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Q31.py
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Q31.py
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# -*- coding: utf-8 -*-
"""
Created on Sun May 26 15:32:40 2024
@author: Gregory_Guo
"""
import gurobipy as gp
from gurobipy import GRB
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use( ['science',"grid","ieee"])
import os
########################################################
#################### Function ######################
########################################################
# 1-风光储联合 2-分时电价
Func = 1
########################################################
#################### Data Load #####################
########################################################
# Load all necessary data for the first question analysis
load_data_path = r'C:/Users/29639/Desktop/2024电工杯/题目/1716500748329333/A题/附件1：各园区典型日负荷数据.xlsx'
generation_data_path = r'C:/Users/29639/Desktop/2024电工杯/题目/1716500748329333/A题/附件2：各园区典型日风光发电数据.xlsx'
# Load the load data
load_data = pd.read_excel(load_data_path)
load_data.columns = ['时间（h）', 'A园区负荷(kW)', 'B园区负荷(kW)', 'C园区负荷(kW)']
# Load the wind and solar generation data
generation_data = pd.read_excel(generation_data_path, header=2)
generation_data.columns = ['时间（h）', 'A园区光伏出力（p.u.）', 'B园区风电出力（p.u.）', 'C园区光伏出力（p.u.）', 'C园区风电出力（p.u.）']
# Combine load and generation data for each park
combined_data = pd.merge(load_data, generation_data, on='时间（h）')
# 将时间列转换为datetime格式
combined_data['时间（h）'] = pd.to_datetime(combined_data['时间（h）'], format='%H:%M:%S').dt.hour
# 汇总联合园区的负荷和发电数据
combined_data['联合园区负荷(kW)'] = combined_data[['A园区负荷(kW)', 'B园区负荷(kW)', 'C园区负荷(kW)']].sum(axis=1)
combined_data['联合园区光伏出力（p.u.）'] = combined_data[['A园区光伏出力（p.u.）', 'C园区光伏出力（p.u.）']].sum(axis=1)
combined_data['联合园区风电出力（p.u.）'] = combined_data[['B园区风电出力（p.u.）', 'C园区风电出力（p.u.）']].sum(axis=1)
combined_data['A园区风电出力（p.u.）'] = 0
combined_data['B园区光伏出力（p.u.）'] = 0
# 初始储能状态
initial_storage_energy = 0.9 # 初始储能能量%
efficiency_charge = 0.95 # 充电效率
efficiency_discharge = 0.95 # 放电效率
purchase_cost = 1 # 购电成本
pv_cost = 0.4 # 光伏发电成本
wind_cost = 0.5 # 风电发电成本
# 储能系统的成本参数
power_cost_per_kw = 80 # 元/kW
energy_cost_per_kwh = 180 # 元/kWh
# 风光系统的成本参数
wind_cost_per_kw = 3000 # 风电容量成本，元/kW
solar_cost_per_kw = 2500 # 光伏容量成本，元/kW
if Func == 1:
pass
elif Func == 2:
# 分时电价设置
def get_time_of_use_price(time):
# 假设高峰电价为1.0元/kWh，低谷电价为0.4元/kWh
peak_price = 1.0
off_peak_price = 0.4
# 定义高峰、平时和低谷时间段
if time in [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]:
return peak_price
else:
return off_peak_price
# 应用分时电价到每个时间点
combined_data['电价'] = combined_data['时间（h）'].apply(get_time_of_use_price)
########################################################
#################### Opt Model #####################
########################################################
# (modelCase, combined_data, park) = ("energy_optimization", combined_data, 'C')
def OptModel(modelCase, combined_data, park):
# 创建模型
model = gp.Model(modelCase)
# 时间步长
time_steps = combined_data.shape[0]
# 创建变量
storage_charge = model.addVars(time_steps, vtype=GRB.BINARY, name="storage_charge")
storage_discharge = model.addVars(time_steps, vtype=GRB.BINARY, name="storage_discharge")
storage_energy = model.addVars(time_steps, vtype=GRB.CONTINUOUS, name="storage_energy")
