CN105207195B - A kind of power distribution network distributed power source addressing constant volume method - Google Patents

A kind of power distribution network distributed power source addressing constant volume method Download PDF

Info

Publication number
CN105207195B
CN105207195B CN201510345059.9A CN201510345059A CN105207195B CN 105207195 B CN105207195 B CN 105207195B CN 201510345059 A CN201510345059 A CN 201510345059A CN 105207195 B CN105207195 B CN 105207195B
Authority
CN
China
Prior art keywords
mrow
msub
power source
mtd
distributed power
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201510345059.9A
Other languages
Chinese (zh)
Other versions
CN105207195A (en
Inventor
孙蓉
卫志农
孙国强
孙永辉
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hohai University HHU
Original Assignee
Hohai University HHU
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hohai University HHU filed Critical Hohai University HHU
Priority to CN201510345059.9A priority Critical patent/CN105207195B/en
Publication of CN105207195A publication Critical patent/CN105207195A/en
Application granted granted Critical
Publication of CN105207195B publication Critical patent/CN105207195B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Landscapes

  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a kind of power distribution network distributed power source addressing constant volume method, and the grid-connected position of power distribution network distributed power source and capacity optimization are realized based on NSGA II algorithms.Consider distributed power source it is grid-connected after economy and stability, realize that grid net loss, installation operating cost and total voltage deviation are optimal the addressing and constant volume calculating of lower distribution distributed power source using the algorithms of NSGA II.This method represents taking into account for economy and stability multiple target in the planning of distribution distributed power source in the form of Pareto optimal solutions, it can obtain stressing economy with stability difference multiple optimal solutions under degree, the weak point of single situation can only be represented by overcoming simple weighted, and actual emulation shows the validity and reasonability of institute's extracting method.

