计及多重不确定性的生物质电厂库存优化模型

doi:10.11930/j.issn.1004-9649.202308050

摘要/Abstract

摘要：

生物质电厂库存优化策略的制定是保障区域电力供应的基础，然而生物燃料的季节性和需求的不确定性给库存优化带来了极大的挑战。为降低多类不确定因素对库存优化的影响，提出一种计及多重不确定性的生物质电厂库存随机-鲁棒优化模型。首先，使用椭球不确定集描述了生物质燃料价格及质量水平的不确定性，并利用情景树法构建典型生物质可用性场景，降低燃料供应季节性对库存策略制定的影响。其次，考虑误差的随机性和模糊性，利用生态云发生器模拟了3种用户的负荷曲线，提高了需求曲线拟合的准确性。最后，以库存总成本最小为目标，建立计及多重不确定性的生物质电厂库存随机-鲁棒优化模型，并通过算例对比确定型、随机和随机-鲁棒3类优化模型的优化结果，验证模型的有效性。结果表明：随机-鲁棒优化模型的生物质电厂库存总成本最低，为269.15万元。相比随机优化模型，所提策略的库存总成本降低了34.59%，能够提升库存优化策略的经济性和可靠性。

关键词: 随机-鲁棒优化, 椭球式不确定集, 正态云模型, 生物质电厂, 库存优化

Abstract:

The formulation of inventory optimization strategies for biomass power plants is the basis for ensuring regional power supply. However, the seasonality and demand uncertainty of biofuels have brought great challenges to inventory optimization. In order to reduce the impact of multiple uncertain factors on inventory optimization, a stochastic-robust inventory optimization model for biomass power plants based on multiple uncertainties was proposed. First, the uncertainties of the price and quality level of biomass fuels are described using ellipsoidal uncertainty sets. And use the scenario tree method to construct typical biomass availability scenarios to reduce the impact of fuel supply seasonality on inventory optimization strategies. Secondly, considering the randomness and ambiguity of the error, three kinds of user load curves are simulated by using the ecological cloud generator, which improves the accuracy of demand curve fitting. Finally, a stochastic-robust optimization model of biomass power plant inventory taking multiple uncertainties into account is developed with the objective of minimizing the total inventory cost. The validity of the model is verified by comparing the optimization results of the deterministic, stochastic and stochastic-robust optimization models through examples. The results show that the total cost of biomass power plant inventory in the stochastic-robust optimization model is the lowest, which is 2.6915 million yuan. Compared with the stochastic optimization model, the total inventory cost of the proposed strategy is reduced by 34.59%, which can improve the economy and reliability of the inventory optimization strategy.

Key words: stochastic-robust optimization, ellipsoidal uncertain set, normal cloud model, biomass power plant, inventory optimization

张金良, 胡泽萍. 计及多重不确定性的生物质电厂库存优化模型[J]. 中国电力, 2024, 57(12): 157-168.

Jinliang ZHANG, Zeping HU. Inventory Optimization Model of Biomass Power Plant Considering Multiple Uncertainties[J]. Electric Power, 2024, 57(12): 157-168.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202308050

https://www.electricpower.com.cn/CN/Y2024/V57/I12/157

图/表 18

图 1 生物质电厂库存优化流程

Fig.1 Biomass power plant inventory optimization process

图 2 多周期库存动态运作流程

Fig.2 Dynamic operation flow of inventory with multiple cycles

图 3 生物质可用性不确定场景集

Fig.3 Uncertainty scenario set for biomass availability

图 4 随机-鲁棒优化模型求解流程

Fig.4 Solution flow of stochastic-robust optimization model

表 1 4种生物质燃料的平均单位购买价格

Table 1 Average unit purchase price of four biomass fuels 单位：元/绿吨

季度	松木纸浆	松木锯材	硬木纸浆	硬木锯材
一	61.2	163.2	68.0	231.2
二	54.4	163.2	74.8	251.6
三	47.6	156.4	74.8	265.2
四	54.4	163.2	74.8	272.0

