WO2015184662A1 - 一种电力系统柔性约束优化方法 - Google Patents

一种电力系统柔性约束优化方法 Download PDF

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WO2015184662A1
WO2015184662A1 PCT/CN2014/081160 CN2014081160W WO2015184662A1 WO 2015184662 A1 WO2015184662 A1 WO 2015184662A1 CN 2014081160 W CN2014081160 W CN 2014081160W WO 2015184662 A1 WO2015184662 A1 WO 2015184662A1
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power
node
flexible
load
optimization
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French (fr)
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王承民
孙伟卿
衣涛
李宏仲
刘涌
段建民
肖定垚
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上海交通大学
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Priority to US14/470,504 priority Critical patent/US9720431B2/en
Publication of WO2015184662A1 publication Critical patent/WO2015184662A1/zh

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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05FSYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES
    • G05F1/00Automatic systems in which deviations of an electric quantity from one or more predetermined values are detected at the output of the system and fed back to a device within the system to restore the detected quantity to its predetermined value or values, i.e. retroactive systems
    • G05F1/66Regulating electric power
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/041Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a variable is automatically adjusted to optimise the performance
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00Systems involving the use of models or simulators of said systems
    • G05B17/02Systems involving the use of models or simulators of said systems electric
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05FSYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES
    • G05F1/00Automatic systems in which deviations of an electric quantity from one or more predetermined values are detected at the output of the system and fed back to a device within the system to restore the detected quantity to its predetermined value or values, i.e. retroactive systems
    • G05F1/70Regulating power factor; Regulating reactive current or power
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations

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  • the invention relates to a power system constraint planning method, in particular to a power system flexible constraint optimization method.
  • the object of the present invention is to provide a power system flexible constraint optimization method with the ultimate goal of economy, safety and reliability as the ultimate goal in order to overcome the shortcomings of the prior art.
  • a flexible constraint optimization method for a power system comprising:
  • Step S2 Select a multi-dimensional flexible optimization model or a power generation cost flexible optimization model according to the actual situation of the power system and the actual purpose of the optimization;
  • Step S3 determining power system operating conditions, including grid structure and generator voltage and power;
  • Step S4 performing power flow calculation based on operating conditions of the power system;
  • Step S5 If the power flow calculation is successful, the corresponding optimization calculation is performed according to the multi-dimensional flexible or power generation cost flexible model selected in step S2, and the comprehensive flexible optimization result or the optimal power generation cost is obtained. If the power flow calculation fails, according to the optimal load shedding model Perform the corresponding optimization calculation to obtain the optimal load shedding.
  • the multidimensional flexible optimization model is:
  • N is the total number of system nodes
  • L is the total number of system lines
  • PG. k is the active power and reactive power of node k, respectively
  • P Lk and G k are the active load and reactive load of node k
  • V k the voltage of node kj, G k]
  • P GI the active load and reactive load deviation of node k
  • P G T 1 are the active power of generator i respectively.
  • AP G T ⁇ ⁇ 3 ⁇ 4 ⁇ is the maximum allowable limit value
  • Q a , Q ⁇ 11 are the reactive power of generator i and its upper and lower limits
  • ⁇ ⁇ 4 ⁇ 7 is 2 ⁇ ⁇ maximum allowable limit value
  • v k , vv k mm are the voltage of node k and its upper and lower limits, respectively
  • ⁇ ⁇ 1 is the maximum allowable limit value of ⁇ x and in , respectively
  • S is line 1
  • AS is the maximum allowable limit.
  • the flexible optimization model of the power generation cost is:
  • N is the total number of system nodes
  • L is the total number of system lines
  • PG. k is the active power and reactive power of node k respectively
  • P LK and G k are the active load and reactive load of node k, respectively
  • v k ⁇ ′′ is the voltage of node kj, and respectively, between nodes k and j
  • the conductance, susceptance and phase angle difference, p Gl and P G T 1 are the active power of generator i and its upper and lower limits, respectively.
  • Q Gl and Q ⁇ 2 are the reactive power of generator i and
  • the lower limit, v k , vr are the voltage of node k and its upper and lower limits, respectively, and s is the power flow value and limit of line 1, respectively.
  • the optimal load shedding model is:
  • N is the total number of system nodes
  • L is the total number of system lines
  • G. k is the active power and reactive power of node k, respectively
  • P Lk and G k are the active load and reactive load of node k, v k , the voltage of node k, j, G k , respectively node k and j
  • the conductance, susceptance and phase angle difference between ⁇ and ⁇ £1 ⁇ are the active load and reactive load deviation of node k, respectively.
  • P Gl , , ⁇ 1 are the active power of generator i and its upper and lower limits respectively.
  • Q is the active power of generator i and its upper and lower limits respectively.
  • V k and vr are the voltage of node k and its upper and lower limits, respectively
  • S sr 1 is the power flow value and limit of line 1, respectively.
  • the present invention has the following advantages:
  • the present invention is insufficient for the rigid constraint boundary of the existing power system operation optimization method, and proposes a power system flexible optimization method, which is a supplement and improvement of the existing power system operation optimization method.
  • the invention is to establish a multi-dimensional flexible optimization model for intelligent grid optimal scheduling, a power generation cost flexible optimization model and an optimal load shedding model, and utilize a flexible analysis method to expand a power system safety constraint boundary, effectively improve the limitation of the rigid constraint condition, and find power.
  • the optimal operating point of the system operation economy, safety and reliability during the operation of the system is exchanged for safety and reliability at the lowest possible economic cost.
  • the present invention considers that in the flexible optimization process, when the power flow calculation fails, the optimal solution of the load shedding amount is solved, and the safety and reliability of the optimization process are improved.
  • the invention does not have the original optimal power flow problem When solvable, the optimal load shedding model can be utilized. Regardless of the economics of system operation and the flexibility of inequality constraints, load shedding measures are taken to restore the system to the feasible domain with minimal load loss.
  • a flexible constraint optimization method for a power system includes:
  • Step S1 The traditional power system economic dispatch problem usually takes the total power generation cost of the system as the standard for measuring the economic performance of the system, and the sum of the quadratic functions of the active output of each generator set in the system represents the total power generation cost of the power system/',
  • the invention is based on the power generation cost of the flexible expression, and regards the operation safety and reliability of the system as an intangible power resource, and the overall goal of economic, security and reliability is integrated.
  • Step S2 Select a multi-dimensional flexible optimization model or a power generation cost flexible optimization model according to the actual situation of the power system and the actual purpose of the optimization;
  • the multi-dimensional flexible optimization model considers the flexibility of the operating cost of the power system, the flexibility of the node voltage, the flexibility of the generator output and the flexibility of the transmission line capacity. It is a comprehensive optimization of the economic, safety and reliability of the system. The form is as follows: 1 1
  • the load flexibility index of node k is the flexibility index of generator i output, which is the voltage flexibility index of node k, which is the power flow flexibility index of line 1
  • N is the total number of system nodes
  • L is the total number of system lines, PG.
  • k is the active power and reactive power of node k, respectively
  • P LK and G k are the active load and reactive load of node k
  • V k the voltage of node kj, and G K , respectively, between nodes k and j
  • the conductance, susceptance and phase angle difference, ⁇ ⁇ £1 ⁇ are the active load and reactive load deviation of node k, respectively
  • P GL , P G T ⁇ 1 are the active power of generator i and its upper and lower limits, respectively.
  • is the maximum allowable limit value, Q GI ⁇ , respectively, the reactive power of generator i and its upper and lower limits
  • ⁇ ⁇ 42 ⁇ is 2
  • the maximum allowable limit value of ⁇ 1 , v k , v vr is the voltage of node k and its upper and lower limits respectively
  • ⁇ ⁇ 1 is the maximum allowable limit value of ⁇ x and in , respectively
  • S is the power flow value and limit of line 1, respectively
  • AS is the maximum allowable limit value.
  • is the generator output flexibility index, which is the node voltage flexibility
  • the index, ⁇ is the line current flexibility index.
