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Article(id=1154038488321221382, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1154038481564197598, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2024.2.273, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1619020800000, receivedDateStr=2021-04-22, revisedDate=1623600000000, revisedDateStr=2021-06-14, acceptedDate=1623772800000, acceptedDateStr=2021-06-16, onlineDate=1753073816613, onlineDateStr=2025-07-21, pubDate=1711728000000, pubDateStr=2024-03-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753073816613, onlineIssueDateStr=2025-07-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753073816613, creator=13701087609, updateTime=1753073816613, updator=13701087609, issue=Issue{id=1154038481564197598, tenantId=1146029695717560320, journalId=1146031654075715584, year='2024', volume='22', issue='2', pageStart='1', pageEnd='455', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1753073815003, creator=13701087609, updateTime=1753780998609, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1157004624629683026, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1154038481564197598, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1157004624629683027, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1154038481564197598, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=273, endPage=282, ext={EN=ArticleExt(id=1154038489508209418, articleId=1154038488321221382, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Optimal Dispatching of Distribution Network Based on Multi-type Demand Response, columnId=1152281496049037440, journalTitle=Journal of Power Supply, columnName=Power System, runingTitle=null, highlight=null, articleAbstract=

Aimed at the problem that the existing incentive pricing compensation mechanism cannot meet the differentiated needs of multiple types of load, a compensation method for interruptible load is proposed in the form of sectional compensatory price. Meanwhile, a two-dimensional alternating function of load transferable time and load transferable power is introduced to establish a transferable load compensation model to quantify the cost of load transfer. A model of multi-type demand response participating in the optimal operation of distribution network considering uncertainties in interruptible load is established. Aimed at the non-convex nonlinearity constraint of the model, it is transformed into a mixed integer second-order cone programming model by the second-order cone relaxation method, which is further solved by the CPLEX solver. In addition, the contribution degree and confidence degree are introduced to evaluate the user responsiveness. Simulation results show that the novel compensation mechanism can more reasonably guide users to adjust the power load, smooth the load curve, and improve the operating economy of distribution network.

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针对当前激励型定价补偿机制无法满足多类型负荷差异化需求的问题,提出了一种分段式补偿价格形式的可中断负荷补偿方法,同时引入负荷转移时间和负荷转移功率的二维交变函数,建立了量化负荷转移成本的可转移负荷补偿模型;计及可中断负荷不确定性,建立了多类型需求响应参与配电网优化运行模型,针对模型中非凸非线性的约束,利用二阶锥松弛方法将模型转化为混合整数二阶锥规划模型,采用CPLEX 求解器对模型进行求解,并引入贡献度和置信度作为评估用户响应能力的评价指标。仿真结果表明,新型补偿机制能更加合理地指导用户调整用电负荷,平滑负荷曲线,提高配电网运行经济性。

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王玉梅(1963-),女,硕士,教授。研究方向:智能供配电技术。E-mail : wangym@ hpu.edu.cn。

王露露(1996-),女,通信作者,硕士研究生。研究方向:配电网调度。E-mail: 2978002510@qq.com。

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王玉梅(1963-),女,硕士,教授。研究方向:智能供配电技术。E-mail : wangym@ hpu.edu.cn。

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王玉梅(1963-),女,硕士,教授。研究方向:智能供配电技术。E-mail : wangym@ hpu.edu.cn。

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王露露(1996-),女,通信作者,硕士研究生。研究方向:配电网调度。E-mail: 2978002510@qq.com。

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王露露(1996-),女,通信作者,硕士研究生。研究方向:配电网调度。E-mail: 2978002510@qq.com。

