CN110570015B - Multi-target planning method for power distribution network - Google Patents

Multi-target planning method for power distribution network Download PDF

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CN110570015B
CN110570015B CN201910725924.0A CN201910725924A CN110570015B CN 110570015 B CN110570015 B CN 110570015B CN 201910725924 A CN201910725924 A CN 201910725924A CN 110570015 B CN110570015 B CN 110570015B
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power distribution
der
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CN110570015A (en
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王世龙
阳细斌
宋双商
刘上嘉
林清华
王彦波
唐晓军
谢其锋
古耀全
曾挺
郑华
敖进财
谢涛
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Guangdong Power Grid Co Ltd
Yangjiang Power Supply Bureau of Guangdong Power Grid Co Ltd
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Yangjiang Power Supply Bureau of Guangdong Power Grid Co Ltd
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Abstract

The invention relates to a multi-objective planning method for a power distribution network, which is based on an energy storage operation strategy in a market environment and comprises the following steps: s1, establishing a power distribution network planning comprehensive evaluation system considering multi-index evaluation; s2, analyzing energy storage operation strategies of different investment subjects in the market environment; s3, establishing a power distribution network distributed energy planning multi-objective optimization model based on the power distribution network planning comprehensive evaluation system; s4, solving the power distribution network distributed energy planning multi-objective optimization model based on the improved particle swarm optimization algorithm to obtain an optimized power distribution network multi-objective planning scheme. The multi-target planning method for the power distribution network is simple and practical, improves the reliability and the power supply quality of the power distribution network, and has strong practical application background and engineering value.

Description

Multi-target planning method for power distribution network
Technical Field
The invention relates to the technical field of power distribution network planning, in particular to a multi-target planning method for a power distribution network.
Background
With the development of a new round of power system innovation, social capital participates in investment construction on the side of a power distribution network, and distributed energy represented by wind power, photovoltaic and energy storage is connected to the power distribution network in large quantity, so that a new challenge is provided for planning and running of the power distribution network. Due to the access of the DER, the power distribution network is changed from one-way power flow to two-way power flow, and the operation management mode in the traditional power distribution network mode is not applicable any more. The large number of DER is connected into the power distribution network, so that the system economy and the safety are all challenged. In order to ensure the economical efficiency and the safety and stability of the system in the power distribution network planning process, scholars at home and abroad carry out a great deal of work.
However, a multi-objective evaluation index system is not established in the existing power distribution network planning, the DER planning is considered with a single objective, most planning models under a single investment subject are considered, other investment subjects introduced by a distribution network side under the power market environment are not considered, and the proposed planning models are lack of persuasion.
Disclosure of Invention
The invention provides the multi-target planning method for the power distribution network, which is simple and practical, improves the reliability and the power supply quality of the power distribution network and has strong practical application background and engineering value in order to solve the problems that the conventional power distribution network planning considers too single target and the provided planning model is lack of persuasion.
In order to solve the technical problems, the invention provides the following technical scheme:
a multi-target planning method for a power distribution network is based on an energy storage operation strategy in a market environment, and comprises the following steps:
s1: establishing a power distribution network planning comprehensive evaluation system considering multi-index evaluation;
s2: analyzing energy storage operation strategies of different investment subjects in the market environment;
s3: establishing a power distribution network distributed energy planning multi-target optimization model based on the power distribution network planning comprehensive evaluation system;
s4: and solving the power distribution network distributed energy planning multi-objective optimization model based on an improved particle swarm optimization algorithm to obtain an optimized power distribution network multi-objective planning scheme.
The multi-objective planning method for the power distribution network firstly evaluates a comprehensive evaluation system of the power distribution network planning from multiple aspects, further analyzes the operation strategies of energy storage under different investment subjects, establishes a DER multi-objective optimization planning model in the power distribution network based on the comprehensive evaluation system and the energy storage operation strategies under different investment subjects, and finally solves the model by adopting an improved particle swarm algorithm.
Further, in step S1, the indexes include an economic index, an environmental protection index, a power supply quality index and a safety index, which are evaluated in many ways, so that the reliability is greatly improved.
Further, in step S2, the investment subjects include a power grid company and a third party company, and the operation strategy of the stored energy under different investment subjects is further analyzed, so that the reliability of the entire method is improved.
Further, the economic indicators include:
(1) equal annual DER investment, operation and maintenance cost
Figure BDA0002158928060000021
In the above formula, N DER Is the total number of DER; r is the discount rate; n is a radical of an alkyl radical i Is the economic life of class i DER; n is a radical of S The total number of scenes; p is a radical of formula s The occurrence probability of the s-th scene;
Figure BDA0002158928060000022
generating capacity for the ith DER at the time of s scene t; t is the working hours of the distributed power supply per day, and 24 hours are taken as a period;
Figure BDA0002158928060000023
and P DER.i Respectively the investment cost, the operation and maintenance cost and the installed capacity of the unit capacity of the i-type DER;
(2) loss reduction benefit
Figure BDA0002158928060000024
In the above formula, N l Is the total number of lines; c. C loss The grid loss electricity price; i is s.t.l And I s ' .t.l Respectively connecting the DER to the current on the l branch at the t time period in the scene s before and after the DER is connected; r is l The resistance of the first branch circuit;
(3) postpone the benefit of power grid upgrading
Figure BDA0002158928060000025
In the above-mentioned formula, the compound has the following structure,
Figure BDA0002158928060000026
to allocate the annual investment delay benefits; phi (phi) of L For the set of all the legs in the network,
Figure BDA0002158928060000027
investing in delay benefits for a single branch;
(4) subsidy income from renewable energy sources
Figure BDA0002158928060000028
In the above formula, c B Subsidizing the price of the unit electric quantity of the renewable energy sources;
Figure BDA0002158928060000029
and
Figure BDA00021589280600000210
respectively generating capacity PV and WG in a t period in a scene s;
(5) DER electricity sales revenue
Figure BDA00021589280600000211
In the above formula, C sell The annual income of electricity selling for the power generator; n is a radical of DG Is the total number of distributed power sources;
Figure BDA0002158928060000031
the price of electricity sold for the i-type DER;
Figure BDA0002158928060000032
the accuracy and reliability of the whole method are high for the power generation amount of the ith class DER in the t period in the scene s.
Further, the environmental protection index is:
Figure BDA0002158928060000033
in the above formula, α r,G The emission coefficient of the polluted gas is the unit generated energy of the main network; r isThe general class of emitted pollution gases; beta is a r The treatment cost for different pollution gases;
Figure BDA0002158928060000034
and
Figure BDA0002158928060000035
the electric quantity is purchased by the main network before and after the DER is accessed, and the accuracy and the reliability of the whole method are high.
Further, the power supply quality index is as follows:
Figure BDA0002158928060000036
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002158928060000037
in the above formula, VPI is a voltage quality index; delta U i A voltage fluctuation improvement value for node i; phi n Is a node number set; dU i.max And dU i ' .max Maximum voltages of a node i before and after DER access are respectively obtained; dU i Is a voltage fluctuation value; u shape i.min And U i.max The minimum value and the maximum value of voltage fluctuation in the day are respectively; u shape N The accuracy and reliability of the whole method are high for rated voltage
Further, the safety indexes are as follows:
Figure BDA0002158928060000038
in the above formula, C SCS The safety index is a comprehensive safety index of the system; c LSS,s,t,n The load safety power supply rate index of the nth branch failure in the t time period in the scene s; s is the total number of scenes; n is the number of branches; t is the number of static safety analysis time periods; converting the comprehensive safety index of the system into a constraint form as follows:
Figure BDA0002158928060000039
in the above formula, the first and second carbon atoms are,
Figure BDA00021589280600000310
the method is the lowest limit of the comprehensive safety index of the system, and the accuracy and the reliability of the whole method are high.
