CN110556847A - Energy storage system planning operation joint optimization method and system in photovoltaic-containing power distribution network - Google Patents
Energy storage system planning operation joint optimization method and system in photovoltaic-containing power distribution network Download PDFInfo
- Publication number
- CN110556847A CN110556847A CN201910905638.2A CN201910905638A CN110556847A CN 110556847 A CN110556847 A CN 110556847A CN 201910905638 A CN201910905638 A CN 201910905638A CN 110556847 A CN110556847 A CN 110556847A
- Authority
- CN
- China
- Prior art keywords
- model
- planning
- energy storage
- optimal
- storage system
- 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.)
- Granted
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 100
- 238000009826 distribution Methods 0.000 title claims abstract description 64
- 238000005457 optimization Methods 0.000 title claims abstract description 60
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 18
- 238000004458 analytical method Methods 0.000 claims abstract description 12
- 208000011580 syndromic disease Diseases 0.000 claims description 28
- 230000006870 function Effects 0.000 claims description 24
- 238000009434 installation Methods 0.000 claims description 21
- 230000008569 process Effects 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 5
- 238000003860 storage Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000009977 dual effect Effects 0.000 description 16
- 238000005520 cutting process Methods 0.000 description 11
- 238000003462 Bender reaction Methods 0.000 description 5
- 238000010276 construction Methods 0.000 description 3
- 238000007599 discharging Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000007728 cost analysis Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000002040 relaxant effect Effects 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B10/00—Integration of renewable energy sources in buildings
- Y02B10/10—Photovoltaic [PV]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The method comprises the steps of establishing a three-layer robust optimization model for planning and operating the energy storage system of the power distribution network, wherein the three-layer robust optimization model comprises a main planning problem model, a safety check sub-problem model and an optimal economic operation sub-problem model; the optimal solution is obtained through three-layer iteration solution by three-layer decomposition of the robust optimization model and considering the worst scenes of different conditions under the voltage out-of-limit safety analysis and the economic optimal operation. The source load uncertainty is fully considered, an optimal configuration scheme under the worst scene is sought on the basis of ensuring that the voltage out-of-limit problem does not occur to the power distribution network, but the problem is complicated due to excessive variable dimensions, so that the original problem is decomposed into a main planning problem, a sub-safety check problem and an optimal sub-economic operation problem through a decomposition theory, and convenient, rapid and effective iterative solution is achieved.
Description
Technical Field
the disclosure relates to the technical field of power distribution network energy storage, in particular to a method and a system for jointly optimizing planning and operation of an energy storage system in a photovoltaic power distribution network.
background
with the massive access of high-permeability distributed photovoltaic, the problem of voltage out-of-limit of a power distribution network becomes more serious. In recent years, however, the energy storage technology is continuously mature, the cost is continuously reduced, and the optical storage integrated system is popularized, so that the problem that the voltage of a power distribution network is out of limit is alleviated, and an operator can perform arbitrage operation through the low-storage high-power characteristic of the energy storage system. However, multiple uncertainties such as source load and the like in the power distribution network bring huge challenges to planning and operation optimization of the energy storage system, so that the energy storage system cannot give full play to the comprehensive value of the energy storage system. Therefore, the deep research on the planning method of the energy storage system in the power distribution network has important significance for solving the problem of voltage out-of-limit of the power distribution network.
currently, the mainstream solutions are the following:
(1) photovoltaic is consumed by adding a grid structure and load transfer, but the transformation cost is high, and economic benefits are difficult to achieve;
(2) the photovoltaic self-participating reactive power regulation and even cutting active power in a key time period solves the overvoltage problem, but the scheme can possibly damage the economic benefit of a photovoltaic investor, and how the photovoltaic is fair and actively participates in the regulation is always a problem to be solved.
disclosure of Invention
The invention aims to provide a combined optimization method for planning and operating an energy storage system in a photovoltaic power distribution network, which comprehensively considers the source load uncertainty and the energy storage system planning problem of the energy storage system operation condition, establishes a corresponding model and solves the corresponding model to obtain an optimal configuration scheme.
In order to achieve the above object, an embodiment of the present specification provides a combined optimization method for planning and operating an energy storage system in a power distribution network including a photovoltaic, which is implemented by the following technical scheme:
the method comprises the following steps:
establishing a three-layer robust optimization model for planning of the energy storage system of the power distribution network, wherein the three-layer robust optimization model comprises a main planning problem model, a safety check sub-problem model and an optimal economic operation sub-problem model;
The planning main problem model takes the installation position and the installation capacity of the energy storage system of the power distribution network as decision variables;
The safety syndrome problem model is used for verifying the voltage out-of-limit problem of the current power distribution network energy storage system under the worst condition until the voltage out-of-limit problem is solved;
The economic optimal sub-problem model is used for obtaining the economic optimal operation condition of the power distribution network under the worst condition of the current power distribution network energy storage system configuration;
The optimal installation position and the optimal installation capacity of the energy storage system of the power distribution network are obtained through three-layer iteration solution by decomposing three layers of the robust optimization model and considering the worst scenes of different conditions under the voltage out-of-limit safety analysis and the economic optimal operation.
