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+---
+categories: [mathematics, optimization]
+tags: [math, python]
+mathjax: true
+---
+
+# 微电网日前优化调度入门：求解一道数学建模题
+
+
+---
+
+最近看了一些微电网优化调度的论文，苦于无从下手之际，看到一道微电网日前优化调度相关的数学建模题；题目提供了一个简单的风光储微电网场景及测试数据，正好拿来练手。本文基于Python第三方库`PuLP`实现题目的混合整数规划模型，并使用默认的`CBC`求解器求解。输入数据及汇总代码，参见文末。
+
+## 问题描述
+
+问题出自第十届“中国电机工程学会杯”全国大学生电工数学建模竞赛A题：微电网日前优化调度。
+
+下图示意了一个含有风机、光伏、蓄电池及常见负荷的微电网系统。日前经济调度问题是指在对风机出力、光伏出力、常规负荷进行日前（未来24h）预测的基础上，考虑电网测的分时电价，充分利用微网中的蓄电池等可调控手段，使微电网运行的经济性最优。
+
+![img](https://img-blog.csdnimg.cn/20200602153042572.png)
+
+题目要求在如下已知条件下，求不同调度方案的平均用电成本：
+
+- 未来24h、每隔15min共96个时间点的负荷、光伏、风机出力预测，及分时电价数据；
+
+- 风机容量250kW，发电成本0.52元/kWh；
+
+- 光伏容量150kW，发电成本0.75元/kWh；
+
+- 蓄电池容量300kWh；SOC初始值0.4，运行范围[0.3, 0.95]；由充电至放电成为为0.2元/kWh；日充放电次数限制均为8；忽略蓄电池损耗；
+
+完整问题描述参考：
+
+- [第十届“中国电机工程学会杯”全国大学生电工数学建模竞赛 A 题：微电网日前优化调度](https://blog.csdn.net/Jian_Yun_Rui/article/details/72529176)
+- [微电网日前优化调度 。算例有代码（0）](https://blog.csdn.net/kobeyu652453/article/details/106497232)
+
+
+## 风光储优化调度模型
+
+题目涉及电网、新能源（风机、光伏）、蓄电池及负荷四类资源，我们依次建立其线性规划模型，然后交给求解器求解。一个线性规划模型包含：
+
+- 设计变量：各类资源的实时出力数据
+- 约束条件：能量平衡及各类资源应该满足的技术参数，例如蓄电池的容量限制、SOC限制、充放电次数限制等
+- 目标函数：运行成本，具体到本题即用电成本
+
+本文基于 Python 第三方库`PuLP`实现。`PuLP`是一个线性规划问题建模库，将数学模型转换为 MPS 或者 LP 文件，然后调用 LP 求解器如 CBC、GLPK、CPLEX、Gurobi 等求解。具体用法参考下面链接，本文不再赘述。
+
+- [PuLP Documentation](https://coin-or.github.io/pulp/)
+- [Python求解线性规划——PuLP使用教程](https://www.cnblogs.com/OnlyAR/p/16196469.html)
+
+
+开始之前先抽象一个模型基类，表示各类调度设备，包含名称、容量、使用成本等基本属性，同时提供一个`create_model()`方法，用于实现设计变量、约束条件、目标函数等线性规划模型三要素。模型求解后，调用`output`属性获取变量值，即每个时刻的出力。
+
+```python
+import pulp
+import numpy as np
+
+
+class Model:
+    def __init__(self, name:str, 
+                        capacity:float,         # resource capacity
+                        unit_cost:float):       # unit cost when using the energy
+        '''Base class for resource model, e.g., Grid, Renewable and Storage.'''
+        # technical parameters
+        self.name = name
+        self.capacity = capacity
+        self.unit_cost = unit_cost
+
+        # linear programming model: variables, constraints and objective
+        self.variables = None
+        self.constraints = None
+        self.objective = None
+
+    def create_model(self, time_points:int, dt:float):
+        '''How to create the LP model.'''
+        raise NotImplementedError
+    
+    @property
+    def output(self): return np.array([v.value() for v in self.variables])
+```
+
+
+
+接下来依次建立电网、新能源（风机、光伏）及蓄电池的模型。
+
+
+### (1) 电网模型
+
+电网模型继承自`Model`基类，同时新增了**卖电收益**属性，并且满足容量约束即每个时刻的出力不能超过限定值，目标函数为运行成本即用电费用与卖电收益的差值。
+
+直观地，任意时刻可以用一个变量 $p^i$ 来表示电网的出力：正值表示从电网买电，或者负值表示卖电给电网。但是，**事先并不知道 $p^i$ 的正负**，也就没法计算此刻的运行成本（不能将线性规划变量直接用于`if-else`语句中）。因此，引入买电、卖电两个中间变量来分开描述：
+
+- $p^i_f \geq 0$ 表示 $i$ 时刻从电网买电量；
+- $p^i_t \geq 0$ 表示 $i$ 时刻向电网卖电量；
+
+因为同一时刻电流只能单向流动，即 $p^i_f$ 和 $p^i_t$ 至少有一个等于0：$p^i_f * p^i_t=0$。
+
+但这并不是一个合法的线性约束，需要再引入一个`0-1`变量：
+
+- $b_i=\{0，1\}$：1 表示从电网买电即 $p^i_t=0$，0 表示卖电到电网即 $p^i_f=0$
+
+于是线性约束表示为：
+
+$$
+0 \leq p^i_f \leq b^i * C \\
+0 \leq p^i_t \leq (1-b^i) * C \\
+$$
+
+其中，$C$为电网容量（交换功率）限制值。
+
+最终，电网 $i$ 时刻实际出力 $p^i$ 及用电成本（买电或卖电）$c^i$：
+
+$$
+p^i = p^i_f - p^i_t \\
+c^i = p^i_f * u_1 * dt - p^i_t * u_2 * dt
+$$
+
+其中，$u_1,u_2$分别为该时刻单位买电成本、卖电收益（元/kWh），$dt$ 为时间步长。
+
+```python
+class Grid(Model):
+    def __init__(self, name:str, 
+                    capacity:float, 
+                    unit_cost:np.ndarray,    # unit cost when buying electricity from utility grid
+                    unit_profit:np.ndarray): # unit profit when selling electricity to utility grid
+        super().__init__(name, capacity, unit_cost)
+        self.unit_profit = unit_profit
+
+
+    def create_model(self, time_points:int, dt:float):
+        # define variables at each time point
+        vars_from = [pulp.LpVariable(name=f'{self.name}_from_{i}', lowBound=0) for i in range(time_points)]
+        vars_to = [pulp.LpVariable(name=f'{self.name}_to_{i}', lowBound=0) for i in range(time_points)]
+        self.variables = [v1-v2 for v1,v2 in zip(vars_from, vars_to)]
+
+        # constraints: capacity limit
+        # 0<=var_from<=C*b
+        # 0<=var_to<=C*(1-b)
+        self.constraints = []
+        vars_b = [pulp.LpVariable(name=f'{self.name}_binary_{i}', cat=pulp.LpInteger) for i in range(time_points)]
+        for v1,v2,b in zip(vars_from, vars_to, vars_b):
+            self.constraints.append(v1<=self.capacity*b)
+            self.constraints.append(v2<=self.capacity*(1-b))
+        
+        # objective
+        self.objective = pulp.lpSum([v*x for v,x in zip(vars_from, self.unit_cost)])*dt - \
+            pulp.lpSum([v*x for v,x in zip(vars_to, self.unit_profit)])*dt
+```
+
+
+### (2) 新能源发电模型
+
+将风机和光伏抽象为新能源发电模型，约束条件为**每一时刻的电力供应不大于预测出力，如果不允许弃风弃光的话，则等于预测出力值**。因此，在`Model`类基础上增加两个输入参数：
+
+- `forecast`：每一时刻的出力预测，即一个列向量/数组/时间序列；
+- `allow_curtailment`：是否允许弃风弃光，默认允许。
+
+相应地，提供一个`utilization`输出属性表示新能源发电的实际利用率。
+
+```python
+class Renewable(Model):
+    def __init__(self, name:str, 
+                    capacity:float,
+                    unit_cost:float,
+                    forecast:np.ndarray,          # forecasting output
