# -*- coding: utf-8 -*- """电工杯 2024 A 题 园区微电网风光储协调优化配置 —— 数字真源。 确定性合成数据（仅用于跑通方法，非官方原题）。提供： - gen_dgcup2024a() -> 返回全部关键数字（供范文附录 import 复现） - 直接运行本脚本 -> 生成 data/dgcup2024a.csv 与 assets/problems/dgcup2024a/figs/*.svg 成本/容量参数（量级贴近典型工程，仅作教学示例）： 光伏 3500 元/kW，风电 6000 元/kW，储能 1500 元/kWh + 功率 800 元/kW（按 2h 率）， 20 年寿命 + 2% 运维。 """ import math, os, csv ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) DATA = os.path.join(ROOT, "assets", "problems", "data") FIGS = os.path.join(ROOT, "assets", "problems", "dgcup2024a", "figs") WIND = 300.0 # 风电装机 kW（赛题给定 300kW 风机） PV_CAP = 1300.0 # 光伏装机 kW（问题1 固定配置） H = list(range(24)) # 园区负荷曲线 (kW)：双峰（早 8-9、晚 19-21） L = [320,300,290,285,290,320,420,600,720,680,600,600,650,580,580,580,620,700,800,780,700,500,400,360] # 光伏出力 (kW)：6-18 时钟形，正午最大 Ppv = [ (PV_CAP*max(0.0, math.sin(math.pi*(h-6)/12.0)) if 6<=h<=18 else 0.0) for h in H ] # 风电出力 (kW)：夜间强、午后弱 Pw = [ 200+100*math.cos(2*math.pi*(h-3)/24.0) for h in H ] R = [Ppv[h]+Pw[h] for h in H] N = [R[h]-L[h] for h in H] # 问题1：24h 净平衡所需储能容量/功率（S 累计净，容量=峰谷差） S = [0.0]*24 S[0] = N[0] for h in range(1,24): S[h] = S[h-1] + N[h] E_cap = max(S) - min(S) P_cap = max(abs(v) for v in N) SOC0 = -min(S) soc = [SOC0 + S[h] for h in H] sumR, sumL = sum(R), sum(L) # 调度基准（无储能）与含储能对比 import_base = [max(0.0, L[h]-R[h]) for h in H] curt_base = [max(0.0, R[h]-L[h]) for h in H] util_base = sum(min(R[h],L[h]) for h in H)/sumR util_with = 1.0 if sumR>=sumL else sumR/sumL def annual_cost(pv, st_e): capex = pv*3500 + WIND*6000 + st_e*1500 + (max(1,st_e/2.0))*800 return capex/20.0 + capex*0.02 def sweep(): pv_opts=[1000,1300,1600,1900] st_opts=[0,1000,2000,3000,4000,5000] rows=[] for pv in pv_opts: PpvS=[ (pv*max(0.0, math.sin(math.pi*(h-6)/12.0)) if 6<=h<=18 else 0.0) for h in H ] for st_e in st_opts: Rs=[PpvS[h]+Pw[h] for h in H] bat=0.0; Pmax=max(1.0, st_e/2.0); curt=0.0 for h in H: net=Rs[h]-L[h] if net>0: ch=min(net, st_e-bat, Pmax); bat+=ch; curt+=net-ch else: d=min(-net, bat, Pmax); bat-=d util = 1.0 - curt/sum(Rs) rows.append((pv,st_e,util,annual_cost(pv,st_e))) cand=[r for r in rows if r[2]>=0.95] best=min(cand, key=lambda r:r[3]) if cand else min(rows, key=lambda r:(abs(r[2]-0.95),r[3])) return rows, best def gen_dgcup2024a(): """返回供范文附录复现的全部关键数字。""" rows, best = sweep() return { "H":H, "L":L, "Ppv":Ppv, "Pw":Pw, "R":R, "N":N, "S":S, "E_cap":E_cap, "P_cap":P_cap, "SOC0":SOC0, "soc":soc, "sumR":sumR, "sumL":sumL, "util_base":util_base, "util_with":util_with, "import_base_total":sum(import_base), "curt_base_total":sum(curt_base), "knee":best, "sweep":rows, } # ---------------- 生成数据 CSV ---------------- def write_csv(): os.makedirs(DATA, exist_ok=True) path=os.path.join(DATA, "dgcup2024a.csv") with open(path,"w",newline="") as f: w=csv.writer(f) w.writerow(["hour","load_kw","pv_kw","wind_kw","renewable_kw","net_kw","soc_kwh"]) for h in H: w.writerow([h, round(L[h],1), round(Ppv[h],1), round(Pw[h],1), round(R[h],1), round(N[h],1), round(soc[h],1)]) print("data ->", path) # ---------------- 生成配图 ---------------- def line_svg(fname, title, series, ymax, xlabel, ylabel, w=760, hgt=440): x0,x1,y0,y1 = 90,710,350,70 P=[] P.append(''%(w,hgt)) P.append(''%(w,hgt)) P.append('%s'%(title)) P.append('') for i in range(6): yy=y0+(y1-y0)*i/5.0; val=ymax*(5-i)/5.0 P.append(''%(x0,yy,x1,yy)) P.append('%.0f'%(x0-8,yy+4,val)) for h in [0,4,8,12,16,20,23]: xx=x0+(x1-x0)*h/23.0 P.append('%d'%(xx,hgt-22,h)) P.append(''%(x0,y0,x1,y0)) P.append(''%(x0,y0,x0,y1)) for ci,(color,label,vals) in enumerate(series): pts=[] for h in H: xx=x0+(x1-x0)*h/23.0; yy=y0+(y1-y0)*(vals[h]/ymax) pts.append("%.1f,%.1f"%(xx,yy)) P.append(''%( " ".join(pts), color)) for h in H: xx=x0+(x1-x0)*h/23.0; yy=y0+(y1-y0)*(vals[h]/ymax) P.append(''%(xx,yy,color)) bx=110+ci*180 P.append(''%(bx,color)) P.append('%s'%(bx+18,label)) P.append('%s'%(hgt-4,xlabel)) P.append('') open(os.path.join(FIGS,fname),"w").write("\n".join(P)) def write_figs(): os.makedirs(FIGS, exist_ok=True) line_svg("dgcup2024a-1-fig1.svg","图1 园区 24h 负荷与风光出力曲线", [("#ea580c","负荷 L",L),("#2563eb","光伏 Ppv",Ppv),("#0d9488","风电 Pw",Pw)], 1000,"时刻 (h)","功率 (kW)") line_svg("dgcup2024a-1-fig2.svg","图2 储能 SOC 24h 变化（容量 %.0f kWh）"%E_cap, [("#4f46e5","SOC",soc)], E_cap*1.05, "时刻 (h)","荷电量 (kWh)") # 图3 容量优化帕累托 rows,best=sweep() fig3=['', '', '图3 容量配置帕累托：年成本 vs 可再生利用率', ''] x0,x1,y0,y1=90,710,350,70 cmax=max(r[3] for r in rows) for i in range(6): yy=y0+(y1-y0)*i/5.0; val=cmax*(5-i)/5.0 fig3.append(''%(x0,yy,x1,yy)) fig3.append('%.0f'%(x0-8,yy+4,val)) for u in [0.5,0.6,0.7,0.8,0.9,1.0]: xx=x0+(x1-x0)*(u-0.5)/0.5 fig3.append('%.0f%%'%(xx,u*100)) fig3.append(''%(xx,y0,xx,y1)) fig3.append(''%(x0,y0,x1,y0)) fig3.append(''%(x0,y0,x0,y1)) for (pv,st_e,util,c) in rows: xx=x0+(x1-x0)*(util-0.5)/0.5; yy=y0+(y1-y0)*(c/cmax) col="#dc2626" if (pv,st_e)==(best[0],best[1]) else "#2563eb" fig3.append(''%(xx,yy,col)) fig3.append('PV%d'%(xx,yy-8,pv)) fig3.append(''%(x0)) fig3.append('最优(利用≈95%%)'%(x0+18)) fig3.append('可再生利用率') fig3.append('') open(os.path.join(FIGS,"dgcup2024a-1-fig3.svg"),"w").write("\n".join(fig3)) # 图4 方法学流程 flow=['', '', '图4 风光储协调优化配置方法学流程', '', ''] boxes=[("赛题","风光储微网"),("负荷/风光","典型日曲线"),("净平衡","储能容量"),("容量优化","帕累托扫描"),("协调调度","规则策略")] bx=60; bw=130; gap=18 for i,(t,s) in enumerate(boxes): x=bx+i*(bw+gap) flow.append(''%(x,bw)) flow.append('%s'%(x+bw/2,t)) flow.append('%s'%(x+bw/2,s)) if i<4: flow.append(''%(x+bw+gap-2, x+bw+2)) flow.append('') open(os.path.join(FIGS,"dgcup2024a-1-fig4.svg"),"w").write("\n".join(flow)) print("figs ->", FIGS) if __name__ == "__main__": write_csv() write_figs() D=gen_dgcup2024a() print("sumR=%.1f sumL=%.1f"%(D["sumR"],D["sumL"])) print("E_cap=%.1f kWh P_cap=%.1f kW SOC0=%.1f kWh"%(D["E_cap"],D["P_cap"],D["SOC0"])) print("util_base=%.4f util_with=%.4f"%(D["util_base"],D["util_with"])) print("curt_base=%.1f kWh import_base=%.1f kWh"%(D["curt_base_total"],D["import_base_total"])) print("knee=", D["knee"])