CN111555369A - Medium-voltage and low-voltage collaborative optimization method for power distribution network - Google Patents

Medium-voltage and low-voltage collaborative optimization method for power distribution network Download PDF

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CN111555369A
CN111555369A CN202010429006.6A CN202010429006A CN111555369A CN 111555369 A CN111555369 A CN 111555369A CN 202010429006 A CN202010429006 A CN 202010429006A CN 111555369 A CN111555369 A CN 111555369A
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voltage
power
distribution network
optimization
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CN111555369B (en
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苏适
马麟
罗恩博
赵磊
张旭东
黄星
杨洋
严玉廷
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Electric Power Research Institute of Yunnan Power Grid Co Ltd
Dali Power Supply Bureau of Yunnan Power Grid Co Ltd
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Electric Power Research Institute of Yunnan Power Grid Co Ltd
Dali Power Supply Bureau of Yunnan Power Grid 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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]

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Abstract

The application discloses a medium-low voltage collaborative optimization method for a power distribution network, which comprises the following steps: a power distribution network medium-voltage-low-voltage resource coordination control system is constructed based on the time-space scale of a power distribution network, and comprises the following steps: a day-ahead stage medium-voltage control center, a day-interior stage low-voltage control center and a real-time stage resource control center; acquiring the running cost of a medium-voltage distribution network system based on a medium-voltage control center in a day-ahead stage, and establishing a day-ahead optimization objective function based on the running cost of the medium-voltage distribution network system; setting optimization constraint conditions in the day ahead; obtaining an optimized dispatching model of the medium-voltage distribution network; establishing a prediction model based on a low-voltage control center in the daytime; sequentially performing rolling optimization and feedback correction on the prediction model to obtain a low-voltage distribution network closed-loop rolling optimization control model; and respectively solving the medium-voltage distribution network optimal scheduling model and the low-voltage distribution network closed-loop rolling optimal control model to realize the coordination control of medium-voltage and low-voltage resources of the distribution network. The method and the device can be used for carrying out optimization control on the medium-voltage and low-voltage flexible resources of the power distribution network.

Description

Medium-voltage and low-voltage collaborative optimization method for power distribution network
Technical Field
The application relates to the field of automation of power distribution systems, in particular to a medium-voltage and low-voltage collaborative optimization method for a power distribution network.
Background
Power distribution networks occupy an important position in power systems, including both the power supply end and the demand side. At a power supply end, distributed photovoltaic is widely connected at present, and the photovoltaic power distribution system has the characteristics of adaptation to complex terrains in power distribution areas and narrow construction space; on the demand side, clean, pollution-free electric vehicles have been incorporated on a large scale into power distribution grids in urban areas of higher development. With the rapid development of the power distribution network, the permeability of the distributed photovoltaic is gradually improved, and network blockage is easily caused, so that the optimization operation of the power distribution network is very important. And flexible loads such as distributed energy storage, air conditioner heat pump and the like can effectively deal with the randomness of electric vehicles and distributed photovoltaic by virtue of the flexible and adjustable characteristics of the flexible loads, and network blockage is relieved.
The current experts have studied the optimal operating problems of the distribution network, for example: the method comprises the steps of defining a power distribution company and a microgrid as two independent systems based on a complex system architecture, establishing an active power distribution network operation control model by taking the benefit maximization of each independent system as an optimization target, and solving the model by adopting a layered optimization algorithm.
However, the inventor of the present application finds that most of flexible loads in the power distribution network are connected to a direct low-voltage distribution station, and most of the flexible loads in the power distribution network in the prior art perform optimized operation processing on the medium-voltage distribution network, and the optimization of the low-voltage distribution network is not considered, so that the cooperative optimization control of medium-voltage and low-voltage flexible resources in the power distribution network cannot be realized.
Disclosure of Invention
The application provides a medium-voltage and low-voltage collaborative optimization method for a power distribution network, which aims to solve the problem that the prior art cannot effectively perform optimization control on medium-voltage and low-voltage flexible resources of the power distribution network.
In order to solve the technical problem, the embodiment of the application discloses the following technical scheme:
the application provides a medium-low voltage collaborative optimization method for a power distribution network, which comprises the following steps: constructing a power distribution network medium-voltage-low-voltage resource coordination control system based on the power distribution network space-time scale; the power distribution network medium-voltage and low-voltage resource coordination control system comprises: a day-ahead stage medium-voltage control center, a day-interior stage low-voltage control center and a real-time stage resource control center;
acquiring the running cost of a medium-voltage distribution network system based on the medium-voltage control center in the day-ahead stage, and establishing a day-ahead optimization objective function based on the running cost of the medium-voltage distribution network system; setting optimization constraint conditions in the day ahead; obtaining an optimized dispatching model of the medium voltage distribution network based on the day-ahead optimization objective function and the day-ahead optimization constraint condition;
establishing a prediction model based on the low-voltage control center in the intraday period; sequentially carrying out rolling optimization and feedback correction on the prediction model to obtain a low-voltage distribution network closed-loop rolling optimization control model;
and respectively solving the medium-voltage distribution network optimized dispatching model and the low-voltage distribution network closed-loop rolling optimized control model to realize the coordination control of medium-voltage and low-voltage resources of the distribution network.
Optionally, the operating cost of the medium voltage distribution network system includes:
cost of electricity purchase from resource operators:
Figure BDA0002499811700000021
αj＝cj·Δt
wherein:
Δ t represents a preset time interval;
g represents a schedulable set of power generation units;
Figure BDA0002499811700000022
representing the active output of the jth schedulable power generation unit during the r period;
cjrepresenting the price of purchasing 1kWh of electric energy from the jth schedulable power generation unit after the national subsidy;
the resource operator cuts the compensation cost:
Figure BDA0002499811700000023
β＝cDER,j·Δt
wherein:
Figure BDA0002499811700000024
the active output reported by the jth schedulable power generation unit in the r time period;
Figure BDA0002499811700000025
the actual active output of the jth schedulable power generation unit in the r time period;
Figure BDA0002499811700000026
active power output reduced for the jth schedulable power generation unit in the r time period;
cDER,jthe price compensated to the power generator by the power distribution company for reducing the 1kWh generated energy according to the contract;
the electricity purchasing cost from the upper-level power grid is as follows:
Figure BDA0002499811700000027
ψ＝cgrid·Δt
wherein:
Figure BDA0002499811700000028
representing the active power which needs to be purchased from a superior power grid in the period r;
cgridrepresents the price at which a distribution company purchases 1kWh of electrical energy from an upper grid;
demand side response cost:
Figure BDA0002499811700000031
λ＝cbid·Δt
μ＝(csell,r-cgrid)·Δt
wherein:
c represents a user set participating in demand side response;
Figure BDA0002499811700000032
representing the active power shed by the vth user in the r period;
cbidthe price compensated by the power distribution company is obtained by cutting 1kWh of electric energy after the user specified in the contract responds to the interrupt request;
csell,rindicating the time-of-use price sold to the user by the distribution company.
Optionally, the method for establishing the day-ahead optimization objective function includes:
Figure BDA0002499811700000033
wherein:
CDERrepresents the cost of purchasing electricity from the resource operator;
CpayDERindicating that the resource operator cuts back the compensation cost;
Cgridrepresents the cost of purchasing electricity from an upper-level power grid;
CDRrepresenting the demand side response cost.
Optionally, the day-ahead optimization constraint condition includes:
electric energy balance constraint:
Figure BDA0002499811700000034
wherein:
NP is the number of non-dispatchable power generating units;
s is the number of electric vehicle charging stations;
Figure BDA0002499811700000041
the active power output of the h electric vehicle charging station in the r time period;
Figure BDA0002499811700000042
the active output of the u-th unscheduled power generation unit in the r-th time period;
Figure BDA0002499811700000043
is the total load of the r-th period;
Figure BDA0002499811700000044
network loss for the r-th period;
and power balance constraint:
Figure BDA0002499811700000045
Figure BDA0002499811700000046
wherein:
Piand QiRespectively injecting active power and reactive power into the node i;
Gij、Bij、ijsequentially setting the conductance, susceptance and voltage phase angle difference between the nodes i and j;
n is the total number of system nodes; u shapeiIs the voltage amplitude of node i; u shapejIs the voltage amplitude of node j;
schedulable power generation unit operation constraints:
Figure BDA0002499811700000047
wherein:
Figure BDA0002499811700000048
representing the minimum active power allowed by the jth schedulable power generation unit during the r period;
Figure BDA0002499811700000049
representing the maximum active power allowed by the jth schedulable power generation unit during the r period;
RN,jthe limit value of active power is increased or reduced in two adjacent time periods for the jth schedulable power generation unit;
operation restraint of an electric vehicle charging station:
Figure BDA00024998117000000410
Figure BDA00024998117000000411
Figure BDA0002499811700000051
wherein:
Figure BDA0002499811700000052
charging/discharging active power for the h electric vehicle centralized charging station;
PEV,hminand PEV,hmaxRespectively the minimum and maximum charging/discharging active power of the h electric vehicle centralized charging station;
Figure BDA0002499811700000053
the residual capacity of all batteries in the r time period is set for the h concentrated charging station;
ηC、ηFthe charging efficiency and the discharging efficiency of the charger are respectively;
Figure BDA0002499811700000054
the charging requirement of the centralized charging station of the electric automobile in the r time period;
SEV,hmaxthe total electric quantity of all batteries of the h electric vehicle centralized charging station;
SOChmax、SOChminmaximum and minimum charge states for the h electric vehicle centralized charging station;
interruptible load shedding load power constraint:
Figure BDA0002499811700000055
wherein:
Figure BDA0002499811700000056
allowing a load shedding minimum active power for the vth user in the vth period;
Figure BDA0002499811700000057
allowing the maximum active power of the load to be reduced for the vth user in the vth period;
and (3) power constraint exchange with a superior power grid:
Figure BDA0002499811700000058
wherein:
Figure BDA0002499811700000059
the minimum active value is the minimum active value of power exchange between the power distribution network and a superior power grid in the r-th time period;
Figure BDA00024998117000000510
the maximum active value of power exchange between the power distribution network and a superior power grid in the r-th time period;
adjustable low-voltage line power constraint:
Figure BDA00024998117000000511
wherein:
Figure BDA0002499811700000061
the minimum active power of the power can be adjusted for the l low-voltage line in the r period;
Figure BDA0002499811700000062
the maximum active power of the power can be adjusted for the l low voltage line during the r period.
Optionally, the obtaining method of the prediction model includes:
Figure BDA0002499811700000063
wherein:
u0(k) energy storage, distributed photovoltaic and adjustable load power optimization control values at the moment k;
delta u (k + t | k) is the increment of energy storage, distributed photovoltaic and adjustable load power in the future [ k + (t-1) at the moment k and k + t ] time period;
and u (k + i | k) predicts energy storage, distributed photovoltaic and adjustable load optimized power at the future k + i moment for the k moment.
Optionally, the performing rolling optimization on the prediction model includes:
establishing a rolling optimization objective function, wherein the rolling optimization objective function is as follows:
Figure BDA0002499811700000064
wherein:
omega is a day rolling optimization control object set; mu.spA cost factor that is a loss of the network;
Figure BDA0002499811700000065
the network loss value at the moment k + i; n is a radical ofRThe number of renewable energy sources;
μuoptimizing the adjusted cost coefficient for the control object;
Figure BDA0002499811700000066
a predicted force value of the nth renewable energy source at the k + i moment;
Figure BDA0002499811700000067
the actual output value of the nth renewable energy source at the k + i moment is obtained;
Figure BDA0002499811700000068
the scheduling value of the jth control object at the k + i moment in the optimization before the day;
u0,j(k + i-1) is the actual optimized control value of the jth control object at the k + i-1 moment in the optimization before the day;
Δuj(k + i) is a control variable and represents the control value increment of the jth control object at the k + i moment in the intraday optimization;
Δ t' represents a preset time interval;
setting a rolling optimization constraint condition, wherein the rolling optimization constraint condition comprises the following steps: voltage power flow constraint, distributed photovoltaic output constraint, energy storage operation constraint and adjustable load power regulation constraint under linear power flow;
constructing a rolling optimization model based on the rolling optimization objective function and the rolling optimization constraint condition, and obtaining an energy storage, distributed photovoltaic and adjustable load power variation column vector delta u at the k + i moment according to the rolling optimization modelT(k + i | k); to obtain TinA set of optimized period control variable column vector sequences:
{ΔuT(k+1|k),ΔuT(k+2|k),…,ΔuT(k+Tin|k)}
executing a first control variable column vector in the control variable sequence set to obtain the optimized power of the energy storage, the distributed photovoltaic and the adjustable load of the low-voltage distribution network at the moment of k + 1:
u(k+1|k)＝u0(k)+ΔuT(k+1|k)。
optionally, the performing feedback correction on the prediction model includes:
updating the actual load and illumination measurement values of the system at the moment k +1, and predicting the load and photovoltaic short-time rolling at the moment k + 1; and taking the energy storage, distributed photovoltaic and adjustable load optimization control values at the moment k +1 as initial values of the rolling optimization at the moment k +1 to realize closed-loop control:
u0(k+1)＝ureal(k+1)
wherein:
u0(k +1) is an initial value of the control object at the time of k + 1;
urealand (k +1) is a force output value of the control object obtained by the optimization control at the moment of k + 1.
Optionally, the respectively solving the medium voltage distribution network optimized scheduling model and the low voltage distribution network closed-loop rolling optimized control model includes:
solving the medium-voltage distribution network optimal scheduling model based on a particle swarm algorithm;
and solving the low-voltage distribution network closed-loop rolling optimization control model based on a preset linear power flow calculation method.