storage_power_charge = model.addVars(time_steps, vtype=GRB.CONTINUOUS, name="storage_power_charge")
storage_power_discharge = model.addVars(time_steps, vtype=GRB.CONTINUOUS, name="storage_power_discharge")
Pv_used = model.addVars(time_steps, lb = 0, vtype=GRB.CONTINUOUS, name="Pv_used")
Pw_used = model.addVars(time_steps, lb = 0, vtype=GRB.CONTINUOUS, name="Pw_used")
Pg_used = model.addVars(time_steps, lb = 0, vtype=GRB.CONTINUOUS, name="Pg_used")
# 新增的储能容量和储能功率决策变量
storage_capacity = model.addVar(vtype=GRB.CONTINUOUS, name="storage_capacity")
storage_power = model.addVar(vtype=GRB.CONTINUOUS, name="storage_power")
# 风光容量
wind_capacity = model.addVar(vtype=GRB.CONTINUOUS, name="wind_capacity")
solar_capacity = model.addVar(vtype=GRB.CONTINUOUS, name="solar_capacity")
# 约束条件
for t in range(time_steps):
load = combined_data.at[t, f'{park}园区负荷(kW)'] * 1.5
pv_gen = combined_data.at[t, f'{park}园区光伏出力（p.u.）'] * solar_capacity
wind_gen = combined_data.at[t, f'{park}园区风电出力（p.u.）'] * wind_capacity
model.addConstr(Pv_used[t] <= pv_gen)
model.addConstr(Pw_used[t] <= wind_gen)
# 功率平衡约束
model.addConstr(storage_power_charge[t] == Pv_used[t] + Pw_used[t] + Pg_used[t] + storage_power_discharge[t] - load)
# 储能不能同时充电和放电
model.addConstr(storage_charge[t] + storage_discharge[t] <= 1)
# 储能充放电功率限制
model.addConstr(storage_power_charge[t] <= storage_charge[t] * storage_power)
model.addConstr(storage_power_discharge[t] <= storage_discharge[t] * storage_power)
# 储能能量约束
model.addConstr(storage_energy[t] <= 0.9 * storage_capacity)
model.addConstr(storage_energy[t] >= 0.1 * storage_capacity)
# 储能状态约束
if t == 0:
model.addConstr(storage_energy[t] == initial_storage_energy*storage_capacity + storage_power_charge[t] * efficiency_charge - storage_power_discharge[t] / efficiency_discharge)
else:
model.addConstr(storage_energy[t] == storage_energy[t-1] + storage_power_charge[t] * efficiency_charge - storage_power_discharge[t] / efficiency_discharge)
# 初始和终端储能状态约束
model.addConstr(storage_energy[0] == initial_storage_energy*storage_capacity)
model.addConstr(storage_energy[time_steps - 1] == initial_storage_energy*storage_capacity)
# 目标函数：最小化总成本（包括购电成本、发电成本和储能系统的投资成本）
total_cost = gp.quicksum(Pg_used[t] * purchase_cost+
Pv_used[t] * pv_cost +
Pw_used[t] * wind_cost
for t in range(time_steps))
total_investment_cost = (storage_power * power_cost_per_kw + storage_capacity * energy_cost_per_kwh)/365*0.2309
wind_investment_cost = wind_capacity * wind_cost_per_kw/365*0.2309
solar_investment_cost = solar_capacity * solar_cost_per_kw/365*0.2309
Total_Construction_Cost = total_investment_cost + wind_investment_cost + solar_investment_cost
Original_cost = gp.quicksum(1.5*purchase_cost*combined_data.at[t, f'{park}园区负荷(kW)'] for t in range(time_steps))
# pay back constraint
model.addConstr(Original_cost - total_cost >= Total_Construction_Cost, name="sum_constraint")
# model.addConstr(wind_capacity == 1000)
# model.addConstr(solar_capacity == 1000)
model.setObjective(total_cost + Total_Construction_Cost, GRB.MINIMIZE)
# 求解模型
model.optimize()
# model.computeIIS()
# # #
# model.write("abc11111.ilp")
return (model, storage_charge, storage_discharge,
storage_power_charge, storage_power_discharge,
storage_energy, storage_capacity, storage_power,
Pv_used, Pw_used, Pg_used,
wind_capacity, solar_capacity)
def analyze_with_storage(data, park, load_col, gen_col, purchase_cost, generation_cost, OptData):