Description

A kind of power distribution network distributed power source addressing constant volume method
Technical field
The present invention relates to a kind of power distribution network distributed power source addressing constant volume method based on NSGA-II algorithms, belong to distribution Formula field of new energy technologies.
Background technology
The grid integration of distributed power source so that distribution net work structure changes, and distribution system is from radial passive network It is changed into being distributed with the active electric network power network of middle-size and small-size power supply, trend distribution, via net loss in former power network, the stable case of voltage Change can all be produced.Its influence all has substantial connection with the position of distributed power source and capacity, therefore for being distributed in power distribution network The planning of formula power supply also turns into focus therewith.Existing research is mainly only for single optimization aim at present, using simple target Optimized algorithm is solved, or considers distributed power source grid-connected stability and economy, using weighting each target Multiple target is converted into single goal and optimizes solution by mode, with actual demand and not meeting.
The content of the invention
In order to solve the above-mentioned technical problem, the invention provides a kind of power distribution network distributed electrical based on NSGA-II algorithms Source addressing constant volume method, economy and stability after consideration distributed power source is grid-connected, power network is realized using the algorithms of NSGA- II Network loss, installation operating cost and total voltage deviation are optimal the addressing and constant volume calculating of lower distribution distributed power source.Should Method represents taking into account for economy and stability multiple target in the planning of distribution distributed power source, energy in the form of Pareto optimal solutions The multiple optimal solutions stressed with stability difference under degree are accessed to economy, single feelings can only be represented by overcoming simple weighted The weak point of condition.
In order to achieve the above object, the technical solution adopted in the present invention is：
1) clear and definite distributed power source addressing constant volume needs Consideration to include distribution network loss minimum, distributed power source installation Operating cost is minimum, power distribution network total voltage deviation is minimum；
2) the power distribution network distributed power source addressing constant volume based on multiple target is determined；
21) distributed power source grid-connected economy and stability are considered.With the total network loss of power network, distributed power source running cost With and the minimum target of total voltage deviation：
min f1=Ploss
min f2=CD
min f3=DetV
PlossFor network loss, CDFor distributed power source operating cost, DetV is total voltage deviation, f1,f2,f3It is divided into the total net of power network Damage, distributed power source operating cost and total voltage deviation minimum target function；
22)PlossFor network loss：
ΔPiFor branch road i active loss.L is power distribution network branch road sum.
23)CDFor distributed power source operating cost：
TmaxFor distributed power source maximum generation hourage；βjFor distributed power source j power factor；SDjFor distributed electrical Source j capacity；CcjFor distributed power source j unit quantity of electricity costs, including the electricity price and construction cost of distributed power source；
24) DetV is total voltage deviation：
Wherein U*For the reference voltage value of power distribution network；UiThe magnitude of voltage of i-th of node.N represents nodes.
3) constraints that distributed power source addressing constant volume should meet is determined：
31) node voltage constrains：
Uimin≤Ui＜ Uimax
UiFor i-node voltage；Uimin、UimaxFor node i voltage bound；
32) feeder current constrains：
Ij≤Ijmax
IjFor branch road j electric current；IjmaxAllow to pass through maximum current for branch road j；
33) distributed power source capacity-constrained：
S∑D≤SL
S∑DFor distributed power source total capacity；SLFor the 10% of power network total capacity；
35) handled for above-mentioned inequality constraints condition, during optimization using penalty factor method；
34) node voltage limits：
UiFor i-node voltage；Uimin、UimaxFor node i voltage bound；KuFor the penalty factor of node voltage, as right Deviate the punishment of operational limit, general value is larger, and when it meets that system voltage limitation requires, permanent value is 0；
35) feeder current limits：
Wherein IjFor branch road j electric current；IjmaxAllow to pass through maximum current for branch road j；KiFor branch current punishment because Son, when electric current is not above allowing maximum current value on branch road, its permanent value is 0；When electric current exceedes maximum on branch road Then it takes a larger numerical value；
36) distributed power source capacity-constrained：
Wherein S∑DFor distributed power source total capacity；SLFor the 10% of power network total capacity；K∑DFor injection capacity punishment because Son, when the distributed power source capacity injected in power distribution network is less than permission injection rate, its value takes 0, when injection capacity exceedes limit Then its value is larger during value processed.