Optionally, the preset linear power flow calculation method includes:
obtaining a tidal current node voltage and a branch tidal current:
Figure BDA0002499811700000081
Figure BDA0002499811700000082
wherein:
Mris a branch incidence matrix;
Figure BDA0002499811700000083
is a node voltage vector;
Figure BDA0002499811700000084
injecting a complex power vector into a line;
Figure BDA0002499811700000085
is a line current vector;
linearizing equations (1) and (2):
v≈Bvpp+Bvqq+kv(3)
pb≤Mrp+Mr(Fpl(p)+Fpl(q)) (4)
qb≤Mrq+Mr(Fql(p)+Fql(q)) (5)
wherein:
Bvp、Bvqand kvIs a constant;
Fplthe active power loss of the actual branch is expressed as a linear function of p;
Fqlthe reactive power loss of the actual branch is expressed as a linear function of q;
neglecting the imaginary part of the node voltage phasor to obtain the real part of the line voltage drop:
Figure BDA0002499811700000086
calculating the current vector of the line:
Figure BDA0002499811700000087
substituting equation (7) into equation (6) converts equation (3) to:
Figure BDA0002499811700000088
calculating the line loss:
Figure BDA0002499811700000091
based on a piecewise linear approximation method, carrying out relaxation treatment on the branch power loss of the formula (2) to obtain:
Figure BDA0002499811700000092
Figure BDA0002499811700000093
wherein:
Figure BDA0002499811700000094
calculating the active power loss F of the actual branchplAnd actual branch reactive power loss Fql：
Figure BDA0002499811700000095
Figure BDA0002499811700000096
Figure BDA0002499811700000097
Figure BDA0002499811700000098
Wherein:
i∈N，eito represent
Figure BDA0002499811700000099
The ith element is 1, and the rest elements are 0;
k, containing K partitions of branch current;
fpkrepresenting a predetermined piecewise linear function of reactive loss, fqkRepresenting a preset reactive loss piecewise linear function.
Compared with the prior art, the beneficial effect of this application is:
the application provides a medium-voltage and low-voltage collaborative optimization method for a power distribution network, which comprises the following steps: a power distribution network medium-voltage-low-voltage resource coordination control system is constructed based on the time-space scale of a power distribution network, and comprises the following steps: a day-ahead stage medium-voltage control center, a day-interior stage low-voltage control center and a real-time stage resource control center; acquiring the running cost of a medium-voltage distribution network system based on a medium-voltage control center in a day-ahead stage, and establishing a day-ahead optimization objective function based on the running cost of the medium-voltage distribution network system; setting optimization constraint conditions in the day ahead; obtaining an optimized dispatching model of the medium voltage distribution network based on a day-ahead optimization objective function and day-ahead optimization constraint conditions; establishing a prediction model based on a low-voltage control center in the daytime; sequentially performing rolling optimization and feedback correction on the prediction model to obtain a low-voltage distribution network closed-loop rolling optimization control model; and respectively solving the medium-voltage distribution network optimal scheduling model and the low-voltage distribution network closed-loop rolling optimal control model to realize the coordination control of medium-voltage and low-voltage resources of the distribution network. The method provided by the embodiment of the application can realize bidirectional support between the medium-voltage distribution network and the low-voltage distribution network: flexible resources of the medium-voltage distribution network are managed in a time scale before the day, the outlet power of the low-voltage distribution network is set in an optimized mode, and the flexible resource optimization of the medium-voltage distribution network is beneficial to improving the consumption level of the low-voltage distribution network on distributed photovoltaic; random output of distributed photovoltaic connected in the low-voltage line can be fully considered in a short time in a day, optimal control of flexible resources in the low-voltage line and effective tracking of a day-ahead scheduling set value are achieved by adopting a model prediction control method, and the influence of distributed photovoltaic randomness on a day-ahead optimization result of the medium-voltage distribution network is reduced. Therefore, the embodiment of the application can effectively perform optimization control on the medium-low voltage flexible resources of the power distribution network.
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without any creative effort.
Fig. 1 is a schematic flow chart of a medium-voltage and low-voltage collaborative optimization method for a power distribution network according to an embodiment of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all 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 application.
Referring to fig. 1, a schematic flow chart of a medium-voltage-low-voltage collaborative optimization method for a power distribution network according to an embodiment of the present application is provided. As can be seen with reference to fig. 1, the method comprises the following steps:
s1, constructing a power distribution network medium-voltage and low-voltage resource coordination control system based on the power distribution network space-time scale; the power distribution network medium-voltage-low-voltage resource coordination control system comprises: a day-ahead stage medium-voltage control center, a day-interior stage low-voltage control center and a real-time stage resource control center;
s2, acquiring the running cost of the medium-voltage distribution network system based on the medium-voltage control center in the day-ahead stage, and establishing a day-ahead optimization objective function based on the running cost of the medium-voltage distribution network system; setting optimization constraint conditions in the day ahead; acquiring an optimized dispatching model of the medium voltage distribution network based on the day-ahead optimization objective function and the day-ahead optimization constraint condition;
s3, establishing a prediction model based on the low-pressure control center in the intraday period; sequentially carrying out rolling optimization and feedback correction on the prediction model to obtain a low-voltage distribution network closed-loop rolling optimization control model;
and S4, respectively solving the medium voltage distribution network optimal scheduling model and the low voltage distribution network closed loop rolling optimal control model, and realizing coordination control of medium voltage-low voltage resources of the distribution network.
Each step is described in detail below.
Step S1: constructing a power distribution network medium-voltage-low-voltage resource coordination control system based on the power distribution network space-time scale; the power distribution network medium-voltage-low-voltage resource coordination control system comprises: a day-ahead stage medium-voltage control center, a day-interior stage low-voltage control center and a real-time stage resource control center.
Specifically, the power distribution network medium-voltage and low-voltage resource coordination control system provided by the embodiment of the application comprises a three-level control center. The method comprises the following steps of (1) dividing according to the space-time scale of the power distribution network: a day-ahead stage medium-voltage control center, a day-interior stage low-voltage control center and a real-time stage resource control center.
Wherein, the day-ahead stage medium-pressure control center is: and the medium voltage distribution network operation management center is positioned in a 10kV transformer substation. The method is a highest-level control center, and the main task is to optimally schedule medium-voltage adjustable resources in a time scale in the day ahead.
The low-pressure control center in the day period comprises: and the low-voltage distribution network operation management center is positioned in each distribution transformer. The system is a second-level control center and mainly aims to set power according to a low-voltage line given in the day-ahead in time scale, coordinate and control flexible resources in the low-voltage line and issue control instructions to all flexible resource equipment. Specifically, the flexible resources in the embodiment of the present application include: including energy storage, distributed photovoltaics, adjustable load.
The real-time stage resource control center is a third-stage control center and is positioned at each flexible resource device, and the real-time stage resource control center controls each flexible resource in situ in the real-time stage according to an instruction issued by the low-voltage distribution network management center.