"""
"""
(model, storage_charge, storage_discharge,
storage_power_charge, storage_power_discharge,
storage_energy, storage_capacity, storage_power,
Pv_used, Pw_used, Pg_used,
wind_capacity, solar_capacity) = OptData
# 运行储能策略
for i in range(0, len(combined_data)):
data.at[i, f'{park}园区_SOC(kWh)'] = storage_energy[i].X
data.at[i, f'{park}园区购电量(kW)'] = Pg_used[i].X
data.at[i, f'{park}光伏用电量(kW)'] = Pv_used[i].X
data.at[i, f'{park}风电用电量(kW)'] = Pw_used[i].X
data.at[i, f'{park}放电(kWh)'] = -storage_power_discharge[i].X
data.at[i, f'{park}充电(kWh)'] = storage_power_charge[i].X
data[f'{park}园区充放电(kWh)'] = data[[f'{park}放电(kWh)', f'{park}充电(kWh)']].sum(axis=1)
# 经济性分析
load = data[load_col]
# 分开处理光伏和风电发电
pv_cost = generation_cost[0]
wind_cost = generation_cost[1]
data[f'{park}园区弃光(kW)'] = data[f'{park}园区光伏出力（p.u.）']*solar_capacity.X - data[f'{park}光伏用电量(kW)']
data[f'{park}园区弃风(kW)'] = data[f'{park}园区风电出力（p.u.）']*wind_capacity.X - data[f'{park}风电用电量(kW)']
data[f'{park}园区弃电量(kW)'] = data[[f'{park}园区弃风(kW)', f'{park}园区弃光(kW)']].sum(axis=1)
total_purchase_cost = (data[f'{park}园区购电量(kW)'] * purchase_cost).sum()
total_generation_cost = (data[f'{park}光伏用电量(kW)']* pv_cost +
data[f'{park}风电用电量(kW)']* wind_cost).sum()
total_storage_cost = (storage_power.X * power_cost_per_kw + storage_capacity.X * energy_cost_per_kwh)/365*0.1295
total_cost = total_purchase_cost + total_generation_cost + total_storage_cost
avg_cost = total_cost / load.sum()
return (total_cost, avg_cost, data,
data[f'{park}园区_SOC(kWh)'],
data[f'{park}园区弃风(kW)'],
data[f'{park}园区弃光(kW)'],
data[f'{park}园区弃电量(kW)'],
data[f'{park}放电(kWh)'],
data[f'{park}充电(kWh)'],
data[f'{park}园区购电量(kW)'],
data[f'{park}光伏用电量(kW)'],
data[f'{park}风电用电量(kW)'])
def picture23(combined_data, park, storage_capacity, wind_capacity, solar_capacity):
"""
"""
# 创建子图
fig, axes = plt.subplots(1, 1, figsize=(15, 0.72*10), sharex=True)
# 定义柱状图的宽度和透明度
bar_width = 0.7
alpha = 0.5
# 字体设置
title_fontsize = 26
label_fontsize = 24
legend_fontsize = 25
tick_labelsize = 24
# 绘制园区C的数据
axes.bar(combined_data['时间（h）'], solar_capacity*combined_data[f'{park}园区光伏出力（p.u.）'], width=bar_width, label='P_V', color='lightpink', alpha=alpha)
axes.bar(combined_data['时间（h）'], wind_capacity*combined_data[f'{park}园区风电出力（p.u.）'], width=bar_width, bottom= solar_capacity*combined_data[f'{park}园区光伏出力（p.u.）'], label='P_W', color='skyblue', alpha=alpha)
bars_charge = axes.bar(combined_data['时间（h）'], combined_data[f'{park}园区充放电(kWh)'], width=bar_width, bottom= 1.5*combined_data[f'{park}园区负荷(kW)'], label='P_C', color='purple', alpha=1)
axes.bar(combined_data['时间（h）'], 1.5*combined_data[f'{park}园区负荷(kW)'], width=bar_width, label='P_L', edgecolor='black', facecolor='none', linewidth=2, linestyle = '--')
axes.bar(combined_data['时间（h）'], combined_data[f'{park}园区购电量(kW)'], width=bar_width, bottom= solar_capacity*combined_data[f'{park}园区光伏出力（p.u.）']+wind_capacity*combined_data[f'{park}园区风电出力（p.u.）'], label='P_G', color='orange', alpha=alpha)
axes.bar(combined_data['时间（h）'], combined_data[f'{park}园区弃电量(kW)'], width=bar_width, bottom = -combined_data[f'{park}园区弃电量(kW)'], label='P_X', color='gray', alpha=alpha)
if park == '联合':
park = 'Joint'
axes.set_title(f'Park {park}', fontsize=title_fontsize)
park = '联合'
else:
axes.set_title(f'Park {park}', fontsize=title_fontsize)
axes.set_xlabel('Time (h)', fontsize=label_fontsize)
axes.set_ylabel('Power (kW)', fontsize=label_fontsize)
# axes.legend(loc='upper left', fontsize=legend_fontsize)
axes.tick_params(axis='both', which='major', labelsize=tick_labelsize) # 调整刻度标签字体大小
axes.legend(loc='upper center', fontsize=legend_fontsize, bbox_to_anchor=(0.5, 1.25), ncol=6)
# 添加阴影效果
for bar in bars_charge:
bar.set_edgecolor('grey')
bar.set_linewidth(1)
bar.set_alpha(0.7)
# 创建次坐标轴
ax2 = axes.twinx()
ax2.plot(combined_data['时间（h）'], combined_data[f'{park}园区_SOC(kWh)']/storage_capacity, label='SOC', color='green', marker='o')
ax2.set_ylim(0, 1)
ax2.set_ylabel('SOC', fontsize=label_fontsize)
ax2.tick_params(axis='y', which='major', labelsize=tick_labelsize) # 调整刻度标签字体大小
ax2.legend(loc='upper right', fontsize=legend_fontsize)
# 调整布局
plt.tight_layout()
plt.show()
def Write(OptResults, EcoResults, fileName):
"""
"""
results = {
'solar_capacity': OptResults[12].X,
'wind_capacity': OptResults[11].X,
'storage_capacity': OptResults[6].X,
'storage_power': OptResults[7].X,
'total_cost': EcoResults[0],
'avg_cost': EcoResults[1],
'PX_pv': EcoResults[5],
'PX_wind': EcoResults[4],
'PX_total': EcoResults[6],
'PC_discharge': EcoResults[7],
'PC_charge': EcoResults[8],
'PG': EcoResults[9],
'PV': EcoResults[10],
'PW': EcoResults[11],
}
df = pd.DataFrame(results)
# 指定导出文件夹路径和文件名
output_folder = r'C:\Users\29639\Desktop\2024电工杯\dataResults' # 请将此路径替换为你需要的文件夹路径
output_filename = fileName
# 创建文件夹（如果不存在）
os.makedirs(output_folder, exist_ok=True)
# 生成完整的文件路径
output_file = os.path.join(output_folder, output_filename)
# 将DataFrame写入Excel文件
df.to_excel(output_file, index=False)
JointOpt = OptModel("energy_optimization", combined_data, '联合')
joint_result_with_storage = analyze_with_storage(combined_data,
"联合",
'联合园区负荷(kW)',
['联合园区光伏出力(kW)', '联合园区风电出力(kW)'],
purchase_cost,
[pv_cost, wind_cost, power_cost_per_kw, energy_cost_per_kwh], JointOpt
)
AOpt = OptModel("energy_optimization", combined_data, 'A')
park_A_result_with_storage = analyze_with_storage(combined_data,
'A',
'A园区负荷(kW)',
['A园区光伏出力（p.u.）','A园区风电出力（p.u.）'],
purchase_cost,
[pv_cost, wind_cost, power_cost_per_kw, energy_cost_per_kwh], AOpt
)
BOpt = OptModel("energy_optimization", combined_data, 'B')
park_B_result_with_storage = analyze_with_storage(combined_data,
'B',
'B园区负荷(kW)',
['B园区光伏出力(kW)','B园区风电出力(kW)'],
purchase_cost,
[pv_cost, wind_cost, power_cost_per_kw, energy_cost_per_kwh], BOpt
)
COpt = OptModel("energy_optimization", combined_data, 'C')
park_C_result_with_storage = analyze_with_storage(combined_data,
'C',
'C园区负荷(kW)',
['C园区光伏出力(kW)','C园区风电出力(kW)'],
purchase_cost,
[pv_cost, wind_cost, power_cost_per_kw, energy_cost_per_kwh], COpt
)
picture23(joint_result_with_storage[2], '联合', JointOpt[6].X, JointOpt[11].X, JointOpt[12].X)
picture23(park_A_result_with_storage[2], 'A', AOpt[6].X, AOpt[11].X, AOpt[12].X)
picture23(park_B_result_with_storage[2], 'B', BOpt[6].X, BOpt[11].X, BOpt[12].X)
picture23(park_C_result_with_storage[2], 'C', COpt[6].X, COpt[11].X, COpt[12].X)
# Write(AOpt, park_A_result_with_storage, f'AwithQ31_{initial_storage_energy}.xlsx')
# Write(BOpt, park_B_result_with_storage, f'BwithQ31_{initial_storage_energy}.xlsx')
# Write(COpt, park_C_result_with_storage, f'CwithQ31_{initial_storage_energy}.xlsx')
# Write(JointOpt, joint_result_with_storage, f'JwithQ31_{initial_storage_energy}.xlsx')