表 2 库存优化相关参数

Table 2 Relevant parameters of inventory optimization

燃料类型	燃料运输成本/ (元·绿吨^–1)	运输里程/ km	库存持有成本/ (元·绿吨^–1)	燃料质量下降惩罚成本/ (元·绿吨^–1)	第一季度生物质可获得量/ 绿吨	初始库存量/ 绿吨
松木纸浆	0.35	35	0.3	6.12	546009	337
松木锯材	0.35	65	1.5	16.30	397304	337
硬木纸浆	0.40	35	0.3	6.80	141754	337
硬木锯材	0.40	65	1.5	23.10	97378	337

表 3 燃料质量水平指标值

Table 3 Indicator values of fuel quality levels

平均水分含量/ %		水分含量上限/ %		水分含量下限/ %		平均较高热值/ ((MW·h)· 绿吨^–1)		较高热值上限/ ((MW·h)· 绿吨^–1)		较高热值下限/ ((MW·h)· 绿吨^–1)
30		35		25		4.98		5.27		4.69

表 4 正态性检验结果

Table 4 Normality test results

名称	样本量/个	平均值/ MW	标准差/ MW	偏度	峰度	Jarque-Bera检验
名称	样本量/个	平均值/ MW	标准差/ MW	偏度	峰度	χ²	自由度	p值
居民负荷	12	1025.261	359.004	0.898	–0.736	1.649	2	0.438
工商业用户负荷	12	798.338	153.482	0.820	–0.817	1.495	2	0.473
大工业用户负荷	12	2218.287	69.852	0.129	–0.701	0.433	2	0.805

图 5 正态分布拟合及典型年负荷需求曲线

Fig.5 Normal distribution fitting diagram and typical annual load demand curve

表 5 10个典型场景发生的概率

Table 5 Occurrence probability of 10 typical scenarios

场景	居民负荷	工商业用户负荷	大工业用户负荷
1	0.10	0.10	0.11
2	0.10	0.11	0.09
3	0.10	0.10	0.08
4	0.10	0.10	0.09
5	0.08	0.10	0.10
6	0.11	0.12	0.10
7	0.09	0.09	0.10
8	0.11	0.09	0.10
9	0.11	0.11	0.13
10	0.09	0.10	0.10

表 6 3种模型下的电厂库存优化结果

Table 6 Power plant inventory optimization results of three models 单位：万元

模型	总库存成本	燃料购买成本	运输成本	存储成本	惩罚成本
确定型模型	365.89	289.40	81.83	8.18	0
随机优化模型	362.26	286.24	67.69	8.34	0
随机-鲁棒优化模型	269.15	201.43	59.48	8.24	0

图 6 不同模型下每月库存总成本对比

Fig.6 Comparison of total monthly inventory costs of different models

表 7 生物质电厂库存优化结果

Table 7 Optimization results of biomass power plant inventory

燃料类型	购买成本/ 万元	运输成本/ 万元	存储成本/ 万元	惩罚成本/ 万元	燃料使用量/ 绿吨
松木纸浆	201.43	59.48	2.48	0	213241
松木锯材	0.00	0.00	2.06	0	9662
硬木纸浆	0.00	0.00	1.65	0	9663
硬木锯材	0.00	0.00	2.06	0	9662
合计	201.43	59.48	8.24	0	242228

图 7 库存成本参数优化结果

Fig.7 Optimization results of inventory cost parameters

图 8 燃料订购及库存量

Fig.8 Fuel ordered quantity and storage quantity

图 9 生物质质量水平及其影响因素变化

Fig.9 Changes of biomass quality level and its influencing factors

图 10 4种燃料的质量水平对比

Fig.10 Comparison of four fuels' quality levels

图 11 不同鲁棒扰动参数下的总成本

Fig.11 Total cost with different robust perturbation parameters

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