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Energy Conversion and Management, 2016. 115: 103-116., articleTitle=Incorporating price-responsive customers in day-ahead scheduling of smart distribution networks, refAbstract=null)], funds=[Fund(id=1154038618130735408, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154038488321221382, awardId=U1804143, language=EN, fundingSource=National Natural Science Foundation of China(U1804143), fundOrder=null, country=null), Fund(id=1154038618193649970, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154038488321221382, awardId=U1804143, language=CN, fundingSource=国家自然科学基金资助项目(U1804143), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1154038604910289879, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154038488321221382, xref=null, ext=[AuthorCompanyExt(id=1154038604914484185, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154038488321221382, companyId=1154038604910289879, 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caption=TOU price, figureFileSmall=null, figureFileBig=null, tableContent=
负荷类型 电价$/\left\lbrack {\text{元}\cdot {\left(\mathrm{{kW}}\cdot \mathrm{h}\right)}^{-1}}\right\rbrack$
峰时段 平时段 谷时段
居民、商业负荷 0.94 0.75 0.30
工业负荷 1.13 0.83 0.45
), ArticleFig(id=1154038617296068889, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154038488321221382, language=CN, label=表1, caption=分时电价, figureFileSmall=null, figureFileBig=null, tableContent=
负荷类型 电价$/\left\lbrack {\text{元}\cdot {\left(\mathrm{{kW}}\cdot \mathrm{h}\right)}^{-1}}\right\rbrack$
峰时段 平时段 谷时段
居民、商业负荷 0.94 0.75 0.30
工业负荷 1.13 0.83 0.45
), ArticleFig(id=1154038617354789147, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154038488321221382, language=EN, label=Tab. 2, caption=Compensation unit price for IL and TL 元/(kW·h), figureFileSmall=null, figureFileBig=null, tableContent=
负荷 类型 ${c}_{1, t}$ (10%) ${c}_{2, t}$ (15%) ${c}_{3, t}$ (20%) ${c}_{4, t}$ (25%) ${c}_{5, t}$ (30%) ${c}^{\text{T, base }}$ ${c}^{\text{ E, base }}$
居民 IL 0.40 0.50 0.65 0.75 0.85
商业 IL 0.45 0.55 0.70 0.80 0.90
工业$\mathrm{{TL}}$ 0.82 0.92
), ArticleFig(id=1154038617405120797, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154038488321221382, language=CN, label=表2, caption=IL 和 TL 补偿价格, figureFileSmall=null, figureFileBig=null, tableContent=
负荷 类型 ${c}_{1, t}$ (10%) ${c}_{2, t}$ (15%) ${c}_{3, t}$ (20%) ${c}_{4, t}$ (25%) ${c}_{5, t}$ (30%) ${c}^{\text{T, base }}$ ${c}^{\text{ E, base }}$
居民 IL 0.40 0.50 0.65 0.75 0.85
商业 IL 0.45 0.55 0.70 0.80 0.90
工业$\mathrm{{TL}}$ 0.82 0.92
), ArticleFig(id=1154038617463841055, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154038488321221382, language=EN, label=Tab. 3, caption=Self and cross elasticity-coefficients, figureFileSmall=null, figureFileBig=null, tableContent=
负荷 类型 自弹性系数 交叉弹性系数
峰-峰 平-平 谷-谷 峰-平 峰-谷 平-谷
居民、 商业 -0.18 -0.24 -0.15 0.07 0.06 0.10
工业 -0.24 -0.28 -0.12 0.12 0.08 0.14
), ArticleFig(id=1154038617551921441, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154038488321221382, language=CN, label=表3, caption=自弹性和交叉弹性系数, figureFileSmall=null, figureFileBig=null, tableContent=
负荷 类型 自弹性系数 交叉弹性系数
峰-峰 平-平 谷-谷 峰-平 峰-谷 平-谷
居民、 商业 -0.18 -0.24 -0.15 0.07 0.06 0.10
工业 -0.24 -0.28 -0.12 0.12 0.08 0.14
), ArticleFig(id=1154038617614836003, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154038488321221382, language=EN, label=Tab. 4, caption=Calculation results under Scheme 1, figureFileSmall=null, figureFileBig=null, tableContent=
可中断 负荷比例/% PBDR 成本/ 万元 IL 补偿成本/ 万元 TL 补偿成本/ 万元 购电成本/ 万元 功耗成本/ 万元 总成本/万元 净收益/万元
10 0.589 3.164 1.673 16.128 2.612 24.166 11.615
15 0.548 3.735 1.109 14.738 2.448 22.578 12.099
20 0.496 4.439 0.922 13.663 2.422 21.942 13.224
25 0.353 4.683 0.583 13.461 2.196 21.276 14.834
30 0.388 6.211 0.400 12.771 2.023 21.793 12.521
), ArticleFig(id=1154038617690333477, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154038488321221382, language=CN, label=表4, caption=方案 1 计算结果, figureFileSmall=null, figureFileBig=null, tableContent=
可中断 负荷比例/% PBDR 成本/ 万元 IL 补偿成本/ 万元 TL 补偿成本/ 万元 购电成本/ 万元 功耗成本/ 万元 总成本/万元 净收益/万元
10 0.589 3.164 1.673 16.128 2.612 24.166 11.615
15 0.548 3.735 1.109 14.738 2.448 22.578 12.099
20 0.496 4.439 0.922 13.663 2.422 21.942 13.224
25 0.353 4.683 0.583 13.461 2.196 21.276 14.834
30 0.388 6.211 0.400 12.771 2.023 21.793 12.521
), ArticleFig(id=1154038617778413863, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154038488321221382, language=EN, label=Tab. 5, caption=Calculation results under Scheme 2, figureFileSmall=null, figureFileBig=null, tableContent=
可中断 负荷比例/% PBDR 成本/ 万元 IL 补偿成本/ 万元 TL 补偿成本/ 万元 购电成本/ 万元 功耗成本/ 万元 总成本/万元 净收益/万元
10 0.469 1.992 0.823 15.575 2.693 21.552 12.673
15 0.412 2.357 0.677 14.865 2.560 20.871 13.545
20 0.359 2.758 0.590 14.127 2.377 20.211 14.831
25 0.315 3.171 0.499 13.957 2.232 20.174 15.329
30 0.331 3.916 0.367 13.627 2.167 20.408 13.793
), ArticleFig(id=1154038617853911337, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154038488321221382, language=CN, label=表5, caption=方案 2 计算结果, figureFileSmall=null, figureFileBig=null, tableContent=
可中断 负荷比例/% PBDR 成本/ 万元 IL 补偿成本/ 万元 TL 补偿成本/ 万元 购电成本/ 万元 功耗成本/ 万元 总成本/万元 净收益/万元
10 0.469 1.992 0.823 15.575 2.693 21.552 12.673
15 0.412 2.357 0.677 14.865 2.560 20.871 13.545
20 0.359 2.758 0.590 14.127 2.377 20.211 14.831
25 0.315 3.171 0.499 13.957 2.232 20.174 15.329
30 0.331 3.916 0.367 13.627 2.167 20.408 13.793
), ArticleFig(id=1154038617916825899, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154038488321221382, language=EN, label=Tab. 6, caption=Calculation results under Scheme 3, figureFileSmall=null, figureFileBig=null, tableContent=
可中断 负荷比例/% PBDR 成本/ 万元 IL 补偿成本/ 万元 TL 补偿成本/ 万元 购电成本/ 万元 功耗成本/ 万元 总成本/万元 净收益/万元
10 0.466 2.030 0.818 15.323 2.660 21.297 12.928
15 0.409 2.435 0.672 14.612 2.527 20.655 13.761
20 0.357 2.818 0.583 13.874 2.344 19.976 15.066
25 0.310 3.231 0.496 13.705 2.199 19.941 15.562
30 0.325 3.976 0.364 13.377 2.134 20.176 14.025
), ArticleFig(id=1154038617988129069, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154038488321221382, language=CN, label=表6, caption=方案 3 计算结果, figureFileSmall=null, figureFileBig=null, tableContent=
可中断 负荷比例/% PBDR 成本/ 万元 IL 补偿成本/ 万元 TL 补偿成本/ 万元 购电成本/ 万元 功耗成本/ 万元 总成本/万元 净收益/万元
10 0.466 2.030 0.818 15.323 2.660 21.297 12.928
15 0.409 2.435 0.672 14.612 2.527 20.655 13.761
20 0.357 2.818 0.583 13.874 2.344 19.976 15.066
25 0.310 3.231 0.496 13.705 2.199 19.941 15.562
30 0.325 3.976 0.364 13.377 2.134 20.176 14.025
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基于多类需求响应的配电网优化调度
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王玉梅 , 王露露
电源学报 | 电力系统 2024,22(2): 273-282
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电源学报 | 电力系统 2024, 22(2): 273-282
基于多类需求响应的配电网优化调度
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王玉梅 , 王露露
作者信息
  • 河南理工大学 电气工程与自动化学院 焦作 454003

