Novel power system multi-target active and reactive power coordinated optimization method and device

Abstract

The invention discloses a novel power system multi-target active and reactive power coordination optimization method and device, and relates to the technical field of novel power system optimization scheduling, and the method comprises the steps: building a multi-target active and reactive power coordination optimization model with the lowest total operation converted power loss and the optimal static stability index of a system as optimization targets, determining an electric network constraint condition; water energy storage active output, thermal power generating unit active output, wind power actual grid-connected power, photovoltaic actual grid-connected power, reactive compensation equipment capacity and converter reactive capacity are used as populations, and the lowest total operation converted power loss and the optimal static stability index of the system are used as population fitness. And generating upper and lower limits of population generation and upper and lower limits of population scheduling change in front and back time periods by using an electric network constraint condition, and solving a multi-target active and reactive coordinated optimization model by using a multi-target differential evolution algorithm. According to the method, the converted power loss and the static stability in a novel power system scheduling decision can be considered.

Description

Technical Field

[0001] The present application relates to the technical field of novel power system optimization and dispatching, and in particular to a novel power system multi-objective active and reactive coordinated optimization method and equipment. Background Art

[0002] In recent years, with the rapid development of renewable energy grid-connected technology, the grid-connected capacity of new energy sources such as wind power and photovoltaics has been continuously improved. However, the fluctuating output characteristics of large-scale new energy sources have brought serious potential risks to the safe operation of the power system. In order to ensure the safe operation of the system, there are often problems of wind and solar abandonment in the optimization decision-making process, which to a certain extent reduces the economic efficiency of new energy grid connection.

[0003] The existing research on the optimization and dispatching of new power systems is mainly divided into two parts: one is to optimize the dispatching of the day-ahead active power of multiple types of active sources, aiming to reduce the converted power loss during the operation of the power system. The optimization is usually set to minimize the operating loss and wind and solar power abandonment under the expected conditions of meeting various energy constraints, which belongs to the dispatching of the economic field of the power system; the other is to optimize the configuration of reactive power compensation for the power system by multiple types of energy. The configuration of reactive power compensation capacity is crucial to the stability of the power system. The existing optimization configuration of reactive power compensation capacity mainly takes the quantitative voltage stability index, voltage deviation and power system network loss as the optimization target, and improves the safety, stability and operation reliability of the power system under the operating constraints of reactive sources such as reactive compensation devices. These contents cannot simultaneously consider the system loss and stability optimization problems of active and reactive power combined. Summary of the invention

[0004] The purpose of this application is to provide a novel multi-objective active and reactive coordinated optimization method and device for a new power system, which can take into account both the converted power loss and static stability in the dispatching decision of the new power system.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a novel multi-objective active and reactive coordinated optimization method for a power system, the novel multi-objective active and reactive coordinated optimization method for a power system comprising:

[0007] Obtain the generator set data and load data of the area to be optimized;

[0008] The total operation converted power loss of the system is calculated by using the generator set data and the load data; the total operation converted power loss of the system includes: the converted loss of various generator sets, the loss of reactive power compensation equipment and the network loss; the various generator sets include: thermal power units, hydropower storage units, wind power units and photovoltaic units; the network loss includes: current meter loss;

[0009] Taking the minimum total system operation converted power loss and the optimal static stability index as the optimization objectives, a multi-objective active and reactive coordination optimization model is established; the coordination content of the multi-objective active and reactive coordination optimization model includes: the active output of hydropower storage, the active output of thermal power units, the actual grid-connected power of wind power, the actual grid-connected power of photovoltaic power, the capacity of reactive compensation equipment and the reactive capacity of converter;

[0010] Determining the power network constraint conditions of the multi-objective active and reactive coordinated optimization model;

[0011] The active output of hydropower storage, the active output of thermal power units, the actual grid-connected power of wind power, the actual grid-connected power of photovoltaic power, the capacity of reactive compensation equipment and the reactive capacity of converter are taken as the population, the minimum total system operation converted power loss and the optimal static stability index are taken as the population fitness, the power network constraints are taken as the upper and lower limits of population generation and the upper and lower limits of population scheduling changes in the previous and next time periods, and the multi-objective differential evolution algorithm is used to solve the multi-objective active and reactive coordination optimization model.

[0012] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the novel multi-objective active and reactive coordinated optimization methods for a power system described above.

[0013] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the novel multi-objective active and reactive coordinated optimization methods for a power system as described above.

[0014] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the novel multi-objective active and reactive coordinated optimization methods for a power system described above.

[0015] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0016] The present application provides a novel multi-objective active and reactive coordinated optimization method and device for a power system, the method comprising: obtaining generator set data and load data of a region to be optimized; using the generator set data and the load data to calculate the system's total operation converted power loss; the system's total operation converted power loss comprises: the converted loss of various generator sets, the reactive compensation equipment loss and the network loss; the various generator sets comprise: thermal power units, hydropower storage units, wind power units and photovoltaic units; the network loss comprises: current meter loss; taking the system's total operation converted power loss as the minimum and the static stability index as the optimal as the optimization objectives, a multi-objective active and reactive coordinated optimization model is established; the multi-objective active and reactive coordinated optimization model The coordination content of the model includes: hydropower storage active output, thermal power unit active output, wind power actual grid-connected power, photovoltaic actual grid-connected power, reactive compensation equipment capacity and converter reactive capacity; determine the power network constraints of the multi-objective active and reactive coordination optimization model; take hydropower storage active output, thermal power unit active output, wind power actual grid-connected power, photovoltaic actual grid-connected power, reactive compensation equipment capacity and converter reactive capacity as the population, take the lowest system total operation converted power loss and the best static stability index as the population fitness, take the power network constraints as the upper and lower limits of population generation and the upper and lower limits of population scheduling changes in the previous and next time periods, and use the multi-objective differential evolution algorithm to solve the multi-objective active and reactive coordination optimization model. This application takes into account the specific contents of the system total operation converted power loss and static stability indicators in both the optimization model construction and solution stages. This application can take into account both the converted power loss and static stability in the scheduling decision of the new power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is an application environment diagram of a novel multi-objective active and reactive coordinated optimization method for a power system in one embodiment of the present application.

