CN114884063A - Distributed power supply and energy storage voltage control method and device considering interval coordination - Google Patents

Distributed power supply and energy storage voltage control method and device considering interval coordination Download PDF

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CN114884063A
CN114884063A CN202210371705.9A CN202210371705A CN114884063A CN 114884063 A CN114884063 A CN 114884063A CN 202210371705 A CN202210371705 A CN 202210371705A CN 114884063 A CN114884063 A CN 114884063A
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energy storage
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voltage
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徐晶
刘聪
张梁
李娟�
李桂鑫
迟福建
张章
崔荣靖
刘英英
王哲
孙阔
李广敏
刘勍
宋关羽
于川航
冀浩然
李鹏
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
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State Grid Tianjin 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected 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
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • 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
    • 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
    • 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
    • H02J3/38—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
    • H02J3/48—Controlling the sharing of the in-phase component
    • 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
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • 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]
    • 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
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/62—The condition being non-electrical, e.g. temperature
    • H02J2310/64—The condition being economic, e.g. tariff based load management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/30—Reactive power compensation

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Abstract

The invention relates to a distributed power supply and energy storage self-adaptive voltage control method considering interval coordination, which comprises the following steps: determining an active power distribution network, and acquiring parameter information of the active power distribution network; partitioning the active power distribution network, and acquiring node voltage measurement values at t moment in each area of the active power distribution network and interaction information among the areas; establishing virtual nodes in each area, uploading mutual information among the areas to the virtual nodes corresponding to the areas, acquiring voltage difference values of voltage measurement values of the nodes in each area and voltage control target reference values of the nodes in each area and voltage difference values of the voltage values of the virtual nodes and the voltage control target reference values of the virtual nodes, and judging whether any voltage difference value exceeds the limit or not; if so, establishing a distributed power supply and energy storage self-adaptive voltage control model considering interval coordination; if not, updating the control time t to t + delta t; the invention realizes the solution of the voltage optimization control problem of the distributed power supply high permeability access power distribution network.

Description

Distributed power supply and energy storage voltage control method and device considering interval coordination
Technical Field
The invention belongs to the technical field of voltage control, and particularly relates to a distributed power supply and energy storage voltage control method and device considering interval coordination.
Background
Under the background that a distributed power supply is connected to a power distribution network at high permeability, the power distribution network is gradually changed from a passive one-way power supply network to a complex active network with bidirectional power flow. On one hand, the access of the distributed power supply brings many challenges to the operation control of the system, wherein the voltage out-of-limit problem becomes a key limiting factor for improving the photovoltaic permeability; on the other hand, obtaining the voltage and power measurement information of all nodes in each area of the power distribution network can cause overlarge data volume and cause communication burden. Therefore, how to realize the full utilization of controllable resources such as a distributed energy storage system and a distributed power supply through a reasonable interval information interaction and coordination control strategy, improve the system operation level and improve the photovoltaic permeability is a key problem which needs to be solved urgently for constructing an intelligent power distribution network.
However, although the traditional centralized control causes less network loss and better voltage control effect, an accurate physical model is required and real-time control cannot be performed to cope with frequently-changing complex operation scenes of the power distribution network; the intra-area coordination control method of the distributed energy storage system and the distributed power supply cannot consider the mutual influence of control strategies among the areas, and can cause the problems of large network loss, poor voltage control effect and the like. The data driving method does not depend on detailed mathematical model information of a controlled system, and only replaces a general nonlinear system with a dynamic linear time-varying model near the trajectory of the controlled system by utilizing measured data, so that the simulation construction of unknown characteristics of a complex link is realized; the combination of the data driving control and the interval coordination control method can effectively solve the adverse effect of the output strategy conflict of the controllable resources of each area on the voltage of the power distribution network in the control process.
Therefore, a distributed power supply and energy storage self-adaptive voltage control method considering interval coordination is provided, a new thought is provided for the voltage optimization problem of the power distribution network, and the safety of the power distribution side and the user experience are improved.
Disclosure of Invention
The invention aims to solve the technical problem that aiming at the voltage optimization control problem of a power distribution network, under the condition that the voltage of the power distribution network is adversely affected by the independent action of a controllable resource control strategy in each region, an adjusting mode capable of realizing the coordination control of a distributed energy storage system and a distributed energy storage system through interval information interaction is established based on a data driving and interval coordination means, and then a distributed power supply and energy storage self-adaptive voltage control strategy considering interval coordination is established.
The invention relates to a distributed power supply and energy storage adaptive voltage control method considering interval coordination, which is a dynamic adaptive voltage coordination control method for a power distribution network based on boundary information interaction.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
the distributed power supply and energy storage voltage control method considering interval coordination comprises the following steps:
determining an active power distribution network, and acquiring parameter information of the active power distribution network; setting iteration step length delta T and control step length delta T c Prediction step length delta T p Optimizing the duration T and the predicted step number N, and initializing a control parameter k to be 1 and an initialization control time T to be 0;
partitioning the active power distribution network, and acquiring node voltage measurement values at t moment in each area of the active power distribution network and interaction information among the areas;
setting virtual nodes in each area in consideration of the limit of actual information transmission conditions of different intervals, uploading mutual information among the areas to the virtual nodes corresponding to the areas, wherein the mutual information among the areas comprises the voltage of the virtual nodes at the time t and the active transmission power of the virtual node connecting line at the time t; the virtual node voltage at the time t is a node voltage measurement value with the maximum deviation from a node voltage control target reference value in each region at the time t, and the active transmission power of the virtual node tie line at the time t is a boundary tie line active transmission power measurement value in each region at the time t;
acquiring voltage difference values of the voltage measurement values of the nodes in each area and the voltage control target reference values of the nodes in each area and voltage difference values of the voltage values of the virtual nodes and the voltage control target reference values of the virtual nodes, and judging whether any voltage difference value exceeds the limit or not;
if so, establishing a distributed power supply and energy storage adaptive voltage control model considering interval coordination under the constraint condition by taking the minimum control target deviation and the minimum energy storage system charge-discharge cost in each area as a target function;
if not, the control time t is updated to t + Δ t.