Step S2: acquiring the running cost of a medium-voltage distribution network system based on the medium-voltage control center in the day-ahead stage, and establishing a day-ahead optimization objective function based on the running cost of the medium-voltage distribution network system; setting optimization constraint conditions in the day ahead; and acquiring an optimized dispatching model of the medium voltage distribution network based on the day-ahead optimization objective function and the day-ahead optimization constraint condition.
When the embodiment of the application is implemented specifically, the method comprises the following steps:
s201: and establishing a day-ahead optimization objective function.
Specifically, the operating cost of the medium voltage distribution network system includes: purchasing electricity charges from a flexible resource operator; the fee for compensating the power generation amount reduction part of the flexible resource operator according to the contract requirement signed with the flexible resource operator; the electricity purchasing cost to the superior power grid; the distribution company sends out a compensation fee (namely the response cost of the demand side) for the user after the scheduling request is responded by the user. Wherein:
(1) the cost of purchasing electricity from a flexible resource operator is related to the amount of electricity purchased and the price quoted for electricity sold by the generator. The country makes a partial compensation for the distribution company purchasing the distributed generators in order to protect the benefit of the operator.
The cost of purchasing electricity from the resource operator is:
Figure BDA0002499811700000111
αj＝cj·Δt
wherein:
Δ t represents a preset time interval; in the embodiment of the present application, the set time T is composed of R time intervals Δ T, that is, T is equal to R · Δ T, where T is equal to 24h, and Δ T is equal to 1 h;
g represents a schedulable set of power generation units; (in the present embodiment, a power generation unit such as a micro gas turbine and a fuel cell is included)
Figure BDA0002499811700000112
Representing the active output of the jth schedulable power generation unit during the r period;
cjrepresenting the price to purchase 1kWh of electrical energy from the jth dispatchable power generation unit after the national subsidy.
(2) Under the background of an active power distribution network, according to a contract which is signed in advance by a flexible resource operator and a power distribution company, a power generator needs to reduce the reported power generation amount to a certain extent according to the scheduling requirement of the power distribution company and can obtain corresponding compensation.
The resource operator reduces the compensation cost as follows:
Figure BDA0002499811700000121
β＝cDER,j·Δt
wherein:
Figure BDA0002499811700000122
the active output reported by the jth schedulable power generation unit in the r time period;
Figure BDA0002499811700000123
the actual active output of the jth schedulable power generation unit in the r time period;
Figure BDA0002499811700000124
active power output reduced for the jth schedulable power generation unit in the r time period;
cDER,jto contractually cut the price that a 1kWh power generation distribution company compensates for the power generator.
(3) The electricity purchasing cost from the superior power grid is as follows:
Figure BDA0002499811700000125
ψ＝cgrid·Δt
wherein:
Figure BDA0002499811700000126
representing the active power which needs to be purchased from a superior power grid in the period r;
cgridrepresenting the price at which the distribution company purchases 1kWh of electrical energy from the upper level grid.
(4) The users such as interruptible loads, electric vehicle charging stations and the like can send interruption request signals to the users in the peak period of the power grid according to the contract agreement of the supply and demand parties in advance, partial power supply is interrupted after the users respond, and corresponding interruption compensation can be obtained.
The demand side response cost is:
Figure BDA0002499811700000127
λ＝cbid·Δt
μ＝(csell,r-cgrid)·Δt
wherein:
c represents a user set participating in demand side response;
Figure BDA0002499811700000131
representing the active power shed by the vth user in the r period;
cbidthe price compensated by the power distribution company is obtained by cutting 1kWh of electric energy after the user specified in the contract responds to the interrupt request;
csell,rindicating the time-of-use price sold to the user by the distribution company.
(λ + μ) may reflect the profit variation of the distribution company.
The embodiment of the application takes the minimized cost as a target, and establishes a day-ahead optimization objective function:
Figure BDA0002499811700000132
wherein:
CDERrepresents the cost of purchasing electricity from the resource operator;
CpayDERindicating that the resource operator cuts back the compensation cost;
Cgridrepresents the cost of purchasing electricity from an upper-level power grid;
CDRrepresenting the demand side response cost.
S202: and setting optimization constraint conditions in the day ahead.
Constraints include power balance constraints, power flow constraints, and operational constraints for the DERs. Wherein the power flow constraint comprises: a power balance constraint; the operational constraints for each of the DERs include: the system comprises a dispatchable power generation unit operation constraint, an electric vehicle charging station operation constraint, an interruptible load reduction load power constraint, an exchange power constraint with a superior power grid and an adjustable low-voltage line power constraint.
Specifically, the day-ahead optimization constraint conditions are respectively:
electric energy balance constraint:
Figure BDA0002499811700000133
wherein:
NP is the number of non-dispatchable power generating units;
s is the number of electric vehicle charging stations;
Figure BDA0002499811700000141
the active power output of the h electric vehicle charging station in the r time period;
Figure BDA0002499811700000142
the active output of the u-th unscheduled power generation unit in the r-th time period;
Figure BDA0002499811700000143
is the total load of the r-th period;
Figure BDA0002499811700000144
is the network loss of the r-th period.
And power balance constraint:
Figure BDA0002499811700000145
Figure BDA0002499811700000146
wherein:
Piand QiRespectively injecting active power and reactive power into the node i;
Gij、Bij、ijsequentially setting the conductance, susceptance and voltage phase angle difference between the nodes i and j;
n is the total number of system nodes; u shapeiIs the voltage amplitude of node i; u shapejIs the voltage magnitude at node j.
Schedulable power generation unit operation constraints:
Figure BDA0002499811700000147
wherein:
Figure BDA0002499811700000148
representing the minimum active power allowed by the jth schedulable power generation unit during the r period;
Figure BDA0002499811700000149
representing the maximum active power allowed by the jth schedulable power generation unit during the r period;
RN,jthe limit value of the active power is increased or decreased in two adjacent time periods for the jth schedulable power generation unit.
Operation restraint of an electric vehicle charging station:
Figure BDA00024998117000001410
Figure BDA00024998117000001411
Figure BDA0002499811700000151
wherein:
Figure BDA0002499811700000152
charging/discharging active power for the h electric vehicle centralized charging station;
PEV,hminand PEV,hmaxRespectively the minimum and maximum charging/discharging active power of the h electric vehicle centralized charging station;
Figure BDA0002499811700000153
the residual capacity of all batteries in the r time period is set for the h concentrated charging station;
ηC、ηFthe charging efficiency and the discharging efficiency of the charger are respectively;
Figure BDA0002499811700000154
the charging requirement of the centralized charging station of the electric automobile in the r time period;
SEV,hmaxthe total electric quantity of all batteries of the h electric vehicle centralized charging station;
SOChmax、SOChminthe maximum and minimum charge states of the h electric vehicle concentrated charging station.
Interruptible load shedding load power constraint:
Figure BDA0002499811700000155
wherein:
Figure BDA0002499811700000156
allowing a load shedding minimum active power for the vth user in the vth period;
Figure BDA0002499811700000157
and allowing the maximum active power of the load to be reduced for the vth user in the vth period.
