A novel multi-objective active and reactive power coordinated optimization method and device for power system

Abstract

The application discloses a novel power system multi-objective active and reactive power coordination optimization method and equipment, relates to the technical field of novel power system optimization scheduling, and comprises the following steps: taking the minimum system total operation converted power loss and the optimal static stability index as optimization objectives, establishing a multi-objective active and reactive power coordination optimization model, and determining power network constraint conditions; taking water storage active power output, thermal power unit active power output, wind power actual grid-connected power, photovoltaic actual grid-connected power, reactive power compensation device capacity and converter reactive power capacity as a population, taking the minimum system total operation converted power loss and the optimal static stability index as population fitness, taking the power network constraint conditions as population upper and lower limits and front and rear time period population scheduling change upper and lower limits, and solving the multi-objective active and reactive power coordination optimization model by using a multi-objective differential evolution algorithm. The application can consider the converted power loss and the static stability in novel power system scheduling decision.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new power system optimal scheduling, in particular to a new power system multi-objective active and reactive power coordinated optimization method and device. BACKGROUND

[0002] In recent years, with the rapid development of renewable energy grid connection technology, the online capacity of new energy such as wind power and photovoltaic power is continuously increasing. However, the fluctuating output characteristics of large-scale new energy have brought serious potential risks to the safe operation of power systems. In order to ensure the safe operation of the system, there is often a problem of curtailment of wind and light in the optimization decision-making process, which reduces the economy of new energy grid connection to some extent.

[0003] The existing research on the optimal scheduling of new power systems mainly includes two parts: one is the optimal scheduling of multi-type active power sources in advance, aiming to reduce the converted power loss in the operation of the power system, and the optimization is usually set to minimize the operation loss and curtailment of wind and light under the condition of meeting the constraints of various energy sources, which belongs to the economic field of power system scheduling; the other is the optimal configuration of multi-type energy sources for power system reactive power compensation. The configuration of reactive power compensation capacity is crucial to the strength of power system stability. 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 and stability and operation reliability of the power system under the condition of meeting the operation constraints of reactive power compensation devices and other reactive power sources. These contents cannot simultaneously consider the system loss and stability optimization problem of active and reactive power joint. SUMMARY

[0004] The purpose of the present application is to provide a new power system multi-objective active and reactive power coordinated optimization method and device, which can consider the converted power loss and static stability in the scheduling decision of new power system.

[0005] To achieve the above purpose, the present application provides the following scheme:

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

[0007] Obtaining generator unit data and load data of a region to be optimized;

[0008] Calculating the total system operation converted power loss by using the generator unit data and the load data; the total system operation converted power loss includes the converted loss of various types of generator units, the loss of reactive power compensation equipment and the network loss; the various types of generator units include thermal power units, pumped storage units, wind power units and photovoltaic units; the network loss includes current transformer loss;

[0009] A multi-objective active and reactive power coordination optimization model is established with the lowest total system operation equivalent power loss and the optimal static stability index as the optimization objectives; the coordination content of the multi-objective active and reactive power coordination optimization model includes: water storage active power output, thermal power unit active power output, actual grid-connected power of wind power, actual grid-connected power of photovoltaic, reactive power compensation device capacity and converter reactive power capacity;

[0010] The electrical network constraint condition of the multi-objective active and reactive power coordination optimization model is determined;

[0011] The water storage active power output, the thermal power unit active power output, the actual grid-connected power of wind power, the actual grid-connected power of photovoltaic, the reactive power compensation device capacity and the converter reactive power capacity are taken as the population, the lowest total system operation equivalent power loss and the optimal static stability index are taken as the population fitness, the electrical network constraint condition is taken as the upper and lower limits of population generation and the upper and lower limits of population scheduling change in the front and rear time periods, and the multi-objective differential evolution algorithm is used to solve the multi-objective active and reactive power coordination optimization model.

[0012] In a second aspect, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the novel power system multi-objective active and reactive power coordination optimization method in any one of the above.

[0013] In a third aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the novel power system multi-objective active and reactive power coordination optimization method in any one of the above.

[0014] In a fourth aspect, a computer program product is provided, which includes a computer program, and the computer program is executed by a processor to implement the novel power system multi-objective active and reactive power coordination optimization method in any one of the above.

[0015] According to the embodiments provided in the present application, the following technical effects are disclosed:

[0016] The application provides a novel power system multi-objective active and reactive power coordinated optimization method and device, the method comprising: obtaining generator unit data and load data of a region to be optimized; calculating system total operation equivalent power loss by using the generator unit data and the load data; the system total operation equivalent power loss comprising: equivalent loss of various types of generator units, reactive power compensation device loss and network loss; the various types of generator units comprising: thermal power generator units, water storage generator units, wind power generator units and photovoltaic generator units; the network loss comprising: current transformer loss; taking the lowest system total operation equivalent power loss and the optimal static stability index as optimization objectives, establishing a multi-objective active and reactive power coordinated optimization model; the coordinated content of the multi-objective active and reactive power coordinated optimization model comprising: water storage active power output, thermal power generator unit active power output, wind power actual grid-connected power, photovoltaic actual grid-connected power, reactive power compensation device capacity and converter reactive power capacity; determining the electric network constraint condition of the multi-objective active and reactive power coordinated optimization model; taking the water storage active power output, the thermal power generator unit active power output, the wind power actual grid-connected power, the photovoltaic actual grid-connected power, the reactive power compensation device capacity and the converter reactive power capacity as a population, taking the lowest system total operation equivalent power loss and the optimal static stability index as population fitness, taking the electric network constraint condition as population generation upper and lower limits and front and rear time period population scheduling change upper and lower limits, and solving the multi-objective active and reactive power coordinated optimization model by using a multi-objective differential evolution algorithm. The application simultaneously considers the specific content of the system total operation equivalent power loss and the static stability index in the construction of the optimization model and the solving stage. The application can take into account the equivalent power loss and the static stability in the dispatching decision of the novel power system. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description only constitute some embodiments of the application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

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

[0019] Figure 2 The flowchart of a novel power system multi-objective active and reactive power coordinated optimization method provided by an embodiment of the application.

[0020] Figure 3 The flowchart of a novel power system multi-objective active and reactive power coordinated optimization method provided by an embodiment of the application.

[0021] Figure 4An improved IEEE 30-node diagram provided by an embodiment of the present application.

[0022] Figure 5 A wind and light output and load curve diagram provided by an embodiment of the present application.

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

[0024] Figure 7 A TS3 active and reactive power optimization scheduling result diagram provided by an embodiment of the present application.

[0025] Figure 8 A 24h active and reactive power coordination optimization multi-objective solution set diagram provided by an embodiment of the present application.

[0026] Figure 9 A different scheme typical solution decomposed power loss diagram provided by an embodiment of the present application.

[0027] Figure 10 A different scheme typical solution static stability L index diagram provided by an embodiment of the present application. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0029] Currently, the research on the optimal dispatching of new power systems mainly includes two parts: one is the optimal dispatching of the active power of multiple types of active power sources in advance, aiming to reduce the converted power loss in the operation of the power system, and the optimal setting is usually to minimize the operation loss and abandoned wind and light under the condition of meeting various energy constraints, which belongs to the economic field of power system dispatching; the other is the optimal configuration of the reactive power compensation of multiple types of energy to the power system. The configuration of the reactive power compensation capacity is crucial to the strength of the stability of the power system. The existing optimal configuration of the 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 condition of meeting the operation constraints of the reactive power compensation device and other reactive power sources. In fact, the active power optimal dispatching strategy considering the uncertainty and volatility of new energy may cause damage to the stability of the power system, and the demand for the configuration of the reactive power compensation capacity under different active dispatching schemes is not the same. The single optimization criterion of loss or stability may cause the reduction of the static stability domain of the system or the long-term deviation from the economic operation. It is particularly important to consider the system loss and stability optimization of active and reactive power joint. In order to consider the converted power loss and static stability in the dispatching decision of the new power system, and fully explore the reactive power regulation capacity of the converter, the application provides a multi-objective active and reactive power coordinated optimization strategy considering the reactive power support capacity of the converter.

[0030] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the application will be further described in detail below in combination with the drawings and specific embodiments.

[0031] The new power system multi-objective active and reactive power coordinated optimization method provided by the embodiments of the application can be applied to, for example Figure 1The application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data required by the server 104 to process. The data storage system can be set up separately, or 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, and the server 104 receives the generator set data and load data to be processed, and calculates the system total running conversion power loss by using the generator set data and the load data; the system total running conversion power loss includes: the conversion loss of various types of generator sets, the loss of reactive power compensation equipment and network loss; the various types of generator sets include: thermal power units, water storage units, wind power units and photovoltaic units; the network loss includes: current transformer loss; the lowest system total running conversion power loss and the optimal static stability index are taken as the optimization goal to establish a multi-objective active and reactive power coordinated optimization model; the coordination content of the multi-objective active and reactive power coordinated optimization model includes: water storage active power output, thermal power unit active power output, wind power actual grid-connected power, photovoltaic actual grid-connected power, reactive power compensation equipment capacity and converter reactive power capacity; the electrical network constraint conditions of the multi-objective active and reactive power coordinated optimization model are determined; the water storage active power output, the thermal power unit active power output, the wind power actual grid-connected power, the photovoltaic actual grid-connected power, the reactive power compensation equipment capacity and the converter reactive power capacity are taken as the population, the lowest system total running conversion power loss and the optimal static stability index are taken as the population fitness, and the electrical network constraint conditions are taken as the upper and lower limits of the population and the upper and lower limits of the population scheduling change in the front and rear periods. The multi-objective differential evolution algorithm is used to solve the multi-objective active and reactive power coordinated optimization model. The server 104 can feed back the final result to the terminal 102. In addition, in some embodiments, the new type of power system multi-objective active and reactive power coordinated optimization method can also be realized by the server 104 or the terminal 102 alone, such as 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] Among them, the terminal 102 can be but not limited to various desktop computers, notebook computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices, and the Internet of Things devices can be smart speakers, smart televisions, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The server 104 can be realized by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0033] In an exemplary embodiment, as Figure 2As shown, a novel multi-objective active and reactive coordinated optimization method for a power system is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. 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 wind and solar output characteristics and daily load characteristics of the area to be optimized are sorted out and studied to predict the day-ahead load curve and the wind and solar output curve. Including the output size of wind turbines and photovoltaic units in each period of 24 hours 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 output data and load curve (this part means that the wind and solar output and load curve have obvious time-varying laws on the time scale. The commonly used prediction method is mainly based on neural networks. Neural networks can effectively learn existing data and predict corresponding outputs based on input data. Here, the load and wind and solar output are predicted based on time series and meteorological forecast factors), the subsequent 24-hour optimization scheduling of wind and solar output and load data in each period is configured and optimized based on the predicted values.

[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, the data 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: conversion 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 transformer losses.

[0040] S3, the lowest total system operation equivalent power loss and the optimal static stability index as the optimization goal, a multi-objective active and reactive power coordinated optimization model is established; the coordination content of the multi-objective active and reactive power coordinated optimization model includes: water storage active power output, thermal power unit active power output, actual grid-connected power of wind power, actual grid-connected power of photovoltaic, reactive power compensation device capacity and converter reactive power capacity.

[0041] In this embodiment, the two targets of total system operation equivalent power loss and stability L index are optimized, and the optimization target can be represented by the following formula:

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

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

[0044] 1) Static stability index:

[0045] The voltage stability L index is commonly used to monitor and evaluate the static stability of the system, and the index divides the nodes of the power system into PV nodes g (including the balance node) 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 by the PQ node (PV node), and Ypp, Ypg, Ygp and Ygg are the admittance submatrices 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 index calculation:

[0052]

[0053] Wherein, α is the PV node set, β is the PQ node set, U j (U k) is the voltage phasor at node j(k). Here according to the practical criterion, L index is located in the interval (0, 1), indicating that the node is static stable, the larger the index, the weaker the stability of the node; L = 1, in the critical state; L > 1, the node is static unstable. Generally, the peak value of each node L index is taken as the system L index, and the stability optimization objective and the constraint condition of the embodiment are further analyzed and described. The static stability of the system can be expressed as:

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

[0055] wherein 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, the lower the stability of the node. Taking the maximum value of L index in the system as the stability index of the system, the decrease of the stability index represents the increase of the stability of the power system.

[0057] 2) Conversion power loss:

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

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

[0060] wherein f M is the system operation conversion loss; f G is the conversion power loss of various types of units; f LOSS is the system network loss; f CP is the loss of system reactive power compensation device.

[0061] ① Conversion power loss of various types of units:

[0062] The operation conversion power loss of each unit mainly covers three aspects: operation loss of thermal power units, operation loss of pumped storage power stations and abandoned loss of wind and solar renewable energy.

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

[0064] wherein f G is the operation conversion power loss of each unit; f1 is the operation loss of thermal power units; f2 is the operation loss of pumped storage power stations; f3 is the abandoned wind and light loss.

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

[0066]

[0067] Wherein, f1 is the total loss of the conventional thermal power unit during operation; f mh is the coal consumption of the thermal power unit; f qt is the start-stop loss of the thermal power unit; P i,t is the output of the i-th unit at time t; N T , N G are the scheduling period and the number of thermal power units participating in scheduling, respectively; a i , b i and c i are the consumption coefficients of the i-th unit; S it is the single start-stop loss; u it is the operation state of the unit.

