CN110556847A - Energy storage system planning operation joint optimization method and system in photovoltaic-containing power distribution network - Google Patents

Abstract

This disclosure proposes a joint optimization method and system for energy storage system planning and operation in a photovoltaic distribution network, and establishes a three-layer robust optimization model for distribution network energy storage system planning, including a planning master problem model, a safety check sub-problem model and an economic Run the optimal sub-problem model; through the three-layer decomposition of the robust optimization model, while considering the worst scenarios of different situations under the safety analysis of voltage over-limit and economic optimal operation, the optimal solution is obtained through three-layer iterative solution. Fully consider the uncertainty of source load, on the basis of ensuring that the distribution network does not have the problem of voltage exceeding the limit, seek the optimal configuration scheme in the worst scenario, but this problem becomes complicated due to too many variable dimensions, so through Decomposition theory decomposes the original problem into the main planning problem, the safety verification sub-problem and the optimal sub-problem of economic operation, which is convenient for fast and effective iterative solution.

Description

Combined optimization method and system for energy storage system planning and operation in photovoltaic distribution network

technical field

The present disclosure relates to the technical field of distribution network energy storage, in particular to a joint optimization method and system for energy storage system planning and operation in a photovoltaic distribution network.

Background technique

With the large-scale access of high-penetration distributed photovoltaics, the voltage limit problem of the distribution network is becoming more and more serious. However, in recent years, the energy storage technology has been continuously matured and the cost has been continuously reduced, which has enabled the promotion of the integrated solar storage system. Carry out arbitrage operations based on the characteristics of reserves and high issuance. However, multiple uncertainties such as source and load in the distribution network have brought huge challenges to the planning and operation optimization of the energy storage system, making it impossible for the energy storage system to fully exert its comprehensive value. Therefore, it is of great significance to deeply study the planning method of energy storage system in distribution network to solve the problem of distribution network voltage exceeding the limit.

Currently, mainstream solutions include the following:

(1) To absorb photovoltaics by increasing the grid structure and load transfer, but the cost of transformation is relatively high, and it is difficult to have economic benefits;

(2) Photovoltaic itself participates in the regulation of reactive power, and even cuts active power in critical periods to solve the overvoltage problem, but this solution may damage the economic interests of photovoltaic investors. How to participate in the regulation fairly, justly and actively has always been a need to solve problem.

Contents of the invention

The purpose of the present invention is to provide a joint optimization method for energy storage system planning and operation in a photovoltaic distribution network, which comprehensively considers the energy storage system planning problem of source load uncertainty and energy storage system operation, establishes a corresponding model and solves it, and obtains the optimal solution. Configuration.

In order to achieve the above purpose, the implementation mode of this specification provides a joint optimization method for the planning and operation of the energy storage system in the photovoltaic distribution network, which is realized through the following technical solutions:

include:

Establish a three-layer robust optimization model for distribution network energy storage system planning, including the planning main problem model, the safety verification sub-problem model and the economic operation optimal sub-problem model;

The planning master problem model takes the distribution network energy storage system installation location and installation capacity as the decision variables;

The safety check sub-problem model is used to check the voltage limit problem of the current distribution network energy storage system configuration in the worst case until the voltage limit problem is solved;

The economic optimal sub-problem model is to obtain the economic optimal operation of the distribution network under the worst case of the current distribution network energy storage system configuration;

Through the three-layer decomposition of the robust optimization model, and considering the worst scenarios of different situations under the safety analysis of voltage violation and optimal economic operation, the optimal installation location of the energy storage system in the distribution network is obtained through three-layer iterative solution and installed capacity.

As a further technical solution, the process of finding the optimal solution through three-layer iterative solution is as follows:

The initial energy storage system configuration scheme of the planning main problem model is brought into the safety verification sub-problem model. If the requirement beyond the limit is not satisfied, return to feasible cut, and adjust the configuration scheme of the planning master problem model until all the constraints are met;

If the decision variables of the planning main problem model can satisfy the safety check sub-problem model, then enter the economic optimal sub-problem model, further perform economic optimization calculations, and return the feasible cut or optimal cut to the planning main problem model until the optimal untie.

In a further technical solution, the objective function of the planning main problem model is to minimize the sum of the objective function values of the economic optimal sub-problem models of investment cost and typical number of days.

In a further technical solution, the constrained conditions of the planning master problem model include planning investment constraints, safety test feasible cut sets, and economically optimal feasible cut sets or economically optimal cut sets.