And (3) power constraint exchange with a superior power grid:
Figure BDA0002499811700000158
wherein:
Figure BDA0002499811700000159
the minimum active value is the minimum active value of power exchange between the power distribution network and a superior power grid in the r-th time period;
Figure BDA00024998117000001510
the maximum active value of the power exchange between the power distribution network and the superior power grid in the r-th time period.
Adjustable low-voltage line power constraint:
Figure BDA0002499811700000161
wherein:
Figure BDA0002499811700000162
the minimum active power of the power can be adjusted for the l low-voltage line in the r period;
Figure BDA0002499811700000163
the maximum active power of the power can be adjusted for the l low voltage line during the r period.
S203: and constructing an optimized dispatching model of the medium-voltage distribution network.
Specifically, a model is established according to the day-ahead optimization objective function and the day-ahead optimization constraint condition.
In the embodiment of the application, the day-ahead optimization scheduling adopts an open-loop optimization mode, the optimization solution of each flexible resource of the medium-voltage distribution network in the future 24 hours is obtained through optimization in the initial stage, and the power optimization set value of the adjustable low-voltage line is issued to the low-voltage line.
Step S3: establishing a prediction model based on the low-voltage control center in the day period; and sequentially carrying out rolling optimization and feedback correction on the prediction model to obtain the low-voltage distribution network closed-loop rolling optimization control model.
When a high-permeability distributed photovoltaic is connected to a low-voltage line, due to the fact that the photovoltaic has a high prediction error in the day-ahead scale, if the low-voltage line carries out optimization scheduling on flexible resources in the day-ahead and the result is directly applied to an actual system, the voltage and power flow out-of-limit risks can be brought to the system, and the economical efficiency of the system is reduced. Therefore, the embodiment of the application adopts the rolling optimization model based on the MPC in the day phase aiming at the low-voltage adjustable line.
When the embodiment of the application is implemented specifically, the method comprises the following steps:
s301: and establishing a prediction model.
The MPC takes the short-time rolling predicted values of the photovoltaic and the load as input variables, takes the actual control values of the stored energy, the distributed photovoltaic and the adjustable load in the low-voltage distribution line as initial values at the moment k, takes the power increment of the stored energy, the distributed photovoltaic and the adjustable load in the future finite time domain as control variables, and solves the control variables through rolling optimization so as to predict the power increment of the stored energy, the distributed photovoltaic and the adjustable load in the future finite time domain.
The prediction model of the embodiment of the application is as follows:
Figure BDA0002499811700000164
wherein:
u0(k) energy storage, distributed photovoltaic and adjustable load power optimization control values at the moment k;
delta u (k + t | k) is the increment of energy storage, distributed photovoltaic and adjustable load power in the future [ k + (t-1) at the moment k and k + t ] time period;
and u (k + i | k) predicts energy storage, distributed photovoltaic and adjustable load optimized power at the future k + i moment for the k moment.
S302: and (4) optimizing rolling.
In the in-day stage, by taking the day ahead as a reference value and 15min as a time interval, and considering a comprehensive target with the minimum network loss, the minimum distributed photovoltaic reduction and the minimum correction of a control object compared with the day ahead optimization as an objective function, the power increment of the energy storage, the distributed photovoltaic and the adjustable load in the future 4 hours is obtained in a rolling mode.
S3021: and establishing a rolling optimization objective function. The above rolling optimization objective function is:
Figure BDA0002499811700000171
wherein:
omega is a day rolling optimization control object set; mu.spA cost factor that is a loss of the network;
Figure BDA0002499811700000172
the network loss value at the moment k + i; n is a radical ofRThe number of renewable energy sources;
μuoptimizing the adjusted cost coefficient for the control object;
Figure BDA0002499811700000173
a predicted force value of the nth renewable energy source at the k + i moment;
Figure BDA0002499811700000174
the actual output value of the nth renewable energy source at the k + i moment is obtained;
Figure BDA0002499811700000175
is the jthControlling the scheduling value of the object at the k + i moment in the day-ahead optimization;
u0,j(k + i-1) is the actual optimized control value of the jth control object at the k + i-1 moment in the optimization before the day;
Δuj(k + i) is a control variable and represents the control value increment of the jth control object at the k + i moment in the intraday optimization;
Δ t 'represents a preset time interval, and in the embodiment of the present application, Δ t' is set to 15 min.
S3022: and setting rolling optimization constraint conditions. The rolling optimization constraints include: voltage power flow constraint under linear power flow, distributed photovoltaic output constraint, energy storage operation constraint and adjustable load power regulation constraint.
Specifically, the voltage-power flow constraint under linear flow is as follows:
v＝Bvpp+Bvqq+kv
pb≤Mrp+Mr(Fpl(p)+Fpl(q))
qb≤Mrq+Mr(Fql(p)+Fql(q))
wherein:
Bvp、Bvqand kvIs a constant;
Fplthe active power loss of the actual branch is expressed as a linear function of p;
Fqland the reactive power loss of the actual branch is expressed as a linear function of q.
The distributed photovoltaic output constraints are:
Figure BDA0002499811700000181
wherein:
Figure BDA0002499811700000182
the minimum active power of the s distributed photovoltaic output in the k + i time period;
Figure BDA0002499811700000183
the maximum active power of the s distributed photovoltaic output in the k + i time period;
Figure BDA0002499811700000184
is the active power of the s distributed photovoltaic contribution during the k + i time period.
The energy storage operation constraints are as follows:
Figure BDA0002499811700000185
Figure BDA0002499811700000186
Figure BDA0002499811700000187
wherein:
Figure BDA0002499811700000188
and
Figure BDA0002499811700000189
respectively the minimum and maximum charging/discharging active power of the c-th stored energy in the k + i time period;
Figure BDA00024998117000001810
the active power of the c-th stored energy in the k + i-th time period.
Figure BDA00024998117000001811
The residual capacity of the c-th stored energy in the k + i-th time period;
λC、λFrespectively charging and discharging efficiency of energy storage;
SES,c.maxthe total electric quantity of the c-th energy storage battery;
SOCES,c.max、SOCES,c.minthe c-th maximum and minimum state of charge of the stored energy.
The adjustable load power adjustment constraints are:
Figure BDA0002499811700000191
wherein:
Figure BDA0002499811700000192
the minimum active power of the power can be adjusted for the l low-voltage line in the k + i period;
Figure BDA0002499811700000193
the minimum active power of the power can be adjusted for the l low-voltage line in the k + i period;
Figure BDA0002499811700000194
the active power of the power can be adjusted for the l low voltage line in the k + i period.
S3023: and constructing a rolling optimization model based on the rolling optimization objective function and the rolling optimization constraint condition.
Obtaining energy storage, distributed photovoltaic and adjustable load power variation column vectors delta u at the moment k + i according to the rolling optimization modelT(k + i | k) to obtain TinA set of optimized period control variable column vector sequences:
{ΔuT(k+1|k),ΔuT(k+2|k),…,ΔuT(k+Tin|k)}
executing a first control variable column vector in the control variable sequence set to obtain the optimized power of the energy storage, the distributed photovoltaic and the adjustable load of the low-voltage distribution network at the moment of k + 1:
u(k+1|k)＝u0(k)+ΔuT(k+1|k)。
s303: and (5) feedback correction.