  • the value of the total power generation cost of the system is related to the value of the system constraint boundary and is mutually constrained. That is, the larger the system constraint domain, the system is running. The better the economy is. However, when the expansion of the system constraint domain can not significantly improve the economics of system operation, the system operation constraint domain is reduced to retain more system operation safety margin. At the same time, the value of each flexible indicator reflects the system. Running status:
  • the above model is equivalent to the traditional power system optimal power flow model.
  • the traditional power system optimal power flow model is a special case of one-dimensional flexible optimization problem. If the optimal power flow model of the traditional power system is solvable, the multi-dimensional flexible optimization model can be solved, and the power generation cost flexible optimization model can be solved.
  • the optimal load shedding model is used when the original optimal power flow problem is unsolvable, and the system is restored to the feasible domain.
  • the form is as follows:
  • Step S3 determining the operating conditions of the power system, including the grid structure and the generator voltage and power;
  • Step S4 based on the operating conditions of the power system, calculating the active power of each bus line by the power flow calculation, Reactive power, node voltage and conductance, susceptance, phase angle difference between nodes;
  • Step S5 If the power flow calculation is successful, perform a corresponding optimization calculation according to the multi-dimensional flexible or power generation cost flexible model selected in step S2, and obtain a comprehensive flexible optimization result or an optimal power generation cost, that is, an optimal power system total power generation cost/' And optimal power consumption, reactive power, flexibility index, etc., if the power flow calculation fails, according to the optimal load shedding model, the corresponding optimization calculation is performed to obtain the optimal load shedding amount and the active power at this time.
  • the reactive power control such as reactive power and flexibility index, the specific steps of multi-dimensional flexibility, power generation cost flexibility or optimal load shedding optimization calculation are:
  • the invention introduces the concept of power system flexibility, and proposes multi-dimensional flexibility, power generation cost flexibility and optimal load shedding optimization model under the premise of ensuring system operation safety and reliability, reasonably expanding the system's constraint boundary and maximizing system operation. At the same time, based on the optimization results, we can find out the bottlenecks affecting the economic performance of the system and provide reference for the upgrading of the power grid.

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Abstract

一种电力系统柔性约束优化方法,包括:步骤S1:以系统中各发电机组有功出力的二次函数之和表示电力系统总发电成本,构造目标函数;步骤S2:根据电力系统的实际情况以及优化的实际目的选择多维柔性优化模型或发电成本柔性优化模型;步骤S3:确定电力系统运行条件;步骤S4:潮流计算;步骤S5:若潮流计算成功,根据步骤S2选择的模型进行相应的优化计算,得到综合柔性优化结果或最优发电成本,若潮流计算失败,根据最优切负荷模型进行相应的优化计算,得到最优切负荷量。与现有技术相比,可以为电力系统运行提供最佳运行点,兼顾系统运行经济性、安全性和可靠性。

Description

一种电力系统柔性约束优化方法
技术领域
本发明涉及一种电力系统约束规划方法, 尤其是涉及一种电力系统柔性约 束优化方法。 背景技术
现代电力系统优化涉及多种学科领域、研究内容广泛, 电力系统潮流分析、 数学优化理论、 运筹学以及系统工程等都是研究中必不可少的学科, 使电力系 统优化问题成为一个复杂而庞大的问题。
随着智能电网的发展, 现代电力系统正向大系统、 超高压、 远距离、 大容 量发展, 大规模可再生能源的接入使电力网架结构和运行方式更加复杂, 系统 运行的各种约束条件日益强化, 对约束条件的要求更加精细与苛刻。 传统的电 力系统优化分析一般通过对系统参数设置刚性约束来保证系统运行的安全性和 可靠性, 但刚性约束边界的整定值缺乏灵活性且往往趋于保守。
在这种情况下, 电力系统优化问题出现了很多新的特点和要求, 采用传统 的优化模型以及常规优化方法很难兼顾系统运行的经济性、 安全性和可靠性, 难以找到最佳运行点。 如文献 《考虑运行可靠性的含风电电力系统优化调度》
(电工技术学报, 2013, 28 (5 ): 58-65 )、 《兼顾安全与经济的电力系统优化调 度协调理论》 (电力系统自动化, 2007, 31 (6): 28-33 ) 均未能完全考虑经济 性、 安全性和可靠性的综合趋优。 发明内容
本发明的目的就是为了克服上述现有技术存在的缺陷而提供一种以经济 性、 安全性和可靠性综合趋优为最终目的电力系统柔性约束优化方法。
本发明的目的可以通过以下技术方案来实现:
一种电力系统柔性约束优化方法, 包括:
歩骤 S1 :以系统中各发电机组有功出力的二次函数之和表示电力系统总发 电成本尸, 其柔性形式如下:
Figure imgf000004_0001