    • 王玉梅(1963-),女,硕士,教授。研究方向:智能供配电技术。E-mail : wangym@ hpu.edu.cn。

    王露露(1996-),女,通信作者,硕士研究生。研究方向:配电网调度。E-mail: 2978002510@qq.com。

Optimal Dispatching of Distribution Network Based on Multi-type Demand Response
Yumei WANG , Lulu WANG
Affiliations
  • School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454003 China
出版时间: 2024-03-30 doi: 10.13234/j.issn.2095-2805.2024.2.273
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摘要
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针对当前激励型定价补偿机制无法满足多类型负荷差异化需求的问题,提出了一种分段式补偿价格形式的可中断负荷补偿方法,同时引入负荷转移时间和负荷转移功率的二维交变函数,建立了量化负荷转移成本的可转移负荷补偿模型;计及可中断负荷不确定性,建立了多类型需求响应参与配电网优化运行模型,针对模型中非凸非线性的约束,利用二阶锥松弛方法将模型转化为混合整数二阶锥规划模型,采用CPLEX 求解器对模型进行求解,并引入贡献度和置信度作为评估用户响应能力的评价指标。仿真结果表明,新型补偿机制能更加合理地指导用户调整用电负荷,平滑负荷曲线,提高配电网运行经济性。

需求响应  /  配电网  /  可中断负荷  /  激励机制

Aimed at the problem that the existing incentive pricing compensation mechanism cannot meet the differentiated needs of multiple types of load, a compensation method for interruptible load is proposed in the form of sectional compensatory price. Meanwhile, a two-dimensional alternating function of load transferable time and load transferable power is introduced to establish a transferable load compensation model to quantify the cost of load transfer. A model of multi-type demand response participating in the optimal operation of distribution network considering uncertainties in interruptible load is established. Aimed at the non-convex nonlinearity constraint of the model, it is transformed into a mixed integer second-order cone programming model by the second-order cone relaxation method, which is further solved by the CPLEX solver. In addition, the contribution degree and confidence degree are introduced to evaluate the user responsiveness. Simulation results show that the novel compensation mechanism can more reasonably guide users to adjust the power load, smooth the load curve, and improve the operating economy of distribution network.