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

[0069]

[0070] Wherein, represents the output of the h-th pumped storage power station at time 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 loss coefficients of the pumped storage power station in the power generation and water storage states; Q cp.ps is the unit operation loss coefficient; P g.ps , P s.ps are the unit loss amounts of the pumped storage power station in the power generation and water storage states; η p is the energy conversion efficiency of the pumped storage power station.

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

[0072]

[0073] Wherein, represents the abandoned wind power of the l-th wind farm at time t; N W is the number of wind farms; N S is the number of photovoltaic power stations; Pm(t) represents the curtailment power of the mth photovoltaic power station at time t; Δt is the scheduling time accuracy; Pm(t) represents the curtailment power of the mth photovoltaic power station at time t; Δt is the scheduling time accuracy;

[0074] ②Network loss:

[0075] The network loss is:

[0076] Wherein, 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] ③The conversion loss of the input reactive power equipment:

[0078] The reactive power compensation equipment loss is:

[0079] Wherein, 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 electric network constraint condition of the multi-objective active and reactive power coordination optimization model.

[0081] The power flow balance constraint is:

[0082]

[0083] Wherein, P i,t is the active power of the ith thermal power unit at time t; is the on-grid active power of the lth wind farm at time t; is the on-grid active power of the hth pumped storage power station at time t; is the on-grid active power of the mth photovoltaic power station at time t; is the load power of the jth node; N G is the number of thermal power units participating in scheduling; N 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 is the network loss.

[0084] The distributed wind and light output constraint is:

[0085]

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

[0087] The thermal unit constraints mainly include unit output constraints and ramping constraints, and the conventional thermal unit constraints are:

[0088]

[0089] wherein, P i,min , P i,max are the upper and lower limits of the active power output of the ith thermal unit; r i,down , r i,up are the maximum upward and downward ramping rates of the thermal unit; P i,t-1 is the active power of the ith thermal unit at time t-1.

[0090] The pumped storage power station constraints mainly include unit output upper and lower limit constraints and operating state constraints, which specifically means that the power meets the upper and lower limit requirements and cannot be in the power generation and energy storage states at the same time. The pumped storage power station operating constraints are:

[0091]

[0092] wherein, K is the total number of pumped storage power stations; and P represent the total power emitted and absorbed by the hth pumped storage power station; P g,h,max and P g,h,min are the maximum and minimum values of the power generated by a single pumped storage unit; P s,h,max and P s,h,min are the maximum and minimum values of the power absorbed by a single pumped storage unit; K g,h,r is the pumped storage power station in the power generation operating state, and K s,h,t is the pumped storage power station in the energy storage operating state.

[0093] The converter operating constraints are:

[0094]

[0095] wherein, Q n,t,min and Q n,t,max are the minimum and maximum values of the reactive power emitted by the converter; P n,t is the active power emitted by the nth converter; is the power factor specification value at time t; Q n,t is the reactive power emitted by the nth converter at time t.

[0096] The static reactive power compensator operating constraints are:

[0097]

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

[0099] S5, taking the water storage active power, the thermal power unit active power, the wind power actual grid-connected power, the photovoltaic actual grid-connected power, the reactive power compensation device capacity and the converter reactive power capacity as the population, taking the minimum total system operation converted power loss and the optimal static stability index as the population fitness, taking the electric network constraint condition as the population generation upper and lower limits and the population scheduling change upper and lower limits of the front and rear periods, and solving the multi-objective active and reactive power coordination optimization model by using a multi-objective differential evolution algorithm.

[0100] In this embodiment, a multi-objective solving algorithm MODEA is configured to perform multi-objective optimization model iteration optimization.

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

[0102] 1) Population initialization stage: To ensure the diversity of the population in the iteration process, the MODEA generates an opposite population with the same number of individuals as the initial population at the same time of generating the initial population. The purpose is to make the initial population closer to the optimal solution at the beginning of generation. Wherein, N represents the population size, and W represents the number of individuals in a single population.

[0103]

[0104] wherein, 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 , wherein X min,j and X max,j represent the upper and lower limits of the jth dimension variable. The initial amplitude of each dimension 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 competitive opposite population is as follows:

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

[0108] where rand(0,1) denotes a random value in [0,1]. In the case of an initial population size of 2N, non-dominated sorting and crowding distance sorting are performed to screen a population of size N for subsequent iterations.

[0109] 2) Mutation crossover:

[0110] v i = x1+F·(x2-x3)

[0111] Three different individuals, x1, x2, and x3, are randomly selected from the population [1, N]. x1 is usually the best individual, better than x2 and x3 in the optimization objective. When the three individuals are not dominated by each other, one of them is randomly selected as x1. F is a mutation scaling factor, with a value range of [0, 1].

[0112] In the process of performing crossover operations, according to the crossover probability, the parent individual xi and the mutation individual vij are subjected to binomial crossover to generate test individuals ui, and the specific expression is as follows:

[0113]

[0114] where Cr represents the crossover probability.

[0115] 3) New population selection: Elite selection strategy is used to screen individuals. After the parent individual xi is subjected to crossover and mutation, the offspring individual μi is generated. When the selection operation is performed, it is evaluated whether the offspring μi is dominated by the parent xi. Those solutions that are not dominated are included in the upper population, while those solutions that are dominated are included in the lower population. After the upper and lower populations are combined, crowding degree sorting and tournament selection are used for screening to regenerate the upper population. The crowding degree sorting formula is as follows:

[0116]

[0117] where 2N is the number of new offspring, f j,max max and f j,min min are the maximum and minimum values of the mth objective function, respectively. f j,i+1 i+1 and f j,i-1 i-1 are the adaptive values of the i+1th and i-1th individuals, respectively, and the individuals with higher crowding degree are selected into the next iteration process.

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

[0119] The MODEA coordination iterative optimization (the optimization process is the continuous updating and iteration of the MODEA population) is performed, and the output result is checked (when the MODEA intelligent algorithm is set, the optimization target is generally set to be within the set value or to meet the iteration number. If the target value is met within the iteration number, the current population value is output as the optimization result. If not, step D is continued to be looped until the requirements are met or the iteration number is reached). If the check requirements are not met, the iteration is returned to the initial stage of the population until the output result meets the check. The active and reactive power coordinated optimization scheduling result (the scheduling result output is the active power output of each unit in each time period and the reactive power compensation capacity output by the 5 reactive power compensation devices and 4 converter devices) is output as the system day-ahead active and reactive power optimization configuration scheme. Under this scheme, while meeting the loss, the reactive power support capability of the new energy converter can be fully explored, and the system operation stability can be improved.

[0120] The embodiment proposes a new type of power system multi-objective active and reactive power coordinated optimization strategy considering the reactive power support capability of the converter, aiming at the problems of system static stability domain reduction and economic operation deviation caused by the active and reactive power decoupling optimization scheme or single-objective optimization scheduling. The strategy takes into account the system loss while exploring the reactive power support capability of the new energy converter, further improving the system operation stability. Through the following examples, it is verified that compared with the decoupling two-stage optimization method, the scheme proposed in the embodiment has sufficient potential reactive power regulation capability of the converter. At the same time, it is verified that the typical solution of the proposed scheme has more significant optimization effect in terms of system loss and stability.

[0121] In a new power system with high renewable energy penetration, the decoupled two-stage active and reactive power optimization scheme has poor applicability. Meanwhile, the single optimization criterion of the converted power loss or stability may cause the reduction of the static stability domain or long-term deviation from the economic operation. Therefore, the embodiment first proposes a multi-objective active and reactive power coordinated optimization model considering the converted power loss and static stability index. The model takes the minimum converted power loss and optimal static stability margin as the target, considers the reactive power support capability of the converter, reactive power compensation equipment, and on-load voltage regulation, and realizes the optimal scheduling of the system. Secondly, the multi-objective differential evolution algorithm (MODEA) is used to overcome the solving difficulty caused by the non-convex and nonlinear characteristics of the model. Compared with the decoupled two-stage optimization method, the scheme has the potential to fully explore the reactive power regulation capability of the converter, and the typical solution obtained by the scheme has more significant optimization effect in system loss and stability.

[0122] In one exemplary embodiment, as shown in Figure 3 , a new power system multi-objective active and reactive power coordinated optimization method is provided, as shown in Figure 4 An improved IEEE-30 grid model using the new multi-objective active and reactive power coordinated optimization strategy considering the reactive power support capability of the converter is configured with 5 thermal power units, 2 wind power units, 2 photovoltaic units, and 1 pumped storage unit. The detailed access nodes are: 1, 2, 5, 8, 11 nodes access conventional thermal power units, the thermal power unit parameters are shown in Table 1; photovoltaic access 26, 15 nodes; wind power access 30, 19 nodes; pumped storage power station access 13 nodes. Taking this network as an example, the effectiveness is verified.

[0123] Table 1 Thermal power unit parameter table

[0124]

[0125] The example verification of the present application is described in detail as follows:

[0126] (1) The wind and light output characteristics and daily load characteristics of the research area are sorted out. According to the time series of wind and light output data and load curve, the day-ahead load curve and wind and light output curve are predicted. In order to verify the effectiveness of the multi-objective active and reactive power coordinated optimization strategy considering the reactive power support capability of the converter, the example analysis part includes two parts of optimization scheduling result analysis and coordinated optimization effectiveness analysis. The predicted wind and light output and load curve is shown in Figure 5 , and the multi-objective active and reactive power coordinated optimization of the area is carried out on the basis of the day-ahead load data and wind and light output data.

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

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

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

[0130] (5) Perform MODEA coordination iteration optimization, check the output results, and if they do not meet the verification requirements, return to the initial stage of the population iteration until the output results meet the verification. Output the active and reactive power coordination optimization scheduling results as the system day-ahead active and reactive power optimization configuration scheme.

[0131] (6) Optimization scheduling result analysis:

[0132] The optimal solution of the converted 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 of stability is defined as typical solution 3 (TS3). As shown in Figure 6 and Figure 7 show the active and reactive power scheduling solutions TS1 and TS3 of the system under two different optimization objectives of converted power loss and stability, respectively, corresponding to the two optimization objectives of focusing on loss and stability. Among them, Figure 6 (a) in (a) is the active power optimization scheduling unit output, Figure 6 (b) in (b) is the node reactive power optimization configuration, and the active power scheduling and reactive power capacity configuration scheme of the system shows significant differences under different optimization objectives. As shown in Figure 6 when the load decreases, to avoid the increase of loss caused by frequent start and stop of thermal power units, pumped storage power stations absorb excess power. At noon, the photovoltaic and wind power generation capacity of new energy is high, and the pumped storage power station absorbs more power for use at the evening load peak. At this time, the reactive power capacity configuration of each node is shown in the figure. Under the premise of meeting the system constraints, the reactive power capacity of the converter of the new energy node is adjusted according to the fluctuation of the new energy output to optimize the influence of load fluctuation and new energy output fluctuation on system stability. The reactive power compensation capacity of other nodes changes little, on the basis of ensuring system stability, to the greatest extent reduces the converted power loss caused by switching frequency and compensation capacity change.

[0133] Figure 7 The active and reactive power optimization scheduling results of TS3 are shown in Figure 7 (a) in (a) is the active power optimization scheduling unit output, Figure 7(b) is the reactive power optimization configuration of the node. In the early morning period, the pumped storage power station is in the operation mode of absorbing electric energy, the input of the reactive power compensation device is relatively low, the stability of the system itself is good, and the demand for reactive power capacity is relatively small. In the period after 8h, the output of new energy power generation such as wind energy and solar energy begins to increase, the stability of the system decreases, and the demand for reactive power gradually increases. At this time, the reactive power compensation device bears the main role of reactive power support, and the new energy node converter is used to balance the influence on the stability of the system due to output fluctuation, and plays an auxiliary support role. Compared with TS1, the reactive power configuration capacity at this time is higher.

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

[0135] (7) Coordination optimization effectiveness analysis:

[0136] As shown in Figure 9 and Figure 10 The converted power loss and static stability L index corresponding to the three typical solutions. The results show that in the scheme considering the reactive power regulation capacity of the converter, the converted power loss of the three typical solutions is lower than that in the scheme without considering the reactive power regulation capacity of the converter. The reactive power support capacity of the converter improves the network static stability, thereby reducing the converted power loss caused by the reactive power compensation device. Compared with the scheme without considering the reactive power regulation capacity of the converter, the three typical solutions respectively reduce 118,000, 325,000 and 261,000 in converted power loss. In the 24-hour scheduling period, the scheme considering the reactive power regulation capacity of the converter reduces 284,000, 779,000 and 625,000 respectively under the three selected solution criteria.

[0137] Figure 10The static stability indexes corresponding to three typical solutions TS1, TS2 and TS3 are shown in the case of considering and not considering the reactive power regulation capability of the converter. The access of the converter improves the reactive power support capability of the system. Compared with the scheme not considering the reactive power regulation capability of the converter, the stability difference is more significant in some periods, especially when the wind and solar new energy output is high, and the maximum difference is 0.233, 0.127 and 0.116 respectively. Unlike the loss consideration, the static stability L index is poor in the optimization effect of the solution not considering the reactive power regulation capability of the converter, and the TS3 solution set not considering the converter is similar to the compromise solution TS2 considering the converter in stability. When the new energy output is high, the optimization effect of the scheme not considering 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] The stability margins of the three solution sets considering and not considering the reactive power regulation capability of the converter are compared according to the parameters shown in Table 2.

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

[0140]

[0141] It can be seen that the average value of the stability L index of the three typical solutions TS1, TS2 and TS3 considering the converter is lower than that of the three typical solutions not considering the converter, which is reduced by 10.9%, 12.3% and 13.6% respectively. In addition, the total stability margin shows a high level, among which TS3 has the highest stability margin of 16.94. The highest stability L index reflects the period when the system stability is the worst. In the scheme considering the reactive power regulation capability of the converter, the maximum value of the L index is reduced by 0.033, 0.124 and 0.116 respectively.

[0142] In the two-stage active and reactive power optimization scheduling, the active power optimization scheduling result is usually taken as the base flow of the system two-stage optimization model. In the two-stage optimization scheduling, the converted power loss is similar to the three typical solutions of the optimization scheme and the TS2 compromise solution. Compared with the TS1 solution with lower loss, the converted power loss is higher. In the static stability L index, in the time period of 0-8 hours with low system stability L index, the two-stage optimization scheduling solution is maintained between 0.22-0.33, which is better than the TS1 solution of about 0.4. After the output of new energy increases, the high proportion of new energy access affects the system stability, and the two-stage optimization scheduling lacks consideration of the two-stage reactive power regulation limit in the first stage, so the optimization effect of the system stability is poor in this period. The maximum value of the optimized static stability L index is 0.707, and the L index is higher than that of TS1 and TS2, and higher than that of TS3 in some periods. Compared with the two-stage optimization scheduling, the multi-objective active and reactive power coordination scheme provided by the application is more effective in improving the optimization scheduling loss and stability.

[0143] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal, and the computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured 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 operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a novel multi-objective active and reactive power coordination optimization method for a power system.

[0144] In an exemplary embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores a computer program. The processor executes the computer program to implement the steps in the above method embodiments.

[0145] In an exemplary embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0146] In an exemplary embodiment, a computer program product is provided, which includes a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

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

[0148] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program. 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 above-mentioned embodiments of each method. Any reference to memory, database or other medium used in each embodiment provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.

[0149] The database involved in each embodiment provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in each embodiment provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0150] Any technical features in the above embodiments can be combined, and for the sake of brevity, not all possible combinations are described above, however, it should be understood that the application encompasses all possible combinations of the technical features described above.

[0151] The principles and implementation manners of the present application are described herein by using specific examples, and the above embodiments are only used to help understand the method of the present application and its core idea; meanwhile, according to the idea of the present application, the specific implementation manners and application scopes will be changed by those skilled in the art. In conclusion, the content of the present specification should not be understood as a limitation of the present application.

Claims

1. A novel multi-objective active and reactive power coordinated optimization method for power systems, characterized by, The new power system multi-objective active and reactive power coordinated optimization method comprises: acquire generator unit data and load data of a region to be optimized; calculate system total operation equivalent power loss by using the generator unit data and the load data; the system total operation equivalent power loss comprises equivalent loss of various types of generator units, loss of reactive power compensation equipment and network loss; the various types of generator units comprise thermal power generator units, pumped storage generator units, wind power generator units and photovoltaic generator units; the network loss comprises current transformer loss; establish a multi-objective active and reactive power coordinated optimization model with system total operation equivalent power loss minimum and static stability index optimal as optimization objectives; the coordinated content of the multi-objective active and reactive power coordinated optimization model comprises pumped storage active output, thermal power generator unit active output, wind power actual grid-connected power, photovoltaic actual grid-connected power, reactive power compensation equipment capacity and converter reactive capacity; determine electric network constraint conditions of the multi-objective active and reactive power coordinated optimization model; use the multi-objective differential evolution algorithm to solve the multi-objective active and reactive power coordinated optimization model by taking pumped storage active output, thermal power generator unit active output, wind power actual grid-connected power, photovoltaic actual grid-connected power, reactive power compensation equipment capacity and converter reactive capacity as a population, taking system total operation equivalent power loss minimum and static stability index optimal as population fitness, and taking the electric network constraint conditions as population upper and lower limits and front and rear time period population scheduling change upper and lower limits.

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

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

4. The new power system multi-objective active and reactive power coordinated optimization method according to claim 2, wherein the equivalent loss of the thermal power generator units is the equivalent loss of the pumped storage generator units is Wherein, f1 is the total loss of conventional thermal power unit operation; f mh is the coal consumption of thermal power unit; f qt is the start-stop loss of thermal power unit; P i,t is the output of the i-th unit at time t; N T , N G are the scheduling period and the number of thermal power units participating in scheduling, respectively; a i , b i and c i are the consumption coefficients of unit i; S it is the single start-stop loss; u it is the unit operating state; the electric network constraint conditions comprise power flow balance constraint, distributed wind and light output constraint, conventional thermal power generator unit constraint, pumped storage power station operation constraint, converter operation constraint and static reactive power compensator operation constraint. wherein, represents the output of the hth pumped storage power station at time 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 stations at time t, respectively; η g , η s are the loss coefficients of the pumped storage power stations in the generating and storing states, respectively; Q cp.ps is the unit operation loss coefficient; P g.ps , P s.ps are the unit operation loss amounts of the pumped storage power stations in the generating and storing states, respectively; η p is the energy conversion efficiency of the pumped storage power station; The equivalent loss of the wind turbine generator and the photovoltaic generator is: wherein, represents the curtailed 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 curtailed wind power of the mth photovoltaic power station at time t; Δt is the scheduling time accuracy; represents the curtailed wind and light unit loss at time t; The network loss is: Wherein, β is the conversion factor; τ max is the annual maximum load loss hours; ΔP LOSS，t is the network loss power at t time The loss of the reactive power compensation device is: wherein, Qc(t) is the reactive power output of the cth reactive power compensation device at time t, c,SVC Qc is the capacity of the cth reactive power compensation device; N C N is the number of reactive power compensation devices.

5. The novel multi-objective active and reactive power coordinated optimization method of power system according to claim 1, characterized in that, 6. The new power system multi-objective active and reactive power coordinated optimization method according to claim 5, wherein the use of the multi-objective differential evolution algorithm to solve the multi-objective active and reactive power coordinated optimization model by taking pumped storage active output, thermal power generator unit active output, wind power actual grid-connected power, photovoltaic actual grid-connected power, reactive power compensation equipment capacity and converter reactive capacity as a population, taking system total operation equivalent power loss minimum and static stability index optimal as population fitness, and taking the electric network constraint conditions as population upper and lower limits and front and rear time period population scheduling change upper and lower limits specifically comprises: the use of the multi-objective differential evolution algorithm to solve the multi-objective active and reactive power coordinated optimization model by taking pumped storage active output, thermal power generator unit active output, wind power actual grid-connected power, photovoltaic actual grid-connected power, reactive power compensation equipment capacity and converter reactive capacity as a population, taking system total operation equivalent power loss minimum and static stability index optimal as population fitness, and taking the electric network constraint conditions as population upper and lower limits and front and rear time period population scheduling change upper and lower limits to obtain output results. The tidal flow balance constraint is: wherein, P i,t is the active power of the ith thermal power unit at time t; is the active power of the lth wind farm on the grid at time t; is the active power of the hth pumped storage power station on the grid at time t; is the active power of the mth photovoltaic power station on the grid at time t; is the load power of the jth node; N G is the number of thermal power units participating in dispatch; W is the number of wind farms; S is the number of photovoltaic power stations; H is the number of pumped storage power stations; L is the number of network nodes, △P Loss is the network loss; The distributed wind-solar power output constraint is: wherein, Pmax, l is the maximum active power of the wind farm l; Pmax, l is the maximum active power of the photovoltaic farm l; and P l and P m are the actual grid-connected powers of the lth and mth units at time t, respectively. The conventional thermal power generating unit constraints are: wherein P i,min , P i,max are the upper and lower limits of the active power output of the i-th thermal power unit; r i,down , r i,up are the maximum rates of upward and downward ramping of the thermal power unit; P i,t-1 is the active power of the i-th thermal power unit at time t-1. The pumped storage power station operation constraints are: Wherein, K is the total number of pumped storage power station unit; respectively represent the h pumped storage power station, the total power absorption; P g,h,max and P g,h,min respectively the maximum, minimum value of single pumped storage unit power generation; P s,h,max and P s,h,min the maximum, minimum value of single pumped storage unit absorption power; K g,h,t pumped storage power station in power generation state, K s,h,t pumped storage power station in energy storage operation state; The converter operation constraints are: Wherein, Q n,t,min , Q n,t,max are respectively the minimum and maximum values of the reactive power sent by the converter; P n,t is the active power sent by the nth converter; is the power factor specification value at time t; Q n,t is the reactive power sent by the nth converter at time t; The static reactive compensator operating constraints are: wherein, respectively the minimum and maximum output of the cth static reactive power compensation device; Qctis the reactive power of the cth reactive power compensation device at time t.

7. The novel multi-objective active and reactive power coordinated optimization method of power system according to claim 1, characterized in that, ​ ​ The output result is checked until a preset condition is met, and a final output result is output; the final output result is: the active power output size of each type of generator set in each period, and the reactive compensation capacity size output by the reactive compensation device and the converter device.

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

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

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

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