+                    allow_curtailment:bool=True): # allow curtailment or not
+        super().__init__(name, capacity, unit_cost)
+        self.forecast = forecast
+        self.allow_curtailment = allow_curtailment
+
+    def create_model(self, time_points:int, dt:float):
+        # define variables at each time point
+        self.variables = [pulp.LpVariable(name=f'{self.name}_{i}', lowBound=0) for i in range(time_points)]
+
+        # constraints:  v<=forecast       
+        if self.allow_curtailment:
+            self.constraints = [v<=x for v,x in zip(self.variables, self.forecast)]
+        else:
+            self.constraints = [v==x for v,x in zip(self.variables, self.forecast)]
+        
+        # objective
+        self.objective = pulp.lpSum(self.variables)*self.unit_cost*dt
+
+    @property
+    def utilization(self): return self.output.sum() / self.forecast.sum()
+```
+
+
+### (3) 蓄电池模型
+
+原题已经给出了蓄电池的混合整数规划数学模型，除了基类中的容量、单位用电成本外，还有如下主要参数：
+
+- `capacity_limit`：爬坡限制值，即原题公式（5）中的数值 20%
+- `init_soc`：初始SOC状态
+- `soc_limit`：电量范围SOC限制
+- `cycle_limit`：充放电次数限制
+
+参考原题的约束：
+
+- 爬坡约束：公式（3）（5）
+- 容量约束：公式（1）（2）
+- 调度周期始末电量相等：公式（4）
+- 充放电次数约束：公式（6）
+
+类比上文对电网买电、卖电行为的建模，同一时刻也需要三个中间变量：充电功率 $p^i_c$、放电功率 $p^i_d$、充放电`0-1`状态 $b^i$ （1-放电，0-充电）来描述电池的出力。前三个约束的实现不再赘述，下面重点解析充放电次数约束。
+
+充放电状态序列 $b=\{b^1, b^2, ..., b^n\}$，引入辅助的`0-1`变量 $t$ 表示相邻状态相减的绝对值，即
+
+$$
+t^i = |b^{i+1} - b^i| \quad 1=1,2,3,...,n-1
+$$
+
+当 $t^i=1$ 时，即相邻的充放电状态由0变成了1，或者由1变成了0，表示完成了一次充放电周期。于是总的充放电次数限制约束可以表示为：
+
+$$
+\Sigma t^i \leq N_c
+$$
+
+至此还剩最后一个问题，如何将含有绝对值的等式 $t^i = |b^{i+1} - b^i|$ 变换为线性约束？
+
+结合本文场景，将等式松弛一下 $t^i \geq |b^{i+1} - b^i|$：
+
+- $t^i=1$ 正是我们需要计数的情况
+- $t^i=0$ 没有增加计数，此时 $b^{i+1} = b^i$ 表明并未发生充放电状态变化，恰好可以对应上
+
+于是，上述绝对值等式约束等效为：
+
+$$
+-t^i \leq b^{i+1} - b^i \leq t^i
+$$
+
+
+```python
+class Storage(Model):
+    def __init__(self, name:str, 
+                    capacity:float, 
+                    unit_cost:float,
+                    capacity_limit:float, # charging / discharging ramping limit
+                    init_soc:float,       # initial state of charge
+                    soc_limit:list,       # SOC limit
+                    cycle_limit:int):     # charing / discharging cycle counts limit
+        super().__init__(name, capacity, unit_cost)
+        self.init_soc = init_soc
+        self.soc_limit = soc_limit
+        self.cycle_limit = cycle_limit
+        self.capacity_limit = capacity_limit
+    
+    def create_model(self, time_points: int, dt: float):
+        # define variables at each time point
+        vars_ch = [pulp.LpVariable(name=f'{self.name}_charge_{i}', lowBound=0) for i in range(time_points)]
+        vars_dis = [pulp.LpVariable(name=f'{self.name}_discharge_{i}', lowBound=0) for i in range(time_points)]
+        self.variables = [v1-v2 for v1,v2 in zip(vars_dis, vars_ch)]
+
+        # constraints 1: ramping limit
+        # 0<=var_dis<=C*b
+        # 0<=var_ch<=C*(1-b)
+        self.constraints = []
+        vars_b = [pulp.LpVariable(name=f'{self.name}_binary_{i}', cat=pulp.LpInteger) for i in range(time_points)]
+        C = self.capacity * self.capacity_limit
+        for v1,v2,b in zip(vars_dis, vars_ch, vars_b):
+            self.constraints.append(v1<=C*b)
+            self.constraints.append(v2<=C*(1-b))
+        
+        # constraints 2: SOC limit
+        soc = self.init_soc
+        s1, s2 = self.soc_limit
+        for v1,v2 in zip(vars_ch, vars_dis):
+            soc += (v1*dt - v2*dt) / self.capacity
+            self.constraints.append(soc>=s1)
+            self.constraints.append(soc<=s2)
+        
+        # constraints 3: same SOC at the scheduling end
+        self.constraints.append(pulp.lpSum(self.variables)==0)
+
+        # constraints 4: charging / discharging cycle limit
+        # t = |b_i-b_{i+1}|
+        # sum(t)<=N
+        vars_db = [vars_b[i+1]-vars_b[i] for i in range(time_points-1)]
+        vars_t = [pulp.LpVariable(name=f'{self.name}_binary_t_{i}', cat=pulp.LpInteger) for i in range(time_points-1)]
+        for db, t in zip(vars_db, vars_t):
+            self.constraints.append(db>=-t)
+            self.constraints.append(db<=t)
+        self.constraints.append(pulp.lpSum(vars_t)<=self.cycle_limit)
+
+        # objective
+        self.objective = pulp.lpSum(vars_dis)*self.unit_cost*dt
+```
+
+
+### (4) 风光储优化调度模型
+
+最后，我们抽象出一个微电网类，包含上述能源设备`resources`及负荷`load`，同时引入系统能量平衡约束，建立最终的优化模型。其中的几个方法：
+
+- `optimize()`：建模和求解过程    
+- `operation_cost`：目标函数值即总用电费用
+- `average_cost`：平均用电成本
+
+```python
+import matplotlib.pyplot as plt
+
+
+class MicroGrid:
+    def __init__(self, resources:list, load:np.ndarray, time_step:float) -> None:
+        self.resources = resources
+        self.load = load
+        self.time_step = time_step
+
+        # create problem: minimize the operation cost
+        self.prob = pulp.LpProblem(name='microgrid_optimization', sense=pulp.LpMinimize)
+    
+    @property
+    def operation_cost(self): return self.prob.objective.value()
+
+    @property
+    def average_cost(self): return self.operation_cost / (self.load.sum()*self.time_step)
+
+    def optimize(self):
+        '''Micro-grid operation optimization.'''
+        # collect resources models
+        d_variables, constraints, objective = [], [], 0.0
+        time_points = self.load.size
+        for resource in self.resources:
+            resource.create_model(time_points, self.time_step)
+            d_variables.append(resource.variables)
+            constraints.extend(resource.constraints)
+            objective += resource.objective
+        
+        # add constraints: resource level
+        for c in constraints: self.prob += c
+
+        # add constraint: energy balance
+        for i in range(time_points):
+            _vars = [variables[i] for variables in d_variables]
+            self.prob += pulp.lpSum(_vars)==self.load[i]
+        
+        # objective
+        self.prob += objective
+
+        # solve
+        self.prob.solve()
+
+        # output
+        self._summary() 
+
+    def _summary(self):
+        print(f'Status: {pulp.LpStatus[self.prob.status]}')
+        print(f'全天总供电费用：{round(self.operation_cost,4)} 元，负荷平均购电单价：{round(self.average_cost,4)} 元/kWh')        
+        # plot
+        for r in self.resources: plt.plot(r.output, label=r.name)
+        plt.plot(self.load, label='load')
+        plt.legend()
+```
+
+## 求解典型场景
+
+本节根据上文建立的优化调度模型，求解提问中的不同调度策略。
+
+先导入全天96个时刻的时间序列数据：
+
+```python
+# read text to list
+with open('input.csv', 'r', encoding='utf-8') as f: lines = f.readlines()
+
+# print the first three lines for example
+for line in lines[:3]: print(line)
+
+# list to numpy 2D array
+data = [list(map(float, line.split(','))) for line in lines[1:]] # except the header line
+data = np.array(data)
+
+data_load, data_wt, data_pv, unit_profit, unit_cost = [data[:, i] for i in range(1,6)]
+
+# output:
+# 序号,负荷(kW),风机(kW),光伏(kW),售电(元/kWh),购电(元/kWh)
+# 1,64.3,163.1,0,0.22,0.25
+# 2,65.5,201.47,0,0.22,0.25
+```
+
+
+### (1) 经济性评估方案
+
+> 问题：微网中蓄电池不作用，微网与电网交换功率无约束，无可再生能源情况下，分别计算各时段负荷的供电构成（kW）、全天总供电费用(元)和负荷平均购电单价（元/kWh）。
+
+这一问不用优化模型也能解，多余的电卖给电网、不足的电从电网购买即可，参考[这篇文章](https://blog.csdn.net/kobeyu652453/article/details/106497539)的解析过程。但既然我们已经建立了统一的优化调度模型，本例只要引入电网一种资源，将其作为一个特例直接求解即可。
+
+因为电网交换功率没有限制，直接设一个较大的数例如$10^6$即可。
+
+```python
+# set a large value 1e6 as no limit on energy exchanging with grid
+grid = Grid(name='grid', capacity=1e6, unit_cost=unit_cost, unit_profit=unit_profit) 
+
+# microgrid
+resources = [grid]
+mg = MicroGrid(resources=resources, load=data_load, time_step=15/60) # 15min
+mg.optimize()
+```
+
+输出：
+
+```
+Status: Optimal
+全天总供电费用：1976.4142 元，负荷平均购电单价：0.5976 元/kWh
+```
+
+
+> 问题：微网中蓄电池不作用，微网与电网交换功率无约束，可再生能源全额利用情况下，分别计算各时段负荷的供电构成（kW）、全天总供电费用(元)和负荷平均购电单价（元/kWh）。
+
+这一问将风机和光伏加入微网，同时注意设置不可弃风弃光（可再生能源全额利用）。
+
+```python
+# set a large value 1e6 as no limit on energy exchanging with grid
+grid = Grid(name='grid', capacity=1e6, unit_cost=unit_cost, unit_profit=unit_profit) 
+
+# wind turbine: allow_curtailment=False
+wt = Renewable(name='wind', capacity=250, unit_cost=0.52, forecast=data_wt, allow_curtailment=False)
+
+# pv: allow_curtailment=False
+pv = Renewable(name='pv', capacity=150, unit_cost=0.75, forecast=data_pv, allow_curtailment=False)
+
+# microgrid
+resources = [grid, wt, pv]
+mg = MicroGrid(resources=resources, load=data_load, time_step=15/60) # 15min
+mg.optimize()
+
+print(f'弃风率：{round(1-wt.utilization,4)}，弃光率：{round(1-pv.utilization, 4)}')
+```
+
+输出：
+
+```
+Status: Optimal
+全天总供电费用：2275.1698 元，负荷平均购电单价：0.6879 元/kWh
+弃风率：0.0，弃光率：0.0
+```
+
+因为限定全额利用可再生能源，所以弃风弃光率都是0。风机、光伏的用电成本较高，即便可以将风机、光伏的电卖给电网，其最终收益还不如低电价时刻从电网直接买电，所以全额利用可再生能源情况下，这一小问的平均用电成本高于上一问的纯网电。
+
+
+
+### (2) 最优日前调度方案一
+
+> 问题：不计蓄电池作用，微网与电网交换功率无约束，以平均负荷供电单价最小为目标（允许弃风弃光），分别计算各时段负荷的供电构成（kW）、全天总供电费用(元)和平均购电单价（元/kWh），分析可再生能源的利用情况。
+
+这个调度方案是在上一问的基础上允许弃风弃光，即合理选择使用新能源发电还是网电。同样，我们设置输入参数，然后交给优化模型即可。注意和上一段代码的唯一区别是设置允许弃风弃光 `allow_curtailment=True`。
+
+```python
+# set a large value 1e6 as no limit on energy exchanging with grid
+grid = Grid(name='grid', capacity=1e6, unit_cost=unit_cost, unit_profit=unit_profit) 
+
+# wind turbine: allow_curtailment=True
+wt = Renewable(name='wind', capacity=250, unit_cost=0.52, forecast=data_wt, allow_curtailment=True)
+
+# pv: allow_curtailment=True
+pv = Renewable(name='pv', capacity=150, unit_cost=0.75, forecast=data_pv, allow_curtailment=True)
+
+# microgrid
+resources=[grid, wt, pv]
+mg = MicroGrid(resources=resources, load=data_load, time_step=15/60) # 15min
+mg.optimize()
+
+print(f'弃风率：{round(1-wt.utilization,4)}，弃光率：{round(1-pv.utilization, 4)}')
+```
+
+输出：
+
+```
+Status: Optimal
+全天总供电费用：1785.1532 元，负荷平均购电单价：0.5397 元/kWh
+弃风率：0.5399，弃光率：0.6923
+```
+
+因为可以根据经济最优选择合适的电力来源，这一调度方案的平均用电成本低于前两问。例如，凌晨时段网电电价本来就低，所以选择直接弃掉此时的风机和光伏电力（参考综合弃风弃光率）；高网电电价时段择机考虑风电和光伏。[这篇文章](https://blog.csdn.net/kobeyu652453/article/details/106525235) 从电价解析的角度分析了这个问题，人为分析的策略与本文优化的结果很接近，可以作为参考。
