CN108470239B - Active power distribution network multi-target layered planning method considering demand side management and energy storage - Google Patents

Active power distribution network multi-target layered planning method considering demand side management and energy storage Download PDF

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CN108470239B
CN108470239B CN201810170942.2A CN201810170942A CN108470239B CN 108470239 B CN108470239 B CN 108470239B CN 201810170942 A CN201810170942 A CN 201810170942A CN 108470239 B CN108470239 B CN 108470239B
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郑洁云
张林垚
邓鋆芃
吴桂联
施鹏佳
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State Grid Fujian Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Fujian Electric Power Co Ltd
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Abstract

The invention relates to a multi-target hierarchical planning method for an active power distribution network, which takes demand side management and energy storage into consideration. A double-layer model is established: the upper-layer planning model takes various economic costs of a power distribution company and the reliability and stability of a power distribution network as upper-layer targets in a planning process, considers investment and benefits of demand side management and energy storage equipment, and determines a target function and constraint conditions; the lower layer planning model takes the minimum cut-off amount of the distributed power supply in the planning process as an objective function, considers the maximum effective utilization of the distributed power supply, reduces the output of the distributed power supply through measures of wind abandoning, light abandoning and the like, and adjusts a transformer tap, reactive power compensation equipment and the like to perform active control. The invention can not only improve the reliability of power supply and the stability of distribution network operation, but also delay the upgrade of the power grid.

Description

Active power distribution network multi-target layered planning method considering demand side management and energy storage
Technical Field
The invention relates to a multi-target hierarchical planning method for an active power distribution network, which takes demand side management and energy storage into consideration.
Background
With the continuous improvement of the ratio of a multi-type Distributed Generation (DG) to be connected to a power distribution network and the continuous increase of the application of controllable equipment such as flexible loads, the traditional power distribution network planning cannot meet the requirement of high-efficiency utilization of the DG with high permeability under the low-carbon economic background. Therefore, research on active power distribution network planning considering demand side management and energy storage is urgently needed, active consumption and multi-stage coordinated utilization of various renewable energy sources are achieved, uncertain factors such as flexible loads are reasonably regulated and controlled, and network topology is changed to improve system reliability and stability.
At present, in the research of an active power distribution network, random uncertainty of distributed power supplies such as fans and photovoltaic power supplies is generally considered, and then constant volume and site selection are carried out on the distributed power supplies. The ADN research also generally adopts double-layer model planning, and the upper layer comprehensively considers the realization of multiple targets from the investment level; the lower layer considers the comprehensive application of DG from the aspect of operation simulation. The demand side management is a new concept that the power of the United states in the last 80 th century is saved on the demand side, and energy is reasonably utilized as replaceable resources on the supply side, so that the consumption of the energy is reduced and the utilization rate is improved by saving the power of the terminal, and the aim of relieving the power shortage of users is fulfilled. The Energy Storage Battery (Battery Energy Storage System, BESS) is also an important interactive resource under the ADN framework, and can optimize the Energy utilization mode and improve the power supply reliability.
Disclosure of Invention
The invention aims to provide an active power distribution network multi-target layered planning method considering demand side management and energy storage, which can improve the power supply reliability and the stability of distribution network operation and delay the upgrading of a power grid.
In order to achieve the purpose, the technical scheme of the invention is as follows: a multi-target layered planning method for an active power distribution network considering demand side management and energy storage comprises the following steps,
s1, determining an objective function and a constraint condition by taking various economic costs of a power distribution company and the reliability and stability of a power distribution network as upper-layer targets and considering investment and benefits of demand side management and energy storage equipment in the planning process, and establishing an upper-layer planning model;
step S2, taking the minimum expected value of annual cutting amount of the distributed power supply as a target, considering the effective utilization of the distributed power supply, and establishing a lower-layer planning model;
and step S3, solving the upper layer model and the lower layer model based on the improved particle swarm optimization.
In an embodiment of the present invention, the step S1 is specifically implemented as follows:
firstly, modeling is carried out on the power requirement of the electric automobile, firstly, a probability model of the charging starting time and the daily mileage is established, and the probability density function is as follows:
Figure BDA0001585545530000021
in the formula, σtRepresents the variance value, mu, of the first trip timetThe average value of the first trip time is represented and is two parameters of normal distribution;
further establishing a probability model of the charging power, and assuming that the charging period is TrIf the charging power of the user is Δ E, the charging load can be obtained by the following equation:
Socs＝Socr-MdEave/(100Bc)
ΔE＝Esoc-Socs
Tr＝ΔE/(Prηc)
in the formula, PrIndicating rated charging power of electric vehicle, ηcRepresents the charging efficiency of the electric vehicle, MdFor daily mileage, EaveFor electric energy consumed per stroke, BcFor the capacity of a lithium battery, Socr is the initial electric quantity before the electric automobile goes out, Socs is the initial charging electric quantity after the electric automobile goes back, and Esoc is the expected value of full electric quantity;
then, establishing an upper-layer planning model, taking various economic costs of a power distribution company and the reliability and stability of a power distribution network as upper-layer targets in a planning process, considering investment and benefits of demand side management and energy storage equipment, determining a target function and constraint conditions, and establishing the upper-layer planning model:
Figure BDA0001585545530000022
wherein f is1Representing an economic objective; f. of2Expressed as reliability target, λreliabilityRepresenting the reliability of the power supply of the distribution network, f3The voltage stability index of the system is represented; cLine、CDG、Csub、Closs、
Figure BDA0001585545530000023
CMA、CS,DSR、CIL、CbThe method comprises the following steps of respectively setting investment and operation maintenance cost of a distribution line, investment and operation maintenance cost of a distributed power supply, operation electricity purchasing cost of a transformer substation, total system loss cost, total annual energy storage battery investment cost, annual energy storage battery operation cost, comprehensive load demand response project management cost, annual electricity selling income reduced after demand response is implemented, compensation cost for interruptible load, and government subsidy cost of unit wind power generation and photovoltaic power generation; lambda [ alpha ]reliabilityReliability of power supply to the distribution network; FVSIkFor resolvability of branch voltage, ΩBIs a set of all branches.
In an embodiment of the present invention, the step S2 is specifically implemented as follows:
establishing a lower-layer planning model, taking the minimum cut-off amount of the distributed power supply in the planning process as an objective function, and considering the maximum effective utilization of the distributed power supply, the lower-layer objective function is expressed as follows:
Figure BDA0001585545530000031
in the formula, omega1、Ω2Is respectively the set of newly built photovoltaic power generation and newly built wind power generation, omegaSFor a set of scenes, Δ tsFor the cumulative running time of the distribution network year under the scene s,
Figure BDA0001585545530000032
the active power cutting amount of the jth photovoltaic power generation and wind power generation under the scene s is obtained;
the lower model reduces the output of the distributed power supply by measures including wind abandoning and light abandoning, and active control is performed by adjusting a transformer tap and reactive compensation equipment, and the constraint conditions are as follows:
(1) DG output resection constraint
Figure BDA0001585545530000033
In the formula:
Figure BDA0001585545530000034
respectively representing the upper limit and the lower limit of the output cutting of photovoltaic power generation and wind power generation;
(2) reactive compensation equipment switching amount constraint
Figure BDA0001585545530000035
In the formula: qCiThe switching quantity of the reactive compensation equipment at the ith node is represented,
Figure BDA0001585545530000036
and
Figure BDA0001585545530000037
respectively represents the lower limit and the upper limit, omega, of the switching quantity of the reactive compensation equipmentCInstalling a node set for reactive compensation;
(3) transformer tap adjustment range constraints
Figure BDA0001585545530000038
In the formula: t iskIndicating the position of the tap of the transformer,
Figure BDA0001585545530000039
and
Figure BDA00015855455300000310
respectively representing the lower and upper limits of the adjustment range of the transformer tap.
Compared with the prior art, the invention has the following beneficial effects: the invention comprehensively considers the economy, reliability AND stability of the power distribution network AND considers the optimization of demand side management AND the energy storage battery on AND. The stability and the reliability of the power distribution network are considered while the maximization of the economic benefits of operators is guaranteed.
Drawings
FIG. 1 is a diagram of a two-level programming relationship.
FIG. 2 is a flow chart of model solution.
Fig. 3 is a modified 33-node diagram.
Fig. 4 is a four season industrial load demand response, where (a) is a spring industrial load demand response where (b) is a summer industrial load demand response, where (c) is a fall industrial load demand response where (d) is a winter industrial load demand response.
