CN110909959B - 一种计及风电运行风险的多能互补电力系统鲁棒优化方法 - Google Patents
一种计及风电运行风险的多能互补电力系统鲁棒优化方法 Download PDFInfo
- Publication number
- CN110909959B CN110909959B CN201911289978.3A CN201911289978A CN110909959B CN 110909959 B CN110909959 B CN 110909959B CN 201911289978 A CN201911289978 A CN 201911289978A CN 110909959 B CN110909959 B CN 110909959B
- Authority
- CN
- China
- Prior art keywords
- wind power
- power
- constraint
- cost
- generating unit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 24
- 230000000295 complement effect Effects 0.000 title claims abstract description 18
- 238000005457 optimization Methods 0.000 title claims abstract description 10
- 239000002356 single layer Substances 0.000 claims abstract description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 19
- 230000005540 biological transmission Effects 0.000 claims description 12
- 238000010248 power generation Methods 0.000 claims description 11
- 239000000446 fuel Substances 0.000 claims description 7
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 abstract description 2
- 230000004308 accommodation Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000029087 digestion Effects 0.000 description 2
- 238000012954 risk control Methods 0.000 description 2
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- 230000009194 climbing Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 238000012887 quadratic function Methods 0.000 description 1
- 239000002351 wastewater Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Educational Administration (AREA)
- General Health & Medical Sciences (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Control Of Eletrric Generators (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
本发明涉及电力系统的运行、分析与调度领域,尤其是涉及一种计及风电运行风险的多能互补电力系统鲁棒机组组合优化方法。本发明计及风电功率超额偏差的CVaR,确保风电消纳的可行性和有效性。同时,本发明通过对风电功率时间/空间不确定性预算的调整策略,控制鲁棒最优解的保守度。此外,引入了风电功率运行风险水平约束,确保风电消纳的经济性与运行风险的可控性。经线性化技术处理,本发明所构建的计及风电运行风险的多能互补电力系统鲁棒机组组合模型最终转化为单层混合整数线性规划模型,可由高效的商业求解器直接求解。
Description
技术领域
本发明涉及电力系统的运行、分析与调度领域,尤其是涉及一种计及风电运行风险的多能互补电力系统鲁棒优化方法。
背景技术
在电力市场环境下,弃风和切负荷被认为是由风电场和消费者提供的辅助服务,并且获得这些服务是有代价的。另一方面,由于风电功率的不确定性,电力公司在制定调度策略和营销计划时必须考虑发电企业、电力市场、电网运行等方面存在的风险并加强风险管理。风-火-水多能互补电力系统受水电典型运行方式的影响,在不同时期对风电消纳能力的影响也不同,因此,有必要权衡风电消纳的运行风险和经济性。
本发明主要解决电力市场环境下风电消纳的风险管理问题,针对风-火-水多能互补电力系统,基于鲁棒优化框架和CVaR理论,提供一种计及风电运行风险和输电线路传输容量安全约束的机组组合方法。
发明内容
本发明主要是解决现有技术所存在的技术问题,基于鲁棒优化框架,提供一种计及风电运行风险和输电线路传输容量安全约束的多能互补电力系统机组组合方法,建立风-火-水多能互补电力系统的鲁棒日前机组组合模型,解决电力市场环境下风电消纳的风险管理问题。