4) model is solved using multi-objective optimization theory
41) Multiobjective Programming describes：
Minf (x)=(f1(x),…,fp(x))T
gi(x)≥0,i∈I
hj(x)=0, j ∈ E
42) using non-dominated sorted genetic algorithm (Non-dominated sorting genetic algorithm, NSGA- II) carry out problem solving, it is not necessary to the weight ratio in each target is weighed, multi-objective problem can be obtained after optimization Pareto optimal solutions.
5) multiple-objection optimization based on NSGA-II algorithms is used, after to the address of distributed power source and capacity coding, with Machine produces an initial population.And Load flow calculation is carried out to the individual in its initial population, arranged according to its target function value Sequence, then carry out genetic manipulation and obtain new population, second generation population is obtained according to the algorithm flows of NSGA- II.Repeatedly operation until Reach greatest iteration number, can obtain the optimum results of distributed power source addressing and constant volume.
The beneficial effect that the present invention is reached：The present invention is it can be considered that economy after distributed power source is grid-connected and stably Property, it is optimal to take into account economy and multiple targets such as stability in the planning of distribution distributed power source, can obtain to economy and surely Qualitative difference stresses multiple optimal solutions under degree, and the weak point of single situation can only be represented by overcoming simple weighted.
Brief description of the drawings
Fig. 1 is the algorithm flows of NSGA- II.
Fig. 2 is improvement IEEE-33 node systems.
Fig. 3 is containing 3 distributed power source optimum results.
Fig. 4 is containing 4 distributed power source optimum results.
Fig. 5 is network loss and voltage deviation.
Fig. 6 is network loss and operating cost.
Fig. 7 is voltage deviation and operating cost.
Embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following examples are only used for clearly illustrating this hair Bright technical scheme, and can not be limited the scope of the invention with this.
As shown in figure 1, a kind of power distribution network distributed power source addressing constant volume method flow based on the algorithms of NSGA- II, it is determined that Multiple-objection optimization and constraints, after address and capacity to distributed power source encode, randomly generate an initial population.It is and right Individual in its initial population carries out Load flow calculation, is ranked up according to its target function value, then carries out genetic manipulation and obtains New population, second generation population is obtained according to the algorithm flows of NSGA- II.Operation can be divided up to reaching greatest iteration number repeatedly The optimum results of cloth site selection of coal fired power plant and constant volume.Including initial algorithm parameter, individual UVR exposure, initial population, non-branch are established at random With individual sequence, sub- population, selected father population, loop iteration are obtained, obtains the steps such as optimal solution set.
1) factor that clear and definite distributed power source addressing constant volume needs to consider includes distribution network loss minimum, distributed power source dress Machine operating cost is minimum, power distribution network total voltage deviation is minimum.
2) the power distribution network distributed power source addressing constant volume based on multiple target is determined
21) with the total network loss of power network, distributed power source operating cost and the minimum target of total voltage deviation：
min f1=Ploss
min f2=CD
min f3=DetV
PlossFor network loss, CDFor distributed power source operating cost, DetV is total voltage deviation, f1,f2,f3It is divided into the total net of power network Damage, distributed power source operating cost and total voltage deviation minimum target function.
22)PlossFor network loss：
ΔPiFor branch road i active loss.L is power distribution network branch road sum.
23)CDFor distributed power source operating cost：
TmaxFor distributed power source maximum generation hourage；βjFor distributed power source j power factor；SDjFor distributed electrical Source j capacity；CcjFor distributed power source j unit quantity of electricity costs, including the electricity price and construction cost of distributed power source.
24) DetV is total voltage deviation：
Wherein U*For the reference voltage value of power distribution network；UiThe magnitude of voltage of i-th of node.N represents nodes.
3) constraints that distributed power source addressing constant volume should meet is determined
31) node voltage constrains：
Uimin≤Ui＜ Uimax
UiFor i-node voltage；Uimin、UimaxFor node i voltage bound；
32) feeder current constrains：
Ij≤Ijmax
IjFor branch road j electric current；IjmaxAllow to pass through maximum current for branch road j；
33) distributed power source capacity-constrained：
S∑D≤SL
S∑DFor distributed power source total capacity；SLFor the 10% of power network total capacity；
34) handled for above-mentioned inequality constraints condition, during optimization using penalty factor method.
35) node voltage limits：
UiFor i-node voltage；Uimin、UimaxFor node i voltage bound；KuFor the penalty factor of node voltage, as right Deviate the punishment of operational limit, general value is larger, and when it meets that system voltage limitation requires, permanent value is 0.
36) feeder current limits：
Wherein IjFor branch road j electric current；IjmaxAllow to pass through maximum current for branch road j；KiFor branch current punishment because Son, when electric current is not above allowing maximum current value on branch road, its permanent value is 0；When electric current exceedes maximum on branch road Then it takes a larger numerical value.
37) distributed power source capacity-constrained：
Wherein S∑DFor distributed power source total capacity；SLFor the 10% of power network total capacity；K∑DFor injection capacity punishment because Son, when the distributed power source capacity injected in power distribution network is less than permission injection rate, its value takes 0, when injection capacity exceedes limit Then its value is larger during value processed.
4) model is solved using multi-objective optimization theory
41) Multiobjective Programming describes：
Min f (x)=(f1(x),…,fp(x))T
gi(x)≥0,i∈I
hj(x)=0, j ∈ E
42) using non-dominated sorted genetic algorithm (Non-dominated sorting genetic algorithm, NSGA- II) carry out problem solving, it is not necessary to the weight ratio in each target is weighed, multi-objective problem can be obtained after optimization Pareto optimal solutions.
5) multiple-objection optimization based on NSGA-II algorithms is used, after to the address of distributed power source and capacity coding, with Machine produces an initial population.And Load flow calculation is carried out to the individual in its initial population, arranged according to its target function value Sequence, then carry out genetic manipulation and obtain new population, second generation population is obtained according to the algorithm flows of NSGA- II.Repeatedly operation until Reach greatest iteration number, can obtain the optimum results of distributed power source addressing and constant volume.
Described above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art For member, without departing from the technical principles of the invention, some improvement and deformation can also be made, these are improved and deformation Also it should be regarded as protection scope of the present invention.
Case verification：
This deletes 5 loops in former 33 node systems using IEEE-33 node systems as example.Its topological diagram is such as Shown in Fig. 2：
The parameter settings of NSGA- II are as follows in this example：The scale of population is 200, iterations 100, and crossing-over rate is 0.9, aberration rate 0.1.Penalty coefficient Ku=Ki=K∑D=1500, the operating cost C of the unit quantity of electricity of distributed power sourcecj= 3800 yuan/KWh.Exemplified by being respectively incorporated into 3 and 4 distributed formula power supplys in power distribution network, matlab software programming meters are used Available optimum results such as Fig. 3 is calculated, shown in Fig. 4：
Plane projection is carried out for Fig. 3 and Fig. 4, can obtain voltage deviation and operating cost relation, as a result such as Fig. 5, Fig. 6, Shown in Fig. 7：By perspective view it can be seen that being incorporated to distributed power source after, can reduce grid net loss and voltage deviation, network loss with Positive correlation is presented in voltage deviation.And as the reduction of voltage deviation and network loss, distributed power source operating cost can increase, voltage is inclined Difference and grid net loss and the presentation of distributed power source operating cost are reversely related, and the variation relation of three is with distributed power source quantity Increase and gradually weaken.
Under being respectively incorporated into power distribution network premised on 3 and 4 distributed power sources, four schemes, its result respectively have chosen As shown in Table 1 and Table 2, wherein bracket inner digital represents that the distributed power source is incorporated to the node number in power distribution network.
Table 1 contains 3 distributed power source prioritization schemes
Table 2 contains 4 distributed power source prioritization schemes
Summary icon can be seen that in first wife's power network after addition distributed power source, and can play reduces former power network Network loss, the effect of burning voltage deviation.In each four kinds of schemes more than, the capacity for the distributed power source that scheme one is incorporated to Smaller, network loss reduces smaller, there is provided voltage support it is little, but operating cost is relatively low.Scheme four is in network loss reduction and voltage Great role is played in both stable, but operating cost is higher, and the optimization that both schemes all represent in a certain respect is extreme.And Scheme two and scheme three then integrate three targets and optimized, and are the schemes for comparing the golden mean of the Confucian school.
It is not simply by multiple target when NSGA- II optimizes for multi-objective problem compared with traditional genetic algorithm Conversion turns to single goal and calculated.Avoid the randomness of the weight ratio in conversion process between each function.And NSGA- Multigroup optimal solution can be calculated by optimization once in II algorithm optimization so that and disaggregation converges to the optimal forward positions of Pareto, Algorithm more efficiency.Operating personnel can choose most suitable scheme according to the condition needed for oneself.
The present invention is disclosed with preferred embodiment above, so it is not intended to limiting the invention, all to use equivalent substitution Or the technical scheme that equivalent transformation mode is obtained, it is within the scope of the present invention.