Further, the parameters of the power distribution network distributed energy planning multi-objective optimization model comprise:
(1) objective function
Figure BDA0002158928060000041
In the above formula, λ 1 -λ 6 The variable is 0-1, and a planning decision maker selects part of indexes as optimization targets according to actual requirements;
(2) constraint conditions
1) DER access capacity constraints
Figure BDA0002158928060000042
In the above formula, i is the access point to be accessed;
Figure BDA0002158928060000043
the DER capacity of the access point i to be accessed is set;
Figure BDA0002158928060000044
accessing an upper capacity limit for the DER of the ith node;
2) voltage quality index constraint
VPI≤λ
In the above formula, λ is a voltage quality index reference value;
3) system security index constraints
C scs ≥β
In the above formula, beta is a system comprehensive safety index constraint reference value, and the accuracy and reliability of the whole method are high.
Further, the energy storage operation strategy in the market environment includes: under the condition that a power grid company invests in energy storage, the strategy of energy storage operation is mainly to promote the local consumption of new energy, stabilize the fluctuation of the new energy and consider economic benefits as an energy optimization target; under the condition that a third-party company invests in energy storage, the strategy of energy storage operation is to track the price of electricity sold by the energy storage and the charge state of the energy storage.
Compared with the prior art, the invention has the following beneficial effects:
the invention constructs a multi-objective planning model of a power distribution network based on different energy storage operation strategies in a market environment, firstly, a comprehensive evaluation system of the power distribution network planning evaluated from the aspects of economy, environmental protection, power supply quality, safety and the like is further analyzed, the operation strategies of energy storage under different investment subjects are further analyzed, based on the comprehensive evaluation index system and the energy storage operation strategies under different investment subjects, a DER multi-objective optimization planning model in the power distribution network is established, and finally, an improved particle swarm algorithm is adopted to solve the model.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart of a multi-objective planning method for a power distribution network according to the present invention.
FIG. 2 is a flow chart of an energy storage operation strategy with a power grid company as an investment subject;
FIG. 3 is a flow chart of an energy storage operation strategy with a third party company as an investment principal;
FIG. 4 is a simplified diagram of a topology;
FIG. 5 is a price of electricity parameter graph;
FIG. 6 is a comparison of installed capacity for different investment entity plans;
FIG. 7 is a diagram illustrating the values of the indices.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The embodiment of the invention comprises the following steps:
the first embodiment is as follows:
as shown in fig. 1, a multi-objective planning method for a power distribution network is based on an energy storage operation strategy in a market environment, and includes the following steps:
s1: establishing a power distribution network planning comprehensive evaluation system considering multi-index evaluation;
s2: analyzing energy storage operation strategies of different investment subjects in the market environment;
s3: establishing a power distribution network distributed energy planning multi-target optimization model based on the power distribution network planning comprehensive evaluation system;
s4: and solving the power distribution network distributed energy planning multi-objective optimization model based on an improved particle swarm optimization algorithm to obtain an optimized power distribution network multi-objective planning scheme.
The multi-objective planning method for the power distribution network firstly evaluates a comprehensive evaluation system of the power distribution network planning from multiple aspects, further analyzes the operation strategies of energy storage under different investment subjects, establishes a DER multi-objective optimization planning model in the power distribution network based on the comprehensive evaluation system and the energy storage operation strategies under different investment subjects, and finally solves the model by adopting an improved particle swarm algorithm.
In the present embodiment, in step S1, the indexes include an economic index, an environmental protection index, a power supply quality index and a safety index, which are evaluated in many ways, so that the reliability is greatly improved.
As shown in fig. 2-3, in step S2, the investment entities include the power grid company and the third party company, and the operation strategy of the stored energy under different investment entities is further analyzed, so as to improve the reliability of the whole method.
In this embodiment, the economic indicators include:
(1) equal annual DER investment, operation and maintenance cost
Figure BDA0002158928060000061
In the above formula, N DER Total number of DER; r is the discount rate; n is a radical of an alkyl radical i Is the economic life of class i DER; n is a radical of hydrogen S Is the total number of scenes; p is a radical of formula s The occurrence probability of the s-th scene;
Figure BDA0002158928060000062
generating capacity of the ith DER at the time of s scene t; t is the working hours of the distributed power supply per day, and 24 hours are taken as a period;
Figure BDA0002158928060000063
and P DER.i Respectively the unit capacity investment cost, the operation and maintenance cost and the installed capacity of the i-type DER;
(2) profit from loss reduction
Figure BDA0002158928060000064
In the above formula, N l Is the total number of lines; c. C loss The grid loss electricity price; I.C. A s.t.l And I s ' .t.l Respectively connecting the DER to the current on the l branch at the t time period in the scene s before and after the DER is connected; r l The resistance of the first branch circuit;
(3) postpone the benefit of power grid upgrading
Figure BDA0002158928060000065
In the above formula, the first and second carbon atoms are,
Figure BDA0002158928060000066
to allocate the annual investment delay benefits; phi (phi) of L For the set of all the legs in the network,
Figure BDA0002158928060000067
investing in delay benefits for a single branch;
(4) subsidy income from renewable energy sources
Figure BDA0002158928060000068
In the above formula, c B Supplementing the price for the unit electric quantity of the renewable energy sources;
Figure BDA0002158928060000069
and
Figure BDA00021589280600000610
respectively generating capacity PV and WG in a t period in a scene s;
(5) DER Electricity sales revenue
Figure BDA00021589280600000611
In the above formula, C sell The annual income of electricity selling for the power generator; n is a radical of DG Is the total number of distributed power sources; electricity selling price for the i-th DER; the accuracy and reliability of the whole method are high for the power generation amount of the ith class DER in the t period in the scene s.
In this embodiment, the environmental protection index is:
Figure BDA0002158928060000071
in the above formula, the emission coefficient of the polluted gas is the unit generated energy of the main network; r is the total class of the discharged pollution gas; the treatment cost for different pollution gases; and the electric quantity is bought by the main network before and after the DER is accessed, and the accuracy and the reliability of the whole method are high.
In this embodiment, the power supply quality index is:
Figure BDA0002158928060000072
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002158928060000073
in the above formula, the voltage quality index is shown; a voltage fluctuation improvement value for node i; is a set of node numbers; and maximum voltages of the DER before and after being connected to the node i respectively; is a voltage fluctuation value; and the minimum value and the maximum value of the voltage fluctuation in the day are respectively; the accuracy and reliability of the whole method are high for rated voltage
In this embodiment, the safety indexes are:
Figure BDA0002158928060000074
in the above formula, the safety index is the comprehensive safety index of the system; the load safety power supply rate index of the nth branch fault in the t period in the scene s; is the total number of scenes; the number of branches; number of time segments for static security analysis; converting the comprehensive safety index of the system into a constraint form as follows:
Figure BDA0002158928060000075
in the above formula, the minimum limit is the comprehensive safety index of the system, and the accuracy and reliability of the whole method are high.
In this embodiment, the parameters of the power distribution network distributed energy planning multi-objective optimization model include:
(1) objective function
Figure BDA0002158928060000076
In the above formula, the-is a variable of 0-1, and a planning decision maker selects part of indexes as optimization targets according to actual requirements;
(2) constraint conditions
1) DER access capacity constraints
Figure BDA0002158928060000077
In the above formula, i is the access point to be accessed; DER capacity of a point to be accessed; accessing an upper capacity limit for the DER of the ith node;
2) voltage quality index constraint
VPI≤λ
In the above formula, the reference value is the voltage quality index;
3) system security index constraints
C scs ≥β
In the above formula, the reference value is constrained for the comprehensive safety index of the system, and the accuracy and reliability of the whole method are high.
In this embodiment, the energy storage operation strategy in the market environment includes: under the condition that a power grid company invests in energy storage, the strategy of energy storage operation is mainly to promote the local consumption of new energy, stabilize the fluctuation of the new energy and consider economic benefits as an energy optimization target; under the condition that a third-party company invests in energy storage, the strategy of energy storage operation is to track the price of electricity sold by the energy storage and the charge state of the energy storage.
Example two:
on the basis of the first embodiment, the multi-objective planning method for the distribution network in the first embodiment is adopted for example, the topology of the 2-supply-1-backup medium-voltage distribution network is adopted for simulation analysis, a simplified topological structure diagram is shown in fig. 4, DER nodes to be selected are L3, L5, L8, L14, L16 and L21, and economic index design electricity price data in a model is shown in fig. 5.
In order to analyze the influence of different energy storage investment bodies on the planning result in the market environment, two planning schemes are designed:
scheme 1: assuming that a power grid company is used as an investment subject, the concerned benefits comprise direct income and indirect income brought by DER, the direct income comprises power selling income and subsidy income, and the indirect income comprises loss reduction income, power grid transformation income delay and environmental protection income, so that the target function index weight is set, and the energy storage operation strategy is shown in figure 2;
scheme 2: assuming that a third-party company is used as an investment subject and the interest of the third-party company is only direct benefits brought by DER access, namely electricity selling and subsidy benefits, the target function index weight is set, and the energy storage operation strategy is shown in FIG. 3.
The results of the configuration under both schemes are shown in fig. 6 and 7, where the data with x represents the indirect benefit.
According to example results, the wind-light configuration capacities under the two schemes are basically consistent and almost maximized access is achieved; and the energy storage capacity configured by the third-party company for the investment subject is far larger than that configured by the power grid company for the investment subject. The reasons for this are mainly that third-party companies focus on the direct benefits of energy storage, in order to maximize the investment benefit of energy storage, control the energy storage to operate in a low-charging and high-discharging mode, and use the peak-valley price difference for arbitrage, the benefit at this time is far greater than the cost, so more energy storage is selected and configured; when the power grid company is used as an investment main body, the stored energy operates in a mode of promoting the new energy to be consumed on the spot, the economic benefit obtained by charging and discharging the stored energy is low in the mode, and the power grid company cannot invest excessive stored energy.
As seen from the voltage quality index in fig. 7, the voltage quality indexes of the two schemes are both negative, which indicates that the DER configured in the two schemes both plays a role in improving voltage fluctuation, and scheme 2 is configured with more energy storage than scheme 1, but since the energy storage in scheme 2 operates in a low charge and high discharge mode and does not play a role in stabilizing power fluctuation, the voltage fluctuation improvement effect is poorer than that in scheme 1.
From the comprehensive safety index of the system, the scheme 2 is slightly higher than the scheme 1 by 0.02, AND the configuration result shows that the mounting capacities of PV AND WG under the two schemes are basically consistent, AND the energy storage of the scheme 2 is more than that of the scheme 1, so that the effect of ensuring the stable power supply of the system when the energy storage is in an AND fault is shown, AND the comprehensive safety of the system can be improved by the energy storage.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by the present specification, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (8)

1. The multi-target planning method for the power distribution network is characterized by being based on an energy storage operation strategy under a market environment and comprising the following steps of:
s1: establishing a power distribution network planning comprehensive evaluation system considering multi-index evaluation;
s2: analyzing energy storage operation strategies of different investment subjects in the market environment;
s3: establishing a power distribution network distributed energy planning multi-target optimization model based on the power distribution network planning comprehensive evaluation system; the parameters of the power distribution network distributed energy planning multi-objective optimization model comprise:
(1) objective function
Figure FDA0003656868880000011
In the above formula, λ 1 -λ 6 The variable is 0-1, and a planning decision maker selects part of indexes as optimization targets according to actual requirements;
(2) constraint conditions
1) DER access capacity constraints
Figure FDA0003656868880000012
In the above formula, i is the access point to be accessed;
Figure FDA0003656868880000013
DER capacity of a point i to be accessed;
Figure FDA0003656868880000014
accessing the capacity upper limit for the DER of the ith node;
2) voltage quality index constraint
VPI≤λ
In the above formula, λ is a voltage quality index reference value;
3) system security index constraints
C scs ≥β
In the above formula, β is a system comprehensive safety index constraint reference value;
s4: and solving the power distribution network distributed energy planning multi-objective optimization model based on an improved particle swarm optimization algorithm to obtain an optimized power distribution network multi-objective planning scheme.
2. The multi-objective power distribution network planning method according to claim 1, wherein in step S1, the indicators include economic indicators, environmental protection indicators, power supply quality indicators, and safety indicators.
3. The multi-objective power distribution network planning method according to claim 1, wherein in step S2, the investment entities include a power grid company and a third party company.
4. The multi-objective power distribution network planning method according to claim 2, wherein the economic indicators include:
(1) equal annual DER investment, operation and maintenance cost
Figure FDA0003656868880000021
In the above formula, N DER Total number of DER; r is the discount rate; n is i Is the economic life of class i DER; n is a radical of hydrogen S The total number of scenes; p is a radical of s The occurrence probability of the s-th scene;
Figure FDA0003656868880000022
generating capacity of the ith DER at the time of s scene t; t is the working hours of the distributed power supply every day, and 24 hours are taken as a period;
Figure FDA0003656868880000023
Figure FDA0003656868880000024
and P DER.i Respectively the investment cost, the operation and maintenance cost and the installed capacity of the unit capacity of the i-type DER;
(2) profit from loss reduction
Figure FDA0003656868880000025
In the above formula, N l Is the total number of lines; c. C loss The grid loss electricity price; i is s.t.l And I s ' .t.l Respectively connecting the DER to the current on the l branch at the t time period in the scene s before and after the DER is connected; r l The resistance of the first branch circuit;
(3) postpone the benefit of power grid upgrading
Figure FDA0003656868880000026
In the above formula, the first and second carbon atoms are,
Figure FDA0003656868880000027
the investment delay benefit of each year is allocated; phi L For the set of all the legs in the network,
Figure FDA0003656868880000028
investing delayed benefits for a single branch;
(4) subsidy income of renewable energy
Figure FDA0003656868880000029
In the above formula, c B Supplementing the price for the unit electric quantity of the renewable energy sources;
Figure FDA00036568688800000210
and
Figure FDA00036568688800000211
respectively generating capacity PV and WG in a t period in a scene s;
(5) DER Electricity sales revenue
Figure FDA00036568688800000212
In the above formula, C sell The annual income of electricity selling for the power generator; n is a radical of DG Is the total number of distributed power sources;
Figure FDA00036568688800000213
the price of electricity sold for the i-type DER;
Figure FDA00036568688800000214
is the power generation amount of the ith DER in the t period in the scene s.
5. The multi-objective planning method for the power distribution network according to claim 2, wherein the environmental protection indexes are as follows:
Figure FDA0003656868880000031
in the above formula, α r,G Unit generated energy of main networkThe emission coefficient of the polluted gas; r is the total class of the discharged pollution gas; beta is a r The treatment cost for different pollution gases;
Figure FDA0003656868880000032
and
Figure FDA0003656868880000033
and respectively purchasing electric quantity for the main network before and after the DER is accessed.
6. The multi-objective planning method for the power distribution network according to claim 2, wherein the power supply quality indexes are as follows:
Figure FDA0003656868880000034
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003656868880000035
in the above formula, VPI is a voltage quality index; delta U i A voltage fluctuation improvement value for node i; phi n Is a set of node numbers; dU i.max And dU' i.max Maximum voltages of a node i before and after DER access are respectively obtained; dU i Is a voltage fluctuation value; u shape i.min And U i.max The minimum value and the maximum value of voltage fluctuation in the day are respectively; u shape N Is a rated voltage.
7. The multi-objective planning method for the power distribution network according to claim 2, wherein the safety indexes are as follows:
Figure FDA0003656868880000036
in the above formula, C SCS The system is a comprehensive safety index; c LSS,s,t,n For the t period in the scene sThe load safety power supply rate index of the fault of the n branches; s is the total number of scenes; n is the number of branches; t is the number of static security analysis periods; the system comprehensive safety index is converted into a constraint form as follows:
Figure FDA0003656868880000037
in the above formula, the first and second carbon atoms are,
Figure FDA0003656868880000038
is the lowest limit of the comprehensive safety index of the system.
8. The multi-objective planning method for the power distribution network according to claim 3, wherein the energy storage operation strategy in the market environment comprises: under the condition that a power grid company invests in energy storage, the strategy of energy storage operation is mainly to promote the local consumption of new energy, stabilize the fluctuation of the new energy and consider economic benefits as an energy optimization target; under the condition that a third-party company invests in energy storage, the strategy of energy storage operation is to track the price of electricity sold by the energy storage and the charge state of the energy storage.
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