其中, Ng为系统发电机总数, i = V∑"."Ng , a,., b,. c,.为发电机组 的发电 成本系数; Rei为发电机 的有功功率; /。表示系统总发电成本的最小期望值; Af 表示系统总发电成本可接受的最大增加量; 为系统发电成本柔性指数, 其取 值范围为 [0,1];
歩骤 S2:根据电力系统的实际情况以及优化的实际目的选择多维柔性优化 模型或发电成本柔性优化模型;
歩骤 S3: 确定电力系统运行条件, 包括电网结构和发电机电压、 功率; 歩骤 S4: 基于电力系统运行条件进行潮流计算;
歩骤 S5: 若潮流计算成功, 根据歩骤 S2选择的多维柔性或发电成本柔性 模型进行对应的优化计算, 得到综合柔性优化结果或最优发电成本, 若潮流计 算失败, 根据最优切负荷模型进行对应的优化计算, 得到最优切负荷量。
所述的多维柔性优化模型为:
1 1
in f(S) = δ) +丄 -—∑ -丄 -丄†
Figure imgf000004_0002
PGk - Vk∑V} (Gk] cos 0k] + Bk] sin 0k] ) = PLk- SLkAPLk QGk - Vk∑V} (Gk] sin 0k] - Bk] cos 0k] ) = QLk- SLkAQLk
+ ≤P ≤PG皿 - ^Δ^Γ ( 2 ) vr + ^vr≤vk≤ vr - svkAvr
S,≤S,max_ ,AS, i = 1,2,...,N ;k,j = 1,2,...,N;/ = 1,2,..., L
其中, 为节点 k的负荷柔性指数, 为发电机 i出力的柔性指数, 为 节点 k的电压柔性指数, 为线路 1的潮流柔性指数, N为系统节点总数, L为 系统线路总数, P G。k分别为节点 k的有功功率和无功功率, PLk 、 Gk分别为 节点 k的有功负荷和无功负荷, Vk、 为节点 k j的电压, Gk]、 、 分别 为节点 k和 j之间的电导、电纳和相角差, Δ Δβ£1ί分别为节点 k的有功负荷、 无功负荷的偏差, PGI , PGT 1分别为发电机 i的有功功率及其上、下限, APGT^ Δ¾Γ为 、 的最大允许越限值, Qa、 Q β^11分别为发电机 i的无功功 率及其上、 下限, Δβ^χ 4^7为2^ β^Γ的最大允许越限值, vk、 v vk mm 分别为节点 k的电压及其上、 下限, Δν Δν 1分别为 \ xin的最大允许 越限值, S 分别为线路 1的潮流值和限值, AS 为 最大允许越限值。
所述的发电成本柔性优化模型为:
mmf(S) = δ s-t-∑ + b, Gi + ) = + sfAf
PGk ~Vk∑ V} (Gkj cos 0k] + Bk] sin 0k] ) = PLk
QGk ~Vk∑ V} (Gk] sin 0k] - Bk] cos 0k] ) = QLk
pmi .n〈 p Jek〈 max (3)
≤ QGi≤ QT
vk min≤vk≤ vk max
S,≤ S,
0≤^≤1
i = l,2,...,N ;k,j = l,2,...,N;l=l,2,...,L
其中, N为系统节点总数, L为系统线路总数, P G。k分别为节点 k 的 有功功率和无功功率, PLK、 Gk分别为节点 k的有功负荷和无功负荷, vk ν」为 节点 k j的电压, 、 、 分别为节点 k和 j之间的电导、 电纳和相角差, pGl、 PGT 1分别为发电机 i的有功功率及其上、 下限, QGl、 Q ^ 2 分别 为发电机 i的无功功率及其上、 下限, vk、 vr 分别为节点 k的电压及其 上、 下限, s 分别为线路 1的潮流值和限值。
所述的最优切负荷模型为:
Figure imgf000006_0001
s-t-PGk - Vk∑V} (Gk] cos 0k] + Bk] sin 0k] ) = PLk - SLkAPLk QGk - Vk∑VJ (Gkj sin 0k] - Bkj cos 0k] ) = QLk - SLkAQLk
pmin〈 p 〈 max (、4) '
≤ QGi≤ Q
vk min≤vk≤ vk max
S,≤ S,max
i = l, 2,..., N ; k, j = l, 2,..., N;l = l, 2,..., L
其中, 为节点 k的负荷柔性指数, N为系统节点总数, L为系统线路总 数, 、 G。k分别为节点 k的有功功率和无功功率, PLk 、 G k分别为节点 k的有 功负荷和无功负荷, vk、 为节点 k、 j的电压, Gk 、 分别为节点 k和 j之间的电导、 电纳和相角差, Δ 、 Δβ£1ί分别为节点 k的有功负荷、 无功负荷 的偏差, PGl 、 、 Γ1分别为发电机 i的有功功率及其上、 下限, Q 、
分别为发电机 i的无功功率及其上、 下限, Vk、 vr 分别为节点 k的 电压及其上、 下限, S sr1分别为线路 1的潮流值和限值。
所述的对应的优化计算的具体歩骤为:
501:根据对应的多维柔性、发电成本柔性或最优切负荷模型建立拉格朗日 目标函数;
502: 求其最优解的对应的库恩 -塔克条件;
503: 采用牛顿法求解求得模型的最优解。
与现有技术相比, 本发明具有以下优点:
1 )本发明对现有电力系统运行优化方法的刚性约束边界不足,提出电力系 统柔性优化方法, 是对现有电力系统运行优化方法的补充与完善。 本发明是通 过建立智能电网最优调度的多维柔性优化模型、 发电成本柔性优化模型和最优 切负荷模型, 利用柔性分析方法, 拓展电力系统安全约束边界, 有效改善刚性 约束条件的限制, 寻找电力系统运行过程中兼顾系统运行经济性、 安全性和可 靠性的最佳运行点, 以尽可能小的经济性代价, 换取安全性和可靠性的提升。
2 )本发明考虑了在柔性优化过程中, 潮流计算失败时, 进行切负荷量最优 解的求解, 提高了优化过程的安全性和可靠性。 本发明在原始最优潮流问题不 可解时, 可以利用最优切负荷模型, 不考虑系统运行的经济性和不等式约束的 柔性, 采取切负荷措施, 以最小的负荷损失代价, 使系统恢复到可行域内。 附图说明
图 1为本发明柔性约束优化方法的流程图。 具体实施方式
下面结合附图和具体实施例对本发明进行详细说明。
如图 1所示, 一种电力系统柔性约束优化方法, 包括:
歩骤 S1 :传统的电力系统经济调度问题通常以系统总发电成本作为衡量系 统运行经济性的标准, 以系统中各发电机组有功出力的二次函数之和表示电力 系统总发电成本 /', 其柔性形式如下:
Figure imgf000007_0001
其中, Ng为系统发电机总数, i = V∑"."Ng , a,. , b,.、 c,.为发电机组 的发电 成本系数; Rei为发电机 的有功功率; /。表示系统总发电成本的最小期望值; Af 表示系统总发电成本可接受的最大增加量; 为系统发电成本柔性指数, 其取 值范围为 [0,1]。
本发明由柔性化表述的发电成本出发, 将系统运行安全性和可靠性视为一 种无形的电力资源, 以经济性、 安全性和可靠性综合趋优为最终目的。
歩骤 S2:根据电力系统的实际情况以及优化的实际目的选择多维柔性优化 模型或发电成本柔性优化模型;
1 ) 多维柔性优化模型同时考虑电力系统的运行成本柔性、 节点电压柔性、 发电机出力柔性和输电线路容量柔性, 是对系统运行经济性、 安全性和可靠性 的综合优化, 其形式如下: 1 1
min f(S) = δ) +丄6 — L∑¾ -丄 丄
f N Ng t G N tr - v Lit ,
Figure imgf000008_0001
PGk - Vk∑V} (Gk] cos 0k] + Bk] sin 0k] ) = PLk- SLkAPLk QGk - Vk∑V} (Gk] sin 0k] - Bk] cos 0k] ) = QLk- SLkAQLk
+ SGiAPT≤PGi≤PGT~ SGiAP- ( 2 ) vr + ^vr≤vk≤ vr - svkAvr
S,≤S,max_ ,AS, i = 1,2,...,N ;k,j = 1,2,...,N;/ = 1,2,..., L 其中, 为节点 k的负荷柔性指数, 为发电机 i出力的柔性指数, 为 节点 k的电压柔性指数, 为线路 1的潮流柔性指数, N为系统节点总数, L为 系统线路总数, P G。k分别为节点 k的有功功率和无功功率, PLK、 Gk分别为 节点 k的有功负荷和无功负荷, Vk、 为节点 k j的电压, GK 、 分别 为节点 k和 j之间的电导、电纳和相角差, Δ Δβ£1ί分别为节点 k的有功负荷、 无功负荷的偏差, PGL , PGT Γ1分别为发电机 i的有功功率及其上、下限,
ΑΡ^Γ为 、 的最大允许越限值, QGI ΩοΓ、 分别为发电机 i的无功功 率及其上、 下限, Δβ χ 42^为2 、 β 1的最大允许越限值, vk、 v vr 分别为节点 k的电压及其上、 下限, Δν Δν 1分别为 \ xin的最大允许 越限值, S 分别为线路 1的潮流值和限值, AS 为 最大允许越限值。
实际问题中, 通常会对柔性优化模型作出简化, 其形式如下: mmf(S) = Sf 2-SG 2-S^-SF 2
(=1
PGk ~Vk∑ V} (Gk] cos 0k] + Bk] sin 6k] ) = PL
Figure imgf000009_0001
Vk mm+SvAVk mm≤Vk≤Vk
S,≤d AS,
0≤SfJSGJSVJSF≤l