本发明解决上述技术问题是采取以下技术方案实现的:
一种计及风电运行风险的多能互补电力系统鲁棒优化方法,包括以下步骤:
步骤1,获取火电机组的基本参数、水电机组的机组参数和水量计划、电力系统网络结构参数、负荷短期预测数据、风电的短期预测信息和时间、空间不确定性预算;
步骤2,描述目标函数以及约束条件,建立计及风电运行风险的多能互补电力系统鲁棒机组组合模型;
步骤2.1,建立以火电机组的发电成本、水电机组的弃水成本以及风电消纳的CVaR成本之和最小的目标函数,火电机组的发电成本包括启停成本、燃料成本和备用成本,基于:
F=min{f1+f2+f3} (1)
式中,T为调度周期;NG为火电机组台数,Nh为水电机组台数,NR为风电场个数;Cst,i和Csd,i分别为火电机组i在第t个时段的启动和停机成本;ui,t和vi,t分别表征火电机组i在时段t的开、停机状态,为二进制变量;gi,t为火电机组i在时段t的计划出力,zi,t为火电机组i在时段t的运行状态;f(gi,t,zi,t)为火电机组i在时段t的燃料成本;和分别为火电机组i的向上和向下旋转备用成本系数;和分别为火电机组i在时段t的向上和向下旋转备用容量;为水电机组h的计划水量,Qh,t为水电机组h在时段t的发电流量,κh为水电站h的水流量惩罚成本系数,Δt为秒级的调度时段;为时段t的风电功率预测功率,Δxj,t为风电预功率测误差,和分别为风电场j在时段t所能消纳的风电功率上、下限;与分别为风电场j在时段t的发电功率上、下限;和分别为低估和高估风电场j在时段t的出力时,系统额外增加的紧急调节成本,yj,t(Δxj,t)表示风电预测误差的概率密度函数(PDF);
步骤2.2,描述约束条件,主要包括:火电机组约束、水电机组约束、风电约束、系统约束;
所述步骤2.2中,火电机组约束条件包括最小启停时间约束、技术出力约束、机组爬坡约束、旋转备用释放约束;
水电机组约束条件包括水电机组技术出力约束、功率与水量转换约束、水量约束;
风电约束条件主要包括风电功率约束和风险水平约束;
风电功率约束被描述为:
f3≤Rlevel (6)
式中:Rlevel为风险水平控制成本,反映了模型的风险偏好程度,它不仅影响着机组组合和调度决策,而且影响着模型的可解性;
系统约束条件主要包括功率平衡约束、系统旋转备用约束、输电线路传输容量安全约束;
步骤3:火电机组燃料成本为二次函数,对其进行分段线性化处理:
式中:No为火电机组的发电功率的分段数;ξo,t和ωo,t分别为各分段在时段t的斜率和发电功率;
步骤4:引入松弛变量uit和vit,将火电机组启停状态及其转换的逻辑关系线性化为:
步骤5:采用分段线性逼近(PLA)法将表征CVaR成本的目标函数逐步线性化:
步骤6:将系统旋转备用约束式扩展为线性约束,并将输电线路传输容量安全约束转化为确定性约束式;
步骤7:调用商业软件包(如CPLEX或GUROBI)求解上述混合整数线性规划问题;输出常规机组的发电计划、旋转备用容量、风电功率的消纳区间及其CVaR成本;
本发明具有以下优点:
(1)本发明方法计及风电功率超额偏差的CVaR,确保风电在可消纳范围内解的可行性和有效性;
(2)本发明方法考虑了风电出力时间和空间不确定性预算的调节策略,避免鲁棒最优解过于保守;此外,引入了风电功率运行风险水平约束,确保风电消纳的经济性与运行风险的可控性;
(3)本发明所构建的计及风电运行风险的多能互补电力系统鲁棒机组组合模型,经线性化技术处理,最终转化为单层混合整数线性规划模型,可由高效的商业求解器直接求解。
附图说明
图1是本发明的实施例中在枯水期和丰水期两种典型运行方式下不同时间不确定性预算下各成本变化趋势;
图2是本发明的实施例中在不同风险水平控制成本及紧急调节成本下的CVaR成本变化趋势;
图3是本发明的实施例中不同风电场个数的CVaR成本与计算时间变化曲线;
附图4是本发明方法实现的流程图。
具体实施方式
下面通过实例,并结合附图,对本发明的技术方案作进一步具体的说明。本实施例以修改的IEEE 30节点测试系统为例,对本发明所提方法的可行性及有效性进行分析及验证。本实施例中,风电场接入节点5,装机容量为150MW。
一种计及风电运行风险的多能互补电力系统鲁棒优化方法,具体包括以下步骤:
步骤1,获取实施例中火电机组的基本参数、水电机组的机组参数和水量计划、电力系统网络结构参数、负荷短期预测数据、风电的短期预测信息和时间、空间不确定性预算。
步骤2,描述实施例的目标函数以及约束条件,建立计及风电运行风险的多能互补电力系统鲁棒机组组合模型。
步骤3,对实施例中火电机组燃料成本进行分段线性化处理。
步骤4,将实施例中火电机组启停状态及其转换的逻辑关系线性化。
步骤5,采用分段线性逼近(PLA)法将实施例中表征CVaR成本的目标函数逐步线性化。
步骤6,将实施例中系统旋转备用约束扩展为线性约束,并将输电线路传输容量安全约束转化为确定性约束。
步骤7,调用商业软件包(如CPLEX或GUROBI)求解上述混合整数线性规划问题。输出常规机组的发电计划、旋转备用容量、风电功率的消纳区间及其CVaR成本。
由图1可知,随着的增加,枯水期火电机组的发电成本呈增大趋势,丰水期由于水电机组水量计划增加,系统的发电成本较枯水期的大大减少,且相对枯水期变化趋势更为平稳,发电计划的鲁棒性更强。在同一时间不确定性预算下,水电机组通过弃水参与调峰后,丰水期的风电消纳运行风险略低于枯水期。随着的增加,各运行方式下的弃水成本保持不变,CVaR成本不断下降并且超过火电机组发电成本的增加,最终导致总成本的逐步降低。