[0019] Figure 2 A flowchart of a novel multi-objective active and reactive coordinated optimization method for a power system provided in one embodiment of the present application.

[0020] Figure 3 A flowchart of a novel multi-objective active and reactive coordinated optimization method for a power system provided in one embodiment of the present application.

[0021] Figure 4A schematic diagram of an improved IEEE 30 node provided in an embodiment of the present application.

[0022] Figure 5 A schematic diagram of wind and solar power output and load curves provided in one embodiment of the present application.

[0023] Figure 6 A schematic diagram of TS1 active and reactive power optimization scheduling results provided in an embodiment of the present application.

[0024] Figure 7 A schematic diagram of TS3 active and reactive power optimization scheduling results provided in one embodiment of the present application.

[0025] Figure 8 A schematic diagram of a multi-objective solution set for 24-hour active and reactive power coordinated optimization provided in one embodiment of the present application.

[0026] Fig. 9 A schematic diagram of typical power loss calculations for different solutions provided in one embodiment of the present application.

[0027] Fig.10 A schematic diagram of the static stability L index of typical solutions provided for different schemes according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] At present, the research on the optimal dispatch of new power systems is mainly divided into two parts: one is the optimal dispatch of the day-ahead active power of multiple types of active sources, which aims to reduce the converted power loss during the operation of the power system. The optimization setting is usually set to minimize the operating loss and wind and solar abandonment under the expected conditions of meeting various energy constraints, which belongs to the dispatch of the economic field of the power system; the other is the optimal configuration of reactive power compensation for the power system by multiple types of energy. The configuration of reactive power compensation capacity is crucial to the stability of the power system. The existing optimal configuration of reactive power compensation capacity mainly takes the quantitative voltage stability index, voltage deviation and power system network loss as the optimization target, and improves the safety, stability and operation reliability of the power system under the operating constraints of reactive sources such as reactive compensation devices. In fact, the active power optimization dispatch strategy taking into account the uncertainty and volatility of new energy may damage the stability of the power system. The requirements for reactive power compensation capacity configuration under different active dispatch schemes are also different. The single optimization criterion of loss or stability may cause the static stability domain of the system to be reduced or deviate from economic operation for a long time. It is particularly important to optimize the system loss and stability taking into account the combined active and reactive power. In order to take into account the converted power loss and static stability in the dispatching decision of the new power system, and to fully explore the reactive power regulation capability of the converter, this application proposes a multi-objective active and reactive coordination optimization strategy that takes into account the reactive support capability of the converter.

[0030] In order to make the above-mentioned objects, features and advantages of the present application more obvious and understandable, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0031] The present invention provides a novel multi-objective active and reactive coordination optimization method for a power system, which can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the generator set data and load data to be processed to the server 104. After the server 104 receives the generator set data and load data to be processed, it uses the generator set data and the load data to calculate the system's total operation converted power loss; the system's total operation converted power loss includes: the converted loss of various types of generator sets, the reactive compensation equipment loss and the network loss; the various types of generator sets include: thermal power units, hydropower units, wind power units and photovoltaic units; the network loss includes: current meter loss; taking the minimum system total operation converted power loss and the optimal static stability index as the optimization objectives, a multi-objective active and reactive coordination optimization model is established; the multi-objective active and reactive coordination optimization The coordination content of the optimization model includes: hydropower storage active output, thermal power unit active output, wind power actual grid-connected power, photovoltaic actual grid-connected power, reactive compensation equipment capacity and converter reactive capacity; determine the power network constraints of the multi-objective active and reactive coordination optimization model; take hydropower storage active output, thermal power unit active output, wind power actual grid-connected power, photovoltaic actual grid-connected power, reactive compensation equipment capacity and converter reactive capacity as populations, take the lowest system total operation converted power loss and the best static stability index as population fitness, take the power network constraints as the upper and lower limits of population generation and the upper and lower limits of population scheduling changes in the previous and next time periods, and use the multi-objective differential evolution algorithm to solve the multi-objective active and reactive coordination optimization model. The server 104 can feedback the final result obtained to the terminal 102. In addition, in some embodiments, the novel power system multi-objective active and reactive coordinated optimization method can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly process the generator set data and load data to be processed, or the server 104 can obtain the generator set data and load data to be processed from the data storage system and process them.

[0032] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0033] In an exemplary embodiment, Figure 2As shown, a novel multi-objective active and reactive coordination optimization method for a power system is provided. The method is executed by a computer device, and specifically can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, and the steps include the following steps S1 to S5.

[0034] in:

[0035] S1. Obtain the generator set data and load data of the area to be optimized.

[0036] In this embodiment, the characteristics of wind and solar power output and daily load characteristics of the area to be optimized are sorted out and studied to predict the load curve and wind and solar power output curve before the day. Including the output size of wind turbines and photovoltaic units in each period of 24h and the corresponding time, air temperature, temperature, light, wind speed and other variables, as well as the corresponding relationship between the load size and the corresponding time, air temperature, humidity and other weather factors. According to the time series of wind and solar power output data and load curve (this part means that the wind and solar power output and load curve have obvious time-varying laws on the time scale. The commonly used prediction method is mainly neural network. Neural network can effectively learn existing data and predict the corresponding output based on input data. Here, the load and wind and solar power output are predicted based on time series and meteorological forecast factors), the subsequent 24h optimization scheduling of wind and solar power output and load data in each period is configured and optimized based on the predicted value.

[0037] As an optional implementation method, the wind, solar, thermal and storage load data are cleaned, and the wind and solar output and load curve of the day before are predicted based on the long short-term memory (LSTM) neural network. After meeting the error check requirements, they are used as the wind and solar output and load curve data of the day before.

[0038] Step S1 is to organize the new energy units and load data in the optimized area as the benchmark value for subsequent optimized scheduling in each period.

[0039] S2. Calculate the system's total operating converted power loss using the generator set data and the load data; the system's total operating converted power loss includes: converted losses of various generator sets, reactive compensation equipment losses, and network losses; the various generator sets include: thermal power units, hydropower storage units, wind power units, and photovoltaic units; the network losses include: current transmitter losses.

[0040] S3. Taking the minimum total system operation converted power loss and the optimal static stability index as the optimization objectives, a multi-objective active and reactive coordination optimization model is established; the coordination content of the multi-objective active and reactive coordination optimization model includes: hydropower storage active output, thermal power unit active output, wind power actual grid-connected power, photovoltaic actual grid-connected power, reactive compensation equipment capacity and converter reactive capacity.

[0041] In this embodiment, the two objectives of the system total operation converted power loss and the stability L index are optimized, and the optimization objective can be expressed by the following formula:

[0042] minf(x)=[f L (x),f M (x)] T ;

[0043] Where f(x) is the objective function; f L (x) is the quantitative L index of the static stability of the system; f M (x) is the total power loss of the system. Based on the two objectives, a multi-objective multi-energy system active and reactive coordination optimization model is established as follows:

[0044] 1) Static stability index:

[0045] The voltage stability index L is often used to monitor and evaluate the static stability of the system. This index divides the power system nodes into PV nodes g (including balance nodes) and PQ nodes p. After classification, the system state equation can be expressed as follows:

[0046]

[0047] The voltage phasor Up (Ug) corresponds to the PQ node (PV node) in the network, the current vector Ip (Ig) is the current injected into the PQ node (PV node), and Ypp, Ypg, Ygp, and Ygg are the admittance sub-matrices of the corresponding node voltage equation. Through further transformation, the following formula can be obtained:

[0048]

[0049] Define the PQ node participation factor:

[0050]

[0051] PQ node L indicator calculation:

[0052]

[0053] Among them, α is the PV node set, β is the PQ node set, and U j (U k) is the voltage phasor at node j(k). Here, according to the practical criterion, the L index is in the interval (0,1), indicating that the node is statically stable. The larger the index, the weaker the stability of the node; when L=1, it is in a critical state; when L>1, the node is statically unstable. Generally, the peak value of the L index of each node is used as the system L index, and the stability optimization target and constraint conditions of this embodiment are further analyzed and explained. The static stability of the system can be expressed as:

[0054] f L =max(L1,L2...L j );

[0055] Among them, f L Is the static stability index; L j is the voltage stability L index of j nodes.

[0056] According to the definition of L index, the larger the node L index is, the lower the stability of the node is. The maximum value of L index in the optimized system is used as the stability index of the system. The decrease of this stability index indicates the increase of power system stability.

[0057] 2) Calculate power loss:

[0058] The system operation conversion loss mainly includes the conversion loss of various types of generator sets, network loss and reactive power compensation equipment loss. It is mainly expressed as:

[0059] f M =f G +f LOSS +f CP ;

[0060] Among them, f M The loss calculated for system operation; f G The converted power loss of each type of unit; f LOSS is the system network loss; f CP It is the loss of the reactive power compensation device of the system.

[0061] ① Converted power loss of various units:

[0062] The converted power loss of each unit mainly covers three aspects: the operating loss of thermal power units, the operating loss of pumped storage power stations, and the abandonment loss of wind and solar renewable energy.

[0063] f G =f1+f2+f3;

[0064] Among them, f G is the converted power loss of each unit; f1 is the operating loss of the thermal power unit; f2 is the operating loss of the pumped storage power station; f3 is the loss of wind and solar power abandonment.

[0065] The converted loss of the thermal power unit is (the operating loss of the thermal power unit mainly includes the coal consumption during operation and the start-stop loss):

[0066]

[0067] Among them, f1 is the total operating loss of conventional thermal power units; f mh is the coal consumption of thermal power units; f qt is the start-stop loss of thermal power units; P i,t is the output of the i-th unit at time t; N T , N G are the dispatching period and the number of thermal power units involved in the dispatching; a i , b i and c i are the consumption coefficients of unit i; S it is the single start-stop loss; u it The unit is in operation status.

[0068] The converted loss of the water storage unit is:

[0069]

[0070] in, represents the output of the hth pumped storage power station in period t; N H is the number of pumped storage power stations; K s,t , K g,t are the working states of the pumped storage power station at time t; η g , η s are the power generation and water storage loss coefficients of the pumped storage power station respectively; Q cp.ps is the unit operation loss coefficient; P g.ps , P s.ps are the unit losses of power generation and water storage operation of the hydropower station respectively; η p Energy conversion efficiency of pumped storage power station.

[0071] The power loss generated by renewable resources such as wind power and photovoltaic power is generally expressed as the abandoned capacity per unit time, that is, the sum of abandoned wind power and photovoltaic power. The more the total amount of unabsorbed energy, the weaker the power system's ability to absorb new energy, the worse the economic benefits of renewable energy, and the higher the loss. The converted loss of the wind turbine and photovoltaic unit is:

[0072]

[0073] in, represents the abandoned wind power of the lth wind farm at time t; N W is the number of wind farms; N S is the number of photovoltaic power stations; represents the wind power abandoned by the mth PV power station at time t; Δt is the scheduling time accuracy; Indicates the unit loss of wind and solar power abandoned at time t.

[0074] ②Network loss:

[0075] The network loss is:

[0076] Among them, β is the conversion coefficient; τ max is the annual maximum load loss hours; ΔP LOSS，t is the network loss power at time t.

[0077] ③ Loss calculation of reactive equipment:

[0078] The reactive power compensation equipment loss is:

[0079] in, is the reactive power output of the cth reactive power compensation device at time t, Q c,SVC is the capacity of the cth reactive power compensation device; N C is the number of reactive power compensation devices.

[0080] S4. Determine the power network constraints of the multi-objective active and reactive power coordinated optimization model.

[0081] The power flow balance constraint is:

[0082]

[0083] Among them, P i,t is the active power of the i-th thermal power unit at time t; is the grid-connected active power of the lth wind farm at time t; is the grid-connected active power of the hth pumped-storage power station at time t; is the grid-connected active power of the mth PV power station at time t; is the load power of the jth node; N G N is the number of thermal power units involved in dispatching; W is the number of wind farms; N S is the number of photovoltaic power stations; N H is the number of pumped storage power stations; N L is the number of network nodes, △P Loss For network loss.

[0084] The distributed wind and solar output constraints are:

[0085]

[0086] in, is the maximum active power of wind farm l; is the maximum active power of the photovoltaic field l; and are the actual grid-connected powers of the lth and mth units at time t respectively.

[0087] The constraints of thermal power units mainly include unit output constraints and ramp constraints. The constraints of conventional thermal power units are:

[0088]

[0089] Among them, P i,min , P i,max are the upper and lower limits of active output of the i-th thermal power unit; r i,down 、r i,up P is the maximum upward and downward climbing rate of the thermal power unit; i,t-1 is the active power of the i-th thermal power unit at time t-1.

[0090] The constraints of a pumped storage power station mainly include the upper and lower limits of the unit output and the operating state constraints, which are specifically manifested as the power meeting the upper and lower limit requirements and cannot be in the power generation and energy storage state at the same time. The operating constraints of the pumped storage power station are:

[0091]

[0092] Among them, K is the total number of units in the pumped storage power station; They represent the total power generated and absorbed by the h-th pumped storage power station; P g,h,max With P g,h,min are the maximum and minimum power generation capacity of a single pumped storage unit respectively; P s,h,max With P s,h,min K is the maximum and minimum value of power absorbed by a single pumped storage unit; g,h,r The pumped storage power station is in power generation operation state, K s,h,t The pumped storage power station is in energy storage operation state.

[0093] The converter operation constraints are:

[0094]

[0095] Among them, Q n,t,min , Q n,t,max are the minimum and maximum reactive power output of the converter; P n,t is the active power generated by the nth converter; is the power factor standard value at time t; Q n,t is the reactive power generated by the nth converter at time t.

[0096] The static VAR compensator operation constraints are:

[0097]

[0098] in, are the minimum and maximum output values ​​of the cth static VAR compensation device respectively; is the reactive power of the cth reactive compensation equipment at time t.

[0099] S5. Taking the active output of hydropower storage, the active output of thermal power units, the actual grid-connected power of wind power, the actual grid-connected power of photovoltaic power, the capacity of reactive compensation equipment and the reactive capacity of converter as the population, taking the lowest total system operation converted power loss and the best static stability index as the population fitness, taking the power network constraints as the upper and lower limits of population generation and the upper and lower limits of population scheduling changes in the previous and next time periods, and using a multi-objective differential evolution algorithm to solve the multi-objective active and reactive coordination optimization model.

[0100] In this embodiment, a multi-objective solution algorithm MODEA is configured to perform iterative optimization of the multi-objective optimization model.

[0101] The multi-objective differential evolution algorithm (MODEA) is a variant of the differential evolution intelligent algorithm. It uses a competitive learning mechanism to generate the initial population and updates the offspring population based on a random positioning mutation mechanism. Finally, it uses the tournament selection method and the crowding ranking method to obtain the Pareto frontier. The main contents are as follows:

[0102] 1) Population initialization phase: To ensure the diversity of the population during the iteration process, MODEA randomly generates a population of N individuals and also generates an equal number of opposing populations during the population initialization phase. This is to make the initial population closer to the optimal solution at the beginning of its generation. N represents the population size, and W represents the number of individuals in a single population.

[0103]

[0104] Among them, x i,j represents the value of the jth dimension of the ith individual, and the solution range is between X min,j and X max,j , where X min,j With X max,j Respectively represent the upper and lower limits of the j-th dimension variable. The initial amplitude of each dimensional solution satisfies:

[0105] x i,j =rand j (0,1)·(X max.j -X min.j )+X min.j ;

[0106] The initial value solution of the initial population of competition is as follows:

[0107] y i,j =X min,j +X max,j -x i,j ;

[0108] Where rand(0,1) represents a random value in [0,1]. When the initial population size is 2N, non-dominated sorting and crowding distance sorting are performed to select a population of size N for subsequent iterations.

[0109] 2) Mutation crossover:

[0110] v i =x1+F·(x2-x3)

[0111] Randomly select three different individuals in the [1,N] population, x1, x2 and x3. Among them, x1 is usually the best individual, which is better than x2 and x3 in terms of optimization objectives. When the three individuals do not dominate each other, one of them is randomly selected as x1. F is the mutation scaling factor, and its value range is [0,1].

[0112] In the process of performing the crossover operation, binomial crossover is performed on the parent individual xi and the mutant individual vij according to the crossover probability, thereby generating the test individual ui, whose specific expression is as follows:

[0113]

[0114] Among them, Cr represents the crossover probability.

[0115] 3) New population selection: Use elite selection strategy to screen individuals. After crossover mutation, the parent individual xi generates the offspring individual μi. During the selection operation, it will be evaluated whether the offspring μi is dominated by the parent xi. Those undominated solutions will be included in the upper population, while the dominated solutions will be included in the lower population. After the upper and lower populations are merged, they are screened by crowding sorting and tournament selection to regenerate the upper population. The crowding sorting formula is as follows:

[0116]

[0117] Among them, 2N is the number of new offspring, f j,max , f j,min are the maximum and minimum values ​​of the mth objective function respectively. j,i+1 With f j,i-1 are the fitness values ​​of the i+1th and i-1th individuals respectively, and the individuals with higher crowding degree are selected to enter the next round of iteration.

[0118] Then, the decision variables are taken as the population, which are the active output of each unit and the reactive output of the reactive compensation equipment (specifically, 1 pumped storage active output, 5 thermal power units active output, 2 wind power actual grid-connected power, 2 photovoltaic actual grid-connected power, 5 reactive compensation equipment capacity, 4 converter reactive capacity). The fitness of the population is the above two optimization objectives. The constraints are the upper and lower limits of population generation, the upper and lower limits of population scheduling changes in the previous and next time periods, and the operating state constraints. Verification is performed when the population is generated and when the population changes, and the boundary value is pulled back to the boundary.

[0119] Perform MODEA coordinated iterative optimization (the optimization process is that the MODEA population is continuously updated and iterated), and verify the output results (when setting the verification, the MODEA intelligent algorithm generally sets the optimization target to the set value or meets the number of iterations. If the target value is met within the number of iterations, the current population value is output as the optimization result. If not, continue to loop step D until the requirements are met or the number of iterations is reached). If the verification requirements are not met, return to the initial stage of the population for iteration until the output result meets the verification. Output the active and reactive coordinated optimization scheduling results (the output scheduling results are the active output of 10 units in each time period and the reactive compensation capacity output by 5 reactive compensation devices and 4 converter devices) as the system's active and reactive optimization configuration plan for the day before. Under this plan, while meeting the loss, the reactive support capacity of the new energy converter can be fully explored to improve the system's operating stability.

[0120] This embodiment proposes a new multi-objective active and reactive coordination optimization strategy for power systems that takes into account the reactive support capability of converters, in order to solve the problems of reduction of the static stability domain of the system and deviation from economic operation caused by active and reactive decoupling optimization schemes or single-objective optimization scheduling. This strategy explores the reactive support capability of new energy converters while taking into account system losses, further improving the operational stability of the system. The following examples verify that the scheme proposed in this embodiment is capable of fully exploring the potential reactive regulation capability of converters compared to the decoupled two-stage optimization method. At the same time, it is verified that the typical solution of the proposed scheme has more significant optimization effects on system losses and stability.

[0121] In new power systems with a high penetration rate of renewable energy, the decoupled two-stage active and reactive power optimization scheme has poor applicability. At the same time, the single optimization criterion of converted power loss or stability may cause the static stability domain of the system to be reduced or deviate from economic operation for a long time. To this end, this embodiment first proposes a multi-objective active and reactive coordination optimization model that takes into account both the converted power loss and the static stability index. The model takes the lowest converted power loss and the optimal static stability margin as the goal, and takes into account the reactive support capacity of the converter, reactive compensation equipment, and on-load voltage regulation to achieve the optimal scheduling of the system. Secondly, a multi-objective differential evolution algorithm (MODEA) is used to overcome the solution difficulties caused by the non-convex nonlinear characteristics of the constructed model. Compared with the decoupled two-stage optimization method, the scheme proposed in the present invention has the ability to fully explore the potential reactive regulation capability of the converter, and the typical solution obtained by the proposed scheme has a more significant optimization effect on system loss and stability.

[0122] In an exemplary embodiment, Figure 3 As shown in the figure, a new multi-objective active and reactive coordination optimization method for power system is provided. Figure 4 In order to adopt the improved IEEE-30 power grid model of a new type of power system multi-objective active and reactive coordinated optimization strategy taking into account the reactive support capacity of the converter proposed in this embodiment, 5 thermal power units, 2 wind power units, 2 photovoltaic units, and 1 pumped storage unit are configured. The detailed access nodes are: 1, 2, 5, 8, 11 nodes are connected to conventional thermal power units, and the parameters of the thermal power units are shown in Table 1; photovoltaic access to nodes 26 and 15; wind power access to nodes 30 and 19; pumped storage power station access to node 13. The network is used as an example object for validity verification.

[0123] Table 1 Thermal power unit parameters

[0124]

[0125] The example verification of the present invention is described in detail below:

[0126] (1) Sorting out the characteristics of wind and solar power output and daily load characteristics in the research area, and predicting the load curve and wind and solar power output curve on the previous day according to the time series of wind and solar power output data and load curve. In order to verify the effectiveness of the multi-objective active and reactive power coordinated optimization strategy taking into account the reactive power support capacity of the converter proposed in this embodiment, the example analysis part includes two parts: optimization scheduling result analysis and coordinated optimization effectiveness analysis. Predicting wind and solar power output and load curves Figure 5 As shown in the figure, multi-objective active and reactive power coordinated optimization is carried out in the area based on the day-ahead load data and wind and solar power output data.

[0127] (2) Establish a multi-objective active and reactive power coordinated optimization model and determine the optimization objective function.

[0128] (3) Establish a multi-objective active and reactive power coordination optimization model and determine the constraints of the power network.

[0129] (4) Configure the multi-objective solution algorithm MODEA to perform iterative optimization of the multi-objective optimization model.

[0130] (5) Perform MODEA coordinated iterative optimization and verify the output results. If the verification requirements are not met, return to the initial stage of the population for iteration until the output results meet the verification requirements. Output the active and reactive power coordinated optimization scheduling results as the system's day-ahead active and reactive power optimization configuration plan.

[0131] (6) Analysis of optimized scheduling results:

[0132] The optimal solution for reduced power loss is defined as typical solution 1 (TS1), the multi-objective compromise solution is defined as typical solution 2 (TS2), and the optimal solution for stability is defined as typical solution 3 (TS3). Figure 6 and Figure 7 The following are the active and reactive dispatch solutions TS1 and TS3 of the system under two different optimization objectives: power loss and stability, which correspond to the two optimization objectives of focusing on loss and stability. Figure 6 (a) in the figure is the output of the active optimization dispatching unit. Figure 6 (b) in the figure is the node reactive power optimization configuration. When focusing on different optimization objectives, the system's active power scheduling and reactive capacity configuration schemes show significant differences. In TS1, Figure 6 As shown in the figure, when the load is reduced, in order to avoid increased losses caused by frequent start-up and shutdown of thermal power units, the pumped storage power station absorbs excess power. During the noon period, the power generation of photovoltaic and wind power new energy is high, and the power absorbed by the pumped storage power station increases accordingly to prepare for the peak load in the evening. At this time, the reactive capacity configuration of each node is shown in the figure. Under the premise of meeting the system constraints, the reactive capacity of the converter of the new energy node is adjusted according to the fluctuation of the new energy output to optimize the impact of load fluctuations and new energy output fluctuations on system stability. The reactive compensation capacity of other nodes changes little. On the basis of ensuring system stability, the converted power loss caused by the number of switching times and compensation capacity changes is minimized.

[0133] Figure 7 The figure shows the results of TS3 active and reactive power optimization scheduling. Figure 7 (a) in the figure is the output of the active optimization dispatching unit. Figure 7(b) in the figure is the node reactive power optimization configuration. In the early morning hours, the pumped storage power station is in the operation mode of absorbing electric energy, the investment in reactive power compensation equipment is relatively low, the system itself is stable, and the reactive power capacity demand is relatively small. After 8 hours, the power generation of new energy sources such as wind and solar energy begins to increase, the system stability decreases, and the reactive power demand gradually increases. At this time, the reactive power compensation equipment assumes the main reactive power support role, while the new energy node converter is used to balance and adjust the impact of output fluctuations on system stability, and play an auxiliary support role. Compared with TS1, the reactive power configuration capacity at this time is higher.

[0134] like Figure 8 The figure shows the multi-objective solution set for active and reactive power coordinated optimization within a 24-hour period. The values ​​of TS1, TS2 and TS3 in each period are shown in the figure. After implementing active and reactive power coordinated optimization scheduling, different typical solutions can be selected as the optimization scheduling strategy according to actual needs to adapt to the requirements for loss and stability under different conditions.

[0135] (7) Analysis of the effectiveness of coordination and optimization:

[0136] like Fig. 9 and Fig.10 The converted power loss and static stability L index corresponding to the three typical solutions are shown in the figure. The results show that in the scheme taking into account the reactive power regulation capability of the converter, the converted power loss of the three typical solutions is lower than that of the scheme not taking into account the reactive power regulation capability of the converter. The reactive support capability of the converter improves the static stability of the network, thereby reducing the converted power loss caused by the reactive compensation equipment. Compared with the scheme not taking into account the reactive power regulation capability of the converter, the three typical solutions reduce the converted power loss by 11,800, 32,500 and 26,100 respectively. Within the 24-hour dispatch cycle, the scheme taking into account the reactive power regulation capability of the converter reduces 284,000, 779,000 and 625,000 respectively under the three selection criteria.

[0137] Fig.10The figure shows the static stability indexes of three typical solutions TS1, TS2 and TS3 when the reactive power regulation capability of the converter is taken into account and when the reactive power regulation capability of the converter is not taken into account. The access of the converter improves the reactive power support capability of the system. Compared with the scheme that does not take the reactive power regulation capability of the converter into consideration, the stability difference is more significant in some periods, especially when the output of wind and solar renewable energy is high, and the maximum difference is 0.233, 0.127 and 0.116 respectively. Different from the loss consideration, the optimization effect of the static stability L index is poor when the reactive power regulation capability of the converter is not taken into account. The TS3 solution set that does not take into account the converter is similar to the compromise solution TS2 that takes into account the converter in terms of stability. When the output of renewable energy is high, the optimization effect of the scheme that does not take into account the converter regulation is poor, and the maximum value of the L index is 0.71. When the L index reaches 1, the system will be in an unstable state. Based on this, the stability margin of the three typical solutions under different schemes is analyzed.

[0138] According to the parameters shown in Table 2, the stability margins of the three solution sets with and without considering the reactive power regulation capability of the converter are compared.

[0139] Table 2 Comparison of stability parameters of typical solutions of different schemes

[0140]

[0141] It can be seen that the average values ​​of the stability L index of the three typical solutions TS1, TS2 and TS3 taking into account the converter are all lower than the average values ​​of the L index of the three typical solutions not taking into account the converter, which are reduced by 10.9%, 12.3% and 13.6% respectively. In addition, the total stability margins all show a high level, among which TS3 has the highest stability margin of 16.94. The highest stability L index reflects the worst period of system stability. Under the scheme taking into account the reactive power regulation capability of the converter, the maximum values ​​of the L index decrease by 0.033, 0.124 and 0.116 respectively.

[0142] In the two-stage active and reactive power optimization scheduling, the active optimization scheduling result is usually used as the base state flow of the two-stage optimization model of the system. In the two-stage optimization scheduling, the converted power loss is similar to the three typical solutions of the optimization scheme of the present invention and the TS2 compromise solution. Compared with the TS1 solution with better loss, the loss is higher. In the static stability L index, in the 0-8 hour time period when the system stability L index is relatively low, the two-stage optimization scheduling solution is maintained between 0.22-0.33, which is better than TS1 of about 0.4. After the increase of new energy output, the high proportion of new energy access has an impact on the stability of the system, and the two-stage optimization scheduling lacks consideration of the reactive regulation limit of the second stage in the first stage. The system stability optimization effect is poor during this period, and the maximum value of the optimized static stability L index is 0.707. The L index size is higher than TS1 and TS2, and higher than TS3 in some periods. Compared with the two-stage optimization scheduling, the multi-objective active and reactive coordination scheme proposed by the present invention is more effective in improving the loss and stability of optimization scheduling.

[0143] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal, and includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a novel multi-objective active and reactive coordinated optimization method for a power system is implemented.

[0144] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0145] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0146] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0147] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0148] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0149] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0150] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0151] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A novel multi-objective active and reactive coordination optimization method for power system, characterized in that: The novel multi-objective active and reactive coordinated optimization method for power system comprises: Obtain the generator set data and load data of the area to be optimized; The total operation converted power loss of the system is calculated by using the generator set data and the load data; the total operation converted power loss of the system includes: the converted loss of various generator sets, the loss of reactive power compensation equipment and the network loss; the various generator sets include: thermal power units, hydropower storage units, wind power units and photovoltaic units; the network loss includes: current meter loss; Taking the minimum total system operation converted power loss and the optimal static stability index as the optimization objectives, a multi-objective active and reactive coordination optimization model is established; the coordination content of the multi-objective active and reactive coordination optimization model includes: the active output of hydropower storage, the active output of thermal power units, the actual grid-connected power of wind power, the actual grid-connected power of photovoltaic power, the capacity of reactive compensation equipment and the reactive capacity of converter; Determining the power network constraint conditions of the multi-objective active and reactive coordinated optimization model; The active output of hydropower storage, the active output of thermal power units, the actual grid-connected power of wind power, the actual grid-connected power of photovoltaic power, the capacity of reactive compensation equipment and the reactive capacity of converter are taken as the population, the minimum total system operation converted power loss and the optimal static stability index are taken as the population fitness, the power network constraints are taken as the upper and lower limits of population generation and the upper and lower limits of population scheduling changes in the previous and next time periods, and the multi-objective differential evolution algorithm is used to solve the multi-objective active and reactive coordination optimization model.

2. The novel multi-objective active and reactive coordinated optimization method for a power system according to claim 1 is characterized in that: The objective function of the multi-objective active and reactive coordinated optimization model is: minf(x)=[f L (x),f M (x)] T ; Where f(x) is the objective function; f L (x) is the quantitative L index of the static stability of the system; f M (x) is the total system operation reduced power loss.

3. The novel multi-objective active and reactive coordinated optimization method for a power system according to claim 2 is characterized in that: The static stability index is: f L =max(L1,L2...L j ); Among them, f L Is the static stability index; L j is the voltage stability L index of j nodes.

4. The novel multi-objective active and reactive coordinated optimization method for a power system according to claim 2 is characterized in that: The converted loss of the thermal power unit is: Among them, f1 is the total operating loss of conventional thermal power units; f mh is the coal consumption of thermal power units; f qt is the start-stop loss of thermal power units; P i,t is the output of the i-th unit at time t; N T , N G are the dispatching period and the number of thermal power units involved in the dispatching; a i , b i and c i are the consumption coefficients of unit i; S it is the single start-stop loss; u it The unit operating status; The converted loss of the water storage unit is: in, represents the output of the hth pumped storage power station in period t; N H is the number of pumped storage power stations; K s,t , K g,t are the working states of the pumped storage power station at time t; η g , η s are the power generation and water storage loss coefficients of the pumped storage power station respectively; Q cp.ps is the unit operation loss coefficient; P g.ps , P s.ps are the unit losses of power generation and water storage operation of the hydropower station respectively; η p Energy conversion efficiency of pumped storage power station; The converted losses of the wind turbine and photovoltaic units are: in, represents the abandoned wind power of the lth wind farm at time t; N W is the number of wind farms; N S is the number of photovoltaic power stations; represents the wind power abandoned by the mth PV power station at time t; Δt is the scheduling time accuracy; It represents the unit loss of wind and solar power abandoned at time t; The network loss is: Among them, β is the conversion coefficient; τ max is the annual maximum load loss hours; ΔP LOSS，t is the network loss power at time t; The reactive power compensation equipment loss is: in, is the reactive power output of the cth reactive power compensation device at time t, Q c,SVC is the capacity of the cth reactive power compensation device; N C is the number of reactive power compensation devices.

5. The novel multi-objective active and reactive coordinated optimization method for a power system according to claim 1 is characterized in that: The power network constraints include: power flow balance constraints, distributed wind and solar power output constraints, conventional thermal power unit constraints, pumped storage power station operation constraints, converter operation constraints and static VAR compensator operation constraints.

6. The novel multi-objective active and reactive coordinated optimization method for a power system according to claim 5 is characterized in that: The power flow balance constraint is: Among them, P i,t is the active power of the i-th thermal power unit at time t; is the grid-connected active power of the lth wind farm at time t; is the grid-connected active power of the hth pumped-storage power station at time t; is the grid-connected active power of the mth PV power station at time t; is the load power of the jth node; N G N is the number of thermal power units involved in dispatching; W is the number of wind farms; N S is the number of photovoltaic power stations; N H is the number of pumped storage power stations; N L is the number of network nodes, △P Loss Network loss; The distributed wind and solar output constraints are: in, is the maximum active power of wind farm l; is the maximum active power of the photovoltaic field l; and are the actual grid-connected powers of the lth and mth units at time t respectively; The constraints of the conventional thermal power unit are: Among them, P i,min , P i,max are the upper and lower limits of active output of the i-th thermal power unit; r i,down 、r i,up P is the maximum upward and downward climbing rate of the thermal power unit; i,t-1 is the active power of the i-th thermal power unit at time t-1; The operating constraints of the pumped storage power station are: Among them, K is the total number of units in the pumped storage power station; They represent the total power generated and absorbed by the h-th pumped storage power station; P g,h,max With P g,h,min are the maximum and minimum power generation capacity of a single pumped storage unit respectively; P s,h,max With P s,h,min K is the maximum and minimum value of power absorbed by a single pumped storage unit; g,h,r The pumped storage power station is in power generation operation state, K s,h,t The pumped storage power station is in energy storage operation state; The converter operation constraints are: Among them, Q n,t,min , Q n,t,max are the minimum and maximum reactive power output of the converter; P n,t is the active power generated by the nth converter; is the power factor standard value at time t; Q n,t is the reactive power generated by the nth converter at time t; The static VAR compensator operation constraints are: in, are the minimum and maximum output values ​​of the cth static VAR compensation device respectively; is the reactive power of the cth reactive compensation equipment at time t.

7. The novel multi-objective active and reactive coordinated optimization method for a power system according to claim 1 is characterized in that: The active output of hydropower storage, active output of thermal power units, actual grid-connected power of wind power, actual grid-connected power of photovoltaic power, capacity of reactive compensation equipment and reactive capacity of converter are used as populations, the lowest total system operation converted power loss and the best static stability index are used as population fitness, the power network constraints are used as the upper and lower limits of population generation and the upper and lower limits of population scheduling changes in the previous and next time periods, and the multi-objective differential evolution algorithm is used to solve the multi-objective active and reactive coordination optimization model, which specifically includes: Taking the active output of hydropower storage, the active output of thermal power units, the actual grid-connected power of wind power, the actual grid-connected power of photovoltaic power, the capacity of reactive compensation equipment and the reactive capacity of converter as the population, taking the lowest total system operation converted power loss and the best static stability index as the population fitness, taking the power network constraint conditions as the upper and lower limits of population generation and the upper and lower limits of population scheduling changes in the previous and next time periods, using the multi-objective differential evolution algorithm to solve the multi-objective active and reactive coordination optimization model, and obtain the output result; The output result is verified until the preset conditions are met, and the final output result is output; the final output result is: the active output of each type of generator set in each time period, and the reactive compensation capacity output by the reactive compensation equipment and the converter equipment.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the novel multi-objective active and reactive coordinated optimization method for a power system as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the novel multi-objective active and reactive coordinated optimization method for a power system described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the novel multi-objective active and reactive coordinated optimization method for a power system described in any one of claims 1 to 7 is implemented.

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