WO2023065113A1 - 风光水多能互补系统灵活性需求量化及协调优化方法 - Google Patents
风光水多能互补系统灵活性需求量化及协调优化方法 Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
Definitions
- the invention relates to the field of electric power system dispatching, in particular to a method for quantifying and coordinating and optimizing flexibility requirements of a wind-solar-water multi-energy complementary system.
- the reserve capacity reservation method can effectively deal with uncertainties such as power and load.
- uncertainties such as power and load.
- the previous reserve capacity method may significantly increase the cost of the system.
- the core issue is how to accurately quantify and absorb uncertain wind and The need for flexibility in power generation.
- some evaluation indicators have been proposed, which can be generally divided into three categories: the first category is the indicators for evaluating the flexible supply capacity of resources, including climbing ability, shortest start-stop time, start-up time, response Time, minimum stable output, etc.
- the second category is indicators for evaluating system flexibility requirements, including net load ramp rate and ramp acceleration etc., which are mainly used to analyze the characteristics of the load curve and quantify the demand for flexibility
- the third category is to evaluate the supply and demand relationship of system flexibility, including the probability and expectation of insufficient flexibility, which can also be subdivided into the probability and expectation of increased flexibility, Lowering the probability and expectation of insufficient flexibility is mainly used to evaluate the overall flexibility level of the system.
- this invention relies on the National Natural Science Foundation of China (52079014) and the actual project of Yunnan Power Grid to propose a flexibility evaluation and short-term complementary scheduling method for a high-proportion renewable energy power system mainly based on water, wind and wind.
- a flexibility demand quantification method considering the uncertainty of wind and solar output is constructed, and the quantile points are used to divide the range of wind and solar output to generate a set of output scenarios, and then the flexibility requirements in each scenario are calculated; the minimum expected expectation considering the lack of system flexibility is constructed.
- the hydro-wind-solar complementary optimal scheduling model through the complementary and coordinated operation of various types of wind-solar power sources, effectively meets the flexibility needs of large-scale intermittent new energy grid-connected consumption.
- the technical problem to be solved by the present invention is the quantification and coordination optimization of the flexibility requirements of wind-solar-water multi-energy complementary systems.
- the purpose is to promote the large-scale consumption of wind-solar and other clean energy and reduce unreasonable curtailment of wind and light.
- a method for quantifying and coordinating optimization of flexibility requirements of a wind-solar-water multi-energy complementary system comprising the following steps:
- Step1 According to the output probability distribution function of the wind power plant cluster in each period, the output of the power station cluster corresponding to the ⁇ quantile point in each period is obtained:
- the power station cluster output is at The following probability is ⁇ m , at The following probability is ⁇ m+1 , so the cluster output is at and The probability between is expressed as ⁇ m+1 - ⁇ m ; according to the above method, the cluster output intervals with different probabilities are obtained, as follows:
- Step1 For the output range obtained above, when the values of the adjacent quantile points ⁇ m and ⁇ m+1 are closer, and The width of the output interval between will be smaller, so take and center line of Represents the output area between the two, see the following formula:
- the present invention Compared with most of the current quantitative methods that focus on the flexibility requirements of the deterministic level, the present invention considers the uncertain output characteristics of the wind power plant cluster, and uses the quantile point method according to the output probability distribution of the wind power plant cluster The output range and probability of occurrence are obtained, and a series of output scenario sets are generated by using the method of replacing the interval with the center line, so as to realize the accurate quantification of the flexibility demand faced by the power generation plan.
- This method can fully consider the intermittence and volatility of the daily sequential power generation output of wind power plants and photovoltaic power plants, rely on the high-quality regulation of hydropower plants, meet the needs of clean energy consumption under different new energy access ratios, and dynamically respond to differentiated conditions. Flexibility needs to reduce curtailment of wind and photovoltaic power, and improve the clean energy consumption level of the whole system.
- Fig. 1 is the overall solution frame diagram of the method of the present invention
- Figure 2 is a schematic diagram of flexibility requirements
- Figure 3 is a chart showing the variation of the expectation and probability of insufficiency of flexibility with the proportion of new energy installed capacity
- Figure 4 is a graph showing the change law of the expectation of lack of flexibility and the probability of lack of flexibility with the proportional coefficient of wind and rain;
- Figure 5 is a typical daily load balance diagram during dry season.
- the output of the wind power plant cluster is converted into a series of continuous output intervals.