由图2可观察到,在满足风险控制水平约束的前提下,随着紧急调节成本的增加,CVaR成本也随之提高。本算例中,当紧急调节成本不超过2000美元时,CVaR成本几乎不受风险水平控制成本的影响。然而,当紧急调节成本逐渐增加,CVaR成本受风险水平控制成本的影响明显,例如:当紧急调节成本超过2000美元且风险水平控制成本低于40000美元时,模型找不到能满足预设风险控制水平的最优解。
图3显示了风电场个数从4增加到24时,系统的CVaR成本及求解时间的变化趋势由图3可以看出,无论在枯水期还是丰水期,随着风电场接入个数的增加,在不增加调峰电源的情况下,系统的CVaR成本均明显提高,系统消纳风电的运行风险随之增加,这与实际运行经验也是相符的。此外,枯水期的平均求解时间为147.90秒,丰水期的平均求解时间为38.41秒。根据观测到的测试结果,所提方法的计算效率适合于含多风电场的多能互补电力系统的短期优化调度。
据上述算例测试结果可以看出,本发明方法可以权衡风电在可消纳范围内解的可行性和所决策结果的经济性。且本发明通过对风电出力时间和空间不确定性预算的调整,控制鲁棒最优解的保守度。本发明方法所构建的模型最终转化为单层混合整数线性规划模型,可通过调用高效的商业求解器直接求解。
Claims (2)
1.一种计及风电运行风险的多能互补电力系统鲁棒优化方法,其特征在于,包括以下步骤:
步骤1,获取火电机组的基本参数、水电机组的机组参数和水量计划、电力系统网络结构参数、负荷短期预测数据、风电的短期预测信息和时间、空间不确定性预算;
步骤2,描述目标函数以及约束条件,建立计及风电运行风险的多能互补电力系统鲁棒机组组合模型;
步骤2.1,建立以火电机组的发电成本、水电机组的弃水成本以及风电消纳的CVaR成本之和最小的目标函数;
步骤2.2,描述约束条件,主要包括:火电机组约束、水电机组约束、风电约束、系统约束;
步骤3,对火电机组燃料成本进行分段线性化处理;
步骤4,将火电机组启停状态及其转换的逻辑关系线性化;
步骤5,采用分段线性逼近法将表征CVaR成本的目标函数逐步线性化;
步骤6,将系统旋转备用约束式扩展为线性约束,并将输电线路传输容量安全约束转化为确定性约束式;
步骤7,调用商业软件包求解混合整数线性规划问题;输出常规机组的发电计划、旋转备用容量、风电功率的消纳区间及其CVaR成本;
所述的步骤2.1中,目标函数被描述为:
F=min{f1+f2+f3}
式中,T为调度周期;NG为火电机组台数,Nh为水电机组台数,NR为风电场个数;Cst,i和Csd,i分别为火电机组i在第t个时段的启动和停机成本;ui,t和vi,t分别表征火电机组i在时段t的开、停机状态,为二进制变量;gi,t为火电机组i在时段t的计划出力,zi,t为火电机组i在时段t的运行状态;f(gi,t,zi,t)为火电机组i在时段t的燃料成本;和分别为火电机组i的向上和向下旋转备用成本系数;和分别为火电机组i在时段t的向上和向下旋转备用容量;为水电机组h的计划水量,Qh,t为水电机组h在时段t的发电流量,κh为水电站h的水流量惩罚成本系数,Δt为秒级的调度时段;为时段t的风电功率预测功率,Δxj,t为风电预功率测误差,和分别为风电场j在时段t所能消纳的风电功率上、下限;与分别为风电场j在时段t的发电功率上、下限;和分别为低估和高估风电场j在时段t的出力时,系统额外增加的紧急调节成本,yj,t(Δxj,t)表示风电预测误差的概率密度函数;
所述的步骤2.2中,风电功率约束被描述为:
风险水平约束被描述为:
f3≤Rlevel
式中:Rlevel为风险水平控制成本。
2.根据权利要求1所述的一种计及风电运行风险的多能互补电力系统鲁棒优化方法,其特征在于,所述的步骤7中,通过调用一种高效的商业求解器求解所构建的单层混合整数线性规划模型。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201911289978.3A CN110909959B (zh) | 2019-12-16 | 2019-12-16 | 一种计及风电运行风险的多能互补电力系统鲁棒优化方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201911289978.3A CN110909959B (zh) | 2019-12-16 | 2019-12-16 | 一种计及风电运行风险的多能互补电力系统鲁棒优化方法 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN110909959A CN110909959A (zh) | 2020-03-24 |
CN110909959B true CN110909959B (zh) | 2023-04-07 |
Family
ID=69824536
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201911289978.3A Active CN110909959B (zh) | 2019-12-16 | 2019-12-16 | 一种计及风电运行风险的多能互补电力系统鲁棒优化方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN110909959B (zh) |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111934361A (zh) * | 2020-07-29 | 2020-11-13 | 国网甘肃省电力公司电力科学研究院 | 一种源网协调调峰优化策略评估方法 |