In a further technical solution, the decision variable constraints of the planning main problem model are: the rated power and rated capacity of the energy storage are both greater than or equal to zero; the objective function value of the economic optimal sub-problem model is greater than or equal to a set minimum value.

In a further technical solution, the security verification sub-problem model introduces slack variables to make the configuration scheme of the planning main problem model feasible, and the objective function is the maximum value of the introduced slack variables as the target.

In a further technical solution, the constraints of the objective function of the safety syndrome problem model include: voltage upper and lower limit constraints, branch power flow constraints, and energy storage system operation constraints.

In a further technical solution, the economic optimal sub-problem model aims at the lowest operating cost of the distribution network, and the constraints include: voltage upper and lower limit constraints, branch power flow constraints, and energy storage system operation constraints.

The implementation mode of this manual provides a joint optimization system for the planning and operation of the energy storage system in the photovoltaic distribution network, which is realized through the following technical solutions:

include:

The three-layer robust optimization model building module is configured to: establish a three-layer robust optimization model for distribution network energy storage system planning, including a planning master problem model, a safety check sub-problem model and an economic operation optimal sub-problem model;

The planning master problem model takes the installation location and installation capacity of the distribution network energy storage system as decision variables;

The safety check sub-problem model is used to check the voltage limit problem of the current distribution network energy storage system configuration in the worst case until the voltage limit problem is solved;

The economic optimal sub-problem model is to obtain the economic optimal operation of the distribution network under the worst case of the current distribution network energy storage system configuration;

The three-layer robust optimization model solving module is configured to: through the three-layer decomposition of the robust optimization model, while considering the worst scenarios of different situations under the safety analysis of voltage over-limit safety and economic optimal operation, through three-layer iteration Solve for the optimal solution.

Compared with the prior art, the beneficial effects of the present disclosure are:

This disclosure proposes a brand-new three-layer robust optimization model for min-max-min distribution network energy storage system planning, fully considers source-load uncertainty, and introduces source-load uncertainty into the planning model in the form of an uncertain set , by configuring the capacity of the energy storage system, the energy is stored when the photovoltaic output is too much, and the energy is released when the load is heavy, so as to ensure that the distribution network does not have voltage over-limit problems, and seek energy storage in the worst scenario The optimal configuration scheme of the system, but this problem becomes complicated due to too many variable dimensions, so the original problem is decomposed into the planning main problem model, the safety verification sub-problem model and the economic operation optimal sub-problem model through the decomposition theory, which is convenient Fast and effective iterative solution to obtain the optimal energy storage system configuration scheme.

This disclosure considers the planning configuration and operation control of the energy storage system at the same time, and proposes a planning-operation joint optimization method for the energy storage system. Due to the different harsh scenarios obtained from the safety analysis and economic analysis of the voltage exceeding the limit, the traditional two-layer decomposition Only considering the impact of uncertainty on operating costs, the impact of uncertainty on the distribution network voltage cannot be considered at the same time, so the three-layer solution framework proposed in this disclosure is convenient for considering both security analysis and economic analysis in two different situations Uncertainty problem.

In view of different uncertainty set intervals, this disclosure analyzes the risks and benefits of the energy storage system planning problem based on the solution of the energy storage system planning problem, which is beneficial for decision makers to make better decisions and reduces the conservatism of robust optimization .

This disclosure comprehensively considers the uncertainty of source load, and proposes a three-layer robust planning-operation optimization model, aiming at the energy storage planning-operation joint optimization problem, and seeks an energy storage system configuration scheme in the worst scenario. On the one hand, Benders decomposition is used to add security constraints to the optimization problem, and on the other hand, the economic optimization is taken into account, so that the problem is more reasonable and can be solved quickly. However, the traditional planning method fails to fully consider the source-load uncertainty, and cannot better deal with the voltage limit problem caused by the source-load uncertainty on the distribution network.

Description of drawings

The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure, and the exemplary embodiments and descriptions of the present disclosure are used to explain the present disclosure, and do not constitute improper limitations to the present disclosure.

Fig. 1 is a detailed flowchart of planning-operation joint optimization provided by the implementation example of the present disclosure;

Fig. 2 is the results of robust optimization of photovoltaic output in typical four seasons under the planning method of the implementation example of the present disclosure;

Fig. 3 is the results of robust optimization of typical daily load power in four seasons under the planning method of the implementation example of the present disclosure;

Fig. 4 is the node voltage situation under the planning method of the implementation example of the present disclosure;

Fig. 5 shows the SOC of the energy storage system under the planning method of the implementation example of the present disclosure;

Fig. 6 shows the charging and discharging power of the energy storage system under the planning method of the implementation example of the present disclosure;

Fig. 7 is an energy storage system configuration scheme under the planning method of the implementation example of the present disclosure.

