Power transmission and distribution network cooperative scheduling method considering wind power uncertainty and frequency modulation constraint

Abstract

The invention discloses a power transmission and distribution network cooperative scheduling method considering wind power uncertainty and frequency modulation constraints. The method comprises the following steps: constructing frequency modulation security constraints by combining the frequency modulation response capabilities of a wind turbine generator, an energy storage system and a thermal power generating unit; acquiring operation data and unit parameters of the power transmission network and the power distribution network; respectively establishing an upper-layer power transmission network unit combination model and a lower-layer active power distribution network dispatching model containing frequency modulation security constraints; performing linearization processing on a nonlinear part in the model by using a second-order cone technology and a large M method; and taking the interconnecting line interaction power and the auxiliary frequency modulation power between the power transmission and distribution grids as coupling variables, and adopting a distributed optimization method based on a target cascade algorithm to realize parallel optimization solution of the transmission and distribution system. According to the method, the solving accuracy is ensured, the efficient linkage of a transmission and distribution network system in resource scheduling and frequency response is realized, and the frequency modulation pressure caused by large-scale wind power grid connection to a traditional thermal power generating unit is remarkably reduced.

Description

Technical Field

[0001] The present invention belongs to the field of power system dispatch optimization, and specifically relates to a transmission and distribution network coordinated dispatch method taking into account wind power uncertainty and frequency regulation constraints. Background Art

[0002] Conventional thermal power units can effectively provide support for grid frequency stability due to their high mechanical inertia. As the proportion of renewable energy generation continues to increase, the proportion of synchronous machines in regional power grids has decreased, resulting in insufficient frequency response capabilities, which in turn increases the risk of low-frequency load shedding accidents in the system. Relying solely on thermal power units cannot meet the frequency support needs of new power systems. In contrast, wind turbines have significant advantages in participating in grid frequency regulation. The energy storage system has an extremely fast response speed and can compensate for the unstable output of wind turbines caused by wind speed fluctuations.

[0003] The popularization of intelligent power grids has promoted the transition from traditional distribution networks to active distribution networks. The coupling between the transmission and distribution networks has been enhanced. Considering the actual situation that the two are managed by different dispatching departments, the integrated dispatching method of the transmission and distribution networks is difficult to implement. Therefore, the present invention adopts a distributed optimization mode of the transmission and distribution network.

[0004] In the field of transmission and distribution coordinated optimization research, most studies only focus on optimizing frequency regulation reserve capacity under steady-state frequency safety conditions. Some studies on transient frequency safety constraints only consider a single-level power grid, ignoring the auxiliary frequency regulation services of the active distribution network, and failing to fully tap the dispatching potential of the rich resources in the distribution network.

[0005] In summary, it is necessary to propose a solution to the problem of frequency stability and economic coupling in a low-inertia power grid environment with a high proportion of renewable energy, so as to alleviate the frequency regulation pressure of traditional thermal power units and indirectly improve the anti-interference ability of the power grid. Summary of the invention

[0006] In view of the deficiencies of the above-mentioned technologies, an object of the present invention is to provide a transmission and distribution network coordinated scheduling method taking into account wind power uncertainty and frequency regulation constraints.

[0007] In order to achieve the above object, the present invention adopts the following technical scheme:

[0008] A transmission and distribution network coordinated dispatching method taking into account wind power uncertainty and frequency regulation constraints comprises the following steps:

[0009] A frequency response model is constructed based on the primary frequency regulation response characteristics of wind turbines, energy storage equipment and thermal power units to analyze the dynamic behavior of frequency changes; frequency safety indicators are determined according to the grid operation requirements; the frequency safety indicators include the minimum value of the primary frequency regulation reserve capacity and the transient frequency safety constraints after interference;

[0010] Obtain transmission and distribution grid operation data, parameters of wind turbines, thermal power units and energy storage equipment;

[0011] Based on the transmission and distribution network operation data and unit parameters, an upper transmission network optimization model and a lower distribution network optimization model are constructed, with the total operation cost as the objective function, and frequency security indicators and wind power uncertainty are comprehensively considered;

[0012] The second-order cone reconstruction technology is used to simplify the lower distribution network model. The frequency minimum point constraint containing strong nonlinear terms in the transient frequency security constraint is linearized by combining the second-order Taylor series expansion and the big-M method, thereby converting the overall problem into a mixed integer linear programming problem.

[0013] Taking the interconnection power and auxiliary frequency regulation power between transmission and distribution networks as coupling variables, distributed optimization is realized through the target cascade (ATC) algorithm, which supports parallel computing and collaborative optimization of transmission and distribution systems.

[0014] Furthermore, the frequency regulation constraints include transient frequency safety constraints and primary frequency regulation reserve constraints. Transient frequency safety constraints include initial frequency change rate, frequency minimum point, and steady-state frequency deviation constraints:

[0015] Initial frequency change rate constraint:

[0016]

[0017] Where: RoCoF, RoCoF max are the system initial frequency change rate and its maximum limit respectively; f N is the initial frequency of the system; ΔP e is the interference power received by the system; H sys is the system equivalent inertia;

[0018] Frequency minimum point constraint:

[0019]

[0020] Where: Δf DB is the frequency dead zone; D, T d are the damping coefficient and the primary frequency modulation response time respectively; R sys is the frequency modulation response rate of the system; Δf max is the maximum frequency deviation limit;

[0021] Steady-state frequency deviation constraint:

[0022]

[0023] Where: Δf ss , Δf ss,max They are the steady-state frequency deviation and its maximum limit respectively; ΔPm is the total amount of primary frequency modulation response;

[0024] Primary frequency regulation reserve constraints:

[0025]

[0026] In the formula, are the primary frequency regulation reserve capacity requirements of the transmission network and the kth distribution network respectively; They are the node sets of thermal power, wind power and energy storage in the transmission network; are the node sets of the kth distribution network C-DG, R-DG, and energy storage respectively; N dis Indicates the number of distribution networks participating in dispatching; It is the primary frequency regulation standby power for each thermal power unit, wind power unit and energy storage equipment in the transmission network; They represent the primary frequency regulation reserve power of the distributed controllable power source, wind power, and energy storage in the kth distribution network respectively; represents the auxiliary frequency regulation power purchased by the transmission network from the kth distribution network; It represents the auxiliary frequency regulation power provided by the kth distribution network to the transmission network.