Fig. 5 is a four-season resident load demand response in which (a) is a spring resident load demand response in which (b) is a summer resident load demand response in which (c) is an autumn resident load demand response in which (d) is a winter resident load demand response.
Fig. 6 is a four-season commercial load demand response, where (a) is a spring commercial load demand response, where (b) is a summer commercial load demand response, where (c) is an autumn commercial load demand response, where (d) is a winter commercial load demand response.
Fig. 7 is a Pareto result without regard to demand response and energy storage planning.
Fig. 8 is Pareto results considering demand response and energy storage planning.
Fig. 9 is a three-dimensional Pareto result considering demand response and energy storage planning.
Fig. 10 is a wind energy storage optimization diagram of the node 6.
Fig. 11 is a wind energy storage optimization diagram of the node 27.
Fig. 12 is a graph of node 8 optical energy storage optimization.
Detailed Description
The technical scheme of the invention is specifically explained in the following by combining the attached drawings 1-12.
The invention relates to a multi-target layered planning method for an active power distribution network, which takes demand side management and energy storage into consideration and comprises the following steps,
s1, determining an objective function and a constraint condition by taking various economic costs of a power distribution company and the reliability and stability of a power distribution network as upper-layer targets and considering investment and benefits of demand side management and energy storage equipment in the planning process, and establishing an upper-layer planning model;
step S2, taking the minimum expected value of annual cutting amount of the distributed power supply as a target, considering the effective utilization of the distributed power supply, and establishing a lower-layer planning model;
and step S3, solving the upper layer model and the lower layer model based on the improved particle swarm optimization.
The following is a specific implementation of the present invention.
As shown in fig. 1 and 2, the multi-objective hierarchical planning method for an active power distribution network, which takes demand side management and energy storage into consideration, is specifically implemented as follows:
(1) and performing time sequence load modeling on the charging and discharging of the electric automobile, and further predicting and obtaining the charging load distribution of the electric automobile 24 hours a day by predicting the probability distribution of the charging starting time and the daily mileage of the electric automobile.
(2) Considering the influence of demand side Management on active power distribution network planning, the invention mainly considers the demand side Interruptible Load Management (ILM) to model the ILM.
(3) BESS has the functions of peak clipping and valley filling and voltage quality improvement, and the BESS charge-discharge model is established in the aspects of residual charge level (SOC) and the like.
(4) And determining a target function and constraint conditions by taking various economic costs of a power distribution company and the reliability and stability of a power distribution network as upper-layer targets and considering investment and benefits of demand side management and energy storage equipment in the planning process, and establishing an upper-layer planning model.
(5) And establishing a lower-layer planning model by taking the minimum expected value of the annual cutting-off quantity of the distributed power supply as a target and considering the effective utilization of the distributed power supply.
(6) And solving the upper layer model and the lower layer model based on an improved particle swarm algorithm.
The active power distribution network multi-target hierarchical planning considering demand side management and energy storage is described as follows:
firstly, modeling is carried out on the power requirement of the electric automobile, firstly, a probability model of the charging starting time and the daily mileage is established, and the probability density function is as follows:
Figure BDA0001585545530000051
in the formula, σtRepresents the variance value, mu, of the first trip timetThe average value of the first trip time is represented and is two parameters of normal distribution;
further establishing a probability model of the charging power, and assuming that the charging period is TrIf the charging power of the user is Δ E, the charging load can be obtained by the following equation:
Socs＝Socr-MdEave/(100Bc)
ΔE＝Esoc-Socs
Tr＝ΔE/(Prηc)
in the formula, PrIndicating the rated charging power of the EV (electric vehicle), ηcM represents the charging efficiency of this EVdFor daily mileage, EaveFor electric energy consumed per stroke, BcFor the capacity of a lithium battery, Socr is the initial electric quantity before the EV travels, Socs is the initial charging electric quantity after the EV travels back, and Esoc is the expected value of full electric quantity;
then, establishing an upper-layer planning model, taking various economic costs of a power distribution company and the reliability and stability of a power distribution network as upper-layer targets in a planning process, considering investment and benefits of demand side management and energy storage equipment, determining a target function and constraint conditions, and establishing the upper-layer planning model:
Figure BDA0001585545530000061
wherein f is1Representing an economic objective; f. of2Of the representationFor reliability purposes, λreliabilityRepresenting the reliability of the power supply of the distribution network, f3The voltage stability index of the system is represented; cLine、CDG、Csub、Closs、
Figure BDA0001585545530000062
CMA、CS,DSR、CIL、CbThe method comprises the following steps of respectively setting investment and operation maintenance cost of a distribution line, investment and operation maintenance cost of a distributed power supply, operation electricity purchasing cost of a transformer substation, total system loss cost, total annual energy storage battery investment cost, annual energy storage battery operation cost, comprehensive load demand response project management cost, annual electricity selling income reduced after demand response is implemented, compensation cost for interruptible load, and government subsidy cost of unit wind power generation and photovoltaic power generation; lambda [ alpha ]reliabilityReliability of power supply to the distribution network; FVSIkFor resolvability of branch voltage, ΩBIs a set of all branches.