In a further aspect of the present invention,
after the step of establishing the distributed power supply and energy storage adaptive voltage control model considering interval coordination, the method further includes:
solving the distributed power supply and the energy storage self-adaptive voltage control model considering interval coordination to obtain a t-moment data-driven energy storage system and a distributed power supply output strategy, and issuing and executing;
and acquiring interaction information between the regions after the energy storage system and the distributed power supply output strategy are issued and executed, and updating the node voltage control target reference value of each region and the boundary power control target reference value of each region in the target function.
Further, in the above-mentioned case,
after the step of obtaining the interaction information between the regions after the energy storage system and the distributed power supply output strategy are issued and executed, and updating the node voltage control target reference value of each region and the boundary power control target reference value of each region in the target function, the method further comprises the following steps:
the update control time t is t + Δ t.
Further, in the above-mentioned case,
after the step of updating the control time t to t + Δ t, the method further includes:
judging T is more than or equal to k delta T c Whether or not the above-mentioned conditions are satisfied,
if yes, let Δ T p ＝ΔT p -ΔT c And updating the control parameter k to k +1, and judging whether the updated control time T is less than the optimization time T.
Further, in the above-mentioned case,
after the step of determining whether the updated control time T is less than the optimized time T, the method further includes:
if yes, partitioning the active power distribution network, acquiring node voltage measurement values at the time t in all areas of the active power distribution network and mutual information among all areas, setting virtual nodes in all areas based on the mutual information among all areas, and acquiring virtual node voltage values at the time t;
if not, the process is ended.
In a further aspect of the present invention,
the distributed power supply and energy storage adaptive voltage control model considering interval coordination is as follows:
Figure BDA0003588836440000041
Figure RE-GDA0003712445770000042
Figure RE-GDA0003712445770000043
wherein, Δ X m,N [t]Representing the energy storage charging and discharging power variation vector of the future N steps from the time t + delta t in the region m,n represents the number of predicted steps and,
Figure BDA0003588836440000044
Figure BDA0003588836440000045
represents T + i Δ T c Charging and discharging power of the distributed energy storage system in the moment area m;
Figure BDA0003588836440000046
indicating the control target reference value at time t + deltat in region m,
Figure BDA0003588836440000047
a control target estimation value representing the region m at time t + deltat,
Figure BDA0003588836440000048
respectively representing the reactive power output of the distributed power supply r in the region m at the time t and the time t-delta t,
Figure BDA0003588836440000049
the reactive power output, lambda, of the distributed energy storage system l in the region m at the time t and the time t-delta t is represented P 、
Figure BDA0003588836440000051
Is a weight coefficient;
Figure BDA0003588836440000052
a control target reference value vector representing N future steps from the time t + Δ t in the region m is expressed as follows:
Figure BDA0003588836440000053
in the formula (I), the compound is shown in the specification,
Figure BDA0003588836440000054
indicating the reference value of the node voltage starting N steps in the future from the time t + delta t in the region mThe amount of the compound (A) is,
Figure BDA0003588836440000055
a vector of voltage reference values representing the virtual node alpha in the region m, starting from the time t + deltat for N future steps,
Figure BDA0003588836440000056
representing a net transmission active power reference value vector of N steps in the future from the moment t + delta t on the boundary beta of the area m, wherein epsilon represents a conversion coefficient;
Figure BDA0003588836440000057
and representing a control target estimation value vector of N future steps starting from the moment t + delta t in the region m, including the voltages of each node and virtual node of the region m and the boundary net transmission active power, wherein the composition expression of the control target estimation value vector is as follows:
Figure BDA0003588836440000058
wherein N represents the number of prediction steps, which is the prediction step length Delta T p And control step length delta T c The ratio of (a) to (b),
Figure BDA0003588836440000059
a vector of node voltage estimate values representing N steps in the future from the time t + Δ t in the region m,
Figure BDA00035888364400000510
a vector of voltage estimation values representing the virtual node alpha in the region m, starting from the time t + deltat for N future steps,
Figure BDA00035888364400000511
the net transmission active power vector of N steps is predicted from the moment t + delta t at the boundary beta of the area m, and epsilon represents a conversion coefficient;
Figure BDA00035888364400000512
the calculation expression is as follows:
Figure BDA00035888364400000513
in the formula, E [ t ]]Representing a unit column vector, Y m,N [t]A vector consisting of control target measurement values in N sets of time t regions m,
Figure BDA00035888364400000514
a pseudo Jacobian estimation matrix, Δ X, representing the region m at time t m,N [t]Representing energy storage charging and discharging power change quantity vectors of N steps in the future from the time t + delta t in the region m;
Figure BDA0003588836440000061
the calculation method of (2) is as follows:
Figure BDA0003588836440000062
in the formula, Y m [t]A control target measurement value indicating the area m at time t,
Figure BDA0003588836440000063
and
Figure BDA0003588836440000064
respectively representing the number of distributed energy storage systems and distributed power supplies in the area m,
Figure BDA0003588836440000065
a pseudo jacobian matrix representing the distributed power sources r in the region m at time t,
Figure BDA0003588836440000066
a pseudo jacobian matrix representing the distributed energy storage system l in the region m at time t,
Figure BDA0003588836440000067
the calculation expression of (c) is as follows:
Figure BDA0003588836440000068
Figure BDA0003588836440000069
in the formula (I), the compound is shown in the specification,
Figure BDA00035888364400000610
a pseudo-Jacobian matrix representing the distributed power supply r in the region m at the time t and the time t-delta t respectively,
Figure BDA00035888364400000611
a pseudo Jacobian matrix, DeltaY, representing the distributed energy storage system l in the region m at the time t and the time t-Deltat m [t]＝Y m [t]-Y m [t-Δt]The difference between the control target measurement values of the area m at the time t and the time t-delta t is expressed,
Figure BDA00035888364400000612
Figure BDA00035888364400000613
representing the difference between the reactive power output of the distributed power source r in the region m at the time t-deltat and the time t-2 deltat,
Figure BDA00035888364400000614
represents the difference between the reactive power output of the distributed energy storage system l in the t-delta t moment and the t-2 delta t moment region m, eta DG 、μ DG 、η ESS And mu ESS Representing the weight coefficients.