And at the moment of k +1, updating the actual load and illumination measurement values of the system, and predicting the load and photovoltaic short-time rolling at the moment of k + 1. And taking the energy storage, distributed photovoltaic and adjustable load optimization control values at the moment k +1 as initial values of the rolling optimization at the moment k +1 to realize closed-loop control:
u0(k+1)＝ureal(k+1)
wherein:
u0(k +1) is an initial value of the control object at the time of k + 1;
urealand (k +1) is a force output value of the control object obtained by the optimization control at the moment of k + 1.
Step S4: and respectively solving the medium-voltage distribution network optimized dispatching model and the low-voltage distribution network closed-loop rolling optimized control model to realize the coordination control of medium-voltage and low-voltage resources of the distribution network.
When the embodiment of the application is implemented specifically, the method comprises the following steps:
s401: and solving the medium-voltage distribution network optimal scheduling model based on a particle swarm algorithm.
The particle swarm algorithm is a self-adaptive evolutionary computing technology based on population search, has the advantages of simple concept, convenient realization and less parameter setting, and is suitable for solving the problems of continuous optimization and multipoint search. The optimized variable dimension in the optimized dispatching model of the medium-voltage distribution network is high and continuous in value, so that the particle swarm algorithm is adopted in the embodiment of the application.
S402: and solving the low-voltage distribution network closed-loop rolling optimization control model based on a preset linear power flow calculation method.
Specifically, the preset linear power flow calculation method in the embodiment of the present application includes:
obtaining a tidal current node voltage and a branch tidal current:
Figure BDA0002499811700000201
Figure BDA0002499811700000202
wherein:
Mris a branch incidence matrix;
Figure BDA0002499811700000203
is a node voltage vector;
Figure BDA0002499811700000204
injecting a complex power vector into a line;
Figure BDA0002499811700000205
is the line current vector.
Linearizing equations (1) and (2):
v≈Bvpp+Bvqq+kv(3)
pb≤Mrp+Mr(Fpl(p)+Fpl(q)) (4)
qb≤Mrq+Mr(Fql(p)+Fql(q)) (5)
wherein:
Bvp、Bvqand kvIs a constant;
Fplthe active power loss of the actual branch is expressed as a linear function of p;
Fqland the reactive power loss of the actual branch is expressed as a linear function of q.
In the distribution line, the influence of the phase angle of the node voltage on the line power flow is small, so that the imaginary part of the node voltage phasor is ignored in the embodiment of the application, and the real part of the line voltage drop is obtained:
Figure BDA0002499811700000211
calculating the current vector of the line:
Figure BDA0002499811700000212
by substituting equation (7) into equation (6), equation (3) can be transformed into:
Figure BDA0002499811700000213
in equation (8), the voltage amplitude of the line is linear with the line active and reactive power expressions.
Calculating the line loss:
Figure BDA0002499811700000214
for the branch power flow formula of the formula (2), a piecewise linear approximation method is adopted to perform relaxation processing on the branch power loss to realize linear expression, and then the formula (7) is substituted into the formula (9) to obtain:
Figure BDA0002499811700000215
Figure BDA0002499811700000216
wherein:
Figure BDA0002499811700000217
equations (10) and (11) are quadratic functions of p and q, respectively, relaxed as piecewise linear functions.
Calculating the active power loss F of the actual branchplAnd actual branch reactive power loss Fql：
Figure BDA0002499811700000218
Figure BDA0002499811700000219
Figure BDA00024998117000002110
Figure BDA00024998117000002111
Wherein:
i∈N，eito represent
Figure BDA00024998117000002112
The ith element is 1, and the rest elements are 0;
Figure BDA00024998117000002113
is a real number set;
k, containing K partitions of branch current;
fpkrepresenting a predetermined piecewise linear function of reactive loss, fqkRepresenting a preset reactive loss piecewise linear function.
In the examples of the present application, fpkAnd fqkThe setting method comprises the following steps:
set of settings k0K ∪ {0}, where K: { 1.., K } contains K partitions of the branch current,
Figure BDA0002499811700000221
Figure BDA0002499811700000222
representing a positive real number set. Calculating the current of the segmented branch circuit:
Figure BDA0002499811700000223
wherein:
Ibmaxis the maximum branch current amplitude.
The expression of the active loss piecewise linear function is defined as follows:
fpk(x)＝Bpkx+dpk
wherein:
Bpkand dpkIs the intermediate coefficient.
Figure BDA0002499811700000224
dpk＝-Rdb1NIb(k-1)Ibkk∈κ
The expression for defining the piecewise linear function of reactive loss is:
fqk(x)＝Bqkx+dqk
wherein:
Bqkand dqkIs the intermediate coefficient.
The embodiment realizes the linear processing of the branch power flow.
It should be noted that, in one embodiment of the present application:
in the day-ahead stage, flexible resources such as distributed photovoltaic, distributed energy storage and adjustable load accessed to the low-voltage distribution network generate flexible resource model parameters according to the prediction result of the next day power demand, renewable energy sources perform output prediction and report the output prediction to the control center of the low-voltage distribution network layer. And the low-voltage power distribution network layer control center generates a set flexible resource model after summing the flexible resource model parameters reported by the resources, and reports the corresponding parameters to the power distribution network layer scheduling center.
Although the errors of the prediction of the demand for electricity in the day ahead and the prediction of the output of renewable energy cannot be avoided due to the influence of various random factors, the prediction of the parameters of the collective flexible resource model is more accurate than the prediction of the demand of a single flexible resource by summing the characteristics of large-scale flexible resources.
After the control center of the medium-voltage distribution network layer receives the next-day flexible resource model of the low-voltage distribution network layer, flexible resources such as distributed power supplies, electric vehicle charging stations and the like of the medium-voltage distribution network layer are optimized, and power consumption curves of distribution transformers in the next day are calculated and sent to the corresponding control center of the low-voltage distribution network. In the step of calculation, the medium-voltage power distribution network layer control center only depends on the parameters of the low-voltage power distribution network layer next-day set flexible resource model to make decisions, and does not need to obtain detailed parameters of each flexible resource accessed in the low-voltage power distribution network layer.
In a short-time phase in the day, the low-voltage distribution network layer control center and all flexible resources need to be coordinately controlled according to the optimized load curve of the medium-voltage distribution network, so that the optimal economic benefit of the distribution network is realized, and the reduction of distributed photovoltaic output is reduced as much as possible. And the low-voltage distribution network layer adopts a model prediction control method to roll and optimize the output of each flexible resource according to the real-time power consumption demand information reported by each flexible resource, the short-time load and the output prediction of the distributed power supply, and simultaneously issues a control instruction to the flexible resource equipment for local control. The MPC carries out repeated rolling optimization within a limited period of time by introducing feedback correction, can effectively solve the influence of renewable energy source prediction errors on the operation of the power distribution network, and is suitable for the rolling optimization stage within the day.
In summary, compared with the prior art, the method has the following beneficial effects:
in the embodiment of the application, a power distribution network medium-voltage and low-voltage resource coordination control system is constructed by considering multiple space-time scales. On a time scale before the day, establishing an optimized dispatching model of the medium voltage distribution network by taking the minimum running cost of the medium voltage distribution network as an optimized target, and providing the set power before the day for the low voltage adjustable line; in the intraday rolling optimization stage, a low-voltage distribution network closed-loop rolling optimization control model is established based on model prediction control, flexible control over flexible resources in a low-voltage line is achieved, the influence of distributed photovoltaic random output is reduced, and optimal operation of the distribution network can be effectively guaranteed.
The embodiment of the application provides a linear power flow solving algorithm based on improved branch loss relaxation on the low-voltage distribution network level, and compared with the existing power flow calculating methods such as Newton Raphson and forward-backward substitution, the linear power flow solving algorithm has small difference on the calculation result, and the accuracy of solving calculation is guaranteed. Meanwhile, in the aspect of calculation speed, compared with an algorithm based on cone optimization, the method is faster, and the solving efficiency of low-voltage distribution network coordination control is effectively improved.
Since the above embodiments are all described by referring to and combining with other embodiments, the same portions are provided between different embodiments, and the same and similar portions between the various embodiments in this specification may be referred to each other. And will not be described in detail herein.
It is noted that, in this specification, relational terms such as "first" and "second," and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such circuit structure, article, or apparatus. The term "comprising" a defined element does not, without further limitation, exclude the presence of other like elements in a circuit structure, article, or device that comprises the element.
Other embodiments of the present application 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 application 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 application being indicated by the following claims. The above-described embodiments of the present application do not limit the scope of the present application.

Claims (9)

1. A medium-voltage and low-voltage collaborative optimization method for a power distribution network is characterized by comprising the following steps:
constructing a power distribution network medium-voltage-low-voltage resource coordination control system based on the power distribution network space-time scale; the power distribution network medium-voltage and low-voltage resource coordination control system comprises: a day-ahead stage medium-voltage control center, a day-interior stage low-voltage control center and a real-time stage resource control center;
acquiring the running cost of a medium-voltage distribution network system based on the medium-voltage control center in the day-ahead stage, and establishing a day-ahead optimization objective function based on the running cost of the medium-voltage distribution network system; setting optimization constraint conditions in the day ahead; obtaining an optimized dispatching model of the medium voltage distribution network based on the day-ahead optimization objective function and the day-ahead optimization constraint condition;
establishing a prediction model based on the low-voltage control center in the intraday period; sequentially carrying out rolling optimization and feedback correction on the prediction model to obtain a low-voltage distribution network closed-loop rolling optimization control model;
and respectively solving the medium-voltage distribution network optimized dispatching model and the low-voltage distribution network closed-loop rolling optimized control model to realize the coordination control of medium-voltage and low-voltage resources of the distribution network.
2. The collaborative optimization method according to claim 1, wherein the medium voltage distribution network system operating costs include:
cost of electricity purchase from resource operators:
Figure FDA0002499811690000011
αj＝cj·Δt
wherein:
Δ t represents a preset time interval;
g represents a schedulable set of power generation units;
Figure FDA0002499811690000012
representing the active output of the jth schedulable power generation unit during the r period;
cjrepresenting the price of purchasing 1kWh of electric energy from the jth schedulable power generation unit after the national subsidy;
the resource operator cuts the compensation cost:
Figure FDA0002499811690000013
β＝cDER,j·Δt
wherein:
Figure FDA0002499811690000014
the active output reported by the jth schedulable power generation unit in the r time period;
Figure FDA0002499811690000015
the actual active output of the jth schedulable power generation unit in the r time period;
Figure FDA0002499811690000016
active power output reduced for the jth schedulable power generation unit in the r time period;
cDER,jthe price compensated to the power generator by the power distribution company for reducing the 1kWh generated energy according to the contract;
the electricity purchasing cost from the upper-level power grid is as follows:
Figure FDA0002499811690000021
ψ＝cgrid·Δt
wherein:
Figure FDA0002499811690000022
representing the active power which needs to be purchased from a superior power grid in the period r;
cgridrepresents the price at which a distribution company purchases 1kWh of electrical energy from an upper grid;
demand side response cost:
Figure FDA0002499811690000023
λ＝cbid·Δt
μ＝(csell,r-cgrid)·Δt
wherein:
c represents a user set participating in demand side response;
Figure FDA0002499811690000024
representing the active power shed by the vth user in the r period;
cbidthe price compensated by the power distribution company is obtained by cutting 1kWh of electric energy after the user specified in the contract responds to the interrupt request;
csell,rindicating the time-of-use price sold to the user by the distribution company.
3. The collaborative optimization method according to claim 2, wherein the method for establishing the day-ahead optimization objective function comprises:
Figure FDA0002499811690000025
wherein:
CDERrepresents the cost of purchasing electricity from the resource operator;
CpayDERindicating that the resource operator cuts back the compensation cost;
Cgridrepresents the cost of purchasing electricity from an upper-level power grid;
CDRrepresenting the demand side response cost.
4. The collaborative optimization method according to claim 3, wherein the day-ahead optimization constraints include:
electric energy balance constraint:
Figure FDA0002499811690000026
wherein:
NP is the number of non-dispatchable power generating units;
s is the number of electric vehicle charging stations;
Figure FDA0002499811690000027
the active power output of the h electric vehicle charging station in the r time period;
Figure FDA0002499811690000031
the active output of the u-th unscheduled power generation unit in the r-th time period;
Figure FDA0002499811690000032
is the total load of the r-th period;
Figure FDA0002499811690000033
network loss for the r-th period;
and power balance constraint:
Figure FDA0002499811690000034
Figure FDA0002499811690000035
wherein:
Piand QiRespectively injecting active power and reactive power into the node i;
Gij、Bij、ijsequentially setting the conductance, susceptance and voltage phase angle difference between the nodes i and j;
n is the total number of system nodes; u shapeiIs the voltage amplitude of node i; u shapejIs the voltage amplitude of node j;
schedulable power generation unit operation constraints:
Figure FDA0002499811690000036
wherein:
Figure FDA0002499811690000037
representing the minimum active power allowed by the jth schedulable power generation unit during the r period;
Figure FDA0002499811690000038
representing the maximum active power allowed by the jth schedulable power generation unit during the r period;
RN,jthe limit value of active power is increased or reduced in two adjacent time periods for the jth schedulable power generation unit;
operation restraint of an electric vehicle charging station:
Figure FDA0002499811690000039
Figure FDA00024998116900000310
Figure FDA00024998116900000311
wherein:
Figure FDA00024998116900000312
charging/discharging active power for the h electric vehicle centralized charging station;
PEV,hminand PEV,hmaxRespectively the minimum and maximum charging/discharging active power of the h electric vehicle centralized charging station;
Figure FDA00024998116900000313
for the h concentrated charging station in the r periodThe remaining capacity of the battery;
ηC、ηFthe charging efficiency and the discharging efficiency of the charger are respectively;
Figure FDA0002499811690000041
the charging requirement of the centralized charging station of the electric automobile in the r time period;
SEV,hmaxthe total electric quantity of all batteries of the h electric vehicle centralized charging station;
SOChmax、SOChminmaximum and minimum charge states for the h electric vehicle centralized charging station;
interruptible load shedding load power constraint:
Figure FDA0002499811690000042
wherein:
Figure FDA0002499811690000043
allowing a load shedding minimum active power for the vth user in the vth period;
Figure FDA0002499811690000044
allowing the maximum active power of the load to be reduced for the vth user in the vth period;
and (3) power constraint exchange with a superior power grid:
Figure FDA0002499811690000045
wherein:
Figure FDA0002499811690000046
the minimum active value is the minimum active value of power exchange between the power distribution network and a superior power grid in the r-th time period;
Figure FDA0002499811690000047
the maximum active value of power exchange between the power distribution network and a superior power grid in the r-th time period;
adjustable low-voltage line power constraint:
Figure FDA0002499811690000048
wherein:
Figure FDA0002499811690000049
the minimum active power of the power can be adjusted for the l low-voltage line in the r period;
Figure FDA00024998116900000410
the maximum active power of the power can be adjusted for the l low voltage line during the r period.