[0027] Furthermore, the construction of the optimization model of the upper transmission network and the lower distribution network is specifically as follows:

[0028] The overall objective function of the optimization model is:

[0029]

[0030] Where: C total is the overall operating cost of the transmission and distribution network; C tran is the transmission network operation cost; C dis,k is the operating cost of the kth distribution network;

[0031] Among them, the objective function of the transmission network optimization model is:

[0032]

[0033] Where: t is the time period; T is the total number of time periods in a scheduling cycle; It is the sum of the generation and start-up and shutdown costs of thermal power units in the transmission network at time t; is the sum of the frequency regulation costs of thermal power units at time t; is the wind power abandonment cost of the transmission network at time t; BD,t Indicates the time-of-use electricity price at the boundary of the transmission and distribution network; It represents the electricity price of auxiliary frequency regulation service at the boundary of transmission and distribution network; represents the active power transmitted from the transmission network to the kth distribution network; represents the auxiliary frequency regulation power purchased by the transmission network from the kth distribution network;

[0034] Among them, the objective function of the kth distribution network optimization model is:

[0035]

[0036] Where: are the power generation cost and frequency regulation backup cost of the controllable distributed generation in the kth distribution network at time t respectively; is the wind curtailment cost of the kth distribution network at time t; represents the transmission power purchased by the kth distribution network from the transmission network; It represents the auxiliary frequency regulation power provided by the kth distribution network to the transmission network.

[0037] Furthermore, the constraints for constructing the upper-layer transmission network optimization model include the following conventional constraints in addition to the above-mentioned frequency regulation constraints:

[0038] Thermal power unit operation constraints:

[0039]

[0040] In the formula, are the active power output of thermal power unit i at time t and t-1 respectively; are the lower upper limits of the active power output of thermal power unit i; Respectively represent the minimum start and stop time of unit i; Respectively represent the start and stop status of thermal power unit i at time t and t-1; Indicates the duration of continuous startup or shutdown of thermal power unit i up to time t-1; are the maximum upward and downward climbing capabilities of thermal power unit i, respectively;

[0041] Wind farm operation constraints:

[0042]

[0043] In the formula, They represent the actual output active power and predicted output of wind power j respectively; are the abandoned wind volume, frequency modulation response power and passive abandoned wind volume of wind power j respectively; is the frequency modulation response rate of wind power j; It represents the maximum response power that wind power j can provide in the primary frequency regulation stage;

[0044] Energy storage operation constraints:

[0045]

[0046] In the formula, are the charge states of the energy storage device z at time t and t-1 respectively; represents the self-damage rate; They represent the charging and discharging efficiency factors of the energy storage device z respectively; represents the charging and discharging power of energy storage device z at time t; Indicates the charging and discharging status; They are the upper and lower limits of energy storage charge state respectively; Indicates the charge and discharge power limit; Δt indicates the time interval; Indicates the rated capacity of the energy storage device; The state of charge of the energy storage device at the beginning and end of a cycle;

[0047] Spinning reserve constraints:

[0048]

[0049] Where: η represents the load forecast error coefficient; represents the total load of the transmission network at time t;

[0050] Active power balance constraints:

[0051]

[0052] Where: represents the set of load nodes in the transmission network; Indicates the load power of the transmission network;

[0053] Network security constraints:

[0054]

[0055] Where: G g,i , G w,j , G ess,z , G l,m , are thermal power, wind power, energy storage, load, and power flow transfer factor with the kth distribution network node; Indicates the line power transfer limit;

[0056] Transmission and distribution grid boundary interactive power constraints:

[0057]

[0058] Where: They represent the lower and upper limits of active power transmission between the kth distribution network and the transmission network coupling branch respectively; represents the active power transmitted from the transmission network to the kth distribution network; represents the auxiliary frequency regulation power purchased by the transmission network from the kth distribution network;

[0059] The constraints of the kth distribution network optimization model include:

[0060] Controllable distributed energy C-DG power constraints:

[0061]

[0062] In the formula, Respectively represent the active power and reactive power output by C-DG; They represent the lower and upper limits of C-DG active output respectively; Respectively represent the lower and upper limits of C-DG reactive output;

[0063] Distributed renewable energy R-DG power constraints:

[0064]

[0065] In the formula, They represent the actual output value and predicted value of distributed wind power in the kth distribution network respectively; Indicates the amount of wind power abandoned by distributed wind power; Indicates the primary frequency regulation response capability of wind power; Indicates the passive abandonment of wind power;

[0066] The energy storage operation constraints are the same as those in the transmission grid and will not be elaborated here;

[0067] Node active power balance constraints:

[0068]

[0069] Where: They represent the active power and reactive power injected by the transmission network into the kth distribution network node j respectively; denote the active power and reactive power injected into node j by C-DG respectively; They represent the active power output and abandoned air volume of the wind turbine at node j respectively; They are the discharging and charging power of energy storage at node j respectively; The power transmitted from node j to node m and the power transmitted from node i to node j are respectively; Represents the square of the current amplitude of branch ij; r ij,k 、x ij,k are the resistance reactance of branch ij respectively; f k (j) l k (j) are the branch head node set and branch terminal node set with node j as the initial node; are C-DG, R-DG and energy storage set on node j respectively;

[0070] Ohm's law and node voltage constraints:

[0071]

[0072] Where: u i,k 、u j,k Represents the square of the voltage amplitude at nodes i and j; V i,k,min 、V i,k,max Respectively represent the minimum and maximum limits of the voltage amplitude at node i;

[0073] Line power flow constraints based on the second-order cone form:

[0074]

[0075] Transmission and distribution grid boundary interactive power constraints:

[0076]

[0077] Where: They represent the lower and upper limits of active power transmission between the kth distribution network and the transmission network coupling branch respectively; represents the transmission power purchased by the kth distribution network from the transmission network; It represents the auxiliary frequency regulation power provided by the kth distribution network to the transmission network.

