）供电，无储能 tot7, _, wuse7, tmin7, tmax7 = scenario_cost([1], load15, wind15, 60) sto7 = storage_size([1], load15, wind15) q7 = dict(load_max=1200.0, wind_inst=1200.0, units=[1], tmax=tmax7, tmin=tmin7, loss_kwh=tot7["loss_kwh"], curt_kwh=tot7["curt_kwh"], total=tot7["total"], sto_capacity=sto7["capacity_MWh"], sto_power=sto7["power_MW"], sto_feasible=sto7["feasible"], load_arr=load15, wind_arr=wind15, daily_loss=sorted(tot7["loss_kwh"] / 15.0 for _ in [0])[0] if False else tot7["loss_kwh"] / 15.0) # ---------- 写 CSV ---------- os.makedirs(OUT_DIR, exist_ok=True) path = os.path.join(OUT_DIR, out_csv) with open(path, "w", newline="", encoding="utf-8-sig") as f: w = csv.writer(f) w.writerow(["模块", "参数", "数值", "单位", "说明"]) w.writerow(["系统", "火电机组数", 3, "台", "装机1050MW"]) w.writerow(["系统", "机组1 最大/最小", "600/180", "MW", "a=0.226 b=30.42 c=786.80 carbon=0.72"]) w.writerow(["系统", "机组2 最大/最小", "300/90", "MW", "a=0.588 b=65.12 c=451.32 carbon=0.75"]) w.writerow(["系统", "机组3 最大/最小", "150/45", "MW", "a=0.785 b=139.6 c=1049.50 carbon=0.79"]) w.writerow(["系统", "电煤价格", COAL_PRICE, "元/t", ""]) w.writerow(["系统", "风电运维", WIND_OM, "元/kWh", ""]) w.writerow(["系统", "储能功率/能量成本", "3000/3000", "元/kW,元/kWh", "寿命10年"]) w.writerow(["系统", "弃风/失负荷损失", "0.3/8", "元/kWh", ""]) w.writerow(["系统", "日负荷最大", LOAD_MAX, "MW", ""]) w.writerow(["系统", "碳捕集单价档位", "0/60/80/100", "元/t", ""]) w.writerow([]) w.writerow(["Q1", "碳价", "单位供电成本", "元/kWh", "总发电成本"]) for cp in CARBON_PRICES: w.writerow(["Q1", cp, round(q1[cp]["unit_supply"], 5), "元/kWh", round(q1[cp]["total"], 1)]) w.writerow([]) w.writerow(["Q2", "风电装机", q2["inst"], "MW", "替代机组3，可用1,2"]) w.writerow(["Q2", "弃风电量", round(q2["curt_kwh"], 1), "kWh", "全天"]) w.writerow(["Q2", "失负荷电量", round(q2["loss_kwh"], 1), "kWh", ""]) w.writerow(["Q2", "无弃风可降装机", round(q2["inst_no_curt"], 1), "MW", ""]) w.writerow(["Q2", "可降低容量", round(q2["reduce_MW"], 1), "MW", ""]) w.writerow(["Q2", "单位供电成本", round(q2["unit_supply"], 5), "元/kWh", "碳价0"]) w.writerow([]) w.writerow(["Q3", "风电装机", q3["inst"], "MW", "替代机组2，可用1,3"]) w.writerow(["Q3", "弃风电量", round(q3["curt_kwh"], 1), "kWh", ""]) w.writerow(["Q3", "失负荷电量", round(q3["loss_kwh"], 1), "kWh", ""]) w.writerow(["Q3", "不失负荷可增装", round(q3["add_MW"], 1), "MW", ""]) w.writerow(["Q3", "单位供电成本", round(q3["unit_supply"], 5), "元/kWh", "碳价0"]) w.writerow([]) w.writerow(["Q4", "场景", "碳价0", "碳价60", "碳价80", "碳价100"]) for tag in ["Q2", "Q3"]: r = q4[tag] w.writerow(["Q4", tag, round(r[0], 5), round(r[60], 5), round(r[80], 5), round(r[100], 5)]) w.writerow([]) w.writerow(["Q5", "风电装机", q5["inst"], "MW", "替代2,3，仅机组1"]) w.writerow(["Q5", "无储能失负荷", round(q5["loss_kwh_no_sto"], 1), "kWh", ""]) w.writerow(["Q5", "无储能弃风", round(q5["curt_kwh_no_sto"], 1), "kWh", ""]) w.writerow(["Q5", "最小储能容量", round(q5["sto_capacity"], 2), "MWh", "往返效率0.9"]) w.writerow(["Q5", "最小储能功率", round(q5["sto_power"], 1), "MW", ""]) w.writerow(["Q5", "储能日成本", round(q5["sto_daily"], 1), "元/天", "含投资平摊+运维"]) w.writerow(["Q5", "含储能单位供电成本", round(q5["unit_supply"], 5), "元/kWh", "碳价60"]) w.writerow([]) w.writerow(["Q6", "风电装机", "渗透率", "单位供电成本", "弃风kWh", "失负荷kWh"]) for s in q6["series"]: w.writerow(["Q6", s["inst"], round(s["pen"], 3), round(s["unit_supply"], 5), round(s["curt_kwh"], 1), round(s["loss_kwh"], 1)]) w.writerow([]) w.writerow(["Q7", "十五日负荷/风电", "1200/1200", "MW", "替代2,3，仅机组1"]) w.writerow(["Q7", "失负荷电量", round(q7["loss_kwh"], 1), "kWh", "十五日合计"]) w.writerow(["Q7", "弃风电量", round(q7["curt_kwh"], 1), "kWh", ""]) w.writerow(["Q7", "所需储能容量", round(q7["sto_capacity"], 2), "MWh", ""]) w.writerow(["Q7", "所需储能功率", round(q7["sto_power"], 1), "MW", ""]) return dict( load_norm=load_norm, wind_norm=wind_norm, load_arr=load_arr, q1=q1, q1_plan=q1_plan, q2=q2, q3=q3, q4=q4, q5=q5, q6=q6, q7=q7, carbon_prices=CARBON_PRICES, load_kwh_day=load_kwh_day, csv_path=path, summary="电工杯2022A：Q1单位供电成本%.4f元/kWh(碳价0)；Q2弃风%.0fkWh可降装机%.0fMW；" "Q5无储能失负荷%.0fkWh，最小储能%.1fMWh/%.0fMW，含储能单位成本%.4f元/kWh" % (q1[0]["unit_supply"], q2["curt_kwh"], q2["reduce_MW"], q5["loss_kwh_no_sto"], q5["sto_capacity"], q5["sto_power"], q5["unit_supply"]), ) if __name__ == "__main__": D = gen_dgcup2022a() print("=" * 70) print(D["summary"]) print("=" * 70) print("[Q1] 无风三机组最小成本运行，单位供电成本(元/kWh) by 碳价:") for cp in D["carbon_prices"]: print(" 碳价 %4d 元/t -> %.5f 元/kWh (总成本 %.1f 元)" % (cp, D["q1"][cp]["unit_supply"], D["q1"][cp]["total"])) print("[Q2] 风电300MW替代机组3: 弃风=%.1f kWh, 失负荷=%.1f kWh, 单位成本=%.5f 元/kWh" % (D["q2"]["curt_kwh"], D["q2"]["loss_kwh"], D["q2"]["unit_supply"])) print(" 为减弃风不失负荷，装机可降 %.1f MW -> %.1f MW" % (D["q2"]["reduce_MW"], D["q2"]["inst_no_curt"])) print("[Q3] 风电600MW替代机组2: 弃风=%.1f kWh, 失负荷=%.1f kWh, 单位成本=%.5f 元/kWh" % (D["q3"]["curt_kwh"], D["q3"]["loss_kwh"], D["q3"]["unit_supply"])) print(" 为不失负荷，可增装风电 %.1f MW" % D["q3"]["add_MW"]) print("[Q4] 单位供电成本(元/kWh) 碳价0/60/80/100:") print(" Q2场景:", {cp: round(D["q4"]["Q2"][cp], 5) for cp in D["carbon_prices"]}) print(" Q3场景:", {cp: round(D["q4"]["Q3"][cp], 5) for cp in D["carbon_prices"]}) print("[Q5] 风电900MW替代2,3(仅机组1):") print(" 无储能失负荷=%.1f kWh, 弃风=%.1f kWh" % (D["q5"]["loss_kwh_no_sto"], D["q5"]["curt_kwh_no_sto"])) print(" 最小储能容量=%.2f MWh, 功率=%.1f MW, 可行=%s" % (D["q5"]["sto_capacity"], D["q5"]["sto_power"], D["q5"]["sto_feasible"])) print(" 储能日成本=%.1f 元, 含储能单位供电成本=%.5f 元/kWh (碳价60)" % (D["q5"]["sto_daily"], D["q5"]["unit_supply"])) print("[Q6] 替代容量递增 -> 单位供电成本/弃风/失负荷:") for s in D["q6"]["series"]: print(" 装机%4dMW 渗透%.3f -> 成本%.5f 元/kWh, 弃风%.0f, 失负荷%.0f" % (s["inst"], s["pen"], s["unit_supply"], s["curt_kwh"], s["loss_kwh"])) print("[Q7] 十五日(负荷/风电1200MW,替代2,3):") print(" 失负荷=%.1f kWh, 弃风=%.1f kWh, 所需储能=%.2f MWh/%.1f MW, 可行=%s" % (D["q7"]["loss_kwh"], D["q7"]["curt_kwh"], D["q7"]["sto_capacity"], D["q7"]["sto_power"], D["q7"]["sto_feasible"])) print("=" * 70) print("CSV ->", D["csv_path"])