Unit combination method, system and equipment considering frequency response constraint and unit maintenance constraint and medium

Abstract

The invention relates to the technical field of power system planning, and discloses a unit combination method, system and equipment considering frequency response constraint and unit maintenance constraint, and a medium, and the method comprises the steps: obtaining basic data of a power system, building a dynamic frequency model of the power system according to the basic data of the power system, and forming frequency safety constraint; constructing a power system unit shutdown model, performing linearization processing on the power system unit shutdown model through logarithm conversion and piecewise linear approximation, and forming unit maintenance constraints; combining the frequency security constraint and the unit maintenance constraint to construct a power system unit combination model; and solving the power system unit commitment model by adopting a first decomposition optimization method to obtain a final optimization result. According to the method, multiple targets of economy, frequency safety, maintenance reliability and the like can be considered, the frequency response performance of the power system can be effectively improved, and a safe, reliable and economical optimal scheduling scheme is provided for the high-proportion renewable energy power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system planning, and in particular to a unit commitment method, system, device and medium considering frequency response constraints and unit maintenance constraints. BACKGROUND

[0002] With the continuous increase of renewable energy such as wind power and photovoltaic in the power system, the power system gradually presents the operation characteristics of "low inertia and weak regulation", which leads to serious challenges to the overall reliability of the system. The reliability of the power system includes two key dimensions of safety and adequacy, both of which are significantly affected by the reduction of traditional thermal power units.

[0003] In terms of frequency safety, the core problem faced by the system is the structural shortage of frequency regulation resources. Although wind turbines can provide certain frequency support through virtual inertia control, there are essential differences in dynamic response characteristics between them and traditional units, which are difficult to completely replace the inertia support and primary frequency modulation function of thermal power units. This contradiction is particularly prominent when new energy output fluctuates or the system is disturbed suddenly, and it is necessary to introduce frequency dynamic response constraints in unit commitment optimization to ensure system frequency safety. In terms of system adequacy, the traditional evaluation method based on deterministic rules has been difficult to adapt to the refined operation requirements of high proportion of renewable energy power grid. The current mainstream research embeds unit commitment into the Monte Carlo simulation framework to improve the evaluation accuracy, but has not realized the collaborative decision of adequacy evaluation and operation optimization. It is worth noting that the regular maintenance strategy of generating units directly affects the equipment failure rate and thus determines the system adequacy level. By optimizing the maintenance plan, the system reliability can be effectively improved, which provides a new optimization dimension for the unit commitment model.

[0004] The existing technology has the following outstanding defects: the frequency safety constraint mostly uses static approximation method, which cannot accurately reflect the dynamic frequency characteristics of high proportion of new energy system; the maintenance plan and operation scheduling are decided in stages, lacking collaborative optimization mechanism; the traditional optimization model is difficult to consider multiple objectives such as economy, frequency safety and maintenance reliability at the same time.

[0005] Therefore, it is urgent to develop a new unit commitment method, which integrates the frequency dynamic response constraint and the unit maintenance constraint on the basis of the traditional economic dispatch model, constructs a multi-time scale optimization decision framework, and thus provides a safe, reliable and economic optimization scheduling scheme for high proportion of renewable energy power system. SUMMARY

[0006] In view of the above existing problems, the present application is proposed.

[0007] Therefore, the application provides a unit combination method, system, device and medium considering frequency response constraints and unit maintenance constraints, which solves the problem that current research simplifies frequency response and adequacy assessment to a certain extent, cannot accurately reflect the dynamic frequency characteristics of a high proportion of new energy systems, ignores the coordinated optimization mechanism of maintenance plans and operation scheduling, and is difficult to simultaneously consider multiple objectives such as economy, frequency safety and maintenance reliability, and therefore cannot guarantee the safe and reliable operation of a power system.

[0008] To solve the above technical problems, the application provides the following technical solutions.

[0009] In a first aspect, the application provides a unit combination method considering frequency response constraints and unit maintenance constraints, which comprises:

[0010] obtaining power system basic data, establishing a power system dynamic frequency model according to the power system basic data, and forming frequency safety constraints;

[0011] constructing a power system unit outage model, linearizing the power system unit outage model through logarithmic conversion and piecewise linear approximation, and forming unit maintenance constraints;

[0012] combining the frequency safety constraints and the unit maintenance constraints to construct a power system unit combination model;

[0013] solving the power system unit combination model by using a first decomposition optimization method to obtain a final optimization result.

[0014] As a preferred scheme of the unit combination method considering frequency response constraints and unit maintenance constraints, the frequency safety constraints comprise:

[0015] the maximum frequency change rate of the system should be less than a first frequency change rate threshold;

[0016] the maximum frequency deviation of the system should be less than a first frequency deviation threshold.

[0017] As a preferred scheme of the unit combination method considering frequency response constraints and unit maintenance constraints, the linearization processing comprises:

[0018] the unit outage rate of the power system unit outage model is calculated through the continuous availability rate of a single unit, and the continuous availability rate is calculated based on the scale parameter and shape parameter of the Weibull distribution, and a risk function affected by the environmental challenge level and the load coefficient;

[0019] when linearizing the unit outage rate, the environmental challenge level and the load coefficient are set to be constant in an interval, the logarithmic outage rate is defined, the outage rate of the multiplication structure is converted into a linear model by taking the natural logarithm.