[0078] Furthermore, the uncertainty of wind power output is comprehensively considered and an adjustable robustness coefficient is introduced to describe the wind power output in the transmission and distribution network:

[0079]

[0080] Where: P w,j,t , They represent the actual output and predicted output of wind turbine j respectively; They represent the maximum upper and lower deviations of wind turbine j output respectively; represents a 0-1 variable; Γ represents an adjustable robust parameter.

[0081] Furthermore, there are strong nonlinear terms in the analytical expression of the lowest frequency point in the transient frequency safety constraint. By introducing the second-order Taylor series expansion and the big-M method to linearize it, the model is converted into a mixed integer linear programming problem. Although the number of constraints is increased, the computational complexity is reduced.

[0082] Further, the distributed optimization is realized by the target cascade (ATC) algorithm, including:

[0083] Under the ATC algorithm framework, the consistency constraint is relaxed into the objective function in the form of a penalty function, and the objective function is modified as follows:

[0084]

[0085] Where: T is the total number of time periods in a scheduling cycle; N dis is the number of distribution networks participating in dispatching; C tran , C dis,k are the operating costs of the transmission network and the kth distribution network respectively; represents the active power transmitted from the transmission network to the kth distribution network; represents the auxiliary frequency regulation power purchased by the transmission network from the kth distribution network; represents the transmission power purchased by the kth distribution network from the transmission network; represents the auxiliary frequency regulation power provided by the kth distribution network to the transmission network; α k,t , The algorithm multiplier representing the linear term of the penalty function; β k,t , The algorithmic multiplier representing the quadratic term of the penalty function; Respectively represent the known quantities obtained by calculation.

[0086] Furthermore, the distributed optimization implemented by the ATC algorithm further includes the following steps:

[0087] Step 1: Set the number of iterations i = 0, initialize the algorithm multiplier and the transmission and distribution network coupling interaction value and

[0088] Step 2: i=i+1, each distribution network solves the distribution network optimization model in parallel to obtain the transmission and distribution boundary interaction power and auxiliary frequency regulation reserve power and transmitted to the transmission grid;

[0089] Step 3: The transmission network solves the transmission network optimization model based on the data transmitted from the distribution network, and obtains the transmission and distribution boundary interaction value and to the distribution grid;

[0090] Step 4: Use the following convergence criteria to check whether the loop has reached the convergence condition; if the condition is met, output the final result, otherwise, proceed to step 5;

[0091]

[0092] Where ε1, ε2, ε3 are the convergence accuracy; N dis Indicates the number of distribution networks participating in dispatching; They represent the boundary interaction power obtained by solving the i-th iteration transmission network and distribution network models respectively; They represent the auxiliary frequency regulation power obtained by solving the i-th iteration transmission network and distribution network models respectively; C tran (i) C tran (i-1) are the transmission network operation costs of the i-th and i-1-th iterations respectively; C dis,k (i) C dis,k (i-1) is the kth distribution network operation cost at the i-th and i-1-th iterations respectively;

[0093] Step 5: Use the following formula to update the algorithm multiplier and return to step 2 for a new round of iteration;

[0094]

[0095] In the formula, γ is a constant, which is 1≤γ≤3; α k,t (i) represents the algorithm multiplier of the first term of the penalty function for the i-th iteration; α k,t (i-1), The algorithm multiplier representing the first term of the penalty function for the i-1th iteration; β k,t (i) The algorithm multiplier representing the quadratic term of the penalty function for the i-th iteration; β k,t (i-1), The algorithmic multiplier representing the quadratic term of the penalty function for the i-1th iteration.

[0096] The present invention also provides a dispatching system, which includes the transmission and distribution network coordinated dispatching method taking into account wind power uncertainty and frequency regulation constraints; by solving the transmission and distribution coordinated dispatching model through the system, the economic operation dispatching result of the transmission and distribution network that ensures the system frequency safety can be obtained.

[0097] The beneficial effects of the present invention are:

[0098] Compared with the traditional single grid level or separate dispatching, the transmission and distribution network collaborative optimization model considering frequency security and economy proposed in the present invention significantly improves the operation safety level, is conducive to efficient collaboration between transmission and distribution systems, and conforms to the future development trend of new power systems.

[0099] Active distribution network participation in power interaction and auxiliary frequency regulation and standby services can not only improve the utilization rate of distributed energy and reduce the operating cost of distribution network, but also improve the economic efficiency of transmission network operation;

[0100] It can fully tap the potential of resource scheduling, effectively respond to frequency fluctuations caused by the uncertainty of load and renewable energy output, and efficiently coordinate the frequency regulation needs of the transmission network and the active distribution network through transmission and distribution coordinated optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0102] Figure 1 It is the framework of the transmission and distribution network coordinated dispatching method taking into account wind power uncertainty and frequency regulation constraints disclosed in the present invention;

[0103] Figure 2 It is a flow chart of the coordinated dispatching method of the transmission and distribution network taking into account wind power uncertainty and frequency regulation constraints disclosed in the present invention. DETAILED DESCRIPTION

[0104] The following will be combined with the drawings in the embodiments of the present invention to describe the technical solutions in the embodiments of the present invention in detail and completely. The described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0105] In this application, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, the elements defined by the sentence "comprise one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0106] The overall research framework of the present invention is as follows Figure 1 As shown in the dispatch optimization part, the transmission network system resources mainly include centralized wind farms, conventional thermal power plants, energy storage units and loads, and the distribution network resources mainly include controllable distributed generation (C-DG), renewable distributed generation (R-DG) and loads.

[0107] In the frequency security constraint construction part, the virtual inertia support and primary frequency regulation response process of wind turbines and energy storage units are taken into account to build a wind power-energy storage-thermal power joint frequency response model. The initial frequency change rate and the lowest frequency point of the system after the disturbance are taken as key indicators as frequency security constraints, and added to the transmission and distribution network coordinated economic dispatch optimization model to obtain the transmission and distribution network coordinated optimization dispatch model taking into account frequency regulation security constraints.

[0108] like Figure 2 As shown, an embodiment of the present invention provides a transmission and distribution network coordinated scheduling method taking into account wind power uncertainty and frequency regulation constraints, comprising the following steps:

[0109] S1: A frequency response model is constructed based on the primary frequency regulation response characteristics of wind turbines, energy storage equipment and thermal power units to analyze the dynamic behavior of frequency changes; frequency safety indicators are determined according to the grid operation requirements, including the minimum value of the primary frequency regulation reserve capacity and the transient frequency safety index after interference;

[0110] S2: Obtain transmission and distribution grid operation data and unit parameters to provide data support for subsequent optimization modeling;

[0111] S3: Based on the collected data, the upper transmission network optimization model and the lower distribution network optimization model are constructed, with the total operating cost as the objective function, and the frequency security index and wind power uncertainty are comprehensively considered;

[0112] S301: The objective function of constructing the transmission network optimization model is:

[0113]

[0114] Where: t is the time period; T is the total number of time periods in a scheduling cycle; It is the sum of the generation and start-up and shutdown costs of thermal power units in the transmission network area at time t; is the sum of the frequency regulation costs of thermal power units at time t; is the wind power abandonment cost of the transmission network at time t; BD,t Indicates the time-of-use electricity price at the boundary of the transmission and distribution network; It represents the electricity price of auxiliary frequency regulation service at the boundary of transmission and distribution network; represents the active power transmitted from the transmission network to the kth distribution network; represents the auxiliary frequency regulation power purchased by the transmission network from the kth distribution network; Represents the set of thermal power units and wind farm nodes in the transmission network; N dis The number of distribution networks participating in dispatching;

[0115] S302: The constraints of the transmission network optimization model include conventional constraints and frequency regulation constraints.

[0116] (I) Conventional constraints:

[0117] (1.1) Output constraints of thermal power units:

[0118]

[0119] Where: They represent the actual output active power of thermal power unit i at time t and t-1 respectively; Indicates the lower upper limit of the active power output of thermal power unit i; Respectively represent the minimum start and stop time of unit i; Respectively represent the start and stop status of thermal power unit i at time t and t-1; Indicates the duration of continuous startup or shutdown of thermal power unit i up to time t-1; are the maximum upward and downward climbing capabilities of thermal power unit i, respectively;

[0120] (1.2) Wind farm operation constraints:

[0121] The uncertainty of wind power output is considered in the model, and wind power error is the main reason for uncertainty. The uncertainty of wind power output is represented as:

[0122]

[0123] Where: They represent the actual output active power and predicted output of wind farm j respectively; are the maximum upper and lower deviations of wind turbine j output respectively; is a 0-1 variable; Γ is an adjustable robust parameter; are the abandoned wind volume, frequency modulation response power and passive abandoned wind volume of wind turbine j respectively; is the frequency modulation response rate of fan j; represents the maximum response power that wind turbine j can provide in the primary frequency modulation stage; T d is the frequency modulation response time;

[0124] (1.3) Energy storage operation constraints:

[0125]

[0126] In the formula, Respectively represent the charge state of the energy storage device z at time t and t-1; represents the self-damage rate; They represent the charging and discharging efficiency factors of the energy storage device z respectively; represents the charging and discharging power of energy storage device z at time t; Indicates the charging and discharging status; Respectively represent the upper and lower limits of the energy storage charge state; Indicates the charge and discharge power limit; Δt indicates the time interval; Indicates the rated capacity of the energy storage device; The state of charge of the energy storage device at the beginning and end of a cycle;

[0127] (1.4) Power balance constraint:

[0128]

[0129] Where: are the node sets of thermal power, wind power, energy storage and load in the transmission network; N dis The number of distribution networks involved in the dispatch; They are the thermal power output of the transmission network, wind power output, wind power abandonment, energy storage discharge and charging power; Indicates the load power of the transmission network; represents the interactive coupling active power between the kth distribution network and the transmission network;

[0130] (1.5) Network security constraints:

[0131]

[0132] Where: G g,i , G w,j , G ess,z , G l,m , are thermal power, wind power, energy storage, load, and power flow transfer factor with the kth distribution network node; Indicates the line power transfer limit.

[0133] (1.6) Spinning reserve constraints:

[0134]

[0135] Where: η represents the load forecast error coefficient, which is generally 0.05; is the total load of the transmission network;

[0136] (1.7) Transmission and distribution boundary power constraints:

[0137]

[0138] Where: They represent the lower and upper limits of active power transmission between the kth distribution network and the transmission network coupling branch respectively; represents the active power transmitted from the transmission network to the kth distribution network; represents the auxiliary frequency regulation power purchased by the transmission network from the kth distribution network;

[0139] (II) Frequency modulation constraints:

[0140] (2.1) Transient frequency safety constraints:

[0141] Wind power and energy storage jointly participate in power system frequency response modeling:

[0142] The dynamic frequency response of the system frequency can be described by the swing equation:

[0143]

[0144] ΔP m =ΔP G +ΔP W +ΔP ess ;

[0145] Where: D is the system damping coefficient; H sys is the system equivalent inertia; ΔP e is the power disturbance received by the system; ΔP m Indicates the total amount of primary frequency modulation response; ΔP G , ΔP W and ΔP ess They are the frequency response quantities of thermal power, wind power and energy storage respectively;

[0146] When the frequency change exceeds the frequency dead zone, ΔP e Still greater than ΔP m , the system unbalanced power still exists, and the system frequency continues to drop. m With ΔP e The difference gradually decreases, and the rotor speed decreases gradually until it drops to 0. At this time, the system frequency drops to the lowest point f nadir . Subsequently, ΔP m Continue to rise, ΔP m With ΔP e The difference causes the rotor speed to increase until a new equilibrium state is reached. m The time domain expression is complex and has nonlinear characteristics. It is necessary to linearize the active output of the primary frequency modulation response of the prime mover piecewise and prove the accuracy of the linearization processing method. In order to ensure a certain degree of accuracy and reduce the complexity of the solution, ΔP m The piecewise linearized function with time t is:

[0147]

[0148] R sys =R G +R W +R ess ;

[0149] Where, T d Indicates the frequency modulation response time; t DB is the dead time; R sys is the system primary frequency modulation response rate; R G , R W , R ess are the frequency regulation response rates of thermal power, wind power and energy storage respectively;

[0150] (2.1.1) Frequency minimum point constraint:

[0151] ΔP m Substitute the piecewise function into the swing equation to obtain the frequency deviation expression:

[0152]

[0153] Where Δf DB is the frequency dead zone;

[0154] Take the derivative of the above formula, and the derivative result is equal to 0, and the lowest frequency point Δf in the first frequency modulation response stage is obtained. nadir The expression is:

[0155]

[0156] In order to ensure that the system minimum frequency is not lower than the minimum frequency protection limit, the following constraints need to be met:

[0157] |Δf nadir |≤|Δf max |;

[0158] Where Δf max is the maximum deviation limit of the system frequency;

[0159] Δf nadir Substitute the expression into the above formula to obtain the frequency minimum point constraint expression:

[0160]

[0161] (2.1.2) Initial frequency change rate constraint:

[0162] The initial frequency change rate RoCoF is directly related to the system inertia level. The value of RoCoF is the largest at the initial moment of the disturbance, when Δf≈0.

[0163]

[0164] Where, RoCoF max The maximum frequency change rate allowed can be obtained by rearranging the inertia requirement that satisfies the RoCoF constraint:

[0165]

[0166] (2.1.3) Steady-state frequency deviation constraint:

[0167] After entering a new steady state, The disturbance is still ΔP e , the frequency modulation response is ΔP m , substituting into the swing equation, we get the steady-state frequency deviation constraint:

[0168]

[0169] Where: Δf ss , Δf ss,max are the system steady-state frequency deviation and limit respectively;

[0170] (2.2) Primary frequency regulation reserve constraints:

[0171] Thermal power units, wind farms and energy storage units need to meet the following frequency regulation and reserve constraints:

[0172]

[0173] Where: They represent the primary frequency regulation reserve power of thermal power units, wind power units and energy storage equipment in the transmission network respectively; It represents the primary frequency regulation reserve power that the energy storage can provide in the charging and discharging states; α is the primary frequency regulation capability coefficient of the thermal power unit, which is generally taken as 0.1; The passive wind abandonment volume of wind turbines; It indicates the primary frequency regulation reserve capacity required by the transmission network; It is the maximum value of the frequency regulation reserve of the wind turbine; is the wind power frequency regulation response rate; They are the frequency modulation response rates under energy storage charging and discharging conditions respectively; It is the frequency modulation response rate of thermal power units;

[0174] S303: The objective function of the kth distribution network optimization model is:

[0175]

[0176] Where: are the power generation cost and frequency regulation backup cost of the controllable distributed generation in the kth distribution network at time t respectively; is the wind curtailment cost of the kth distribution network at time t; represents the transmission power purchased by the kth distribution network from the transmission network; It represents the auxiliary frequency regulation power provided by the kth distribution network to the transmission network.

[0177] S304: The constraints of the kth distribution network optimization model include conventional constraints and frequency regulation constraints:

[0178] Among them, the operating output constraints of wind power and energy storage equipment are similar to those in the transmission network and will not be repeated here.

[0179] (I) Conventional constraints:

[0180] (3.1) C-DG operation constraints:

[0181]

[0182] In the formula, Respectively represent the active power and reactive power output by C-DG; They represent the lower and upper limits of C-DG active output respectively; Respectively represent the lower and upper limits of C-DG reactive output;

[0183] (3.2) R-DG operation constraints:

[0184]

[0185] In the formula, They represent the actual output value and predicted value of distributed wind power respectively; They are total wind power abandonment, passive wind abandonment, and primary frequency regulation capacity; are the maximum upper and lower deviations of wind turbine j output respectively; is a 0-1 variable; Γ is an adjustable robust parameter;

[0186] (3.3) The energy storage operation constraints are similar to those in the transmission network optimization model and will not be repeated here;

[0187] (3.4) Node active power balance constraint:

[0188]

[0189] Where: They represent the active power and reactive power injected by the transmission network into the kth distribution network node j respectively; denote the active power and reactive power injected into node j by C-DG respectively; They represent the active power output and abandoned air volume of the wind turbine at node j respectively; They are the discharging and charging power of energy storage at node j respectively; The power transmitted from node j to node m and the power transmitted from node i to node j are respectively; Represents the square of the current amplitude of branch ij; r ij,k 、x ij,k are the resistance reactance of branch ij respectively; f k (j) l k(j) are the branch head node set and branch terminal node set with node j as the initial node; are C-DG, R-DG and energy storage set on node j respectively;

[0190] (3.5) Ohm’s law and node voltage constraints:

[0191]

[0192] Where: u i,k 、u j,k Represents the square of the voltage amplitude at nodes i and j; V i,k,min 、V i,k,max Respectively represent the minimum and maximum limits of the voltage amplitude at node i;

[0193] (3.6) Line power flow constraint based on the second-order cone form:

[0194]

[0195] (3.7) Transmission and distribution boundary power constraints:

[0196]

[0197] Where: are the lower and upper limits of active power transmission between the kth distribution network and the transmission network coupling branch respectively; represents the transmission power purchased by the kth distribution network from the transmission network; It represents the auxiliary frequency regulation power provided by the kth distribution network to the transmission network.

[0198] (II) Frequency modulation constraints:

[0199]

[0200] Where: are the primary frequency regulation capabilities of C-DG, R-DG, and energy storage in the k-th distribution network, respectively; represents the auxiliary frequency regulation power purchased by the transmission network from the kth distribution network; represents the auxiliary frequency regulation power provided by the kth distribution network to the transmission network; are the node sets of the kth distribution network C-DG, R-DG, and energy storage respectively;

[0201] S4: The second-order cone technique and the large M method are used to linearize the power flow constraint part and the frequency minimum point constraint of the distribution network respectively to simplify the solution process;

[0202] S401: Further technical restrictions are made on the linearization process of the frequency minimum point constraint in S4. The frequency minimum point constraint condition has strong nonlinearity and is difficult to solve directly using a commercial solver. It needs to be linearized preprocessed. First, it is simplified using a second-order Taylor series expansion:

[0203]

[0204] In the formula, ΔP e,t is the power disturbance of the system at time t; H sys,t , R sys,t is the system equivalent inertia and frequency modulation response rate at time t. They are estimated by the following formulas:

[0205]

[0206] Where: H g,i , H w,j , H ess,z are the inertia coefficients of thermal power units, fans and energy storage; R BD,k is the auxiliary frequency regulation response rate of the kth distribution network;

[0207] Among them, H sys,t R sys,t The term contains nonlinear factors and needs further linearization. Let z t =H sys,t R sys,t , whose expansion is:

[0208]

[0209] Where: They represent the start and stop status of thermal power generation units i and j in the transmission network at time t respectively;

[0210] and The first two items are linearized using the big M method. Both M are large positive numbers, so we do not distinguish them here. We set M = 1000.

[0211] First, introduce a new variable y w,i,t and ess,i,t Replace the nonlinear part in the original formula to achieve the linearization purpose, let

[0212]

[0213]

[0214] Item 3 By introducing a new variable w ij,t ,make

[0215]

[0216] In summary, the frequency minimum point constraint after linearization is:

[0217]

[0218] S5: The interconnection power of the interconnection lines between the transmission and distribution networks and the auxiliary frequency regulation power are used as coupling variables, and distributed optimization is achieved through the ATC algorithm to support parallel computing and collaborative optimization of the transmission and distribution system.

[0219] S501: As a further technical limitation, the ATC algorithm is used in S5 to parallelize the solution of the transmission and distribution network model. When the convergence criterion is met, the optimal solution of the model is obtained. Under the framework of the ATC algorithm, the consistency constraint is relaxed into the objective function in the form of a penalty function, and the objective function is modified as follows:

[0220]

[0221] The steps to solve the transmission and distribution network model using the ATC algorithm are as follows:

[0222] Step 1: Set the number of iterations i = 0, initialize the algorithm multiplier and the transmission and distribution network coupling interaction value and

[0223] Step 2: i = i + 1, each active distribution network solves its dispatch optimization model in parallel to obtain the interactive active power of the transmission and distribution boundary and auxiliary frequency regulation reserve power passing it to the transmission grid;

[0224] Step 3: The transmission network solves the transmission network dispatch optimization model based on the data received from the distribution network to obtain the transmission and distribution boundary interaction value. and to the distribution grid;

[0225] Step 4: Use the following criteria to check whether the convergence is achieved. If the conditions are met, output the final result. Otherwise, proceed to step 5.

[0226]

[0227] Where ε1, ε2, ε3 are the convergence accuracy; are the boundary interaction powers obtained by solving the transmission network and distribution network models for the i-th iteration respectively; are the auxiliary frequency regulation powers obtained by solving the i-th iteration transmission network and distribution network models respectively; C tran (i) C tran (i-1) are the transmission network operation costs of the i-th and i-1-th iterations, respectively; are the kth distribution network operation costs at the i-th and i-1-th iterations, respectively;

[0228] Step 5: Update the multiplier using the following formula and return to step 2 for a new round of iteration;

[0229]

[0230] In the formula, γ is a constant, which is 1≤γ≤3; α k,t (i) The algorithm multiplier representing the first-order term of the penalty function for the i-th iteration; α k,t (i-1), The algorithm multiplier representing the first term of the penalty function for the i-1th iteration; β k,t (i) The algorithm multiplier representing the quadratic term of the penalty function for the i-th iteration; β k,t (i-1), The algorithmic multiplier representing the quadratic term of the penalty function for the i-1th iteration.

[0231] An embodiment of the present invention also provides a dispatching system, which includes the transmission and distribution network coordinated dispatching method taking into account wind power uncertainty and frequency regulation constraints as described in any embodiment; by solving the transmission and distribution coordinated dispatching model through the system, the economic operation dispatching result of the transmission and distribution network that ensures the system frequency safety can be obtained.

[0232] The present invention takes into account the transmission and distribution coordination mode in the optimization of power system dispatch, and adds frequency regulation constraints to conventional constraints, rationally utilizes the frequency regulation resources of wind power, energy storage and thermal power, effectively alleviates the problem of power system frequency stability under a high proportion of new energy access, ensures that the frequency does not fall below the safety threshold when interference occurs, and ensures system safety and stability while pursuing operational economy.

[0233] Through the above description, those skilled in the art can already implement it.

[0234] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A transmission and distribution network coordinated dispatching method taking into account wind power uncertainty and frequency regulation constraints, characterized in that: The following steps are involved: A frequency response model is constructed based on the primary frequency modulation response characteristics of wind turbines, energy storage equipment and thermal power units to analyze the dynamic behavior of frequency changes; Determine frequency safety indicators according to grid operation requirements; the frequency safety indicators include the minimum value of primary frequency regulation reserve capacity and transient frequency safety constraints after interference; Obtain transmission and distribution grid operation data, parameters of wind turbines, thermal power units and energy storage equipment; Based on the transmission and distribution network operation data and unit parameters, an upper transmission network optimization model and a lower distribution network optimization model are constructed, with the total operation cost as the objective function, and frequency security indicators and wind power uncertainty are comprehensively considered; The second-order cone reconstruction technology is used to simplify the lower-layer distribution network model. The frequency minimum point constraint containing strong nonlinear terms in the transient frequency security constraint is linearized by combining the second-order Taylor series expansion and the big-M method, thereby converting the overall problem into a mixed integer linear programming problem. Taking the interconnection power between transmission and distribution networks and the auxiliary frequency regulation power as coupling variables, distributed optimization is achieved through the target cascade algorithm, supporting parallel computing and collaborative optimization of transmission and distribution systems.

2. A transmission and distribution network coordinated dispatching method taking into account wind power uncertainty and frequency regulation constraints as claimed in claim 1, characterized in that: The frequency regulation constraints include transient frequency safety constraints and primary frequency regulation standby constraints; wherein the transient frequency safety constraints include: Initial frequency change rate constraint: Where: RoCoF, RoCoF max are the system initial frequency change rate and its maximum limit respectively; f N is the initial frequency of the system; ΔP e is the interference power received by the system; H sys is the system equivalent inertia; Frequency minimum point constraint: Where: Δf DB is the frequency dead zone; D, T d are the damping coefficient and the primary frequency modulation response time respectively; R sys is the system's primary frequency modulation response rate; Δf max is the maximum frequency deviation limit; Steady-state frequency deviation constraint: Where: Δf ss , Δf ss,max They are the steady-state frequency deviation and its maximum limit respectively; ΔP m It is the total amount of primary frequency modulation response of the system; Primary frequency regulation reserve constraints: In the formula, are the primary frequency regulation reserve capacity requirements of the transmission network and the kth distribution network respectively; are the collection of thermal power, wind power and energy storage nodes in the transmission network; N dis The number of distribution networks participating in dispatching; Provide primary frequency regulation backup capacity for thermal power, wind power and energy storage in the transmission network; are the auxiliary frequency regulation power purchased by the transmission network from the kth distribution network and the auxiliary frequency regulation power provided by the kth distribution network to the transmission network; They respectively represent the primary frequency regulation response capabilities of distributed controllable power sources, wind power, and energy storage in the kth distribution network.

3. A transmission and distribution network coordinated dispatching method taking into account wind power uncertainty and frequency regulation constraints as claimed in claim 1, characterized in that: The construction of the upper layer transmission network optimization model and the lower layer distribution network optimization model is specifically as follows: The overall objective function of the optimization model is: Where: C total is the total operating cost of the transmission and distribution network; C tran is the transmission network operation cost; C dis,k is the operating cost of the kth distribution network; N dis The number of distribution networks participating in dispatching; Among them, the objective function of the transmission network optimization model is: Where: t is the time period; T is the total number of time periods in a scheduling cycle; are the start-up and shutdown and frequency regulation costs of thermal power units in the transmission network area at time t; is the wind power abandonment cost of the transmission network at time t; BD,t , They are the transmission and distribution network boundary time-of-use electricity price and the auxiliary frequency regulation service price; are the active power transmitted from the transmission network to the kth distribution network and the auxiliary frequency regulation power purchased; Among them, the objective function of the kth distribution network optimization model is: Where: are the generation and frequency regulation backup costs of the controllable distributed generation in the kth distribution network at time t, respectively; is the wind curtailment cost of the kth distribution network at time t; is the transmission power purchased by the kth distribution network from the transmission network; It represents the auxiliary frequency regulation power provided by the kth distribution network to the transmission network.

4. A transmission and distribution network coordinated dispatching method taking into account wind power uncertainty and frequency regulation constraints as claimed in claim 1, characterized in that: The constraints of the upper-level transmission network optimization model include the following conventional constraints in addition to the above-mentioned frequency regulation constraints: Output constraints of thermal power units: In the formula, is the active power output of thermal power unit i at time t; are the lower and upper limits of the output active power of thermal power unit i respectively; Thermal power unit start and stop constraints: In the formula, are the minimum start and stop time of thermal power unit i respectively; are the start and stop states of thermal power unit i at time t and t-1 respectively; is the continuous start-up or shutdown time of thermal power unit i up to time t-1; Thermal power unit climbing constraints: In the formula, They represent the maximum upward and downward climbing capabilities of thermal power unit i respectively; are the active power output of thermal power unit i at time t and t-1 respectively; Wind farm operation constraints: In the formula, are the actual output and predicted output of wind farm j respectively; They represent the wind curtailment, frequency modulation response power and passive wind curtailment of wind farm j respectively; represents the frequency modulation response rate of wind farm j; represents the maximum response power that wind farm j can provide in the primary frequency regulation stage; T d is the frequency modulation response time; Energy storage operation constraints: In the formula, are the charge states of energy storage z at time t and t-1 respectively; represents the self-damage rate; They represent the charging and discharging efficiency factors of energy storage z respectively; is the charging and discharging power of energy storage z at time t; It is a 0-1 variable for the charge and discharge status; are the upper and lower limits of the state of charge of the energy storage z respectively; are the charge and discharge power limits respectively; Δt is the time interval; is the rated capacity of energy storage z; They are the charge states of the energy storage at the beginning and end of a cycle respectively; Spinning reserve constraints: Where: η is the load forecast error coefficient; is the total load of the transmission network at time t; Power balance constraints: Where: are the node sets of thermal power, wind power, energy storage and load in the transmission network; N dis The number of distribution networks participating in dispatching; They are thermal power output, wind power output, wind abandonment, energy storage discharge and charging power; is the load power of the transmission network; represents the interactive coupling active power between the kth distribution network and the transmission network; Network security constraints: Where: G g,i , G w,j , G ess,z , G l,m , are thermal power, wind power, energy storage, load, and power flow transfer factor with the kth distribution network node; is the line power transfer limit; Transmission and distribution grid boundary interactive power constraints: Where: are the lower and upper limits of active power transmission between the kth distribution network and the transmission network coupling branch respectively; represents the active power transmitted from the transmission network to the kth distribution network; represents the auxiliary frequency regulation power purchased by the transmission network from the kth distribution network; The constraints for lower-level distribution network optimization include: Controllable distributed energy C-DG power constraints: In the formula, The active and reactive power outputs of C-DG are respectively; They are the lower and upper limits of C-DG active and reactive output respectively; Distributed renewable energy R-DG power constraints: In the formula, They represent the actual output value and predicted value of distributed wind power in the kth distribution network respectively; Indicates the amount of wind power abandoned by distributed wind power; Indicates the primary frequency regulation response capability of wind power; Indicates the passive abandonment of wind power; Node active power balance constraints: Where: are the active power and reactive power injected into the kth distribution network node j by the transmission network respectively; are the active and reactive power injected into node j by C-DG respectively; are the active power output and abandoned air volume of the fan at node j respectively; They are the discharging and charging power of energy storage at node j respectively; The power transmitted from node j to node m and the power transmitted from node i to node j are respectively; Represents the square of the current amplitude of branch ij; r ij,k 、x ij,k are the resistance reactance of branch ij respectively; f k (j) l k (j) are the branch head node set and branch terminal node set with node j as the initial node; are C-DG, R-DG and energy storage set on node j respectively; Ohm's law and node voltage constraints: Where: u i,k 、u j,k are the squares of the voltage amplitudes at nodes i and j respectively; V i,k,min 、V i,k,max are the minimum and maximum limits of the voltage amplitude at node i, respectively; The line power flow constraint based on the second-order cone form can be expressed as: Transmission and distribution grid boundary interactive power constraints: Where: They represent the lower and upper limits of active power transmission between the kth distribution network and the transmission network coupling branch respectively; represents the transmission power purchased by the kth distribution network from the transmission network; It represents the auxiliary frequency regulation power provided by the kth distribution network to the transmission network.

5. A transmission and distribution network coordinated dispatching method taking into account wind power uncertainty and frequency regulation constraints as claimed in claim 1, characterized in that: The comprehensive consideration of wind power output uncertainty includes: introducing an adjustable robustness coefficient to describe the wind power output in the transmission and distribution network: Where: P w,j,t , They represent the actual output and predicted output of wind turbine j respectively; They represent the maximum upper and lower deviations of wind turbine j output respectively; represents a 0-1 variable; Γ represents an adjustable robust parameter.

6. A transmission and distribution network coordinated dispatching method taking into account wind power uncertainty and frequency regulation constraints as claimed in claim 1, characterized in that: There are strong nonlinear terms in the analytical expression of the lowest frequency point in the transient frequency safety constraint. The second-order Taylor series expansion and the big-M method are introduced to linearize the expression and transform the model into a mixed integer linear programming problem.

7. A transmission and distribution network coordinated dispatching method taking into account wind power uncertainty and frequency regulation constraints as claimed in claim 1, characterized in that: The distributed optimization implemented by the target cascade algorithm includes: Under the framework of the target cascade ATC algorithm, the consistency constraint is relaxed into the objective function in the form of a penalty function, and the objective function is modified as follows: Where: t is the time period; T represents the total time period of a scheduling cycle; N dis Indicates the number of distribution networks participating in the dispatch; C tran represents the transmission network operation cost; C dis,k represents the operating cost of the kth distribution network; represents the active power transmitted from the transmission network to the kth distribution network; represents the auxiliary frequency regulation power purchased by the transmission network from the kth distribution network; represents the transmission power purchased by the kth distribution network from the transmission network; represents the auxiliary frequency regulation power provided by the kth distribution network to the transmission network; α k,t , The algorithm multiplier representing the linear term of the penalty function; β k,t , The algorithmic multiplier representing the quadratic term of the penalty function; Respectively represent the known quantities obtained by calculation.

8. A transmission and distribution network coordinated dispatching method taking into account wind power uncertainty and frequency regulation constraints as claimed in claim 1, characterized in that: The distributed optimization implemented by the target cascade algorithm also includes the following steps: Step 1: Set the number of iterations i = 0, initialize the algorithm multiplier and the transmission and distribution network coupling interaction value and Step 2: i=i+1, each distribution network solves the distribution network optimization model in parallel to obtain the transmission and distribution boundary interaction power and auxiliary frequency regulation reserve power and transmitted to the transmission grid; Step 3: The transmission network solves the transmission network optimization model based on the data transmitted from the distribution network, and obtains the transmission and distribution boundary interaction value and to the distribution grid; Step 4: Use the following convergence criteria to check whether the loop has reached the convergence condition; if the condition is met, output the final result, otherwise, proceed to step 5; Where ε1, ε2, ε3 are the convergence accuracy; N dis The number of distribution networks participating in dispatching; They represent the boundary interaction power obtained by solving the transmission network and distribution network models in the i-th iteration respectively; They represent the auxiliary frequency regulation power obtained by solving the i-th iteration transmission network and distribution network models respectively; C tran (i) C tran (i-1) are the transmission network operation costs of the i-th and i-1-th iterations respectively; C dis,k (i) C dis,k (i-1) is the kth distribution network operation cost at the i-th and i-1-th iterations respectively; Step 5: Use the following formula to update the algorithm multiplier and return to step 2 for a new round of iteration; Where N dis Indicates the number of distribution networks participating in the dispatch; γ is a constant, 1≤γ≤3; α k,t (i) The algorithm multiplier representing the first-order term of the penalty function for the i-th iteration; α k,t (i-1), The algorithm multiplier representing the first term of the penalty function for the i-1th iteration; β k,t (i) The algorithm multiplier representing the quadratic term of the penalty function for the i-th iteration; β k,t (i-1), The algorithmic multiplier representing the quadratic term of the penalty function for the i-1th iteration.

9. A scheduling system, characterized in that: The system includes the transmission and distribution network coordinated dispatching method taking into account wind power uncertainty and frequency regulation constraints as described in any one of claims 1-8; by solving the transmission and distribution coordinated dispatching model through the system, the economic operation dispatching result of the transmission and distribution network that ensures the system frequency security can be obtained.

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