An electro-mechanical coupling system day-ahead interval optimization scheduling method and system

Abstract

The application discloses a kind of electric-gas coupling system day-to-day interval optimization scheduling methods, by with the minimum generation cost of power system as objective function, establish the first electric-gas coupling system day-to-day interval optimization scheduling model, introduce minimum warning time constraint in the first electric-gas coupling system, establish the second electric-gas coupling system day-to-day interval optimization scheduling model, solve the second electric-gas coupling system day-to-day interval optimization scheduling model, obtain the operation scheme of electric-gas coupling system day-to-day interval optimization scheduling optimization, according to the operation scheme, electric-gas coupling system day-to-day interval optimization is scheduled, the uncertainty of non-ignorable renewable energy injection is considered, improve the stability of power system operation.In addition, the application also discloses a kind of electric-gas coupling system day-to-day interval optimization scheduling system.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a day-ahead interval optimization scheduling method and system for an electro-pneumatic coupled system. Background Technology

[0002] With the increasing global demand for green energy, the proportion of power generation from renewable energy sources such as wind and solar power is growing. The rapid response characteristics of gas-fired power units bring significant benefits to the absorption of renewable energy. However, the uncertainty of renewable energy injection also poses a growing threat to the power-gas coupling system. In traditional day-ahead interval optimization dispatch studies of power systems, only the impact of wind and electricity uncertainties on power system operation is usually considered. The output of gas-fired power units is used as a boundary input through capacity constraints, without considering the impact of natural gas pipeline network operation constraints. The resulting dispatch schemes are often overly optimistic and cannot guarantee the safe operation of the power system. This may cause many devices in the natural gas pipeline network to exceed their operating pressure limits, leading to equipment damage or loss of load risk.

[0003] Against this backdrop, it is imperative to study the impact of renewable energy uncertainties on the operation of electro-gas coupled systems. It is urgent to develop a scheduling method that takes into account the uncertainties of renewable energy injection, based on the comprehensive safety assessment and control of electro-gas coupled systems. Summary of the Invention

[0004] The purpose of this invention is to provide a dispatching method that takes into account the uncertainty of renewable energy. This technical solution is based on the comprehensive safety assessment and control of the electric-gas coupling system, which solves the problem that the safe operation of the power system cannot be guaranteed because the impact of the natural gas pipeline network operation constraints is not considered.

[0005] To achieve the above objectives, embodiments of the present invention provide a day-ahead interval optimization scheduling method and system for an electro-pneumatic coupling system.

[0006] In a first aspect, the day-ahead interval optimization scheduling method for an electro-pneumatic coupled system provided by embodiments of the present invention includes the following steps:

[0007] S101, with the objective function of minimizing the power generation cost of the power system, establish the first day-ahead interval optimization scheduling model of the electric-gas coupled system;

[0008] S102, Based on the first day-ahead interval optimization scheduling model of the electro-pneumatic coupling system, a minimum warning time constraint is introduced to establish a second day-ahead interval optimization scheduling model of the electro-pneumatic coupling system;

[0009] S103, Solve the day-ahead interval optimization scheduling model of the second electro-pneumatic coupling system to obtain the optimized operation scheme of the day-ahead interval optimization scheduling of the electro-pneumatic coupling system;

[0010] S104, According to the aforementioned operating scheme, the day-ahead interval optimization of the electro-pneumatic coupling system is scheduled.

[0011] Preferably, the day-ahead interval optimal scheduling model for the first electro-gas coupled system is established with the objective function of minimizing the power generation cost of the power system, including:

[0012] Considering the constraints of the power system and the natural gas system, the quadratic function of the output of each thermal power unit in the power generation system is approximated as the power generation cost function of each thermal power unit, and a first electric-gas coupled system optimization scheduling model is established.

[0013] Preferably, the power system constraints include thermal power unit power balance constraints, thermal power unit capacity constraints, thermal power unit ramping constraints, branch transmission capacity constraints, renewable energy range constraints, and coupling constraints.

[0014] Preferably, the natural gas system constraints include flow balance constraints, gas source supply flow constraints, pipeline pressure constraints, and pipeline transmission flow constraints.

[0015] Preferably, the objective function expressions for both the day-ahead interval optimization scheduling model of the first electro-pneumatic coupling system and the day-ahead interval optimization scheduling model of the second electro-pneumatic coupling system are:

[0016] Where T is the scheduling period, N G This represents the total number of thermal power units. Let a be the active power output of the i-th thermal power unit at time t. i b i and c i Let be the coefficients of the quadratic function corresponding to the power generation cost of the i-th thermal power unit.

[0017] Preferably, the expression for the minimum warning time constraint is: min <SADT i ,t>≥SCT i,t SADT i,t SCT is the fault warning time of the i-th thermal power unit at time t. i,t Let t be the safety control time for the i-th thermal power unit at time t.

[0018] Secondly, the electro-pneumatic coupling system day-ahead interval optimization scheduling system provided by the embodiments of the present invention includes:

[0019] The first module is used to establish a day-ahead interval optimization scheduling model for the first electro-gas coupled system with the objective function of minimizing the power generation cost of the power system.

[0020] The second establishment module is used to establish a second day-ahead interval optimization scheduling model for the electric-gas coupling system by introducing a minimum warning time constraint based on the first day-ahead interval optimization scheduling model for the electric-gas coupling system.

[0021] The solution module is used to solve the day-ahead interval optimal scheduling model of the second electro-pneumatic coupling system to obtain the optimized operation scheme of the day-ahead interval optimal scheduling of the electro-pneumatic coupling system.

[0022] The scheduling module is used to schedule the day-ahead interval optimization of the electro-pneumatic coupling system according to the operation plan.

[0023] Preferably, the first establishment module is specifically used for:

[0024] Considering the constraints of the power system and the natural gas system, the quadratic function of the output of each thermal power unit in the power generation system is approximated as the power generation cost function of each thermal power unit, and a first electric-gas coupled system optimization scheduling model is established.

[0025] Preferably, the objective function expression of the first electro-pneumatic coupling system optimization scheduling model is:

[0026] Where T is the scheduling period, N G This represents the total number of thermal power units. Let a be the active power output of the i-th thermal power unit at time t. i b i and c i Let be the coefficients of the quadratic function corresponding to the power generation cost of the i-th thermal power unit.

[0027] Preferably, the objective function expression of the second electro-pneumatic coupling system optimization scheduling model is:

[0028] Where T is the scheduling period, N G This represents the total number of thermal power units. Let a be the active power output of the i-th thermal power unit at time t. i b i and c i Let be the coefficients of the quadratic function corresponding to the power generation cost of the i-th thermal power unit.

[0029] Preferably, the power system constraints include thermal power unit power balance constraints, thermal power unit capacity constraints, thermal power unit ramping constraints, branch transmission capacity constraints, renewable energy range constraints, and coupling constraints.

[0030] Preferably, the natural gas system constraints include flow balance constraints, gas source supply flow constraints, pipeline pressure constraints, and pipeline transmission flow constraints.

[0031] The above technical solution has the following beneficial effects:

[0032] By introducing early warning safety constraints and considering the uncertainty of renewable energy injection, an interval optimization scheduling scheme for an electric-gas coupled system with early warning safety constraints is studied, which improves the stability of power system operation. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0034] Figure 1 This is a schematic diagram of a day-ahead interval optimization scheduling method for an electro-pneumatic coupling system proposed in an embodiment of the present invention;

[0035] Figures 2a-2b A schematic diagram of the dynamic process of a natural gas system under different load fluctuation patterns;

[0036] Figure 3 This is a schematic diagram of a day-ahead interval optimization scheduling system for an electro-pneumatic coupling system proposed in an embodiment of the present invention. Detailed Implementation

[0037] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0038] like Figure 1 As shown, the day-ahead interval optimization scheduling method for an electro-pneumatic coupling system provided in this embodiment of the invention includes the following steps:

[0039] S101, with the objective function of minimizing the power generation cost of the power system, establishes the day-ahead interval optimization scheduling model for the first electro-gas coupled system.

[0040] In one embodiment of the present invention, the thermal power units include gas-fired thermal power units and non-gas-fired thermal power units. Considering the constraints of the power system and the natural gas system, the quadratic function of the output of each thermal power unit in the power generation system is approximated as the power generation cost function of each thermal power unit, and a first electric-gas coupled system optimization scheduling model is established. Wherein:

[0041] Power system constraints include thermal power unit power balance constraints, thermal power unit capacity constraints, thermal power unit ramping constraints, branch transmission capacity constraints, renewable energy range constraints, and coupling constraints.

[0042] Natural gas system constraints include flow balance constraints, gas source supply flow constraints, pipeline pressure constraints, and pipeline transmission flow constraints.

[0043] Furthermore, the day-ahead interval optimal scheduling model for the first electric-gas coupled system is established without considering the correlation between various uncertain variables. The day-ahead interval optimal scheduling scheme for the electric-gas coupled system is based on the uncertainty of renewable energy to obtain the range of output changes. When the actual output of renewable energy deviates from the predicted output within a certain range, the output of gas-fired power units will also change within a certain range. This makes the gas consumption of gas-fired power units in the natural gas system an interval value, no longer a fixed value, and the various state variables in the natural gas system also interval variables.

[0044] S102, introduce the minimum warning time constraint into the first electro-pneumatic coupling system, and establish the day-ahead interval optimization scheduling model of the second electro-pneumatic coupling system.

[0045] In one embodiment of the present invention, the constraint of the day-ahead interval optimization scheduling model of the second electro-pneumatic coupling system is expressed as: min <SADT i,t >≥SCT i,t SADT i,t SCT is the fault warning time of the i-th thermal power unit at time t. i,t Let t be the safety control time for the i-th thermal power unit at time t.

[0046] Furthermore, when the state variable of the natural gas system is a range quantity, the warning signal for the power system after a fault in the natural gas system will also necessarily be a range value. The range warning signal can be understood as follows: when the output of renewable energy is any value within a set range, a scheduling scheme for the electric-gas coupled system under that output can be obtained through range-optimized scheduling decisions, thereby acquiring the warning signals for each fault under that scheduling scheme. After considering all possible values ​​of renewable energy within the range, all possible values ​​of the warning signal are obtained. These possible values ​​constitute the range of the warning signal, i.e., the range warning signal.

[0047] In reality, due to the slow dynamic response characteristics of natural gas systems, when the operating output of a natural gas system changes, the system often cannot quickly reach a steady state. Furthermore, the dynamic process of a natural gas system is related not only to the stable values ​​before and after the change in its operating state, but also to the process of that change. Figure 2a and Figure 2b As shown, different load fluctuation patterns will affect the pressure change process of each node in the natural gas system. When using the interval method to model the uncertainty of the natural gas system, it is impossible to know the actual change process of each state variable within the interval. Therefore, it is necessary to obtain accurate early warning signals based on the real-time dynamic simulation of the natural gas system.

[0048] S103, solve the day-ahead interval optimization scheduling model of the second electro-pneumatic coupling system to obtain the optimized operation scheme of the day-ahead interval optimization scheduling of the electro-pneumatic coupling system.

[0049] S104. According to this operation plan, the day-ahead interval optimization of the electro-pneumatic coupling system is scheduled.

[0050] In one embodiment of the present invention, the objective functions of the day-ahead interval optimization scheduling model of the first electro-pneumatic coupling system and the objective functions of the day-ahead interval optimization scheduling model of the second electro-pneumatic coupling system are both:

[0051]

[0052] Where T is the scheduling period, N G This represents the total number of thermal power units. Let a be the active power output of the i-th thermal power unit at time t. i b i and c i Let be the coefficients of the quadratic function corresponding to the power generation cost of the i-th thermal power unit.

[0053] Furthermore, the constraints of the day-ahead interval optimal scheduling model for the first electro-pneumatic coupling system include equations (2) to (16):

[0054]

[0055]

[0056] P lk,t =(θ i,t -θ j,t ) / x ij ,i,j∈k (4)

[0057]

[0058]

[0059] -Rd Ggi ≤P Ggi,t+1 -P Ggi,t ≤Ru Ggi (7)

[0060]

[0061]

[0062]

[0063] m gG =η -1 T Gg P Gg (11)

[0064]

[0065] m bk,t =(p i.t -p j.t ) / R ij ,i,j∈k (13)

[0066]

[0067]

[0068]

[0069] Where t = 1, 2, 3…T, equations (2)-(11) are power system constraints, where:

[0070] Equation (2) represents the power balance constraints for each thermal power unit in the power system. P represents the active power injected into the i-th gas-fired power unit at time t. Ggi,t Let be the active power output of the i-th gas-fired power unit at time t. Let P be the active power output vector of the i-th gas-fired power unit at time t, and P be the active power output vector of the non-gas-fired power unit. lk,t Let P be the active power of branch lk at time t, where branch lk is the branch formed between the l-th and k-th gas-fired power units. REi,t For the active power output of the i-th gas-fired power unit at time t, P edi,t The active load of the i-th gas-fired power unit at time t;

[0071] Equation (3) is the equation for absorbing the uncertainty of renewable energy forecasts for gas-fired power units, where P Ggi，t Let be the active power output of the l-th gas-fired power unit at time t. Let G be the initial dispatch output of the l-th gas-fired power unit under the renewable energy forecast at time t, and let G be the allocation ratio factor. Assume that the i-th row and j-th column of matrix G is G. ijThis indicates that when the output of the j-th gas-fired power unit experiences a power deficit in the renewable energy power unit, the change in output of the i-th gas-fired power unit should be 1, and the sum of the elements in the j-th column of matrix G should be 1. REi，t Let be the active power output vector of the i-th gas-fired power unit at time t, which is the output of renewable energy. Let G be the vector of active power output from renewable energy sources for all gas-fired power units preceding the i-th unit at time t. In fact, this factor is the same concept as the proportional factor used by Automatic Generator Control (AGC) to regulate and distribute unbalanced power among power units. Generally, it is desirable that G... ij ≥0 means that when fluctuations in renewable energy cause a power deficit in the system, the output of gas-fired power units increases to prevent a further decrease in the power generation capacity of the power system.

[0072] Equation (4) is the power flow equation for a branch of the power system, θ i，t Let θ be the voltage phase angle of the i-th gas-fired power unit at time t. j，t Let x be the voltage phase angle of the j-th gas-fired power unit at time t. ij Let be the reactance of the branch formed between the i-th gas-fired power unit and the j-th gas-fired power unit;

[0073] Equation (5) represents the capacity constraint for gas-fired power units. This is the upper limit of the active power output of gas-fired thermal power units; P Ggi This is the lower limit of the active power output of gas-fired thermal power units;

[0074] Equation (6) represents the capacity constraint for non-gas-fired thermal power units. This represents the upper limit of the active power output vector of non-gas-fired thermal power units. P Goi This represents the lower limit of the active power output vector of non-gas-fired thermal power units.

[0075] Equation (7) represents the ramp-up constraint for gas-fired power units, where R is a diagonal matrix formed by the "gas resistance" of the natural gas system branches, and d Ggi As the lower limit of the ramping constraint for gas-fired power units, u Ggi The upper limit of the ramping constraint for gas-fired power units;

[0076] Equation (8) represents the ramping constraint for non-gas-fired thermal power units, d Goi For non-gas-fired thermal power units, the lower limit of the ramping constraint, u Goi This is the upper limit of the ramping constraint for non-gas-fired thermal power units.

[0077] Equation (9) represents the tributary transmission capacity constraint. This is the upper limit of the active power of the branch circuit; This is the lower limit of the active power of the branch circuit.

[0078] Equation (10) represents the range constraint for renewable energy. Let be the prediction error of renewable energy for the i-th gas-fired power unit at time t.

[0079] Equation (11) is the coupling equation between gas consumption and active power output of a gas turbine unit, T Gg Let η be the connection matrix between gas-fired power unit nodes in the power system and gas-fired power unit load nodes in the natural gas system, and m be the efficiency parameter vector of the gas-fired power unit. gG P is the mass flow vector of gas consumed by the node gas-fired power unit. Gg For the active power output of gas-fired thermal power units;

[0080] Equations (12)-(16) represent the constraints of the natural gas system, where:

[0081] Equation (12) represents the gas flow balance constraint at the nodes of the natural gas system. Let m be the mass flow vector of injected natural gas for the i-th gas-fired power unit at time t. gsi，t Let m be the mass flow rate vector of the gas source output of the i-th gas-fired power unit at time t (the vector value corresponding to non-gas source nodes is zero; the following flow rate vectors are processed in the same way). gGi，t Let m be the mass flow rate vector of the gas consumed by the i-th gas-fired power unit at time t. gdi,t Let m be the natural gas load mass flow vector of the i-th non-gas-fired thermal power unit at time t. bk,t Let be the natural gas flow rate of branch bk at time t;

[0082] Equation (13) is the hydraulic equation for a "circuit-like" model of natural gas pipeline transmission, p i.t Let R be the pressure of the i-th natural gas pipeline at time t. ij Represents air resistance parameters;

[0083] Equation (14) represents the gas supply flow constraint, m gsk,t Let t be the gas supply flow rate at time t. This is the upper limit of the gas supply volume.

[0084] Equation (15) represents the flow rate constraint in the pipeline. In practical engineering, flow velocity constraints are often more important and can be converted using the pipeline flow rate, m bk,t Let be the natural gas pipeline flow rate at time t. This is the upper limit for the flow rate of natural gas pipelines;

[0085] Equation (16) represents the pipeline pressure constraint, p i,t For natural gas pipeline node pressure, p i This represents the lower limit of the pressure at a natural gas pipeline node. This represents the upper limit of the pressure at the natural gas pipeline node.

[0086] The first day-ahead interval optimization scheduling model of the electric-gas coupling system demonstrates that when the output of renewable energy varies arbitrarily within a certain range, the corresponding output of the gas-fired power unit can be adjusted by AGC to ensure the real-time energy balance of the electric-gas coupling system, while not violating the capacity and safety constraints of each device in the system.

[0087] In the day-ahead interval optimal scheduling model of the first electro-gas coupled system, the variables are difficult to solve directly because they are included in the equality constraints. Since the constraints in the above model are all linear interval equations, and the coefficients of the uncertain interval variables are all constants, this provides a possibility for simplifying the day-ahead interval optimal scheduling model of the first electro-gas coupled system. Wherein:

[0088] Equation (5) can be equivalently simplified to:

[0089]

[0090] Right now:

[0091]

[0092] Equation (7) can be equivalently simplified to:

[0093]

[0094] Right now:

[0095] in,

[0096]

[0097] According to the formula Equations (2) and (4) can be transformed into:

[0098]

[0099] Right now:

[0100] in, Se1, Se2, Sg1, and Sg2 are network transfer distribution factors, which can be obtained from the system network topology correlation matrix and network parameters. The symbol "<>" represents the interval number, and the same applies below.

[0101] Therefore, equation (9) can be simplified to:

[0102]

[0103] Right now:

[0104] When Ce k,i When >0,

[0105]

[0106] When Ce k,i When <0,

[0107]

[0108] Introduce variable x lk,i , making x lk,i satisfy:

[0109] (I). (II)

[0110] When Ce k,i When Ce > 0, equation (II) is a redundant constraint; when Ce k,i When <0, equation (I) is a redundant constraint.

[0111] Therefore, equation (26) can be equivalently transformed into:

[0112]

[0113] Right now:

[0114] According to the formula From equations (11)-(13), we can obtain:

[0115]

[0116] in, Y is the nodal admittance matrix constructed from the branch "gas resistance". The superscript s corresponds to the gas source node (constant pressure node) in the natural gas system. This corresponds to the load node (constant flow node) in the natural gas system. Equation (16) can then be simplified to the equivalent:

[0117]

[0118]

[0119] Right now:

[0120] According to the formula From equations (11)-(13), we can obtain:

[0121]

[0122] in:

[0123] Therefore, equation (15) can be simplified to:

[0124]

[0125] Right now:

[0126]

[0127] When Cg k,i When >0,

[0128]

[0129] When Cg k,i When <0,

[0130]

[0131] Introduce variable x bk,i , making x bk,i satisfy:

[0132] (I) (II)

[0133] When Cg k,i When Cg > 0, equation (II) is a redundant constraint; when Cg k,i When <0, equation (I) is a redundant constraint.

[0134] Therefore, equation (35) can be equivalently transformed into:

[0135]

[0136] Right now:

[0137] The gas source output flow rate can be measured via the pipeline flow vector m. b,t We obtain, that is:

[0138]

[0139] in:

[0140] Introduce variable x sk,i Using m b,t A similar approach simplifies equation (14) to the following equivalent form:

[0141]

[0142] In summary, by simplifying the process, the day-ahead interval optimization scheduling model of the first electro-pneumatic coupling system is equivalently transformed into a quadratic programming model with convex constraints. Then, optimization toolkits such as Cplex are used to solve the quadratic programming model with convex constraints.

[0143] Furthermore, at the day-ahead level, approximate estimation of the warning time for each fault remains of great significance. Inter-regional warning signals can provide decision-makers with estimated information on the safety margin of each fault under uncertainty, and provide an analytical basis for formulating day-ahead inter-regional optimal scheduling schemes.

[0144] In one embodiment of the present invention, the focus is on passive turbine tripping faults, i.e., faults in pipeline branches leading to interruption of gas supply to the gas-fired power unit, causing the operating gas pressure level in the pipeline to gradually decrease below the minimum inlet pressure limit of the power unit, resulting in turbine tripping. Without loss of generality, taking a fault affecting multiple loads as an example, the approximate estimation method for the interval warning signal is as follows:

[0145]

[0146] Among them, SADT i,t SCT is the fault warning time of the i-th thermal power unit at time t. i,t Let ALP be the safety control time of the i-th thermal power unit at time t. i，t Let be the available gas storage capacity of the i-th thermal power unit at time t.

[0147] Day-ahead interval optimization scheduling of the electric-gas coupled system can yield scheduling schemes that do not violate the system's safety constraints even when renewable energy output varies arbitrarily within an uncertain interval. The warning times for each fault will naturally also fall within a certain range. To ensure that the power system has sufficient emergency control time after a daytime natural gas system fault, <sadt>The lower bound should be greater than its safety control time SCT. Therefore, based on the aforementioned first electro-pneumatic coupling system day-ahead interval optimization scheduling model, this invention introduces a minimum warning time constraint (Equation (41)) and establishes a day-ahead interval optimization scheduling model considering the warning safety constraint.

[0148] min <SADT i,t >≥SCT i,t (41)

[0149] min <SADT i,t >That is, in equation (40) <SADT i,t The lower bound of the interval > is intuitively observed to be that equation (40) contains an interval division equation, which is difficult to solve directly. However, the numerator term in the division equation is negatively correlated with the uncertainty, while the denominator term is positively correlated with the uncertainty. Therefore, the min term can be directly solved by simplifying the derivation. <SADT i,t This avoids the problem of conservatism. The specific derivation is as follows:

[0150]

[0151] Where, N gd N represents the number of non-gas-fired power unit loads in the faulty section of the pipeline network. gG m represents the number of gas-fired power units in the faulty pipeline network. gdk,t Let m be the mass flow rate of the k-th non-gas-fired power unit load at time t. gGk,t Let be the mass flow rate of the load of the kth gas-fired power unit at time t.

[0152] The equivalent simplified formula (42) is obtained first. <lp>The lower bound:

[0153]

[0154] Where, N LP This represents the number of pipelines in the faulty natural gas pipeline network. Based on equations (29)-(32):

[0155]

[0156] in:

[0157]

[0158] Then, based on equation (11) and equations (17)-(19), we can obtain... <m gd,k The upper bound of >:

[0159] max <m gGk,t >=η k -1 ·T Ggk ·max(P Gg,t (45)

[0160] Right now:

[0161] Therefore, the lower bound of SADT can be obtained as:

[0162]

[0163] Therefore, equation (41) is equivalent to:

[0164]

[0165] For each anticipated fault n, the constraint of equation (48) should be satisfied.

[0166] In summary, the constraints of the day-ahead interval optimization scheduling model for the second electro-pneumatic coupling system, considering early warning safety constraints, include the following functions:

[0167]

[0168] P lk,t =(θ i,t -θ j,t ) / x ij ,i,j∈k (50)

[0169]

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176]

[0177]

[0178] Where, N LP,n N represents the number of natural gas pipelines affected by fault n. gd,n N represents the number of non-gas-fired power unit loads affected by fault n. gG,n Let n be the number of gas-fired power unit loads affected by fault n. Let represent the remaining available gas storage capacity for the i-th load under fault n.

[0179] By simplifying, the day-ahead interval optimization scheduling model of the second electro-pneumatic coupling system is transformed into a quadratic programming model with convex constraints, which can be solved directly using optimization toolkits such as Cplex.

[0180] The day-ahead interval optimization scheduling method for an electric-gas coupled system provided in this invention establishes a first day-ahead interval optimization scheduling model for the electric-gas coupled system with the objective function of minimizing the power generation cost of the power system. A minimum warning time constraint is introduced into the first electric-gas coupled system to establish a second day-ahead interval optimization scheduling model. The second electric-gas coupled system day-ahead interval optimization scheduling model is solved to obtain an optimized operation scheme for the electric-gas coupled system day-ahead interval optimization scheduling. Based on this operation scheme, the day-ahead interval of the electric-gas coupled system is optimized for scheduling. This method considers the non-negligible uncertainty of renewable energy injection, thereby improving the stability of power system operation.

[0181] like Figure 3 As shown, the day-ahead interval optimization scheduling system for an electro-pneumatic coupling system provided in this embodiment of the invention includes:

[0182] The first module is used to establish a day-ahead interval optimization scheduling model for the first electro-gas coupled system with the objective function of minimizing the power generation cost of the power system.

[0183] The second module is used to introduce minimum warning time constraints into the first electro-pneumatic coupling system and establish a day-ahead interval optimization scheduling model for the second electro-pneumatic coupling system.

[0184] The solution module is used to solve the day-ahead interval optimal scheduling model of the second electro-pneumatic coupling system and obtain the optimized operation scheme of the day-ahead interval optimal scheduling of the electro-pneumatic coupling system.

[0185] The scheduling module is used to optimize the day-ahead interval of the electro-pneumatic coupling system according to the operation plan.