i = \,2"..,N ;k,j = l,2,...,N;l=l,2,...,L 其中, ^是发电机出力柔性指数, 是节点电压柔性指数, ^是线路潮流 柔性指数。 上述多维柔性优化模型中, 系统总发电成本的值是与系统约束边界 的值联系在一起的, 并且相互制约。 也就是说, 系统约束域越大, 系统运行的 经济性就越好。 但是, 当系统约束域的扩大不能明显改善系统运行经济性时, 即缩小系统运行约束域以保留更多的系统运行安全裕度。 同时各个柔性指标的 值反映了系统的运行状态:
(1) 系统发电成本柔性指数 越小, 说明系统总发电成本越小, 经济性 越好;
(2) 负荷柔性指数 越小, 说明系统切负荷功率越小, 对电力用户的影 响也就越小;
( 3 )发电机出力柔性指数 SG、节点电压柔性指数 和线路潮流柔性指数 δΡ 越大, 说明系统运行安全裕度越大, 系统的安全性和可靠性也就越高;
2)发电成本柔性优化模型仅从系统发电成本柔性角度考虑,约束条件采用 刚性约束条件, 其形式如下: minf(S) = Sf
s-t-∑ + btPGi +c,.) = 0+ SfAf
PGk ~Vk∑ V} (Gk] cos 0k] + Bk] sin 0k] ) = PL
QGk ~Vk∑ V} (Gkj sin 6kj - Bkj cos 6k] ) = Qt
(3)
Figure imgf000010_0001
< V < vma
S,≤ S,
0≤^≤1
i = l,2,...,N ;k,j = l,2,...,N;l=l,2,...,L
上述模型与传统电力系统最优潮流模型等价, 传统电力系统最优潮流模型 是一维柔性优化问题的特例。 若传统电力系统最优潮流模型可解, 则多维柔性 优化模型可解, 进而发电成本柔性优化模型可解。
3)最优切负荷模型在原始最优潮流问题不可解时利用,使系统恢复到可行 域内, 其形式如下:
N k=l
s-t-PGk - Vk∑V} (Gk] cos 0k] + Bk] sin 0k] ) = PLk- SLkAPLk
QGk - Vk∑V} (Gk] sin 0k] - Bk] cos 0k] ) = QLk- SLkAQ -LLk
Figure imgf000010_0002
max
vr ≤y ≤v
Figure imgf000010_0003
i=l,2,...,N ;k,j = l,2,...,N;l = l,2,...,L
在原始最优潮流问题不可解的情况下, 只考虑负荷的柔性, 不考虑系统运 行的经济性和不等式约束的柔性了, 即必须采取切负荷措施, 以最小的负荷损 失代价, 将系统恢复到可行域内。
歩骤 S3: 确定电力系统运行条件, 包括电网结构和发电机电压、 功率; 歩骤 S4:基于电力系统运行条件,通过潮流计算得到各母线上的有功功率、 无功功率、 节点电压和节点间的电导、 电纳、 相角差;
歩骤 S5 : 若潮流计算成功, 根据歩骤 S2选择的多维柔性或发电成本柔性 模型进行对应的优化计算, 得到综合柔性优化结果或最优发电成本, 即最优的 电力系统总发电成本 /'和最优时的有功功率、 无功功率、 柔性指数等电网运行 状态控制量, 若潮流计算失败, 根据最优切负荷模型进行对应的优化计算, 得 到最优切负荷量和此时的有功功率、 无功功率、 柔性指数等电网运行状态控制 量, 其中多维柔性、 发电成本柔性或最优切负荷优化计算的具体歩骤为:
501:根据对应的多维柔性、发电成本柔性或最优切负荷模型建立拉格朗日 目标函数;
502: 求其最优解的对应的库恩 -塔克条件;
503: 采用牛顿法求解求得模型的最优解。
本发明引入电力系统柔性的概念, 在保证系统运行安全性和可靠性的前提 下, 提出多维柔性、 发电成本柔性和最优切负荷优化模型, 合理拓展系统的约 束边界, 最大程度地提高系统运行的经济性, 同时可以基于优化结果找出影响 系统运行经济性的瓶颈, 为电网升级改造提供参考。

Claims

权 利 要 求
1 . 一种电力系统柔性约束优化方法, 其特征在于, 包括:
歩骤 S 1 :以系统中各发电机组有功出力的二次函数之和表示电力系统总发 电成本尸, 其柔性形式如下:
其中, Ng为系统发电机总数, i = V∑"."Ng , a,. , b,.、 c,.为发电机组 的发电 成本系数; Rei为发电机 ^的有功功率; /。表示系统总发电成本的最小期望值; Af 表示系统总发电成本可接受的最大增加量; 为系统发电成本柔性指数, 其取 值范围为 [0,1] ;
歩骤 S2:根据电力系统的实际情况以及优化的实际目的选择多维柔性优化 模型或发电成本柔性优化模型;
歩骤 S3 : 确定电力系统运行条件, 包括电网结构和发电机电压、 功率; 歩骤 S4: 基于电力系统运行条件进行潮流计算;