Demand response  /  distribution network  /  interruptible load  /  incentive mechanism
王玉梅, 王露露. 基于多类需求响应的配电网优化调度. 电源学报, 2024 , 22 (2) : 273 -282 . DOI: 10.13234/j.issn.2095-2805.2024.2.273
Yumei WANG, Lulu WANG. Optimal Dispatching of Distribution Network Based on Multi-type Demand Response[J]. Journal of Power Supply, 2024 , 22 (2) : 273 -282 . DOI: 10.13234/j.issn.2095-2805.2024.2.273
正文
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随着电力市场的深化改革,需求响应$\mathrm{{DR}}$ (demand response)作为配电系统的虚拟灵活资源, 基于价格信号或激励机制引导电力用户主动调整用电方式, 促进电力资源合理配置, 提升系统可靠性和经济性,是适应竞争市场发展的必然要求[14]。
根据不同的实现机制, DR 可分为价格型需求响应 PBDR(price-based demand response)和激励型需求响应 IBDR(incentive-based demand response)[5]。 分时电价在价格型需求响应中起主导作用, 用户参与分时电价转移部分峰时负荷至平、谷时段, 达到削峰填谷、提高系统运行稳定性的目的[6-7]。IBDR 主要包括可中断负荷 IL(interruptible load) 和可转移负荷 TL(transferable load)。IBDR 的实施依赖于通过与电力用户签订合同以削减电量, 同时用户获得电网的相应补偿[8-9]。文献[10]基于用户满意度,建立了配电网-负荷聚合商的多目标优化调度模型以确定最佳的 IL 合同。文献[11]针对需求侧资源的优化配置问题, 提出了一种新的基于最优潮流的电价机制以实现能源供给侧与配电网运营商的协调灵活调度。文献[12]提出了一种优化源-荷-储需求响应资源的日前-日内优化调度策略,可有效实现微电网参与短时需求响应市场。文献[13]通过分析电网中多类型可调资源的出力特性, 提出了一种可实时协调可调资源有功无功的主动配电网分布式优化控制策略, 提高了电网运行经济性与可靠性。文献 [14]提出了一种多时间尺度需求响应协同优化策略, 实现了居民用户群合理、有序、低偏差地参与需求响应, 同时降低调度成本。
上述研究大多未考虑不同类型负荷响应的差异性及不确定性。文献[15]针对多类型负荷在不同时段的响应差异性, 提出了基于指数变化的差异化价格型需求响应机制。文献[16]基于不同类型负荷响应特性, 提出了多类负荷联合参与需求响应的协调优化策略。文献[17]基于电价折扣补偿机制建立了计及响应参与率不确定性的 DR 模型,但采用了固定电价进行负荷响应量的折扣补偿。综上分析,针对用户中断负荷及转移负荷的激励补偿机制的研究较少, 亟需提出新的灵活性激励型需求响应机制。
本文考虑不同类型负荷的差异化弹性需求, 针对激励型需求响应提出一种新型补偿机制, 建立计及可中断负荷不确定性的多类型需求响应参与配电网优化运行模型。首先建立价格型需求响应模型和激励型需求响应模型; 然后, 计及用户参与 IL 响应不确定性, 建立以配电网运营商获利最大为目标函数的调度模型,采用贡献度、置信度作为评价指标评估用户响应能力;最后通过仿真分析验证新型补偿机制的可行性。
1 多类型需求响应建模
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1.1 价格型需求响应模型
价格型需求响应主要通过实施分时电价引导用户调整用电时段, 负荷响应量与价格变化量的关系用弹性系数来描述, 为
$\varepsilon =\frac{{p}^{0}}{{L}^{0}}\frac{\Delta L}{\Delta p}$
式中:$\varepsilon$ 为需求响应价格弹性系数;${L}^{0}$ 为电量;${p}^{0}$ 为电价;${\Delta L}\text{、}{\Delta p}$ 分别为电量和电价的相对增量。
用户在某时刻的用电需求量不仅与当前电价有关, 也受到其他时段电价的影响, 因此用户在多时段用电需求与电价的关联关系为
${\varepsilon }_{th}= \frac{{p}_{h}^{0}}{{L}_{t}^{0}}\frac{\Delta {L}_{t}}{\Delta {p}_{h}}= \frac{\Delta {L}_{t}/{L}_{t}^{0}}{\Delta {p}_{h}/{p}_{h}^{0}}$
式中:${\varepsilon }_{th}$ 为互弹性系数;${L}_{t}^{0}\text{、}{p}_{h}^{0}$ 分别为实施 PBDR 前$t$ 时刻的用电负荷需求和$h$ 时刻的电价;$\Delta {L}_{t}\text{、}\Delta {p}_{h}$ 分别为实施 PBDR 前后$t$ 时刻的用电变化量、$h$ 时刻的价格变化量。用户参与 PBDR 后的负荷变化量为
$\left\lbrack \begin{matrix}\Delta {L}_{1}/{L}_{1}^{0}\\\Delta {L}_{2}/{L}_{2}^{0}\\\vdots \\\Delta {L}_{24}/{L}_{24}^{0}\end{matrix}\right\rbrack =\left\lbrack \begin{matrix}{\varepsilon }_{1,1}& \cdots &{\varepsilon }_{1,{24}}\\\vdots && \vdots \\{\varepsilon }_{{24},1}& \cdots &{\varepsilon }_{{24},{24}}\end{matrix}\right\rbrack \left\lbrack \begin{matrix}\Delta {p}_{1}/{p}_{1}^{0}\\\Delta {p}_{2}/{p}_{2}^{0}\\\vdots \\\Delta {p}_{24}/{p}_{24}^{0}\end{matrix}\right\rbrack $
根据文献[18],实施 PBDR 后用户$f$ 在$t$ 时刻的用电负荷${L}_{f, t}$ 公式为
${L}_{f, t}= {L}_{f, t}^{0}\left\{{1 +{\varepsilon }_{f,{tt}}\frac{\left\lbrack {p}_{f, t}- {p}_{f, t}^{0}\right\rbrack }{{p}_{f, t}^{0}}+ \mathop{\sum }\limits_{{h = 1, h \neq t}}^{T}{\varepsilon }_{f,{th}}\frac{\left\lbrack {p}_{f, h}- {p}_{f, h}^{0}\right\rbrack }{{p}_{f, h}^{0}}}\right\}$
由于用户负荷会随电价的变化而变动, 本文引入浮动因子${\tau }_{f, t}$ 模拟不同时段内响应价格变化。
$\left\{\begin{array}{l}{L}_{f}^{\text{sum }}= \mathop{\sum }\limits_{{t = 1}}^{T}{L}_{f, t}^{0}\\{L}_{f}^{\text{su }}= \frac{{L}_{f}^{\text{sum }}}{T}\\{\tau }_{f, t}= \frac{{L}_{f, t}^{0}}{{L}_{f}^{x}}\\{p}_{t, t}= {\tau }_{f, t}{p}_{t, t}^{0}\end{array}\right.$
式中:${L}_{f}^{\text{sum }}$ 为实施 PBDR 前用户$f$ 总用电负荷;${L}_{f}^{\text{av }}$ 为平均用电负荷;${p}_{f, t}$ 为用户$f$ 在$t$ 时刻的最终响应价格。
