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PuLP 模型不遵循指定的约束

时间:2024-07-31 14:34:59浏览次数:6  
标签:python optimization model pulp

代码: def add_constraints(概率,发电机,power_vars,需求): # 绝对差异变量 绝对差异变量 = {} 对于发电机中的 gen: 名称 = gen["名称"] 对于 t in range(1, len(demand)): abs_diff_vars[(name, t)] = mud.LpVariable(f"abs_diff_{name}_{t}", lowBound=0)

# Demand Satisfaction Constraints
for t in range(len(demand)):
    prob += pulp.lpSum([power_vars[(gen["name"], t)] for gen in generators]) >= demand[t], f"Demand_Satisfaction_{t}"

# Ramp-Up and Ramp-Down Constraints
for t in range(1, len(demand)):
    for gen in generators:
        name = gen["name"]
        rampup = gen["rampup"]
        rampdown = gen["rampdown"]

        # Ramp-up constraint
        prob += power_vars[(name, t)] - power_vars[(name, t-1)] <= rampup f"Ramp_Up_Constraint_{name}_{t}"

        # Ramp-down constraint
        prob += power_vars[(name, t-1)] - power_vars[(name, t)] <= rampdown f"Ramp_Down_Constraint_{name}_{t}"
    
        # Absolute Difference Constraints
        prob += abs_diff_vars[(name, t)] >= (power_vars[(name, t)] - power_vars[(name, t-1)]), f"Abs_Diff_Upper_{name}_{t}"
        prob += abs_diff_vars[(name, t)] >= (power_vars[(name, t-1)] - power_vars[(name, t)]), f"Abs_Diff_Lower_{name}_{t}"

# Capacity Constraints
for t in range(len(demand)):
    for gen in generators:
        name = gen["name"]
        max_power = gen["max"][t]
        min_power = gen["tech_min"][t]
        
        # Max Power Constraint
        prob += power_vars[(name, t)] <= max_power, f"Max_Power_{name}_{t}"

        # Min Power Constraint
        prob += power_vars[(name, t)] >= min_power, f"Min_Power_{name}_{t}"

需求输入 - [ 17116.3, 16940.71, 16948.98, 16839.5, 16808.51, 16800.25, 16782.69, 16768.23, 16762.03, 16559.6, 16566.83, 16583.35, 16357.16, 16340.63, 16342.7, 16121.67, 16250.77, 16479.03, 16721.75, 17053.29, 17425.12, 17859.95, 18199.75, 18375.34, 18425.95, 18440.4, 18453.83, 18461.06, 18764.72, 18746.13, 18648.01, 18786.41, 18678.99, 18636.65, 18827.72, 18818.43, 19127.25, 19337.95, 19317.29, 19294.57, 19243.96, 19224.33, 19229.5, 19254.29, 19225.37, 19146.87, 19002.27, 18857.67, 18675.89, 18712.04, 18732.7, 18582.94, 18465.19, 18114.03, 18125.39, 18194.59, 18249.33, 18276.18, 18274.12, 18015.91, 18042.76, 18089.24, 18456.93, 18492.05, 18510.64, 18197.69, 18192.52, 18191.49, 18193.56, 18255.53, 18317.5, 18548.85, 18781.24, 19061.14, 19222.27, 19378.23, 19523.86, 19711.84, 19824.42, 19614.75, 19239.83, 18839.08, 18714.11, 18619.09, 18526.13, 18373.27, 18215.24, 18119.19, 18089.24, 18076.84, 18007.64, 17849.62, 17693.66, 17604.83, 17572.82, 17579.01 ]