CN112952919B (zh) * | 2021-04-12 | 2022-06-17 | 浙江大学 | 多区域电网动态经济调度方法及装置、电子设备、介质 |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106655246A (zh) * | 2016-10-18 | 2017-05-10 | 国网黑龙江省电力有限公司哈尔滨供电公司 | 基于风电预测与需求响应的鲁棒双层优化模型的求解方法 |
CN108985524A (zh) * | 2018-08-07 | 2018-12-11 | 南京南瑞继保电气有限公司 | 一种多能互补系统的协调控制方法 |
CN110165711A (zh) * | 2019-02-28 | 2019-08-23 | 重庆大学 | 考虑发电运行弹性空间的风水火联合调度优化方法 |
Family Cites Families (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102004048341A1 (de) * | 2004-10-01 | 2006-04-13 | Repower Systems Ag | Windpark mit robuster Blindleistungsregelung und Verfahren zum Betrieb |
US7991512B2 (en) * | 2007-08-28 | 2011-08-02 | General Electric Company | Hybrid robust predictive optimization method of power system dispatch |
CN105550766A (zh) * | 2015-12-04 | 2016-05-04 | 山东大学 | 一种含可再生能源的微网鲁棒多目标运行优化方法 |
CN106253352B (zh) * | 2016-08-17 | 2017-09-29 | 山东大学 | 计及风电概率分布特征的鲁棒实时调度方法 |
CN106886839B (zh) * | 2017-02-20 | 2020-07-28 | 国网青海省电力公司电力科学研究院 | 基于混合整数规划的水火电机组组合优化调度方法 |
CN110571789B (zh) * | 2018-06-06 | 2020-10-20 | 南京工程学院 | 数据驱动下基于风电不确定性的电热气网三阶段调度方法 |
CN110137955B (zh) * | 2019-05-21 | 2021-04-02 | 国网能源研究院有限公司 | 一种计及CVaR的鲁棒机组组合调度的决策方法 |
-
2019
- 2019-12-16 CN CN201911289978.3A patent/CN110909959B/zh active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106655246A (zh) * | 2016-10-18 | 2017-05-10 | 国网黑龙江省电力有限公司哈尔滨供电公司 | 基于风电预测与需求响应的鲁棒双层优化模型的求解方法 |
CN108985524A (zh) * | 2018-08-07 | 2018-12-11 | 南京南瑞继保电气有限公司 | 一种多能互补系统的协调控制方法 |
CN110165711A (zh) * | 2019-02-28 | 2019-08-23 | 重庆大学 | 考虑发电运行弹性空间的风水火联合调度优化方法 |
Non-Patent Citations (1)
Title |
---|
夏沛 ; 邓长虹 ; 龙志君 ; 姚维为 ; 张凯 ; 王维洲 ; .含抽水蓄能机组的风电消纳鲁棒机组组合.电力系统自动化.2018,(第19期),59-71. * |
Also Published As
Publication number | Publication date |
---|---|
CN110909959A (zh) | 2020-03-24 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Li et al. | A coordinated dispatch method with pumped-storage and battery-storage for compensating the variation of wind power | |
He et al. | Optimal offering strategy for concentrating solar power plants in joint energy, reserve and regulation markets | |
Reddy | Optimal power flow with renewable energy resources including storage | |
Chen | Optimal wind–thermal generating unit commitment | |
Castronuovo et al. | On the optimization of the daily operation of a wind-hydro power plant | |
Pérez-Díaz et al. | Trends and challenges in the operation of pumped-storage hydropower plants | |
