CN115333085A - Double-layer optimized scheduling method considering flexibility constraint and including distributed new energy power distribution network - Google Patents

Double-layer optimized scheduling method considering flexibility constraint and including distributed new energy power distribution network Download PDF

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CN115333085A
CN115333085A CN202210958621.5A CN202210958621A CN115333085A CN 115333085 A CN115333085 A CN 115333085A CN 202210958621 A CN202210958621 A CN 202210958621A CN 115333085 A CN115333085 A CN 115333085A
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power
distribution network
constraint
flexibility
power distribution
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万黎
周鲲鹏
蔡德福
王涛
董航
刘海光
王文娜
陈汝斯
杨玺
李航
孙冠群
王尔玺
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Hubei Electric Power Co Ltd
Wuhan Power Supply Co of State Grid Hubei Electric Power Co Ltd
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Hubei Electric Power Co Ltd
Wuhan Power Supply Co of State Grid Hubei Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

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Abstract

The invention provides a double-layer optimization scheduling method for a distribution network containing distributed new energy resources, which considers flexibility constraints, and firstly calculates the flexibility of each system of the distribution network; secondly, an upper-layer optimization model without considering flexibility constraints is established, an optimization scheduling strategy is obtained by solving the model, and the most severe net load curve is obtained by solving the lower-layer optimization model based on the optimization scheduling strategy; secondly, converting an upper-layer optimization model considering flexibility constraint into an intuitive fuzzy planning model by adopting intuitive fuzzy planning, and then obtaining a new optimized scheduling strategy; and comparing the a operation costs corresponding to the two times of optimization scheduling strategies, and repeating iteration until the absolute value of the difference between the operation costs is smaller than a threshold value. The comprehensive most satisfactory solution of the model is solved by introducing intuitive fuzzy planning, and the flexibility and the economy of the power distribution network can be considered.

Description

Double-layer optimized scheduling method for distribution network containing distributed new energy resources considering flexibility constraints
Technical Field
The invention relates to the technical field of optimal scheduling of a power distribution network, in particular to a double-layer optimal scheduling method for a power distribution network containing distributed new energy, which considers flexibility constraints.
Background
Wind power and photovoltaic are distinguished from a plurality of renewable energy sources by the advantages of wide distribution, mature technology, low cost and the like. Because wind and light output has the characteristics of strong uncertainty such as intermittence and volatility, large-scale access of the wind and light output not only brings new challenges to the power quality, relay protection and scheduling of the power distribution network, but also is not beneficial to low-carbon and economic operation of the power distribution network. In order to inhibit the volatility of the distributed new energy and promote the consumption of the new energy, not only the electric power company can make time-of-use prices to guide demand response loads to participate in the operation scheduling of the power grid, but also a virtual energy storage system such as an electric vehicle charging station can be utilized to absorb or release energy to optimize the operation scheduling of the power distribution network. Therefore, under the condition that wind and light combined output is uncertain, how to fully schedule flexible resources in the power distribution network to improve low-carbon economy of the power distribution network has important research significance.
The flexibility of the power system refers to the capability of the power system to respond to the large-amplitude fluctuation of both supply and demand sides in the system, and foreseeable or unforeseeable changes and events of the power system under the boundary constraint. At present, the flexibility of a power system is mainly researched in two aspects of flexibility level evaluation and flexibility-considered power system optimization scheduling, wherein the main research of the flexibility level evaluation mainly provides reliable and reasonable system flexibility evaluation indexes and calculates the margin of the flexibility indexes, so that reference is provided for formulating an efficient optimization scheduling strategy; the latter is mainly to consider renewable energy, an adjustable power supply and a controllable load to establish a coordinated optimization scheduling model, so that an optimization scheduling strategy meeting the flexibility required by all scenes with uncertain fluctuation of new energy output is obtained by solving. The probability of the extreme scene with uncertain fluctuation of wind-light combined output in actual operation is extremely low, and the optimal scheduling strategy which needs to meet the flexibility required by the extreme scene usually pays extremely high economic cost. How to give consideration to the flexibility and the economy of an optimal scheduling strategy of an electric power system is a problem which must be concerned in the construction process of a novel electric power system.
The traditional method for solving the double-layer optimization scheduling model of the power distribution network is that an upper layer model and a lower layer model are alternately iterated to obtain an optimal solution meeting the distributed new energy extreme output scene, but the optimal solution is not the most satisfactory solution due to higher economic cost of an optimization scheduling strategy obtained by solving through the method. Therefore, intuitionistic fuzzy planning is introduced to solve the comprehensive most satisfactory solution of the model so as to take the flexibility and the economy of the power distribution network into consideration.
Disclosure of Invention
The invention aims to provide a double-layer optimized scheduling method of a distributed new energy power distribution network considering flexibility constraints aiming at the problem that the economic cost of an optimized scheduling strategy is higher due to the extreme scene with uncertain fluctuation of wind-light combined output with extremely low probability, and an optimized scheduling strategy which considers the flexibility requirement and economic operation of most scenes is satisfied by introducing intuitive fuzzy planning and solving.