In the further technical scheme, the process of finding the optimal solution through three-layer iterative solution comprises the following steps:
The method comprises the steps that an initialized energy storage system configuration scheme for planning a main problem model is brought into a safety check subproblem model, safety check is carried out on the subproblem model firstly, whether decision variables of the planned main problem model can meet the voltage non-out-of-limit requirement in the worst scene is judged, if the decision variables cannot meet the voltage non-out-of-limit requirement, a feasible cut is returned, and the planned main problem model configuration scheme is adjusted until all constraint conditions are met;
And if the decision variables of the planning main problem model can meet the safety syndrome problem model, entering an economic optimal sub problem model, further performing economic optimization calculation, and returning a feasible cut or an optimal cut to the planning main problem model until an optimal solution is found.
according to the further technical scheme, the objective function of the planning main problem model aims to minimize the sum of the investment cost and the objective function value of the economic optimal sub-problem model in typical days.
according to the further technical scheme, the constraint conditions of the planning main problem model comprise planning investment constraint, a safety inspection feasible cut set and an economic optimal feasible cut set or economic optimal cut set.
in a further technical scheme, the decision variable constraint conditions of the planning main problem model are as follows: the rated power and the rated capacity of the stored energy are both greater than or equal to zero; and the objective function value of the economic optimal subproblem model is more than or equal to the set minimum value.
In a further technical scheme, in order to enable the configuration scheme of the planning main problem model to be feasible, a relaxation variable is introduced, and the maximum value of the introduced relaxation variable serving as an objective function is a target.
in a further technical solution, the constraint of the safety syndrome problem model objective function includes: voltage upper and lower limit constraints, branch power flow constraints and energy storage system operation constraints.
In a further technical scheme, the economic optimal sub-problem model takes the lowest operation cost of the power distribution network as a target, and the constraint comprises the following steps: voltage upper and lower limit constraints, branch power flow constraints and energy storage system operation constraints.
The embodiment of the specification provides a combined optimization system for planning and operating an energy storage system in a photovoltaic power distribution network, which is realized by the following technical scheme:
the method comprises the following steps:
a three-layer robust optimization model building module configured to: establishing a three-layer robust optimization model for planning of the energy storage system of the power distribution network, wherein the three-layer robust optimization model comprises a main planning problem model, a safety check sub-problem model and an optimal economic operation sub-problem model;
The planning main problem model takes the installation position and the installation capacity of the energy storage system of the power distribution network as decision variables;
the safety syndrome problem model is used for verifying the voltage out-of-limit problem of the current power distribution network energy storage system under the worst condition until the voltage out-of-limit problem is solved;
the economic optimal sub-problem model is used for obtaining the economic optimal operation condition of the power distribution network under the worst condition of the current power distribution network energy storage system configuration;
a three-layer robust optimization model solving module configured to: the optimal solution is obtained through three-layer iteration solution by three-layer decomposition of the robust optimization model and considering the worst scenes of different conditions under the voltage out-of-limit safety analysis and the economic optimal operation.
Compared with the prior art, the beneficial effect of this disclosure is:
The invention provides a brand-new min-max-min power distribution network energy storage system planning three-layer robust optimization model, which fully considers source charge uncertainty, introduces the source charge uncertainty into a planning model in an uncertain set mode, stores energy when photovoltaic output is excessive by configuring the capacity of an energy storage system, and releases energy when the load is heavy, so that an optimal configuration scheme of the energy storage system under the worst scene is sought on the basis of ensuring that the power distribution network does not have the problem of voltage out-of-limit, but the problem is complicated due to excessive variable dimensions, so that an original problem is decomposed into a planning main problem model, a safety syndrome problem model and an economic operation optimal sub-problem model through a decomposition theory, the fast and effective iterative solution is facilitated, and the optimal energy storage system configuration scheme is obtained.
The planning configuration and the operation control of the energy storage system are simultaneously considered, the planning-operation combined optimization method of the energy storage system is provided, the adverse scenes obtained by the voltage out-of-limit safety analysis and the economic analysis are different, the influence of uncertainty on the operation cost is only considered in the traditional two-layer decomposition, and the influence of uncertainty on the voltage of the power distribution network cannot be simultaneously considered, so that the three-layer solving framework provided by the disclosure is convenient to simultaneously consider the uncertainty problems of the safety analysis and the economic analysis under two different conditions.
According to the method and the device, based on the solution of the energy storage system planning problem, the cost analysis is carried out on the risk and the income of the energy storage system planning problem in view of different uncertain set intervals, so that a decision maker can make a better decision, and the conservation of robust optimization is reduced.