[0186] Although the invention has been described by way of embodiments, those skilled in the art will recognize that the invention has many variations and modifications without departing from its spirit, and it is intended that the appended claims cover such variations and modifications without departing from the spirit of the invention.< / lp> < / sadt>

Claims

1. A method for day-ahead interval optimal dispatch of an electro-mechanical coupling system, characterized in that, The method comprises the following steps: S101, a first electricity-gas coupled system day-ahead interval optimization scheduling model is established with the lowest power generation cost of the power system as an objective function; S102, a second electricity-gas coupled system day-ahead interval optimization scheduling model is established based on the first electricity-gas coupled system day-ahead interval optimization scheduling model and by introducing a minimum warning time constraint; S103, the second electricity-gas coupled system day-ahead interval optimization scheduling model is solved to obtain an optimized operation scheme of the electricity-gas coupled system day-ahead interval optimization scheduling; S104, the electricity-gas coupled system day-ahead interval optimization is scheduled according to the operation scheme. The objective function of the first electricity-gas coupled system day-ahead interval optimization scheduling model and the objective function expression of the second electricity-gas coupled system day-ahead interval optimization scheduling model are as follows: wherein, is the dispatching period, is the total number of thermal power units, is the first thermal power unit active power output at the time, , and is the first coefficient of the quadratic function corresponding to the generation cost of the nth thermal power unit. The expression of the minimum warning time constraint is: wherein, the first fault early warning time of the unit at time t, the first safety control time of the unit at time t. 2.The EAC system day-ahead interval optimal dispatching method according to claim 1, wherein, The step S101 comprises: In the case of considering power system constraints and natural gas system constraints, a quadratic function of the output of each thermal power unit of the power generation system is approximated as a power generation cost function of each thermal power unit to establish a first electricity-gas coupled system optimization scheduling model. 3.The EAC system day-ahead interval optimal dispatching method of claim 2, wherein, The power system constraints comprise thermal power unit power balance constraints, thermal power unit capacity constraints, thermal power unit ramping constraints, branch transmission capacity constraints, renewable energy interval constraints and coupling constraints. 4.The electric-gas coupling system day-ahead interval optimization scheduling method of claim 2, wherein, The natural gas system constraints comprise flow balance constraints, gas source gas supply flow constraints, pipeline pressure constraints and pipeline transmission flow constraints.

5. A day-ahead interval optimal dispatch system for an electro-mechanical coupling system, characterized in that, The method comprises the following modules: A first establishing module is configured to establish a first electricity-gas coupled system day-ahead interval optimization scheduling model with the lowest power generation cost of the power system as an objective function; A second establishing module is configured to establish a second electricity-gas coupled system day-ahead interval optimization scheduling model based on the first electricity-gas coupled system day-ahead interval optimization scheduling model and by introducing a minimum warning time constraint; A solving module is configured to solve the second electricity-gas coupled system day-ahead interval optimization scheduling model to obtain an optimized operation scheme of the electricity-gas coupled system day-ahead interval optimization scheduling; A scheduling module is configured to schedule the electricity-gas coupled system day-ahead interval optimization according to the operation scheme. The objective function of the first electricity-gas coupled system day-ahead interval optimization scheduling model and the objective function expression of the second electricity-gas coupled system day-ahead interval optimization scheduling model are as follows: wherein, is a dispatching period, is a total number of thermal power units, is the first thermal power unit active power output at the time, , and is the first coefficient of the quadratic function corresponding to the generation cost of the thermal power unit The expression of the minimum warning time constraint is: wherein, the first warning time of the unit at time t, the first safety control time of the unit at time t.

6. The electro-mechanical coupled system day-ahead interval optimal dispatch system of claim 5, wherein, The first establishing module is specifically configured to: In the case of considering power system constraints and natural gas system constraints, a quadratic function of the output of each thermal power unit of the power generation system is approximated as a power generation cost function of each thermal power unit to establish a first electricity-gas coupled system optimization scheduling model.

7. The electro-mechanical coupled system day-ahead interval optimal dispatching system of claim 6, wherein, The power system constraints comprise thermal power unit power balance constraints, thermal power unit capacity constraints, thermal power unit ramping constraints, branch transmission capacity constraints, renewable energy interval constraints and coupling constraints.

8. The electro-mechanical coupled system day-ahead interval optimal dispatch system of claim 6, wherein, The natural gas system constraints comprise flow balance constraints, gas source gas supply flow constraints, pipeline pressure constraints and pipeline transmission flow constraints.

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