Claims (3)

  1. A kind of 1. power distribution network distributed power source addressing constant volume method, it is characterised in that：Comprise the following steps：
    1) constraints that determining distributed power source addressing constant volume should meet includes：
    1-1) node voltage constrains：
    Ui min≤Ui＜ Ui max
    UiFor i-node voltage；Uimin、UimaxFor node i voltage bound；
    1-2) feeder current constrains：
    Ij≤Ij max
    IjFor branch road j electric current；Ij maxAllow to pass through maximum current for branch road j；
    1-3) distributed power source capacity-constrained：
    S∑D≤SL
    S∑DFor distributed power source total capacity；SLFor the 10% of power network total capacity；
    Handled 1-4) for above-mentioned inequality constraints condition, during optimization using penalty factor method,
    <mrow> <msub> <mi>K</mi> <mi>u</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>U</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>K</mi> <mi>u</mi> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>U</mi> <mrow> <mi>i</mi> <mi>min</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>U</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mtd> <mtd> <mrow> <msub> <mi>U</mi> <mi>i</mi> </msub> <mo>&lt;</mo> <msub> <mi>U</mi> <mrow> <mi>i</mi> <mi>min</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>K</mi> <mi>u</mi> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>U</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>U</mi> <mrow> <mi>i</mi> <mi>max</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mtd> <mtd> <mrow> <msub> <mi>U</mi> <mi>i</mi> </msub> <mo>&gt;</mo> <msub> <mi>U</mi> <mrow> <mi>i</mi> <mi>max</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <msub> <mi>U</mi> <mrow> <mi>i</mi> <mi>min</mi> </mrow> </msub> <mo>&amp;le;</mo> <msub> <mi>U</mi> <mi>i</mi> </msub> <mo>&lt;</mo> <msub> <mi>U</mi> <mrow> <mi>i</mi> <mi>max</mi> </mrow> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
    UiFor i-node voltage；Uimin、UimaxFor node i voltage bound；KuFor the penalty factor of node voltage, as to deviateing The punishment of operational limit, general value is larger, and when it meets that system voltage limitation requires, permanent value is 0；
    1-5) feeder current limits：
    <mrow> <msub> <mi>K</mi> <mi>l</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>I</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>K</mi> <mi>i</mi> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>I</mi> <mi>j</mi> </msub> <mo>-</mo> <msub> <mi>I</mi> <mrow> <mi>j</mi> <mi>max</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mtd> <mtd> <mrow> <msub> <mi>I</mi> <mi>j</mi> </msub> <mo>&gt;</mo> <msub> <mi>I</mi> <mrow> <mi>j</mi> <mi>max</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <msub> <mi>I</mi> <mi>j</mi> </msub> <mo>&amp;le;</mo> <msub> <mi>I</mi> <mrow> <mi>j</mi> <mi>max</mi> </mrow> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
    Wherein IjFor branch road j electric current；Ij maxAllow to pass through maximum current for branch road j；KiFor the penalty factor of branch current, when When electric current is not above allowing maximum current value on branch road, its permanent value is 0；When electric current exceedes maximum on branch road, then it takes One larger numerical value；
    1-6) distributed power source capacity-constrained：
    <mrow> <msub> <mi>K</mi> <mrow> <mi>&amp;Sigma;</mi> <mi>D</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>S</mi> <mrow> <mi>&amp;Sigma;</mi> <mi>D</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = '{' close = ''> <mtable> <mtr> <mtd> <mrow> <msub> <mi>K</mi> <mrow> <mi>&amp;Sigma;</mi> <mi>D</mi> </mrow> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>S</mi> <mrow> <mi>&amp;Sigma;</mi> <mi>D</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>S</mi> <mi>L</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mtd> <mtd> <mrow> <msub> <mi>S</mi> <mrow> <mi>&amp;Sigma;</mi> <mi>D</mi> </mrow> </msub> <mo>&gt;</mo> <msub> <mi>S</mi> <mi>L</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <msub> <mi>S</mi> <mrow> <mi>&amp;Sigma;</mi> <mi>D</mi> </mrow> </msub> <mo>&amp;le;</mo> <msub> <mi>S</mi> <mi>L</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
    Wherein S∑DFor distributed power source total capacity；SLFor the 10% of power network total capacity；K∑DFor the penalty factor of injection capacity, When the distributed power source capacity injected in power distribution network is less than permission injection rate, its value takes 0, when injection capacity exceedes limits value Then its value is larger；
    2) multiple-objection optimization based on NSGA-II algorithms is used, after to the address of distributed power source and capacity coding, random production A raw initial population；
    3) Load flow calculation is carried out to the individual in its initial population, be ranked up according to its target function value, then carry out heredity Operation obtains new population, and second generation population is obtained according to NSGA-II algorithm flows；
    4) operation up to reaching greatest iteration number, can obtain the optimum results of distributed power source addressing and constant volume repeatedly.
  2. 2. site selection of coal fired power plant constant volume method according to claim 1, it is characterised in that：Distributed power source addressing constant volume needs to examine The factor of worry includes the total loss minimization of power distribution network, distributed power source installation operating cost minimum and power distribution network total voltage deviation most It is small, i.e.,
    min f1=Ploss
    min f2=CD
    min f3=DetV
    PlossFor network loss, CDFor distributed power source operating cost, DetV is total voltage deviation, f1, f2, f3It is divided into the total network loss of power network, Distributed power source operating cost and total voltage deviation minimum target function；
    <mrow> <msub> <mi>P</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>l</mi> </munderover> <msub> <mi>&amp;Delta;P</mi> <mi>i</mi> </msub> </mrow>
    ΔPiFor branch road i active loss.L is power distribution network branch road sum；
    <mrow> <msub> <mi>C</mi> <mi>D</mi> </msub> <mo>=</mo> <msub> <mi>T</mi> <mi>max</mi> </msub> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>&amp;beta;</mi> <mi>j</mi> </msub> <msub> <mi>S</mi> <mrow> <mi>D</mi> <mi>j</mi> </mrow> </msub> <msub> <mi>C</mi> <mrow> <mi>c</mi> <mi>j</mi> </mrow> </msub> </mrow>
    TmaxFor distributed power source maximum generation hourage；βjFor distributed power source j power factor；SDjFor distributed power source j's Capacity；CcjFor distributed power source j unit quantity of electricity costs, including the electricity price and construction cost of distributed power source,
    <mrow> <mi>D</mi> <mi>e</mi> <mi>t</mi> <mi>V</mi> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mo>|</mo> <msup> <mi>U</mi> <mo>*</mo> </msup> <mo>-</mo> <msub> <mi>U</mi> <mi>i</mi> </msub> <mo>|</mo> </mrow>
    Wherein U*For the reference voltage value of power distribution network；UiThe magnitude of voltage of i-th of node.N represents nodes.
  3. 3. site selection of coal fired power plant constant volume method according to claim 1, it is characterised in that：Using multi-objective optimization theory to model Solved, it is specific as follows：
    Min f (x)=(f1..., f (x)p(x))T
    gi(x) >=0, i ∈ I
    hj(x)=0, j ∈ E
    Problem solving is carried out using non-dominated sorted genetic algorithm, it is not necessary to the weight ratio in each target is weighed, after optimization Obtain the Pareto optimal solutions of multi-objective problem.
CN201510345059.9A 2015-06-19 2015-06-19 A kind of power distribution network distributed power source addressing constant volume method Active CN105207195B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201510345059.9A CN105207195B (en) 2015-06-19 2015-06-19 A kind of power distribution network distributed power source addressing constant volume method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201510345059.9A CN105207195B (en) 2015-06-19 2015-06-19 A kind of power distribution network distributed power source addressing constant volume method

Publications (2)

Publication Number Publication Date
CN105207195A CN105207195A (en) 2015-12-30
CN105207195B true CN105207195B (en) 2018-02-06

Family

ID=54954705

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201510345059.9A Active CN105207195B (en) 2015-06-19 2015-06-19 A kind of power distribution network distributed power source addressing constant volume method

Country Status (1)

Country Link
CN (1) CN105207195B (en)

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106451556B (en) * 2016-11-24 2021-10-12 中国电力科学研究院有限公司 Method and device for determining capability of power distribution network feeder line for accepting distributed power supply
CN106532772B (en) * 2016-12-01 2020-04-24 三峡大学 Distributed power supply planning method based on improved orthogonal optimization group intelligent algorithm
CN107179464B (en) * 2017-07-20 2019-10-18 南方电网科学研究院有限责任公司 A kind of detection method of the voltage sensitivity of grid nodes
CN107612016B (en) * 2017-08-08 2020-01-14 西安理工大学 Planning method of distributed power supply in power distribution network based on maximum voltage correlation entropy
CN107394797A (en) * 2017-08-30 2017-11-24 华侨大学 A kind of appraisal procedure for the distributed power source access power distribution network for considering idle work optimization
CN107766941A (en) * 2017-10-31 2018-03-06 天津大学 A kind of facility site selecting method based on genetic algorithm
CN108599235B (en) * 2018-04-20 2021-03-09 国网湖北省电力有限公司宜昌供电公司 Constant volume method for distributed photovoltaic network access
CN109447307A (en) * 2018-08-24 2019-03-08 广西大学 It is a kind of to be colonized the distributed generation resource site selection model optimization methods of Competitive Algorithms based on improving empire
CN109409616A (en) * 2018-11-22 2019-03-01 南方电网科学研究院有限责任公司 A kind of distributed generation resource addressing constant volume method and Related product
CN109412207A (en) * 2018-12-21 2019-03-01 南京工程学院 A kind of roof photovoltaic orderly accesses power distribution network addressing constant volume method
CN112365196A (en) * 2020-12-03 2021-02-12 国网河北省电力有限公司信息通信分公司 Spark-based power supply planning method for distributed improved differential evolution algorithm
CN113690930B (en) * 2021-07-27 2022-08-19 国网江苏省电力有限公司扬州供电分公司 NSGA-III algorithm-based medium and long term locating and sizing method for distributed photovoltaic power supply