+
+![img](images/2022-10-22-01.png)
+
+
+### (3) 最优日前调度方案二
+
+> 问题：考虑蓄电池作用，且微网与电网允许交换功率不超过150kW，在可再生能源全额利用的条件下，以负荷平均供电单价最小为目标，建立优化模型，给出最优调度方案，包括各时段负荷的供电构成（kW）、全天总供电费用(元)和平均购电单价（元/kWh），分析蓄电池参与调节后产生的影响。
+
+这个调度方案在基础场景（1）第二问的基础上引入了蓄电池，同时限制了电网交换功率。
+
+```python
+# grid
+grid = Grid(name='grid', capacity=150, unit_cost=unit_cost, unit_profit=unit_profit) 
+
+# wind turbine
+wt = Renewable(name='wind', capacity=250, unit_cost=0.52, forecast=data_wt, allow_curtailment=False)
+
+# pv: allow_curtailment=False
+pv = Renewable(name='pv', capacity=150, unit_cost=0.75, forecast=data_pv, allow_curtailment=False)
+
+# battery: allow_curtailment=False
+bt = Storage(name='battery', capacity=300, unit_cost=0.2, capacity_limit=0.2, init_soc=0.4, soc_limit=[0.3,0.95], cycle_limit=8)
+
+# microgrid
+resources=[grid, wt, pv, bt]
+mg = MicroGrid(resources=resources, load=data_load, time_step=15/60) # 15min
+mg.optimize()
+
+print(f'弃风率：{round(1-wt.utilization,4)}，弃光率：{round(1-pv.utilization, 4)}')
+```
+
+输出：
+
+```
+Status: Optimal
+全天总供电费用：2210.4672 元，负荷平均购电单价：0.6683 元/kWh
+弃风率：0.0，弃光率：0.0
+```
+
+相比基础场景（1）第二问的平均用电成本0.6879，本方案用电成本有所降低。结合下图具体调度结果可知，蓄电池将凌晨高成本的新能源电力转移到了网电的峰电时段，因此比直接在凌晨时段卖高成本的新能源电力更为划算。[这篇文章](https://blog.csdn.net/kobeyu652453/article/details/106562951)也有对这一问的解答，可以作为参考。
+
+
+![img](images/2022-10-22-02.png)
+
+
+
+### (4) 最优日前调度方案三
+
+> 问题：考虑蓄电池作用，且微网与电网允许交换功率不超过150kW，以负荷供电成本最小为目标（允许弃风弃光），建立优化模型，给出最优调度方案，包括各时段负荷的供电构成（kW）、全天总供电费用(元)和平均购电单价（元/kWh），分析可再生能源的利用情况及蓄电池参与调节后产生的影响。
+
+这一问是在上一调度方案的基础上允许弃风弃光，同理略作修改即可。
+
+```python
+# grid
+grid = Grid(name='grid', capacity=150, unit_cost=unit_cost, unit_profit=unit_profit) 
+
+# wind turbine
+wt = Renewable(name='wind', capacity=250, unit_cost=0.52, forecast=data_wt, allow_curtailment=True)
+
+# pv: allow_curtailment=True
+pv = Renewable(name='pv', capacity=150, unit_cost=0.75, forecast=data_pv, allow_curtailment=True)
+
+# battery: allow_curtailment=True
+bt = Storage(name='battery', capacity=300, unit_cost=0.2, capacity_limit=0.2, init_soc=0.4, soc_limit=[0.3,0.95], cycle_limit=8)
+
+# microgrid
+resources=[grid, wt, pv, bt]
+mg = MicroGrid(resources=resources, load=data_load, time_step=15/60) # 15min
+mg.optimize()
+
+print(f'弃风率：{round(1-wt.utilization,4)}，弃光率：{round(1-pv.utilization, 4)}')
+```
+
+输出：
+
+```
+Status: Optimal
+全天总供电费用：1733.5558 元，负荷平均购电单价：0.5241 元/kWh
+弃风率：0.5383，弃光率：0.6494
+```
+
+相比问题（3），本方案允许放弃高电价的新能源电力，因此可以进一步降低平均用电成本；相比问题（2），本方案多了蓄电池的调节作用（峰谷电价转移），因此也降低了平均用电成本，同时因为电池对新能源的消纳，本方案相对问题（2）也略微降低了弃风弃光率。
+
+
+![img](images/2022-10-22-03.png)
+
+
+## 代码汇总
+
+- [input.csv](https://github.com/dothinking/blog/blob/master/samples/microgrid/input.csv)
+
+- [microgrid_optimization.ipynb](https://github.com/dothinking/blog/blob/master/samples/microgrid/microgrid_optimization.ipynb)
\ No newline at end of file
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diff --git a/samples/microgrid/input.csv b/samples/microgrid/input.csv
new file mode 100644
index 0000000..52f4c14
--- /dev/null
+++ b/samples/microgrid/input.csv
@@ -0,0 +1,97 @@
+序号,负荷(kW),风机(kW),光伏(kW),售电(元/kWh),购电(元/kWh)
+1,64.3,163.1,0,0.22,0.25
+2,65.5,201.47,0,0.22,0.25
+3,66.7,154.26,0,0.22,0.25
+4,66.9,140.29,0,0.22,0.25
+5,67.5,200.29,0,0.22,0.25
+6,67.7,250,0,0.22,0.25
+7,68,154.26,0,0.22,0.25
+8,68.2,125.64,0,0.22,0.25
+9,70.2,182.87,0,0.22,0.25
+10,71.9,211.67,0,0.22,0.25
+11,71.9,214.11,0,0.22,0.25
+12,71.9,224.41,0,0.22,0.25
+13,70.7,158.26,0,0.22,0.25
+14,70.7,135.45,0,0.22,0.25
+15,71.3,163.1,0,0.22,0.25
+16,72,175.49,0,0.22,0.25
+17,76.5,219.38,0,0.22,0.25
+18,77.6,250,0,0.22,0.25
+19,78.7,168.04,0,0.22,0.25
+20,78.8,124.56,0.06,0.22,0.25
+21,90.6,170.15,0.96,0.22,0.25
+22,93.8,201.47,2.11,0.22,0.25
+23,94.7,231.44,4.04,0.22,0.25
+24,94.8,250,6.54,0.22,0.25
+25,110.5,235.01,9.18,0.22,0.25
+26,113.1,227.59,13.4,0.22,0.25
+27,113.9,135.9,16.29,0.22,0.25
+28,114.3,106.25,20.19,0.22,0.25
+29,132.2,213.81,28.8,0.42,0.53
+30,145.4,250,34.78,0.42,0.53
+31,145.2,221.25,31.62,0.42,0.53
+32,145.1,204.14,39.61,0.42,0.53
+33,154.1,246.62,46.08,0.42,0.53
+34,157.4,250,53.66,0.42,0.53
+35,156.5,179.02,36.29,0.42,0.53
+36,155.5,144.06,49.64,0.42,0.53
+37,144,197.36,76.96,0.42,0.53
+38,142.2,227.91,66.81,0.42,0.53
+39,142.1,215.96,55.57,0.42,0.53
+40,142.1,218.44,88.62,0.42,0.53
+41,125.3,212.28,87.02,0.65,0.82
+42,118.9,210.15,54.04,0.65,0.82
+43,116.9,153.76,63.44,0.65,0.82
+44,115.9,124.77,101.59,0.65,0.82
+45,115.4,90.46,104.11,0.65,0.82
+46,115,57.35,90.68,0.65,0.82
+47,124.1,96.22,91.85,0.65,0.82
+48,127.1,114.66,66.78,0.65,0.82
+49,130.2,94.39,77.14,0.65,0.82
+50,131.6,86.98,63.76,0.65,0.82
+51,140.7,69.47,75.46,0.65,0.82
+52,141.8,55.77,110.46,0.65,0.82
+53,143.9,74.36,70.57,0.65,0.82
+54,145.5,83.41,103.15,0.65,0.82
+55,145.5,50.33,72.79,0.65,0.82
+56,145.6,37.21,67.41,0.65,0.82
+57,144.7,9.1,28.94,0.65,0.82
+58,144.4,1.34,23.89,0.65,0.82
+59,145.2,19.54,19.75,0.65,0.82
+60,145.3,33.06,31.53,0.65,0.82
+61,149.6,2.02,40.48,0.42,0.53
+62,150.3,0,63.95,0.42,0.53
+63,150.1,10.47,59.41,0.42,0.53
+64,150,16.35,50.76,0.42,0.53
+65,203.5,21.07,41.64,0.42,0.53
+66,207.2,27.11,23.39,0.42,0.53
+67,207,43.75,24.86,0.42,0.53
+68,206.9,53.45,20.6,0.42,0.53
+69,215.5,19.61,17.4,0.42,0.53
+70,223.9,9.95,15.06,0.42,0.53
+71,225,72.19,13.59,0.42,0.53
+72,225.5,120.28,22.08,0.42,0.53
+73,233.9,81.91,18.2,0.65,0.82
+74,237.5,76.88,12.15,0.65,0.82
+75,236.6,62.81,5.37,0.65,0.82
+76,236.1,56.82,2.07,0.65,0.82
+77,215.4,34.9,0,0.65,0.82
+78,211,23.98,0,0.65,0.82
+79,210.9,25.11,0,0.65,0.82
+80,210.8,23.43,0,0.65,0.82
+81,198,58.69,0,0.65,0.82
+82,197.9,93.67,0,0.65,0.82
+83,198.5,93.49,0,0.65,0.82
+84,198.6,99.55,0,0.65,0.82
+85,180.8,56.82,0,0.42,0.53
+86,177.2,26.01,0,0.42,0.53
+87,177.8,16.74,0,0.42,0.53
+88,177.9,6.97,0,0.42,0.53
+89,161.5,18.98,0,0.42,0.53
+90,147.3,23.12,0,0.42,0.53
+91,147.2,44.43,0,0.42,0.53
+92,147.2,55.64,0,0.42,0.53
+93,117.2,92.41,0,0.42,0.53
+94,107.5,109.01,0,0.42,0.53
+95,62,73.42,0,0.42,0.53
+96,58.7,63.8,0,0.42,0.53
diff --git a/samples/microgrid/microgrid_optimization.ipynb b/samples/microgrid/microgrid_optimization.ipynb
new file mode 100644
index 0000000..c126f52
--- /dev/null
+++ b/samples/microgrid/microgrid_optimization.ipynb
@@ -0,0 +1,627 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 微电网日前优化调度\n",
+    "\n",
+    "基于`PuLP`建立混合整数规划模型，求解微电网日前调度问题。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 问题\n",
+    "\n",
+    "[第十届“中国电机工程学会杯”全国大学生电工数学建模竞赛 A 题：微电网日前优化调度](https://blog.csdn.net/Jian_Yun_Rui/article/details/72529176)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 优化调度模型\n",
+    "\n",
+    "基于 Python 第三方库 `PuLP`，依次建立电网、新能源（风机、光伏）及蓄电池的模型，最后组合为微电网系统优化调度模型。\n",
+    "\n",
+    "\n",
+    "开始之前先抽象一个模型基类，表示各类调度设备，包含`名称`、`容量`及`使用成本`等基本属性，同时提供一个`create_model()`方法用于实现`设计变量`、`约束`及`目标函数`三个模型相关的属性。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pulp\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "class Model:\n",
+    "    def __init__(self, name:str, \n",
+    "                        capacity:float,         # resource capacity\n",
+    "                        unit_cost:float):       # unit cost when using the energy\n",
+    "        '''Base class for resource model, e.g., Grid, Renewable and Storage.'''\n",
+    "        # technical parameters\n",
+    "        self.name = name\n",
+    "        self.capacity = capacity\n",
+    "        self.unit_cost = unit_cost\n",
+    "\n",
+    "        # linear programming model: variables, constraints and objective\n",
+    "        self.variables = None\n",
+    "        self.constraints = None\n",
+    "        self.objective = None\n",
+    "\n",
+    "    def create_model(self, time_points:int, dt:float):\n",
+    "        '''How to create the LP model.'''\n",
+    "        raise NotImplementedError\n",
+    "    \n",
+    "    @property\n",
+    "    def output(self): return np.array([v.value() for v in self.variables])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1 电网模型\n",
+    "\n",
+    "继承自`Model`基类，电网模型新增了`卖电收益`属性，并且满足容量约束即每个时刻的出力不能超过限定值，目标函数为运行成本即用电费用与卖电收益的差值。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Grid(Model):\n",
+    "    def __init__(self, name:str, \n",
+    "                    capacity:float, \n",
+    "                    unit_cost:np.ndarray,    # unit cost when buying electricity from utility grid\n",
+    "                    unit_profit:np.ndarray): # unit profit when selling electricity to utility grid\n",
+    "        super().__init__(name, capacity, unit_cost)\n",
+    "        self.unit_profit = unit_profit\n",
+    "\n",
+    "\n",
+    "    def create_model(self, time_points:int, dt:float):\n",
+    "        # define variables at each time point\n",
+    "        vars_from = [pulp.LpVariable(name=f'{self.name}_from_{i}', lowBound=0) for i in range(time_points)]\n",
+    "        vars_to = [pulp.LpVariable(name=f'{self.name}_to_{i}', lowBound=0) for i in range(time_points)]\n",
+    "        self.variables = [v1-v2 for v1,v2 in zip(vars_from, vars_to)]\n",
+    "\n",
+    "        # constraints: capacity limit\n",
+    "        # 0<=var_from<=C*b\n",
+    "        # 0<=var_to<=C*(1-b)\n",
+    "        self.constraints = []\n",
+    "        vars_b = [pulp.LpVariable(name=f'{self.name}_binary_{i}', cat=pulp.LpInteger) for i in range(time_points)]\n",
+    "        for v1,v2,b in zip(vars_from, vars_to, vars_b):\n",
+    "            self.constraints.append(v1<=self.capacity*b)\n",
+    "            self.constraints.append(v2<=self.capacity*(1-b))\n",
+    "        \n",
+    "        # objective\n",
+    "        self.objective = pulp.lpSum([v*x for v,x in zip(vars_from, self.unit_cost)])*dt - \\\n",
+    "            pulp.lpSum([v*x for v,x in zip(vars_to, self.unit_profit)])*dt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2 新能源发电模型\n",
+    "\n",
+    "将风机和光伏抽象为新能源发电模型，特征为每一时刻的电力供应不大于该时刻的预测出力，如果不允许弃风弃光的话，则等于预测出力值。因此，在`Model`类基础上增加两个属性：`预测出力`及`是否允许弃风弃光`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Renewable(Model):\n",
+    "    def __init__(self, name:str, \n",
+    "                    capacity:float,\n",
+    "                    unit_cost:float,\n",
+    "                    forecast:np.ndarray,          # forecasting output\n",
+    "                    allow_curtailment:bool=True): # allow curtailment or not\n",
+    "        super().__init__(name, capacity, unit_cost)\n",
+    "        self.forecast = forecast\n",
+    "        self.allow_curtailment = allow_curtailment\n",
+    "\n",
+    "    def create_model(self, time_points:int, dt:float):\n",
+    "        # define variables at each time point\n",
+    "        self.variables = [pulp.LpVariable(name=f'{self.name}_{i}', lowBound=0) for i in range(time_points)]\n",
+    "\n",
+    "        # constraints:  v<=forecast\n",
+    "        if self.allow_curtailment:\n",
+    "            self.constraints = [v<=x for v,x in zip(self.variables, self.forecast)]\n",
+    "        else:\n",
+    "            self.constraints = [v==x for v,x in zip(self.variables, self.forecast)]\n",
+    "        \n",
+    "        # objective\n",
+    "        self.objective = pulp.lpSum(self.variables)*self.unit_cost*dt\n",
+    "\n",
+    "    @property\n",
+    "    def utilization(self): return self.output.sum() / self.forecast.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 储能模型\n",
+    "\n",
+    "根据题中对蓄电池数学模型的描述，新增`爬坡限制`、`初始SOC`、`SOC限制`、`充放电次数限制`等属性，相应的4个约束：`爬坡约束`、`容量约束`、`充放电次数约束`及`调度周期始末电量相等约束`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Storage(Model):\n",
+    "    def __init__(self, name:str, \n",
+    "                    capacity:float, \n",
+    "                    unit_cost:float,\n",
+    "                    capacity_limit:float, # charging / discharging ramping limit\n",
+    "                    init_soc:float,       # initial state of charge\n",
+    "                    soc_limit:list,       # SOC limit\n",
+    "                    cycle_limit:int):     # charing / discharging cycle counts limit\n",
+    "        super().__init__(name, capacity, unit_cost)\n",
+    "        self.init_soc = init_soc\n",
+    "        self.soc_limit = soc_limit\n",
+    "        self.cycle_limit = cycle_limit\n",
+    "        self.capacity_limit = capacity_limit\n",
+    "    \n",
+    "    def create_model(self, time_points: int, dt: float):\n",
+    "        # define variables at each time point\n",
+    "        vars_ch = [pulp.LpVariable(name=f'{self.name}_charge_{i}', lowBound=0) for i in range(time_points)]\n",
+    "        vars_dis = [pulp.LpVariable(name=f'{self.name}_discharge_{i}', lowBound=0) for i in range(time_points)]\n",
+    "        self.variables = [v1-v2 for v1,v2 in zip(vars_dis, vars_ch)]\n",
+    "\n",
+    "        # constraints 1: ramping limit\n",
+    "        # 0<=var_dis<=C*b\n",
+    "        # 0<=var_ch<=C*(1-b)\n",
+    "        self.constraints = []\n",
+    "        vars_b = [pulp.LpVariable(name=f'{self.name}_binary_{i}', cat=pulp.LpInteger) for i in range(time_points)]\n",
+    "        C = self.capacity * self.capacity_limit\n",
+    "        for v1,v2,b in zip(vars_dis, vars_ch, vars_b):\n",
+    "            self.constraints.append(v1<=C*b)\n",
+    "            self.constraints.append(v2<=C*(1-b))\n",
+    "        \n",
+    "        # constraints 2: SOC limit\n",
+    "        soc = self.init_soc\n",
+    "        s1, s2 = self.soc_limit\n",
+    "        for v1,v2 in zip(vars_ch, vars_dis):\n",
+    "            soc += (v1*dt - v2*dt) / self.capacity\n",
+    "            self.constraints.append(soc>=s1)\n",
+    "            self.constraints.append(soc<=s2)\n",
+    "        \n",
+    "        # constraints 3: same SOC at the scheduling end\n",
+    "        self.constraints.append(pulp.lpSum(self.variables)==0)\n",
+    "\n",
+    "        # constraints 4: charging / discharging cycle limit\n",
+    "        # t = |b_i-b_{i+1}|\n",
+    "        # sum(t)<=N\n",
+    "        vars_db = [vars_b[i+1]-vars_b[i] for i in range(time_points-1)]\n",
+    "        vars_t = [pulp.LpVariable(name=f'{self.name}_binary_t_{i}', cat=pulp.LpInteger) for i in range(time_points-1)]\n",
+    "        for db, t in zip(vars_db, vars_t):\n",
+    "            self.constraints.append(db>=-t)\n",
+    "            self.constraints.append(db<=t)\n",
+    "        self.constraints.append(pulp.lpSum(vars_t)<=self.cycle_limit)\n",
+    "\n",
+    "        # objective\n",
+    "        self.objective = pulp.lpSum(vars_dis)*self.unit_cost*dt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4 优化调度模型\n",
+    "\n",
+    "抽象出一个微电网类，包含上述能源设备及负荷，综合各设备的约束及系统能量平衡，建立最终的优化模型。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "class MicroGrid:\n",
+    "    def __init__(self, resources:list, load:np.ndarray, time_step:float) -> None:\n",
+    "        self.resources = resources\n",
+    "        self.load = load\n",
+    "        self.time_step = time_step\n",
+    "\n",
+    "        # create problem: minimize the operation cost\n",
+    "        self.prob = pulp.LpProblem(name='microgrid_optimization', sense=pulp.LpMinimize)\n",
+    "    \n",
+    "    @property\n",
+    "    def operation_cost(self): return self.prob.objective.value()\n",
+    "\n",
+    "    @property\n",
+    "    def average_cost(self): return self.operation_cost / (self.load.sum()*self.time_step)\n",
+    "\n",
+    "    def optimize(self):\n",
+    "        '''Micro-grid operation optimization.'''\n",
+    "        # collect resources models\n",
+    "        d_variables, constraints, objective = [], [], 0.0\n",
+    "        time_points = self.load.size\n",
+    "        for resource in self.resources:\n",
+    "            resource.create_model(time_points, self.time_step)\n",
+    "            d_variables.append(resource.variables)\n",
+    "            constraints.extend(resource.constraints)\n",
+    "            objective += resource.objective\n",
+    "        \n",
+    "        # add constraints: resource level\n",
+    "        for c in constraints: self.prob += c\n",
+    "\n",
+    "        # add constraint: energy balance\n",
+    "        for i in range(time_points):\n",
+    "            _vars = [variables[i] for variables in d_variables]\n",
+    "            self.prob += pulp.lpSum(_vars)==self.load[i]\n",
+    "        \n",
+    "        # objective\n",
+    "        self.prob += objective\n",
+    "\n",
+    "        # solve\n",
+    "        self.prob.solve()\n",
+    "\n",
+    "        # output\n",
+    "        self._summary()\n",
+    "\n",
+    "    def _summary(self):\n",
+    "        print(f'Status: {pulp.LpStatus[self.prob.status]}')\n",
+    "        print(f'全天总供电费用：{round(self.operation_cost,4)} 元，负荷平均购电单价：{round(self.average_cost,4)} 元/kWh')        \n",
+    "        # plot\n",
+    "        for r in self.resources: plt.plot(r.output, label=r.name)\n",
+    "        plt.plot(self.load, label='load')\n",
+    "        plt.legend()\n",
+    "        plt.savefig('plot.png', dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3 计算结果\n",
+    "\n",
+    "先导入时间序列数据，然后计算不同场景。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "序号,负荷(kW),风机(kW),光伏(kW),售电(元/kWh),购电(元/kWh)\n",
+      "\n",
+      "1,64.3,163.1,0,0.22,0.25\n",
+      "\n",
+      "2,65.5,201.47,0,0.22,0.25\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# read text to list\n",
+    "with open('input.csv', 'r', encoding='utf-8') as f: lines = f.readlines()\n",
+    "\n",
+    "# print the first three lines for example\n",
+    "for line in lines[:3]: print(line)\n",
+    "\n",
+    "# list to numpy 2D array\n",
+    "data = [list(map(float, line.split(','))) for line in lines[1:]] # except the header line\n",
+    "data = np.array(data)\n",
+    "\n",
+    "data_load, data_wt, data_pv, unit_profit, unit_cost = [data[:, i] for i in range(1,6)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1 经济性评估方案\n",
+    "\n",
+    "若微网中蓄电池不作用，且微网与电网交换功率无约束，在**无可再生能源**情况下，计算各时段负荷的供电构成（kW）、全天总供电费用(元)和负荷平均购电单价（元/kWh）。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status: Optimal\n",
+      "全天总供电费用：1976.4142 元，负荷平均购电单价：0.5976 元/kWh\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# set a large value 1e6 as no limit on energy exchanging with grid\n",
+    "grid = Grid(name='grid', capacity=1e6, unit_cost=unit_cost, unit_profit=unit_profit) \n",
+    "\n",
+    "# microgrid\n",
+    "resources=[grid]\n",
+    "mg = MicroGrid(resources=resources, load=data_load, time_step=15/60) # 15min\n",
+    "mg.optimize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "若微网中蓄电池不作用，且微网与电网交换功率无约束，在**可再生能源全额利用**情况下，计算各时段负荷的供电构成（kW）、全天总供电费用(元)和负荷平均购电单价（元/kWh）。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status: Optimal\n",
+      "全天总供电费用：2275.1698 元，负荷平均购电单价：0.6879 元/kWh\n",
+      "弃风率：0.0，弃光率：0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# set a large value 1e6 as no limit on energy exchanging with grid\n",
+    "grid = Grid(name='grid', capacity=1e6, unit_cost=unit_cost, unit_profit=unit_profit) \n",
+    "\n",
+    "# wind turbine\n",
+    "wt = Renewable(name='wind', capacity=250, unit_cost=0.52, forecast=data_wt, allow_curtailment=False)\n",
+    "\n",
+    "# pv\n",
+    "pv = Renewable(name='pv', capacity=150, unit_cost=0.75, forecast=data_pv, allow_curtailment=False)\n",
+    "\n",
+    "# microgrid\n",
+    "resources=[grid, wt, pv]\n",
+    "mg = MicroGrid(resources=resources, load=data_load, time_step=15/60) # 15min\n",
+    "mg.optimize()\n",
+    "\n",
+    "print(f'弃风率：{round(1-wt.utilization,4)}，弃光率：{round(1-pv.utilization, 4)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2 最优日前调度方案一\n",
+    "\n",
+    "若不计蓄电池作用，且微网与电网交换功率无约束，以平均负荷供电单价最小为目标（允许弃风弃光），分别计算各时段负荷的供电构成（kW）、全天总供电费用(元)和平均购电单价（元/kWh），分析可再生能源的利用情况。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status: Optimal\n",
+      "全天总供电费用：1785.1532 元，负荷平均购电单价：0.5397 元/kWh\n",
+      "弃风率：0.5399，弃光率：0.6923\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# set a large value 1e6 as no limit on energy exchanging with grid\n",
+    "grid = Grid(name='grid', capacity=1e6, unit_cost=unit_cost, unit_profit=unit_profit) \n",
+    "\n",
+    "# wind turbine\n",
+    "wt = Renewable(name='wind', capacity=250, unit_cost=0.52, forecast=data_wt, allow_curtailment=True)\n",
+    "\n",
+    "# pv\n",
+    "pv = Renewable(name='pv', capacity=150, unit_cost=0.75, forecast=data_pv, allow_curtailment=True)\n",
+    "\n",
+    "# microgrid\n",
+    "resources=[grid, wt, pv]\n",
+    "mg = MicroGrid(resources=resources, load=data_load, time_step=15/60) # 15min\n",
+    "mg.optimize()\n",
+    "\n",
+    "print(f'弃风率：{round(1-wt.utilization,4)}，弃光率：{round(1-pv.utilization, 4)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3 最优日前调度方案二\n",
+    "\n",
+    "若考虑蓄电池作用，且微网与电网允许交换功率不超过 150kW，在可再生能源全额利用的条件下，以负荷平均供电单价最小为目标，建立优化模型，给出最优调度方案，包括各时段负荷的供电构成（kW）、全天总供电费用(元)和平均购电单价（元/kWh），分析蓄电池参与调节后产生的影响。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status: Optimal\n",
+      "全天总供电费用：2210.4672 元，负荷平均购电单价：0.6683 元/kWh\n",
+      "弃风率：0.0，弃光率：0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# grid\n",
+    "grid = Grid(name='grid', capacity=150, unit_cost=unit_cost, unit_profit=unit_profit) \n",
+    "\n",
+    "# wind turbine\n",
+    "wt = Renewable(name='wind', capacity=250, unit_cost=0.52, forecast=data_wt, allow_curtailment=False)\n",
+    "\n",
+    "# pv: allow_curtailment=False\n",
+    "pv = Renewable(name='pv', capacity=150, unit_cost=0.75, forecast=data_pv, allow_curtailment=False)\n",
+    "\n",
+    "# battery: allow_curtailment=False\n",
+    "bt = Storage(name='battery', capacity=300, unit_cost=0.2, capacity_limit=0.2, init_soc=0.4, soc_limit=[0.3,0.95], cycle_limit=8)\n",
+    "\n",
+    "# microgrid\n",
+    "resources=[grid, wt, pv, bt]\n",
+    "mg = MicroGrid(resources=resources, load=data_load, time_step=15/60) # 15min\n",
+    "mg.optimize()\n",
+    "\n",
+    "print(f'弃风率：{round(1-wt.utilization,4)}，弃光率：{round(1-pv.utilization, 4)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.4 最优日前调度方案三\n",
+    "\n",
+    "若考虑蓄电池作用，且微网与电网允许交换功率不超过 150kW，以负荷供电成本最小为目标（允许弃风弃光），建立优化模型，给出最优调度方案，包括各时段负荷的供电构成（kW）、全天总供电费用(元)和平均购电单价（元/kWh），分析可再生能源的利用情况及蓄电池参与调节后产生的影响。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status: Optimal\n",
+      "全天总供电费用：1733.5558 元，负荷平均购电单价：0.5241 元/kWh\n",
+      "弃风率：0.5383，弃光率：0.6494\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# grid\n",
+    "grid = Grid(name='grid', capacity=150, unit_cost=unit_cost, unit_profit=unit_profit) \n",
+    "\n",
+    "# wind turbine\n",
+    "wt = Renewable(name='wind', capacity=250, unit_cost=0.52, forecast=data_wt, allow_curtailment=True)\n",
+    "\n",
+    "# pv: allow_curtailment=True\n",
+    "pv = Renewable(name='pv', capacity=150, unit_cost=0.75, forecast=data_pv, allow_curtailment=True)\n",
+    "\n",
+    "# battery: allow_curtailment=True\n",
+    "bt = Storage(name='battery', capacity=300, unit_cost=0.2, capacity_limit=0.2, init_soc=0.4, soc_limit=[0.3,0.95], cycle_limit=8)\n",
+    "\n",
+    "# microgrid\n",
+    "resources=[grid, wt, pv, bt]\n",
+    "mg = MicroGrid(resources=resources, load=data_load, time_step=15/60) # 15min\n",
+    "mg.optimize()\n",
+    "\n",
+    "print(f'弃风率：{round(1-wt.utilization,4)}，弃光率：{round(1-pv.utilization, 4)}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.6.8 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "1a4fb1221fb574de367dddd9fedcf37002c6fd4c322463eb95ebed0730eac503"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}