结合式(4)、式(5)可得用户参与 PBDR 的总用电负荷${L}_{t}^{\mathrm{{PBDR}}}$ 为
${L}_{t}^{\mathrm{{PBDR}}}= \mathop{\sum }\limits_{{f = 1}}^{{N}_{\mathrm{f}}}\left({{L}_{f, t}- {L}_{f, t}^{0}}\right)$
式中,${N}_{\mathrm{f}}$ 为参与$\mathrm{{PBDR}}$ 的用户总个数。
综上所述, PBDR 的补偿成本 (用户峰谷套利)${C}^{\mathrm{{PBDR}}}$ 为
${C}^{\mathrm{{PBDR}}}= {L}_{f, t}^{0}{p}_{t}^{0}- {L}_{f, t}{p}_{t}$
1.2 激励型需求响应模型
IBDR 比 PBDR 更加灵活,便于直接控制。电网对参与 IBDR 的用户提供相应补偿以引导用户积极响应调度需求, 降低配电网运行成本。
1.2.1 可中断负荷补偿机制建模
由于负荷削减给用户造成一定的经济损失, 因此电网采取一些激励机制进行补偿。对于 IL, 用户向配电网运营商提交分步式补偿价格报价, 包括减少负荷的数量和相关报价, 如图1 所示, 其中第一阶段$\left({m = 1}\right)$ 为基本响应阶段,其他阶段$\left({m > 1}\right)$ 为弹性响应阶段, 配电网运营商在接受报价的同时, 将要求 IL 用户削减负荷。
IL 补偿成本${C}^{\mathrm{{IL}}}$ 为
${C}^{\mathrm{{IL}}}= \mathop{\sum }\limits_{{t = 1}}^{T}\mathop{\sum }\limits_{{l = 1}}^{{N}_{\mathrm{l}}}\mathop{\sum }\limits_{{m = 1}}^{{N}_{\mathrm{m}}}{c}_{t, l, m}\Delta {L}_{t, l, m}^{\mathrm{{IL}}}$
式中:${N}_{1}$ 为参与 IL 的总用户个数;${N}_{\mathrm{m}}$ 为用户逐步报价的总阶段数;${c}_{t, l, m}$ 为用户$l$ 提供的第$m$ 阶段的价格报价;$\Delta {L}_{t, l, m}^{\mathrm{{IL}}}$ 为用户$l$ 在时段$t$ 的计划响应电量。
1.2.2 可转移负荷补偿机制建模
本文采用基于负荷可转移时间 LTT(load transferable time) 和负荷可转移功率 LTP(load transferable power)的二维交变函数量化 TL 补偿成本, 负荷转移成本是 LTT 和 LTP 的非线性函数, 随着 LTT 和 LTP 的增加,成本增加更大, TL 补偿成本${C}^{\mathrm{{TL}}}$ 公式为
${C}^{\mathrm{{TL}}}= \mathop{\sum }\limits_{{g = 1}}^{{N}_{\mathrm{g}}}\mathop{\sum }\limits_{{{t}_{\mathrm{r}}= 1}}^{{T}_{\mathrm{r}}}\left({{c}_{g}^{\mathrm{E}}{c}_{g}^{\mathrm{T}}{L}_{g,{t}_{\mathrm{r}}}^{\mathrm{{TL}}}}\right)$
$\left\{\begin{array}{ll}{c}_{g}^{\mathrm{E}}= {c}^{\mathrm{E},\text{ base }}+ \mathop{\sum }\limits_{{n = 1}}^{{E}_{\mathrm{n}}}{r}_{n}^{\mathrm{E}}\left({\Delta {L}_{g,{t}_{t}}^{\mathrm{{TL}}}}\right)& {E}_{\mathrm{n}}\leq {N}_{\mathrm{E}}\\{c}_{g}^{\mathrm{T}}= {c}^{\mathrm{T},\text{ base }}+ \mathop{\sum }\limits_{{n = 1}}^{{T}_{\mathrm{n}}}{r}_{n}^{\mathrm{E}}\left({\Delta {T}_{g,{t}_{t}}^{\mathrm{{TL}}}}\right)& {T}_{\mathrm{n}}\leq {N}_{\mathrm{T}}\end{array}\right.$
式中:${N}_{\mathrm{g}}$ 为参与$\mathrm{{TL}}$ 的用户总个数;${T}_{\mathrm{r}}$ 为负荷转移时段；${c}_{g}^{\mathrm{T}}\text{、}{c}_{g}^{\mathrm{E}}$ 分别为 LTT 和 LTP 的最终价格；${L}_{g,{t}_{i}}^{\mathrm{{TL}}}$ 为用户$g$ 在时段${t}_{\mathrm{r}}$ 的负荷转移功率;${c}^{\mathrm{T},\text{base }}\text{、}{c}^{\mathrm{E},\text{base }}$ 分别为基于 LTT 和 LTP 的基础价格;${r}_{n}^{\mathrm{E}}$ 为单位价格增量;$\Delta {L}_{g,{t}_{i}}^{\mathrm{{TL}}}$ 为用户$g$ 在时段${t}_{\mathrm{r}}$ 原计划转移电量的增量;$\Delta {T}_{g,{t}_{\mathrm{r}}}^{\mathrm{{IL}}}$ 为用户$g$ 在原计划转移时段${t}_{\mathrm{r}}$ 的时段增量;${E}_{\mathrm{n}}\text{、}{T}_{\mathrm{n}}$ 分别为实际负荷转移量和实际负荷转移时段；${N}_{\mathrm{E}}$ 、${N}_{\mathrm{T}}$ 分别为用户原计划的负荷转移量和负荷转移时段。
1.3 可中断负荷不确定性建模
考虑用户响应的不确定性, IBDR 的实际响应能力将与预期有所不同, 本文提出一种基于固定约束和柔性约束的激励机制以避免不确定性的影响, 如图2 所示,${\omega }_{l, m, t}$ 为实时范围内不确定响应量的波动范围。
基于图2,用户在第$m$ 阶段的计划响应电量应满足合同中第$m$ 阶段的容量限制。
$ 0 \leq \Delta {L}_{t, l, m}^{\mathrm{{IL}},\text{ before }}\leq \Delta {L}_{t, l, m}^{\mathrm{{IL}},\text{ before, max }}$
式中:$\Delta {L}_{t, l, m}^{\mathrm{{IL}},\text{ before }}$ 为用户$l$ 在第$m$ 阶段的计划响应电量;$\Delta {L}_{t, l, m}^{\mathrm{{IL}},\text{ before, max }}$ 为合同中第$m$ 阶段的最大响应量。
用户实际响应电量以计划响应电量为参考, 允许在合同规定的范围内有波动, 采用随机规划方法描述具有正态分布特征的 IL 不确定性。
$\left\{\begin{array}{l}{\omega }_{t, l, m}^{\mathrm{{IL}}}\sim \left\lbrack {0,{\delta }_{t, l, m}^{2}}\right\rbrack \\\Delta {\widetilde{L}}_{t, l, m}^{\mathrm{{IL}}}\sim \left\lbrack {\Delta {\widetilde{L}}_{t, l, m}^{\mathrm{{IL}}},{\delta }_{t, l, m}^{2\left(\mathrm{{IL}}\right)}}\right\rbrack \end{array}\right.$
式中:${\omega }_{t, l, m}^{\mathrm{{IL}}}$ 为实际响应电量与计划响应电量的偏差;$\Delta {\widetilde{L}}_{t, l, m}^{\mathrm{{IL}}}$ 为计及 IL 不确定性后用户$l$ 在第$m$ 阶段$t$ 时段的实际响应电量;$\delta$ 为正态分布的标准差。