- the specific steps are as follows:
- Step1 According to the output probability distribution function of the wind power plant cluster in each period, the output of the power station cluster corresponding to the ⁇ quantile point in each period is obtained:
- the power station cluster output is at The following probability is ⁇ m , at The following probability is ⁇ m+1 , so the cluster output is at and The probability between is expressed as ⁇ m+1 - ⁇ m ; according to the above method, the cluster output intervals with different probabilities are obtained, as follows:
- the demand for flexibility reduction at this moment can be expressed as formula (14), and the demand for flexibility increase is 0 at this time; on the contrary, if the output of the scenario is lower than the planned output, the demand for flexibility up-regulation at this moment can be expressed as formula (15), and the demand for flexibility down-regulation is 0, as shown in Figure 2.
- the flexibility of the power system is evaluated by using the expectations of flexibility up-regulation and flexibility down-regulation.
- Insufficient flexibility upward adjustment expectation refers to the expectation of the difference between the flexibility upward adjustment demand and the flexibility upward adjustment ability caused by insufficient upward adjustment ability at time t, and the calculation formula is as follows:
- Insufficient flexibility down-regulation expectation refers to the expectation of the difference between the flexibility down-regulation demand and the flexibility down-regulation capacity caused by the lack of down-regulation capacity at time t, and the calculation formula is as follows:
- V n,t+1 and V n,t represent the storage capacity of the nth hydropower station at time t+1 and t respectively, m 3 ;
- QI n,t represent the inflow flow of the nth hydropower station at time period t, m 3 /s;
- QU n,t represents the discharge flow of the nth hydropower station in the period t, m 3 /s;
- ⁇ t represents the hours of the period t;
- QD n,t represents the power generation flow of the nth hydropower station in the period t , m 3 /s;
- QS n,t represents the abandoned water flow of the nth hydropower station in period t, m 3 /s.
- Z n,1 and Z n,T+1 respectively represent the water level at the beginning and end of the dispatching period of the nth hydropower station, m; Respectively represent the water level at the beginning and end of the dispatching period of the given nth hydropower station, m.
- Z n,t represents the water level of the nth hydropower station at time t, m; Respectively represent the lower limit and upper limit of the water level of the nth hydropower station at time t, m.
- QD n,t represents the power generation flow of the nth hydropower station in the time period t, m 3 /s; Respectively represent the minimum power generation flow and maximum power generation flow of the nth hydropower station in time period t, m 3 /s.
- n,t represents the power generation flow of the nth hydropower station in the time period t, m 3 /s; Respectively represent the lower limit and upper limit of the outflow flow of the nth hydropower station in the period t, m 3 /s.
- N n,t represents the output of the nth hydropower station in time period t, MW; Respectively represent the output lower limit and output upper limit of the nth hydropower station in period t, MW.
- N n,t+1 and N n,t represent the output of the nth hydropower station in time period t+1 and time period t, MW respectively; Indicates the climbing capacity of the nth hydropower station, MW.
- R max and R min represent the maximum value and minimum value of residual load respectively, MW; ⁇ R represents the peak-valley difference control demand of residual load, MW; R t represents the residual load of time period t, MW; PL t represents the system Total load, MW.
- the above model is solved by mixed integer linear programming, and the output process of 96 points before each power station is obtained.
- Yunnan is an area rich in clean energy in my country.
- the total amount of clean energy that can be developed is about 200 million kW, of which water energy resource reserves are 97.95 million kW, ranking second in the country, wind energy resources are 123 million kW, and solar energy resources are 2.14 ⁇ 10MJ/a.
- Yunnan's full-caliber installed capacity of hydropower, wind power, and photovoltaic power was 75.56 million kW, 8.95 million kW, and 3.51 million kW, accounting for 88% of the total installed capacity of the entire network, and the proportion of power generation exceeded 90%.
- the data used to construct the probability distribution function of the wind power plant cluster in the present invention are the historical measured output of the month to which a typical day belongs, and the input parameters of the model come from the actual parameters of the power plant.
- the present invention respectively selects new energy installed capacity ratios of 10% to 70% for comparative analysis. Introduce the index of electricity abandonment rate (abandoned electricity/total power generation) to represent the utilization of new energy.
- the calculation formula is as follows:
- EB t represents the actual curtailed wind and photovoltaic power generated due to insufficient flexibility and adjustment capacity during the t period, kWh.
- E t represents the total power generation of the wind and solar power plant during the t period, kWh.
- Table 1 shows the flexibility indicators under different proportions of new energy installed capacity. It can be seen that when the proportion of new energy installed capacity is less than 20%, the flexibility demand can be met; when the proportion of new energy installed capacity reaches 30%, the expectation of insufficient flexibility adjustment is 26.1MW, and the probability of insufficient flexibility adjustment is 4.22% , the adjustment flexibility gap is relatively small. When the proportion of installed capacity of new energy reaches more than 30%, due to the limited flexibility and adjustment ability of hydropower, the expectation and probability of insufficient flexibility adjustment will continue to increase with the increase of the proportion of installed capacity of new energy. In the typical daily verification, the power curtailment rate also increases. Although the proportion of power curtailment is not large, it is very considerable for the huge power generation.
- Figure 3 shows the change trend of the expectation and probability of insufficient flexibility under different proportions of new energy installed capacity. It can be seen that after the proportion of new energy installed capacity exceeds 20%, the probability of insufficient system flexibility increases roughly linearly with the proportion of new energy installed capacity. Insufficient flexibility is expected to show a quadratic growth with the proportion of new energy installed capacity.
- the wind and solar limit capacity that the power system can accommodate is about 30% of the total installed capacity of the system. If it is higher than this ratio, the system flexibility and adjustment capability will be seriously insufficient, threatening the safe and stable operation of the power system, and resulting in a large amount of curtailed wind and light. This result can provide decision-making support for the future planning of the installed capacity of new energy power stations such as wind and solar in the power grid.
- P w , P s respectively represent the installed capacity of the wind power station and the photovoltaic power station.
- Table 2 shows the expectations and probabilities of insufficient flexibility regulation under different wind-wind ratio coefficients. It can be seen that when the wind-wind ratio coefficient is between 0.5-0.6, the system has the least flexibility regulation expectations.
- Fig. 4 shows the change law of the expectation of insufficiency of flexibility and the probability of insufficiency of flexibility with the wind-solar scale coefficient. The reason is that when the installed capacity of photovoltaic power generation accounts for a large proportion, it will have a greater impact on the shape of the load curve, which will increase the peak-to-valley difference of the system load, as shown in Figure 5.
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Abstract
本发明属于电力系统调度领域,提供了一种风光水多能互补系统灵活性需求量化及协调优化方法,首先构建考虑风光出力不确定性的灵活性需求量化方法,采用分位点对风光出力区间进行划分,生成出力场景集,进而计算各场景下的灵活性需求;以灵活性需求量化指标为基础,构建考虑系统灵活性不足期望最小的水风光互补优化调度模型,实现水风光互补优化计算。依托云南电网实际风光水互补系统,针对不同新能源接入比例进行了模型验证,结果表明本发明方法能够给出不同条件下的多类型电源互补运行调度方案,有效满足系统的灵活性需求,减少弃电量,提高清洁能源的消纳水平。
Description
本发明涉及电力系统调度领域,特别涉及一种风光水多能互补系统灵活性需求量化及协调优化方法。
“双碳”目标下,风、光等间歇性可再生能源并网规模将急剧增大,预计2030年、2050年新能源装机比重将分别达到38%、70%,如此大规模间歇性电源势必导致电力系统短期运行的调节灵活性需求也随之大幅增加,如何量化不确定性风光发电面临的灵活性需求、如何开展风光与可调节电源短期互补运行,已成为建设以新能源为主新型电力系统的重大挑战,事关我国电力系统安全、稳定和清洁能源消纳。
对于风光电源规模较小的系统,采用备用容量预留方式可以有效应对功率、负荷等不确定性。然而,随着系统中风光比重不断加大,受其发电出力的不确定性与大幅波动影响,以往的备用容量方法可能会使系统成本显著增加,核心问题在于如何准确量化消纳不确定性风光发电的灵活性需求。目前已有成果开展了这方面研究,提出了一些评价指标,总体可分为三类:第一类是评价资源灵活性供给能力的指标,包括爬坡能力、最短启停时间、开机时间、响应时间、最小稳定出力等,主要用于比较不同资源的灵活性调节能力大小,常作为调度模型的输入参数;第二类是评价系统灵活性需求的指标,包括净负荷爬坡率、爬坡加速度等,主要用于分析负荷曲线的特征,量化灵活性需求;第三类是评价系统灵活性供需关系的指标,包括灵活性不足概率及期望,也可细分为上调灵活性不足概率及期望、下调灵活性不足概率及期望,主要用于评价系统整体的灵活性水平。
总体来看,目前对于灵活性需求的量化大多侧重确定性层面,但由于风光出力的不确定性,灵活性需求实际也是时空动态变化的,因此在多能互补系统调度运行考虑调节灵活性随机特性也是非常重要的。另一方面,对于风光等间歇性电源占比较大的互补系统,当灵活性调节能力不足时,如何在时空多维尺度上合理配置灵活性调节能力,对于维持系统安稳性、提升新能源发电消纳能力也是至关重要的。
针对上述问题,本发明依托国家自然科学基金(52079014)和云南电网实际工程,提出一种以水风光为主的高比例可再生能源电力系统灵活性评价及短期互补调度方法。构建了考虑风光出力不确定性的灵活性需求量化方法,采用分位点对风光出力区间进行划分,生成出力场景集,进而计算各场景下的灵活性需求;构建了考虑系统灵活性不足期望最小的水风光互补优化调度模型,通过风光水多种类型电源的互补协调运行,有效满足了大规模间歇性新能源并网消纳的灵活性需求。
发明内容
本发明要解决的技术问题是风光水多能互补系统灵活性需求量化及协调优化问题,目的是促进风光等清洁能源大规模消纳,减少不合理弃风、弃光。
本发明技术方案:
一种风光水多能互补系统灵活性需求量化及协调优化方法,包括如下步骤:
(1)以风光电站集群的出力分布函数为基准,将风光电站集群的出力转换为一系列连续的出力区间,具体步骤如下:
Step1.根据风光电站集群各时段的出力概率分布函数,得到α分位点对应的电站集群各时段的出力:
(2)生成风光电站集群出力场景集,具体步骤如下:
Step2.取α
1=0,α
2=0.01,α
3=0.02,…,α
M=1,由此得到一系列电站集群出力场景及其对应的概率:
(3)风光电站间歇性发电的灵活性需求;对于集群出力场景m,若该场景某时段实际出力大于计划出力,则该时段灵活性下调需求表示为式(6),此时灵活性上调需求为0;反之,若该场景某时段出力小于计划出力,则该时段灵活性上调需求表示为式(5),灵活性下调需求则为0;
(4)计算间歇性风光发电的灵活性评价指标;本方法提出两个指标:灵活性上调不足期望与灵活性下调不足期望;灵活性上调不足期望表示任一t时刻系统可提供的灵活性上调能力不能满足风光发电消纳需求的概率,见式(7);灵活性下调不足期望表示任一t时刻系统可提供的灵活性下调能力不能满足风光发电消纳需求的概率,见式(8):
式中:
表示集群i在t时段的灵活性上调不足期望,MW;Pr
i,m表示场景m的概率;
表示灵活性资源在t时段为集群i提供的灵活性上调能力,MW;
表示集群i在t时段的灵活性下调不足期望,MW;Pr
i,m表示场景m的概率;
表示灵活性资源在t时段为集群i提供的灵活性下调能力,MW;
(5)构建风光水电站群短期互补灵活性协调模型,采用灵活性不足期望最小目标,目的是希望通过水风光多种电源互补运行,尽可能减小消纳大规模风光发电带来的系统灵活性不足问题,见下式:
(6)采用混合整数线性规划求解上述模型,获得各类电站的发电出力过程。
目前对于灵活性需求的量化大多侧重确定性层面,但由于风光出力的不确定性,灵活性需求实际也是时空动态变化的,因此在多能互补系统调度运行考虑调节灵活性随机特性也是非常重要的。另一方面,对于风光等间歇性电源占比较大的互补系统,当灵活性调节能力不足时,如何在时空多维尺度上合理配置灵活性调节能力,对于维持系统安稳性、提升新能源发电消纳能力也是至关重要的
本发明成果有如下有益效果:与目前多数侧重确定性层面灵活性需求的量化方法相比,本发明考虑风光电站集群的不确定性出力特性,根据风光电站集群出力概率分布,利用分位点法得到出力区间及发生概率,采用中心线代替区间的方法生成系列出力场景集,从而实现了面临发电计划灵活性需求的准确量化。这种方式能够充分考虑风电站、光伏电站日内时序发电出力的间歇性和波动性,依靠水电站的优质调节作用,满足不同新能源接入比例条件下的清洁能源消纳需要,动态响应差异化的灵活性需求,减少弃风、弃光电量,提高全系统的清洁能源消纳水平。
图1是本发明方法总体求解框架图;
图2是灵活性需求示意图;
图3是灵活性不足期望与灵活性不足概率随新能源装机占比变化规律图;
图4是灵活性不足期望与灵活性不足概率随风光比例系数变化规律图;
图5是枯期典型日日负荷平衡图。
下面结合附图和技术方案,进一步说明本发明的具体实施方式。
以风光电站集群的出力分布函数为基准,将风光电站集群的出力转换为一系列连续的出力区间,具体步骤如下:
Step1.根据风光电站集群各时段的出力概率分布函数,得到α分位点对应的电站集群各时段的出力:
拟以步长0.01设置分位点,即取α
1=0,α
2=0.01,α
3=0.02,…,α
M=1。由此可得到一系列出力场景及其对应的概率:
以场景m为例,若该场景某时刻出力大于计划出力,则该时刻灵活性下调需求可表示为式(14),此时灵活性上调需求为0;反之,若该场景某时刻出力小于计划出力,则该时刻灵活性上调需求可表示为式(15),灵活性下调需求则为0,见图2。
考虑到间歇性电源并网的灵活性需求快速增大与系统可调节资源有限的矛盾,采用灵活性上调不足期望、灵活性下调不足期望对电力系统的灵活性进行评价。
灵活性上调不足期望是指t时刻因上调能力不足而导致的灵活性上调需求与灵活性上调能力之间差值的期望,计算公式如下:
灵活性下调不足期望是指t时刻因下调能力不足而导致的灵活性下调需求与灵活性下调能力之间差值的期望,计算公式如下:
灵活性不足期望越小,意味着间歇性新能源并网带来的安稳运行影响越小,因此构建风光水电站群短期互补灵活性协调模型,采用灵活性不足期望最小目标,目的是希望通过水风光多种电源互补运行,尽可能减小消纳大规模风光发电带来的系统灵活性不足问题,见下式:
约束条件如下:
灵活性供需关系
灵活性调节能力
水量平衡约束
式中:V
n,t+1、V
n,t分别表示第n个水电站在t+1和t时刻的库容,m
3;QI
n,t表示第n个水电站在时段t的入库流量,m
3/s;QU
n,t表示第n个水电站在时段t的出库流量,m
3/s;Δt表示t时段的小时数;QD
n,t表示第n个水电站在时段t的发电流量,m
3/s;QS
n,t表示第n个水电站在时段t的弃水流量,m
3/s。
始末水位约束
库水位约束
发电流量约束
出库流量约束
水电站出力约束
水电站出力爬坡约束
调峰控制需求
式中:R
max、R
min分别表示剩余负荷的最大值和最小值,MW;ΔR表示剩余负荷峰谷差控制需求,MW;R
t表示时段t的剩余负荷,MW;PL
t表示t时段系统总负荷,MW。
采用混合整数线性规划求解上述模型,获得各电站的日前96点出力过程。
以云南电网实际工程为背景,对本发明模型方法进行分析验证。云南是我国清洁能源富集地区,清洁能源可开发总量约2亿kW,其中水能资源储量9795万kW,居全国第二位,风能资源1.23亿kW,太阳能资源2.14×10MJ/a,居全国第三位。截止2020年底,云南全口径水电、风电、光伏电装机容量分别为7556万kW、895万kW、351万kW,合计占全网总装机比重高达88%,发电量比重更是超过90%,是清洁能源居绝对主导地位的省级电网,清洁能源消纳问题突出且极具代表性。本发明用于构建风光电站集群概率分布函数的数据分别为典型日所属月份的历史实测出力,模型输入参数来源于电站实际参数。
由于用电需求不断增大以及国家政策的号召,未来将继续大力开发风光能源,间歇性能源接入电网的比例将进一步增大。因此分析不同新能源装机占比情况下的灵活性调节关系是十分必要的。为此,本发明分别选取新能源装机占比为10%至70%进行对比分析。引入弃电率(弃电量/总发电量)指标,来表示新能源的利用情况,计算公式如下:
式中:EB
t表示由于t时段由于灵活性调节能力不足而产生的实际弃风、弃光电量,kWh。E
t表示t时段风光电站总发电量,kWh。
表1为不同新能源装机占比情况下的灵活性指标。可以看出,当新能源装机占比小于20%时,灵活性需求能够得到满足;当新能源装机占比达到30%时,灵活性调节不足期望为26.1MW,灵活性调节不足概率为4.22%,调节灵活性缺口相对较小。当新能源装机占比达到30%以上,由于水电的灵活性调节能力有限,灵活性调节不足期望及概率随着新能源装机占比地增加不断增大。而在典型日验证中,弃电率也随之增大,虽然弃电比例不大,但对于巨大的发电量来说,弃电量十分可观。图3为不同新能源装机占比下的灵活性不足期望和概 率变化趋势,可以看出,新能源装机占比超过20%后,系统灵活性不足概率随新能源装机占比大致呈线性增长,灵活性不足期望随新能源装机占比大致呈二次增长。
表1不同新能源装机占比下的计算结果
综合上述计算与分析结果,当前灵活性水平下,该电力系统能够接纳的风光极限容量约为系统总装机容量的30%。若高于此比例,则系统灵活性调节能力将会严重不足,威胁电力系统的安全稳定运行,且产生大量弃风、弃光。这一结果可以为电网未来规划风光等新能源电站装机容量提供决策支持。
由于风光出力的互补特性,不同比例的风光发电出力特性区别较大,因此对电力系统的影响也不尽相同,研究不同风光装机比例对电力系统灵活性的影响。本部分重点研究风光发电装机比例对结果的影响,为便于分析,将不同装机比例的风光电站汇聚为一个集群进行研究,风光装机比例设置为30%。为方便阐述,定义风光比例系数λ,计算公式如下:
式中:P
w、P
s分别表示风电站和光电站的装机容量。
表2为不同风光比例系数下的灵活性调节不足期望及概率,可以看出,风光比例系数在0.5-0.6之间时,系统的灵活性调节不足期望最小。图4给出了灵活性不足期望与灵活性不足概率随风光比例系数变化规律,可以看出,灵活性不足期望与灵活性不足概率随风光比例系数变化规律基本一致,即随风光比例系数的变大先减后增,原因是当光伏发电装机占比较大时,对负荷曲线形状影响较大,会增大系统负荷的峰谷差,如图5所示。为了达到调峰控制需求,水电站会在负荷低谷以较小的出力运行,因此灵活性下调能力缺额较大;当风电装机占比较大时,由于风电的不确定性更大,灵活性需求更大,同样会导致系统的灵活性调节能力不足。上说结果表明,不同风光发电装机比例对电力系统的调度影响是不同的,在实际应用时,需要结合具体的工程确定适合的新能源发电装机比例。
表2不同风光比例系数下的灵活性调节不足期望和概率
Claims (1)
- 一种风光水多能互补系统灵活性需求量化及协调优化方法,其特征在于,包括如下步骤:(1)以风光电站集群的出力分布函数为基准,将风光电站集群的出力转换为一系列连续的出力区间,具体步骤如下:Step1.根据风光电站集群各时段的出力概率分布函数,得到α分位点对应的电站集群各时段的出力:(2)生成风光电站集群出力场景集,具体步骤如下:Step2.取α 1=0,α 2=0.01,α 3=0.02,…,α M=1,由此得到一系列电站集群出力场景及其对应的概率:(3)风光电站间歇性发电的灵活性需求;对于集群出力场景m,若该场景某时段实际出力大于计划出力,则该时段灵活性下调需求表示为式(6),此时灵活性上调需求为0;反之,若该场景某时段出力小于计划出力,则该时段灵活性上调需求表示为式(5),灵活性下调需求则为0;(4)计算间歇性风光发电的灵活性评价指标;本方法提出两个指标:灵活性上调不足期望与灵活性下调不足期望;灵活性上调不足期望表示任一t时刻系统可提供的灵活性上调能力不能满足风光发电消纳需求的概率,见式(7);灵活性下调不足期望表示任一t时刻系统可提供的灵活性下调能力不能满足风光发电消纳需求的概率,见式(8):式中: 表示集群i在t时段的灵活性上调不足期望,MW;Pr i,m表示场景m的概率; 表示灵活性资源在t时段为集群i提供的灵活性上调能力,MW; 表示集群i在t时段的灵活性下调不足期望,MW;Pr i,m表示场景m的概率; 表示灵活性资源在t时段为集群i提供的灵活性下调能力,MW;(5)构建风光水电站群短期互补灵活性协调模型,采用灵活性不足期望最小目标,目的是希望通过水风光多种电源互补运行,尽可能减小消纳大规模风光发电带来的系统灵活性不足问题,见下式:(6)采用混合整数线性规划求解上述模型,获得各类电站的发电出力过程。
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