(1) Economic objective f1
1) Distribution line investment and operation maintenance cost CLine
Figure BDA0001585545530000063
Figure BDA0001585545530000064
In the formula:
Figure BDA0001585545530000065
investment cost for distribution lines;
Figure BDA0001585545530000066
the maintenance cost is equal to the annual operation cost of the line; omegaL1、ΩL2And ΩLRespectively setting a newly-built circuit, an upgraded circuit and a set of all circuits;
Figure BDA0001585545530000067
the costs required for newly building a line, upgrading the line and the unit length of all the lines are respectively; lijIs the line ij length; r is the discount rate; t isLineThe investment recovery period of the distribution line is set; gamma rayMMaintaining the cost rate for the line operation;
2) investment and operation maintenance cost C of distributed power supplyDG
Figure BDA0001585545530000068
Figure BDA0001585545530000071
In the formula:
Figure BDA0001585545530000072
equal annual value investment cost for a distributed power supply of a power distribution network;
Figure BDA0001585545530000073
operating and maintaining costs for the distributed power supply at an annual value; cPVGAnd CWTGInstallation costs per unit capacity for photovoltaic power generation (PVG) and wind power generation (WTG), respectively; omega1、Ω2The method comprises the steps of respectively integrating newly built photovoltaic power generation and newly built wind power generation;
Figure BDA0001585545530000074
respectively the installation capacity of each photovoltaic and fan of the jth; t isDGA recovery period for the investment of the distributed power supply;
Figure BDA0001585545530000075
and
Figure BDA0001585545530000076
respectively unit operation and maintenance costs of photovoltaic power generation and wind power generation; omegaSIs a collection of scenes; Δ tsAccumulating the running time of the distribution network in a scene s;
Figure BDA0001585545530000077
the active output of the jth photovoltaic power generation under the scene s is obtained;
Figure BDA0001585545530000078
the active power output of the jth wind power generation under the scene s is obtained;
3) cost C for purchasing electricity during operation of transformer substationsub
Figure BDA0001585545530000079
In the formula: ceThe unit electricity purchasing cost; n is the total number of the load nodes of the power distribution network;
Figure BDA00015855455300000710
the active load power of the distribution network node j under the scene s is shown. Due to the distributed power supply in the system, the cost required by the transformer substation for supplying power to the system is that all load power and the power which is subtracted from the distributed power supply are supplied.
4) Total loss cost of system Closs
Figure BDA00015855455300000711
In the formula: omegaSubA substation node set in the system is obtained; i isij,sAnd Ij,sThe magnitudes of the currents flowing through line ij and node j in the s-th scenario, respectively. ZSubIs the equivalent resistance of the substation, rijIs the resistance of line ij.
5) Investment cost and profit for energy storage equipment
Figure BDA00015855455300000712
The upper type
Figure BDA00015855455300000713
In order to realize the annual investment total cost of the energy storage battery,
Figure BDA00015855455300000714
the unit investment cost of the wind energy storage equipment of the distributed wind power node,
Figure BDA0001585545530000081
unit investment cost, omega, of optical energy storage devices for distributed photovoltaic nodesWTGAnd ΩPVGRespectively a distributed wind power generator node and a photovoltaic node,
Figure BDA0001585545530000082
investing decision variables at this point for energy storage when
Figure BDA0001585545530000083
The energy storage device is invested in the distributed power node,
Figure BDA0001585545530000084
it means that no energy storage equipment is invested at this point.
Figure BDA0001585545530000085
Figure BDA0001585545530000086
The annual running cost of the energy storage battery is saved;
Figure BDA0001585545530000087
and
Figure BDA0001585545530000088
the unit cost of wind storage equipment and light storage charging in unit time respectively;
Figure BDA0001585545530000089
and
Figure BDA00015855455300000810
respectively obtaining unit benefits of wind storage equipment and light storage and discharge electricity in unit time; t isi chargeAnd Ti unchargeRespectively representing the charging time and the discharging time of the node i.
6) Demand response interrupt load cost
Figure BDA00015855455300000811
Figure BDA00015855455300000812
Figure BDA00015855455300000813
Above formula CMAManagement of costs for integrated load demand response projects, CS,DSRReduced annual electricity sales revenue after implementing demand response for the s-th scenario; ccurA compensation fee for interruptible loads; cmaThe cost is managed for the demand response item per unit power. Cp,s,tThe electricity selling price provided by the power distribution network company in the tth scene time is provided; the original total load of the network of the power grid company in the t-th period of the s scene is large; pCur,s,tActive power of active interrupt load for a scene s time period t, PIncre,s,tAnd active power actively increasing the load in the second period in the s-th scene.
7) Subsidy cost C for new energy power generationb
Figure BDA00015855455300000814
In the formula: c. CbThe method is the government subsidy cost of wind power generation and photovoltaic power generation.
(2) Reliability target f2
f2The middle ASAI represents the average power supply availability of the power distribution network, the larger the ASAI is, the higher the reliability of the power distribution network is, and the ASAI is converted into f2The smaller the reliability target, the higher the reliability of the distribution network.
The specific calculation steps are as follows:
1) inputting various parameters of the power distribution network;
2) dividing areas by taking the switch element as a boundary to form element groups and load areas, and numbering each area for each parameter;
3) the component group is equivalent upwards from the maximum level to the level 1, and the final equivalent is the simplest radiation network;
4) calculating the reliability of the element group on the main feeder;
5) the element group is equivalent downwards, and the reliability parameters of the maximum level load area can be calculated in sequence;
6) calculating a load point reliability parameter;
7) and then calculates the system reliability index ASAI.
The specific calculation formula is as follows:
ASAI＝(N×8760-T)/(N×8760)
in the formula: n is the total number of power supply users, and T is the total power failure time of the users.
(3) Stability goal f3
f3Medium FVSI refers to a rapid voltage stabilization index, and the smaller the FVSI, the more stable the power distribution network is. FVSIijThe solvability of the branch voltage is represented, the value of which may represent the voltage stability of the branch. Firstly, the FVSI of each branch is calculatedijThen, the largest of them is FVSI, and the calculation formula is as follows:
FSVIij＝4Z2Qj/Vi 2X
FSVI＝max(FSVIij)
in the formula: z and X represent the impedance and reactance of the branch, respectively; qjRepresenting reactive power flow of the tail end branch; viRepresenting the branch head voltage magnitude.
In order to accelerate the calculation speed, a weight method is introduced to process a reliability target and a stability target, three targets are converted into double targets to be processed, and the specific calculation formula is as follows:
F'＝βF2+(1-β)F3
in the formula: f' represents the comprehensive index after the weight is introduced; β is a weight, and is taken herein to be 0.98.
Considering that the distributed power supply is connected into a system and then impacts on the original electrical characteristics of the power distribution network, such as power flow distribution, voltage level, short-circuit capacity and the like, the permeability is required to meet a certain range. The specific constraints are expressed as follows:
(1) distribution network flow constraint
Figure BDA0001585545530000101
Figure BDA0001585545530000102
In the formula: x is the number ofijIs the reactance of line ij, ΩBThe node sets are all node sets of the system, (j) is a branch end node set taking j as a head end node, and pi (j) is a branch head end node set taking j as a tail end node; qjk,s、Qij,sReactive power for branches jk and ij;
Figure BDA0001585545530000103
Figure BDA0001585545530000104
respectively providing reactive power for a transformer substation node, a wind power generation node, a photovoltaic node and a load node; u shapej,sIs the node voltage.
(2) Probability constraint of upper and lower limits of node voltage
P{Umin≤U≤Umax}＝k1/N≥β1
In the formula: u shapeminAnd UmaxThe lower limit and the upper limit of the node voltage are respectively; k is a radical of1The number of scenes satisfying the voltage upper and lower limit constraint in all scenes β1Is the confidence level of the node voltage constraint.
(3) Branch power probability constraint
P{Pij≤Pij,max}＝k2/N≥β2
In the formula: pij,maxThe upper power limit allowed for branch ij; k is a radical of2For the number of scenarios satisfying the branch power constraint among all scenarios β2Confidence level for branch power constraints。
(4) Inhibiting reverse power probabilistic constraints
P{PΣDG≤PΣL}＝kB/N≥βB
In the formula: pΣDGAnd PΣLRespectively the total active power of the distributed power supply and the total active power of the load; k is a radical ofBFor the number of scenarios satisfying the power back-off prohibition constraint among all scenarios βBTo inhibit confidence levels of the reverse power constraint.
(5) Distributed power source installation capacity constraints
Figure BDA0001585545530000111
In the formula:
Figure BDA0001585545530000112
and
Figure BDA0001585545530000113
PVG and WTG total installation capacity respectively; sigma is the maximum permeability of new energy access;
Figure BDA0001585545530000114
the sum of the maximum active load of the distribution network;
Figure BDA0001585545530000115
and
Figure BDA0001585545530000116
respectively obtaining the maximum installation capacity of PVG and WTG of the grid-connected node i to be selected; pi PVGAnd Pi WTGThe installation capacities of PVG and WTG, respectively, for the candidate installation node i.
(6) Energy storage charge and discharge restraint
Figure BDA0001585545530000117
Figure BDA0001585545530000118
Figure BDA0001585545530000119
Figure BDA00015855455300001110
Above formula Ti chargeAnd Ti unchargeRespectively representing the charging time and the discharging time of the node i;
Figure BDA00015855455300001111
and
Figure BDA00015855455300001112
for the charging power and the discharging power of the energy storage device per unit time,
Figure BDA00015855455300001113
and
Figure BDA00015855455300001114
is the maximum charging power per unit time of the energy storage device,
Figure BDA00015855455300001115
the total charge capacity of the energy storage device.
(7) Demand response management constraint
Figure BDA00015855455300001116
Figure BDA00015855455300001117
PTL,s,t＝PTLO,s,t-PTLI,s,t
Figure BDA00015855455300001118
Figure BDA00015855455300001119
In the formula: pTLO,s,tIs PTLI,s,tLoad transfer-out and load transfer-in for the tth scene period respectively,
Figure BDA00015855455300001120
Figure BDA0001585545530000121
respectively transferring the upper limit value and the lower limit value of the load transferring response coefficient and the load transferring response coefficient for the t-th time period of the s-th scene;
Figure BDA0001585545530000122
and (4) loading interruption power upper and lower limit values for the t-th time period of the s-th scene.
(8) Radial confinement
Firstly, generating an undirected graph by using a minimum spanning tree algorithm, and then generating a directed graph based on a Kruskal idea.
(9) Connectivity constraints
And (4) solving an adjacency matrix and a reachability matrix of the graph, and then judging the connectivity of the graph through the reachability matrix of the graph.
Then, a lower-layer planning model is established, the minimum cut-off amount of the distributed power supply in the planning process is taken as an objective function, and the maximum effective utilization lower-layer objective function of the distributed power supply is considered to be expressed as follows:
Figure BDA0001585545530000123
in the formula:
Figure BDA0001585545530000124
the active power cut-off amount of the jth PVG and WTG under the scene s is shown.
The lower layer model reduces the output of the distributed power supply by measures such as abandoning wind and abandoning light, and carries out active control by adjusting a transformer tap, reactive compensation equipment and the like, and the constraint conditions are as follows:
(1) DG output resection constraint
Figure BDA0001585545530000125
In the formula:
Figure BDA0001585545530000126
respectively representing the upper limit and the lower limit of the output cutting of photovoltaic power generation and wind power generation;
(2) reactive compensation equipment switching amount constraint
Figure BDA0001585545530000127
In the formula: qCiThe switching quantity of the reactive compensation equipment at the ith node is represented,
Figure BDA0001585545530000128
and
Figure BDA0001585545530000129
respectively represents the lower limit and the upper limit, omega, of the switching quantity of the reactive compensation equipmentCInstalling a node set for reactive compensation;
(3) transformer tap adjustment range constraints
Figure BDA00015855455300001210
In the formula: t iskIndicating the position of the tap of the transformer,
Figure BDA00015855455300001211
and
Figure BDA00015855455300001212
respectively representing the lower and upper limits of the adjustment range of the transformer tap.