图2 的第一阶段为固定补偿阶段, 其他阶段为柔性补偿阶段, 每个阶段的数量范围由上述正态分布的概率密度函数决定。计及 IL 不确定性后的补偿成本为
${C}^{\mathrm{{IL}}}= \mathop{\sum }\limits_{{t = 1}}^{T}\mathop{\sum }\limits_{{l = 1}}^{{N}_{1}}\mathop{\sum }\limits_{{m = 1}}^{{N}_{\mathrm{m}}}{c}_{t, l, m}\Delta {\widetilde{L}}_{t, l, m}^{\mathrm{{IL}}}$
2 多类型需求响应参与配电网优化运行模型
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2.1 目标函数
本文以配电网运营商获取利润最大为目标函数, 通过向用户售电获取收益, 成本包括向上级电网的购电成本${C}^{\text{sub }}$ 、电力损耗成本${C}^{\text{loss }}$ 、需求响应补偿成本${C}^{\mathrm{{DR}}}$ 等。
$\left\{\begin{array}{l}\max F =\mathop{\sum }\limits_{{t = 1}}^{T}{p}_{t}\left({{P}_{t, i}^{\text{total }}+ {L}_{t, i}^{\mathrm{{PBDE}}}- {\bar{L}}_{t, i}^{\mathrm{{IL}}}+ {L}_{t, i}^{\mathrm{{TL}}}}\right)- \\\;\left({{C}^{\text{sub }}+ {C}^{\text{loss }}+ {C}^{\mathrm{{DR}}}}\right)\\{C}^{\text{sub }}= \mathop{\sum }\limits_{{t = 1}}^{T}{c}_{t}^{\text{sub }}{P}_{t, i}^{\text{sub }}\\{C}^{\text{loss }}= \mathop{\sum }\limits_{{t = 1}}^{T}\mathop{\sum }\limits_{{i, j}}\mathop{\sum }\limits_{{r, j \in {C}^{\text{loss }}}}{c}_{t}^{\text{loss }}{r}_{ij}{l}_{i, i}^{\text{col }}\\{C}^{\text{loss }}= {C}^{\text{loss }}+ {C}^{\text{loss }}\end{array}\right.$
式中:${P}_{t, j}^{\text{Load }}$ 为$t$ 时段节点$j$ 的用户负荷有功需求量;${P}_{t, j}^{\text{sub }}$ 为$t$ 时段节点$j$ 的变电站注入的有功功率;${c}_{t}^{\text{sub }}$ 为$t$ 时段变电站注入有功功率的单位成本;${c}_{t}^{\text{loss }}$ 为$t$ 时段电力损耗的单位成本;${r}_{ij}$ 、${I}_{t,{ij}}$ 分别为支路电阻、电流。
2.2 约束条件
2.2.1 支路潮流约束
本文所建等式潮流约束属于典型的混合整数非凸约束,应用分布式优化算法难以保证收敛性。 因此, 本文采用二阶锥松弛技术将其转化为凸二阶锥约束[19],并采用拓展 Distflow 方程进行求解计算。 引入锥优化变量${\widetilde{I}}_{t,{ij}}\text{、}{\widetilde{V}}_{t, j}$,定义为
$\left\{\begin{array}{l}{\widetilde{I}}_{t,{ij}}= {I}_{t,{ij}}^{2}\\{\widetilde{V}}_{t, j}= {V}_{t, j}^{2}\end{array}\right.$
$\begin{cases}& \mathop{\sum }\limits_{{k \in \gamma \left( j\right)}}{P}_{t,{jk}}- \mathop{\sum }\limits_{{i \in \pi \left( j\right)}}\left({{P}_{t,{ij}}- {\widetilde{I}}_{t,{ij}}{r}_{ij}}\right)= {P}_{t, j}^{\text{sub }}+ {P}_{t, j}^{\mathrm{{WT}}}- {P}_{t, j}^{\mathrm{{WT}}}- \\& \;\left({{P}_{t, j}^{\text{load }}+ {L}_{t, j}^{\mathrm{{PBRD}}}- {\widetilde{L}}_{t, j}^{\mathrm{{LL}}}+ {L}_{t, j}^{\mathrm{{TL}}}}\right)\\& \mathop{\sum }\limits_{{k \in \gamma \left( j\right)}}{Q}_{t,{jk}}- \mathop{\sum }\limits_{{i \in \pi \left( j\right)}}\left({{Q}_{t,{ij}}- {\widetilde{I}}_{t,{ij}}{x}_{ij}}\right)+ {b}_{j}{\widetilde{V}}_{t, j}= {Q}_{t, j}^{\text{sub }}+ \\& \;{Q}_{t, j}^{\mathrm{{WT}}}+ {Q}_{t, j}^{\mathrm{{WT}}}- \left({{Q}_{t, j}^{\text{LAD 2 }}+ {Q}_{t, j}^{\mathrm{{PBRD}}}- {\widetilde{Q}}_{t, j}^{\mathrm{{LL}}}+ {Q}_{t, j}^{\mathrm{{LL}}}}\right)\\& \mathop{\sum }\limits_{{k \in \gamma \left( j\right)}}{x}_{k, j}+ {b}_{j}{x}_{k, j}+ {b}_{j}{x}_{k, j}+ {b}_{j}\left({{x}_{k, j}^{\mathrm{{LAD}}}- {\widetilde{x}}_{k, j}^{\mathrm{{LAD}}}}\right)\end{cases}$
${\widetilde{I}}_{t,{ij}}= \frac{{\left({P}_{t,{ij}}\right)}^{2}+ {\left({Q}_{t,{ij}}\right)}^{2}}{{\widetilde{V}}_{t, i}}$
式(17)可转化为标准二阶锥松弛形式
${\widetilde{I}}_{t,{ij}}+ {\widetilde{V}}_{t, i}\geq {\begin{Vmatrix} 2{P}_{t,{ij}}\\ 2{Q}_{t,{ij}}\\{\widetilde{I}}_{t,{ij}}- {\widetilde{V}}_{t, i}\end{Vmatrix}}_{2}$
式中:$\gamma \left( j\right)$ 为以$j$ 为首端节点的支路末端节点集合;$\pi \left( j\right)$ 为以$j$ 为末端节点的支路首端节点集合;${P}_{t,{ij}}$ 、${Q}_{t,{ij}}$ 分别为支路${ij}$ 的有功功率和无功功率;${P}_{t, j}^{\mathrm{{PV}}}\text{、}{P}_{t, j}^{\mathrm{{WT}}}$ 分别为$t$ 时段节点$j$ 的光伏、风电有功出力;${V}_{t, j}$ 为节点$j$ 的电压;${x}_{ij}$ 为支路电抗;${Q}_{t, j}^{\text{sub }}$ 为$t$ 时段变电站注入的无功功率;${Q}_{t, j}^{\text{Load }}$ 为$t$ 时段节点$j$ 的无功负荷;${Q}_{t, j}^{\mathrm{{PBDR}}}$ 为$t$ 时段节点$j$ 的$\mathrm{{PBDR}}$ 无功出力;${\widetilde{Q}}_{t, j}^{\mathrm{{IL}}}$ 为$t$ 时段节点$j$ 的 IL 无功出力;${Q}_{i, j}^{\mathrm{{TL}}}$ 为$t$ 时段节点$j$ 的 TL 无功出力;$\parallel {\parallel }_{2}$ 为二范数。