Further, in the above-mentioned case,
the expression of the distributed power supply and energy storage self-adaptive voltage control model for solving and considering interval coordination is as follows:
Figure BDA0003588836440000071
Figure BDA0003588836440000072
in the formula (I), the compound is shown in the specification,
Figure BDA0003588836440000073
a reference value indicating a control target of the area m at time t,
Figure BDA0003588836440000074
representing an estimate of the voltage of region m at time t,
Figure BDA0003588836440000075
respectively representing the reactive power output of the distributed power supply r in the region m at the time t and the time t-delta t,
Figure BDA0003588836440000076
the reactive power output of the distributed energy storage system l in the region m at the time t and the time t-delta t is shown,
Figure BDA0003588836440000077
Figure BDA0003588836440000078
the difference between the reactive power output of the distributed energy storage system b in the region m at the time t-delta t and the time t-2 delta t is shown,
Figure BDA0003588836440000079
representing the difference between the reactive power of the distributed power source a in the region m at the time t-deltat and the time t-2 deltat,
Figure BDA00035888364400000710
and
Figure BDA00035888364400000711
respectively representing the number of distributed energy storage systems and distributed power supplies in the area m,
Figure BDA00035888364400000712
a pseudo jacobian matrix representing the distributed power source a in the area m at time t,
Figure BDA00035888364400000713
the pseudo Jacobian matrix, ρ, representing the distributed energy storage system b in the region m at time t DG 、λ DG 、ρ ESS And λ ESS Representing the weight coefficients.
Further, in the above-mentioned case,
the updating of the mutual information among the areas is represented as follows:
Figure BDA00035888364400000714
Figure BDA00035888364400000715
in the formula (I), the compound is shown in the specification,
Figure BDA0003588836440000081
a vector of voltage reference values representing the virtual node alpha in the region i, starting from time t for N future steps,
Figure BDA0003588836440000082
a vector of voltage estimation values representing N steps in the future from time t in region j,
Figure BDA0003588836440000083
represents the vector of net transmitted active power reference values N steps in the future starting from time t on the boundary beta of the region i,
Figure BDA0003588836440000084
and representing the net transmission active power estimated value vector of N steps in the future from the time t on the boundary beta of the region j.
Further, in the above-mentioned case,
including a prediction step size Δ T under the constraint p Upper and lower limit of internal energy storage system state of chargeThe active power output upper and lower limits of the energy storage system, the operation constraint of the energy storage system converter and the operation constraint of the distributed power supply converter.
A computing device, comprising:
one or more processing units;
a storage unit for storing one or more programs,
wherein the one or more programs, when executed by the one or more processing units, cause the one or more processing units to perform the method of any of claims 1-9.
The invention has the advantages and positive effects that:
the invention relates to a distributed power supply and energy storage self-adaptive voltage control method considering interval coordination, which comprehensively considers information interaction among all areas of a power distribution network and coordination control of a distributed energy storage system and a distributed power supply, and solves the problem of voltage optimization control of a distributed power supply with high permeability connected to the power distribution network by establishing a distributed power supply and energy storage self-adaptive voltage control strategy considering interval coordination.
Description of the drawings:
the technical solution of the present invention will be described in further detail with reference to the accompanying drawings and examples, but it should be understood that the drawings are designed for illustrative purposes only and thus do not limit the scope of the present invention. Furthermore, unless otherwise indicated, the drawings are intended to be illustrative of the structural configurations described herein and are not necessarily drawn to scale.
Fig. 1 is a flowchart of a distributed power supply and energy storage voltage control method considering interval coordination according to the present invention;
fig. 2 is a topological diagram of an interval coordination area in embodiment 2;
FIG. 3 is a predicted curve of the distributed power supply output and load variation in example 2;
FIG. 4 is a comparison of the voltage results before and after the control of node 33 for 24 hours in example 2;
fig. 5 shows the active charge-discharge power and the reactive power change of the 24-hour 33-node distributed energy storage system in embodiment 2;
FIG. 6 is the reactive power of the 24-hour area distributed power supply in example 2;
FIG. 7 is a comparison result of the maximum node voltages before and after the 24-hour voltage control in example 2;
fig. 8 is the 24-hour change in state of charge for each distributed energy storage system in example 2.
Wherein, in fig. 3: load represents Load, PV represents photovoltaic, WT represents wind turbine; in fig. 5: P-ES represents the active output of the energy storage system, and Q-ES represents the reactive output of the energy storage system;
Detailed Description
First, it should be noted that the specific structures, features, advantages, etc. of the present invention will be specifically described below by way of example, but all the descriptions are for illustrative purposes only and should not be construed as limiting the present invention in any way. Furthermore, any individual technical features described or implicit in the embodiments mentioned herein may still be continued in any combination or subtraction between these technical features (or their equivalents) to obtain still further embodiments of the invention that may not be mentioned directly herein. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Example 1
The invention relates to a distributed power supply and energy storage adaptive voltage control method considering interval coordination, which comprises the following steps as shown in figure 1:
1) acquiring parameter information of the power distribution network according to the selected active power distribution network, and setting iteration step length delta T and control step length delta T c Prediction step length delta T p Optimizing the duration T and the predicted step number N, initializing a control parameter k to be 1, and initializing a control time T to be 0; wherein, the parameter information of distribution network includes: the method comprises the following steps of (1) active power distribution network partition information, the type, the capacity, the access position and the initial charge state of an energy storage system and the charging and discharging power limit value, and the type, the capacity and the access position of a distributed power supply; each area control target reference value
Figure BDA0003588836440000101
The method comprises the following steps: each region node voltage control target reference value
Figure BDA0003588836440000102
Virtual node voltage control target reference value of each region
Figure BDA0003588836440000103
Power control target reference value for each zone boundary
Figure BDA0003588836440000104
2) Partitioning the active power distribution network, and acquiring node voltage measurement values at t moment in each area of the active power distribution network and interaction information among the areas;
setting virtual nodes in each area in consideration of the limit of actual information transmission conditions of different intervals, uploading mutual information among the areas to the virtual nodes corresponding to the areas, wherein the mutual information among the areas comprises the voltage of the virtual nodes at the time t and the active transmission power of the virtual node connecting line at the time t; the virtual node voltage at the time t is a node voltage measured value with the maximum deviation from a node voltage control target reference value in each region at the time t, and the active transmission power of the virtual node tie line at the time t is a boundary tie line active transmission power measured value in each region at the time t;
wherein, the virtual node is used for: in order to perform interval coordination control on each area of the power distribution network and consider the limitation of actual information transmission conditions of different intervals, a method of setting virtual nodes in each interval is adopted, a node voltage measurement value with the maximum deviation between each area and a node voltage control target reference value and a boundary connecting line active transmission power measurement value are uploaded to the virtual nodes corresponding to each area, and information contained in the virtual nodes of each area is transmitted and interacted, so that coordination voltage control among the areas of the power distribution network is realized under the background that the information transmission conditions are limited;
3) according to the reference value of each area control target in the step 1) and the voltage measurement value of the node in each area and the voltage value of the virtual node at the time t in the step 2), calculating the voltage difference value between the voltage measurement value of each area node and the voltage control target reference value of each area node and the voltage difference value between the voltage value of the virtual node and the voltage control target reference value of the virtual node, judging whether any voltage difference value is out of limit, if so, executing the step 4), otherwise, turning to the step 7);
4) taking the minimum deviation of each control target in each area and the minimum charge-discharge cost of the energy storage system as a target function, and considering the prediction step length delta T p The method comprises the following steps of (1) restraining upper and lower limits of the charge state of an internal energy storage system, upper and lower limits of the active power output of the energy storage system, restraining operation of a current converter of the energy storage system and restraining of a current converter of a distributed power supply, and establishing a distributed power supply and energy storage adaptive voltage control model considering interval coordination;
in this step, the objective function with the minimum node voltage deviation, the minimum charging and discharging cost of the energy storage system, and the minimum boundary net transmission power in each region as the control targets is expressed as follows.
Figure BDA0003588836440000111
Figure RE-GDA0003712445770000112
Figure RE-GDA0003712445770000113
In the formula (1), the reaction mixture is,
Figure BDA0003588836440000114
represents T + i Δ T c And charging and discharging power of the distributed energy storage system in the time area m. In the formulas (2) and (3),
Figure BDA0003588836440000115
indicating the control target reference value at time t + deltat in region m,
Figure BDA0003588836440000116
a control target estimation value representing the region m at time t + deltat,
Figure BDA0003588836440000117
respectively representing the reactive power output of the distributed power supply r in the region m at the time t and the time t-delta t,
Figure BDA0003588836440000121
the reactive power output, lambda, of the distributed energy storage system l in the region m at the time t and the time t-delta t is represented P 、
Figure BDA0003588836440000122
Is a weight coefficient;
Figure BDA0003588836440000123
a control target reference value representing N steps in the future from the time t + Δ t in the region m is expressed as follows:
Figure BDA0003588836440000124
in the formula (I), the compound is shown in the specification,
Figure BDA0003588836440000125
represents the node voltage reference value vector of the N future steps starting from the time t + delta t in the region m,
Figure BDA0003588836440000126
a vector of voltage reference values representing the virtual node alpha in the region m, starting from the time t + deltat for N future steps,
Figure BDA0003588836440000127
representing a vector of net transmission active power reference values of N steps in the future from the moment t + delta t on the boundary beta of the area m, wherein epsilon represents a conversion coefficient;
Figure BDA0003588836440000128
and a control target estimation value vector which represents that the area m is in the area m, and N steps in the future are started from the moment t + delta t, comprises the voltages of each node and virtual node of the area m, and the boundary net transmission active power, and is composed of the following expressions:
Figure BDA0003588836440000129
wherein N represents the number of prediction steps, which is the prediction step length Delta T p And control step length delta T c The ratio of (a) to (b),
Figure BDA00035888364400001210
a node voltage estimated value vector representing N steps in the future from the time t + Δ t in the region m,
Figure BDA00035888364400001211
a vector of voltage estimation values representing the virtual node alpha in the region m, starting from the time t + deltat for N future steps,
Figure BDA00035888364400001212
the net transmission active power vector of N steps is predicted from the moment t + delta t at the boundary beta of the area m, and epsilon represents a conversion coefficient;
Figure BDA00035888364400001213
the calculation expression is as follows:
Figure BDA00035888364400001214
in the formula, E [ t ]]Representing a unit column vector, Y m,N [t]A vector consisting of control target measurement values in N sets of time t regions m,
Figure BDA0003588836440000131
a pseudo Jacobian estimation matrix, Δ X, representing the region m at time t m,N [t]Representing energy storage charging and discharging power change quantity vectors of N steps in the future from the time t + delta t in the region m;
Figure BDA0003588836440000132
the calculation method of (2) is as follows:
Figure BDA0003588836440000133
in the formula, Y m [t]A control target measurement value indicating the area m at time t,
Figure BDA0003588836440000134
and
Figure BDA0003588836440000135
respectively representing the number of distributed energy storage systems and distributed power supplies in the area m,
Figure BDA0003588836440000136
a pseudo jacobian matrix representing the distributed power sources r in the region m at time t,
Figure BDA0003588836440000137
a pseudo jacobian matrix representing the distributed energy storage system l in the region m at time t,
Figure BDA0003588836440000138
the calculation expression of (a) is as follows:
Figure BDA0003588836440000139
Figure BDA00035888364400001310
in the formula (I), the compound is shown in the specification,
Figure BDA00035888364400001311
a pseudo jacobian matrix representing the distributed power sources r in the region m at time t,
Figure BDA00035888364400001312
a pseudo Jacobian matrix, Δ Y, representing the distributed energy storage system l in the region m at time t m [t]＝ Y m [t]-Y m [t-Δt]The difference between the control target measurement values of the region m at time t and time t- Δ t is expressed,
Figure BDA00035888364400001313
the difference between the reactive power of the distributed power r in the region m at the time t-delta t and the time t-2 delta t is shown,
Figure BDA00035888364400001314
Figure BDA0003588836440000141
represents the difference between the reactive power of the distributed energy storage system l in the t-delta t moment and the t-2 delta t moment region m, eta DG 、μ DG 、η ESS And mu ESS Representing a weight coefficient;
Figure BDA0003588836440000142
the calculation method of (2) is as follows:
Figure BDA0003588836440000143
in the formula (I), the compound is shown in the specification,
Figure BDA0003588836440000144
the iterative calculation expression is as follows:
Figure BDA0003588836440000145
in the formula (I), the compound is shown in the specification,
Figure BDA0003588836440000146
respectively represents time t and t-The pseudo Jacobian matrix of the first distributed energy storage of the region mth at the time of Deltat, DeltaU [ t [ ]]＝U[t]-U[t-Δt]The difference between the voltage measurement of each node at time t and time t- Δ t is shown,
Figure BDA0003588836440000147
the difference between the reactive power output of the first distributed energy storage in the t-delta t moment and the t-2 delta t moment is expressed, eta ESS And mu ESS Representing the weight coefficients.
In the formula (10)
Figure BDA0003588836440000148
Represents T + w Δ T c The pseudo jacobian prediction matrix of the ith distributed energy storage in the time region m, w is 1, …, N, and the calculation method is as follows:
Figure BDA0003588836440000149
in the formula (I), the compound is shown in the specification,
Figure BDA00035888364400001410
represents T + w Δ T c The time region mth pseudo jacobian prediction matrix (w is 1, …, N), θ q [t]Denotes a prediction coefficient at time T, where q is 1, …, ξ, m denotes the number of days of the required historical measurement data when constructing the estimation sequence, T d The time interval at which the historical data is represented,
Figure BDA0003588836440000151
and
Figure BDA0003588836440000152
respectively representing T + w delta T calculated by using historical measurement data of the active power distribution network at the same moment in Xi day c -T d Time T + w Δ T c -2T d Time sum T + w Δ T c -ξT d The ith pseudo Jacobian matrix of distributed energy storage in the time zone m. Theta [ t ]]＝(θ 1 [t],…,θ ξ [t]) T ,θ[t]The iterative solution formula is as follows:
Figure BDA0003588836440000153
in the formula (I), the compound is shown in the specification,
Figure BDA0003588836440000154
δ represents a weight coefficient.
The energy storage system state of charge constraints in this step are expressed as follows:
Figure BDA0003588836440000155
Figure BDA0003588836440000156
in the formula (14), SOC 0 Initial state of charge, SOC, representing distributed energy storage max And SOC min Respectively represent the upper and lower limits of the state of charge,
Figure BDA0003588836440000157
representing the current transformer capacity of the first distributed energy storage in the area m, E t]The unit column vector is represented by a unit column vector,
Figure BDA0003588836440000158
represents the functional capacity of the ith distributed storage of the region m at time t,
Figure BDA0003588836440000159
ΔT c for controlling the step size, N represents the number of predicted steps,
Figure BDA00035888364400001510
and
Figure BDA00035888364400001511
Figure BDA00035888364400001512
respectively represent T- Δ T + Δ T c 、t-Δt+2ΔT c And T- Δ T + N Δ T c And (3) the variation of the l distributed energy storage charging and discharging power in the time area m. In the formula (15), the reaction mixture is,
Figure BDA00035888364400001513
represents the initial value of the state of charge of the ith distributed stored energy in region m,
Figure BDA0003588836440000161
and the state of charge value of the ith distributed energy storage in the area m after one period of work is shown.
The distributed energy storage active power output constraint is expressed as follows:
Figure BDA0003588836440000162
in the formula (I), the compound is shown in the specification,
Figure BDA0003588836440000163
respectively representing the active output upper and lower limits of the first distributed energy storage in the region m, Et]The unit column vector is represented by a unit column vector,
Figure BDA0003588836440000164
and the active output of the ith distributed energy storage in the area m at the moment t is shown.
The expression formulas of the reactive power output constraint of the energy storage system, the capacity constraint of the converter of the energy storage system, the reactive power output constraint of the distributed power supply and the capacity constraint of the converter of the distributed power supply in the step are as follows.
The reactive power output constraint of the energy storage system and the capacity constraint of a current converter of the energy storage system are expressed as follows:
Figure BDA0003588836440000165
in the formula (I), the compound is shown in the specification,
Figure BDA0003588836440000166
respectively represent in the region mUpper and lower limit of reactive power output, Et, of the first distributed energy storage]The unit column vector is represented by a unit column vector,
Figure BDA0003588836440000167
the reactive power output of the l distributed energy storage in the area m at the time t is shown,
Figure BDA0003588836440000168
representing the i-th distributed energy storage converter capacity in region m,
Figure BDA0003588836440000169
and the active output of the ith distributed energy storage at the time t in the area m is shown.
The distributed power reactive power output constraints and the distributed power converter capacity constraints are expressed as:
Figure BDA00035888364400001610
in the formula (I), the compound is shown in the specification,
Figure BDA0003588836440000171
representing the upper and lower limits of reactive power output, Et, of the r-th distributed power supply]The unit column vector is represented by a unit column vector,
Figure BDA0003588836440000172
representing the active power of the r-th distributed power source at time t,
Figure BDA0003588836440000173
representing the capacity of the r-th distributed power converter in region m,
Figure BDA0003588836440000174
and the reactive power output of the r-th distributed power supply in the area m at the time t is shown.
5) Solving the distributed power supply and energy storage self-adaptive voltage control model considering interval coordination in the step 4), obtaining an energy storage system driven by data at the time t and a distributed power supply output strategy, and issuing and executing;
the expression of solving the distributed power supply and energy storage adaptive voltage control model with consideration of interval coordination in the step is as follows.
Figure BDA0003588836440000175
Figure BDA0003588836440000176
In the formula (I), the compound is shown in the specification,
Figure BDA0003588836440000177
a reference value indicating a control target of the area m at time t,
Figure BDA0003588836440000178
representing an estimate of the voltage of region m at time t,
Figure BDA0003588836440000179
respectively represents the reactive power output of the distributed power supply r in the region m at the time t and the time t-delta t,
Figure BDA00035888364400001710
the reactive power output of the distributed energy storage system l in the region m at the time t and the time t-delta t is shown,
Figure BDA00035888364400001711
Figure BDA00035888364400001712
the difference between the reactive power output of the distributed energy storage system b in the region m at the time t-delta t and the time t-2 delta t is represented,
Figure BDA00035888364400001713
representing the difference between the reactive power of the distributed power source a in the region m at the time t-deltat and the time t-2 deltat,
Figure BDA00035888364400001714
and
Figure BDA00035888364400001715
respectively representing the number of distributed energy storage systems and distributed power supplies in the area m,
Figure BDA0003588836440000181
a pseudo jacobian matrix representing the distributed power source a in the area m at time t,
Figure BDA0003588836440000182
the pseudo Jacobian matrix, ρ, representing the distributed energy storage system b in the region m at time t DG 、λ DG 、ρ ESS And λ ESS Representing the weight coefficients.
6) Acquiring interaction information among the regions after the energy storage system and the distributed power supply output strategy are issued and executed, and updating the node voltage control target reference value of each region and the boundary power control target reference value of each region in the target function;
the update of the mutual information between the regions in this step is shown as follows.
Figure BDA0003588836440000183
Figure BDA0003588836440000184
In the formula (I), the compound is shown in the specification,
Figure BDA0003588836440000185
a vector of voltage reference values representing the virtual node alpha in the region i, starting from time t for N future steps,
Figure BDA0003588836440000186
a vector of voltage estimation values representing N steps in the future from time t in region j,
Figure BDA0003588836440000187
represents the vector of net transmitted active power reference values N steps in the future starting from time t on the boundary beta of the region i,
Figure BDA0003588836440000188
and representing the net transmission active power estimated value vector of N steps in the future from the time t on the boundary beta of the region j.
7) Updating control time T to T + delta T, and judging T to be more than or equal to k delta T c If true, let Δ T p ＝ΔT p -ΔT c If k is k +1, step 8) is executed, and if not, step 8) is directly executed;
8) and judging whether the updated control time T is less than the optimization time T, if so, turning to the step 2), and if not, ending.
Example 2
For this embodiment, the distribution network includes 33 nodes, the topological connection condition is as shown in fig. 2, the nodes 18, 25, and 33 are respectively connected to a distributed energy storage system with a capacity of 4MVA, the upper and lower limits of active charge and discharge power are 5kW and-5 kW, and the upper and lower limits of reactive output power are 5kvar and-5 kvar, respectively; 4. the nodes 12 and 21 are connected into the photovoltaic system; 9. the 15, 24 and 29 nodes are connected with a fan; the iteration step length delta T is 5min, and the control step length delta T c 1h, the initial value of the prediction step Δ T p 24h, and 24h is the optimized time length T; the voltage reference value of the distribution network is set to 1.0p.u. Active power charge-discharge cost C of unit time distributed energy storage system ESS 0.05 yuan/kWh. Weight coefficient lambda P The value of the amount of the carbon dioxide is 15,
Figure BDA0003588836440000191
value 1, ρ DG 、 ρ ESS Taking the value of 1, eta DG 、η ESS The value is 0.8, mu DG 、μ ESS A value of 1 and a threshold number epsilon of 4; lambda [ alpha ] P The value of the active control parameter is generally between 10 and 20;
Figure BDA0003588836440000192
the value of the reactive power control parameter is generally between 0 and 1; eta DG 、η ESS The value of (A) is generally between 0 and 1; rho DG 、ρ ESS Generally, the value is between 0 and 1; epsilon is a conversion coefficient and generally takes a value between 1 and 5. And optimizing by adopting a data-driven distributed energy storage system and distributed power supply coordinated voltage control method, and obtaining a distributed energy storage system charge-discharge strategy and a distributed power supply reactive power output strategy through the steps. In order to verify the effectiveness of the method, the following two control schemes are adopted for comparison aiming at the power distribution network:
the first scheme comprises the following steps: the controllable resources of the power distribution network are not controlled, and the initial running state of the active power distribution network is obtained;
scheme II: the distributed power supply and energy storage data driving voltage control method considering interval coordination is adopted.
The computer hardware environment for executing the optimization calculation is Intel (R) core (TM) CPU i5-10210U, the dominant frequency is 1.6GHz, and the memory is 16 GB; the software environment is the Windows10 operating system.
The topology of the interval coordination area adopted in the present embodiment is shown in fig. 2. The change of the prediction curve of the distributed power supply output and load information is shown in fig. 3. The result of voltage before and after the control of the 24-hour energy storage access node 33 is shown in fig. 4, and the active charge and discharge power and the reactive power change of the 24-hour 33-node distributed energy storage system are shown in fig. 5. The 24 hour zone 2 distributed power reactive power is shown in fig. 6. The comparison result of the maximum node voltages before and after the 24-hour voltage control is shown in fig. 7. The 16-hour global voltage deviation distribution is shown in fig. 8. As can be seen from fig. 4 to 8, the second scheme can effectively adjust the distributed power supply and energy storage data driving voltage control method for the voltage level consideration interval coordination of the power distribution network of the embodiment, so that the voltage optimization problem can be effectively solved.
The results of the above two schemes are shown in table 1 below:
TABLE 1
Figure BDA0003588836440000201
Example 3
A computing device, comprising:
one or more processing units;
a storage unit for storing one or more programs,
wherein the one or more programs, when executed by the one or more processing units, cause the one or more processing units to perform the above-described interval-coordinated distributed power supply and energy storage voltage control method; it is noted that the computing device may include, but is not limited to, a processing unit, a storage unit; those skilled in the art will appreciate that the inclusion of a computing device as a processing unit, a memory unit, or a combination of both, does not constitute a limitation of computing devices, and may include further components, or some components in combination, or different components, e.g., a computing device may also include input output devices, network access devices, buses, etc.
A computer-readable storage medium having non-volatile program code executable by a processor, the computer program, when executed by the processor, implementing the steps of the above-described interval-coordinated distributed power supply and energy storage voltage control method; it should be noted that the readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof; the program embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. For example, program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the C programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, or entirely on a remote computing device or server. In situations involving remote computing devices, the remote computing devices may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to external computing devices (e.g., through the internet using an internet service provider).

Claims (10)

1. The distributed power supply and energy storage voltage control method considering interval coordination is characterized by comprising the following steps of:
determining an active power distribution network, and acquiring parameter information of the active power distribution network; setting iteration step length delta T and control step length delta T c Prediction step length delta T p Optimizing the duration T and the predicted step number N, initializing a control parameter k to be 1, and initializing a control time T to be 0;
partitioning the active power distribution network, and acquiring node voltage measurement values at t moment in each area of the active power distribution network and interaction information among the areas;
setting virtual nodes in each area in consideration of the limit of actual information transmission conditions of different intervals, uploading mutual information among the areas to the virtual nodes corresponding to the areas, wherein the mutual information among the areas comprises the voltage of the virtual nodes at the time t and the active transmission power of the virtual node connecting line at the time t; the virtual node voltage at the time t is a node voltage measured value with the maximum deviation from a node voltage control target reference value in each region at the time t, and the active transmission power of the virtual node tie line at the time t is a boundary tie line active transmission power measured value between each region at the time t;
acquiring voltage difference values of the voltage measurement values of the nodes in each area and the voltage control target reference values of the nodes in each area and voltage difference values of the voltage values of the virtual nodes and the voltage control target reference values of the virtual nodes, and judging whether any voltage difference value exceeds the limit or not;
if so, establishing a distributed power supply and energy storage self-adaptive voltage control model considering interval coordination under the constraint condition by taking the minimum control target deviation and the minimum energy storage system charge-discharge cost in each region as a target function;
if not, the control time t is updated to t + Δ t.
2. The interval-coordinated distributed power supply and energy storage voltage control method of claim 1, wherein:
after the step of establishing the distributed power supply and energy storage adaptive voltage control model considering interval coordination, the method further includes:
solving the distributed power supply and the energy storage self-adaptive voltage control model considering interval coordination to obtain an energy storage system driven by data at the time t and a distributed power supply output strategy, and issuing and executing the strategy;
and acquiring interaction information between the regions after the energy storage system and the distributed power supply output strategy are issued and executed, and updating the node voltage control target reference value of each region and the boundary power control target reference value of each region in the target function.
3. The interval-coordinated distributed power supply and energy storage voltage control method according to claim 2, characterized in that:
after the step of obtaining the mutual information between the regions after the energy storage system and the distributed power supply output strategy are issued and executed, and updating the node voltage control target reference value of each region and the boundary power control target reference value of each region in the target function, the method further comprises the following steps:
the update control time t is t + Δ t.
4. The interval-coordinated distributed power supply and energy storage voltage control method according to claim 1 or 3, characterized in that:
after the step of updating the control time t to t + Δ t, the method further includes:
judging T is more than or equal to k delta T c Whether or not the above-mentioned conditions are satisfied,
if yes, let Δ T p ＝ΔT p -ΔT c And updating the control parameter k to k +1, and judging whether the updated control time T is less than the optimization time T.
5. The interval-coordinated distributed power supply and energy storage voltage control method according to claim 4, wherein:
after the step of determining whether the updated control time T is less than the optimized time T, the method further includes:
if yes, partitioning the active power distribution network, acquiring node voltage measurement values at the time t in all areas of the active power distribution network and mutual information among all areas, setting virtual nodes in all areas based on the mutual information among all areas, and acquiring virtual node voltage values at the time t;
if not, the process is ended.
6. The interval-coordinated distributed power supply and energy storage voltage control method of claim 1, wherein: the distributed power supply and energy storage adaptive voltage control model considering interval coordination is as follows:
Figure FDA0003588836430000031
Figure DEST_PATH_FDA0003712445760000032
Figure DEST_PATH_FDA0003712445760000033
wherein, Δ X m,N [t]Indicating the energy storage charge and discharge power variation vector of N steps in the future from the time t + delta t in the region m, wherein N represents the predicted step number,
Figure FDA0003588836430000034
Figure FDA0003588836430000035
represents T + i Δ T c Charging and discharging power of the distributed energy storage system in the moment area m;
Figure FDA0003588836430000036
indicating the control target reference value at time t + deltat in region m,
Figure FDA0003588836430000037
a control target estimation value representing the region m at time t + deltat,
Figure FDA0003588836430000038
respectively representing the reactive power output of the distributed power supply r in the region m at the time t and the time t-delta t,
Figure FDA0003588836430000039
the reactive power output, lambda, of the distributed energy storage system l in the region m at the time t and the time t-delta t is represented P 、
Figure FDA00035888364300000310
Is a weight coefficient;
Figure FDA00035888364300000311
a control target reference value vector representing N future steps from the time t + Δ t in the region m is expressed as follows:
Figure FDA00035888364300000312
in the formula (I), the compound is shown in the specification,
Figure FDA00035888364300000313
a vector of node voltage reference values representing N future steps from time t + at in region m,
Figure FDA00035888364300000314
a vector of voltage reference values representing the virtual node alpha in the region m, starting from the time t + deltat for N future steps,
Figure FDA00035888364300000315
representing a net transmission active power reference value vector of N steps in the future from the moment t + delta t on the boundary beta of the area m, wherein epsilon represents a conversion coefficient;