The method comprehensively considers source load uncertainty, provides a three-layer robust planning-operation optimization model, and seeks an energy storage system configuration scheme under the worst scene aiming at the problem of energy storage planning-operation combined optimization. Safety constraints are added in the optimization problem by means of Benders decomposition, and economic optimization is taken into account, so that the problem is more reasonable and can be solved quickly. The traditional planning method cannot fully consider source load uncertainty and can not better deal with the problem that the source load uncertainty generates voltage out-of-limit on the power distribution network.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a detailed flow chart of planning-operation joint optimization provided by an embodiment of the present disclosure;
FIG. 2 is a four-season typical sunlight photovoltaic output robust optimization result under the planning method of the embodiment of the disclosure;
FIG. 3 is a four-season typical daily load power robustness optimization result under the planning method of the embodiment of the disclosure;
FIG. 4 is a node voltage condition under a planning method according to an embodiment of the present disclosure;
FIG. 5 shows the SOC condition of the energy storage system under the planning method of the embodiment of the disclosure;
FIG. 6 shows the charging and discharging power of the energy storage system according to the planning method of the embodiment of the disclosure;
fig. 7 is an energy storage system configuration scheme under a planning method of an embodiment of the disclosure.
Detailed Description
it should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
example of implementation 1
The embodiment discloses a combined optimization method for planning and operating an energy storage system in a photovoltaic power distribution network, source load uncertainty cannot be fully considered in the prior art, a traditional method is difficult to process, and in order to solve the technical problems, the energy storage planning-operation combined optimization method comprehensively considering the source load uncertainty based on three-layer decomposition is provided.
Referring to the attached figure 1, the three-layer decomposition-based high-permeability photovoltaic power distribution network energy storage system planning-operation combined optimization method comprises the following steps:
(1) Establishing an energy storage system planning model which can be mainly divided into an energy storage system planning investment model, a power distribution network safety check operation model and a power distribution network optimal economic operation model, respectively representing by a planning main problem model, a safety check sub-problem model and an economic operation optimal sub-problem model in order to enable context to be consistent, and then carrying out iterative solution through a three-layer decomposition algorithm;
(2) The planning main problem model is an energy storage system planning problem which takes the installation position and the installation capacity of an energy storage system as decision variables, and a current optimal energy storage system configuration scheme is obtained based on the constraints generated by the lower sub-problem model, but the scheme is not necessarily the global optimal;
(3) The safety syndrome problem model is used for verifying the voltage out-of-limit problem of the optimal energy storage system configuration scheme transmitted by the main problem model under the worst condition, and generating safety operation cut constraint to be transmitted to the main problem model until the voltage out-of-limit problem is solved;
(4) the economic optimal sub-problem model is used for obtaining the economic optimal operation condition of the power distribution network under the worst condition of the optimal energy storage system configuration scheme transmitted based on the safety check sub-problem model, so that whether the scheme is globally optimal or not is verified;
through three-layer decomposition, the worst scenes of different conditions under the voltage out-of-limit safety analysis and the economic optimal operation can be considered at the same time, and the optimal solution can be found through three-layer iterative solution.
Further, bringing the cutting constraints generated by the sub-problem model into the main problem model, and sequentially iterating until the problem is optimal, specifically:
The main problem model initializes the configuration scheme of the energy storage system and brings the main problem model into the sub problem model, the sub problem model firstly carries out security check, and whether the decision variable of the main problem model can meet the requirement of not exceeding the limit of the voltage in the worst scene is judged. If the constraint conditions cannot be met, returning to the feasible cutting, and adjusting the configuration scheme of the main problem model until all the constraint conditions are met. And if the decision variables of the main problem model can meet the requirements of the safety syndrome problem model, entering an economic optimal sub problem model, further performing economic optimization calculation, and returning feasible cutting or optimal cutting to the main problem model until an optimal solution is found.
The constraint conditions of the first layer planning main problem model comprise planning investment constraint, a safety inspection feasible cut set, an economic optimal feasible cut set and an economic optimal cut set, and can be constructed in the following forms:
(1) An objective function:
wherein,
In the formula, CINrepresenting the investment cost of a main problem model, and being determined by the energy storage installation power and the installation capacity; cPAnd CEunit prices of unit energy storage power and capacity are respectively expressed; piand EiThe rated power and the rated capacity of the ith stored energy are represented; n represents the number of energy storage installations; zjThe introduced auxiliary variable is related to the objective function value of the jth economic optimum subproblem model; day represents typical days of selection.
(2) And (4) decision variable constraint:
Pi≥0 (3)
Ei≥0 (4)
Zjphi is more than or equal to phi, and phi is a very small value (5)
(3) the safety provided by the second layer of safety syndrome problem model is feasible:
In the formula,expressing the sum of relaxation variables in the second layer sub-problem model, wherein the relaxation variables are auxiliary variables introduced for solving the problem of the undesolvable condition possibly occurring under the strong voltage constraint, and relaxing the voltage upper and lower limit constraint to enable the problem to be solved, and specifically expressed by the formulas (10) and (11);Showing a dual variable returned by the second layer of the subproblem model, wherein the dual variable is an auxiliary variable introduced into a dual linear programming problem and can refer to relevant books of operational research; x(k)And X(k-1)respectively representing the energy storage system configuration scheme generated by the main planning problem model of the kth iteration and the kth-1 iteration; g is the introduced coefficient matrix, which is described in detail later.
(4) the third layer of economic operation optimal subproblem model provides economic feasible cutting or economic optimal cutting:
Zj≥Λj+Πo,j*G*(X(k)-X(k-1)) (8)
In the formula,Representing the sum of relaxation variables introduced in the third layer of sub-problem models; lambdajan objective function value representing a third layer of sub-problem models; II typef2,jand pio,jRepresenting the dual variables returned by the third layer of sub-problem models. The dual variables are auxiliary variables that are automatically generated in the optimization process.
In order to enable the configuration scheme obtained by the first layer planning main problem model to be feasible, the second layer safety syndrome problem model introduces a relaxation variable, and the construction problem is as follows:
(1) An objective function:
in the formula, S1,i(t) and S2,iand (t) respectively representing relaxation variables introduced into the constraints of the second-layer subproblem model so as to deal with the situation that the second-layer subproblem model is not solvable.
(2) And (3) limiting the upper and lower voltage limits:
Vi(t)+S1,i(t)≥Vmin (10)
Vi(t)-S2,i(t)≤Vmax (11)
in the formula, VminAnd VmaxRespectively representing the upper and lower limit constraints of the voltage; vi(t) represents a voltage value of the ith node at the t-th time.
(3) Branch flow constraint:
V1(t)＝1 (13)
Qi+1(t)-Qi(t)＝-qi(t) (15)
In the formula, riAnd xiRespectively representing the resistance and reactance of the branch i; pi(t) and Qi(t) respectively representing the active power and the reactive power of the branch i at the moment t; v0A reference value representing a voltage;Representing the active power output of the ith photovoltaic at the moment t;Representing the discharge power of the ith energy storage system at the moment t;representing the charging power of the ith energy storage system at the moment t; p is a radical ofi(t) and qiand (t) represents the load active power and reactive power on the node i at the moment t.
(4) and (4) operation restraint of the energy storage system:
In the formula, Ci(t) represents the electric quantity of the energy storage system i at the moment t; etachAnd ηdisrespectively representing the charging and discharging efficiency of the energy storage system; sSOCmaxand SSOCminrepresenting upper and lower limits of the state of charge of the energy storage system;Indicating the rated capacity, P, of the energy storage system ii Nrepresenting the power rating of the energy storage system i.
(5) other constraints are:
S1,i(t)≥0 (23)
S2,i(t)≥0 (24)
In the formula, S1,i(t) and S2,i(t) represents the relaxation variations introduced by the second layer subproblem modelamount of the compound (A).
The third layer of economic operation optimal sub-problem model is used for solving the optimal solution of the economic operation problem when the target value of the second layer of sub-problem model is 0, and the construction problem is as follows:
(1) An objective function:
Wherein,
In the formula, COPrepresenting the operating cost of the power distribution network; price (t) represents the electricity purchase and sale price of the first node at time t, P1(t) represents the value of the power flowing in or out of the first node at time t, ULAnd UPVand representing a load power uncertain set and a photovoltaic output uncertain set.
(2) constraint conditions are as follows:
The same constraints as the second-tier safety syndrome problem model, namely (10) - (22), except that there are no slack variables in (10) and (11), are of the form:
Vi(t)≥Vmin (27)
Vi(t)≤Vmax (28)
To better introduce the mechanism of the three-layer decomposition method proposed by the present invention, the following explanation is made in a compact form of the three-layer decomposition problem, specifically as follows:
the energy storage system planning-operation combined optimization model provided by the invention can be written as the following min-max-min optimization problem, which is the original problem form of the optimization problem:
In the formula, X and Y respectively represent a planning problem decision variable and an operation problem decision variable; a and B respectively represent coefficient matrixes of decision variables; D. e, F, G, K, M, F, G, h, pPV、pLCoefficient matrixes corresponding to equality and inequality constraints about Y are respectively; phi represents a constraint set of decision variables of the planning problem; Ω represents a constraint set of running problem decision variables; r is a real number set; u shapePVAnd ULrespectively representing a source load uncertainty set.
The problem is a three-layer nonlinear non-convex problem, and because the problem variable dimension is too much, the solving is difficult, so the method decomposes the original problem into the three-layer problem, and efficiently solves the problem through iteration. Because multiple uncertainties are considered, but the worst uncertainty scene considered by the safety syndrome problem model and the economic optimum syndrome problem model is different, for convenience, the Benders decomposition theory is adopted to decompose the original problem into an investment planning main problem model, a safety syndrome problem model and an economic operation optimum syndrome problem model. The main problem model is an energy storage system planning problem which takes the installation position and the installation capacity of the energy storage system as decision variables; the safety syndrome problem model is used for verifying the voltage out-of-limit problem under the worst condition of the current configuration until the voltage out-of-limit problem is solved; the optimal economic operation sub-problem model is used for obtaining the optimal economic operation condition of the power distribution network under the worst condition of the current configuration. Through three-layer decomposition, the worst scenes of different conditions under the conditions of voltage out-of-limit safety analysis and economic optimal operation can be considered at the same time, but the traditional two-layer decomposition is not easy to realize, and the optimal solution can be found through three-layer iterative solution.
in order to realize the above process, the following decomposition is further carried out:
(1) First layer planning main problem model
The first layer planning main problem model obtains an optimal configuration scheme of the energy storage system, and the considered constraint conditions comprise planning layer decision variable constraint, feasible cutting provided by the second layer safety check sub problem model and optimal cutting provided by the third layer economic operation optimal sub problem model. The first layer planning main problem model aims at minimizing the sum of investment and operation costs of an energy storage system in a planning period, an equation (30) is an objective function of the first layer main problem model, equations (31) and (32) are planning layer decision variable constraints, equation (33) represents a safety feasible cut, equations (34) and (35) represent an economic feasible cut and an economic optimum cut, only one of equations (33), (34) and (35) is generated by the sub problem model in each iteration and added into the constraints of the main problem model, and the Benders cut constraint is not generated in the first iteration.
1) planning layer decision variable constraints
X≥0 (31)
ZjPhi is a very small value (32)
2) Safety feasible cut provided by second-layer safety syndrome problem model
3) Economically feasible cutting or economically optimal cutting provided by third-layer economic operation optimal sub-problem model
Zj≥Λj+Πo,j*G*(X(k)-X(k-1)) (35)
wherein,andthe sum of all constraint relaxation amounts in the second layer safety check subproblem model and the third layer economic operation optimal subproblem model is the optimization problemA target function value; zjIntroducing the variable as a new variable into an objective function of the main problem model to represent the running cost; lambdajRepresenting the objective function value obtained by the last iteration economy running of the optimal subproblem model;and pioAnd dual variables obtained from the second-layer safety syndrome problem model and the third-layer economic operation optimal syndrome problem model are respectively obtained.
(2) second-layer safety syndrome problem model
and (3) substituting the optimal configuration scheme X (the k-1 st iteration) obtained by the first layer of planning main problem model into a second layer of safety check subproblem model, wherein the objective function of the second layer of subproblem model is to meet the condition that the sum of relaxation quantity introduced by all constraint conditions under the worst scene is minimum, and the formula (36) is the objective function and the constraint conditions of the second layer of subproblem model.
Wherein,a set of decision variables representing a second layer of sub-problem models,The sum of the relaxation variables after the configuration scheme obtained by planning the main problem model is substituted into the second layer of the sub-problem model is calculated. It can be seen that the second layer sub-problem model is a max-min optimization problem, which is not easy to solve, so the original problem is changed into the easily-solved max problem by the dual theory. To facilitate the transformation, the original problem is first changed into a dual strict form, and the transformation is as follows:
due to P in the original problemiAnd QiThe symbol of (A) is undetermined, does not meet the dual requirement, so some modifications are made to Pi＝Pi'-Pi”，Qi＝Qi'-Qi", wherein Pi'≥0，Pi”≥0，Qi'≥0，QiIf' is greater than or equal to 0, the decision variable of the second layer subproblem model is changed intoAnd Y is1' > 0, and the dual treatment can obtain the following form:
s.t.
a1≥0,a2≥0,a3≥0,a4≥0,a5≥0,a6≥0,a7≥0,a8≥0 (39)
-DTa1+ΕTa2-ETa3-FTa4+KTa5-KTa6+MTa7-MTa8＝B (40)
Wherein,the dual variable array obtained for the dual problem is known to have the product of the dual variable and the variable 0-1 in the uncertain set and bilinear form by the target function of the dual problemThe second layer of sub-problem model is a nonlinear optimization problem which is difficult to solve, and dual questions are asked by using a large M methodQuestions (38) - (40) translate into a linear problem.
so as to obtain the composite material with the characteristics of,
s.t.
a1≥0,a2≥0,a3≥0,a4≥0,a5≥0,a6≥0,a7≥0,a8≥0 (47)
-DTa1+ETa2-ETa3-FTa4+KTa5-KTa6+MTa7-MTa8＝B (48)
the above equations (46) to (50) are linear problems obtained by converting the equations (41) to (45) into the equations (38) to (40), where M is a constant having a sufficiently large value.
(3) Third-layer economic operation optimal subproblem model
The third layer of economic optimal sub-problem model seeks an economic optimal solution on the basis of meeting the safety syndrome problem model, the objective function of the problem is the economic optimal operation under the maximum uncertainty, and a Benders cut (a feasible cut or an optimal cut) is added to the planning main problem model once every time, wherein the feasible cut is generated when the economic optimal sub-problem model is not feasible; when the economically optimal sub-problem model is feasible, the optimal cut can be generated.
And the economic operation optimal sub-problem model is used for optimizing the economic optimization after the energy storage configuration meets the safety check, so that the operation cost under the worst scene is obtained, whether convergence occurs is judged, and otherwise, the optimal cut is returned to the planning main problem model. Equation (51) is the objective function of the optimal subproblem model for economic operation and its constraint conditions, and is in the form:
Wherein,set of decision variables, Λ, representing a third layer of sub-problem modelsjAnd the operation cost is obtained by substituting the configuration scheme obtained by planning the main problem model into the third layer of the sub problem model.
the max-min form of the problem still needs to be processed by a dual theory to become the max problem, and the processing mode is the same as that of the safety checker problem model, and is not repeated herein.
here, we derive the construction form of Benders' cut, taking the optimal cut as an example, the form is as follows:
suppose that at the k-1 iteration, X ═ X(k-1)The optimization result of the optimal subproblem model for economic operation is lambdajLet Δ X be X-X(k-1)Then equation (52) can be converted to:
Similarly, the feasible segmentations returned by the safety syndrome problem model and the economic feasibility syndrome problem model are as follows:
in a specific implementation example, the four-season typical sunlight photovoltaic output robust optimization result is shown in fig. 2; the result of the robust optimization of the load power in the typical day of the four seasons is shown in the attached figure 3; the node voltage situation is shown in figure 4, and the problem of voltage out-of-limit is reasonably solved; the SOC situation of the energy storage system under the planning method of the present disclosure is shown in fig. 5; the charging and discharging power conditions of the energy storage system under the planning method of the invention are shown in the attached figure 6; the energy storage system configuration scheme under the planning method of the invention is shown in fig. 7, and the optimal configuration of the energy storage system can be changed under the adjustment based on different uncertainties.
Example II
the embodiment of the specification provides a combined optimization system for planning and operating an energy storage system in a photovoltaic power distribution network, which is realized by the following technical scheme:
The method comprises the following steps:
A three-layer robust optimization model building module configured to: establishing a three-layer robust optimization model for planning of the energy storage system of the power distribution network, wherein the three-layer robust optimization model comprises a main planning problem model, a safety check sub-problem model and an optimal economic operation sub-problem model;
the planning main problem model takes the installation position and the installation capacity of the energy storage system of the power distribution network as decision variables;
the safety syndrome problem model is used for verifying the voltage out-of-limit problem of the current power distribution network energy storage system under the worst condition until the voltage out-of-limit problem is solved;
The optimal economic operation sub-problem model is used for obtaining the optimal economic operation condition of the power distribution network under the worst condition of the current power distribution network energy storage system configuration;
A three-layer robust optimization model solving module configured to: the optimal solution is obtained through three-layer iteration solution by three-layer decomposition of the robust optimization model and considering the worst scenes of different conditions under the voltage out-of-limit safety analysis and the economic optimal operation.
The specific implementation process of the module in this embodiment can be referred to as the specific formula expression in the first embodiment, and is not described in detail here.
Example III
The embodiment of the present specification provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for jointly optimizing the planned operation of the energy storage system in the photovoltaic power distribution network according to the first embodiment.
Example four
The embodiment of the present specification provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of implementing the method for jointly optimizing the planning and operation of an energy storage system in a power distribution grid including photovoltaic in example one.
It is to be understood that throughout the description of the present specification, reference to the term "one embodiment", "another embodiment", "other embodiments", or "first through nth embodiments", etc., is intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, or materials described may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. the method for jointly optimizing the planning and operation of the energy storage system in the photovoltaic power distribution network is characterized by comprising the following steps:
Establishing a three-layer robust optimization model for planning of the energy storage system of the power distribution network, wherein the three-layer robust optimization model comprises a main planning problem model, a safety check sub-problem model and an optimal economic operation sub-problem model;
The planning main problem model takes the installation position and the installation capacity of the energy storage system of the power distribution network as decision variables;
The safety syndrome problem model is used for verifying the voltage out-of-limit problem of the current power distribution network energy storage system under the worst condition until the voltage out-of-limit problem is solved;
the optimal economic operation sub-problem model is used for obtaining the optimal economic operation condition of the power distribution network under the worst condition of the current power distribution network energy storage system configuration;
the optimal installation position and the optimal installation capacity of the energy storage system of the power distribution network are obtained through three-layer iteration solution by decomposing three layers of the robust optimization model and considering the worst scenes of different conditions under the voltage out-of-limit safety analysis and the economic optimal operation.
2. the method for jointly optimizing the planning and operation of the energy storage system in the photovoltaic power distribution network as claimed in claim 1, wherein the process of finding the optimal solution through three-layer iterative solution comprises the following steps:
the method comprises the steps that an initialized energy storage system configuration scheme for planning a main problem model is brought into a safety check subproblem model, safety check is carried out on the subproblem model firstly, whether decision variables of the planned main problem model can meet the voltage non-out-of-limit requirement in the worst scene is judged, if the decision variables cannot meet the voltage non-out-of-limit requirement, a feasible cut is returned, and the planned main problem model configuration scheme is adjusted until all constraint conditions are met;
and if the decision variables of the planning main problem model can meet the safety syndrome problem model, entering an economic operation optimal sub problem model, further performing economic optimization calculation, and returning a feasible cut or an optimal cut to the planning main problem model until an optimal solution is found.
3. The method of claim 1, wherein the objective function of the main problem model is designed to minimize the sum of the investment cost and the objective function value of the optimal sub-problem model for economic operation in typical days.
4. The method of claim 1, wherein the planning major problem model constraints comprise planning investment constraints, security check feasible cut sets, and economically optimal feasible cut sets or economically optimal cut sets.
5. The method for jointly optimizing the planning and operation of the energy storage system in the photovoltaic power distribution network, according to claim 4, wherein the decision variable constraints of the planning main problem model are as follows: the rated power and the rated capacity of the stored energy are both greater than or equal to zero; and the objective function value of the optimal subproblem model for economic operation is more than or equal to the set minimum value.
6. the method for jointly optimizing the planning and operation of the energy storage system in the photovoltaic power distribution network, according to claim 1, wherein the safety syndrome problem model introduces slack variables for enabling the configuration scheme of the planning main problem model, and the objective function is the maximum value of the introduced slack variables;
The constraints of the safety syndrome problem model objective function comprise: voltage upper and lower limit constraints, branch power flow constraints and energy storage system operation constraints.
7. The method of claim 1, wherein the economic operation optimization sub-problem model aims at minimizing the operation cost of the distribution network, and the constraints comprise: voltage upper and lower limit constraints, branch power flow constraints and energy storage system operation constraints.
8. energy storage system planning operation joint optimization system among distribution network including photovoltaic, characterized by includes:
A three-layer robust optimization model building module configured to: establishing a three-layer robust optimization model for planning of the energy storage system of the power distribution network, wherein the three-layer robust optimization model comprises a main planning problem model, a safety check sub-problem model and an optimal economic operation sub-problem model;
The planning main problem model takes the installation position and the installation capacity of the energy storage system of the power distribution network as decision variables;
The safety syndrome problem model is used for verifying the voltage out-of-limit problem of the current power distribution network energy storage system under the worst condition until the voltage out-of-limit problem is solved;
the optimal economic operation sub-problem model is used for obtaining the optimal economic operation condition of the power distribution network under the worst condition of the current power distribution network energy storage system configuration;
A three-layer robust optimization model solving module configured to: the optimal solution is obtained through three-layer iteration solution by three-layer decomposition of the robust optimization model and considering the worst scenes of different conditions under the voltage out-of-limit safety analysis and the economic optimal operation.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method for jointly optimizing the planning and operation of energy storage systems in a distribution network containing photovoltaic power according to any one of claims 1 to 7.
10. a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for jointly optimizing the planning and operation of energy storage systems in a distribution network comprising photovoltaic power, as set forth in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910905638.2A CN110556847B (en) | 2019-09-19 | 2019-09-19 | Energy storage system planning operation joint optimization method and system in photovoltaic-containing power distribution network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910905638.2A CN110556847B (en) | 2019-09-19 | 2019-09-19 | Energy storage system planning operation joint optimization method and system in photovoltaic-containing power distribution network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110556847A true CN110556847A (en) | 2019-12-10 |
| CN110556847B CN110556847B (en) | 2021-04-30 |
Family
ID=68741235
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910905638.2A Active CN110556847B (en) | 2019-09-19 | 2019-09-19 | Energy storage system planning operation joint optimization method and system in photovoltaic-containing power distribution network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110556847B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111725826A (en) * | 2020-07-02 | 2020-09-29 | 国网青海省电力公司 | Energy storage comprehensive constant volume method based on high-proportion photovoltaic access power system |
| CN112581039A (en) * | 2020-12-30 | 2021-03-30 | 国网河北省电力有限公司南宫市供电分公司 | Active power distribution network planning and operation joint optimization method |
| CN113421004A (en) * | 2021-06-30 | 2021-09-21 | 国网山东省电力公司潍坊供电公司 | Transmission and distribution cooperative active power distribution network distributed robust extension planning system and method |
| CN113762792A (en) * | 2021-09-13 | 2021-12-07 | 山东大学 | Fixed and mobile hybrid energy storage system optimal configuration system and method |
| CN114048809A (en) * | 2021-11-10 | 2022-02-15 | 天津大学 | Hybrid power distribution network adaptability planning method and device considering interval voltage overlimit |
| CN114707783A (en) * | 2021-11-09 | 2022-07-05 | 天津大学 | Two-stage robust planning method for solar energy reduction equipment of regional electricity-heat comprehensive energy system |
| CN114759602A (en) * | 2022-04-24 | 2022-07-15 | 国网山东省电力公司潍坊供电公司 | Power distribution network acceptance capacity assessment method considering photovoltaic extreme scene |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20070037210A (en) * | 2005-09-30 | 2007-04-04 | 고려대학교 산학협력단 | Optimal allocation method of facts devices considering both corrective and preventive mode |
| CN107979111A (en) * | 2017-07-21 | 2018-05-01 | 天津大学 | A kind of energy management method for micro-grid based on the optimization of two benches robust |
| CN108964012A (en) * | 2017-05-22 | 2018-12-07 | 武汉大学 | The security constraint Unit Combination dual blank-holder decomposed based on Benders |
| CN109740786A (en) * | 2018-11-15 | 2019-05-10 | 国网江苏省电力有限公司电力科学研究院 | A kind of multiterminal flexible interconnection distribution network planning method and system |
-
2019
- 2019-09-19 CN CN201910905638.2A patent/CN110556847B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20070037210A (en) * | 2005-09-30 | 2007-04-04 | 고려대학교 산학협력단 | Optimal allocation method of facts devices considering both corrective and preventive mode |
| CN108964012A (en) * | 2017-05-22 | 2018-12-07 | 武汉大学 | The security constraint Unit Combination dual blank-holder decomposed based on Benders |
| CN107979111A (en) * | 2017-07-21 | 2018-05-01 | 天津大学 | A kind of energy management method for micro-grid based on the optimization of two benches robust |
| CN109740786A (en) * | 2018-11-15 | 2019-05-10 | 国网江苏省电力有限公司电力科学研究院 | A kind of multiterminal flexible interconnection distribution network planning method and system |
Non-Patent Citations (2)
| Title |
|---|
| ZHE LIN: "Optimal ESS allocation in distribution network using accelerated generalised Benders decomposition", 《IET GENERATION, TRANSMISSION & DISTRIBUTION》 * |
| 沙强益: "基于机会约束的考虑N–1安全约束的储能优化配置方法", 《工程科学与技术》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111725826A (en) * | 2020-07-02 | 2020-09-29 | 国网青海省电力公司 | Energy storage comprehensive constant volume method based on high-proportion photovoltaic access power system |
| CN112581039A (en) * | 2020-12-30 | 2021-03-30 | 国网河北省电力有限公司南宫市供电分公司 | Active power distribution network planning and operation joint optimization method |
| CN113421004A (en) * | 2021-06-30 | 2021-09-21 | 国网山东省电力公司潍坊供电公司 | Transmission and distribution cooperative active power distribution network distributed robust extension planning system and method |
| CN113762792A (en) * | 2021-09-13 | 2021-12-07 | 山东大学 | Fixed and mobile hybrid energy storage system optimal configuration system and method |
| CN114707783A (en) * | 2021-11-09 | 2022-07-05 | 天津大学 | Two-stage robust planning method for solar energy reduction equipment of regional electricity-heat comprehensive energy system |
| CN114707783B (en) * | 2021-11-09 | 2024-10-18 | 天津大学 | Two-stage robust planning method for solar energy absorption equipment of regional electric-thermal comprehensive energy system |
| CN114048809A (en) * | 2021-11-10 | 2022-02-15 | 天津大学 | Hybrid power distribution network adaptability planning method and device considering interval voltage overlimit |
| CN114048809B (en) * | 2021-11-10 | 2024-04-19 | 天津大学 | Series-parallel power distribution network adaptive planning method and device considering interval voltage beyond limit |
| CN114759602A (en) * | 2022-04-24 | 2022-07-15 | 国网山东省电力公司潍坊供电公司 | Power distribution network acceptance capacity assessment method considering photovoltaic extreme scene |
| CN114759602B (en) * | 2022-04-24 | 2024-04-05 | 国网山东省电力公司潍坊供电公司 | Power distribution network acceptance assessment method considering photovoltaic extreme scenes |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110556847B (en) | 2021-04-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110556847B (en) | Energy storage system planning operation joint optimization method and system in photovoltaic-containing power distribution network | |
| Sun et al. | Optimal local volt/var control for photovoltaic inverters in active distribution networks | |
| Gao et al. | Robust coordinated optimization of active and reactive power in active distribution systems | |
| Patrinos et al. | Stochastic MPC for real-time market-based optimal power dispatch | |
| CN107546743B (en) | Distributed power flow optimization method for radial power distribution network | |
| Erlich et al. | Optimal dispatch of reactive sources in wind farms | |
| CN107069814B (en) | Fuzzy opportunity constraint planning method and system for distribution network distributed power capacity distribution | |
| CN108306303A (en) | Voltage stability evaluation method considering load increase and new energy output randomness | |
| CN107330568A (en) | Energy storage, distributed power source and the power distribution network coordinated planning method decoupled based on Benders | |
| Tang et al. | Study on day-ahead optimal economic operation of active distribution networks based on Kriging model assisted particle swarm optimization with constraint handling techniques | |
| CN109829560B (en) | Renewable energy power generation cluster access planning method for power distribution network | |
| CN107947192A (en) | A kind of optimal reactive power allocation method of droop control type isolated island micro-capacitance sensor | |
| CN105207573A (en) | Quantitative optimal configuration method of wind-solar hybrid power system based on discrete probability model | |
| CN109002913A (en) | A kind of more virtual plant joint optimal operation methods | |
| CN111291978A (en) | Two-stage energy storage method and system based on Benders decomposition | |
| CN112467748A (en) | Double-time-scale distributed voltage control method and system for three-phase unbalanced active power distribution network | |
| Giannelos et al. | Option value of dynamic line rating and storage | |
| Kermani et al. | Energy management system for smart grid in the presence of energy storage and photovoltaic systems | |
| Hou et al. | Peer-to-peer energy trading among multiple microgrids considering risks over uncertainty and distribution network reconfiguration: A fully distributed optimization method | |
| Yumbla et al. | Optimal operational planning of distribution systems: A neighborhood search-based matheuristic approach | |
| Mellouk et al. | Overview of mathematical methods for energy management optimization in smart grids | |
| Bhende et al. | Optimal power flow management and control of grid connected photovoltaic-battery system | |
| CN116049298A (en) | Power distribution network carbon footprint analysis method and system of flexible power distribution substation carbon monitoring model | |
| CN112734590B (en) | Two-stage regional power grid reactive power optimization method based on interior point method and dynamic programming method of variable domain search | |
| Ramesh et al. | Cost Optimization by Integrating PV-System and Battery Energy Storage System into Microgrid using Particle Swarm Optimization |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |