Power system dispatching method and device and electronic equipment

Abstract

The application discloses a power system scheduling method and device and electronic equipment. The method comprises the following steps: determining an objective function based on economic scheduling in a predetermined power system; determining a constraint condition based on the objective function; determining a first solution set of the objective function under the constraint condition based on a mixed integer programming algorithm; and determining a second solution set of the objective function according to the first solution set and a slime mold algorithm. The application solves the technical problem that the effect after implementing a scheduling strategy is greatly different from the actual expectation when the power system scheduling is performed in the related art.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more specifically, to a power system dispatching method, apparatus, and electronic equipment. Background Technology

[0002] Currently, with the development of the digital economy and clean energy, the demand for load dispatching of power systems is becoming increasingly widespread in production and daily life. However, in related technologies, load dispatching is mainly carried out by considering economic dispatching. However, when dispatching is carried out in this way and the corresponding algorithm is used to determine the dispatching strategy, the effect after implementing the dispatching strategy is quite different from the actual expectation. Therefore, how to more accurately determine the load dispatching of power systems is a current challenge.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a power system dispatching method, apparatus, and electronic device to at least address the technical problem in related technologies where the effect of implementing a dispatching strategy differs significantly from the actual expectation.

[0005] According to one aspect of the present invention, a power system dispatching method is provided, comprising: determining an objective function based on economic dispatching in a predetermined power system, wherein the predetermined power system is a wind power generation and photovoltaic power generation power system, the objective function is constructed based on the load transfer cost of a target load, the fuel cost, operating cost, maintenance cost, and depreciation cost of a target unit, the target load including multiple types of load, the multiple types of load being classified according to time and space transferability; determining constraints based on the objective function, wherein the constraints include power system operation constraints of the predetermined power system, load transfer constraints of the target load, and unit constraints of the target unit; determining a first solution set of the objective function under the constraints based on a mixed integer programming algorithm, wherein the first solution set includes load dispatching data corresponding to the target load and unit power data corresponding to the target unit; and determining a second solution set of the objective function based on the first solution set and a slime mold algorithm.

[0006] Optionally, determining the second solution set of the objective function based on the first solution set and the slime mold algorithm includes: determining the third solution set of the objective function under the power flow equality constraint based on the holomorphic embedding algorithm; configuring slime mold parameters corresponding to the slime mold algorithm based on the first solution set and the third solution set, wherein the slime mold parameters include the initial position of the highest food odor concentration corresponding to the slime mold algorithm, the constraint violation penalty value, and the maximum number of iterations, the initial position of the highest food odor concentration is determined based on the first solution set, and the constraint violation penalty value is determined based on the third solution set; iteratively operating the slime mold algorithm to determine the second solution set of the objective function.

[0007] Optionally, the iterative operation of the slime mold algorithm to determine the second solution set of the objective function includes: determining the fitness value sequence and the position update rate; determining the weight value of the slime mold algorithm at the current iteration number based on the fitness value sequence; and determining the second solution set of the objective function based on the first solution set, the weight value, and the position update rate.

[0008] Optionally, determining the fitness value sequence includes: determining multiple sets of fourth solution sets that have a predetermined relationship with the first solution set; and determining the fitness value sequence based on the multiple sets of fourth solution sets.

[0009] Optionally, determining the fitness value sequence based on the multiple sets of fourth solution sets includes: determining multiple sets of initial fitness values ​​corresponding to the multiple sets of fourth solution sets based on the violation penalty values; normalizing the multiple sets of initial fitness values ​​to obtain multiple sets of target fitness values ​​corresponding to the multiple sets of fourth solution sets; and arranging the multiple sets of target fitness values ​​in a predetermined order to obtain the fitness value sequence.

[0010] Optionally, determining the position update rate includes: determining the current iteration number; and determining the position update rate based on the current iteration number and the maximum iteration number.

[0011] Optionally, before determining the objective function based on economic dispatch in the predetermined power system, the method further includes: determining the target load in the predetermined power system and the target generating unit used for load transfer; determining the load transfer cost of the target load, and the fuel cost, operating cost, maintenance cost, and depreciation cost of the target generating unit.

[0012] According to one aspect of the present invention, a power system dispatching device is provided, comprising: a first determining module, configured to determine an objective function based on economic dispatching in a predetermined power system, wherein the predetermined power system is a wind power generation and photovoltaic power generation power system, the objective function is constructed based on the load transfer cost of a target load, the fuel cost, operating cost, maintenance cost, and depreciation cost of a target generator, the target load including multiple types of loads, the multiple types of loads being classified according to time and space transferability; a second determining module, configured to determine constraints based on the objective function, wherein the constraints include power system operation constraints of the predetermined power system, load transfer constraints of the target load, and generator constraints of the target generator; a third determining module, configured to determine a first solution set of the objective function under the constraints based on a mixed integer programming algorithm, wherein the first solution set includes load dispatching data corresponding to the target load and generator power data corresponding to the target generator; and a fourth determining module, configured to determine a second solution set of the objective function based on the first solution set and a slime mold algorithm.

[0013] According to one aspect of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the power system dispatching method described in any of the preceding claims.

[0014] According to one aspect of the present invention, a computer-readable storage medium is provided, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the power system dispatching method described above.

[0015] In this embodiment of the invention, an objective function based on economic dispatch in a predetermined power system is determined; constraints based on the objective function are determined; a first solution set of the objective function under the constraints is determined based on a mixed-integer programming algorithm; and a second solution set of the objective function is determined based on the first solution set and the slime mold algorithm. Since the first solution set is determined based on a mixed-integer programming algorithm, which considers the efficiency of load dispatch, and the second solution set is determined based on the first solution set and the slime mold algorithm instead of directly using the first solution set, a solution set with better implementation effect than the first solution set can be determined. Therefore, considering economic dispatch, the implementation effect of load dispatch is maximized, thereby solving the technical problem in related technologies where the effect of implementing dispatch strategies differs significantly from the actual expectations when conducting power system dispatch. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 This is a flowchart of a power system dispatching method according to an embodiment of the present invention;

[0018] Figure 2 This is a structural block diagram of a power system dispatching device according to an embodiment of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] Example 1

[0022] According to an embodiment of the present invention, an embodiment of a power system dispatching method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0023] Figure 1 This is a flowchart of a power system dispatching method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0024] Step S102: Determine the objective function based on economic dispatch in the predetermined power system;

[0025] Step S104: Determine the constraints based on the objective function, wherein the constraints include the power system operation constraints of the predetermined power system, the load transfer constraints of the target load, and the unit constraints of the target unit.

[0026] Step S106: Based on the mixed integer programming algorithm, determine the first solution set of the objective function under the constraints, wherein the first solution set includes the load scheduling data corresponding to the target load and the unit power data corresponding to the target unit;

[0027] Step S108: Determine the second solution set of the objective function based on the first solution set and the slime mold algorithm.

[0028] Through the above steps, the objective function based on economic dispatch in the predetermined power system is determined; the constraints based on the objective function are determined; a first solution set of the objective function under the constraints is determined based on a mixed-integer programming algorithm; and a second solution set of the objective function is determined based on the first solution set and the slime mold algorithm. Since the first solution set is determined based on a mixed-integer programming algorithm, which considers the efficiency of load dispatch, and the second solution set is determined based on the first solution set and the slime mold algorithm instead of directly using the first solution set, a solution set with better implementation effect than the first solution set can be determined. Therefore, considering economic dispatch, the implementation effect of load dispatch is maximized, thus solving the technical problem in related technologies where the effect of implementing dispatch strategies differs significantly from the actual expectations.

[0029] It should be noted that the aforementioned planned power system is a wind power and photovoltaic power system capable of transmitting wind power, solar power, etc. The target unit can be any equipment in the planned power system that can generate electricity and / or transmit power, and the target load can be any electrical load equipment.

[0030] As an optional implementation, the target load in the predetermined power system and the target generating units used for load transfer are determined. The load transfer cost of the target load, and the fuel cost, operating cost, maintenance cost, and depreciation cost of the target generating units are determined. Based on the above data, an objective function based on economic dispatch in the predetermined power system can be constructed. This objective function considers multiple costs and can better determine the solution set to achieve better cost savings. The target load includes various types of loads. For example, loads can be classified into different types according to different classification rules, such as by time of use, including industrial loads, residential loads, and commercial loads. Optionally, the load type can also be classified according to time and space transferability. For example, dishwashers, air conditioners, and water heaters are time-transferable loads, while electric vehicle loads are time- and space-transferable loads, and so on. The objective function determined in this way can better guide the load dispatch of the predetermined power system with the goal of minimizing costs.

[0031] As an optional implementation, constraints based on the objective function are determined. These constraints include power system operation constraints of the predetermined power system, load transfer constraints of the target load, and unit constraints of the target generating units. Constraints determined in this way are more comprehensive and complete. By determining these constraints, the economically optimal solution set can be determined under the condition that the predetermined power system, target generating units, and target load are all operating normally, ensuring the orderly operation of the predetermined power system.

[0032] As an optional implementation, a mixed-integer programming algorithm is used to determine the first solution set of the objective function under constraints. Mixed-integer programming refers to an integer programming problem where some decision variables are restricted to integers. The first solution set includes load scheduling data corresponding to the target load and unit power data corresponding to the target unit. These data can be presented as curves to illustrate power or load settings at different times. The mixed-integer programming algorithm does not consider AC power flow during the process of solving the first solution set. Because AC power flow is not considered, the process of determining the first solution set is faster, improving efficiency.

[0033] As an optional implementation, based on the first solution set and the slime mold algorithm, a second solution set of the objective function can be determined. The second solution set represents the scheduling strategy and method of the objective, namely, the target load scheduling data corresponding to the target load and the target unit power data corresponding to the target unit. Since the slime mold algorithm can perform iterative calculations based on the first solution set, the second solution set is superior to the first solution set.

[0034] As an optional embodiment, when the second solution set of the objective function can be determined based on the first solution set and the slime mold algorithm, other algorithms can also be involved to improve the accuracy of the results provided by the algorithm in this application. For example, the third solution set of the objective function under the power flow equality constraint can be determined based on the holomorphic embedding algorithm. That is, based on the holomorphic function construction principle, a holomorphic AC power flow model is constructed by embedding parameters, and then the third solution set is determined based on the holomorphic AC power flow model. When using the holomorphic embedding algorithm to solve for the third solution set, AC power flow is taken into account, making the final solution set more accurate and more in line with expectations.

[0035] As an optional embodiment, slime mold parameters corresponding to the slime mold algorithm are configured based on the first and third solution sets. The slime mold algorithm is an optimization algorithm derived from the feeding behavior of slime molds. The slime mold parameters include the initial position of the highest food odor concentration, the penalty value for violating constraints, and the maximum number of iterations. The initial position of the highest food odor concentration is determined based on the first solution set, and the penalty value for violating constraints is determined based on the third solution set. The slime mold algorithm is iteratively operated to determine the second solution set of the objective function. The second solution set determined in this way does not violate the constraints and ensures that the obtained second solution set is a reasonable and applicable solution set determined during the iteration process.

[0036] As an optional implementation, multiple sets of fourth solution sets that have a predetermined relationship with the first solution set are determined; these can be understood as solution sets that differ slightly from the first solution set. Based on the penalty value for violating constraints, multiple sets of initial fitness values ​​are determined, each corresponding to one of the multiple sets of fourth solution sets. These initial fitness values ​​are normalized to obtain multiple sets of target fitness values, each corresponding to one of the multiple sets of fourth solution sets. The multiple sets of target fitness values ​​are arranged in a predetermined order to obtain a fitness value sequence. That is, the predetermined order can be to arrange the multiple sets of fitness values ​​from largest to smallest, with higher fitness values ​​tending to be better performing solution sets.

[0037] As an optional implementation, the current iteration number is determined. Based on the current iteration number and the maximum iteration number, the position update rate is determined, where the position update rate is the update rate within the current iteration number. Based on the fitness value sequence, the weight values ​​of the slime mold algorithm at the current iteration number are determined. Based on the first solution set, the weight values, and the position update rate, a second solution set of the objective function is determined. In this way, a more optimal solution set for economic scheduling can be determined.

[0038] An optional embodiment of the present invention provides a method for power system dispatching, which is described in detail below:

[0039] S1, determine the objective function based on economic dispatch in the predetermined power system, where the predetermined power system is a power system for wind power generation and photovoltaic power generation. The objective function is constructed based on the load transfer cost of the target load, the fuel cost, operating cost, maintenance cost and depreciation cost of the target unit. The target load includes multiple types of loads, which are classified according to their time and space transferability.

[0040] Define the objective function of the predetermined power system based on economic dispatch as follows:

[0041]

[0042] In the formula, s represents the transferable load, n and N are the indices and sets of the generating units (same as the target generating units mentioned above); k and K are the indices and sets of the transferable load nodes (same as the target load mentioned above); T and T are the sets and periods of the scheduling time periods, respectively; P n (t) represents the unit output, P s,k (t) represents the transferable load size of node k after the transfer, where the node is a node with active and / or reactive power output in the predetermined power system; C fuel,n C is the unit's fuel cost function; S,n C is the startup cost function for the unit; OM,n C DP,n These represent the unit's maintenance costs and depreciation costs, respectively.

[0043] The objective function includes the following functions for unit fuel cost, start-up cost (same as the operating cost mentioned above), maintenance cost, and depreciation cost:

[0044]

[0045] C S,n (P n (t))=S n ×U start,n (t) n∈N;

[0046] C OM,n (P n (t))=K OM,n ·P n (t)·Δt n∈N;

[0047]

[0048] In the formula: a, b, and c are the coefficients of the cost function, which can be provided by the manufacturer or obtained through fitting; Sn is the unit startup cost; U start,n(t)Let be the 0 / 1 decision variables for unit startup at time t, where 1 represents startup; Caz,n is the present value of the unit capacity installation cost of the nth unit; kn is the capacity factor of the nth unit; yn is the service life of the nth unit; K OM,n This is the unit power operation and maintenance cost coefficient for the nth unit.

[0049] The load transfer costs in the objective function are as follows:

[0050] C L,k (P s,k (t))=ρ k,t ·P s,k (t)k∈K;

[0051] In the formula: ρ k,t The transfer load electricity price at transferable load node k.

[0052] S2, determine the constraints based on the objective function, where the constraints include the power system operation constraints of the predetermined power system, the load transfer constraints of the target load, and the unit constraints of the target unit.

[0053] (a) Load transfer constraints of the target load:

[0054] 1) General constraints for spatiotemporally transferable loads

[0055] The total electricity consumption of all spatiotemporally transferable loads before and after the transfer should be equal. Simultaneously, there are constraints on the size of the spatiotemporally transferable load, as specified in the following formula:

[0056]

[0057]

[0058] In the formula: k and K are the subscripts and sets of transferable loads, respectively; P s,k (t) represents the power of the transferable load k during time period t; P s,k,0 (t) represents the original transferable load before optimization; T0 represents the set of working times of the transferable load before optimization; T1 represents the set of working times of the transferable load after optimization. P s,k (t) and These are the minimum and maximum permissible power for transferable loads, respectively.

[0059] If the working time range of the transferable load is unrestricted, the set of working times of the transferable load can be regarded as the scheduling period T, i.e., T1 = T; if the working time range of the transferable load is restricted, the set of working times of the transferable load satisfies T1 ∈ T. The transferable load can only operate within the specified time T1, and the load value is 0 during the time period outside the working time T1, as shown in the following formula.

[0060]

[0061] 2) Constraints on total electricity consumption of transferable loads in different times and spaces

[0062] ① Spatial Transferable Load. Based on the latency sensitivity of data, data center loads are divided into offline loads and online loads. For online loads, the total power consumption before and after the transfer should be equal at each moment, as specified in the following formula:

[0063]

[0064] ② Time-transferable loads. For time-transferable loads such as dishwashers, air conditioners, and water heaters, the total electricity consumption before and after the transfer within the working time set should be equal. The specific formula is as follows:

[0065]

[0066] ③ Spatiotemporally transferable loads. For offline data center loads, the total power consumption before and after the transfer should be equal within the set of working hours for transferable loads. For electric vehicle loads, the charging power requirements of each electric vehicle also need to be met.

[0067] 3) Continuity constraints of transferable loads in different times and spaces

[0068] Some transferable loads require continuous operation, meaning that power supply to the load cannot be interrupted once it begins. Assume the load power remains constant during the operating time (denoted as P). s,k,0 For this type of load, a constraint on the continuous operation time of the load needs to be added. The specific constraints are as follows:

[0069]

[0070] Where: T0 is the operating time of the transferable load; U s,sta,k (t′) represents the decision variable for when the transferable load starts operating (1 for starting, 0 otherwise). A transferable load can only receive one start-up instruction within a scheduling cycle, therefore U s,sta,k (t′) must satisfy the following constraints:

[0071]

[0072] If the power of the transferable load is not constant during the operating time, but rather exhibits a power variation curve, then the assumption above that the load power remains constant during the operating time (let's call it P) is invalid. s,k,0 If this condition is not met, then the formula is:

[0073] This does not apply to situations where the load is not constant during working hours. The transferable load constraint in this case is as follows:

[0074]

[0075] In the formula: U s,k,t′ (t) is a 0 / 1 auxiliary variable for the transferable load k in time period t. A value of 1 indicates that the value of the transferable load k in time period t is the value of the original transferable load in time period t′. To ensure the uniqueness and continuity of the transferable load operation, U s,k,t′ (t) needs to satisfy several of the following formulas simultaneously:

[0076]

[0077]

[0078]

[0079] In the formula: with U s,k,t′ (t) is defined similarly, with its subscript t 0,sta This indicates the first time period during which the original transferable load begins operation; t 1,sta With t 1,end These are the allowable start and end times for the transferable load, i.e., T1 = [t 1,sta ,t 1,end ].

[0080] (II) Power System Operation Constraints:

[0081] The power system flow constraints, voltage and capacity upper and lower limit constraints, and reserve constraints are as follows:

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] In the formula: and Let these represent the sum of active power output and the sum of reactive power output of the units at node i, respectively. and P represents the active load and reactive load at node i, respectively; s,i This represents the amount of load transferred at node i, with positive values ​​indicating the amount of load transferred in and negative values ​​indicating the amount of load transferred out. The reactive power injected into the capacitor or reactor bank at node i; G ij and B ij V represents the real and imaginary parts of the element in the i-th row and j-th column of the admittance matrix, respectively; i and V j θ represents the voltage magnitudes at nodes i and j, respectively; ij The voltage phase angle difference between node i and node j; V i and Indicates the lower and upper limits of voltage; Indicates the upper limit of power flow on the line; and P n R+ and R represent the upper and lower limits of the output of the nth conventional generator unit, respectively; - These represent the system's upper and lower backup requirements, respectively; Δ down,n Δ up,n These represent the maximum downward and upward ramp rates of the controllable unit n, respectively.

[0088] (III) Unit constraints of the target unit:

[0089] Controllable unit operation constraints include: upper and lower limits of unit output, minimum downtime constraint, minimum running time constraint, and ramp-up constraint.

[0090]

[0091]

[0092]

[0093] -Δ down,n ≤P n (t)-P n (t-1)≤Δ up,n n∈N;

[0094] In the formula: P n , U represents the lower and upper limits of the output of unit n, respectively; n (t) represents the operating status of unit n, where 0 indicates shutdown and 1 indicates operation; U start,n (t), U shut,n (t) represents the start-up decision variable and the shutdown decision variable of unit n, respectively; MOTn is the minimum start-up time of unit n; MDTn is the minimum start-up time of unit n.

[0095] The day-ahead dispatch values ​​of wind power and photovoltaic power output are constrained by the predicted values ​​of wind power and photovoltaic power output. The constraints of wind power and photovoltaic power output are shown in the following formulas.

[0096] P w (t)≤P WT,fore (t);

[0097] P p (t)≤P PV,fore (t);

[0098] In the formula: P WT,fore (t) and P PV,fore (t) represents the predicted values ​​of wind power output and photovoltaic power output, respectively.

[0099] S3, based on the mixed integer programming algorithm, determines the first solution set of the objective function under the constraints. The first solution set includes the load scheduling data corresponding to the target load and the unit power data corresponding to the target unit, that is, the optimal output of the unit and the load.

[0100] S4. Based on the fully morphological embedding algorithm, determine the third solution set of the objective function under the power flow equality constraint;

[0101] Consider the power balance equations at the PQ nodes of an AC system:

[0102]

[0103] In the formula: Y ik V is the admittance between node i and node k; k , S represents the conjugate of the voltage at node k and the voltage at node i, respectively; i Power injection for node i.

[0104] By embedding a complex variable s into the above equation and expressing the voltage unknown as a holomorphic function V(s) of the embedded complex variable, the power balance equation at the PQ node can be constructed in the following form:

[0105]

[0106] In the formula: Y iktrans Y is the admittance corresponding to the series branch between node i and node k; ishunt The admittance corresponding to the parallel branch associated with node i; to ensure complete purity, the conjugate node voltage... Represented as conjugate complex variable s * holomorphic functions

[0107] When s = 1, the above equation can revert to the initial power flow equation represented by the power balance equation; when s = 0, the above equation corresponds to the case where the system is unloaded, has no generators, and has no shunt branches. All node voltages are equal to the voltage values ​​of the relaxed nodes at this time. For simplicity, let the voltage value at this time be V. k(0)=1∠0°, therefore, for a relaxed node with constant voltage, its node model can be expressed as follows:

[0108]

[0109] In the formula: V is the given voltage of the relaxation node; sl (s) is a holomorphic function of the relaxed node voltage.

[0110] Analogous to PQ nodes and relaxed nodes, for a known node, the injected active power P i and voltage amplitude For the PV nodes, reconstruct their power flow equations as follows:

[0111]

[0112]

[0113] In the formula: The magnitude of the voltage given to the PV node; Q i (s) is a holomorphic function for injecting reactive power into PV nodes.

[0114] Based on the properties of holomorphic functions, the constructed holomorphic function of the unknown can be expanded into the following equations: V(S) and V * (S * The power series shown is given, and W(s) is defined as the reciprocal of the holomorphic voltage function V(s). It is also expanded into a power series:

[0115]

[0116]

[0117]

[0118] At this point, the holomorphic function of the unknown can be expanded into a power series, and let both sides of the equation correspond to s, s 2 ,s 3 ,…s n Since the coefficients are all equal, we can obtain the following recursive relationships for solving the coefficients of the power series:

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] In the formula: V ire [n] represents the real part of the nth coefficient of the voltage power series at node i.

[0125] Y iktrans The real and imaginary parts are separated into conductance G. ik and susceptance B ik V k [n] is separated into the real part V kre [n] and the imaginary part V kim [n], for an N-node AC system, can be transformed into a 2N-order recursive solution matrix as shown below:

[0126]

[0127] Combined with the initial value V when s=0 k [0] = 1∠0°, Q i By using [0] = 0 and the recursive relationship between W[n] and the above matrix, the coefficients of the power series of the unknown quantity can be obtained. The Pad approximation is used to accelerate the convergence of the voltage power series and the reactive power power series, and the numerical solutions of the steady-state voltage and reactive power of the AC system when s = 1 are obtained.

[0128] The obtained numerical solution is substituted into the original power flow equation to calculate the power mismatch. If the accuracy requirement is not met, the number of power series terms and the Pad approximation order are increased and the calculation is repeated until the accuracy requirement is met.

[0129] Check the reactive power constraints of the PV nodes. If the obtained reactive power solution exceeds the limit, update the corresponding PV node to a PQ node and update the corresponding elements in the recursion matrix. Repeat the recursive calculation until all conditions are met, and finally output the calculated third solution set.

[0130] S5. Based on the first solution set and the third solution set, configure the slime mold parameters corresponding to the slime mold algorithm. The slime mold parameters include the initial position of the highest food odor concentration corresponding to the slime mold algorithm, the penalty value for violating the constraint, and the maximum number of iterations. The initial position of the highest food odor concentration is determined based on the first solution set, and the penalty value for violating the constraint is determined based on the third solution set.

[0131] S6, the iterative computation slime mold algorithm, determines the second solution set of the objective function.

[0132] The slime mold algorithm is used to solve for the optimal output of the unit and load (same as the second solution set mentioned above). It simulates the food-approaching behavior of slime molds during their foraging process, and solves for the second solution set X. The position update equation for X is as follows:

[0133]

[0134] p = tanh|S(i) - FD |;

[0135] In the formula: m is the current iteration number; X, X b X represents the current location of the slime mold and the location with the highest concentration of the currently detected food odor, respectively. The location with the highest concentration of the currently detected food odor corresponds to the location in the first solution set. A and X B This represents two randomly selected slime mold locations. It should be noted that other slime mold locations are obtained from a fourth solution set that is close to the first solution set; UB and LB represent the upper and lower limits of the search range, respectively; rand and r represent random values ​​in [0,1]; z is a constant, typically taken as 0.03; v b The range of values ​​for is [-a, a], where m max v is the maximum number of iterations. c The fitness of X decreases linearly from 1 to 0; S(i) represents the fitness of X; F D This represents the optimal fitness obtained across all iterations. W is the fitness weight of the slime mold individual, calculated as follows:

[0136]

[0137] SmellIndex = sort(S);

[0138] In the formula: bF represents the best fitness obtained in the current iteration; wF represents the worst fitness obtained in the current iteration; C represents the set of individuals with fitness S(i) in the top half; SmellIndex represents the fitness sequence (an increasing sequence in the minimum problem).

[0139] That is, after configuring the slime mold parameters corresponding to the slime mold algorithm, set the current iteration number to 1, iterate the slime mold algorithm, and determine the second solution set of the objective function.

[0140] Optionally, during the iterative computation, parameters such as the fitness value sequence and position update speed are determined. Based on the fitness value sequence, the weight values ​​of the algorithm at the current iteration number are determined. Multiple sets of fourth solution sets with predetermined relationships to the first solution set are determined. Based on the constraint violation penalty value, multiple sets of initial fitness values ​​corresponding to the multiple sets of fourth solution sets are determined; the multiple sets of initial fitness values ​​are normalized to obtain multiple sets of target fitness values ​​corresponding to the multiple sets of fourth solution sets; the multiple sets of target fitness values ​​are arranged in descending order to obtain the fitness value sequence, and the currently obtained bF, wF, and F are recorded. D X bParameter values. Then, based on the first solution set, the weight values ​​and the position update rate, update the position with the highest odor concentration, and determine whether the maximum number of iterations has been reached or the iteration stopping condition has been met. If not, repeat the above steps. If it is met, determine the second solution set of the objective function.

[0141] Through the above optional implementation methods, at least the following beneficial effects can be achieved: the scheduling method represented by the determined second solution set can meet the requirements of the power system for wind power generation and photovoltaic power generation, and can reduce costs as much as possible to achieve the best economic efficiency.

[0142] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0143] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0144] Example 2

[0145] According to embodiments of the present invention, an apparatus for implementing the above-described power system dispatching method is also provided. Figure 2 This is a structural block diagram of a power system dispatching device according to an embodiment of the present invention, such as... Figure 2 As shown, the device includes: a first determining device 202, a second determining device 204, a third determining device 206 and a fourth determining device 208. The device will be described in detail below.

[0146] The first determining module 202 is used to determine the objective function based on economic dispatch in a predetermined power system, wherein the predetermined power system is a wind power generation and photovoltaic power generation power system, and the objective function is constructed based on the load transfer cost of the target load, the fuel cost, operating cost, maintenance cost and depreciation cost of the target unit, and the target load includes multiple types of load, which are classified according to their time and space transferability; the second determining module 204 is connected to the first determining module 202 and is used to determine the constraints based on the objective function, wherein the constraints include the power system operation constraints of the predetermined power system, the load transfer constraints of the target load, and the unit constraints of the target unit; the third determining module 206 is connected to the second determining module 204 and is used to determine the first solution set of the objective function under the constraints based on a mixed integer programming algorithm, wherein the first solution set includes the load dispatch data corresponding to the target load and the unit power data corresponding to the target unit; the fourth determining module 208 is connected to the third determining module 206 and is used to determine the second solution set of the objective function based on the first solution set and the slime mold algorithm.

[0147] It should be noted that the first determining device 202, the second determining device 204, the third determining device 206 and the fourth determining device 208 mentioned above correspond to steps S102 to S108 in the implementation of the power system dispatching method. The multiple modules and the corresponding steps are the same in terms of implementation examples and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0148] Example 3

[0149] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to execute instructions to implement the power system scheduling method of any of the above embodiments.

[0150] Example 4

[0151] According to another aspect of the present invention, a computer-readable storage medium is also provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform any of the above-described power system dispatching methods.

[0152] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0153] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0154] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0155] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0156] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0157] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0158] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A power system dispatching method characterized by, include: Determine the objective function based on economic dispatch in a predetermined power system, wherein the predetermined power system is a power system for wind power generation and photovoltaic power generation, and the objective function is constructed based on the load transfer cost of the target load, the fuel cost of the target unit, the operating cost, the maintenance cost and the depreciation cost, wherein the target load includes multiple types of load, and the multiple types of load are classified according to the transferability in time and space; Determine the constraints based on the objective function, wherein the constraints include the power system operation constraints of the predetermined power system, the load transfer constraints of the target load, and the unit constraints of the target unit; Based on the mixed integer programming algorithm, a first solution set of the objective function under the constraints is determined, wherein the first solution set includes the load scheduling data corresponding to the target load and the unit power data corresponding to the target unit; Based on the first solution set and the slime mold algorithm, the second solution set of the objective function is determined; The step of determining the second solution set of the objective function based on the first solution set and the slime mold algorithm includes: determining the third solution set of the objective function under the power flow equality constraint based on the holomorphic embedding algorithm; configuring slime mold parameters corresponding to the slime mold algorithm based on the first solution set and the third solution set, wherein the slime mold parameters include the initial position of the highest food odor concentration corresponding to the slime mold algorithm, the constraint violation penalty value, and the maximum number of iterations, wherein the initial position of the highest food odor concentration is determined based on the first solution set, and the constraint violation penalty value is determined based on the third solution set; and iteratively operating the slime mold algorithm to determine the second solution set of the objective function.

2. The method of claim 1, wherein, The iterative operation of the slime mold algorithm to determine the second solution set of the objective function includes: Determine the fitness value sequence and the position update rate; Based on the fitness value sequence, determine the weight value of the slime mold algorithm at the current iteration number; Based on the first solution set, the weight values, and the position update rate, the second solution set of the objective function is determined.

3. The method of claim 2, wherein, The determination of the fitness value sequence includes: Determine multiple sets of fourth solutions that have a predetermined relationship with the first solution set; The fitness value sequence is determined based on the multiple sets of fourth solution sets.

4. The method of claim 3, wherein, The step of determining the fitness value sequence based on the multiple sets of fourth solution sets includes: Based on the penalty value for violating the constraint, determine multiple sets of initial fitness values ​​corresponding to the multiple sets of fourth solution sets respectively; Normalize the multiple sets of initial fitness values ​​to obtain multiple sets of target fitness values ​​corresponding to the multiple sets of fourth solution sets respectively; The multiple sets of target fitness values ​​are arranged in a predetermined order to obtain the fitness value sequence.

5. The method of claim 2, wherein, Determine the location update speed, including: Determine the current iteration number; The position update speed is determined based on the current iteration number and the maximum iteration number.

6. The method according to any one of claims 1 to 5, characterized in that, Before determining the objective function based on economic dispatch in the predetermined power system, the following steps are also included: Determine the target load in the predetermined power system, and the target generating unit used to transfer the load; Determine the load transfer cost of the target load, the fuel cost of the target unit, the operating cost, the maintenance cost, and the depreciation cost.

7. A power system dispatching device characterized by comprising: include: The first determining module is used to determine the objective function based on economic dispatch in a predetermined power system, wherein the predetermined power system is a power system for wind power generation and photovoltaic power generation, and the objective function is constructed based on the load transfer cost of the target load, the fuel cost of the target unit, the operating cost, the maintenance cost and the depreciation cost, and the target load includes multiple types of load, which are classified according to the transferability in time and space; The second determining module is used to determine the constraints based on the objective function, wherein the constraints include the power system operation constraints of the predetermined power system, the load transfer constraints of the target load, and the unit constraints of the target unit; The third determining module is used to determine the first solution set of the objective function under the constraints based on the mixed integer programming algorithm, wherein the first solution set includes the load scheduling data corresponding to the target load and the unit power data corresponding to the target unit; The fourth determining module is used to determine the second solution set of the objective function based on the first solution set and the slime mold algorithm; The fourth determining module is further configured to: determine the third solution set of the objective function under the power flow equality constraint based on the fully pure embedding algorithm; configure the slime mold parameters corresponding to the slime mold algorithm based on the first solution set and the third solution set, wherein the slime mold parameters include the initial position of the highest food odor concentration corresponding to the slime mold algorithm, the penalty value for violating the constraint, and the maximum number of iterations, wherein the initial position of the highest food odor concentration is determined based on the first solution set, and the penalty value for violating the constraint is determined based on the third solution set; and iteratively operate the slime mold algorithm to determine the second solution set of the objective function.

8. An electronic device, comprising: include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the power system scheduling method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the power system dispatching method as described in any one of claims 1 to 6.

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