2.2.2 安全运行约束
$\left\{\begin{array}{l}{V}_{t, j}^{\min }\leq {V}_{t, j}\leq {V}_{t, j}^{\max }\\ 0 \leq {I}_{t,{ij}}\leq {I}_{t,{ij}}^{\max }\end{array}\right.$
式中:${V}_{t, j}^{\max }\text{、}{V}_{t, j}^{\min }$ 分别为节点$j$ 电压幅值上下限;${I}_{t,{ij}}^{\max }$ 为流过支路 ij 电流幅值最大值。
2.2.3 变压器节点功率约束
$\left\{\begin{array}{l}{P}_{t, j}^{\mathrm{{sub}},\min }\leq {P}_{t, j}^{\mathrm{{sub}}}\leq {P}_{t, j}^{\mathrm{{sub}},\max }\\{Q}_{t, j}^{\mathrm{{sub}},\min }\leq {Q}_{t, j}^{\mathrm{{sub}}}\leq {Q}_{t, j}^{\mathrm{{sub}},\max }\end{array}\right.$
式中,${P}_{t, j}^{\mathrm{{sub}},\max }$ 、${P}_{t, j}^{\mathrm{{sub}},\min }$ 和${Q}_{t, j}^{\mathrm{{sub}},\max }$ 、${Q}_{t, j}^{\mathrm{{sub}},\min }$ 分别为变压器有功功率和无功功率的上、下限值。
2.2.4 可中断负荷约束
为避免用户参与 IL 后形成新的负荷高峰, IL 用户负荷应满足
$\left\{\begin{array}{l}{L}_{t, j}^{\text{before }}+ \mathop{\sum }\limits_{{l = 1}}^{{N}_{1}}\mathop{\sum }\limits_{{m = 1}}^{{N}_{\mathrm{m}}}\Delta {\widetilde{L}}_{t, l, m}^{+ }\leq {\widetilde{L}}_{t, j}^{\mathrm{{II}},\max }\\{L}_{t, j}^{\text{before }}- \mathop{\sum }\limits_{{m = 1}}^{{N}_{1}}\mathop{\sum }\limits_{{m = 1}}^{{N}_{\mathrm{m}}}\Delta {\widetilde{L}}_{t, l, m}^{- }\leq -{\widetilde{L}}_{t, j}^{\mathrm{{II}},\max }\end{array}\right.$
式中:${L}_{t, j}^{\text{before }}$ 为用户参与响应前节点$j$ 的负荷功率;$\Delta {\widetilde{L}}_{t, l, m}^{+ }\smallsetminus \Delta {\widetilde{L}}_{t, l, m}^{- }$ 分别为用户$l$ 在第$m$ 阶段负荷增加和减少的上限;${\widetilde{L}}_{t, j}^{\mathrm{{IL}},\max }$ 为用户参与响应后节点$j$ 的负荷功率。
2.2.5 节点负荷约束
${P}_{t, j}^{\mathrm{{Load}},\min }\leq {P}_{t, j}^{\mathrm{{Load}}}+ {L}_{t, j}^{\mathrm{{PBDR}}}- {\widetilde{L}}_{t, j}^{\mathrm{{IL}}}+ {L}_{t, j}^{\mathrm{{TL}}}\leq {P}_{t, j}^{\mathrm{{Load}},\max }$
式中,${P}_{t, j}^{\mathrm{{Load}},\max }$ 、${P}_{t, j}^{\mathrm{{Load}},\min }$ 分别为节点$j$ 在$t$ 时段的有功负荷上下限。
3 调度策略评价指标
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本文引入贡献度和置信度作为评估用户响应能力的指标。
3.1 贡献度
贡献度指某些特定用户的响应能力, 参与需求响应的负荷量越大,用户贡献值越大。
${\alpha }_{t, l}^{\mathrm{{IL}}}= \frac{{\widetilde{L}}_{t, l}^{\mathrm{{IL}}}}{{\widetilde{L}}_{t}^{\mathrm{{IL}}}}$
${\alpha }_{t, f}^{\mathrm{{PBDR}}}= \frac{{L}_{f, t}- {L}_{f, t}^{0}}{{L}_{t}^{\mathrm{{PBDR}}}}$
式中:${\alpha }_{t, l}^{\mathrm{{IL}}}$ 为参与 IL 用户的响应能力;${\alpha }_{t, f}^{\mathrm{{PBDR}}}$ 为参与 PBDR 用户的响应能力;${\widetilde{L}}_{t}^{\mathrm{{IL}}}$ 为合同中$t$ 时段的负荷中断量;${\widetilde{L}}_{t, l}^{\mathrm{{IL}}}$ 为用户$l$ 在$t$ 时段的响应负荷量。
3.2 置信度
计及用户上报的可中断负荷存在违约和执行的随机风险,本文引入基于用户违约电量和违约时间定义的用电置信度${\beta }_{t, l}^{\mathrm{{IL}}}$ 以表示参与$\mathrm{{IL}}$ 用户的违约风险[20]。
${\beta }_{t, l}^{\mathrm{{IL}}}= \left({1 -\frac{\left|{L}_{t, l}^{\mathrm{{IL}},\text{ before }}- {\widetilde{L}}_{t, l}^{\mathrm{{IL}}}\right|}{\left|{L}_{t, l}^{\mathrm{{IL}},\text{ before }}\right|}}\right)\left({1 -{T}_{t, l}^{\mathrm{{IL}}}}\right)$
式中:${L}_{t, l}^{\mathrm{{IL}},\text{ before }}$ 为用户$l$ 在时段$t$ 上报的负荷中断量;${T}_{t, l}^{\mathrm{{IL}}}$ 为用户$l$ 的历史统计平均违约时段百分数。
4 算例分析
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基于 IEEE-33 节点配电系统进行仿真分析, 采用 CPLEX 求解混合整数二阶锥规划问题, 系统参数见文献[21],基准电压为${12.66}\mathrm{{kV}}$,分布式能源接入位置、各时段出力等参数参考文献[22],负荷端用电情况分为峰、平、谷 3 个时段,总调度时段为一天${24}\mathrm{\;h}$,其中,峰时段为${12}: {00}- {14}: {00}\text{、}{19}: {00}- {22}: {00}$, 平时段为 08:00-11:00、15:00-18:00,谷时段为 23:00-07:00,各时段电价[23] 如表1 所示, IL 和 TL 补偿价格及弹性系数${}^{\left({24}\right)}$ 如表2 和表3 所示, IL 的最大可中断百分比为${30}\%$。TL 的功率范围为$-{500}\sim {500}\mathrm{\;{kW}}$。