Detailed ways

It should be noted that the following detailed description is exemplary and intended to provide further explanation of the present 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 should be noted that the terminology used herein is only for describing specific embodiments, and is not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and/or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and/or combinations thereof.

Implementation example one

This embodiment discloses a joint optimization method for the planning and operation of energy storage systems in photovoltaic distribution networks. The uncertainty of source and load is not fully considered in the prior art, and the traditional method is difficult to deal with. In order to solve the above technical problems, this application proposes A joint energy storage planning-operation optimization method based on a three-level decomposition that comprehensively considers source-load uncertainty is proposed.

As shown in Figure 1, the planning-operating joint optimization method for high-permeability photovoltaic distribution network energy storage system based on three-layer decomposition includes:

(1) Establish the energy storage system planning model, which can be mainly divided into energy storage system planning investment model, distribution network safety verification operation model and distribution network optimal economic operation model. In order to make the context coherent, the planning main problem is used model, the safety check sub-problem model and the optimal sub-problem model of economic operation are represented separately, and then solved iteratively through a three-layer decomposition algorithm;

(2) The planning master problem model is an energy storage system planning problem in which the energy storage system installation location and installation capacity are decision variables. Based on the constraints generated by the lower sub-problem model, a current optimal energy storage system configuration scheme is obtained, but This scheme may not be the global optimal;

(3) The safety verification sub-problem model is used to verify the voltage limit problem of the optimal energy storage system configuration scheme transferred from the main problem model in the worst case, and generates a safe operation cut constraint and passes it to the main problem model until The voltage limit problem is solved;

(4) The economic optimal sub-problem model is to obtain the optimal economic operation of the distribution network in the worst case of the optimal energy storage system configuration scheme transferred from the security check sub-problem model, so as to verify whether the scheme is global optimal;

Through the three-layer decomposition, the worst scenarios of different situations under the safety analysis of voltage violation and the optimal economic operation can be considered at the same time, and the optimal solution can be found through the three-layer iterative solution.

Furthermore, the cut constraint generated by the sub-problem model is brought into the main problem model, and iterated successively until the problem is optimal, specifically:

The main problem model initializes the configuration scheme of the energy storage system and brings it into the sub-problem model. The sub-problem model first conducts safety verification to judge whether the decision variables of the main problem model can meet the voltage limit requirement in the worst scenario. If it cannot be satisfied, return to feasible cut, and adjust the configuration scheme of the main problem model until all constraints are satisfied. If the decision variables of the main problem model can meet the requirements of the safety check sub-problem model, then enter the economic optimal sub-problem model, further perform economic optimization calculations, and return the feasible cut or optimal cut to the main problem model until the optimal untie.

The constraints of the first-level planning master problem model include planning investment constraints, safety test feasible cut sets, economic optimal feasible cut sets and economic optimal cut sets, which can be constructed as follows:

(1) Objective function:

in,

In the formula, C _IN represents the investment cost of the main problem model, which is determined by the installed power and installed capacity of energy storage; C _P and C _E represent the unit price of unit energy storage power and capacity respectively; P _i and E _i represent the cost of the i-th energy storage Rated power and rated capacity; n represents the number of energy storage installations; Z _j is the introduced auxiliary variable, which is related to the objective function value of the jth economic optimal sub-problem model; Day represents the number of typical days selected.

(2) Decision variable constraints:

P _i ≥ 0 (3)

E _i ≥ 0 (4)

Z _j ≥ φ, φ is a very small value (5)

(3) The security feasibility provided by the second layer security syndrome problem model:

In the formula, Indicates the sum of the slack variables in the second-layer sub-problem model. The slack variables are the auxiliary variables introduced to solve the unsolvable situation that may occur under the strong voltage constraint. The upper and lower limits of the voltage are relaxed to make the problem solvable, as shown in the formula ( 10) and formula (11); Indicates the dual variable returned by the second-level sub-problem model. The dual variable is the auxiliary variable introduced in the dual linear programming problem. For details, please refer to related books on operations research; X ^(k) and X ^(k-1) represent the k-th time The energy storage system configuration scheme generated by the planning master problem model of iteration and k-1 iteration; G is the coefficient matrix introduced, which will be introduced in detail later.

(4) Economically feasible cut or economically optimal cut provided by the optimal sub-problem model of the third layer of economic operation:

Z _j ≥Λ _j +Π ^o,j *G*(X ^(k) -X ^(k-1) ) (8)

In the formula, Indicates the sum of slack variables introduced in the third-level sub-problem model; Λ _j represents the objective function value of the third-level sub-problem model; Πf2 ^,j and Πo ^,j represent the dual variables returned by the third-level sub-problem model . Dual variables are auxiliary variables that are automatically generated during the optimization process.

In order to make the configuration scheme obtained by the first-level planning main problem model feasible, the second-level security verification sub-problem model introduces slack variables, and the construction problem is as follows:

(1) Objective function:

In the formula, S _1,i (t) and S _2,i (t) are the slack variables introduced in the constraints of the second-level sub-problem model to deal with the unsolvable situation of the second-level sub-problem model.

(2) Voltage upper and lower limit constraints:

V _i (t)+S _1,i (t)≥V _min (10)

V _i (t)-S _2,i (t)≤V _max (11)

In the formula, V _min and V _max represent the upper and lower limit constraints of the voltage respectively; V _i (t) represents the voltage value of the i-th node at the t-th time.

(3) Branch flow constraints:

V ₁ (t)=1 (13)

Q _i+1 (t)-Q _i (t)＝-q _i (t) (15)

In the formula, r _i and x _i represent the resistance and reactance of branch i respectively; P _i (t) and Q _i (t) represent the active power and reactive power of branch i at time t respectively; V ₀ represents the voltage reference value; Indicates the active output of the i-th photovoltaic at time t; Indicates the discharge power of the i-th energy storage system at time t; Represents the charging power of the i-th energy storage system at time t; p _i (t) and q _i (t) represent the active power and reactive power of the load on node i at time t.

(4) Energy storage system operating constraints:

In the formula, C _i (t) represents the electric quantity of the energy storage system i at time t; η _ch and η _dis represent the charging and discharging efficiency of the energy storage system respectively; S _SOCmax and S _SOCmin represent the upper and lower limits of the state of charge of the energy storage system; Indicates the rated capacity of the energy storage system i, and P _i ^N indicates the rated power of the energy storage system i.

(5) Other constraints:

S _1,i (t)≥0 (23)

S _2,i (t)≥0 (24)

In the formula, S _1,i (t) and S _2,i (t) respectively represent the slack variables introduced by the second-level sub-problem model.

The third-level economic operation optimal sub-problem model is to solve the optimal solution of the economic operation problem when the target value of the second-level sub-problem model is 0. The construction problem is as follows:

(1) Objective function:

in,

In the formula, C _OP represents the operating cost of the distribution network; price(t) represents the purchase and sale price of the first node at time t, P ₁ (t) represents the inflow or outflow power value of the first node at time t, U ^L and U ^PV represents an uncertain set of load power and an uncertain set of photovoltaic output.

(2) Constraints:

The model constraints are the same as those of the second-level safety check subproblem, that is, (10)-(22), except that (10) and (11) do not contain slack variables, and the form is as follows:

V _i (t)≥V _min (27)

V _i (t)≤V _max (28)

In order to better introduce the mechanism of the three-layer decomposition method proposed by the present invention, the following uses the compact form of the three-layer decomposition problem to explain, as follows:

The energy storage system planning-operation joint optimization model proposed by the present 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 represent the decision variables of the planning problem and the decision variables of the operation problem respectively; A and B represent the coefficient matrix of the decision variables respectively; D, E, F, G, K, M, f, g, h, p ^PV , p ^L is the coefficient matrix corresponding to the equality and inequality constraints on Y, respectively; ^Φ represents the constraint set of decision variables in planning problems; Ω represents the constraint set of decision variables in operation problems; ^R is a set of real numbers; Uncertain set of source charges.

The above problem is a three-layer non-linear and non-convex problem, and it is difficult to solve due to too many dimensions of the problem variables. Therefore, the present invention decomposes the original problem into three-layer problems and solves it efficiently through iteration. Due to the consideration of multiple uncertainties, the safety check sub-problem model and the economic optimal sub-problem model consider different worst scenarios of uncertainty. For convenience, this paper uses Benders decomposition theory to decompose the original problem into the main problem model of investment planning , the safety check sub-problem model, and the optimal sub-problem model of economic operation. The main problem model is an energy storage system planning problem with the energy storage system installation location and installation capacity as decision variables; the safety check sub-problem model is used to verify the voltage limit problem of the current configuration in the worst case, until the voltage limit is exceeded The problem is solved; the optimal sub-problem model of economic operation is to obtain the optimal economic operation of the current configuration of the distribution network in the worst case. Through the three-level decomposition, the worst scenarios of different situations under the safety analysis of voltage violation and the optimal economic operation can be considered at the same time. However, the traditional two-level decomposition is not easy to achieve, and the optimal solution can be found through the three-level iterative solution.

In order to realize the above process, the following decomposition needs to be further carried out:

(1) The main problem model of the first level planning

The first-level planning master problem model obtains the optimal configuration scheme of the energy storage system, and the constraints considered include the planning-level decision variable constraints, the second-level safety check sub-problem model provided by the feasible cut and the third-level economic operation optimal sub-problem The optimal cut provided by the problem model. The first-level planning master problem model aims to minimize the sum of investment and operation costs of the energy storage system during the planning period. Equation (30) is the objective function of the first-level master problem model. Equations (31) and (32) are The decision variable constraints of the planning layer, the formula (33) represents the security feasible cut, the formulas (34) and (35) represent the economically feasible cut and the economic optimal cut, each iteration sub-problem model only generates the formula (33 ), (34) and (35), and added to the constraints of the main problem model, there is no Benders cut constraint in the first iteration.

1) Planning layer decision variable constraints

X≥0 (31)

Z _j ≥ φ, φ is a very small value (32)

2) The security feasibility provided by the second layer security syndrome problem model

3) The economically feasible or economically optimal cut provided by the optimal sub-problem model of the third layer of economic operation

Z _j ≥Λ _j +Π ^o,j *G*(X ^(k) -X ^(k-1) ) (35)

in, and are respectively the sum of all constraint slacks in the second-level security verification sub-problem model and the third-level economic operation optimal sub-problem model, which is the objective function value of the optimization problem; Z _j is introduced into the main problem model as a new variable In the objective function of , it represents the operating cost; Λ _j represents the objective function value obtained from the optimal sub-problem model of the last iterative economic operation; and Π ^o are the dual variables obtained from the second-level safety syndrome sub-problem model and the third-level economic operation optimal sub-problem model respectively.

(2) The second layer security check sub-problem model

Bring the optimal configuration plan X (iteration k-1) obtained from the main problem model of the first layer planning into the second layer security check sub-problem model, the objective function of the second layer sub-problem model is to satisfy the In the bad scenario, the sum of the slack introduced by all constraints is the smallest, and Equation (36) is the objective function and constraints of the second-level sub-problem model.

in, Represents the decision variable set of the second-level sub-problem model, is the sum of the slack variables after substituting the configuration scheme obtained from the planning main problem model into the second-level sub-problem model. It can be seen that the second-level sub-problem model is a max-min optimization problem, which is not easy to solve. Therefore, this paper uses the dual theory to transform the original problem into a max problem that is easy to solve. In order to facilitate the transformation, the original problem is first transformed into a strict dual form, and the transformation is as follows:

Since the signs of P _i and Q _i in the original problem are undecided and do not meet the requirements of duality, some deformations are pretended, so that P _i =P _i '-P _i ", Q _i =Q _i '-Q _i ", where P _i ' ≥0, P _i ”≥0, Q _i '≥0, Q _i ”≥0, then the decision variable of the second layer sub-problem model becomes And Y ₁ '≥0, then the dual treatment can get the following form:

s.t.

a ₁ ≥ 0, a ₂ ≥ 0, a ₃ ≥ 0, a ₄ ≥ 0, a ₅ ≥ 0, a ₆ ≥ 0, a ₇ ≥ 0, a ₈ ≥ 0 (39)

-D ^T a ₁ +Ε ^T a ₂ -E ^T a ₃ -F ^T a ₄ +K ^T a ₅ -K ^T a ₆ +M ^T a ₇ -M ^T a ₈ ＝B (40)

in, The dual variable array obtained for the dual problem can be known from the objective function of the dual problem, there is a product of the dual variable and the 0-1 variable in the uncertain set, and the bilinear form The second-layer sub-problem model is a nonlinear optimization problem that is difficult to solve, and the dual problems (38)-(40) are transformed into linear problems by using the big M method.

can be obtained,

s.t.

a ₁ ≥0,a ₂ ≥0,a ₃ ≥0,a ₄ ≥0,a ₅ ≥0,a ₆ ≥0,a ₇ ≥0,a ₈ ≥0 (47)

-D ^T a ₁ +E ^T a ₂ -E ^T a ₃ -F ^T a ₄ +K ^T a ₅ -K ^T a ₆ +M ^T a ₇ -M ^T a ₈ ＝B (48)

The above formulas (46)-(50) are the linear problems after bringing the formulas (41)-(45) into the formulas (38)-(40). In the formula, M is a constant with a large enough value .

(3) Optimal sub-problem model of the third-level economic operation

The third-level economic optimal sub-problem model is to seek the economic optimal solution on the basis of satisfying the safety check sub-problem model. Add a Benders cut (feasible cut or optimal cut) to the planning main problem model, where the feasible cut is generated when the optimal sub-problem model of economic operation is not feasible; when the optimal sub-problem model of economic operation is feasible, the optimal sub-problem model can be generated. cut.

The optimal sub-problem model of economic operation is to optimize the economic optimum after the energy storage configuration meets the safety check, so as to obtain the operating cost in the worst scenario, and judge whether it is converged, otherwise return the optimal cut to the planning main problem model . Equation (51) is the objective function and its constraints of the optimal sub-problem model of economic operation, in the following form:

in, Indicates the decision variable set of the third-level sub-problem model, and Λ _j is the operating cost after substituting the configuration scheme obtained from the planning main problem model into the third-level sub-problem model.

The max-min form of this problem still needs to be dealt with by dual theory, and becomes a max problem, which is the same as the processing method of the safety check sub-problem model, so it will not be repeated here.

Here we deduce the construction form of Benders cut, taking the optimal cut as an example, the form is as follows:

Assuming that at the k-1th iteration, X=X ^(k-1) , the optimization result of the optimal sub-problem model of economic operation is Λ _j , let ΔX=XX ^(k-1) , then the formula (52) can be transform into:

In the same way, the feasible cuts returned by the security syndrome problem model and the economic feasibility syndrome problem model are as follows:

In the specific implementation example, the results of robust optimization of photovoltaic output in four seasons typical day are shown in Figure 2; the results of robust optimization of typical daily load power in four seasons are shown in Figure 3; The limit problem has been reasonably solved; the SOC of the energy storage system under the planning method of the present disclosure is shown in Figure 5; the charge and discharge power of the energy storage system under the planning method of the present invention is shown in Figure 6; the planning method of the present invention is shown in Figure 6. The configuration scheme of the energy storage system is shown in Figure 7. Under the adjustment of different uncertainties, the optimal configuration of the energy storage system will also change.

Implementation Example 2

The implementation mode of this manual provides a joint optimization system for the planning and operation of the energy storage system in the photovoltaic distribution network, which is realized through the following technical solutions:

include:

The three-layer robust optimization model building module is configured to: establish a three-layer robust optimization model for distribution network energy storage system planning, including a planning master problem model, a safety verification sub-problem model and an economic operation optimal sub-problem model;

The planning master problem model takes the distribution network energy storage system installation location and installation capacity as the decision variables;

The safety check sub-problem model is used to check the voltage limit problem of the current distribution network energy storage system configuration in the worst case until the voltage limit problem is solved;

The economical operation optimal sub-problem model is to obtain the economical optimal operation of the distribution network under the worst case of the current distribution network energy storage system configuration;

The three-layer robust optimization model solving module is configured to: through the three-layer decomposition of the robust optimization model, while considering the worst scenarios of different situations under the safety analysis of voltage over-limit safety and economic optimal operation, through three-layer iteration Solve for the optimal solution.

For the specific implementation process of the modules in this embodiment example, refer to the specific formula expression in Embodiment 1, and no detailed description is given here.

Implementation example three

The implementation mode of this specification provides a computer device, including a memory, a processor, and a computer program stored on the memory and operable on the processor. It is characterized in that, when the processor executes the program, it implements the The steps of the joint optimization method for the planning and operation of the energy storage system in the photovoltaic distribution network are included.

Implementation Example 4

The implementation mode of this specification provides a computer-readable storage medium on which a computer program is stored, which is characterized in that, when the program is executed by a processor, the joint optimization of the planning and operation of the energy storage system in the photovoltaic power distribution network in the first implementation example is realized method steps.

It can be understood that, in the description of this specification, references to the terms "an embodiment", "another embodiment", "other embodiments", or "the first embodiment to the Nth embodiment" mean that A specific 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, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the described specific features, structures, and characteristics of materials may be combined in any suitable manner in any one or more embodiments or examples.

The above descriptions are only preferred embodiments of the present disclosure, and are not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present disclosure shall be included within 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.