The invention relates to the following concrete contents:
a double-layer optimization scheduling method considering flexibility constraint and including a distributed new energy distribution network comprises the following steps:
the method comprises the following steps: calculating the flexibility of each system in the power distribution network, wherein the flexibility provided for the power distribution network by the main network access point comprises the flexibility provided for the power distribution network by the main network access point, the flexibility provided for the power distribution network by an electric vehicle charging station and the flexibility provided for the power distribution network by demand response load;
step two: establishing an upper-layer optimization model without considering flexibility constraints so as to solve a scheduling strategy for minimizing the operation cost of the power distribution network under a certain scene of wind-solar combined output;
step three: establishing a lower-layer optimization model, and solving to obtain a corresponding most severe net load curve based on the initial optimization scheduling strategy obtained in the step two;
step four: converting an upper layer model considering flexibility constraint into an intuitive fuzzy planning model by adopting intuitive fuzzy planning;
step five: combining the severest net load curve obtained in the third step with the related parameter z of the intuitive fuzzy programming model 0 、z 1 、y 0 、y 1 Solving the intuitive fuzzy planning model to obtain a new optimized scheduling strategy and operation cost;
step six: comparing the operation cost obtained by calculation in the step two with the operation cost obtained in the step five, and if the absolute value of the difference between the two is smaller than a threshold value, obtaining the most satisfactory optimal scheduling strategy; and otherwise, continuously repeating the third step to the sixth step until the absolute value of the difference of the operation costs is smaller than the threshold value.
Furthermore, the flexibility of each system in the power distribution network is embodied on the climbing rate of adjustable power in the power distribution network, namely the fluctuation amplitude of a net load curve cannot exceed the maximum climbing rate of adjustable total power in the power distribution network, and the net load curve refers to the difference between corresponding points of a daily load curve and a wind-light combined output curve in a time sequence and a corresponding curve in the time sequence formed by the difference.
Further, the detailed calculation steps of the operation flexibility of the power distribution network in the first step are as follows:
(a) Flexibility provided to distribution networks from a primary network access point
The main determinants are: the maximum climbing rate provided by the main network access point to the power distribution network and the maximum climbing rate limited by the capacity of the contact transformer and the climbing rate upper limit of the power enterprise at the main network access point according to actual conditions are the minimum of two determining factors, namely:
Figure BDA0003789424010000031
Figure BDA0003789424010000032
in the formula: s. the 1max 、
Figure BDA0003789424010000033
Respectively representing the rated capacity and the rated power factor of the interconnection transformer at the access point of the main network;
Figure BDA0003789424010000034
the active power injected into the power distribution network by the main network access point in the time period t is represented; r Bupmax 、R Bdnmax Respectively representing the maximum ascending and descending climbing rates of active power injected into the distribution network by the power enterprises and the main network access point set according to actual conditions;
(b) Flexibility provided for power distribution network by electric vehicle charging station
The maximum climbing rate is mainly determined by the residual electric quantity and the charging and discharging power in the current time period t station, namely:
Figure BDA0003789424010000035
Figure BDA0003789424010000036
in the formula: p cimax 、P dimax Respectively representing the maximum charging power and the maximum discharging power of the ith electric vehicle charging station;
Figure BDA0003789424010000037
respectively representing the charging power and the discharging power of the ith electric vehicle charging station in a time period tth;
Figure BDA0003789424010000038
representing the residual capacity of the ith electric vehicle charging station in a time period t;
Figure BDA0003789424010000039
respectively represent
Figure BDA00037894240100000310
Upper and lower limits of (d);
(c) Flexibility in providing demand responsive load to power distribution grid
The method mainly depends on the upper limit and the lower limit of the adjustable power of the demand response load and the total power consumption limit in the whole time period, in the current time period t, the climbing rate of the demand response load cannot enable the power fluctuation amplitude of the demand response load to exceed the adjustable potential, and meanwhile, the sum of the demand response power of the demand response load in the t time periods which cannot be 1-t exceeds the total power consumption in the whole time period, the maximum climbing rate provided for the power distribution network is as follows:
Figure BDA00037894240100000311
Figure BDA00037894240100000312
in the formula:
Figure BDA0003789424010000041
representing the actual active power of the jth demand responsive load during time period tth;
Figure BDA0003789424010000042
respectively representing the upper limit value and the lower limit value of the adjustable power of the demand response load; e DjΣ Representing the total power usage of the jth demand-responsive load over time.
Further, the upper model optimization in the second step is optimized with the lowest operation cost of the power distribution network as a target, the target function of the upper model optimization comprises the electricity purchase cost of the upper power grid, the operation cost of an electric vehicle charging station and the dispatching compensation cost of demand response load, and the constraint conditions comprise conventional power balance and safety constraint conditions, so that the initial optimized dispatching strategy is obtained.
Further, the upper layer optimization model objective function:
Figure BDA0003789424010000043
in the formula: n is a radical of hydrogen T Representing the total time segment number of the scheduling period; Δ t represents the period length;
Figure BDA0003789424010000044
the unit cost of purchasing electricity from the power distribution network to the main network access point in the time period t;
Figure BDA0003789424010000045
the active power injected into the power distribution network by the main network access point in a time period t is represented; k is ci 、K di Respectively representing the charge and discharge cost coefficients of the electric vehicle charging station;
Figure BDA0003789424010000046
respectively representing the t ith powerCharging and discharging power of a charging station of the electric vehicle; k is Dj The compensation cost of the active power of the jth demand response load unit is represented;
Figure BDA0003789424010000047
representing the original power consumption before the jth demand response user responds in the time interval t;
the constraint conditions of the upper-layer optimization model comprise: node power balance constraint, safety constraint, interconnection transformer capacity constraint, electric vehicle charging station charge and discharge constraint and demand response load schedulable potential constraint are respectively as follows:
(a) Node power balance constraint:
Figure BDA0003789424010000048
(b) Safety restraint:
Figure BDA0003789424010000049
in the formula: u shape i 、U j 、U k Respectively representing the voltage amplitudes of the nodes i, j and k;
Figure BDA00037894240100000410
represents the active power on line i;
Figure BDA00037894240100000411
respectively representing the upper limit and the lower limit of active power on a line i; g jk 、b jk Respectively representing the conductance and susceptance of the ith line. Theta jk Representing the voltage phase difference between nodes j, k;
(c) And (3) restraint of the interconnection transformer:
Figure BDA0003789424010000051
in the formula:
Figure BDA0003789424010000052
the reactive power injected into the power distribution network at the time interval t and the main network access point is represented;
(d) Electric vehicle charging and discharging restraint:
Figure BDA0003789424010000053
in the formula: eta ci 、η di Respectively representing the charging efficiency and the discharging efficiency of the electric vehicle charging station;
e) Demand response load constraints:
Figure BDA0003789424010000054
in the formula:
Figure BDA0003789424010000055
respectively representing the upper and lower limit values of the power consumption of the jth demand response load in the time period tth.
Further, the third step specifically includes:
the wind-light actual combined contribution is fluctuated within a certain error level range of the predicted combined contribution mean value, and a net load curve fluctuation range is obtained by the difference between the fluctuation range of each moment of the wind-light combined contribution curve and the load value of each corresponding moment on the internal load curve;
the most severe net load curve refers to a net load curve which corresponds to the maximum sum of all parts of which the climbing rates of all time periods in the whole scheduling cycle are greater than the maximum adjustable total power climbing rate of the power distribution network in the fluctuation range of the net load curve, the severity degree of the net load curve is measured by an index S, S represents the sum of all parts of which the climbing rates of all time periods in the whole scheduling cycle are greater than the maximum adjustable total power climbing rate of the power distribution network in the fluctuation range of the net load curve, namely:
Figure BDA0003789424010000056
after an optimization scheduling strategy with the minimum operation cost of the power distribution network is determined, the climbing rate of the adjustable power in each period of the whole period can be obtained by the upper-layer optimization model, the most severe net load curve is searched by the lower-layer optimization model in the net load curve fluctuation range, and the expression is as follows:
Figure BDA0003789424010000057
in the formula:
Figure BDA0003789424010000061
represents the ramp rate of the most severe net load curve at time period t;
Figure BDA0003789424010000062
the maximum and minimum values of the power of the net load curve in the fluctuation range of the time t are shown.
Further, the flexibility constraint in the fourth step means that the maximum climbing rate of each time period of the most severe net load curve cannot exceed the total adjustable power maximum climbing rate of the power distribution network within the wind-solar combined output fluctuation range, and the expression is as follows:
Figure BDA0003789424010000063
in the formula:
Figure BDA0003789424010000064
a magnitude of a net load representing a most severe net load curve at a time t;
the flexibility constraint corresponds to the fuzzy form, namely:
Figure BDA0003789424010000065
Figure BDA0003789424010000066
in the formula:
Figure BDA0003789424010000067
converting the objective function in the upper-layer optimization model considering the flexibility constraint into a corresponding fuzzy form by adopting an intuitive fuzzy programming method, namely:
Figure BDA0003789424010000068
in the formula: z is a radical of formula 0 、z 1 、y 0 、y 1 Model parameters are planned for intuitive blur;
the intuitive fuzzy planning considers a membership function and a non-membership function, namely, the difference between the integral satisfaction degree and the dissatisfaction degree is taken as a target function, so that an optimal scheduling strategy which is simultaneously satisfied in two aspects is obtained;
overall satisfaction degree alpha optimization model:
Figure BDA0003789424010000069
overall dissatisfaction β optimization model:
Figure BDA0003789424010000071
the upper layer optimization model considering the flexibility constraint can be converted into an intuitive fuzzy programming form, namely:
Figure BDA0003789424010000072
and converting the upper-layer optimization model considering the flexibility constraint into an intuitive fuzzy planning model, and performing optimization iteration according to the intuitive fuzzy planning model.
Further, step five wherein z 0 Denotes a conservative solution within an acceptable range, z 1 Representing an optimal solution, calculated model thereof such asThe following optimization problem is shown:
Figure BDA0003789424010000073
Figure BDA0003789424010000074
in the formula: h 1 B represents conventional constraint; h 2i ≤b i And H 2i ≤b i +p i Representing fuzzy constraints, whereinb i A threshold value representing the membership function for the ith intuitive fuzzy constraint,p i representing the maximum deviation acceptable by the ith fuzzy membership constraint;
y 0 、y 1 the computational model is shown as the following optimization problem:
Figure BDA0003789424010000081
Figure BDA0003789424010000082
in the formula:
Figure BDA0003789424010000083
and
Figure BDA0003789424010000084
represent fuzzy constraints in which
Figure BDA0003789424010000085
A threshold value representing the membership function for the ith intuitive fuzzy constraint,
Figure BDA0003789424010000086
representing the maximum deviation acceptable by the ith fuzzy non-membership constraint.
The traditional alternating iteration method can obtain an optimized scheduling strategy meeting the flexibility requirement of the extreme scene by solving the double-layer model, but the probability of the extreme scene is extremely low, and if the obtained optimized scheduling strategy is considered in the way, the corresponding scheduling cost is higher. The method converts the upper-layer optimization model into an intuitive fuzzy planning form, solves the comprehensive most satisfactory solution which gives consideration to economy and flexibility in most scenes, solves the comprehensive most satisfactory solution of the model by introducing the intuitive fuzzy planning, and can give consideration to both the flexibility and the economy of the power distribution network.
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Fig. 1 is a flowchart of a double-layer optimization scheduling method for a distribution new energy distribution network with flexibility constraints taken into consideration.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The specific implementation mode of the method comprises the following steps:
the method comprises the following steps: calculating the flexibility of the operation of the power distribution network, wherein the flexibility provided for the power distribution network by the main network access point comprises the flexibility provided for the power distribution network by the main network access point, the flexibility provided for the power distribution network by an electric vehicle charging station and the flexibility provided for the power distribution network by demand response load.
The net load curve refers to the difference between corresponding points of the daily load curve and the wind-light combined output curve in time sequence, and the curve corresponding to the time sequence is formed by the difference. The larger the fluctuation amplitude of the wind-solar combined output is, the more the fluctuation amplitude of the net load curve fluctuates sharply, and at the moment, the flexible resource stabilization in the power distribution network needs to be optimized and scheduled, and the severe fluctuation of the net load curve needs to be absorbed. The more flexible the distribution network, the greater the ability to quickly respond to changes in the system. The flexibility of each system in the power distribution network is mainly reflected on the climbing rate of adjustable power in the power distribution network, namely the fluctuation amplitude of a net load curve cannot exceed the maximum climbing rate of adjustable total power in the power distribution network, otherwise, the power distribution network can cause wind abandonment, light abandonment and even load shedding due to the insufficient maximum climbing rate.
The total adjustable power in the power distribution network is the sum of the adjustable power at the main network access point, the adjustable power of the electric vehicle charging station and the adjustable power of the demand response load, and the maximum ascending and descending climbing rates of the adjustable power in the power distribution network at the moment t are respectively as follows:
Figure BDA0003789424010000091
Figure BDA0003789424010000092
in the formula:
Figure BDA0003789424010000093
respectively representing the maximum ascending and descending climbing rates of the adjustable total power of the power distribution network in a time period t;
Figure BDA0003789424010000094
the maximum ascending and descending climbing rates provided for the power distribution network by the main network access point in the time period t are represented;
Figure BDA0003789424010000095
Figure BDA0003789424010000096
respectively representing the maximum ascending and descending climbing rates which can be provided for the power distribution network by the ith electric vehicle charging station in the time period t;
Figure BDA0003789424010000097
respectively representing the maximum ascending and descending ramp rates of the jth demand response load which can be provided for the power distribution network in the time period tth; n is a radical of S 、N D Respectively representing the total number of electric vehicle charging stations and the total number of demand response loads in the power distribution network.
The detailed calculation steps of the operation flexibility of the power distribution network are as follows:
(a) Flexibility provided to distribution networks from a primary network access point
The main determinants are: the capacity of the interconnection transformer is limited, and the climbing rate of the power enterprise is limited according to the actual situation and the climbing rate of the main network access point is limited. The maximum ramp rate provided to the distribution network at the main network access point is the minimum of two determining factors, namely:
Figure BDA0003789424010000098
Figure BDA0003789424010000099
in the formula: s 1max 、
Figure BDA00037894240100000910
Respectively representing the rated capacity and the rated power factor of the interconnection transformer at the access point of the main network;
Figure BDA00037894240100000911
representing the active power injected into the distribution network at the time period t and the main network access point; r Bupmax 、R Bdnmax The maximum ascending and descending climbing rates of active power injected into the distribution network at the main network access point set by the power enterprises according to actual conditions are respectively represented.
(b) Flexibility provided for power distribution network by electric vehicle charging station
The maximum climbing rate is mainly determined by the residual electric quantity and the charging and discharging power in the current time period t station, namely:
Figure BDA0003789424010000101
Figure BDA0003789424010000102
in the formula: p cimax 、P dimax Respectively representing the maximum charging power and the maximum discharging power of the ith electric vehicle charging station;
Figure BDA0003789424010000103
respectively representing the charging power and the discharging power of the ith electric vehicle charging station in a time period tth; e i t represents the residual capacity of the ith electric vehicle charging station in the time period tth;
Figure BDA0003789424010000104
respectively represent
Figure BDA0003789424010000105
Upper and lower limits of (3).
(c) Flexibility in providing demand response load to power distribution grid
It is primarily dependent on the adjustable potential (i.e., adjustable power upper and lower limits) of the demand-responsive load and the total power usage limit over time. In the current time period t, the climbing rate of the demand response load cannot enable the power fluctuation amplitude of the demand response load to exceed the adjustable potential, and meanwhile, the sum of the demand response electric quantities in the t time periods of the climbing rate of the demand response load cannot be 1-t exceeds the total electric quantity in the whole time period, so that the maximum climbing rate provided for the power distribution network is as follows:
Figure BDA0003789424010000106
Figure BDA0003789424010000107
in the formula:
Figure BDA0003789424010000108
representing actual active power of the jth demand-responsive load during time period tth；
Figure BDA0003789424010000109
Respectively representing the upper limit value and the lower limit value of the adjustable power of the demand response load; e DjΣ Representing the total power usage of the jth demand-responsive load over time.
Step two: and establishing an upper-layer optimization model without considering flexibility constraints so as to solve a scheduling strategy for minimizing the operation cost of the power distribution network under a certain scene of wind-solar combined output.
The upper model optimization aims at the lowest operation cost of the power distribution network, the objective function of the upper model optimization comprises the electricity purchase cost of the upper power distribution network, the operation cost of an electric vehicle charging station and the dispatching compensation cost of demand response load, and the constraint conditions comprise conventional power balance and safety constraint conditions, so that the initial optimized dispatching strategy is obtained.
The upper layer optimization model objective function is as follows:
Figure BDA00037894240100001010
in the formula: n is a radical of hydrogen T Representing the total time segment number of the scheduling period; Δ t represents the period length;
Figure BDA0003789424010000111
unit cost for purchasing electricity from the power distribution network to the main network access point in a time period t;
Figure BDA0003789424010000112
the active power injected into the power distribution network by the main network access point in a time period t is represented; k ci 、K di Respectively representing the charging and discharging cost coefficients of the electric vehicle charging station;
Figure BDA0003789424010000113
respectively representing charging and discharging power of the ith electric vehicle charging station in a time period t; k is Dj The compensation cost of the active power of the jth demand response load unit is represented;
Figure BDA0003789424010000114
representing the original power usage before the jth demand response user responds for a period of time tth.
The constraint conditions comprise: node power balance constraint, safety constraint, interconnection transformer capacity constraint, electric vehicle charging station charge and discharge constraint and demand response load schedulable potential constraint are respectively shown as follows.
(a) Node power balance constraint:
Figure BDA0003789424010000115
(b) Safety restraint:
Figure BDA0003789424010000116
in the formula: u shape i 、U j 、U k Respectively representing the voltage amplitudes of the nodes i, j and k;
Figure BDA0003789424010000117
represents the active power on line i;
Figure BDA0003789424010000118
respectively representing an upper limit and a lower limit of active power on a line i; g jk 、b jk Respectively represents the ith line conductance and susceptance. Theta jk Representing the voltage phase difference between nodes j, k.
(c) And (3) constraint of the interconnection transformer:
Figure BDA0003789424010000119
in the formula:
Figure BDA00037894240100001110
and the time period t represents the reactive power injected into the power distribution network at the main network access point.
(d) Electric vehicle charging and discharging restraint:
Figure BDA00037894240100001111
in the formula: eta ci 、η di Respectively shows the charging efficiency and the discharging efficiency of the electric vehicle charging station.
e) Demand response load constraints:
Figure BDA0003789424010000121
in the formula:
Figure BDA0003789424010000122
respectively representing the upper and lower limit values of the power consumption of the jth demand response load in the time period tth.
An initial optimal scheduling strategy can be solved based on the model.
Step three: and (4) establishing a lower-layer optimization model, and solving to obtain a corresponding most severe net load curve based on the initial optimization scheduling strategy obtained in the step two (if the step is circulated, the optimization scheduling strategy is the latest iteration result).
The wind-solar actual joint contribution is assumed to fluctuate within a certain error level of the mean value of the predicted joint contribution. And obtaining the fluctuation range of the net load curve according to the fluctuation range of the wind-light combined output curve at each moment and the difference of the load values corresponding to each moment on the internal load curve.
The most severe net load curve refers to a net load curve corresponding to the maximum sum of all parts of which the climbing rate of all time periods in the whole scheduling cycle is larger than the adjustable total power maximum climbing rate of the power distribution network in the fluctuation range of the net load curve. The severity is measured by an index S, wherein S represents the sum of the areas of all parts of which the climbing rate of each time period in the whole scheduling period is greater than the maximum climbing rate of the adjustable total power of the power distribution network in the fluctuation range of the net load curve. Namely:
Figure BDA0003789424010000123
after an optimization scheduling strategy with the minimum operation cost of the power distribution network is determined, the climbing rate of the adjustable power in each period of the whole period can be obtained by the upper-layer optimization model, and the most severe net load curve is searched by the lower-layer optimization model in the net load curve fluctuation range. The expression is as follows:
Figure BDA0003789424010000124
in the formula:
Figure BDA0003789424010000125
represents the ramp rate of the most severe net load curve at time period t;
Figure BDA0003789424010000126
the maximum and minimum values of the power of the net load curve in the fluctuation range of the time t are shown.
Therefore, the power distribution network optimal scheduling strategy meeting the flexibility constraint under the extreme scene of wind-solar combined output can be obtained through the alternate iterative solution of the double-layer model. Although the optimal scheduling strategy meets the flexibility of the wind-solar combined output extreme scene, the occurrence probability is not high, and therefore higher economic cost is required.
Step four: and converting the upper layer model considering the flexibility constraint into an intuitive fuzzy planning model by adopting intuitive fuzzy planning.
Besides conventional power balance and safety constraints, flexibility constraints are also considered in the constraint conditions.
The flexibility constraint means that the maximum climbing rate of each time period of the most severe net load curve cannot exceed the maximum climbing rate of the total adjustable power of the power distribution network within the wind-solar combined output fluctuation range. The expression is as follows:
Figure BDA0003789424010000131
in the formula:
Figure BDA0003789424010000132
representing the magnitude of the net load of the most severe net load curve at time t.
The flexibility constraint corresponds to the fuzzy form, namely:
Figure BDA0003789424010000133
Figure BDA0003789424010000134
in the formula:
Figure BDA0003789424010000135
converting the objective function in the upper-layer optimization model considering the flexibility constraint into a corresponding fuzzy form by adopting an intuitive fuzzy programming method, namely:
Figure BDA0003789424010000136
in the formula: z is a radical of formula 0 、z 1 、y 0 、y 1 Model parameters are planned for intuitive blur.
In addition, the ordinary fuzzy planning usually only considers a membership function, while the intuitive fuzzy planning starts from the positive and negative aspects, and simultaneously considers the membership function and a non-membership function, namely, the difference between the integral satisfaction degree and the dissatisfaction degree is taken as a target function, so that an optimized scheduling strategy which is simultaneously satisfied by the two aspects is obtained.
Overall satisfaction degree alpha optimization model:
Figure BDA0003789424010000137
overall dissatisfaction β optimization model:
Figure BDA0003789424010000141
the upper layer optimization model considering the flexibility constraint can be converted into an intuitive fuzzy planning form, namely:
Figure BDA0003789424010000142
and converting the upper-layer optimization model considering the flexibility constraint into an intuitive fuzzy planning model, and performing optimization iteration according to the intuitive fuzzy planning model.
Step five: combining the severest net load curve obtained in the third step with the related parameter z of the intuitive fuzzy programming model 0 、z 1 、y 0 、y 1 And solving the intuitive fuzzy planning model to obtain a new optimized scheduling strategy and operation cost.
z 0 Denotes a conservative solution (worst solution) within an acceptable range, z 1 Representing the optimal solution, the calculation model of which is shown as the following optimization problem:
Figure BDA0003789424010000143
Figure BDA0003789424010000144
in the formula: h 1 B represents conventional constraint; h 2i ≤b i And H 2i ≤b i +p i Representing fuzzy constraints, whereinb i A threshold value representing the membership function for the ith intuitive fuzzy constraint,p i representing the maximum deviation acceptable for the ith fuzzy membership constraint.
y 0 、y 1 The computational model is shown as the following optimization problem:
Figure BDA0003789424010000151
Figure BDA0003789424010000152
in the formula:
Figure BDA0003789424010000153
and
Figure BDA0003789424010000154
represent fuzzy constraints in which
Figure BDA0003789424010000155
A threshold value representing the membership function for the ith intuitive fuzzy constraint,
Figure BDA0003789424010000156
representing the maximum deviation acceptable by the ith fuzzy non-membership constraint.
Step six: and comparing the operation cost obtained by calculation in the second step with the operation cost obtained in the fifth step (if the step is circulated, the operation cost corresponding to the latest optimized scheduling strategy at this time is compared with the operation cost corresponding to the last optimized scheduling strategy). If the absolute value of the difference between the two is smaller than the threshold value, the most satisfactory optimal scheduling strategy is obtained; and otherwise, continuously repeating the third step to the sixth step until the absolute value of the difference of the operation costs is smaller than the threshold value.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (8)

1. A double-layer optimization scheduling method considering flexibility constraint and including a distributed new energy distribution network is characterized by comprising the following steps:
the method comprises the following steps: calculating the flexibility of each system in the power distribution network, wherein the flexibility provided for the power distribution network by the main network access point comprises the flexibility provided for the power distribution network by the main network access point, the flexibility provided for the power distribution network by an electric vehicle charging station and the flexibility provided for the power distribution network by demand response load;
step two: establishing an upper-layer optimization model without considering flexibility constraints so as to solve a scheduling strategy for minimizing the operation cost of the power distribution network under a certain scene of wind-solar combined output;
step three: establishing a lower-layer optimization model, and solving to obtain a corresponding most severe net load curve based on the initial optimization scheduling strategy obtained in the second step;
step four: converting an upper layer model considering flexibility constraint into an intuitive fuzzy planning model by adopting intuitive fuzzy planning;
step five: combining the severest net load curve obtained in the third step with the related parameter z of the intuitive fuzzy programming model 0 、z 1 、y 0 、y 1 Solving the intuitive fuzzy planning model to obtain a new optimized scheduling strategy and operation cost;
step six: comparing the operation cost obtained by calculation in the step two with the operation cost obtained in the step five, and if the absolute value of the difference between the two is smaller than a threshold value, obtaining the most satisfactory optimal scheduling strategy; and otherwise, continuously repeating the third step to the sixth step until the absolute value of the difference of the operation costs is smaller than the threshold value.
2. The double-layer optimized dispatching method for the power distribution network containing the distributed new energy resources, which considers the flexibility constraint, as claimed in claim 1, wherein the flexibility of each system in the power distribution network is embodied in the ramp rate of the adjustable power in the power distribution network, that is, the fluctuation amplitude of the net load curve cannot exceed the maximum ramp rate of the adjustable total power in the power distribution network, and the net load curve refers to the difference between the corresponding points of the daily load curve and the wind-light combined output curve in the time sequence, and the curve corresponding to the time sequence is composed of the difference.
3. The double-layer optimization scheduling method for the distribution network containing the new energy resources considering the flexibility constraint is characterized in that in the step one, the detailed calculation steps of the operation flexibility of the distribution network are as follows:
(a) Flexibility offered to distribution networks at main network access points
The main determinants are: the maximum climbing rate provided by the power distribution network and the main network access point to the power distribution network is the minimum value of two determining factors, namely:
Figure FDA0003789407000000021
Figure FDA0003789407000000022
in the formula: s. the 1max 、
Figure FDA0003789407000000023
Respectively representing the rated capacity and the rated power factor of the interconnection transformer at the access point of the main network;
Figure FDA0003789407000000024
the active power injected into the power distribution network by the main network access point in the time period t is represented; r Bupmax 、R Bdnmax Respectively representing the maximum ascending and descending climbing rates of active power injected into the distribution network by the power enterprises and the main network access point set according to actual conditions;
(b) Flexibility provided for power distribution network by electric vehicle charging station
The maximum climbing rate is mainly determined by the residual electric quantity and the charging and discharging power in the current time period t station, namely:
Figure FDA0003789407000000025
Figure FDA0003789407000000026
in the formula: p is cimax 、P dimax Respectively representing the maximum charging power and the maximum discharging power of the ith electric vehicle charging station;
Figure FDA00037894070000000212
respectively representing the charging power and the discharging power of the ith electric vehicle charging station in a time period tth; e i t Representing the residual capacity of the ith electric vehicle charging station in a time period t;
Figure FDA0003789407000000027
respectively represent E i t Upper and lower limits of (d);
(c) Flexibility in providing demand response load to power distribution grid
The method mainly depends on the upper limit and the lower limit of the adjustable power of the demand response load and the total power consumption limit in the whole time period, in the current time period t, the climbing rate of the demand response load cannot enable the power fluctuation amplitude of the demand response load to exceed the adjustable potential, and meanwhile, the sum of the demand response power of the demand response load in the t time periods which cannot be 1-t exceeds the total power consumption in the whole time period, the maximum climbing rate provided for the power distribution network is as follows:
Figure FDA0003789407000000028
Figure FDA0003789407000000029
in the formula:
Figure FDA00037894070000000210
is shown in the time periodtth actual active power of the demand response load;
Figure FDA00037894070000000211
respectively representing the upper limit value and the lower limit value of the adjustable power of the demand response load; e DjΣ Representing the total power usage of the jth demand-responsive load over time.
4. The double-layer optimized dispatching method of the distribution network containing the new energy in consideration of the flexibility constraint, as claimed in claim 1, wherein the upper layer model optimization in the second step is optimized with the objective of lowest operation cost of the distribution network, the objective function includes the electricity purchase cost of the upper layer power grid, the operation cost of the electric vehicle charging station and the dispatching compensation cost of the demand response load, and the constraint condition includes the conventional power balance and the safety constraint condition, so as to obtain the initial optimized dispatching strategy.
5. The double-layer optimization scheduling method for the distribution new energy distribution network considering the flexibility constraint is characterized in that the upper-layer optimization model objective function is as follows:
Figure FDA0003789407000000031
in the formula: n is a radical of T Representing the total time segment number of the scheduling period; Δ t represents a period length;
Figure FDA0003789407000000032
unit cost for purchasing electricity from the power distribution network to the main network access point in a time period t;
Figure FDA0003789407000000033
the active power injected into the power distribution network by the main network access point in a time period t is represented; k is ci 、K di Respectively representing the charging and discharging cost coefficients of the electric vehicle charging station;
Figure FDA0003789407000000034
respectively representing charging and discharging power of the ith electric vehicle charging station in a time period tth; k is Dj The compensation cost of the active power of the jth demand response load unit is represented;
Figure FDA0003789407000000035
representing the original power consumption before the jth demand response user responds in the time period tth;
the constraint conditions of the upper-layer optimization model comprise: node power balance constraint, safety constraint, interconnection transformer capacity constraint, electric vehicle charging station charge and discharge constraint and demand response load schedulable potential constraint are respectively as follows:
(a) Node power balance constraint:
Figure FDA0003789407000000036
(b) Safety restraint:
Figure FDA0003789407000000037
in the formula: u shape i 、U j 、U k Respectively representing the voltage amplitudes of the nodes i, j and k;
Figure FDA0003789407000000038
represents the active power on line i;
Figure FDA0003789407000000039
respectively representing the upper limit and the lower limit of active power on a line i; g jk 、b jk Respectively represents the ith line conductance and susceptance. Theta jk Representing the voltage phase difference between nodes j, k;
(c) And (3) restraint of the interconnection transformer:
Figure FDA0003789407000000041
in the formula:
Figure FDA0003789407000000042
the reactive power injected into the power distribution network at the time interval t and the main network access point is represented;
(d) Electric vehicle charging and discharging restraint:
Figure FDA0003789407000000043
in the formula: eta ci 、η di Respectively representing the charging efficiency and the discharging efficiency of the electric vehicle charging station;
e) Demand response load constraints:
Figure FDA0003789407000000044
in the formula:
Figure FDA0003789407000000045
and the upper limit value and the lower limit value of the power consumption of the jth demand response load in the time interval tth are respectively represented.
6. The double-layer optimized scheduling method for the distribution network containing the new energy resources considering the flexibility constraint, according to claim 1, wherein the third step specifically includes:
the wind-light actual combined output fluctuates within a certain error level range of the predicted combined output mean value, and the net load curve fluctuation range is obtained according to the fluctuation range of the wind-light combined output curve at each moment and the difference of the load values of the internal load curve at each corresponding moment;
the most severe net load curve refers to a net load curve which corresponds to the maximum sum of all parts of which the climbing rates of all time periods in the whole scheduling cycle are greater than the maximum adjustable total power climbing rate of the power distribution network in the fluctuation range of the net load curve, the severity degree of the net load curve is measured by an index S, S represents the sum of all parts of which the climbing rates of all time periods in the whole scheduling cycle are greater than the maximum adjustable total power climbing rate of the power distribution network in the fluctuation range of the net load curve, namely:
Figure FDA0003789407000000046
after an optimization scheduling strategy with the minimum operation cost of the power distribution network is determined, the climbing rate of the adjustable power in each period of the whole period can be obtained by the upper-layer optimization model, the most severe net load curve is searched by the lower-layer optimization model in the net load curve fluctuation range, and the expression is as follows:
Figure FDA0003789407000000051
in the formula:
Figure FDA0003789407000000052
represents the ramp rate of the most severe net load curve at time period t;
Figure FDA0003789407000000053
the maximum and minimum values of the power of the net load curve in the fluctuation range of the time t are shown.
7. The double-layer optimized scheduling method for the distribution network containing the new energy in consideration of the flexibility constraint in claim 1, wherein the flexibility constraint in step four means that the maximum climbing rate of each time interval of the most severe net load curve cannot exceed the maximum climbing rate of the total adjustable power of the distribution network within the fluctuation range of the wind-solar combined output, and the expression is as follows:
Figure FDA0003789407000000054
in the formula:
Figure FDA0003789407000000055
a payload size representing a most severe payload curve at time t;
the flexibility constraint corresponds to the fuzzy form, namely:
Figure FDA0003789407000000056
Figure FDA0003789407000000057
in the formula:
Figure FDA0003789407000000058
converting the objective function in the upper-layer optimization model considering the flexibility constraint into a corresponding fuzzy form by adopting an intuitive fuzzy planning method, namely:
Figure FDA0003789407000000059
in the formula: z is a radical of formula 0 、z 1 、y 0 、y 1 Model parameters are planned for intuitive blur;
the intuition fuzzy planning considers a membership function and a non-membership function, namely, the difference between the integral satisfaction degree and the dissatisfaction degree is taken as a target function, so that an optimal scheduling strategy which is simultaneously satisfied in two aspects is obtained;
overall satisfaction degree alpha optimization model:
Figure FDA0003789407000000061
overall dissatisfaction β optimization model:
Figure FDA0003789407000000062
the upper layer optimization model considering the flexibility constraint can be converted into an intuitive fuzzy planning form, namely:
Figure FDA0003789407000000063
and converting the upper-layer optimization model considering the flexibility constraint into an intuitive fuzzy planning model, and performing optimization iteration according to the intuitive fuzzy planning model.
8. The double-layer optimization scheduling method for the distribution network containing the new distributed energy resources considering the flexibility constraint as claimed in claim 1, wherein z in the fifth step 0 Denotes a conservative solution within an acceptable range, z 1 Representing the optimal solution, the calculation model of which is shown as the following optimization problem:
Figure FDA0003789407000000064
Figure FDA0003789407000000071
in the formula: h 1 B represents conventional constraint; h 2i ≤b i And H 2i ≤b i +p i Representing fuzzy constraints, whereinb i A threshold value representing the membership function for the ith intuitive fuzzy constraint,p i representing the maximum deviation acceptable by the ith fuzzy membership constraint;
y 0 、y 1 the computational model is shown as the following optimization problem:
Figure FDA0003789407000000072
Figure FDA0003789407000000073
in the formula:
Figure FDA0003789407000000074
and
Figure FDA0003789407000000075
represent fuzzy constraints in which
Figure FDA0003789407000000076
A threshold value representing the membership function for the ith intuitive fuzzy constraint,
Figure FDA0003789407000000077
represents the maximum deviation acceptable for the ith fuzzy non-membership constraint.
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Publication number Priority date Publication date Assignee Title
CN117811105A (en) * 2024-02-29 2024-04-02 国网山东省电力公司东营供电公司 Double-layer optimal scheduling method based on distributed photovoltaic flexibility and related equipment

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