5. The collaborative optimization method according to claim 1, wherein the building of a predictive model based on the in-day low pressure control center includes:
the prediction model was constructed as follows:
Figure FDA00024998116900000411
wherein:
u0(k) energy storage, distributed photovoltaic and adjustable load power optimization control values at the moment k;
delta u (k + t | k) is the increment of energy storage, distributed photovoltaic and adjustable load power in the future [ k + (t-1) at the moment k and k + t ] time period;
and u (k + i | k) predicts energy storage, distributed photovoltaic and adjustable load optimized power at the future k + i moment for the k moment.
6. The collaborative optimization method according to claim 5, wherein the rolling optimization of the prediction model includes:
establishing a rolling optimization objective function, wherein the rolling optimization objective function is as follows:
Figure FDA0002499811690000051
wherein:
omega is a day rolling optimization control object set; mu.spA cost factor that is a loss of the network;
Figure FDA0002499811690000052
the network loss value at the moment k + i; n is a radical ofRThe number of renewable energy sources;
μuoptimizing the adjusted cost coefficient for the control object;
Figure FDA0002499811690000053
a predicted force value of the nth renewable energy source at the k + i moment;
Figure FDA0002499811690000054
the actual output value of the nth renewable energy source at the k + i moment is obtained;
Figure FDA0002499811690000055
the scheduling value of the jth control object at the k + i moment in the optimization before the day;
u0,j(k + i-1) is the actual optimized control value of the jth control object at the k + i-1 moment in the optimization before the day;
Δuj(k + i) is a control variable and represents the control value increment of the jth control object at the k + i moment in the intraday optimization;
Δ t' represents a preset time interval;
setting a rolling optimization constraint condition, wherein the rolling optimization constraint condition comprises the following steps: voltage power flow constraint, distributed photovoltaic output constraint, energy storage operation constraint and adjustable load power regulation constraint under linear power flow;
constructing a rolling optimization model based on the rolling optimization objective function and the rolling optimization constraint condition, and obtaining an energy storage, distributed photovoltaic and adjustable load power variation column vector delta u at the k + i moment according to the rolling optimization modelT(k + i | k); to obtain TinA set of optimized period control variable column vector sequences:
{ΔuT(k+1|k),ΔuT(k+2|k),…,ΔuT(k+Tin|k)}
executing a first control variable column vector in the control variable sequence set to obtain the optimized power of the energy storage, the distributed photovoltaic and the adjustable load of the low-voltage distribution network at the moment of k + 1:
u(k+1|k)＝u0(k)+ΔuT(k+1|k)。
7. the collaborative optimization method according to claim 6, wherein the feedback correction of the prediction model comprises:
updating the actual load and illumination measurement values of the system at the moment k +1, and predicting the load and photovoltaic short-time rolling at the moment k + 1; and taking the energy storage, distributed photovoltaic and adjustable load optimization control values at the moment k +1 as initial values of the rolling optimization at the moment k +1 to realize closed-loop control:
u0(k+1)＝ureal(k+1)
wherein:
u0(k +1) is an initial value of the control object at the time of k + 1;
urealand (k +1) is a force output value of the control object obtained by the optimization control at the moment of k + 1.
8. The collaborative optimization method according to claim 1, wherein the solving the medium voltage distribution network optimized scheduling model and the low voltage distribution network closed loop rolling optimized control model separately comprises:
solving the medium-voltage distribution network optimal scheduling model based on a particle swarm algorithm;
and solving the low-voltage distribution network closed-loop rolling optimization control model based on a preset linear power flow calculation method.
9. The collaborative optimization method according to claim 8, wherein the preset linear power flow calculation method includes:
obtaining a tidal current node voltage and a branch tidal current:
Figure FDA0002499811690000061
Figure FDA0002499811690000062
wherein:
Mris a branch incidence matrix;
Figure FDA0002499811690000063
is a node voltage vector;
Figure FDA0002499811690000064
injecting a complex power vector into a line;
Figure FDA0002499811690000065
is a line current vector;
linearizing equations (1) and (2):
v≈Bvpp+Bvqq+kv(3)
pb≤Mrp+Mr(Fpl(p)+Fpl(q)) (4)
qb≤Mrq+Mr(Fql(p)+Fql(q)) (5)
wherein:
Bvp、Bvqand kvIs a constant;
Fplrepresenting the actual branch as a linear function of pActive power loss;
Fqlthe reactive power loss of the actual branch is expressed as a linear function of q;
neglecting the imaginary part of the node voltage phasor to obtain the real part of the line voltage drop:
Figure FDA0002499811690000066
calculating the current vector of the line:
Figure FDA0002499811690000071
substituting equation (7) into equation (6) converts equation (3) to:
Figure FDA0002499811690000072
calculating the line loss:
Figure FDA0002499811690000073
based on a piecewise linear approximation method, carrying out relaxation treatment on the branch power loss of the formula (2) to obtain:
Figure FDA0002499811690000074
Figure FDA0002499811690000075
wherein:
Figure FDA0002499811690000076
calculating the active power loss F of the actual branchplAnd actual branch reactive power loss Fql：
Figure FDA0002499811690000077
Figure FDA0002499811690000078
Figure FDA0002499811690000079
Figure FDA00024998116900000710
Wherein:
i∈N，eito represent
Figure FDA00024998116900000711
The ith element is 1, and the rest elements are 0;
k, containing K partitions of branch current;
fpkrepresenting a predetermined piecewise linear function of reactive loss, fqkRepresenting a preset reactive loss piecewise linear function.
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