歩骤 S5 : 若潮流计算成功, 根据歩骤 S2选择的多维柔性或发电成本柔性 模型进行对应的优化计算, 得到综合柔性优化结果或最优发电成本, 若潮流计 算失败, 根据最优切负荷模型进行对应的优化计算, 得到最优切负荷量。
2. 根据权利要求 1所述的一种电力系统柔性约束优化方法, 其特征在于, 所述的多维柔性优化模型为:
min f(S) = δ) +丄 — L∑¾ -丄
f N6 Ng t G N tr -丄
v Lit ,
Figure imgf000013_0001
PGk - Vk∑V} (Gk] cos 0k] + Bk] sin 0k] ) = PLk- SLkAPLk QGk - Vk∑V} (Gk] sin 0k] - Bk] cos 0k] ) = QLk- SLkAQLk
+ SGiAPT≤PGi≤PGT~ SGiAP- ( 2 ) vr + ^vr≤vk≤ vr - svkAvr
S,≤S,max_ ,AS, i = l,2,...,N ;k,j = l,2,...,N;l=l,2,...,L 其中, 为节点 k的负荷柔性指数, 为发电机 i出力的柔性指数, 为 节点 k的电压柔性指数, 为线路 1的潮流柔性指数, N为系统节点总数, L为 系统线路总数, P G。k分别为节点 k的有功功率和无功功率, PLK、 Gk分别为 节点 k的有功负荷和无功负荷, Vk、 为节点 k j的电压, GK 、 分别 为节点 k和 j之间的电导、电纳和相角差, Δ Δβ£1ί分别为节点 k的有功负荷、 无功负荷的偏差, PGL , PGT Γ1分别为发电机 i的有功功率及其上、下限,
ΑΡ^Γ为 、 的最大允许越限值, QGI ΩοΓ、 分别为发电机 i的无功功 率及其上、 下限, Δβ χ 42^为2 、 β 1的最大允许越限值, vk、 v vr 分别为节点 k的电压及其上、 下限, Δν Δν 1分别为 \ xin的最大允许 越限值, S 分别为线路 1的潮流值和限值, AS 为 最大允许越限值。
3. 根据权利要求 1所述的一种电力系统柔性约束优化方法, 其特征在于, 所述的发电成本柔性优化模型为:
11 minf(S) = δ s-t∑ + PGi +c,.) = 0+ SfAf
PGk ~Vk∑ V} (Gk] cos 0k] + Bk] sin 6k] ) = PL
QGk ~Vk∑ V} (Gkj sin 6kj - Bkj cos 6k] ) = Qt min〈 p 〈 pmax (3)
≤ QGi
vk min≤vk≤ vk max
S,≤ S,
0≤^≤1
i = \,2"..,N k, j = 1,2,..., N l= 1,2,..., L
其中, N为系统节点总数, L为系统线路总数, P G。k分别为节点 k 的 有功功率和无功功率, PLK、 Gk分别为节点 k的有功负荷和无功负荷, vk ν」为 节点 k j的电压, GK]、 、 分别为节点 k和 j之间的电导、 电纳和相角差,
PGL、 PGT 1分别为发电机 i的有功功率及其上、 下限, QGL、 Q ^ 2 分别 为发电机 i的无功功率及其上、 下限, vk、 vr 分别为节点 k的电压及其 上、 下限, s 分别为线路 1的潮流值和限值。
4. 根据权利要求 1所述的一种电力系统柔性约束优化方法, 其特征在于, 所述的最优切负荷模型为:
"•PGk - Vk∑V} (Gk] cos 0k] + Bk] sin 6k] ) = PLk- SLkAPLk
QGk ~Vk∑ V} (Gkj sin 6kj - Bkj cos 6k] ) = QLk- SLkAQt
(4)'
≤vk≤v-
S,≤ S,max
i = l,2,...,N ;k,j = l,2,...,N;l = l,2,...,L
其中, 为节点 k的负荷柔性指数, N为系统节点总数, L为系统线路总 数, G。k分别为节点 k的有功功率和无功功率, PLK、 Gk分别为节点 k的有 功负荷和无功负荷, Vk、 为节点 k、 j的电压, Gk]、 、 分别为节点 k和 j之间的电导、 电纳和相角差, Δ 、 Δβ£1ί分别为节点 k的有功负荷、 无功负荷 的偏差, PGl、 Ρ , 1分别为发电机 i的有功功率及其上、 下限, Q 、
分别为发电机 i的无功功率及其上、 下限, Vk、 vr 分别为节点 k的 电压及其上、 下限, s 分别为线路 1的潮流值和限值。
5. 根据权利要求 1所述的一种电力系统柔性约束优化方法, 其特征在于, 所述的对应的优化计算的具体歩骤为:
501:根据对应的多维柔性、发电成本柔性或最优切负荷模型建立拉格朗日 目标函数;
502: 求其最优解的对应的库恩 -塔克条件;
503: 采用牛顿法求解求得模型的最优解。
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