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103606014A (en) * 2013-10-18 2014-02-26 国家电网公司 Multi-target-based island distributed generation optimization method

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103606014A (en) * 2013-10-18 2014-02-26 国家电网公司 Multi-target-based island distributed generation optimization method

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
A new crowded comparison operator in constrained multiobjective optimization for capacitors sizing and siting in electrical distribution systems;Salvatore Favuzza, Mariano Giuseppe Ippolito, Eleonora Riva;《INNOVATIONS IN APPLIED ARTIFICIAL INTELLIGENCE》;20051231;第3533卷;第678-680页 *
Comparative Application of Multi-Objective Evolutionary Algorithms to the Voltage and Reactive Power Optimization Problem in Power Systems;S.B.D.V.P.S.Anauth, Robert T.F.Ah King;《Lecture Notes in Computer Science》;20101231;第6457卷;第424-434页 *
Crowded comparison operators for constraints handling in NSGA-II for optimal design of the compensation system in electrical distribution networks;S.Favuzza, M.G.Ippolito, E.Riva Sanseverino;《ADVANCED ENGINEERING INFORMATICS》;20060430;第20卷(第2期);第201-220页 *
Locating and Capacity Determination of Distributed Generations Using None-Dominated Sorting Genetic Algorithm;Farzad Banihashemi, Hamid Lesani, Amir Salar Makhzani;《IEEE GRENOBLE POWERTECH (POWERTECH)》;20131231;第1-8页 *
Multi Objective Distributed Generation Planning Using NSGA-II;Muhammad Ahmadi等;《INTERNATIONAL POWER ELECTRONICS AND MOTION CONTROL CONFERENCE》;20081231;第1-5卷(第13期);第1847-1851页 *

Also Published As

Publication number Publication date
CN105207195A (en) 2015-12-30

Similar Documents

Publication Publication Date Title
CN105207195B (en) A kind of power distribution network distributed power source addressing constant volume method
Zhou et al. Distributionally robust co-optimization of energy and reserve for combined distribution networks of power and district heating
CN106372742A (en) Power-to-gas multi-source energy storage type microgrid day-ahead optimal economic dispatching method
CN109510196B (en) Fault recovery game model based on electric-gas coupling system
CN106773704A (en) Multisystem combined Optimization Scheduling and device
CN106529737A (en) Planning and distribution method for peak load regulation power source on supply side of power distribution network
CN108876026A (en) Take into account the electricity optimization configuration method sent a telegram here outside extra-high voltage grid and area
CN109034587A (en) A kind of active distribution system Optimization Scheduling for coordinating a variety of controllables
CN112821470A (en) Micro-grid group optimization scheduling strategy based on niche chaos particle swarm algorithm
Xiong et al. Risk-based multi-objective optimization of distributed generation based on GPSO-BFA algorithm
Dong et al. Hierarchical multi-objective planning for integrated energy systems in smart parks considering operational characteristics
Yuan et al. Hybrid AC/DC microgrid energy management based on renewable energy sources forecasting
Ikram et al. Optimizing energy consumption in smart homes: Load scheduling approaches
Pandya et al. Stochastic wind-thermal power plants integrated multi-objective optimal power flow
Wei et al. Bi-level hybrid stochastic/robust optimization for low-carbon virtual power plant dispatch
Huang et al. Constrained multiobjective optimization for microgrid based on nondominated immune algorithm
Chen et al. Optimal dispatch of medium-voltage microgrid using an adaptive PSO algorithm
Li et al. The expansion planning of wind-thermal co-generation system based on harmony search algorithm under smart grid
Pandya et al. Stochastic renewable energy resources integrated multi-objective optimal power flow
Sedaghati et al. Study of network reconfiguration in distribution systems using an adaptive modified firefly algorithm
Miracle et al. Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System
Ye et al. Research on optimal design and control method of integrated energy system based on improved cloud adaptive particle swarm
Wenyue et al. Optimal scheduling strategy for virtual power plant considering voltage control
CN110365007B (en) Battery energy storage system capacity planning method for IEEE-33 node system
Li et al. Multi-objective optimal operation of combined cooling, heating and power microgrid

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant