CN114638502A - Power distribution network line reinforcement strategy considering flexibility of demand side resources - Google Patents

Power distribution network line reinforcement strategy considering flexibility of demand side resources Download PDF

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CN114638502A
CN114638502A CN202210266048.1A CN202210266048A CN114638502A CN 114638502 A CN114638502 A CN 114638502A CN 202210266048 A CN202210266048 A CN 202210266048A CN 114638502 A CN114638502 A CN 114638502A
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艾欣
王昊洋
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North China Electric Power University
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Abstract

The invention discloses a power distribution network line reinforcement strategy considering the flexibility of demand side resources, which comprises the following steps: acquiring frame information and the ground meteorological data of a certain power distribution network, performing load flow calculation based on node load data and network topology information of the power distribution network, and judging whether a voltage out-of-limit node and an overload branch exist in the network; defining the flexibility requirement of the system by the net load difference value of adjacent time periods of the system; predicting the output of a photovoltaic power station based on a multi-Markov chain Monte Carlo model, and clustering to generate a typical flexibility demand scene based on a neighbor propagation algorithm; aggregating and modeling the adjustable boundary of the multi-energy heterogeneous resources at the demand side based on the virtual battery model; optimizing the gears of the on-load tap changing transformer by taking the minimum voltage deviation degree of the node of the power distribution network as a target; if the node voltage is out of limit or the line is overloaded, then the day-ahead economic dispatching of the power distribution network for considering the resource flexibility of the demand side is carried out; searching a line reinforcement economic scheme by adopting an evolutionary particle swarm algorithm, introducing the maximum annual insufficient electric quantity index to evaluate risks and selecting a reinforcement scheme with the minimum index; and updating network topology information, repeating the steps until all the years in the traversal planning period, and outputting a line reinforcement optimal scheme. By adopting the strategy of the invention, the flexibility of the demand side resources can be effectively utilized to slow down the high-volume investment of the strengthening of the power distribution network lines, and the strategy has positive significance on the economic planning and decision of the strengthening of the power distribution network lines.

Description

Power distribution network line reinforcing strategy considering flexibility of resources on demand side
Technical Field
The invention relates to the field of power distribution network extension planning, in particular to a power distribution network line strengthening strategy considering the flexibility of demand side resources.
Background
Various distributed resources (DERs) are connected into the power distribution network, so that the environmental benefit is obviously improved, and severe challenges are brought to the planning and operation of the power distribution network. Taking a power distribution network line expansion plan as an example, the traditional scheme is to make a line strengthening scheme according to the load prediction result and the worst running condition of the power distribution network; the scheme formulated based on the strategy often has economic loss, and uncertainty of Distributed Generation (DG) output and load demand and flexibility of various resources on a demand side are not considered.
At present, a power distribution network planning research considering DG output and load demand uncertainty exists, a risk assessment model of low-probability high-loss events caused by DERs access is included in a planning problem in the proposed scheme, but the influence of flexibly adjustable resources on a demand side on power distribution network extension planning is not considered. The accurate depiction of the adjustable boundary of various resources on the demand side is the key for participating in optimized scheduling and reducing the investment of extension planning, and Chinese patent publication No. CN112883566A discloses a flexible aggregation method of electric vehicles based on a virtual battery model, which accurately describes the capacity and power boundary of a cluster electric vehicle. However, the modeling idea is not applied to the field of power distribution network line reinforcement. Therefore, in the aspect of the power distribution network line expansion planning, the flexibility of various resources on the demand side and the result of participation in optimization scheduling are considered, and the method is necessary for relieving the high investment of power distribution network line reinforcement.
Disclosure of Invention
The invention aims to provide a power distribution network line strengthening strategy considering the flexibility of demand side resources, the traditional power distribution network line strengthening strategy is improved, and the high investment of line strengthening is effectively reduced by utilizing the flexibility of demand side cluster resources.
In order to achieve the purpose, the invention provides the following scheme:
acquiring frame information and the ground meteorological data of a certain power distribution network, and performing load flow calculation based on load data and network topology information of the power distribution network so as to judge whether a voltage out-of-limit node and an overload branch exist in the network;
defining system flexibility requirements by net load difference values of adjacent time periods of the system; predicting the output of a photovoltaic power station based on a multi Markov Chain Monte Carlo (MCMC) model, and clustering to generate a typical flexibility demand scene based on a neighbor propagation algorithm; performing aggregation modeling on the adjustable boundary of the multi-energy heterogeneous resources on the demand side based on the virtual battery model so as to quantify the available flexibility of various resources;
optimizing the gears of the on-load tap changing transformer by taking the minimum voltage deviation degree of the node of the power distribution network as a target;
if the network still has overload or voltage problems, carrying out day-ahead economic dispatching of the power distribution network considering the flexibility of resources on the demand side;
inputting the gear information of the on-load tap changer and the load data after scheduling into a power distribution network line reinforcement model, searching a line economic reinforcement scheme through an evolutionary particle swarm algorithm, selecting a line reinforcement scheme by taking the maximum annual power shortage as a target, updating topology information of the power distribution network, repeating the process until all years in a traversal planning period are reached, and outputting an optimal line reinforcement scheme.
Optionally, the distribution network frame information and the ground meteorological data include:
the power distribution network topology information, power supply and load composition, node load data, meteorological data and the like.
The network topology information of the power distribution network comprises a grid structure, line parameters and geographic information; the power supply and load components comprise power supply and load types and proportion; the node load data and the meteorological data comprise node load data and meteorological data such as net sunlight intensity and total sunlight intensity which take hours as time scales within a planning year;
the load flow calculation takes the load data of the power distribution network and the network topology information as input, and the calculation aims at judging whether a voltage out-of-limit node and an overload branch exist in the network.
Optionally, the system flexibility requirement is a difference between a net load of the system t and a net load of the t +1 time period:
Figure BDA0003544275950000021
when in use
Figure BDA0003544275950000022
The system net load increases, reflected in an upward flexibility requirement; when in use
Figure BDA0003544275950000023
The system payload is reduced, which is reflected in a downward flexibility requirement; t represents the corresponding time interval of different time scales, and T schedules the total time interval number of the cycle;
the method for predicting the output of the photovoltaic power station based on the multi-Markov-chain Monte Carlo model comprises the steps of photovoltaic power station historical data preprocessing, discrete state division, discrete transfer state learning, Monte Carlo state sampling, meteorological state sequence correction and photovoltaic output reduction, and specifically comprises the following steps:
the photovoltaic power station historical data preprocessing and discrete state division comprises the following steps:
dividing the whole year into H periods according to months, and dividing the time period within each day of the period, within which the earth surface photovoltaic power station can receive the net load data, into TirrTime periods within which the photovoltaic output is constrained
Figure BDA0003544275950000024
Equally dividing the constraint interval into NsDiscrete states, then a single state range can be expressed as
Figure BDA0003544275950000025
The discrete transition state learning includes:
the quantity of the photovoltaic power stations which are supposed to be connected into the power distribution network is MPVThen, the k (k ═ 1.., M)PV) The relevance of the meteorological conditions of the power station t +1 time period to other power stations can be represented by the following distribution:
Figure BDA0003544275950000026
where s is the discrete output state of the photovoltaic power plant, Pr(s)1|s2) Is shown in state s2Under the conditions generated, state s1The probability of generation can be generated from the distribution
Figure BDA0003544275950000031
A dimensional state transition matrix;
the Monte Carlo state samples include:
extracting variables from the state transition matrix in sequence to generate a Markov state chain;
suppose that the threshold of the number of state transitions and the number of required samples are n1And n2The initial state sequence is
Figure BDA0003544275950000032
The sampling procedure is described by the following equation:
Figure BDA0003544275950000033
where → represents the right state extracted by the left probability;
and generating a Markov chain reference sample set through sampling so as to obtain the following meteorological state sequence:
Figure BDA0003544275950000034
the meteorological state sequence correction and photovoltaic output reduction comprises the following steps:
Figure BDA0003544275950000035
wherein psitIn order to account for the instantaneous disturbance of the atmospheric state caused by the cloud layer change, the white noise sequence of the meteorological state is corrected;
the typical flexibility requirement scenario generation includes:
clustering by adopting a neighbor propagation algorithm to generate a flexibility demand scene, inputting flexibility demands under different scenes, and calculating the similarity between elements to be clustered:
Figure BDA0003544275950000036
wherein s(s)i，sj) Representing different flexibility requirement scenarios si、sjThe similarity between the two groups is similar to each other,
Figure BDA0003544275950000037
storing the similarity in a matrix form for the flexibility requirement under the corresponding scene;
Figure BDA0003544275950000038
introducing a reference degree p to describe the possibility that a sample point is selected as a class representative point, wherein the value of p is positively correlated with the number of clustering centers, and usually the median or mean value of a similarity matrix can be taken;
Figure BDA0003544275950000039
Figure BDA0003544275950000041
for searching suitable clustering center, attraction information r(s) is introducedi，sj) With attribution degree information a(s)i，sj) And (4) competing. Attraction degree information r(s)i，sj) Representing a scene sjIs suitable as siDegree of representation of cluster center; attribution degree information a(s)i，sj) By the cluster center siPointing to a sample point sjExpressible scenes sjSelection of siDegree of willingness as a clustering center; s isi′、sj′Respectively representing scenes that are potentially more suitable as clustering centers;
Figure BDA0003544275950000042
wherein lambda is the damping ratio added in each iteration, and the aim is to effectively inhibit numerical oscillation occurring in algorithm iteration;
when r(s)i，si)+a(si，si) If > 0, the scene siSetting as an alternative clustering center;
outputting an alternative clustering center set after traversing all scenes;
the aggregation modeling of the demand side multipotential heterogeneous resource adjustable boundary comprises the following steps:
modeling the isothermal control load boundaries of the cluster electric automobile, the water heater and the heating ventilation air conditioner based on the virtual battery model;
the adjustable boundary modeling result of the cluster electric automobile is as follows:
Figure BDA0003544275950000043
wherein
Figure BDA0003544275950000044
The adjustable power and the adjustable capacity of the cluster electric automobile based on the virtual battery model are respectively;
the modeling result of the cluster temperature control load adjustable boundary is (taking a water heater as an example):
Figure BDA0003544275950000045
wherein
Figure BDA0003544275950000046
The adjustable power and capacity r of the cluster water heater based on the virtual battery modelWH、cWHThe equivalent thermal resistance and the equivalent thermal capacity of the water heater are respectively, the heating ventilation air conditioner is also a temperature control load, and the modeling result can refer to the water heater;
taking minkowski and integrating the flexibility boundaries of each resource at multiple time scales quantifies the flexibility that the demand side can supply.
Figure BDA0003544275950000051
Wherein
Figure BDA0003544275950000052
Representing a minkowski sum.
Optionally, the gear optimization of the on-load tap changer (OLTC) specifically includes:
the minimum node voltage offset is used as an OLTC gear optimization target;
Figure BDA0003544275950000053
wherein f isVOFor the node voltage deviation degree, V, of the distribution networkNRepresents a rated voltage;
the constraints comprise on-load tap changer operation constraints, power flow constraints, power balance constraints and the like.
Optionally, the day-ahead economic scheduling of the power distribution network considering the flexibility of the resource on the demand side specifically includes:
constructing a demand side flexible resource optimization scheduling model aiming at minimizing the operation cost in the scheduling period of the power distribution network;
Figure BDA0003544275950000054
Figure BDA0003544275950000055
wherein
Figure BDA0003544275950000056
The method comprises the following steps of respectively scheduling operation cost, interaction cost with a superior power grid, power generation cost of a gas turbine, demand response cost and light abandoning cost in a time period;
Figure BDA0003544275950000057
respectively unit electricity purchasing/selling, fuel, demand response and light abandoning costs; considering the bidirectional energy interaction of the power distribution network and a superior power distribution network,
Figure BDA0003544275950000058
Figure BDA0003544275950000059
respectively consumes power of the distribution network and returns power to the grading power grid,
Figure BDA00035442759500000510
respectively representing the output, the required response power and the abandoned light power of the micro gas turbine; omegaMT、ΩDR、ΩPVRespectively representing node sets for configuring a micro gas turbine, a demand response load and a photovoltaic;
the constraint conditions include: interactive power constraints, network security constraints, power balance constraints, micro gas turbine constraints, demand response constraints, photovoltaic output and light rejection constraints, and flexibility constraints.
Optionally, the power distribution network line reinforcement model includes:
searching a line reinforcement economic scheme through an evolutionary particle swarm algorithm, and assisting in selecting the line reinforcement scheme by taking the annual maximum insufficient electric quantity as an index;
updating the topological information of the power distribution network, repeating the process until each year in the traversal planning period, and outputting a line reinforcement optimal scheme;
the Evolved Particle Swarm (EPSO) algorithm specifically includes:
the algorithm is given a particle XjIn the case of the particles, new particles are generated
Figure BDA0003544275950000061
The rule of (1) is:
Figure BDA0003544275950000062
Figure BDA0003544275950000063
Figure BDA0003544275950000064
wherein j is an individual number, and k represents an algebra; the expression parameters will evolve during the mutation process, bjRepresents the optimal ancestor particle of the individual j up to this generation, bgRepresents the optimal point of the population up to this generation;
Figure BDA0003544275950000065
denotes bgThe two relations are:
Figure BDA0003544275950000066
n (0, 1) represents a standard normal distribution; w is aj1Is the weight of the inertia term, wj2To memorize item weights, wj3Weights for cooperative and information-interactive items, wj4The method is used for describing the weight influence dispersion degree around the current optimal point;
Figure BDA0003544275950000067
for the particle j at the kth generation position,
Figure BDA0003544275950000068
a diagonal matrix formed by the main diagonal elements of the particle j at the kth generation speed and P, wherein the elements are subjected to binomial distribution;
and (3) providing an EPSO fitness evaluation function with the aim of minimizing line strengthening and punishment cost:
Figure BDA0003544275950000069
wherein omegabranchIs a set of power distribution network lines,
Figure BDA00035442759500000610
the line hardening costs are enforced for each of the solutions,
Figure BDA00035442759500000611
unit penalty cost, N, for the overload line and the voltage off-limit node, respectivelyOB、NBVIs a corresponding number; z1.. Z represents the number of possible consolidation schemes;
the minimum annual maximum energy shortage (ENS) objective function specifically includes:
Figure BDA0003544275950000071
wherein
Figure BDA0003544275950000072
Denotes the y thnThe maximum electricity shortage in the year can be realized,
Figure BDA0003544275950000073
for the load of each node, it is,
Figure BDA0003544275950000074
is the ssTypical scenes correspond to days, OB, in a yeardsRepresenting the downstream node of the overloaded branch.
Compared with the prior art, the invention has the following beneficial effects:
(1) generating a typical flexibility demand scene based on a system flexibility demand concept and photovoltaic power station output prediction, performing aggregation modeling on multi-energy heterogeneous flexibility resource adjustable boundaries on a demand side, quantifying the flexibility available on the demand side, and laying a foundation for analyzing the flexibility supply and demand relationship of a power distribution network and realizing efficient utilization of various resources on the demand side;
(2) the scheme for reinforcing the power distribution network line is improved, a day-ahead economic dispatching-line reinforcing integrated strategy of gear optimization-consideration of flexibility of resources on a demand side is provided, and the decision on line reinforcing investment is effectively delayed.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a power distribution network line strengthening strategy considering flexibility of resources on a demand side according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of photovoltaic power plant output prediction based on a multi-markov-chain monte carlo model according to embodiment 2 of the present invention.
Fig. 3 is a flowchart for generating a scenario based on typical flexibility requirements of a neighbor propagation algorithm according to embodiment 3 of the present invention.
Fig. 4 is a diagram of a structure of a power distribution network reinforcement model provided in embodiment 4 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The following will describe the embodiments of the present invention in further detail with reference to the accompanying drawings, and as shown in fig. 1, a power distribution network line strengthening strategy flow chart considering flexibility of resources on a demand side provided in embodiment 1 of the present invention is provided, where the strategy includes:
step 101 is to obtain the frame information of a certain distribution network and the meteorological data of the ground.
The power distribution network framework information comprises network topology information of the power distribution network, power supply and load composition, node load data and meteorological data; the network topology information of the power distribution network comprises but is not limited to a grid structure, line parameters and geographic information; the power and load components include but are not limited to power and load types and proportions; the node load data and the meteorological data include, but are not limited to, node load data and meteorological data such as net sunshine intensity and total sunshine intensity within a planning year and taking hours as a time scale.
Step 102 is to perform load flow calculation on the power distribution network.
In the step, the load data of the power distribution network and the network topology information set are used as input in the load flow calculation, and the calculation aim is to judge whether a voltage out-of-limit node and an overload branch exist in the network.
Step 103 is modeling for generating typical flexibility demand scenarios and demand side multipotent heterogeneous resource adjustable boundary aggregation.
In this step, the generating of the typical flexibility demand scenario includes defining a system flexibility demand, predicting the photovoltaic power plant output based on a multi-markov-chain monte carlo model, and generating the typical flexibility demand scenario based on a neighbor propagation algorithm.
The system flexibility requirement is defined by the difference between the net load of the system in the period of t and t + 1:
Figure BDA0003544275950000081
wherein
Figure BDA0003544275950000082
The system net loads are respectively T +1 and T periods, T represents the corresponding periods of different time scales, and T is the total period number of the scheduling cycle.
The photovoltaic power station output prediction based on the multi-Markov-chain Monte Carlo model comprises the following steps: the method comprises the following four steps of photovoltaic power station historical data preprocessing, discrete state division, discrete transition state learning, Monte Carlo state sampling, meteorological state sequence correction and photovoltaic output reduction.
The typical flexibility demand scene generation based on the neighbor propagation algorithm mainly comprises the steps of similarity calculation, information competition, algorithm iteration, alternative clustering center output and the like.
The demand side multi-energy heterogeneous resource adjustable boundary aggregation modeling comprises cluster electric automobile and water heater and heating ventilation air conditioner isothermal control load modeling based on a virtual battery model;
the cluster electric automobile modeling part is as follows:
assuming that there is N in the distribution networkEVConsidering the traveling situation, the power and energy feasible region of the kth electric vehicle is described as follows:
Figure BDA0003544275950000083
Figure BDA0003544275950000084
the energy of the electric automobile is limited at the upper limit and the lower limit,
Figure BDA0003544275950000085
in order to allow a lower limit on the energy,
Figure BDA0003544275950000086
the electric quantity is satisfied by the owner when the owner leaves the network;
Figure BDA0003544275950000087
and with
Figure BDA0003544275950000088
To representGrid connection and grid disconnection time periods;
Figure BDA0003544275950000089
respectively, the energy lower limit of each time period before the grid disconnection and each time period after the grid connection.
Upper and lower limits of actual charge and discharge power
Figure BDA0003544275950000091
Is different from the upper limit and the lower limit of theoretical charge-discharge power
Figure BDA0003544275950000092
The specific expression is as follows:
Figure BDA0003544275950000093
Figure BDA0003544275950000094
when considering cluster EV grid connection and off-grid connection, the energy of the virtual battery model is stepped, and can be described as follows:
Figure BDA0003544275950000095
wherein
Figure BDA0003544275950000096
Describing energy changes in grid-connected and off-grid stages, tauk，tFor representing binary variables of EV networking state, 0 is off-grid, 1 is on-grid, and
Figure BDA0003544275950000097
correcting the electric quantity deviation, the virtual battery model of the cluster EV is as follows:
Figure BDA0003544275950000098
the cluster water heater virtual battery model is modeled as follows:
adopting a first-order resistance-capacitance model to describe the temperature of a water tank of the water heater:
Figure BDA0003544275950000099
wherein r isWH、cWHRespectively equivalent thermal resistance and thermal capacity of the water heater;
Figure BDA00035442759500000910
is the hot water temperature, TatIs ambient temperature; q. q ofWH、 qwhdThe temperature-power relation of the electric water heater can be obtained by integrating the heat energy consumed by the water heater and the heat energy consumed by the required hot water.
Figure BDA00035442759500000911
Assuming a total of NWHThe electric water heater deduces the power-temperature relation of the kth electric water in the t' th time period based on the formula:
Figure BDA00035442759500000912
wherein etaWHIs an energy efficiency parameter of the water heater,
Figure BDA00035442759500000913
for the power of the water heater, τWHIn order to describe the variable 0-1 of the using state of the water heater, the water temperature change range of the water heater is described by the following formula:
Figure BDA0003544275950000101
Figure BDA0003544275950000102
wherein
Figure BDA0003544275950000103
Representing the set temperature of the water heater, and describing a dead zone around the set temperature by +/-sigma/2; meanwhile, in order to ensure the comfort of users, the set temperature fluctuation range is not too large,
Figure BDA0003544275950000104
respectively representing the upper limit and the lower limit of the water temperature comfort interval; mu.shigh、μlow∈[0，1]Respectively adjusting the water temperature of the water heater up and down;
Figure BDA0003544275950000105
respectively shows the maximum temperature of the water heater which can be adjusted up and down on the basis of the comfortable water temperature interval.
In modeling cluster water heater, discrete variables are separated
Figure BDA0003544275950000106
Performing a continuous process, i.e. ordering
Figure BDA0003544275950000107
Then there is
Figure BDA0003544275950000108
And the charge-discharge state of the virtual battery model of the water heater is measured by reference power, and the mathematical expression is as follows:
Figure BDA0003544275950000109
when in use
Figure BDA00035442759500001010
Judging the state of charge, otherwise, judging the state of discharge; when energy dissipation is caused by considering the influence of the ambient temperature on the water tank temperature of the water heater, the virtual battery model capacity of a single water heater is described as follows:
Figure BDA00035442759500001011
to accurately calculate the reference power, it is necessary to make the tank temperature
Figure BDA00035442759500001012
As close as possible to the set value
Figure BDA00035442759500001013
The minimum two-norm difference value between the temperature of the water tank of the cluster water heater and a set value is used as a model auxiliary target, and mathematical expression is as follows:
Figure BDA00035442759500001014
the upper and lower limits of the power of the cluster water heater virtual battery model can be represented by the difference value between the upper and lower limits of the total power and the reference power, and the capacity boundary of the cluster water heater virtual battery model is represented by the average power in the whole scheduling period:
Figure BDA00035442759500001015
in summary, the virtual battery model of the cluster water heater is:
Figure BDA0003544275950000111
the heating, ventilating and air conditioning and the water heater are both temperature control loads, and the specific derivation of the virtual battery model can refer to a water heater part, which is not described again;
considering three demand side flexibility resources of electric car, water heater and HVAC, by Minkowski and integrating the flexibility boundaries of each resource at multiple time scales, the demand side can supply flexibility in quantification:
Figure BDA0003544275950000112
wherein
Figure BDA0003544275950000113
Representing a minkowski sum.
Step 104 is gear optimization of the on-load tap changer.
The gear optimization of the on-load tap changing transformer takes the minimum node voltage deviation degree as a target:
Figure BDA0003544275950000114
fVOfor the node voltage deviation degree, V, of the distribution networkNRepresenting the nominal voltage.
The constraint conditions comprise OLTC operation constraint, power flow constraint, power balance constraint and the like; where the OLTC operating constraints are:
Figure BDA0003544275950000115
Figure BDA0003544275950000116
indicating the voltage regulator gear of the branch ij equipped with the on-load tap-changing transformer in the period t,
Figure BDA0003544275950000117
is the upper limit; o isij，tFor voltage regulator transformation ratio, Oij，stepA single gear adjustment value; omegaOThe on-load tap changer branch set is configured.
When the problem that the node voltage of the power distribution network cannot be solved by adjusting the OLTC gears, the demand side flexibility resources are considered to be optimally scheduled.
Step 105 is the day-ahead economic dispatching of the distribution network considering the flexibility of the resources on the demand side.
In the economic dispatching model, a secondary side bus of a transformer in the power distribution network is regarded as a source node of the network, the load of the node is regarded as the total load of the power distribution network, and the total N of the power distribution network is assumedBThe specific model of the node is as follows:
Figure BDA0003544275950000118
Figure BDA0003544275950000121
in the above formula
Figure BDA0003544275950000122
The method comprises the following steps of respectively scheduling operation cost in a time period, interaction cost with a superior power grid, power generation cost of a gas turbine, demand response cost and light abandoning cost;
Figure BDA0003544275950000123
respectively unit electricity purchasing/selling, fuel, demand response and light abandoning costs; considering the bidirectional energy interaction of the power distribution network and a superior power distribution network,
Figure BDA0003544275950000124
respectively consuming power for the distribution network and returning power to the upper-level distribution network,
Figure BDA0003544275950000125
respectively representing the output, the required response power and the abandoned light power of the micro gas turbine; omegaMT、ΩDR、ΩPVAnd respectively representing node sets for configuring the micro gas turbine, the demand response load and the photovoltaic.
The constraints are as follows:
(1) interactive power constraints
Figure BDA0003544275950000126
In order to ensure that the consumed power of the power distribution network and the return power to the upper-level power grid are not 0 simultaneously, introducing a binary variable alpha t;
Figure BDA0003544275950000127
Figure BDA0003544275950000128
the upper and lower limits of the consumed power and the foldback power are respectively.
(2) Network security constraints
Figure BDA0003544275950000129
Network security constraints include node voltage magnitude constraints and line transmission power constraints: wherein Vi，tWhich is indicative of the magnitude of the voltage at the node,
Figure BDA00035442759500001210
is its boundary; sijRepresenting the apparent power transmitted between lines ij,
Figure BDA00035442759500001211
representing the maximum value thereof.
(3) Power balance constraint
Figure BDA00035442759500001212
Wherein
Figure BDA00035442759500001213
The net load power for node i during time t,
Figure BDA00035442759500001214
and with
Figure BDA00035442759500001215
Respectively increased or decreased load power by demand response,
Figure BDA00035442759500001216
the power is generated by photovoltaic and micro gas turbine.
(4) Micro gas turbine constraint
Figure BDA0003544275950000131
The constraint of the micro gas turbine comprises climbing constraint and output constraint:
Figure BDA0003544275950000132
is the maximum climbing power of the micro gas turbine,
Figure BDA0003544275950000133
is a variable 0-1 representing the start-stop state of a micro gas turbine, PMT min、PMT maxIs the output limit.
(5) Demand response constraints
Figure BDA0003544275950000134
Load requirements are divided into rigid loads that cannot participate in scheduling and flexible loads that can participate in scheduling,
Figure BDA0003544275950000135
and the ratio of the flexible load power of the node i in the t period. Assuming a ratio R in the compliant loadSLCan translate the load and introduce
Figure BDA0003544275950000136
Figure BDA0003544275950000137
The compliant load boundary after translation for the t period is described.
Figure BDA0003544275950000138
Figure BDA0003544275950000139
And the load can only be transferred to the t-1 time period or the t +1 time period by adopting the following two formulas to restrict the t time period.
Figure BDA00035442759500001310
Figure BDA00035442759500001311
Wherein
Figure BDA00035442759500001312
For shifting the load in each time interval and ensuring the total energy consumption to be unchanged, scheduling the load in the previous time interval which is unacceptable in the last time interval, and adopting the following formula for constraint:
Figure BDA00035442759500001313
(6) photovoltaic output and abandoned light constraint
Figure BDA00035442759500001314
Wherein gamma isPVcurtail、
Figure BDA00035442759500001315
And respectively representing the allowable light abandon proportion in the scheduling day, the photovoltaic predicted output in each period and the rated capacity of each node configured photovoltaic.
(7) Flexibility constraints
Figure BDA0003544275950000141
Wherein
Figure BDA0003544275950000142
And the demand side in the period t can supply flexibility margin, the maximum flexibility supply of a superior power grid and the flexibility demand of a power distribution network.
Step 106 is a power distribution network line reinforcement model.
In the power distribution network line reinforcement model, whether a feasible line reinforcement scheme exists or not is judged, if yes, an economic scheme is searched by adopting an evolutionary particle swarm algorithm, and a final reinforcement scheme is selected based on a minimum maximum insufficient electric quantity index; and repeating the steps until each year in the planning period is traversed, and outputting an optimal reinforcement scheme.
Fig. 2 is a flow chart of photovoltaic output prediction based on a multi-markov-chain monte carlo model according to embodiment 2 of the present invention, where the flow chart includes:
step 201 is photovoltaic power plant historical data preprocessing and discrete state division.
The photovoltaic power station historical data processing and discrete state division comprises the following steps: dividing the whole year into H periods according to months, and dividing the time period within each day of the period, within which the earth surface photovoltaic power station can receive the net load data, into TirrTime periods within which the photovoltaic output is constrained
Figure BDA0003544275950000143
Equally dividing the constraint interval into NsDiscrete states, then a single state range can be expressed as
Figure BDA0003544275950000144
Step 202 is state transition rule learning.
The state transition rule learning includes: the quantity of the photovoltaic power stations which are supposed to be connected into the power distribution network is MPVThen, the k (k ═ 1.., M)PV) The relevance of the meteorological conditions of the power station t +1 time period to other power stations can be represented by the following distribution:
Figure BDA0003544275950000145
where s is the discrete output state of the photovoltaic power plant, Pr(s)1|s2) Is shown in state s2Under the conditions generated, state s1The probability of generation can be generated from the distribution
Figure BDA0003544275950000146
The state transition matrix is maintained.
Step 203 is a monte carlo state sample.
The Monte Carlo state sampling comprises: extracting variables from the state transition matrix in sequence to generate a Markov state chain;
suppose that the threshold of the number of state transitions and the number of required samples are n1And n2The initial state sequence is
Figure BDA0003544275950000147
The sampling procedure is described as follows:
Figure BDA0003544275950000151
where → represents the right state extracted by the left probability;
and generating a Markov chain reference sample set through sampling so as to obtain the following meteorological state sequence:
Figure BDA0003544275950000152
step 204 is meteorological state sequence correction and photovoltaic output reduction.
The meteorological state sequence correction and photovoltaic output reduction comprises the following steps:
Figure BDA0003544275950000153
wherein psitThe method is used for correcting a white noise sequence of a meteorological state in order to account for instantaneous disturbance of atmospheric state caused by cloud layer change.
Fig. 3 is a flowchart for generating a scenario based on the exemplary flexibility requirement of the neighbor propagation algorithm according to embodiment 3 of the present invention, where the flowchart includes:
step 301 defines the system flexibility requirement, which is described in detail in step 103 of embodiment 1 shown in fig. 1, and is not described herein again.
Step 302 is to predict the annual photovoltaic output based on the multi-markov-chain monte carlo model, which is described in detail in step 201-204 of invention example 2 shown in fig. 2, and is not described herein again.
Step 303 is to generate a year-round flexibility requirement matrix based on which a flexibility requirement similarity matrix is generated.
The annual flexibility requirement matrix is 365T, and each element in the matrix represents the flexibility requirement of the system in a certain period.
The similarity between the elements to be clustered can be represented by the following formula:
Figure BDA0003544275950000154
wherein s(s)i，sj) Representing different flexibility requirement scenarios si、sjThe similarity between the two groups is similar to each other,
Figure BDA0003544275950000155
storing the similarity in a matrix form for the flexibility requirement under the corresponding scene;
Figure BDA0003544275950000156
the reference degree p is introduced to describe the possibility that the sample point is selected as the class representative point, and the value of p is positively correlated with the number of cluster centers, and can be generally the median or mean value in the similarity matrix.
Step 304 is competition for "attractiveness" and "attribution" information.
The "attraction degree" information r(s)i，sj) Representing a scene sjSuitable as scene siThe degree of representation of the cluster center is as follows:
Figure BDA0003544275950000161
the "degree of attribution" information a(s)i，sj) Representing a scene sjSelection of siThe willingness degree of the cluster center can be expressed as follows:
Figure BDA0003544275950000162
wherein s isi′、sj′Respectively representing scenes that are potentially more suitable as clustering centers;
the damping ratio λ is introduced to effectively suppress numerical oscillations that occur in the iteration of the algorithm.
Figure BDA0003544275950000163
Step 305 selects links for the cluster centers.
The selection rule of the alternative cluster center is as follows: r(s)i，si)+a(si，si) If > 0, otherwise, returning to the step 304;
and after a candidate cluster center is searched, judging whether all scenes are traversed, if so, outputting a candidate cluster center set, otherwise, returning to the step 304 to continuously search the next cluster center.
Fig. 4 is a diagram of a structure of a reinforcing model of a power distribution network according to embodiment 4 of the present invention, where the structure includes:
the information input module 401: the information of gear optimization and demand side flexible resource optimization scheduling of the on-load tap changer is input, and the information can be obtained in step 104 and step 105 of the invention example 1 shown in fig. 1, which is not described herein again.
Initial solution determination and economic solution search module 402: and determining a line reinforcement initial scheme, and searching an economic scheme by adopting an evolutionary particle swarm algorithm.
The method for determining the initial reinforcement scheme comprises the following steps:
taking the line which needs to be reinforced and solves the node voltage out-of-limit as an example, firstly, a branch with a voltage problem at an upstream node or a downstream node is searched as a branch which potentially needs to be reinforced; selecting a group of initial strengthening schemes from the alternative line models, and if the node voltage problem cannot be solved, considering the installation of parallel lines and correcting a strengthening plan; if the node voltage is out of limit, the calculation is terminated and no feasible scheme is determined.
The evolutionary particle swarm algorithm searches the economic scheme as follows:
if the proposed scheme solves the node voltage out-of-limit problem, a more economical scheme is searched by using a particle swarm optimization (EPSO) algorithm, and the algorithm is used for setting a particle XjIn the case of generating new particles
Figure BDA0003544275950000171
The rule of (1) is:
Figure BDA0003544275950000172
Figure BDA0003544275950000173
Figure BDA0003544275950000174
wherein j is an individual number, and k represents an algebra; the expression parameters will evolve during the mutation process, bjRepresents the optimal ancestor particle of the individual j up to this generation, bgRepresenting the population up to the optimum point of this generation,
Figure BDA0003544275950000175
denotes bgThe two relations are:
Figure BDA0003544275950000176
n (0, 1) represents a standard normal distribution; w is aj1Is the weight of the inertia term, wj2To memorize item weights, wj3Weights for cooperative and information-interactive items, wj4The method is used for describing the weight influence dispersion degree near the current optimal point;
Figure BDA0003544275950000177
for the particle j at the kth generation position,
Figure BDA0003544275950000178
a diagonal matrix formed by the main diagonal elements of the particle j at the kth generation speed and P, wherein the elements are subjected to binomial distribution;
and (3) providing an EPSO fitness evaluation function with the aim of minimizing line strengthening and punishment cost:
Figure BDA0003544275950000179
wherein omegabranchIs a set of power distribution network lines,
Figure BDA00035442759500001710
for each of the solutions the line consolidation costs,
Figure BDA00035442759500001711
unit penalty cost, N, for the overload line and the voltage off-limit node, respectivelyOB、NBVIs a corresponding number; z1, where Z represents the number of possible consolidation schemes.
Risk assessment and aid decision module 403: and introducing the maximum insufficient electric quantity index evaluation risk of the power distribution network to assist the decision of the line strengthening scheme.
The indexes and decision ideas are as follows:
after one or more economic optimal or suboptimal line reinforcement schemes are searched, in order to resist uncertainty brought by distributed renewable resource access, the risk is evaluated by taking the maximum insufficient electric quantity as an index, and the reinforcement scheme with the minimized index is selected;
Figure BDA00035442759500001712
wherein
Figure BDA00035442759500001713
Denotes the y thnThe maximum electricity shortage in the year can be realized,
Figure BDA00035442759500001714
for the load of each node, it is,
Figure BDA00035442759500001715
is the ssA typical scenario corresponds to days in the year.
The solution traversal and optimal solution determination module 404: and updating the topological information of the power distribution network, repeating the whole line reinforcement strategy until each year in the traversal planning period, and outputting an optimal reinforcement scheme.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
The new rural power distribution network planning strategy based on robust optimization provided by the embodiment of the invention is described in detail above, and the principle of the invention is described in the present document by using specific examples for explaining the core idea of the invention, and the content of the description should not be construed as limiting the protection scope of the invention.

Claims (8)

1. A power distribution network line strengthening strategy considering flexibility of resources on a demand side is characterized by comprising the following steps:
acquiring frame information and the ground meteorological data of a certain power distribution network, and performing load flow calculation based on load data and network topology information of the power distribution network so as to judge whether a voltage out-of-limit node and an overload branch exist in the network;
defining system flexibility requirements by net load difference values of adjacent time periods of the system; predicting the output of a photovoltaic power station based on a multi-Markov chain Monte Carlo model, and clustering to generate a typical flexibility demand scene based on a neighbor propagation algorithm; performing aggregation modeling on the adjustable boundary of the multi-energy heterogeneous resources on the demand side based on the virtual battery model so as to quantify the available flexibility of various resources;
optimizing the gears of the on-load tap changing transformer by taking the minimum voltage deviation degree of the node of the power distribution network as a target;
if the line overload or voltage problem still exists, carrying out day-ahead economic dispatching on the power distribution network considering the flexibility of the resources on the demand side;
inputting the gear information of the on-load tap changer and the load data after scheduling into a power distribution network line reinforcement model, searching a line economic reinforcement scheme by adopting an evolutionary particle swarm algorithm, selecting a line reinforcement scheme by taking the maximum annual insufficient power as an index, updating the topological information of the power distribution network, repeating the process until all years in a traversal planning period are reached, and outputting an optimal line reinforcement scheme.
2. The power distribution network line strengthening strategy considering the flexibility of resources on the demand side according to claim 1, wherein the power distribution network frame information and the ground meteorological data specifically include:
the network topology information, power supply and load composition, node load data and meteorological data of the power distribution network;
the network topology information of the power distribution network comprises but is not limited to a grid structure, line parameters and geographic information;
the power and load components include but are not limited to power and load types and proportions;
the node load data and the meteorological data include, but are not limited to, node load data and meteorological data such as net sunshine intensity and total sunshine intensity within a planning year and taking hours as a time scale.
3. The strategy according to claim 1, wherein the system flexibility requirement specifically includes:
describing the flexibility requirement of the system by the difference value of the net load of the system in the time period of t and t + 1:
Figure FDA0003544275940000011
wherein T represents the corresponding time interval of different time scales, and T is the total time interval number of the scheduling cycle.
4. The power distribution network line strengthening strategy considering resource flexibility on the demand side and the definition of system flexibility requirement in claim 3 in accordance with claim 1 are characterized in that photovoltaic power plant output is predicted based on a multi-markov chain monte carlo model, and a typical flexibility requirement scenario is generated based on a neighbor propagation algorithm clustering, which specifically includes:
carrying out discrete state division on the photovoltaic output in each time period:
Figure FDA0003544275940000012
where H1, H denotes the number of cycles divided in months, T1irrRepresenting the non-0 time interval of the net radiation data in each day of a cycle and the photovoltaic output in each time interval
Figure FDA0003544275940000021
NsThe number of discrete states equally divided for each interval;
generating a state transition rule matrix to describe the relevance of the output state of the photovoltaic power station:
Figure FDA0003544275940000022
wherein s represents the photovoltaic power plant separationScattered power state, k 1PVRepresenting the number of photovoltaic power stations, Pr (-) representing the probability distribution, the matrix being
Figure FDA0003544275940000023
Maintaining;
performing Monte Carlo sampling on the matrix to generate a Markov state chain:
the sampling procedure is described as follows:
Figure FDA0003544275940000024
where → denotes the extraction of the right state by the left probability, n1And n2Respectively a threshold value of the number of state transitions and the number of required samples,
Figure FDA0003544275940000025
is an initial state sequence;
generating a Markov chain reference sample set to obtain a meteorological state sequence:
Figure FDA0003544275940000026
correcting the state sequence and generating power data:
Figure FDA0003544275940000027
the psi is a white noise sequence and is used for correcting transient disturbance of cloud layer change on photovoltaic output;
a typical flexibility requirement scenario generation approach based on a neighbor propagation algorithm is as follows:
calculating the similarity between the elements to be clustered:
Figure FDA0003544275940000028
s(si，sj) Is a scene si、sjThe degree of similarity of (a) to (b),
Figure FDA0003544275940000029
storing the similarity in a matrix form for the flexibility requirement under the corresponding scene;
Figure FDA00035442759400000210
introducing a reference degree p to describe the possibility that a sample point is selected as a class representative point, wherein the value of p is positively correlated with the number of clustering centers, and usually the median or mean value of a similarity matrix can be taken;
Figure FDA00035442759400000211
Figure FDA00035442759400000212
Figure FDA0003544275940000031
to search for a suitable cluster center, attraction information r(s) is introducedi，sj) With attribution degree information a(s)i，sj) Competition, si′、sj′Respectively representing scenes that are potentially more suitable as clustering centers;
Figure FDA0003544275940000032
introducing a damping ratio lambda to effectively suppress numerical oscillations occurring in the iteration of the algorithm;
when r(s)i，si)+a(si，si) If > 0, the scene siSet as the candidate cluster center.
5. The power distribution network line strengthening strategy considering the flexibility of the demand side resources according to claim 1, wherein the demand side multipotential heterogeneous resource adjustable boundary aggregation modeling based on the virtual battery model specifically comprises:
adopting a virtual battery model to aggregate and model the adjustable boundary of the isothermal control load of the electric automobile, the water heater and the heating ventilation air conditioner:
Figure FDA0003544275940000033
Figure FDA0003544275940000034
the adjustable power and the adjustable capacity of the cluster electric automobile are respectively based on the virtual battery model;
Figure FDA0003544275940000035
Figure FDA0003544275940000036
the adjustable power and capacity r of the cluster water heater based on the virtual battery modelWH、cWHThe equivalent thermal resistance and the equivalent thermal capacity of the water heater are respectively, and the water heater can be referred to in the modeling conclusion of the heating ventilation air conditioner.
By Minkowski and integrating the flexibility boundaries of resources at multiple time scales to quantify the flexibility that the demand side can supply.
Figure FDA0003544275940000037
Wherein
Figure FDA0003544275940000038
Representing a minkowski sum.
6. The power distribution network line strengthening strategy considering the flexibility of resources on the demand side according to claim 1, wherein the on-load tap changer is optimized in gear, and specifically comprises:
the method takes the minimization of the voltage deviation degree of a power distribution network node as an OLTC gear optimization target:
Figure FDA0003544275940000039
wherein f isVOFor the node voltage deviation degree, V, of the distribution networkNRepresents a rated voltage;
the constraints comprise on-load tap changer operation constraints, power flow constraints, power balance constraints and the like.
7. The strategy for strengthening the line of the power distribution network considering the flexibility of the resources on the demand side according to claim 1, generating the photovoltaic output prediction and typical flexibility demand scenario according to claim 4, modeling the adjustable boundary aggregation of the multi-energy heterogeneous resources on the demand side according to claim 5, and optimizing the OLTC class according to claim 6, wherein a day-ahead economic dispatching model of the power distribution network considering the flexibility of the resources on the demand side is constructed.
Figure FDA0003544275940000041
Figure FDA0003544275940000042
In the formula
Figure FDA0003544275940000043
Respectively the operation cost of the scheduling period,Interaction cost with a superior power grid, power generation cost of a gas turbine, demand response cost and light abandonment cost;
Figure FDA0003544275940000044
respectively unit electricity purchasing/selling, fuel, demand response and light abandoning costs; considering the bidirectional energy interaction of the power distribution network and a superior power distribution network,
Figure FDA0003544275940000045
Figure FDA0003544275940000046
respectively consuming power for the distribution network and returning power to the upper-level distribution network,
Figure FDA0003544275940000047
respectively representing the output, the required response power and the abandoned light power of the micro gas turbine; omegaMT、ΩDR、ΩPVRespectively representing node sets for configuring a micro gas turbine, a demand response load and a photovoltaic;
constraints include interactive power constraints, network security constraints, power balance constraints, micro gas turbine constraints, demand response constraints, photovoltaic output and light rejection constraints, and flexibility constraints.
8. The strategy for strengthening the power distribution network line considering the flexibility of resources on the demand side as claimed in claim 1, the OLTC gear optimization as claimed in claim 6, and the future economic dispatch result as claimed in claim 7 are characterized in that a model for strengthening the power distribution network line is constructed, specifically:
taking the line which needs to be reinforced and solves the node voltage out-of-limit as an example, firstly, a branch circuit with voltage problems at an upstream node or a downstream node is searched as a branch circuit which potentially needs to be reinforced; making an initial reinforcement plan; if the node voltage problem cannot be solved, the installation of a parallel line is considered and a reinforcement plan is corrected; on the basis, if the node voltage is still out of limit, the calculation is stopped and no feasible scheme is judged;
if it is liftedThe out-of-range problem of node voltage is solved by the scheme, and a more economic scheme is searched by adopting a particle swarm optimization (EPSO) algorithm which gives a particle XjIn the case of the particles, new particles are generated
Figure FDA0003544275940000048
The rule of (1) is:
Figure FDA0003544275940000051
Figure FDA0003544275940000052
Figure FDA0003544275940000053
wherein j is an individual number, and k represents an algebra; the expression parameters will evolve during the mutation process, bjRepresents the optimal ancestor particle of the individual j up to this generation, bgRepresenting the population up to the optimum point of this generation,
Figure FDA0003544275940000054
denotes bgThe two relations are:
Figure FDA0003544275940000055
n (0, 1) represents a standard normal distribution; w is aj1Is the weight of the inertia term, wj2To memorize item weights, wj3Weights for cooperative and information-interactive items, wj4The method is used for describing the weight influence dispersion degree near the current optimal point;
Figure FDA0003544275940000056
is a granuleThe sub-j is in the k-th generation position,
Figure FDA0003544275940000057
a diagonal matrix formed by the main diagonal elements of the particle j at the kth generation speed and P, wherein the elements are subjected to binomial distribution;
an EPSO fitness evaluation function with the aim of minimizing line strengthening and punishment cost is provided:
Figure FDA0003544275940000058
Ωbranchis a set of power distribution network lines,
Figure FDA0003544275940000059
the line hardening costs are enforced for each of the solutions,
Figure FDA00035442759400000510
unit penalty cost, N, for the overload line and the voltage off-limit node, respectivelyOB、NBVIs a corresponding number; z1.. Z represents the number of possible consolidation schemes;
after one or more economic optimal or suboptimal line reinforcement schemes are searched, in order to resist uncertainty brought by distributed renewable resource access, risks are evaluated by the maximum annual shortage electric quantity index, and the reinforcement scheme with the minimum index is selected;
Figure FDA00035442759400000511
Figure FDA00035442759400000512
denotes the y thnThe maximum electricity shortage in the year can be realized,
Figure FDA00035442759400000513
for the load of each node, it is,
Figure FDA00035442759400000514
is the ssA typical scenario corresponds to days in a year.
CN202210266048.1A 2022-03-14 2022-03-14 Power distribution network line reinforcement strategy considering flexibility of demand side resources Pending CN114638502A (en)

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CN114825469A (en) * 2022-06-20 2022-07-29 华北电力大学 Distributed power supply cluster output evaluation method and system
CN115117931A (en) * 2022-07-14 2022-09-27 华北电力大学 Power distribution network planning method and system considering electric vehicle flexibility and photovoltaic access
CN115378042A (en) * 2022-10-25 2022-11-22 国网江西省电力有限公司电力科学研究院 Distributed flexible resource coordination control method
CN115577996A (en) * 2022-12-12 2023-01-06 广东电网有限责任公司中山供电局 Risk assessment method, system, equipment and medium for power grid power failure plan
CN116523279A (en) * 2023-07-05 2023-08-01 国网湖北省电力有限公司经济技术研究院 Determination method of flexible resource allocation scheme considering frequency modulation requirement

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114825469A (en) * 2022-06-20 2022-07-29 华北电力大学 Distributed power supply cluster output evaluation method and system
CN115117931A (en) * 2022-07-14 2022-09-27 华北电力大学 Power distribution network planning method and system considering electric vehicle flexibility and photovoltaic access
CN115117931B (en) * 2022-07-14 2023-07-25 华北电力大学 Power distribution network planning method and system considering electric automobile flexibility and photovoltaic access
CN115378042A (en) * 2022-10-25 2022-11-22 国网江西省电力有限公司电力科学研究院 Distributed flexible resource coordination control method
CN115378042B (en) * 2022-10-25 2023-02-17 国网江西省电力有限公司电力科学研究院 Distributed flexible resource coordination control method
CN115577996A (en) * 2022-12-12 2023-01-06 广东电网有限责任公司中山供电局 Risk assessment method, system, equipment and medium for power grid power failure plan
CN116523279A (en) * 2023-07-05 2023-08-01 国网湖北省电力有限公司经济技术研究院 Determination method of flexible resource allocation scheme considering frequency modulation requirement
CN116523279B (en) * 2023-07-05 2023-09-22 国网湖北省电力有限公司经济技术研究院 Determination method of flexible resource allocation scheme considering frequency modulation requirement

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