[0020] The piecewise linear approximation is adopted for the load coefficient nonlinear term, and the piecewise linear constraint is constructed based on the piecewise coefficient value and the piecewise activation state.

[0021] As a preferred scheme of the unit commitment method considering frequency response constraints and unit maintenance constraints, the unit maintenance constraints comprise:

[0022] The outage is triggered when the accumulated logarithmic outage rate of the unit exceeds a first threshold value, and the accumulated logarithmic outage rate is converted into a linear inequality constraint through a first conversion operation;

[0023] The maintenance action needs to cover a specific period after the outage, and the outage rate accumulation during the maintenance is reset, so the influence of the maintenance on the outage rate is disassembled into linear inequality constraints through a mixed integer linear constraint.

[0024] As a preferred scheme of the unit commitment method considering frequency response constraints and unit maintenance constraints, the objective function of the power system unit commitment model is the sum of the operation cost, standby cost, start-up cost of the thermal power unit and the renewable energy curtailment cost of the system.

[0025] The constraint conditions of the power system unit commitment model comprise node phase angle constraints and line transmission capacity constraints, renewable energy output constraints, output and standby constraints of each thermal power unit at each time, minimum start-stop time constraints of the thermal power unit and unit maintenance constraints.

[0026] The preferred technical scheme has the beneficial effects that multiple targets such as economy, frequency safety and maintenance reliability can be considered, and a safe, reliable and economic optimal scheduling scheme is provided for a high-proportion renewable energy power system.

[0027] As a preferred scheme of the unit commitment method considering frequency response constraints and unit maintenance constraints, the solving of the power system unit commitment model by using the first decomposition optimization method comprises:

[0028] When the power system unit commitment model is solved, it is decomposed into a main problem and a frequency safety evaluation sub-problem, the main problem contains all the constraints except the frequency safety constraints, and the frequency safety evaluation sub-problem is used to judge whether the frequency safety index constraint under a given unit commitment start-stop scheme is satisfied;

[0029] In the iteration process of the main problem and the sub-problem, if the maximum frequency change rate of the system in the sub-problem does not meet the requirement, it indicates that the system inertia is insufficient, and if the maximum frequency deviation of the system in the sub-problem does not meet the requirement, it indicates that the primary frequency modulation capacity of the system is insufficient, and accordingly an optimization cut is formed to obtain the final optimization result.

[0030] As a preferred scheme of the unit commitment method considering frequency response constraints and unit maintenance constraints, the dynamic frequency model of the power system comprises a simulation model of dynamic frequency response of the power system, a frequency response model of a wind turbine, a frequency response model of a load, and a system frequency response model.

[0031] The simulation model of dynamic frequency response of the power system is expressed as:

[0032]

[0033] wherein subscript i represents the number of a thermal power unit, superscript * represents a unit value, Δf * is a system frequency variation, and respectively represent a mechanical power variation and an electromagnetic power variation of the thermal power unit, H sgi represents an inertia time constant of the thermal power unit, represents a primary frequency response of the thermal power unit, R Gi is a droop coefficient of the thermal power unit, T Gi , T Ci and T Ri are time constants of a speed governor, a prime mover and a reheater, respectively, F HPi is a high-pressure cylinder work proportionality coefficient;

[0034] The frequency response model of the wind turbine is expressed as:

[0035]

[0036] wherein subscript j represents the number of a wind turbine, ΔP Wj represents a frequency response power of a wind farm, H Wj represents a virtual inertia time constant of the wind farm;

[0037] The frequency response model of the load is expressed as:

[0038] ΔP L =k L P base Δf *

[0039] wherein k L is a proportionality coefficient, P base is a total active load of the system, and ΔP L is a frequency response power of the system load;

[0040] The system frequency response model is expressed as:

[0041]

[0042] wherein H eqN is the equivalent inertia time constant of the system W N is the number of wind farms G u is the number of thermal power units i and Pi, u represents the start-stop state and rated output of the thermal power unit i.

[0043] The beneficial effects of the preferred technical solution are: effectively improving the frequency response performance of the power system, and the frequency simulation model has replaceability and is more widely applied than the traditional analytical method.

[0044] In a second aspect, the present application provides a unit commitment system considering frequency response constraints and unit maintenance constraints, comprising:

[0045] A dynamic frequency model construction module is configured to obtain power system basic data, establish a power system dynamic frequency model according to the power system basic data, and form a frequency safety constraint;

[0046] An adequacy assessment module is configured to construct a power system unit outage model, linearize the power system unit outage model through logarithmic conversion and piecewise linear approximation, and form a unit maintenance constraint;

[0047] A unit commitment model construction module is configured to combine the frequency safety constraint and the unit maintenance constraint to construct a power system unit commitment model;

[0048] An optimization solution module is configured to solve the power system unit commitment model by using a first decomposition optimization method to obtain a final optimization result.

[0049] In a third aspect, the present application provides an electronic device comprising a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the steps of the unit commitment method considering frequency response constraints and unit maintenance constraints.

[0050] In a fourth aspect, the present application provides a computer readable storage medium storing computer executable instructions, which are executed by a processor to implement the steps of the unit commitment method considering frequency response constraints and unit maintenance constraints.

[0051] Compared with the prior art, the application has the beneficial effects that: the application provides a unit combination method, system, device and medium considering frequency response constraints and unit maintenance constraints, in terms of frequency safety, by establishing a power system dynamic frequency model, setting frequency safety constraints of the maximum frequency change rate and the maximum frequency deviation of the system, and proposing an optimization cut solving algorithm, the frequency response performance of the power system is effectively improved, and the frequency simulation model has replaceability and is more widely applied than the traditional analytic method; in terms of system adequacy, a unit outage model based on Weibull distribution is constructed, linearization processing is performed through logarithmic conversion and piecewise linear approximation, linearization constraints of unit outage and maintenance are established, and the proposed model and unit combination solving algorithm can effectively reduce the adequacy risk of the power system; in addition, the application combines the frequency safety constraints and the unit maintenance constraints to establish a unit combination model, which can consider multiple targets such as economy, frequency safety and maintenance reliability, and provides a safe, reliable and economic optimal scheduling scheme for a high-proportion renewable energy power system. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed to be used in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0053] Figure 1 The overall flow logic diagram of the unit combination method considering frequency response constraints and unit maintenance constraints provided by an embodiment of the application is shown.

[0054] Figure 2 The power system frequency response model diagram of the unit combination method considering frequency response constraints and unit maintenance constraints provided by an embodiment of the application is shown.

[0055] Figure 3 The specific flow diagram of the unit combination method considering frequency response constraints and unit maintenance constraints provided by an embodiment of the application is shown.

[0056] Figure 4 The improved IEEE39 node system diagram of the unit combination method considering frequency response constraints and unit maintenance constraints provided by an embodiment of the application is shown.

[0057] Figure 5 The unit start-stop scheme diagram of the FACUC of the unit combination method considering frequency response constraints and unit maintenance constraints provided by an embodiment of the application is shown.

[0058] Figure 6A system frequency safety index calculation result diagram of a unit combination method considering frequency response constraints and unit maintenance constraints is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0060] Embodiment 1, reference Figures 1-2 For an embodiment of the present application, a unit combination method considering frequency response constraints and unit maintenance constraints is provided, as shown in Figure 1 which specifically includes the following steps:

[0061] S100: Obtain power system basic data, establish a power system dynamic frequency model according to the power system basic data, and form a frequency safety constraint;

[0062] S200: Construct a power system unit outage model, linearize the power system unit outage model through logarithmic conversion and piecewise linear approximation, and form a unit maintenance constraint;

[0063] S300: Combine the frequency safety constraint and the unit maintenance constraint to construct a power system unit combination model;

[0064] S400: Solve the power system unit combination model by using a first decomposition optimization method to obtain a final optimization result;

[0065] It should be noted that the current research simplifies the frequency response and adequacy assessment to a certain extent, which cannot accurately reflect the dynamic frequency characteristics of the high proportion of new energy system, ignores the collaborative optimization mechanism of maintenance plan and operation scheduling, and is difficult to balance multiple objectives such as economy, frequency safety and maintenance reliability, so it cannot guarantee the safe and reliable operation of the power system. The steps S100-S400 establish a dynamic frequency model of the power system, set the frequency safety constraints of the maximum frequency change rate and the maximum frequency deviation of the system, and propose an optimization cut solution algorithm, which effectively improves the frequency response performance of the power system, and the frequency simulation model has replaceability and is more widely used than the traditional analytical method; in terms of system adequacy, a unit outage model based on Weibull distribution is constructed, which is linearized by logarithmic conversion and piecewise linear approximation to establish linear constraints of unit outage and maintenance, and the proposed model and unit commitment solution algorithm can effectively reduce the adequacy risk of the power system; in addition, the frequency safety constraint and the unit maintenance constraint are combined to establish a unit commitment model, which can balance multiple objectives such as economy, frequency safety and maintenance reliability, and provide a safe, reliable and economic optimal scheduling scheme for high proportion of renewable energy power system.

[0066] In the embodiment of the application, the step S100 of acquiring the basic data of the power system, establishing a dynamic frequency model of the power system according to the basic data of the power system, and forming a frequency safety constraint comprises:

[0067] Specifically, the dynamic frequency model of the power system includes a simulation model of the dynamic frequency response of the power system, a frequency response model of the wind turbine, a frequency response model of the load, and a system frequency response model.

[0068] Specifically, the frequency response of the thermal power unit includes inertia response and primary frequency modulation. In the inertia response stage, the unit releases rotational kinetic energy and provides inertia support for the system in a short time. In the primary frequency modulation response stage, the reheater, prime mover and governor of the unit cooperate to increase the mechanical power of the thermal power unit, thereby reducing the power deviation. The simulation model of the dynamic frequency response of the power system is represented as:

[0069]

[0070] Wherein, subscript i represents the number of thermal power units, superscript * represents the unit value, Δf * is the system frequency change, and represent the mechanical power change and electromagnetic power change of the thermal power unit, respectively, H sgi represents the inertia time constant of the thermal power unit, represents the primary frequency modulation response of the thermal power unit, R Gi is the droop coefficient of the thermal power unit, T Gi , TCi and T Ri are the time constants of the governor, prime mover and reheater respectively, F HPi is the high pressure cylinder work proportion coefficient;

[0071] Specifically, the fan in the embodiment is controlled by virtual inertia, and a link related to frequency change is added in the fan control, so that the fan obtains inertia response ability similar to the thermal power generating unit, and the fan frequency response model is expressed as:

[0072]

[0073] wherein subscript j represents the fan number, ΔP Wj represents the wind power plant frequency response power, H Wj represents the virtual inertia time constant of the wind power plant;

[0074] Specifically, the loads in the power system are various, and the frequency response characteristics are quite different, and the embodiment makes certain simplification, considers that the frequency response of the load is in linear relationship with the total active load, and the load frequency response model is expressed as:

[0075] ΔP L =k L P base Δf *

[0076] wherein k L is a proportional coefficient, P base is the total active load of the system, and ΔP L is the frequency response power of the system load;

[0077] Specifically, the embodiment establishes the frequency response model as shown in Figure 2 . In the figure, f base is the system frequency base value; ΔP d represents the power shortage of the system, and in the embodiment, the sum of the maximum error of the power system load and the renewable energy prediction is considered; N G is the number of thermal power generating units; u i and represent the start-stop state and rated output of the thermal power generating unit i, u i is 1 in the start state and 0 in the shutdown state; H eq is the equivalent inertia time constant of the system, and is calculated as shown in the following formula:

[0078]

[0079] wherein H eq is the equivalent inertia time constant of the system, N W is the number of wind power plants, and N GFor the number of thermal power units, u i And indicates the start-stop state and rated output of the thermal power unit i.

[0080] It should be noted that since the frequency response model of the present application does not need to be solved in the subsequent optimization problem, a more detailed simulation model can be used instead without affecting the solution method of the final FACUC problem.

[0081] In the embodiments of the present application, the frequency safety constraint includes: the maximum frequency change rate of the system should be less than a first frequency change rate threshold; the maximum frequency deviation of the system should be less than a first frequency deviation threshold.

[0082] Specifically, the dynamic frequency response indicators of the power system mainly include the maximum frequency change rate (RoCoF) and the maximum frequency deviation (f nadir ), which are defined as:

[0083]

[0084] f nadir = maxΔf(t)

[0085] Where t0 is the time when the power shortage occurs;

[0086] Therefore, the frequency safety constraint of the power system can be represented as:

[0087] RoCoF≤TH RoCoF

[0088] f nadir ≤TH nadir

[0089] Where TH RoCoF represents the first frequency change rate threshold, and TH nadir represents the first frequency deviation threshold.

[0090] In an optional embodiment, the setting of the first frequency change rate threshold and the first frequency deviation threshold is mainly based on the safe operation standard and equipment tolerance of the power system.

[0091] Exemplarily, the first frequency change rate threshold is generally based on the withstand limit of the generator set and the electrical equipment to the rapid fluctuation of the frequency, and is generally controlled within 0.5-1.0 Hz / s, so as to prevent the key equipment such as turbine blades from being damaged due to excessive mechanical stress; the first frequency deviation threshold is set according to the International Electrotechnical Commission (IEC) and the national power grid standard, and is generally ±0.5 Hz (normal operating condition) and ±0.8 Hz (transient operating condition), which can ensure that the protection device correctly acts, and can also avoid generator splitting or load off-grid. At the same time, the determination of the threshold also needs to consider the dynamic characteristics such as the inertia level of the system and the response speed of the regulation resources, and is verified through time domain simulation and actual operation data, so as to ensure that the system stability can still be maintained when a high proportion of renewable energy is accessed.

[0092] It should be noted that the step S100 breaks through the limitation of the traditional static frequency constraint by establishing an accurate power system dynamic frequency model, and can accurately reflect the dynamic frequency response characteristics of the system under high proportion of new energy access.

[0093] In the embodiment of the present application, the step S200 constructs a power system unit outage model, linearizes the power system unit outage model through logarithmic conversion and piecewise linear approximation, and forms a unit maintenance constraint, including:

[0094] In the embodiment of the present application, the step of linearization includes:

[0095] The unit outage rate of the power system unit outage model is calculated by the continuous availability rate of a single unit, and the continuous availability rate is calculated based on the scale parameter and shape parameter of the Weibull distribution, and the risk function affected by the environmental challenge level and the load coefficient;

[0096] When linearizing the unit outage rate, the environmental challenge level and the load coefficient are set to be constant in the interval, the logarithmic outage rate is defined, and the multiplication structure of the outage rate is converted into a linear model by taking the natural logarithm;

[0097] The piecewise linear approximation is adopted for the nonlinear term of the load coefficient, and the piecewise linear constraint is constructed based on the piecewise coefficient value and the piecewise activation state.

[0098] Specifically, the unit outage rate F(t) is calculated as follows:

[0099] F(t)=1-R(t)

[0100] Wherein, R(t) represents the continuous availability rate of a single unit, and is calculated as:

[0101] R(t)=R(t|t-1)R(t-1)

[0102]

[0103] where a and b are scale and shape parameters of Weibull distribution, Z(t) e {0, 1, 2} is the level of environmental challenge, p t is the load coefficient, which affects the risk function through weights γ1 and γ2, and h(t) is the instantaneous sustained unavailability rate of the unit.

[0104] The unit outage rate F(t) can be expressed as:

[0105]

[0106] Specifically, let Z(t), p t be constant in the interval [t-1, t], then the unit outage rate F(t) can be expressed as:

[0107] F(t) = 1 - e -h(t-1) (1 - F(t-1))

[0108] Define the logarithmic outage rate as LF t = -ln(1 - F(t)), which converts the multiplication structure of the outage rate into a linear model LF t = h(t-1) + LF t-1 , and the load coefficient nonlinear term p t is adopted for piecewise linear approximation: The piecewise linear constraint is obtained as:

[0109]

[0110] where p t,s and l t,s represent the load coefficient value and the activation state of the s-th segment, respectively, a s and b s are the coefficients of piecewise linear approximation.

[0111] In the embodiments of the present application, the unit maintenance constraints include:

[0112] When the cumulative aging logarithmic outage rate of the unit exceeds a first threshold, the outage (o t = 1) is triggered, and the cumulative aging logarithmic outage rate is converted into a linear inequality constraint through a first conversion operation, which is expressed as:

[0113]

[0114] where M is a sufficiently large positive number.

[0115] The repair action (r t = 1) needs to cover a specific period of time after the outage, i.e., T r periods, which is expressed as:

[0116]

[0117] and the cumulative outage rate of the unit during the maintenance period is reset to 0;

[0118] The impact of maintenance on the outage rate is disassembled into linear inequality constraints by mixed integer linear constraints, and the formula is:

[0119]

[0120] Wherein, M f and m f are the upper and lower bounds of H t +LF t .

[0121] It should be noted that the setting of the first threshold value is mainly based on the reliability engineering data and historical operation statistical law of the unit equipment, and the aging failure characteristics of the unit are quantitatively analyzed by Weibull distribution and other probability models to determine.

[0122] Exemplarily, the threshold value is based on the failure rate curve provided by the equipment manufacturer, the average failure-free operation time (MTBF) of the same type of unit, and other reliability parameters, combined with the accumulated unit maintenance records and state monitoring data in the actual operation of the power system, under the premise of ensuring system adequacy, the inflection point value (usually corresponding to the critical state of 3%-5% annual forced outage rate) corresponding to the significant increase of the device failure probability on the aging cumulative logarithmic outage rate curve is selected, which not only avoids the waste of resources caused by premature maintenance, but also prevents the sudden increase of failure risk caused by delayed maintenance.

[0123] In an optional embodiment, the first conversion operation can also use McCormick relaxation method to convert the nonlinear logarithmic function into linear constraints by introducing auxiliary variables and convex envelope technology, which is suitable for processing specific nonlinear structures such as bilinear terms.

[0124] In another optional embodiment, the first conversion operation can also use piecewise linear approximation method, based on the convexity of the logarithmic function, to construct linear approximation by constructing tangent or secant line at key working points, and to flexibly balance the calculation accuracy and solving efficiency by increasing the number of segments, which is particularly suitable for processing smooth and monotonous nonlinear functions.

[0125] It should be noted that the unit outage model proposed in the above step S200 innovatively combines Weibull distribution with operation scheduling, and solves the problem that traditional probability models are difficult to be embedded in optimization problems through logarithmic conversion and piecewise linearization processing.

[0126] In the embodiments of the present application, the above step S300 combines frequency safety constraints and unit maintenance constraints to build a power system unit commitment model, which includes:

[0127] The objective function of the unit commitment model of the power system is to minimize the sum of the operation cost, standby cost, start-up cost of the thermal power unit and the renewable energy abandonment cost of the system;

[0128] The constraint conditions of the unit commitment model of the power system include the node phase angle constraint and the line transmission capacity constraint, the renewable energy output constraint, the output and standby constraint of each thermal power unit at each time, the minimum start-stop time constraint of the thermal power unit and the unit maintenance constraint.

[0129] Specifically, the objective function of the unit commitment model established in the embodiment is as follows:

[0130] minCost=C G,run +C G,reserve +C G,start +C RES

[0131] Wherein, G G,run ,G G,reserve ,G G,start ,G RES respectively represent the operation cost, standby cost, start-up cost of the thermal power unit and the renewable energy abandonment cost of the system, and the calculation formulas are as follows:

[0132]

[0133] Wherein, T is the number of time nodes of unit commitment, which is set to 24 in the embodiment; a i , b i , c i , d i and S i are the constant coefficient of the generation cost first term and the constant coefficient of the thermal power unit i, the standby cost coefficient and the start-up cost; K RES is the renewable energy abandonment penalty coefficient; N PV is the number of photovoltaic stations;

[0134] Specifically, the node phase angle constraint and the line transmission capacity constraint are represented as:

[0135] T τ =X -1 A T θ τ

[0136] θ min ≤θ τ ≤θ max

[0137] |T τ |≤T max

[0138]

[0139] Specifically, the renewable energy output constraint, the output of each thermal power unit at each moment, and the reserve constraint are expressed as follows:

[0140]

[0141] Specifically, the formula for the minimum start-stop time constraint of thermal power units is expressed as:

[0142]

[0143] τ=L i +1,...,TT i off +1

[0144]

[0145] Specifically, the formula for unit maintenance constraint is expressed as:

[0146]

[0147] Wherein, the subscript τ represents the time τ; P L,τ is the node load; T τ and θ τ are line flow and node phase angle respectively; P τ W is the optimal output of the fan; P f G is the predicted value of the wind turbine output; RD i and RU i are the minimum technical output, down-ramp rate and up-ramp rate of thermal power units respectively; and is the unit output, lower reserve and upper reserve capacity; X, A, C G and C W are line impedance matrix, node-branch correlation matrix, node-thermal power unit correlation matrix and node-wind turbine correlation matrix respectively; θ min ,θ max and T max They represent the minimum and maximum node phase angles and the upper limit of line capacity respectively. i on and T i off are the minimum startup time and minimum shutdown time of thermal power unit i respectively; T i on,init and T i off,init are the startup and shutdown time of the thermal power unit at the initial moment; is the switch-on / off state of the thermal power unit i at the initial moment. t and v t respectively represent the unit state and the switch-on action, P d is the maximum power drop rate under normal working condition, M d and M u are respectively the absolute value of the maximum power drop range and the minimum operation time period number allowed to switch on during maintenance.

[0148] It should be noted that the above step S300 innovatively coordinates the economy, safety and reliability targets under a single optimization framework, solves the compatibility problem of different dimensional constraints by using a multi-objective normalization method, and realizes the trinity optimization of "operation-safety-maintenance".

[0149] In the embodiment of the present application, the above step S400 solves the power system unit commitment model by using a first decomposition optimization method to obtain the final optimization result, including:

[0150] When solving the power system unit commitment model, it is decomposed into a main problem and a frequency safety evaluation sub-problem, the main problem contains all constraints except the frequency safety constraint, and the frequency safety evaluation sub-problem is used to judge whether the frequency safety index constraint under the given unit commitment start-stop scheme is satisfied;

[0151] In the iteration process of the main problem and the sub-problem, if the system maximum frequency change rate in the sub-problem does not meet the requirement, it indicates that the system inertia is insufficient, and if the system maximum frequency deviation in the sub-problem does not meet the requirement, it indicates that the system primary frequency modulation capability is insufficient, and accordingly an optimization cut is formed to obtain the final optimization result.

[0152] It should be noted that the power system unit commitment model is solved. In the FACUC model established in the embodiment, the dynamic frequency analysis is a nonlinear process, and the adequacy evaluation is an NP-hard problem containing a large number of optimization models, so the FACUC model is very complex. In order to solve the model, the FACUC model is first decomposed into a main problem and two sub-problems. The main problem is as follows: the objective function, the constraints include all constraints except the frequency safety constraint, and obviously the main problem is an MILP problem. The frequency safety evaluation sub-problem is to use the simulation model proposed in the present application to judge whether the frequency safety index constraint under the given unit commitment start-stop scheme is satisfied.

[0153] Specifically, the formula for forming the optimization cut is as follows:

[0154]

[0155] Wherein, is the unit start-stop state obtained by the current main problem.

[0156] It should be noted that the above step S400 converts the original problem into the iterative solving of the main problem and the sub-problem through problem decomposition, which not only maintains the integrity of global optimization, but also significantly improves the calculation efficiency.

[0157] Embodiment 2, refer to Figures 3-6 Based on the previous embodiment, the embodiment provides an application example of a unit commitment method, system, device and medium considering frequency response constraints and unit maintenance constraints, which verifies the technical effects adopted in the method.

[0158] As Figure 3 shown is a specific flowchart of a unit commitment strategy considering frequency response constraints and unit maintenance constraints, the steps include: step 1: reading the basic data of the system; step 2: establishing a dynamic frequency simulation model of the power system; step 3: forming a frequency safety constraint; step 4: establishing a power system unit continuous unavailability rate model to form a piecewise linear constraint; step 5: establishing a power system outage and maintenance constraint; step 6: constructing a power system unit commitment model considering frequency safety constraints and unit maintenance constraints; step 7: solving the optimization model established in step 6 by using the decomposition optimization method proposed in the embodiment; step 8: outputting the optimization result; and step 9: ending.

[0159] The embodiment applies the proposed unit commitment strategy considering frequency response constraints and unit maintenance constraints to the IEEE 39-node system as an example to illustrate the effectiveness of the proposed model and algorithm:

[0160] Some basic data of the improved IEEE 39-node system are obtained. The modified IEEE 39 system has 39 nodes, 46 lines and 10 thermal power units, and a distributed photovoltaic proportional to the size of the load is installed at each node with load, three wind farms are installed at nodes 9, 18 and 24, and the maximum transmission capacity of all lines is reduced to half of the original. The topology of the improved IEEE 39-node system is shown in Figure 4 , all the parameters used in the embodiment are shown in Tables 2-5. In the frequency analysis, the situation that the frequency drops due to the lack of active power is considered, the power shortage is represented by the sum of the prediction error of the load (5% of the maximum load) and the prediction error of the maximum output of renewable energy (15% of the maximum output of renewable energy), and the index RoCoF is represented by the average change rate of the first ten cycles in the simulation.

[0161] The method proposed in the embodiment is used to simulate the frequency index results, in which the unit scheduling scheme is directly obtained by the main problem and obtained by iteration. The optimization cut method proposed in the embodiment is iterated for 3 times, and the results are shown in Figure 5 , and the verification results are shown in Table 1.

[0162] Table 1 verifies the effectiveness of the optimization cut solution

[0163]

[0164] Two unit start-stop schemes are obtained by using the model and method of the present application, and are compared, and the frequency index of each period before and after iteration is calculated, as can be seen from the chart, the number of started thermal power units in the system is increased after iteration, although the operation cost is increased, but the RoCoF index and fnadir index are effectively improved, so that the frequency safety risk is reduced, and the results are shown in Figure 5 and Figure 6 .

[0165] It should be noted that Tables 2-5 are as follows:

[0166] Table 2: System setting parameters.

[0167]

[0168] Table 3: Improved thermal power unit parameters of IEEE39 node system.

[0169]

[0170]

[0171] Table 4: Load and renewable energy output prediction.

[0172]

[0173] Table 5: Line parameters of part of the improved IEEE39 node system.

[0174]

[0175] From the above examples, in terms of frequency safety, the present application establishes a power system dynamic frequency model, sets frequency safety constraints of maximum frequency change rate and maximum frequency deviation, and proposes an optimization cut solving algorithm, which effectively improves the frequency response performance of the power system, and the frequency simulation model has replaceability and is more widely applied than traditional analytical methods; in terms of system adequacy, a unit outage model based on Weibull distribution is constructed, linearization processing is performed through logarithmic conversion and piecewise linear approximation, linearization constraints of unit outage and maintenance are established, the proposed model and unit commitment solving algorithm can effectively reduce the adequacy risk of the power system; in addition, the present application combines frequency safety constraints and unit maintenance constraints to establish a unit commitment model, which can consider multiple objectives such as economy, frequency safety and maintenance reliability, and provides a safe, reliable and economic optimal dispatching scheme for a high-proportion renewable energy power system.

[0176] In the embodiment, a unit commitment system considering frequency response constraints and unit maintenance constraints is provided, comprising:

[0177] a dynamic frequency model construction module, configured to acquire power system basic data, establish a power system dynamic frequency model according to the power system basic data, and form a frequency safety constraint;

[0178] a sufficiency evaluation module, configured to construct a power system unit outage model, linearize the power system unit outage model through logarithmic conversion and piecewise linear approximation, and form a unit maintenance constraint;

[0179] a unit commitment model construction module, configured to combine the frequency safety constraint and the unit maintenance constraint, and construct a power system unit commitment model;

[0180] an optimization solution module, configured to solve the power system unit commitment model by using a first decomposition optimization method, and obtain a final optimization result.

[0181] It should be noted that the technical scheme of the unit commitment system considering frequency response constraints and unit maintenance constraints is the same as the technical scheme of the unit commitment method considering frequency response constraints and unit maintenance constraints described above, and the technical scheme of the unit commitment system considering frequency response constraints and unit maintenance constraints in the embodiment is not described in detail, and the details can be referred to the description of the technical scheme of the unit commitment method considering frequency response constraints and unit maintenance constraints.

[0182] The above-mentioned various unit modules can be embedded in or independent of the processor in the electronic device in hardware form, or can be stored in the memory in the electronic device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned various modules.

[0183] The embodiment also provides an electronic device, which comprises a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved through WIFI, an operator network, NFC (Near Field Communication) or other technologies. The computer program is executed by the processor to implement the unit commitment method considering the frequency response constraint and the unit maintenance constraint. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the electronic device, or an external keyboard, touchpad or mouse, etc.

[0184] The embodiment also provides a computer readable storage medium, which stores a computer program. The program is executed by a processor to implement the method proposed in the above embodiment.

[0185] The storage medium proposed in the embodiment belongs to the same inventive concept as the method proposed in the above embodiment. The technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0186] From the above description about the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary universal hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a ROM, a RAM, a FLASH, a hard disk or an optical disc, and includes a number of instructions for making an electronic device (which can be a personal computer, a server or a network device, etc.) execute the methods of the embodiments of the present application.

[0187] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A unit commitment method taking into account frequency response constraints and unit maintenance constraints, characterized in that: include: Acquiring basic data of the power system, establishing a dynamic frequency model of the power system based on the basic data of the power system, and forming frequency security constraints; Constructing a power system unit outage model, linearizing the power system unit outage model through logarithmic transformation and piecewise linear approximation, and forming unit maintenance constraints; Combining the frequency security constraint and the unit maintenance constraint, constructing a power system unit combination model; The first decomposition optimization method is used to solve the power system unit combination model to obtain a final optimization result.

2. The unit commitment method taking frequency response constraints and unit maintenance constraints into account according to claim 1, characterized in that: The frequency safety constraints include: The system's maximum frequency change rate should be less than the first frequency change rate threshold; The system maximum frequency deviation should be less than the first frequency deviation threshold.

3. The unit commitment method taking frequency response constraints and unit maintenance constraints into account according to claim 2, characterized in that: The linearization process includes: The unit outage rate of the power system unit outage model is calculated by the continuous availability rate of a single unit, and the continuous availability rate is calculated based on the scale parameter and shape parameter of the Weibull distribution and the risk function affected by the environmental challenge level and the load factor; When linearizing the outage rate of the unit, the environmental challenge level and the load factor are set to be constant within the interval, a logarithmic outage rate is defined, and the outage rate with a multiplicative structure is converted into a linear model by taking the natural logarithm; The piecewise linear approximation is adopted for the nonlinear term of the load coefficient, and the piecewise linear constraints are constructed based on the piecewise coefficient values ​​and piecewise activation states.

4. The unit commitment method taking frequency response constraints and unit maintenance constraints into account according to claim 3, characterized in that: The unit maintenance constraints include: When the aging cumulative logarithmic outage rate of the unit exceeds a first threshold, an outage is triggered, and the aging cumulative logarithmic outage rate is converted into a linear inequality constraint through a first conversion operation; The maintenance action needs to cover a specific period after the outage, and the cumulative outage rate of the unit is reset during the maintenance period. The impact of maintenance on the outage rate is decomposed into linear inequality constraints through mixed integer linear constraints.

5. The unit commitment method taking frequency response constraints and unit maintenance constraints into account according to claim 4, characterized in that: The objective function of the power system unit combination model is to minimize the sum of the operating cost, standby cost, startup cost and system renewable energy abandonment cost of the thermal power units; The constraints of the power system unit combination model include node phase angle constraints and line transmission capacity constraints, renewable energy output constraints, output and reserve constraints of each thermal power unit at each moment, minimum start and stop time constraints of thermal power units, and unit maintenance constraints.

6. The unit commitment method taking frequency response constraints and unit maintenance constraints into account according to claim 5, characterized in that: The adopting the first decomposition optimization method to solve the power system unit combination model includes: When solving the power system unit combination model, it is decomposed into a main problem and a frequency security assessment subproblem. The main problem contains all constraints except the frequency security constraint. The frequency security assessment subproblem is used to determine whether the frequency security index constraints under the given unit combination start-stop plan are met. During the iterative process of the main problem and the sub-problems, if the maximum frequency change rate of the system in the sub-problem is not satisfied, it indicates that the system inertia is insufficient; if the maximum frequency deviation of the system in the sub-problem is not satisfied, it indicates that the system's primary frequency regulation capability is insufficient. Based on this, optimization cuts are formed to obtain the final optimization result.

7. The unit commitment method taking frequency response constraints and unit maintenance constraints into account according to claim 1, characterized in that: The power system dynamic frequency model includes a simulation model of the power system dynamic frequency response, a wind turbine frequency response model, a load frequency response model and a system frequency response model; The simulation model of the power system dynamic frequency response is expressed as: Where, the subscript i represents the number of the thermal power unit, the superscript * represents the per-unit value, and Δf * is the system frequency variation, and They represent the mechanical power variation and electromagnetic power variation of the thermal power unit, H sgi represents the inertia time constant of the thermal power unit, Represents the primary frequency modulation response of the thermal power unit, R Gi is the regulation coefficient of thermal power unit, T Gi 、T Ci and T Ri are the time constants of the governor, prime mover and reheater respectively, F HPi is the work proportional coefficient of the high-pressure cylinder; The fan frequency response model is expressed as: Where, subscript j represents the fan number, ΔP Wj Represents the frequency response power of the wind farm, H Wj represents the virtual inertia time constant of the wind farm; The load frequency response model is expressed as: ΔP L =k L P base Δf * Among them, k L is the proportional coefficient, P base is the total active load of the system, ΔP L is the frequency response power of the system load; The system frequency response model is expressed as: Among them, H eq is the equivalent inertia time constant of the system, N W is the number of wind farms, N G is the number of thermal power units, u i and Indicates the start / stop status and rated output of thermal power unit i.

8. A unit commitment system taking frequency response constraints and unit maintenance constraints into account, applying the unit commitment method taking frequency response constraints and unit maintenance constraints into account according to any one of claims 1 to 7, characterized in that: include: A dynamic frequency model building module is used to obtain basic data of the power system, establish a dynamic frequency model of the power system based on the basic data of the power system, and form frequency security constraints; an adequacy assessment module for constructing a power system unit outage model, linearizing the power system unit outage model through logarithmic transformation and piecewise linear approximation, and forming unit maintenance constraints; a unit commitment model building module, configured to build a power system unit commitment model by combining the frequency security constraint and the unit maintenance constraint; The optimization solution module is used to solve the power system unit combination model using the first decomposition optimization method to obtain a final optimization result.

9. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and when the processor executes the computer-executable instructions, it implements the steps of the unit combination method taking into account frequency response constraints and unit maintenance constraints as described in any one of claims 1 to 7.

10. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that: When the computer executable instructions are executed by a processor, the steps of the unit commitment method taking into account frequency response constraints and unit maintenance constraints as described in any one of claims 1 to 7 are implemented.

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