Continuous time aggregation method of building micro-grid containing distributed photovoltaic

Abstract

The invention discloses a continuous time aggregation method for a building micro-grid containing distributed photovoltaics, which relates to the field of micro-grids and comprises the following steps of: modeling heating and ventilation loads and distributed photovoltaics in the building micro-grid to obtain all decision variables Psi D and corresponding constraints A Psi D < = B, and delimiting each scheduling time period as 1 hour; modeling the Psi D into a curve Psi D (t) based on Bernstein interpolation in each time period, taking # imgabs0 # as a vector formed by interpolation of the curve Psi D (t), obtaining a continuous time model # imgabs1 #, obtaining an aggregated power relational expression # imgabs2 # and a constraint # imgabs4 # of an initial feasible region # imgabs3 #, and enabling FiB to be a boundary parameter obtained by ith iteration; and solving a min-max problem with an objective function of 0, obtaining a point # imgabs5 # farthest from the optimal feasible region omega opt on the current initial feasible region, iterating FiB according to the point # imgabs6 #, updating the constraint # imgabs7 #, repeating the steps until a solving result is 0, stopping iteration, and outputting an mth iteration result # imgabs8 #. The method has the characteristics of high accuracy and suitability for economic dispatching.

Description

Technical Field

[0001] The present invention relates to the field of microgrids, and in particular to a continuous time aggregation method for a building microgrid containing distributed photovoltaics. Background Art

[0002] As an important part of the new power system, a microgrid can be defined as a single controllable entity, which includes a group of interconnected loads and distributed energy sources. These energy sources can be solar photovoltaic, wind power generation, energy storage systems, diesel generators, etc., which work together and coordinate with the power grid. Microgrids can also be used in buildings. Many universities in China have carried out research and construction related to campus microgrids. This type of building-based microgrid can be called a building microgrid, such as Figure 2 shown.

[0003] There are two main types of distributed energy sources integrated into the loads of building microgrids. One is the distributed photovoltaic installed on the exterior wall or roof, which is used to consume new energy locally; the other is the HVAC load, which is generally air conditioning, used for cooling and heating. At present, this type of building microgrid faces two major challenges: First, the access of distributed photovoltaics causes a bidirectional flow at the point of common coupling (PCC), which requires the feasible domain of building microgrid operation to be considered during scheduling; second, the power of HVAC loads and distributed photovoltaics is significantly affected by the environment. The former is affected by outdoor temperature, and the latter is affected by light intensity, such as Figure 3 , Figure 4 Moreover, environmental factors are time-varying, but the current discrete time scheduling model divides each scheduling time into 1 hour, and assumes that the power remains unchanged in each scheduling period, and power jumps occur at the junction of the time periods, which does not conform to the actual operation of the building microgrid.

[0004] If a discrete time dispatch model is used, it is easy to mislead the dispatch, reduce the power supply quality, and increase the operating cost of the building microgrid. When the building microgrid participates in the economic dispatch of the power grid, such as the coordinated dispatch of "main-distribution-micro", it is also necessary to consider its safe operation boundary, that is, the feasible domain. Summary of the invention

[0005] The object of the present invention is to provide a continuous time aggregation method for a building microgrid containing distributed photovoltaics to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A continuous time aggregation method for a building microgrid containing distributed photovoltaics, comprising:

[0008] S1. Model the HVAC load and distributed photovoltaic in the building microgrid to obtain all decision variables Ψ D and the corresponding constraint AΨ D ≤B, where A and B are coefficient matrices and vectors, and each scheduling period is defined as 1 hour.

[0009] S2, will D Modeled as a curve Ψ based on Bernstein interpolation in each period D (t), For the curve Ψ D (t) is a vector of interpolated values, resulting in a continuous-time model Among them A B , B B are coefficient matrices and vectors.

[0010] S3. Get the power relationship after aggregation With the initial feasible region Constraints Among them, C B is the summation coefficient matrix, D B is the load factor, matrix E B is the feasible region constraint parameter, is the boundary parameter obtained at the mth iteration.

[0011] S4. Solve the min-max problem with an objective function of 0 and obtain the distance from the optimal feasible domain ω on the current initial feasible domain. opt The farthest point And based on this point Iteration And update the constraints

[0012] S5. Repeat the above steps until the solution is 0, stop the iteration and output the mth iteration result

[0013] Preferably, the building microgrid is connected to the upper grid through a common coupling point PCC, as shown in the conceptual diagram Figure 2 shown.

[0014] Specifically, the decision variable Ψ D and constraint EΨ D ≤F constitutes a discrete-time building microgrid model.

[0015] Preferably, all decision variables in the building microgrid are modeled as Bezier curves, resulting in a continuous time curve Ψ D (t).

[0016] Specifically, each curve Ψ D(t) are all piecewise curves, which are segmented into each scheduling period t∈[0,1] based on the scheduling period.

[0017] Preferably, the curve Ψ D The interpolation vector of (t) is obtained based on the fourth Bernstein interpolation,

[0018] in is interpolation.

[0019] Specifically, according to the properties of the Bezier curve, the curve Ψ D (t) is uniquely determined by its set of control points in each time period t∈[0,1].

[0020] Specifically, the curve Ψ D The control points and their interpolation vectors of (t) One-to-one correspondence, the control points are

[0021] Preferably, each interpolation can be obtained by the curve Ψ in the time period [0,1] D The values ​​Ψ at the beginning and end of (t) D (0),Ψ D (1) and the slope Ψ' D (0), Ψ' D (1) Calculation:

[0022]

[0023] Preferably, the analytical expression of the curve in the time period t∈[0,1] is given by its interpolation vector The only way to determine is as follows:

[0024]

[0025] Specifically, Ψ D (t) has the following integral, differential, equality and inequality properties on t∈[0,1]:

[0026]

[0027] Preferably, all prediction curves in the building microgrid model are also modeled as described in equations (1)-(3) to match the decision variables, and the interpolation of all prediction curve modeling also complies with the properties of equations (4)-(7).

[0028] Specifically, all decision variables obtained are HVAC load P AC , indoor temperature T AC , energy storage power P E , energy storage E E , photovoltaic output value P pvB , transmission line power PLB , power purchase quantity P exB ; Other parameters are the indoor temperature fluctuation range [Tem low ,Tem up ], HVAC load power upper and lower limits P ACmax / P ACmin , specific heat capacity α, thermal conductivity β, temperature coefficient γ, upper and lower limits of energy storage charge / discharge power P ch / P dis , energy storage limit E max , transmission line capacity P Lmax , the upper limit of power purchase and sale capacity P max , P D P is the power load other than HVAC load. pvA is the total power generation of distributed photovoltaics.

[0029] Preferably, the continuous-time model is obtained by transforming the constraints in the discrete-time model according to the properties described in equations (1), (4)-(7) to obtain the constraints for each interpolation vector (·) B The analytical expressions of the curves can be obtained by using equations (2) and (3).

[0030] Specifically, the discrete time model of the building microgrid is as follows:

[0031]

[0032]

[0033]

[0034]

[0035] Among them, equations (8)-(10) are the charging and discharging power and storage capacity constraints of the energy storage unit; equation (11) is the power balance constraint; equations (12)-(14) are the power and temperature constraints of the HVAC load; equation (15) is the transformed upper grid power purchase and sales constraint; equation (16) is the transmission line flow constraint; equations (17)-(18) are the distributed photovoltaic abandonment constraints. i is the number of each component. K () is the parameter matrix of power balance constraint, and delta is the parameter limiting the photovoltaic output ratio.

[0036] Specifically, the continuous time model of the building microgrid is as follows:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] Among them, equations (19)-(27) are the constraints of the continuous time model corresponding to the discrete time model; (28)-(29) are the first-order continuous constraints to ensure that the beginning and the end of the curve are smoothly and continuously connected; for the convenience of solving, the continuous time constraints corresponding to equations (10) and (14) are equivalent to inequality constraints (20) and (24).

[0043] Preferably, is the compact matrix form corresponding to the continuous-time building distribution network model described by equations (19)-(29). Specifically, the matrix A B is a constant coefficient matrix, vector B B is a constant coefficient vector. The matrix C B is the sum coefficient matrix, vector D B is the load factor matrix.

[0044] Preferably, in order to remove the internal variables in the building microgrid, it is necessary to adopt a projection method to aggregate the building microgrid to obtain a feasible domain.

[0045] Specifically, P 聚 is the power value at the common coupling point PCC after the building microgrid is aggregated, that is, P 总 Describes the overall feasible domain, is the parameter of the projection domain, that is, the object that needs to be solved iteratively, and the constant coefficient matrix E B as follows:

[0046] E B =[I 4T -I 4T Δ T -Δ T ] T (30)

[0047]

[0048] Where T is the total number of scheduling periods.

[0049] Preferably, the iterative solution The first step is to find the infeasible point The details are as follows:

[0050]

[0051] Among them, P obtained by solving equation (32) 聚 That is

[0052] Preferably, the iterative solution The second step is to solve the boundary points The details are as follows:

[0053]

[0054] Among them, G obtained by solving equation (33) is

[0055] Preferably, the iterative solution The third step is to solve the new The details are as follows:

[0056]

[0057] Among them, H obtained by solving equation (34) is

[0058] Preferably, the iterative process is to update the constraints for That is, repeatedly solve equations (32)-(34).

[0059] Specifically, the end mark of the iterative solution is that the solution sol of formula (32) is 0, that is, the constraint condition corresponding to the formula has a solution. Satisfying the requirements is the parameter of the feasible domain.

[0060] The continuous time aggregation method of a building microgrid containing distributed photovoltaics according to the present invention has the following beneficial effects:

[0061] 1. The present invention provides a continuous-time model of a building microgrid, which can characterize the continuous change of power of each component in the building microgrid, and is suitable for providing a more accurate reference for power market clearing and economic dispatch.

[0062] 2. The present invention provides a continuous-time feasible domain of a building microgrid, which can provide a reference for the safe operating range of the microgrid level for the coordinated optimization scheduling of "main-distribution-micro". BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is a flow chart of a continuous time aggregation method for a building microgrid containing distributed photovoltaics;

[0064] Figure 2 Conceptual diagram of building microgrid

[0065] Figure 3 Comparison between the discrete time power curve and the actual power curve of distributed photovoltaics;

[0066] Figure 4 is the curve of intraday temperature;

[0067] Figure 5 This is a schematic diagram of the Bezier curve and its corresponding quartic Bernstein interpolation;

[0068] Figure 6-1 , 6-2, 6-3, and 6-4 are conceptual diagrams of iterative solutions of a continuous time aggregation method for a building microgrid containing distributed photovoltaics provided by the present invention;

[0069] Figure 7 This is the improved IEEE-13 node building microgrid topology diagram;

[0070] Figure 8 Comparison of feasible domains of building microgrids obtained by continuous time and discrete time aggregation; DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0072] The present invention discloses a continuous time aggregation method for a building microgrid containing distributed photovoltaics, which relates to the field of electrical engineering: HVAC loads and distributed photovoltaics in the building microgrid are modeled to obtain all decision variables Ψ D and the corresponding constraint AΨ D ≤B, and define each scheduling period as 1 hour; D Modeled as a curve Ψ based on Bernstein interpolation in each period D (t), For the curve Ψ D (t) is a vector of interpolated values, resulting in a continuous-time model The power relationship after aggregation is obtained The initial feasible region Constraints Let F i B is the boundary parameter obtained in the i-th iteration; solve the min-max problem with the objective function being 0 and obtain the distance from the optimal feasible domain ω on the current initial feasible domain opt The farthest point And based on this point Iteration F i B And update the constraints Repeat the above steps until the solution is 0, stop the iteration and output the mth iteration result The example verifies that the method of the present invention can accurately describe the time-varying feasible domain of the building microgrid after aggregation, has the characteristics of high accuracy and suitability for economic dispatch.

[0073] In order to achieve the above object, the present invention provides a continuous time aggregation method for a building microgrid containing distributed photovoltaics, comprising:

[0074] S1. Model the HVAC load and distributed photovoltaic in the building microgrid to obtain all decision variables Ψ D and the corresponding constraint AΨ D ≤B, where A and B are coefficient matrices and vectors, and each scheduling period is defined as 1 hour;

[0075] S2, will D Modeled as a curve Ψ based on Bernstein interpolation in each period D (t), For the curve Ψ D (t) is a vector of interpolated values, resulting in a continuous-time model Among them A B , B B are coefficient matrices and vectors;

[0076] S3. Get the power relationship after aggregation With the initial feasible region Constraints Among them, C B is the summation coefficient matrix, D B is the load factor, matrix E B is the feasible region constraint parameter, is the boundary parameter obtained at the mth iteration;

[0077] S4. Solve the min-max problem with an objective function of 0 and obtain the distance from the optimal feasible domain ω on the current initial feasible domain. opt The farthest point And based on this point Iteration And update the constraints

[0078] S5. Repeat the above steps until the solution is 0, stop the iteration and output the mth iteration result

[0079] Preferably, the building microgrid is connected to the upper grid through a common coupling point PCC, as shown in the conceptual diagram Figure 2 shown.

[0080] Specifically, the decision variable Ψ D and constraint EΨ D ≤F constitutes a discrete-time building microgrid model.

[0081] Preferably, all decision variables in the building microgrid are modeled as Bezier curves, resulting in a continuous time curve Ψ D (t).

[0082] Preferably, the curve Ψ D The interpolation vector of (t) is obtained based on the fourth Bernstein interpolation, in is interpolation.

[0083] Specifically, the curve Ψ D (t) is uniquely determined by its control point set in each time period t∈[0,1], such as Figure 4 shown.

[0084] Specifically, the curve Ψ D The control points and their interpolation vectors of (t) One-to-one correspondence, the control points are

[0085] Preferably, each interpolation can be obtained by the curve Ψ in the time period [0,1] D The values ​​Ψ at the beginning and end of (t) D (0),Ψ D (1) and the slope Ψ' D (0), Ψ' D (1) Calculation:

[0086]

[0087] Preferably, the analytical expression of the curve in the time period t∈[0,1] is given by its interpolation vector The only way to determine is as follows:

[0088]

[0089] Specifically, Ψ D (t) has the following integral, differential, equality and inequality properties on t∈[0,1]:

[0090]

[0091]

[0092] Preferably, all power prediction curves in the building microgrid model are also modeled as described in equations (1)-(3) to match the decision variables, and the interpolation of all prediction curve modeling also complies with the properties of equations (4)-(7).

[0093] Preferably, all decision variables are HVAC load P AC , indoor temperature T AC , energy storage power PE , energy storage E E , photovoltaic output value P pvB , transmission line power P LB , power purchase quantity P exB ; Other parameters are the indoor temperature fluctuation range

[0094] [Tem low ,Tem up ], HVAC load power upper and lower limits P ACmax / P ACmin , specific heat capacity α, thermal conductivity β, temperature coefficient γ, upper and lower limits of energy storage charge / discharge power P ch / P dis , energy storage limit E max , transmission line capacity P Lmax , the upper limit of power purchase and sale capacity P max , P D P is the power load other than HVAC load. pvA is the total power generation of distributed photovoltaics.

[0095] Specifically, the continuous-time model is to transform the constraints in the discrete-time model according to the properties described in equations (1), (4)-(7), and obtain the constraints for each interpolation vector (·) B The analytical expressions of the curves can be obtained by using equations (2) and (3).

[0096] Specifically, the discrete time model of the building microgrid is as follows:

[0097]

[0098]

[0099]

[0100]

[0101] Formulas (8)-(10) are the charging and discharging constraints and energy storage constraints. Formula (11) is the power balance constraint; Formulas (12)-(14) are the power and temperature constraints of the HVAC load; Formula (15) is the power purchase and sales constraint of the upper power grid; Formula (16) is the transmission line flow constraint; Formulas (17)-(18) are the distributed photovoltaic abandonment constraints. i is the number of each component. K () is the parameter matrix of power balance constraint, and delta is the parameter limiting the photovoltaic output ratio.

[0102] Specifically, the continuous time model of the building microgrid is as follows:

[0103]

[0104]

[0105]

[0106]

[0107]

[0108] Among them, (19)-(27) are the constraints of the continuous time model corresponding to the discrete time model; Equations (28)-(29) are first-order continuous constraints to ensure that the beginning and the end of the curve are smoothly and continuously connected; for the convenience of solving, the continuous time constraints corresponding to equations (10) and (14) are equivalent to inequality constraints (20) and (24).

[0109] Preferably, is the compact matrix form corresponding to the continuous-time building distribution network model described by equations (19)-(29). Specifically, the matrix A B is a constant coefficient matrix, vector B B is a constant coefficient vector. The matrix C B is the sum coefficient matrix, vector D B is the load factor matrix, all of which are constants.

[0110] Preferably, in order to remove the internal variables in the building microgrid, it is necessary to adopt the projection method to aggregate the building microgrid to obtain the feasible domain, such as Figure 6-1 Shown

[0111] Specifically, P 聚 is the power value at the common coupling point PCC after the building microgrid is aggregated, that is, P 总 Describes the overall feasible domain, is the parameter of the feasible domain, that is, the object that needs to be solved iteratively, and the constant coefficient matrix E B as follows:

[0112] E B =[I 4T -I 4T Δ T -Δ T ] T (30)

[0113]

[0114] Where T is the total number of scheduling periods.

[0115] Preferably, the iterative solution The first step is to find the infeasible point The details are as follows:

[0116]

[0117] Among them, P obtained by solving equation (32) 聚 That is

[0118] Preferably, the iterative solution The second step is to solve the boundary points The details are as follows:

[0119]

[0120] Among them, G obtained by solving equation (33) is

[0121] Preferably, the iterative solution The third step is to solve the new The details are as follows:

[0122]

[0123] Among them, H obtained by solving equation (34) is

[0124] Preferably, the iterative process is to update the constraints for That is, repeatedly solve equations (32)-(34), as Figure 6-2 , as shown in 6-3 and 6-4.

[0125] Specifically, the end mark of the iterative solution is that the solution sol of formula (32) is 0, that is, the constraint condition corresponding to the formula has a solution. Satisfying the requirements is the parameter of the feasible domain. The area enclosed by the upper and lower curves can be depicted, which is the feasible domain of the building microgrid.

[0126] The following further describes the beneficial effects that can be achieved by this embodiment in conjunction with a specific application scenario.

[0127] This embodiment is verified in a building distribution network based on the improved IEEE-13 node. The building microgrid refers to an office building in Wuhan City, which includes 1 energy storage unit, 5 HVAC loads, and 4 distributed photovoltaics. Figure 7 As shown. The corresponding program is written on the computing software MATLAB R2024a, and the Yalmip solver equipped with Gurobi is called for solution. To solve the optimization problem, the computing device used is: a HUAWEI matebook laptop equipped with an Intel Core i7-10510U processor, 16GB of running memory, and running the Windows 10 Professional operating system. The aggregate solution result is as follows Figure 8 As shown, it can be seen that the continuous time aggregation result is a time-varying feasible domain surrounded by continuous curves, which is more accurate than the discrete time aggregation result and closer to the actual operation of the power system.

[0128] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0129] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A continuous time aggregation method for a building microgrid containing distributed photovoltaics, characterized in that: The steps include: S1. Model the HVAC load and distributed photovoltaic in the building microgrid to obtain all decision variables Ψ D and the corresponding constraint AΨ D ≤B, where A and B are coefficient matrices and vectors, and each scheduling period is defined as 1 hour; S2, will D Modeled as a curve Ψ based on Bernstein interpolation in each period D (t), For the curve Ψ D (t) is a vector of interpolated values, resulting in a continuous-time model Among them A B , B B are coefficient matrices and vectors; S3. Get the power relationship after aggregation With the initial feasible region Constraints Among them, C B is the summation coefficient matrix, D B is the load factor, matrix E B is the feasible region constraint parameter, is the boundary parameter obtained at the mth iteration; S4. Solve the min-max problem with an objective function of 0 and obtain the distance from the optimal feasible domain ω on the current initial feasible domain. opt The farthest point And based on this point Iteration And update the constraints S5. Repeat the above steps until the solution is 0, stop the iteration and output the mth iteration result 2. A continuous time aggregation method for a building microgrid containing distributed photovoltaics as claimed in claim 1, characterized in that: The building microgrid is connected to the upper grid through a common coupling point; the building microgrid contains HVAC loads, distributed photovoltaics, and energy storage units to ensure the reliability of power supply for isolated island operation; the constraints of the building microgrid are D ≤B includes air conditioning temperature and power constraints, energy storage charging and discharging constraints, distributed photovoltaic abandonment constraints, transmission line flow constraints, upper-level power grid power purchase constraints and power balance constraints.

3. A continuous time aggregation method for a building microgrid containing distributed photovoltaics as claimed in claim 2, characterized in that: All decision variables are modeled as continuous time curves Ψ D (t); The interpolation vector of the decision variables in the period is based on the fourth Bernstein interpolation and is in is the interpolation; each interpolation can be obtained by the curve Ψ in the time period [0,1] D The values ​​Ψ at the beginning and end of (t) D (0),Ψ D (1) and the slope Ψ' D (0), Ψ' D (1) Calculation: The analytical expression of the curve in the time period t∈[0,1] is given by its interpolation vector The only way to determine is as follows: Ψ D (t) at t∈[0,1] 上 After integration, it is independent of time t: Curve Ψ D The differential of (t) Ψ' D The curve corresponding to (t) is the one corresponding to the Bernstein interpolation after reducing the order by one: If the curve Ψ D (t) is always equal to 0, then its interpolated value must also be 0: If the curve Ψ D (t) is less than a certain constant δ, then its interpolated value is also less than δ:

4. A continuous time aggregation method for a building microgrid containing distributed photovoltaics as claimed in claim 3, characterized in that: All other decision variables obtained are HVAC load P AC , indoor temperature T AC , energy storage power P E , energy storage E E , photovoltaic output value P pvB , transmission line power P LB , power purchase quantity P exB ; Other parameters are the indoor temperature fluctuation range [Tem low ,Tem up ], HVAC load power upper and lower limits P ACmax / P ACmin , specific heat capacity α, thermal conductivity β, temperature coefficient γ, upper and lower limits of energy storage charge / discharge power P ch / P dis , energy storage limit E max , transmission line capacity P Lmax , the upper limit of power purchase and sale capacity P max , P D P is the power load other than HVAC load. pvA is the total power generation of distributed photovoltaics.

5. A continuous time aggregation method for a building microgrid containing distributed photovoltaics as claimed in claim 4, characterized in that: The model before building microgrid conversion is as follows: Where i is the number of each component. () is the parameter matrix of power balance constraint, and delta is the parameter limiting the photovoltaic output ratio.

6. A continuous time aggregation method for a building microgrid containing distributed photovoltaics as claimed in claim 5, characterized in that: The continuous time model of the building microgrid is as follows:

7. A continuous time aggregation method for a building microgrid containing distributed photovoltaics as claimed in claim 6, characterized in that: It is the compact matrix form corresponding to the continuous-time building distribution network model; the matrix A B is a constant coefficient matrix, vector B B is a constant coefficient vector, all determined by formulas (19)-(29), the matrix C B is the sum coefficient matrix, vector D B is the load factor matrix, all of which are constants; P 聚 is the power value at the common coupling point PCC after the building microgrid is aggregated, that is, P 总 Describes the overall feasible domain, is the parameter of the projection domain, that is, the object that needs to be solved iteratively, and the constant coefficient matrix E B as follows: E B =[I 4T -I 4T D T -D T ] T (30) Iterative solution The first step is to find the infeasible point The details are as follows: Solving equation (32) yields P 聚 That is Iterative solution The second step is to solve the boundary points The details are as follows: Solving equation (33) yields G: Iterative solution The third step is to solve the new The details are as follows: Solving equation (34) yields H The iterative process is to update the constraints That is, repeatedly solve formulas (32)-(34).

8. A continuous time aggregation method for a building microgrid containing distributed photovoltaics as claimed in claim 7, characterized in that: The end mark of the iterative solution is that the solution sol of equation (32) is 0, that is, the constraint condition corresponding to the equation has a solution.

9. A continuous time aggregation method for a building microgrid containing distributed photovoltaics as claimed in claim 8, characterized in that: The iterative solution obtained The area enclosed by the upper and lower time-varying curves can be drawn, which is the continuous-time feasible domain of the building microgrid.

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