本文设计了 2 种方案进行比较分析, 以得到不同需求响应机制对结果的影响, 方案 1: 考虑PBDR, 采用传统定价补偿机制, 不考虑 IL 不确定性; 方案 2: 考虑 PBDR, 采用新型补偿机制, 不考虑 IL 不确定性; 方案 3: 考虑 PBDR, 采用本文提出的补偿机制, 并考虑 IL 不确定性。
4.1 不同需求响应下负荷变化分析
本文在不同方案下对多类型负荷实施不同需求响应分析其负荷变化情况。负荷预测曲线如图3 所示,图4~图6 分别为居民、商业、工业 3 种用户的负荷变化曲线。
从图4 中可看出, 采用方案 1, 居民用户参与 IBDR 将时段 12:00-14:00 及 19:00-22:00 的负荷转移至谷时段, 同时获得中断负荷及转移负荷补偿收益, 多类型需求响应实施后, 用户在时段 19:00- 22:00 削减约 65%的用电量以获取高额补偿, 而平、 谷时段用电量未增加, 反而增大了负荷峰谷差, 不符合实际情况；方案 2 采用新型补偿机制, 实施 IB-DR 后,居民用户转移负荷量受负荷功率补偿约束的限制, 用户在合理范围内削减负荷, 但未考虑 IL 不确定性, 用户在峰时段的用电量增加, 违约风险升高；方案 3 采用新型补偿机制, 居民用户参与响应仅在峰时段 19:00-22:00 合理削减负荷,由于考虑了 IL 不确定性, 实施不同类型的需求响应后, 负荷峰谷差都明显降低, 负荷曲线更加平滑。
从图5 中可看出, 仅实施 IBDR, 在方案 1 的定价补偿方式下, 商业用户为获取最大需求响应补偿收益,在时段 11:00-21:00 削减的负荷功率超过 50%, 导致调度成本大幅度增加; 采用新型补偿机制,商业用户在峰时段 12:00-14:00、19:00-21:00 削减负荷, 方案 2 和方案 3 下用户削减负荷功率分别为 28.10%、24.53%,但方案 3 的负荷功率曲线更加平坦,用户响应行为更加合理。
从图6 中可看出,在时段 08:00-15:00,方案 1 的工业用户转移大量负荷, 甚至在 10:00-11:00 时段的负荷转移率超过 50%,在 20:00-06:00 时段, 负荷功率达到原负荷的 1.5 ~ 2.0 倍,这将导致调度成本大大增加, 不符合实际情况; 采用新型补偿机制, 在实现基于 LTT 和 LTP 的二维交变函数之后, 调度周期内的负荷转移功率将受到限制, 用户在 07:00-19:00 时段削减少量负荷,在 20:00-22:00、 02:00-04:00 时段增加部分负荷, 由于工业负荷的出力特性, 受不确定性影响较小, 因此方案 2 和方案 3 的负荷曲线大致相同, 但方案 3 考虑了 IL 不确定性, 用户负荷曲线更加符合实际。综上所述, 本文提出的补偿机制能指导用户更加合理地进行负荷调整,平滑负荷曲线。
4.2 经济性分析
在不同方案下对总有功负荷进行经济性分析, 计算结果如表4~表6 所示。
由于 TL 相比 IL 对评估结果影响较小, 本文仅分析 IL 对 IBDR 评估结果的影响, 从表4~表6 可看出, 不同 IL 可中断百分比下, 相比方案 1, 方案 2 的 PBDR 成本分别降低了 20.37%、24.82%、27.62%、 10.76%、14.69%, IL 补偿成本分别降低了 37.04%、 36.89%、37.87%、32.29%、36.95%, TL 补偿成本分别降低了 50.80%、38.95%、36.01%、14.41%、8.25%,总成本分别下降了 10.94%、9.88%、7.89%、5.18%、6.36%, 净收益分别增加了 8.35%、10.68%、10.70%、3.23%、 9.22%, 同样, 方案 3 的以上成本均低于方案 1, 不再赘述, 由于方案 3 考虑了 IL 不确定性, 用户响应可靠程度高, 配电网运营商所需支付的 IL 补偿成本高于方案 2, 但方案 2 的用户违约风险较高, 配电网运营商需向上级电网购买更多的电力来维持网络安全稳定性, 不同 IL 可中断百分比下, 相比方案 2, 方案 3 的购电成本分别降低了 2.20%、1.70%、1.79%、 1.81%、1.83%,总成本分别降低了 1.18%、1.06%、 1.16%、1.15%、1.14%,净收益分别增加了 1.97%、 1.57%、1.56%、1.50%、1.65%。综上所述,相比传统定价补偿机制, 实施新型补偿机制并考虑 IL 不确定性后, 配电网运行经济性更高, 当 IL 可中断百分比为 25%时可获得最优调度策略,此时配电网运行总成本最低,净收益最大。
4.3 贡献度和置信度分析
本文以居民负荷为例, 采用贡献度和置信度作为评估用户响应能力和违约风险的评价指标, 对居民用户提供的申报信息和历史数据在统一基准下进行标准化处理。评估结果如图7~图9 所示。
对于 IBDR,图8 和图9 中任何时段的 IL 用电贡献度值都大于图7,即实施新型补偿机制后,用户具有更大的响应能力,同时,考虑 IL 不确定性提高了用户响应可靠性,图9 中峰时段的 IL 用电贡献度值均高于图8; 此外,图7 中 IL 用电置信度的最大值为 0.94,而图8、9 中 IL 用电置信度的最大值分别为 0.95、0.97,因此计及 IL 响应不确定性可最大程度地降低用户违约风险, 提高调度计划的可信度, 但由于高峰时段的功耗增加, 用户于 21:00、 22:00 违约风险大大增加,导致 IL 用电贡献度和置信度较低。对于 PBDR, 从图中可以看出 3 种方案下的用电贡献度值相差不大, 在高峰时段 19:00- 22:00 和低谷时段 23:00-06:00,贡献度值均处于较高水平,证明了 PBDR 具有负荷转移、削峰填谷的特性。
5 结论
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本文主要基于可中断负荷和可转移负荷提出了一种新型补偿机制, 并建立了计及可中断负荷不确定性的多类需求响应参与配电网运行优化模型, 通过仿真得出以下结论:
(1)新型补偿机制通过有效的负荷转移补偿,可约束负荷转移功率在一定范围内,指导用户更加合理地进行负荷调整,参与负荷削减调度。
(2)相比定价补偿机制,采用新型补偿机制提高了用户用电贡献度和用电置信度, 增强了用户的响应能力, 提高了调度计划的可信度, 改善了配电网运行经济性。
(3)多类需求响应协同参与配电网的优化运行可降低负荷峰谷差,平滑负荷曲线。
基金
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  • 国家自然科学基金资助项目(U1804143)
文献
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2024年第22卷第2期
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doi: 10.13234/j.issn.2095-2805.2024.2.273
  • 接收时间：2021-04-22
  • 首发时间：2025-07-21
  • 出版时间：2024-03-30
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  • 收稿日期：2021-04-22
  • 修回日期：2021-06-14
  • 录用日期：2021-06-16
基金
National Natural Science Foundation of China(U1804143)
国家自然科学基金资助项目(U1804143)
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    河南理工大学 电气工程与自动化学院 焦作 454003
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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2024.2.273
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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