The following is illustrated by specific examples:
the simulation system of the invention adopts an improved 33-node system, and the topological diagram of the system is shown in figure 3.
For example, the improved 33-node system has 61 lines and 39 nodes, where the nodes 34-39 are newly added load nodes, and the branches 38-61 are lines to be newly added. The installation reference capacity of the fan and the photovoltaic power supply in the system is 100kW, and the maximum allowable permeability is 50%. Setting the nodes 3, 6, 16 and 27 as the nodes for accessing the fan, wherein the upper limit of the installation number of the nodes is respectively 20, 18, 28 and 18; the number of the nodes to be connected into the photovoltaic power supply is 8, 10, 28 and 30, and the upper limits of the installation number of the nodes are 10, 20, 8 and 10 respectively. The line operation and maintenance cost rate and the discount rate are respectively set to be 3% and 0.1. As shown in tables 1 and 2, the fixed investment recovery periods for the line and DG were 20 years and 10 years, respectively.
TABLE 1 line parameters
Figure BDA0001585545530000131
TABLE 2 DG parameters
Figure BDA0001585545530000132
In this example, the nodes 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, and 37 are assumed to be the residential load nodes; nodes 2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35, 38 are commercial load nodes; nodes 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39 are industrial loads.
The improved PSO algorithm parameters are set to: and (4) iterating for 50 times, wherein the population size is 80, the initial value and the final value of the inertia weight are respectively 0.8 and 0.4, the initial value and the final value of one learning factor are respectively 2.5 and 0.5, and the initial value and the final value of the other learning factor are respectively 0.5 and 2.5.
Through the solution simulation of the model, demand response time sequence curves before and after optimization can be obtained, and the demand response time sequence curves are shown in fig. 4, 5 and 6. Four-season demand response curves for industrial, residential and commercial loads, respectively.
It can be seen from the curve results of the above graph that, through the demand response, after the user responds to the electricity price strategy, the interruption or reduction of the electricity consumption is allowed at some high electricity price moments, and the overall power balance of the system is improved; and the power consumption requirement is increased at certain low-price stages, the resource distribution of a power distribution system can be improved, and the phenomena of wind abandonment and power abandonment are prevented. If the price incentive strategy is adjusted, the demand response distribution curve changes along with the strategy, and reasonable control of part of loads in the active power distribution network can be realized.
The same system is respectively planned, and planning results without considering the demand response and the energy storage equipment and with considering the demand response and the energy storage equipment are compared. Fig. 7 and fig. 8 can be obtained through simulation, and it can be seen that the Pareto optimal boundary considering demand response and energy storage is obviously better than the Pareto optimal boundary not considering demand response and energy storage, and all solutions are non-dominant solutions relative to the situation not considered, that is, all the former solutions answer economic cost and comprehensive indexes smaller than the latter.
Meanwhile, fig. 9 also shows an optimal solution set in a three-dimensional space, i.e., a comprehensive index is decomposed into reliability and stability indexes.
The pareto optimal frontier shown in the upper graph proves the effectiveness of the solution of the method of the invention, the set of machines for obtaining the optimal solution for planning can be shown in table 3,
TABLE 3 planning scenario results
Figure BDA0001585545530000141
Figure BDA0001585545530000151
Scheme 5 is the optimal scheme through an ideal ordering method. In the optimal scheme, the original branches 6, 9, 16, 18, 22, 26, 31 and 36 are upgraded, and the line model is upgraded to be 1; upgrading original branches 1, 3, 4, 14, 27, 28, 35 and 37, wherein the line upgrading model is 2; the newly-built lines are branches 39, 43, 45, 46, 48, 49, 50, 52, 56, 59 and 60; the mounting position and capacity of DG are: 3, (1), 6, (7), 16, (15), 27, (11), 8, (8), 10, (6), 28, (4) and (4), 1 fan is installed on the node 3, 7 fans are installed on the node 6, 15 fans are installed on the node 16, 11 fans are installed on the node 27, 8 photovoltaic power supplies are installed on the node 8, 6 photovoltaic power supplies are installed on the node 10, 4 photovoltaic power supplies are installed on the node 28, and 4 photovoltaic power supplies are installed on the node 30. The installation condition of the energy storage equipment is as follows: the node 6 and the node 27 are provided with wind energy storage equipment, and the node 8 is provided with optical energy storage equipment.
The results in the table above show that scheme 5, although there are more upgraded lines and newly built lines, leads to lower overall cost through energy storage and demand response management.
To further illustrate the optimization effect of energy storage on the distributed power source, fig. 10 to 12 show the comprehensive curves of the energy storage device power in the planning scheme 5. The dotted line is the output power of the distributed power supply after the energy storage device is added, the star curve is the output power before the energy storage is not added, and the solid line is the charge and discharge power of the energy storage device. Through the charging and discharging of the energy storage equipment, the output of the distributed power supply is improved to a certain extent, the input cost of the newly-built distributed power supply is saved, the system loss cost is reduced in a specific period, and the planning is influenced significantly.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.

Claims (1)

1. A multi-target layered planning method for an active power distribution network considering demand side management and energy storage is characterized by comprising the following steps,
s1, determining an objective function and a constraint condition by taking various economic costs of a power distribution company and the reliability and stability of a power distribution network as upper-layer targets and considering investment and benefits of demand side management and energy storage equipment in the planning process, and establishing an upper-layer planning model;
step S2, taking the minimum expected value of annual cutting amount of the distributed power supply as a target, considering the effective utilization of the distributed power supply, and establishing a lower-layer planning model;
s3, solving an upper layer model and a lower layer model based on an improved particle swarm algorithm;
the step S1 is specifically implemented as follows:
establishing an upper-layer planning model, taking various economic costs of a power distribution company and the reliability and stability of a power distribution network into comprehensive consideration in the planning process as upper-layer targets, considering investment and benefits of demand side management and energy storage equipment, determining a target function and constraint conditions, and establishing the upper-layer planning model:
Figure FDA0002536928100000011
wherein f is1Representing an economic objective; f. of2Expressed as reliability target, λreliabilityRepresenting the reliability of the power supply of the distribution network, f3The voltage stability index of the system is represented; cLine、CDG、Csub、Closs、
Figure FDA0002536928100000012
CMA、CS,DSR、CIL、CbThe method comprises the following steps of respectively setting investment and operation maintenance cost of a distribution line, investment and operation maintenance cost of a distributed power supply, operation electricity purchasing cost of a transformer substation, total system loss cost, total annual energy storage battery investment cost, annual energy storage battery operation cost, comprehensive load demand response project management cost, annual electricity selling income reduced after demand response is implemented, compensation cost for interruptible load, and government subsidy cost of unit wind power generation and photovoltaic power generation; lambda [ alpha ]reliabilityReliability of power supply to the distribution network; FVSIkFor resolvability of branch voltage, ΩBA set composed of all branches;
the step S2 is specifically implemented as follows:
establishing a lower-layer planning model, taking the minimum cut-off amount of the distributed power supply in the planning process as an objective function, and considering the maximum effective utilization of the distributed power supply, the lower-layer objective function is expressed as follows:
Figure FDA0002536928100000013
in the formula, omega1、Ω2Is respectively the set of newly built photovoltaic power generation and newly built wind power generation, omegaSFor a set of scenes, Δ tsFor the cumulative running time of the distribution network year under the scene s,
Figure FDA0002536928100000021
the active power cutting amount of the jth photovoltaic power generation and wind power generation under the scene s is obtained;
the lower model reduces the output of the distributed power supply by measures including wind abandoning and light abandoning, and active control is performed by adjusting a transformer tap and reactive compensation equipment, and the constraint conditions are as follows:
(1) DG output resection constraint
Figure FDA0002536928100000022
In the formula:
Figure FDA0002536928100000023
respectively representing the upper limit and the lower limit of the output cutting of photovoltaic power generation and wind power generation;
(2) reactive compensation equipment switching amount constraint
Figure FDA0002536928100000024
In the formula: qCiThe switching quantity of the reactive compensation equipment at the ith node is represented,
Figure FDA0002536928100000025
and
Figure FDA0002536928100000026
respectively represents the lower limit and the upper limit, omega, of the switching quantity of the reactive compensation equipmentCInstalling a node set for reactive compensation;
(3) transformer tap adjustment range constraints
Figure FDA0002536928100000027
In the formula: t iskIndicating the position of the tap of the transformer,
Figure FDA0002536928100000028
and
Figure FDA0002536928100000029
respectively representing the lower and upper limits of the transformer tap adjustment range.
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