Figure FDA0003588836430000041
and representing a control target estimation value vector of N future steps starting from the moment t + delta t in the region m, including the voltages of each node and virtual node of the region m and the boundary net transmission active power, wherein the composition expression is as follows:
Figure FDA0003588836430000042
wherein N represents the number of prediction steps, which is the prediction step size Δ T p And control step length delta T c The ratio of (a) to (b),
Figure FDA0003588836430000043
a vector of node voltage estimate values representing N future steps from time t + at in region m,
Figure FDA0003588836430000044
a vector of voltage estimation values representing the virtual node alpha in the region m, starting from the time t + deltat for N future steps,
Figure FDA0003588836430000045
the net transmission active power vector of N steps is predicted from the moment t + delta t at the boundary beta of the area m, and epsilon represents a conversion coefficient;
Figure FDA0003588836430000046
the calculation expression is as follows:
Figure FDA0003588836430000047
in the formula, E [ t ]]Representing a unit column vector, Y m,N [t]A vector consisting of control target measurement values in N sets of time t regions m,
Figure FDA0003588836430000048
a pseudo Jacobian estimation matrix, Δ X, representing the region m at time t m,N [t]The energy storage charging and discharging power variation vector of N steps in the future is shown from the time t + delta t in the area m;
Figure FDA0003588836430000049
the calculation method of (2) is as follows:
Figure FDA00035888364300000410
in the formula, Y m [t]A control target measurement value indicating the area m at time t,
Figure FDA00035888364300000411
and
Figure FDA00035888364300000412
respectively representing the number of distributed energy storage systems and distributed power supplies in the area m,
Figure FDA00035888364300000413
a pseudo jacobian matrix representing the distributed power sources r in the region m at time t,
Figure FDA00035888364300000414
a pseudo jacobian matrix representing the distributed energy storage system l in the region m at time t,
Figure FDA00035888364300000415
the calculation expression of (a) is as follows:
Figure FDA00035888364300000416
Figure FDA0003588836430000051
Figure FDA0003588836430000052
Figure FDA0003588836430000053
in the formula (I), the compound is shown in the specification,
Figure FDA0003588836430000054
respectively representing the pseudo Jacobian matrixes of the distributed power sources r in the region m at the time t and the time t-delta t,
Figure FDA0003588836430000055
a pseudo Jacobian matrix, Δ Y, representing the distributed energy storage system l in the region m at time t and time t- Δ t m [t]＝Y m [t]-Y m [t-Δt]The difference between the control target measurement values of the region m at time t and time t- Δ t is expressed,
Figure FDA0003588836430000056
Figure FDA0003588836430000057
representing the difference between the reactive power output of the distributed power source r in the region m at the time t-deltat and the time t-2 deltat,
Figure FDA0003588836430000058
represents the difference between the reactive power output of the distributed energy storage system l in the t-delta t moment and the t-2 delta t moment region m, eta DG 、μ DG 、η ESS And mu ESS Representing the weight coefficients.
7. The interval-coordinated distributed power supply and energy storage voltage control method according to claim 2, characterized in that:
the expression of the distributed power supply and energy storage self-adaptive voltage control model for solving and considering interval coordination is as follows:
Figure FDA0003588836430000059
Figure FDA00035888364300000510
in the formula (I), the compound is shown in the specification,
Figure FDA0003588836430000061
a reference value indicating a control target of the area m at time t,
Figure FDA0003588836430000062
an estimate of the voltage of region m at time t,
Figure FDA0003588836430000063
respectively representing the reactive power output of the distributed power supply r in the region m at the time t and the time t-delta t,
Figure FDA0003588836430000064
the reactive power output of the distributed energy storage system l in the region m at the time t and the time t-delta t is shown,
Figure FDA0003588836430000065
Figure FDA0003588836430000066
the difference between the reactive power output of the distributed energy storage system b in the region m at the time t-delta t and the time t-2 delta t is shown,
Figure FDA0003588836430000067
representing the difference between the reactive power output of the distributed power source a in the region m at the time t-delta t and the time t-2 delta t,
Figure FDA0003588836430000068
and
Figure FDA0003588836430000069
respectively representing the number of distributed energy storage systems and distributed power supplies in the area m,
Figure FDA00035888364300000610
a pseudo jacobian matrix representing the distributed power source a in the region m at time t,
Figure FDA00035888364300000611
representing a pseudo Jacobian matrix, rho, of the distributed energy storage system b in the region m at time t DG 、λ DG 、ρ ESS And λ ESS Representing the weight coefficients.
8. The interval-coordinated distributed power supply and energy storage voltage control method according to claim 2, characterized in that: the updating of the mutual information among the areas is represented as follows:
Figure FDA00035888364300000612
Figure FDA00035888364300000613
in the formula (I), the compound is shown in the specification,
Figure FDA00035888364300000614
a vector of voltage reference values representing the virtual node alpha in the region i, starting from time t for N future steps,
Figure FDA00035888364300000615
a vector of voltage estimation values representing N steps in the future from time t in region j,
Figure FDA00035888364300000616
representing the vector of net transmitted active power reference values for N steps in the future starting from time t on the boundary beta of the region i,
Figure FDA00035888364300000617
and representing the net transmission active power estimated value vector of N steps in the future from the time t on the boundary beta of the region j.
9. The interval-coordinated distributed power supply and energy storage voltage control method of claim 1, wherein:
including a prediction step size Δ T under the constraint p The method comprises the following steps of internal energy storage system charge state upper and lower limit restriction, energy storage system active power output upper and lower limit restriction, energy storage system converter operation restriction and distributed power supply converter operation restriction.
10. A computing device, characterized by: the method comprises the following steps:
one or more processing units;
a storage unit for storing one or more programs,
wherein the one or more programs, when executed by the one or more processing units, cause the one or more processing units to perform the method of any of claims 1-9.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115940212A (en) * 2022-12-07 2023-04-07 贵州大学 Intelligent coordination control system of energy storage system
WO2024060344A1 (en) * 2022-09-22 2024-03-28 广东电网有限责任公司 Data-physics fusion-driven adaptive voltage control system for flexible power distribution system

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024060344A1 (en) * 2022-09-22 2024-03-28 广东电网有限责任公司 Data-physics fusion-driven adaptive voltage control system for flexible power distribution system
CN115940212A (en) * 2022-12-07 2023-04-07 贵州大学 Intelligent coordination control system of energy storage system
CN115940212B (en) * 2022-12-07 2024-01-23 贵州大学 Intelligent coordination control system of energy storage system

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