A power power balance economic dispatch method considering flywheel frequency modulation response model

Abstract

The application provides a power and energy balance economic dispatch method considering a flywheel frequency modulation response model, relates to the technical field of power system economic dispatch, and comprises the following steps: S1, a frequency modulation response model of a unit combined with a flywheel energy storage is constructed; S2, a power and energy balance economic dispatch model considering the frequency modulation response model of the unit combined with the flywheel energy storage is established; and S3, the economic dispatch model is solved based on a fuzzy optimization algorithm of a flywheel state of charge (SOC) probability to obtain an optimal dispatch plan. The application can effectively improve the frequency stability of a power system under high proportion of new energy access, a refined frequency modulation response model of a unit combined with a flywheel energy storage is established, and an intelligent algorithm based on flywheel SOC state adaptive optimization is used for solving, so that the system frequency deviation is significantly reduced, the economy of the frequency modulation cost is considered, and the overall optimization of the system frequency modulation performance and operation efficiency is realized.

Description

Technical Field

[0001] This invention relates to the field of economic dispatch of power systems, and in particular to an economic dispatch method for power balance that takes into account a flywheel frequency regulation response model. Background Technology

[0002] With the large-scale integration of renewable energy into the power grid, the frequency fluctuation characteristics and regulation of the power system are becoming increasingly complex. The gap between existing unit power operation control and monitoring methods and the requirements of power grid development is gradually widening, failing to meet the requirements of power system stability and economic operation under the high proportion of renewable energy absorption, such as photovoltaic and wind power. Therefore, it is urgent to seek new frequency regulation methods to assist traditional power sources in smoothing power frequency fluctuations and improve the overall frequency regulation capability of the power grid.

[0003] While large-scale energy storage for frequency regulation in power systems has garnered widespread attention in the industry, the capabilities of energy storage technology in different frequency regulation application scenarios may be limited by physical or design constraints. Therefore, the selection of energy storage technology should vary depending on the application scenario. Primary frequency regulation involves frequent power fluctuations on a timescale ranging from seconds to minutes. Therefore, energy storage technologies with short response times and high cycle life should be selected whenever possible. Simultaneously, the charging and discharging efficiency, economics, and energy density of the energy storage equipment must also be considered.

[0004] Flywheel energy storage, as an advanced physical energy storage technology, features high power density, fast response speed, long lifespan, maintenance-free operation, good scalability, and zero pollution. It boasts a cycle life of up to 150,000 cycles and a response time of less than ten milliseconds, making it well-suited for the frequent power fluctuations and short timescales required by traditional frequency regulation. In recent years, flywheel energy storage technology has matured significantly, leading to a reduction in cost, which is now essentially on par with battery energy storage.

[0005] However, in the real-time operation of the system, the existing AGC command scheduling generally issues frequency adjustment commands simply based on the current operating status of the system and pre-set rules. It is difficult to take into account both the system frequency performance and the lifespan protection of energy storage, a high-quality frequency regulation resource. Furthermore, it is difficult to consider the adjustment characteristics of different frequency regulation resources, which makes it difficult to promote the use of energy storage equipment with high investment costs in frequency regulation services.

[0006] Therefore, the actual frequency regulation needs of the system will be reflected in the "intraday-real-time" scheduling decision in a matching manner, thereby giving full play to the advantages of energy storage equipment to achieve time coordination between adjacent scheduling, and thus formulating a system operation mode that can effectively cope with the random fluctuations of new energy. Summary of the Invention

[0007] The application provides a power and energy balance economic dispatch method considering a frequency modulation response model of a flywheel, aiming to solve the problems of intensified system frequency fluctuation and high frequency modulation cost under high proportion of new energy access, and realize collaborative optimization of frequency stability and economic dispatch.

[0008] The application provides a power and energy balance economic dispatch method considering a frequency modulation response model of a flywheel, and comprises the following steps:

[0009] Step S1: constructing a frequency modulation response model of a combination of a unit and a flywheel energy storage;

[0010] Step S2: establishing a power and energy balance economic dispatch model considering the frequency modulation response model of the combination of the unit and the flywheel energy storage;

[0011] Step S3: solving the economic dispatch model based on a fuzzy optimization algorithm of a state of charge (SOC) probability of the flywheel to obtain an optimal dispatch plan.

[0012] Further, the specific method for constructing the frequency modulation response model of the combination of the unit and the flywheel energy storage in step S1 comprises:

[0013] S1-1: constructing a unit output model, wherein the unit output model represents a relationship between an output of a unit in a current period and an output in a previous period and a frequency modulation mileage required to be provided;

[0014] The unit output model is:

[0015]

[0016] In the formula, k is a unit number; is a dispatch period number; is a short period number; is an output of the unit in the kth short period in the ith dispatch period; is an up / down frequency modulation mileage provided by the unit; is an output of the unit in the kth short period in the ith dispatch period; is an output of the unit in the kth short period in the ith dispatch period; is an output of the unit in the kth short period in the ith dispatch period; is an up / down frequency modulation mileage provided by the unit;

[0017] S1-2: constructing a flywheel energy storage output model, wherein the flywheel energy storage output model represents a relationship between an output of a flywheel energy storage in a current period and an output in a previous period and a frequency modulation mileage required to be provided;

[0018] The flywheel energy storage output model is:

[0019]

[0020] In the formula, k is a flywheel energy storage number; is a dispatch period number; is a short period number; is an output of the flywheel energy storage in the kth short period in the ith dispatch period; Short-term output; For flywheel energy storage Provided up / down frequency regulation mileage;

[0021] S1-3: Set the output upper and lower limit constraints of the unit and the flywheel energy storage;

[0022] Wherein, for the unit planned output Its upper and lower limits are limited by the maximum / minimum output of the unit and the provided up / down regulation capacity, which can be expressed as:

[0023]

[0024] In the formula, The unit In the dispatching period The up / down regulation capacity of the winning bid; The maximum / minimum output of the unit;

[0025] The upper and lower limits of the unit output in the dispatching period can be expressed as:

[0026]

[0027] For the frequency regulation unit, its output adjustment in the dispatching period should be within the range of the winning bid regulation capacity;

[0028] Wherein, the flywheel energy storage output at the initial time of the dispatching period is expressed as Its upper and lower limits Depend on the charge / discharge power corresponding to different state of charge (SOC) of the flywheel and the limit of the up / down regulation capacity provided by the upper-level command, the upper and lower limits of the output of the flywheel energy storage can be expressed as:

[0029]

[0030] The upper and lower limits of the flywheel energy storage output in the dispatching period can be expressed as:

[0031]

[0032] Its output adjustment in the dispatching period should be maintained within the range of the winning bid regulation capacity;

[0033] S1-4: Set the unit ramping constraint and introduce a relaxation factor to represent the alleviating effect of flywheel energy storage access on the unit ramping pressure;

[0034] The unit ramping constraint can be expressed as:

[0035]

[0036]

[0037]

[0038] wherein, is the short-term ramping capability of the unit; is the number of flywheel energy storage.

[0039] Further, the specific method of establishing the power and energy balance economic dispatch model considering the frequency regulation response model of the combination of the unit and the flywheel energy storage in step S2 comprises:

[0040] embedding the frequency regulation response model of the combination of the unit and the flywheel energy storage into the intraday marketized dispatch decision model to obtain an intraday power and energy balance economic dispatch model considering frequency regulation response;

[0041] The objective function of the intraday power and energy balance economic dispatch model considering frequency regulation response is to minimize the system operation cost; and the net load fluctuation in the dispatch period and the frequency regulation cost caused thereby are considered; the objective function can be constructed as follows:

[0042]

[0043]

[0044]

[0045] wherein, is the number of units; is the number of dispatch periods; is the number of short periods in a single dispatch period; is the unit energy offer ($ / MWh); is the flywheel energy storage energy offer ($ / MWh); is the unit up / down frequency regulation mileage offer ($ / MW); is the flywheel up / down frequency regulation mileage offer ($ / MW); is the unit up / down frequency regulation capacity offer ($ / MWh); is the flywheel up / down frequency regulation capacity offer ($ / MWh); is the proportion of the duration of a single dispatch period in the intraday marketized dispatch decision model to 1 hour, for example, the intraday dispatch period is generally 5 minutes, so that will be 12; similarly, is the proportion of the duration of a short period to 1 hour; is the compensation term weight coefficient; is the average error in the cost calculation process;

[0046] Operating costs include energy costs, frequency regulation mileage costs, and frequency regulation capacity costs.

[0047] Furthermore, the relevant constraints of the intraday power balance economic dispatch model that takes into account frequency regulation response are as follows:

[0048] (1) Operational constraints;

[0049]

[0050] In the formula, Number the nodes; The number of nodes; The net load at the beginning of the scheduling period;

[0051] (2) Frequency modulation capacity constraints;

[0052] The frequency regulation capacity provided by the generator set and flywheel energy storage is limited by the maximum frequency regulation capacity reported by the generator set, as shown below:

[0053]

[0054]

[0055]

[0056] In the formula, For the unit The maximum reported up / down frequency modulation capacity; Energy storage for flywheels The maximum up / down frequency modulation capacity reported; of which, the winning frequency modulation capacity should not be less than 0, and at the same time not greater than the maximum frequency modulation capacity reported.

[0057] Meanwhile, the combined frequency regulation capacity of the generating unit and flywheel energy storage should meet the system's frequency regulation capacity requirements, as shown below:

[0058]

[0059]

[0060] In the formula, To meet the system's up / down frequency modulation capacity requirements;

[0061] (3) Frequency regulation response constraints of the generator-flywheel combination;

[0062] This part of the constraint consists of the power output adjustment model of the unit, the power output adjustment model of the flywheel energy storage, and the upper and lower limits and ramp constraints of the power output of the two as described in step S1, which are used to describe the power output of the unit and the flywheel energy storage during the scheduling period.

[0063] (4) Frequency modulation mileage constraint;

[0064] The system power imbalance is compared with the regulation deadband to determine the system frequency regulation range requirement in each short time period;

[0065] The system power imbalance and the regulation deadband can be compared by the following equation:

[0066]

[0067]

[0068]

[0069]

[0070] wherein, is the regulation deadband; is a very large constant; and are integer variables used to determine the relative size of the system power imbalance and the regulation deadband; if the system power imbalance is greater than the positive deadband, then will be limited to 1, indicating that the system needs to be up-regulated at this time; otherwise, will be limited to 0; similarly, if the system power imbalance is less than the negative deadband, then will be limited to 1, indicating that the system needs to be down-regulated at this time; otherwise, will be limited to 0;

[0071] Based on the comparison results described above, the system frequency regulation range requirement in each short time period is determined as follows:

[0072]

[0073]

[0074]

[0075]

[0076] wherein, is the system up / down frequency regulation range requirement;

[0077] if is 1, indicating that the system needs to be up-regulated, then the up frequency regulation range requirement will be equal to the system power imbalance ; otherwise, the up frequency regulation range requirement will be 0; similarly, if is 1, indicating that the system needs to be down-regulated, then the down frequency regulation range requirement The system power imbalance amount ; otherwise, down-regulate the frequency regulation mileage requirement will be 0;

[0078] In addition, the sum of the frequency regulation mileage provided by the unit and the flywheel energy storage should meet the system frequency regulation mileage requirement as shown below:

[0079]

[0080]

[0081] At the same time, the output adjustment should be within the scope of the bid frequency regulation capacity, therefore, the frequency regulation mileage provided by the unit in a single short period should not be greater than the bid frequency regulation capacity:

[0082]

[0083]

[0084]

[0085] (5) High-pass and low-pass filtering constraints;

[0086] The up / down frequency regulation mileage is filtered by using a high-pass and low-pass filtering power distribution method to decompose it into a high-frequency component and a low-frequency component, wherein the high-frequency component is the frequency regulation mileage instruction of the flywheel energy storage , and the low-frequency component is the frequency regulation mileage instruction of the frequency regulation unit ; according to the filtering formula, the mathematical expression of the frequency regulation mileage in the frequency domain is as follows:

[0087]

[0088] In the formula, is a differential operator, is a high-pass filter time constant, usually in seconds to minutes;

[0089] Rewriting the above expression in the time domain can be obtained:

[0090]

[0091] .

[0092] Further, the specific method of solving the economic dispatch model in step S3 based on the fuzzy optimization algorithm based on the flywheel state of charge (SOC) probability to obtain the optimal dispatch plan includes:

[0093] ​The fuzzy optimization algorithm based on the flywheel state of charge (SOC) probability is a fuzzy biased positive particle swarm optimization algorithm based on the flywheel state of charge (SOC) value probability.

[0094] (1) When the flywheel SOC value is too low, the particle population is biased towards the charging direction. At the same time, the probabilistic fuzzy positive particle swarm correction method will consider the SOC change rate caused by the solution set at this time. When the rate of change When the value is large, slightly reduce the probability value Prob of flywheel power generation; when the rate of change... Slightly increase the Prob value when it is low, as shown below:

[0095]

[0096] In the formula, This indicates that the flywheel's behavior at this time is trending towards charging; This indicates that the flywheel's behavior at this time tends to be discharging.

[0097] (2) When the flywheel SOC value is within the set range, according to the rate of change The trend is to maintain a flat flywheel SOC: At this time, Prob tends to be on the discharge side, that is, Prob is greater than 0.5. When the value of Prob is greater than 0.5, the Prob tends to be on the charging side.

[0098] (3) When the flywheel SOC value is too high, the direction of particle swarm generation will be biased towards the energy storage and discharge direction; at the same time, this method will consider the SOC change rate in the solution set at this time. When the rate of change Slightly decrease the Prob value when it is large;

[0099] Based on the above methods, fuzzy theory is combined with the chaotic particle swarm optimization algorithm to solve the power balance economic dispatch model that takes into account the frequency regulation response model; the steps are as follows:

[0100] (1) The particle population is initialized with the system power imbalance as the independent variable and the output upper and lower limits of the unit and flywheel energy storage, the ramp rate constraint, the system power balance constraint, the frequency regulation capacity bid constraint, the frequency regulation mileage demand allocation constraint and the high-pass and low-pass filter constraint as the boundaries.

[0101] (2) Let the number of iterations be... ;

[0102] (3) Calculate the fitness value of each particle at this iteration number. , Given the total number of particles for each independent variable; find the local optimum for that particle population. ,Will and Comparison is made, if The result is better than , it is set as the global optimal solution ;

[0103] (4) judge Whether the number of iterations reaches the termination condition, when the termination condition is met, the result is output and the process is ended.

[0104] (5) according to the fuzzy theory, update The speed and position of the particle swarm under the constraint condition;

[0105] (6) update the particle swarm according to the chaos rule, and the number of iterations ;

[0106] (7) jump to step 3.

[0107] Compared with the prior art, the present application has the following beneficial effects:

[0108] The power and energy balance economic dispatching method considering the frequency modulation response model of the flywheel can effectively improve the frequency stability of the power system with high proportion of new energy access, by establishing a refined frequency modulation response model of the cooperation of the unit and the flywheel energy storage, and using an intelligent algorithm based on the adaptive optimization of the SOC state of the flywheel for solving, the system frequency deviation is significantly reduced, the economy of the frequency modulation cost is considered, and the overall optimization of the system frequency modulation performance and the operation efficiency is realized.

[0109] On the basis of the implementation manners of the above aspects provided by the present application, further combinations can be made to provide more implementation manners. BRIEF DESCRIPTION OF DRAWINGS

[0110] The above and other objects, features and advantages of the exemplary embodiments of the present application will be readily understood through reading the detailed description below, with reference to the accompanying drawings. In the drawings, several embodiments of the present application are shown by way of example and not limitation, in which the same or corresponding elements are designated by the same or corresponding reference numerals, in which:

[0111] Figure 1 is the topological graph of IEEE30 node system;

[0112] Figure 2 is the frequency simulation model graph;

[0113] Figure 3 is the frequency deviation graph of M1 and M2 in IEEE30 node system;

[0114] Figure 4 is the comparison graph of actual output of M1 and M2 unit and system operation cost in time period S in IEEE30 node system;

[0115] Figure 5 is a flow chart of the power and energy balance economic dispatch method of the application considering the flywheel frequency modulation response model. DETAILED DESCRIPTION

[0116] The exemplary embodiments of the present application will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it is understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present application can be more thoroughly understood, and so that the scope of the present application can be conveyed to those skilled in the art. Unless specifically indicated, the technical means used in the examples are conventional means known to those skilled in the art.

[0117] The present application relates to a power and energy balance economic dispatch strategy considering a flywheel frequency modulation response model, which is oriented to a combined frequency modulation mode of a unit and a flywheel, considers power fluctuations within a dispatch period and frequency modulation mileage requirements and costs caused thereby, and reduces system operation costs.

[0118] In combination Figure 5 As shown in the drawings, an optional embodiment of the present application provides a power and energy balance economic dispatch method considering a flywheel frequency modulation response model, and the specific steps of the method include:

[0119] (1) a frequency modulation response model considering an AGC control strategy oriented to a combination of a unit and a flywheel energy storage is proposed to describe the output of the unit and the flywheel during frequency modulation;

[0120] (2) a power and energy balance economic dispatch strategy considering a flywheel frequency modulation response model is proposed to consider net load fluctuations within a dispatch period and frequency modulation costs caused thereby;

[0121] (3) a fuzzy bias particle swarm optimization algorithm based on a flywheel SOC probability is proposed, which can make the population quickly and efficiently reach an optimal solution under the constraint conditions.

[0122] In step (1) of the present application, a frequency modulation response model considering an AGC control strategy oriented to a combination of a unit and a flywheel energy storage is proposed to describe the output of the unit and the flywheel energy storage during frequency modulation:

[0123] In a market environment, whether the unit and the flywheel energy storage participate in frequency regulation in the real-time operation stage depends on whether they win the frequency modulation capacity in the intraday market dispatch decision stage. If the frequency modulation capacity is won, the unit needs to reserve a certain capacity for output adjustment in the real-time operation stage, and such a unit is referred to as a frequency modulation unit in the present application. The flywheel energy storage provides corresponding frequency support according to the winning situation.

[0124] The power output adjustment of the unit during the scheduling period can be modeled as follows (1):

[0125]

[0126] In the formula, Assign unit number; Number the scheduling period; For short time periods; For the unit During the scheduling period The Middle Short-term output; The up / down frequency regulation mileage provided for the generator unit.

[0127] For frequency-regulating generator units, the unit output will be limited by equation (1), that is, if the frequency-regulating generator unit provides frequency regulation mileage service in the current short period, its output will be adjusted based on the previous short period. In the refined model of generator unit frequency regulation response proposed in this invention, the frequency-regulating generator unit will not adjust its own output in every short period, but will only adjust when the system power imbalance exceeds the regulation dead zone.

[0128] The flywheel energy storage output model within the same scheduling period can be modeled as follows (2):

[0129]

[0130] In the formula, Energy storage for flywheels During the scheduling period The Middle Short-term output; Energy storage for flywheels The frequency regulation mileage provided. Since flywheel energy storage does not participate in the power generation output during the normal operation phase, it can be assumed that its output during the frequency regulation phase depends on the output status of the previous short-term frequency regulation and the frequency regulation mileage required from the flywheel energy storage.

[0131] Regarding the output range of the generator set and flywheel energy storage, the upper and lower limits of the generator set output can be expressed by the following formulas (3)-(4):

[0132] :

[0133]

[0134] In the formula, For the unit During the scheduling period The winning bid for up / down frequency modulation capacity; This refers to the unit's maximum / minimum output.

[0135] For the unit scheduled power (i.e. the unit power at the beginning of the dispatch period, denoted as ), its upper and lower limits are restricted by the maximum / minimum unit power and the provided up / down frequency regulation capacity. Since the unit providing frequency regulation needs to reserve power capacity, as shown in equation (3). The unit power in the dispatch period is restricted by equation . For the frequency regulation unit, its power adjustment in the dispatch period should be within the marked frequency regulation capacity.

[0136] The upper and lower limits of the flywheel energy storage power can be represented as equations (5)-(6) as follows:

[0137] :

[0138]

[0139] In which, the flywheel energy storage power at the beginning of the dispatch period is denoted as , whose upper and lower limits depend on the charge / discharge power of the flywheel corresponding to different SOC and the restriction of the up / down frequency regulation capacity provided by the upper level command, as shown in equation (5). The flywheel energy storage power in the dispatch period is restricted by equation (6), and the power adjustment should be maintained within the marked frequency regulation capacity.

[0140] The unit power change in the dispatch period is also restricted by the unit ramping capability, as shown in equations (7)-(9)

[0141]

[0142]

[0143]

[0144] In which, is the unit short-time ramping capability; is the number of flywheel energy storages. Equation (7) limits the unit power change between adjacent short-time periods in the same dispatch period. Equation (8) limits the power change between the end of the previous dispatch period and the beginning of the next dispatch period. Due to the participation of the flywheel energy storage, the ramping constraint of the unit is relaxed, is the relaxation factor.

[0145] In summary, the frequency regulation response model for the combination of the unit and the flywheel energy storage can realize the characterization of the unit and the flywheel energy storage power in the dispatch period.

[0146] The power and energy balance economic dispatch strategy considering the flywheel frequency regulation response model is proposed in step (2) to consider the net load fluctuation in the dispatch period and the frequency regulation cost caused thereby:

[0147] The frequency modulation response model of the unit facing the flywheel energy storage combination is embedded into the intraday marketization scheduling decision model, so as to obtain an intraday power and energy balance economic scheduling model capable of considering the frequency modulation response.

[0148] The objective function of the model is to minimize the system operation cost, and the high and low frequency components in the power shortage are allocated by the combination of frequency modulation units and flywheels in the case of considering the net load fluctuation in the scheduling period and the frequency modulation cost caused thereby, so as to realize the purpose of reducing the system operation cost. The objective function can be constructed as follows formula (10)-(12):

[0149]

[0150]

[0151]

[0152] In the formula, is the number of units; is the number of scheduling periods; is the number of short periods in a single scheduling period; is the unit energy offer ($ / MWh); is the flywheel energy storage energy offer ($ / MWh); is the unit up / down frequency modulation mileage offer ($ / MW); is the flywheel up / down frequency modulation mileage offer ($ / MW); is the unit up / down frequency modulation capacity offer ($ / MWh); is the flywheel up / down frequency modulation capacity offer ($ / MWh); is the proportion of the duration of a single scheduling period in the intraday marketization scheduling decision model to 1 hour, for example, the intraday scheduling period is generally 5 minutes, so will be 12; similarly, is the proportion of the duration of a short period to 1 hour; is the compensation term weight coefficient; is the average error in the cost calculation process.

[0153] The cost function shown in formula and formula contains 3 items, which are energy cost, frequency modulation mileage fee and frequency modulation capacity cost.

[0154] The related constraints of the model are as follows:

[0155] 1) The operation constraint is as follows formula (13):

[0156]

[0157] wherein, is the node number; is the number of nodes; is the net load at the beginning of the dispatch period.

[0158] 2) Frequency regulation capacity constraint

[0159] The frequency regulation capacity provided by the unit and the flywheel energy storage is limited by the maximum frequency regulation capacity reported by it, as shown in the following formulas (14)-(17). The frequency regulation capacity should not be less than 0, and at the same time, it should not be greater than the maximum frequency regulation capacity reported by it.

[0160]

[0161]

[0162]

[0163]

[0164] wherein, is the unit reported maximum up / down frequency regulation capacity; is the flywheel energy storage reported maximum up / down frequency regulation capacity.

[0165] At the same time, the sum of the frequency regulation capacity jointly bid by the unit and the flywheel energy storage should meet the system frequency regulation capacity demand, as shown in formulas (18)-(19).

[0166]

[0167]

[0168] wherein, is the system up / down frequency regulation capacity demand.

[0169] 3) Unit-flywheel combination frequency regulation response constraint

[0170] This part of the constraint is composed of formulas (1)-(9) in step (1), which is used to describe the output of the unit and the flywheel energy storage in the dispatch period.

[0171] 4) Frequency regulation mileage constraint

[0172] The system power imbalance needs to be determined according to the system power imbalance, to check whether it exceeds the regulation dead zone, and then determine the system frequency regulation mileage demand in each frequency regulation short period.

[0173] The size of the system power imbalance and the regulation dead zone can be compared by formulas (20)-(23).

[0174]

[0175]

[0176]

[0177]

[0178] where, is the regulating dead zone; is a very large constant; and is an integer variable used to determine the relative size of the system power imbalance and the regulating dead zone. If the system power imbalance is greater than the positive dead zone, then will be limited to 1, indicating that the system needs to be up-regulated at this time; otherwise, will be limited to 0. Similarly, if the system power imbalance is less than the negative dead zone, then will be limited to 1, indicating that the system needs to be down-regulated at this time; otherwise, will be limited to 0.

[0179] Based on the above comparison results, the system frequency regulation mileage demand for each short period can be determined, as shown in equations (24)-(27):

[0180]

[0181]

[0182]

[0183]

[0184] where, is the system up / down frequency regulation mileage demand.

[0185] If is 1, indicating that the system needs to be up-regulated, then the up-regulation mileage demand will be equal to the system power imbalance , as shown in equation (24). Otherwise, the up-regulation mileage demand will be 0, as shown in equation (25). Similarly, if is 1, indicating that the system needs to be down-regulated, then the down-regulation mileage demand will be equal to the system power imbalance , as shown in equation (26). Otherwise, the down-regulation mileage demand will be 0, as shown in equation (27).

[0186] In addition, the sum of the frequency modulation mileage provided by the unit and the flywheel energy storage should meet the system frequency modulation mileage demand as follows formula (28)-(29):

[0187]

[0188]

[0189] At the same time, the output adjustment should be within the scope of the bid frequency modulation capacity, therefore, the frequency modulation mileage provided by the unit in a single short period should not be greater than the bid frequency modulation capacity as follows formula (30)-(33):

[0190]

[0191]

[0192]

[0193]

[0194] 5) High-pass low-pass filter constraint

[0195] In order to determine the frequency modulation mileage of the unit and the flywheel respectively, and reduce the overall operation cost of the system, the role of different types of frequency modulation devices should be fully played. The system frequency modulation instruction is divided into fast dynamic and slow dynamic two parts, wherein the fast dynamic component is compensated by the flywheel energy storage with fast response ability, and the slow dynamic component is compensated by the frequency modulation unit with low cost and slow response.

[0196] The up / down frequency modulation mileage is filtered by using high-pass low-pass filter power distribution method , which is decomposed into high frequency component and low frequency component, wherein the high frequency component is the frequency modulation mileage instruction of the flywheel energy storage , and the low frequency component is the frequency modulation mileage instruction of the frequency modulation unit . According to the filtering formula, the mathematical expression of the frequency modulation mileage in the frequency domain is shown in the following (34)-(35):

[0197]

[0198]

[0199] In the formula, is the differential operator, is the high-pass filter time constant, usually in seconds to minutes.

[0200] The above expression is rewritten in the time domain as follows formula (36)-(37):

[0201]

[0202]

[0203] In summary, the power and energy balance economic dispatch model considering the flywheel frequency modulation response model is established, the objective function is shown as formula (10), the constraints are shown as formula (1)-(9), (11)-(33), (36)-(37). The model belongs to a mixed integer linear programming model, and the output is the scheduling plan output of the unit and the flywheel energy storage And And the reserved frequency modulation capacity And .

[0204] The step (3) of the application proposes a fuzzy bias particle swarm optimization algorithm based on the SOC probability of the flywheel, which can make the population quickly and efficiently reach the optimal solution under the constraint condition:

[0205] In the process of solving the power and energy balance economic dispatch strategy considering the flywheel frequency modulation response model, integer variables And Need to be introduced to describe the system frequency modulation mileage demand of each short period, so the economic operation solving process is a nonlinear solving process. Obviously, the introduction of integer variables will increase the solving burden of the dispatch decision model, and the dispatch decision has a higher requirement for the solving efficiency of the dispatch decision model. Therefore, considering the practicability of the particle swarm algorithm in solving nonlinear equations, the application selects the more effective chaotic particle swarm algorithm to solve the problem, and proposes a fuzzy bias particle swarm optimization algorithm based on the SOC value probability, which can guide the moving direction of the particle population, so that the population quickly and efficiently reaches the optimal solution under the constraint condition.

[0206] (1) When the SOC value of the flywheel is too low, the particle population is biased to the charging direction, and the probability fuzzy bias particle swarm correction method will consider the SOC change rate caused by the solution set at this time When the change rate Is relatively large, the probability value Prob of the flywheel power generation is slightly reduced, and when the change rate Is relatively small, the Prob value is slightly increased. This method can reduce the fluctuation of the SOC value of the flywheel, as shown in the following formula (38).

[0207]

[0208] In the formula, Indicates that the behavior trend of the flywheel at this time is charging; Indicates that the behavior trend of the flywheel at this time is discharging;

[0209] (2) When the SOC value of the flywheel is in the set range, the SOC of the flywheel is kept as flat as possible according to the change rate Trend: When Prob tends to the discharge side, i.e. Prob is greater than 0.5, When Prob tends to the discharge side, i.e. Prob is greater than 0.5;

[0210] (3) When the flywheel SOC value is too high, the direction of the particle swarm will be biased towards the energy storage discharge direction. At the same time, this method will consider the SOC change rate in the solution set at this time When the change rate is too large, the Prob value is slightly reduced.

[0211] Based on the above method, the fuzzy theory and the chaotic particle swarm algorithm are combined to solve the power and energy balance economic dispatch strategy considering the flywheel frequency modulation response model. The steps can be summarized as follows:

[0212] 1) Taking the system power imbalance as the independent variable (particle), and the constraint conditions described in formulas (1)-(9), (11)-(33), (36)-(37) as the constraints, the particles are initialized;

[0213] 2) Let the iteration number ;

[0214] 3) Calculate the fitness value of each particle at this iteration number , and find the local optimal solution of the particle population. Compare with , if the result is better than , set it as the global optimal solution ; ;

[0215] 4) Judge whether or the iteration number reaches the termination condition. When the termination condition is met, the result is output and the process is ended;

[0216] 5) According to the fuzzy theory, update the speed and position of the particle swarm under the condition of based on the constraint conditions described above;

[0217] 6) Update the particle swarm according to the chaotic rule, and set the iteration number ;

[0218] 7) Jump to step 3.

[0219] The application carries out simulation analysis in an IEEE30 node system, and is tested based on a hardware environment of Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz 32GB RAM, so as to verify the effectiveness of the power and energy balance economic dispatch strategy considering the flywheel frequency modulation response model.

[0220] The IEEE30 node system topology is as shown in Figure 1 In the simulation process, the following two methods are compared:

[0221] M1: the power and energy balance economic dispatch decision model considering the flywheel frequency modulation response model proposed in the application;

[0222] M2: the dispatch decision model only considering the frequency modulation mileage demand.

[0223] (1) Comparison of time domain simulation results

[0224] Firstly, the scheduling results of the whole day are obtained through the rolling scheduling strategy, and then the time domain simulation of the real-time operation of the power system is simulated through the frequency simulation model shown in Figure 2 to evaluate the system operation performance under different scheduling decision methods.

[0225] In the simulation model, is the actual net load curve; is the rotor time constant related to the moment of inertia; is the active frequency response coefficient of the load; is the frequency deviation; is the frequency deviation coefficient (MW / 0.1Hz) of the AGC system, used to determine the system ACE; and are the proportional coefficient and integral coefficient of the PI controller in the AGC system respectively, and the ACE can obtain the area regulation requirement (ARR) through the PI controller, that is, the total regulation amount of the system; the ARR dead zone is set in the simulation model to simulate the actual operation of the power system, which means that the AGC instruction is only issued when the ARR exceeds a certain threshold, so as to avoid frequent regulation of the system; the participation factor is determined according to the proportion of the unit-flywheel reserved frequency modulation capacity in the scheduling decision stage; the capacity limitation module is used to limit the regulation range of the unit-flywheel; is the transfer function; is the unit-flywheel planned output determined in the scheduling decision stage; the speed limitation module is used to limit the regulation speed of the unit-flywheel; the output upper and lower limit module is used to limit the maximum and minimum output of the unit-flywheel; is the droop characteristic of the unit governor, which represents the primary frequency modulation capacity of the unit.

[0226] The operational performance of the proposed method M1 and the existing method M2 was tested using actual operating data from the power system on a given day. The system frequencies obtained from M1 and M2 are as follows: Figure 3 As shown.

[0227] Table 1 shows the frequency deviation indices obtained from M1 and M2, as well as the system operating cost calculated from the generator-flywheel combination quotation.

[0228] Table 1. Frequency deviation indices and system operating costs obtained from M1 and M2 in the IEEE 30-node system.

[0229] Method M1 M2 Performance improvement Maximum absolute frequency deviation (Hz) 0.178 0.305 41.64% Frequency deviation standard deviation (Hz) 0.042 0.045 6.67% System operating cost ($) 1.368 x 10 5 ]] 1.370 x 10 5 ]]> 0.15%

[0230] Depend on Figure 3 As shown in Table 1, the proposed method M1 can maintain the frequency deviation within a smaller range, especially for certain periods when the frequency deviation is prone to exceed the normal range. Simultaneously, it can reduce system operating costs, verifying the effectiveness of the proposed method.

[0231] (2) Scheduling Result Analysis

[0232] Compared to the existing method M2, the proposed method M1 improves the system frequency performance. For M1, the unit scheduling result is required to meet the frequency regulation mileage requirement for each short period. By adding flywheel energy storage, a relaxed short-term ramp constraint is established to ensure that the unit has sufficient adjustment capability to cope with the short-term fluctuation characteristics of net load. In contrast, the existing method M2 only requires meeting the frequency regulation mileage requirement for the entire scheduling period, as shown in equations (39)-(40), where the frequency regulation mileage requirement is the product of the frequency regulation capacity requirement and the system empirical coefficient.

[0233]

[0234]

[0235] In the formula, The system's up / down frequency regulation mileage requirements for the entire scheduling period; This represents the system's empirical coefficient.

[0236] In contrast, because M2 does not perform detailed modeling of the unit's regulation capacity and does not consider the combined effect of flywheel energy storage and the unit, it is difficult to guarantee that the dispatch results can cope with the short-term fluctuation characteristics of net load.

[0237] M1 also has certain advantages in saving system operation cost. In M2, the units providing up-regulation capacity need to follow AGC instructions to increase power output to cope with the upward fluctuation of net load in real-time operation stage. The up-regulation of unit output will generate up-regulation mileage cost, which is not considered in M2. Unlike M2, the method M1 of the present application takes into account the output adjustment of the unit-flywheel in the dispatching period, so it can take into account the frequency regulation mileage cost and the corresponding energy cost change, thereby reducing the system energy cost, as shown in Figure 4

[0238] In summary, thanks to the consideration of the frequency regulation response model for the unit-flywheel combination, M1 can reduce the output up-regulation of the unit-flywheel in the real-time operation stage. Compared with M2, the system operation cost of M1 in the period S is reduced by 1.79%.

[0239] The above merely illustrates the specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which shall be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.​

Claims

1. A power energy balance economic dispatch method considering flywheel frequency modulation response model, characterized in that, The method comprises the following steps: Step S1: constructing a frequency modulation response model of a combination of a unit and a flywheel energy storage; Step S2: establishing an economic dispatching model of power and energy balance considering the frequency modulation response model of the combination of the unit and the flywheel energy storage; Step S3: solving the economic dispatching model based on a fuzzy optimization algorithm of a state of charge (SOC) probability of the flywheel to obtain an optimal dispatching plan; The specific method of constructing the frequency modulation response model of the combination of the unit and the flywheel energy storage in step S1 comprises: S1-1: constructing a unit output model, which represents a relationship between an output of the unit in a current period and an output in a previous period and a frequency modulation mileage required to be provided; The unit output model is as follows: wherein, is the unit number; is the dispatch period number; is the short period number; is the unit in the dispatch period the output of the short period; is the up / down frequency regulation mileage provided for the unit; S1-2: constructing a flywheel energy storage output model, which represents a relationship between an output of the flywheel energy storage in the current period and an output in the previous period and the frequency modulation mileage required to be provided; The flywheel energy storage output model is as follows: In the formula, Flywheel energy storage At dispatch period In the middle of the day Short period of time output; Flywheel energy storage Provided up / down frequency mileage; S1-3: setting an upper and lower limit constraint of outputs of the unit and the flywheel energy storage; where, for the unit schedule power whose upper and lower limits are limited by the maximum / minimum power of the unit and the provided up / down spinning reserve capacity, is expressed as: In the formula, is the unit In the dispatch period The up / down frequency capacity of the winning bid; is the maximum / minimum output of the unit The upper and lower limit of the unit output in a dispatching period is represented as follows: For a frequency modulation unit, an output adjustment thereof in the dispatching period should be within a marked frequency modulation capacity range; wherein the flywheel energy storage output at the beginning of the dispatch period is denoted as its upper and lower limits Depending on the limits of the charge / discharge power of the flywheel corresponding to its different state of charge SOC and the up / down regulation capacity provided by the upper-level command, the upper and lower limits of the flywheel energy storage output are denoted as: The upper and lower limit of the flywheel energy storage output in the dispatching period is represented as follows: An output adjustment thereof in the dispatching period should be maintained within the marked frequency modulation capacity range; S1-4: setting a unit ramping constraint and introducing a relaxation factor to represent a relieving effect of the flywheel energy storage access on a unit ramping pressure; The unit ramping constraint is represented as follows: In the formula, is the short-time climbing ability of the unit; is the number of flywheel energy storage; is the relaxation factor.

2. The economic dispatch method of power supply and energy balance considering the flywheel frequency modulation response model according to claim 1, characterized in that, The specific method of establishing the economic dispatching model of power and energy balance considering the frequency modulation response model of the combination of the unit and the flywheel energy storage in step S2 comprises: Embedding the frequency modulation response model of the combination of the unit and the flywheel energy storage into an intraday marketized dispatching decision model to obtain an economic dispatching model of intraday power and energy balance considering frequency modulation response; An objective function of the economic dispatching model of intraday power and energy balance considering frequency modulation response is to minimize system operation cost; and considering a net load fluctuation in the dispatching period and a frequency modulation fee caused thereby; the objective function is constructed as follows: wherein, is the number of units is the number of scheduling periods is the output of the short period in the scheduling period ; is the number of units is the number of scheduling periods is the up / down frequency regulation capacity of the unit ; is the number of flywheel energy storages is the number of scheduling periods is the output of the short period in the scheduling period ; are the cost functions of the units and flywheel energy storages, respectively is the number of units is the number of scheduling periods is the number of short periods in a single scheduling period is the unit energy offer is the flywheel energy storage energy offer is the unit up / down frequency regulation mileage offer is the flywheel up / down frequency regulation mileage offer is the unit up / down frequency regulation capacity offer is the flywheel up / down frequency regulation capacity offer is the proportion of the duration of a single scheduling period in the intra-day market-oriented scheduling decision model to 1 hour; similarly, is the proportion of the duration of a short period to 1 hour is the compensation item weight coefficient is the average error in the cost calculation process The operation cost comprises an energy cost, a frequency modulation mileage fee and a frequency modulation capacity cost.

3. The economic dispatch method of power supply and energy balance considering the flywheel frequency modulation response model according to claim 2, characterized in that, Related constraints of the economic dispatching model of intraday power and energy balance considering frequency modulation response are as follows: (1) an operation constraint; wherein is the node number; is the number of nodes; is the net load at the beginning of the dispatch period; is the flywheel energy storage output at the beginning of the dispatch period; (2) a frequency modulation capacity constraint; The frequency modulation capacity provided by the unit and the flywheel energy storage is limited by a maximum frequency modulation capacity reported thereby, as follows: In the formula, is a machine set The maximum up / down frequency regulation capacity reported; is a flywheel energy storage The maximum up / down frequency regulation capacity reported; wherein the middle frequency regulation capacity should not be less than 0, and at the same time not more than the maximum frequency regulation capacity reported; Meanwhile, a sum of the frequency modulation capacities jointly marked by the unit and the flywheel energy storage should satisfy a system frequency modulation capacity demand, as follows: In the formula, is the system up / down frequency capacity requirement; (3) a unit-flywheel combination frequency modulation response constraint; This constraint is used to describe an output condition of the unit and the flywheel energy storage in the dispatching period, which is composed of the unit output adjustment model, the flywheel energy storage output adjustment model and the upper and lower limit and ramping constraints of the unit and the flywheel energy storage; (4) a frequency modulation mileage constraint; The system frequency modulation mileage demand in each frequency modulation short period is determined according to a system power imbalance and whether it exceeds a regulation dead zone; The system power imbalance and the regulation dead zone are compared by the following formula; where is the adjusted deadband; is a very large constant; and is an integer variable used to determine the relative size of the system power imbalance and the adjusted deadband; if the system power imbalance is greater than the positive deadband, then will be limited to 1, indicating that the system needs to be up-tuned at this time; otherwise, will be limited to 0; similarly, if the system power imbalance is less than the negative deadband, then will be limited to 1, indicating that the system needs to be down-tuned at this time; otherwise, will be limited to 0; Based on the above comparison results, the system frequency regulation mileage requirements of each short period are determined, as shown below: In the formula, is the system up / down frequency mileage requirement; is the unit maximum / minimum output. If is 1, indicating that the system needs to tune up, then the tune up mileage requirement will equal the system power imbalance ; otherwise, the tune up mileage requirement will be 0; similarly, if is 1, indicating that the system needs to tune down, then the tune down mileage requirement will equal the system power imbalance ; otherwise, the tune down mileage requirement will be 0; In addition, the sum of the frequency regulation mileage provided by the units and the flywheel energy storage should meet the system frequency regulation mileage requirements as shown below: At the same time, the output adjustment should be within the scope of the bid frequency capacity, therefore, the frequency regulation mileage provided by the units in a single short period should not be greater than the bid frequency capacity: (5) High-pass low-pass filter constraint; High-pass and low-pass filter power allocation method for up / down frequency modulation mileage The sample is filtered and decomposed into high-frequency and low-frequency components, with the high-frequency component serving as the frequency regulation mileage command for flywheel energy storage. The low-frequency component serves as the frequency regulation mileage command for the frequency regulation unit. According to the filtering formula, the mathematical expression for the frequency domain allocation of FM mileage is as follows: wherein is a differential operator, is a high-pass filter time constant, typically in the order of seconds to minutes; Rewrite the above expression in the time domain: 。 4. The economic dispatch method of power supply and energy balance considering the flywheel frequency modulation response model according to claim 1, characterized in that, The specific method for solving the economic dispatch model in step S3 based on the flywheel state of charge SOC probability fuzzy optimization algorithm includes: The flywheel state of charge SOC probability fuzzy optimization algorithm is a fuzzy bias particle swarm optimization algorithm based on the flywheel state of charge SOC value probability; (1) When the flywheel SOC value is too low, the particle population is biased towards the charging direction, and the probability fuzzy positive particle swarm correction method will consider the SOC change rate caused by the solution set at this time When the change rate is large, the probability value Prob of the flywheel power generation is slightly reduced, and when the change rate is small, the Prob value is slightly increased, as follows: wherein represents that the flywheel behavior trend at this time is charging; represents that the flywheel behavior trend at this time is discharging; (2) When the flywheel SOC value is in the set range, according to the change rate Trend, keep the flywheel SOC smooth: When Prob tends to the discharge side, that is, Prob is greater than 0.5, When Prob tends to the charge side, that is, Prob is greater than 0.5; (3) When the flywheel SOC value is too high, the direction of the particle swarm will be biased towards the energy storage discharge direction; at the same time, the method will consider the SOC change rate in the solution at this time When the change rate is large, the Prob value is slightly reduced; Based on the above method, the fuzzy theory and the chaotic particle swarm algorithm are combined to solve the power and energy balance economic dispatch model considering the frequency regulation response model; The steps are as follows: (1) Taking the system power imbalance as the independent variable, and taking the output upper and lower limit constraints of the units and the flywheel energy storage, the climbing rate constraint, the system power balance constraint, the frequency capacity bid constraint, the frequency regulation mileage requirement distribution constraint, and the high-pass low-pass filter constraint as the boundary, the particle population is initialized; (2) Let the number of iterations ; (3) Calculate the fitness value of each particle at this iteration , Find the local optimal solution of this particle population Compare with and , if the result is better than , set it as the global optimal solution ; (4) determining or whether the number of iterations reaches a termination condition, and outputting the result and ending when the termination condition is met; (5) updating the velocity and position of the particle swarm based on the constraint conditions according to the fuzzy theory in the case of the particle swarm (6) updating the particle swarm according to the chaos rule and setting the iteration number ; (7) Jump to step 3.

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