发电机输入 - [ { "cn_unit_source": "内部", "cn_dispatch_type": "必须", “变量费用”:0, “斜坡”:0, “斜坡下降”:0, “tech_min”:[3204.93,3181.24,3173.94,3189.28,3168,3129.09,3149.76,3116.75,3098.06,3093.31,3057.82,3070.67,3038.04,3043.26,3025。 4,2968.14,2962,2972.11,3128.8,3172.06,3213.99,3205.06,3182.21 ,3168.66,3126.87,3198.76,3239.04,3375.79,3583.01,3770.13,3741.18,3921.2,4029.8,4232.19,4400.67,4600.04,4666.72,4818.1,501 3.12,5201.65,5267.81,5396.66,5517.87,5591.76,5601.87,5641.31,5681.82,5725.98 ,5767.36,5723.67,5751.78,5776.97,5672.74,5681.85,5570.83,5552.37,5426.99,5402.41,5296.31,5255.07,5089.96,4988.26,4791.9,4 700.34,4400.67,4235.78,4079.16,3959.17,3801.92,3607.67,3451.74,3302.87,3290 ,3189.38,3061.46,2987.27,2970.09,2967.8,2977.63,2986.67,2947.17,2946.03,2956.67,2962.26,2963.86,2973.09,2972.52,2974.3,29 32.38,2929.72,2952.9,2948.59,2961.63,2960.09,2960.11,2964.6], “名称”:“REMC”, “最大”:[3204.93,3181.24,3173.94,3189.28,3168,3129.09,3149.76,3116.75,3098.06,3093.31,3057.82,3070.67,3038.04,3043.26,3025.4,2 968.14,2962,2972.11,3128.8,3172.06,3213.99,3205.06,3182.21 ,3168.66,3126.87,3198.76,3239.04,3375.79,3583.01,3770.13,3741.18,3921.2,4029.8,4232.19,4400.67,4600.04,4666.72,4818.1,501 3.12,5201.65,5267.81,5396.66,5517.87,5591.76,5601.87,5641.31,5681.82,5725.98 ,5767.36,5723.67,5751.78,5776.97,5672.74,5681.85,5570.83,5552.37,5426.99,5402.41,5296.31,5255.07,5089.96,4988.26,4791.9,4 700.34,4400.67,4235.78,4079.16,3959.17,3801.92,3607.67,3451.74,3302.87,3290 ,3189.38,3061.46,2987.27,2970.09,2967.8,2977.63,2986.67,2947.17,2946.03,2956.67,2962.26,2963.86,2973.09,2972.52,2974.3,29 32.38,2929.72,2952.9,2948.59,2961.63,2960.09,2960.11,2964.6] },

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]

旨在满足需求要求,同时遵守加速和减速约束


很抱歉,在 PuLP 模型中遇到约束问题。如果没有看到的完整代码和生成的输出的示例,很难确切地说出问题出在哪里。但是,我可以根据提供的信息指出一些常见的陷阱和调试步骤。

可能的约束问题:

  1. 索引不匹配: 确保在所有循环和约束中使用一致的索引。例如,正在使用 t 迭代需求,并使用 t-1 访问前一个时间段。仔细检查这些索引,尤其是在斜坡和绝对差约束中,以确保它们与预期的相符。

  2. 变量边界: 确认所有的变量( power_vars abs_diff_vars )都有适当的边界。如果变量边界设置得太窄或定义不正确,则可能会导致不可行性。

  3. 约束公式: 仔细检查所有约束公式,以确保它们正确表示要建模的条件。请特别注意坡道向上和坡道向下约束中使用的比较运算符 ( <= >= )。

  4. 绝对值约束: 正在使用线性约束来处理绝对差。检查是否已为每个绝对值约束正确添加了 两个 不等式,以涵盖正差和负差。

  5. 需求满足约束: 确保的需求满足约束 ( Demand_Satisfaction ) 正确地将所有发电机的总功率加起来,并将其与每个时间段的相应需求进行比较。

调试步骤:

  1. 打印求解状态: 使用 prob.solve() 后使用 print(pulp.LpStatus[prob.status]) 打印 PuLP 问题的求解状态。这将告诉问题是“最优”,“不可行”还是其他。

  2. 检查不可行的约束(如果适用): 如果问题不可行,PuLP 可以帮助识别有问题的约束。有多种方法可以做到这一点,包括:

  3. 使用 .infeasibleConstraints() 方法(可能需要安装额外的软件包)。
  4. 通过添加约束松弛并检查哪些松弛变量在最优解中为非零来手动检查。

  5. 简化问题: 尝试通过减少时间段数或发电机数来简化问题。这可以帮助隔离问题并更容易地识别错误。

  6. 打印变量值: 打印求解变量的值以查看它们是否有意义。这可以帮助发现公式或约束中的逻辑错误。

其他技巧:

  • 使用有意义的名称作为变量和约束,以提高代码的可读性和可调试性。
  • 添加注释以解释约束背后的逻辑。
  • 考虑使用 IDE 或调试器来逐步执行代码并检查变量值。

如果能提供更多信息,例如完整代码、示例数据和生成的输出,我可以提供更有针对性的帮助。

标签:python,optimization,model,pulp
From: 78814692

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