Palma-Behnke et al. | Energy management system for a renewable based microgrid with a demand side management mechanism | |
US9026259B2 (en) | Power generation optimization in microgrid including renewable power source | |
Moazeni et al. | Optimal energy management of water-energy networks via optimal placement of pumps-as-turbines and demand response through water storage tanks | |
Xie et al. | Greedy energy management strategy and sizing method for a stand-alone microgrid with hydrogen storage | |
Yuan et al. | Cross-regional integrated transmission of wind power and pumped-storage hydropower considering the peak shaving demands of multiple power grids | |
Li et al. | Optimal trade-off between regulation and wind curtailment in the economic dispatch problem | |
Xie et al. | A short-term optimal scheduling model for wind-solar-hydro hybrid generation system with cascade hydropower considering regulation reserve and spinning reserve requirements | |
CN103997039A (zh) | 基于概率区间预测的计及风电接纳的旋转备用区间预测方法 | |
CN110909959B (zh) | 一种计及风电运行风险的多能互补电力系统鲁棒优化方法 | |
Liang et al. | Interval based transmission contingency-constrained unit commitment for integrated energy systems with high renewable penetration | |
Nikoobakht et al. | Flexible Co-Scheduling of integrated electrical and gas energy networks under continuous and discrete uncertainties | |
Wang et al. | Flexible transmission expansion planning for integrating wind power based on wind power distribution characteristics | |
Zhang et al. | A multiobjective robust scheduling optimization mode for multienergy hybrid system integrated by wind power, solar photovoltaic power, and pumped storage power | |
Rahmati et al. | Pumped-storage units to address spinning reserve concerns in the grids with high wind penetration | |
Qian et al. | Benefits of energy storage in power systems with high level of intermittent generation | |
Akhlaghi et al. | Maximizing wind energy utilization in smart power systems using a flexible network-constrained unit commitment through dynamic lines and transformers rating | |
Chakir et al. | Managing a hybrid energy smart grid with a renewable energy source | |
Jonas | Predictive power dispatch for 100% renewable electricity scenarios using power nodes modeling framework | |
Hooshmand et al. | Power system dynamic scheduling with high penetration of renewable sources |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |