Optimized regulation and control method and system for industrial park source network load storage polymer

Abstract

The invention discloses an industrial park source network load storage polymer optimization regulation and control method and system, and relates to the technical field of power system operation, and the method comprises the steps: obtaining source network load storage operation data, and constructing an industrial park source network load storage resource power characteristic model; constructing a source network load storage polymer regulation and control boundary equivalent calculation model considering network security constraints; solving an industrial park source network load storage polymer regulation boundary; and constructing a source network load storage polymer optimization regulation and control model considering the network security constraint. According to the industrial park source network load storage polymer optimization regulation and control method provided by the invention, unified modeling is carried out on power regulation characteristics of source network load storage resources, and a flexible and adjustable polymer model is constructed. Therefore, the flexibility and adaptability of the power system in large-scale new energy access can be improved, and challenges caused by new energy output volatility and randomness can be better dealt with.

Description

Technical Field

[0001] The present invention relates to the technical field of operation of power systems, and in particular to an optimization control method and system for source-grid-load-storage aggregates in industrial parks. Background Art

[0002] The current power system is evolving towards adapting to large-scale high-proportion renewable energy, and building a new power system has become an inevitable trend. At the same time, with the continuous evolution of the "source-grid-load-storage" form of the power grid, the types and quantities of flexible adjustment resources in the distribution network are gradually increasing, and the opportunities and risks that come with it are also gradually increasing. On the one hand, renewable resources are conducive to low-carbon power supply and sustainable development; however, on the other hand, relatively small unit capacity, limited visibility and huge quantity may bring pressure to the operation and management of the distribution system. Distributed power sources such as photovoltaic panels and wind turbines have the disadvantages of large output fluctuations, strong randomness and weak controllability. The high proportion of renewable energy grid connection not only seriously affects the power quality, but also brings safety problems to the normal operation of the power grid. In order to improve the problem of different spatial dispersion characteristics of distributed resources, by constructing an equivalent model of flexible resources, it is first necessary to calculate the adjustable range of its external power. The aggregation algorithm needs to calculate the adjustable power range formed by aggregating all device characteristics under the premise of considering the internal network constraints of the distribution network.

[0003] Considering the coupling constraints of safe operation within the system will introduce difficulties such as high-dimensional variables and nonlinear constraints to the solution of the control boundary of the aggregate. At present, the solutions to the above problems mainly include the following three categories: First, empirical methods based on assumptions use some preset regular geometric shapes, such as polyhedrons, ellipsoids, and strip areas to approximate the control boundary. The calculation speed is fast but the accuracy is low; the second is a sampling-based method that scans the control boundary of the aggregate by repeatedly solving the optimal power flow problem with different objective functions. The solution accuracy of this method depends on the number of sampling points and the calculation complexity is large; the third is various state space projection algorithms, which reduce the redundant constraints in the Fourier-Motzkin elimination method through umbrella constraint identification, effectively improving the computational efficiency of the solution of the control boundary of the aggregate.

[0004] Based on the above research, the patent takes into account the power regulation characteristics of distributed resources in the industrial park and the grid constraints (line transmission capacity constraints, voltage limit constraints, power balance constraints, etc.), and proposes a method for calculating the active and reactive coupling output boundary considering the internal network constraints of the distributed resource aggregate, which effectively solves the problem of high variable dimension. The control boundary of the model output can be fully characterized by a group of linear inequalities and is fully compatible with the existing dispatching system. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: the existing distributed energy scheduling and management methods in industrial parks fail to effectively consider network security constraints, inflexible coordinated scheduling of source, grid, load and storage resources, and power quality and grid stability problems caused by a high proportion of renewable energy access, as well as the optimization problem of how to achieve safe and efficient energy optimization and regulation in a complex power grid environment.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for optimizing and controlling a source-grid-load-storage aggregate in an industrial park, comprising:

[0008] Obtain source-grid-load-storage operation data, and conduct unified modeling of distributed adjustable resources such as source-load-storage in the industrial park to facilitate unified regulation of resource aggregates;

[0009] Acquire the source, grid, load and storage operation data, build the power characteristic model of the source, load and storage resources in the industrial park, and embed it into the control boundary solution model as a specific constraint; based on the power characteristic model of the source, load and storage resources in the industrial park, build an equivalent calculation model of the control boundary of the source, grid, load and storage aggregate considering network security constraints; solve the equivalent calculation model of the control boundary of the source, grid, load and storage aggregate based on the high-dimensional aggregate boundary approximate projection method to obtain the aggregate control boundary; based on the aggregate control boundary, build an optimized control model of the source, grid, load and storage aggregate considering network security constraints to achieve coordinated control of source, grid, load and storage resources in the industrial park.

[0010] As a preferred scheme of the industrial park source-grid-load-storage aggregate optimization and control method described in the present invention, the power characteristic model of the industrial park source-load-storage resource includes at least one of the industrial park time-shiftable load characteristic modeling, distributed photovoltaic characteristic modeling, energy storage resource characteristic modeling, and controllable distributed power supply characteristic modeling.

[0011] As a preferred solution of the method for optimizing and controlling the source-grid-load-storage aggregate of an industrial park described in the present invention, wherein: the time-shiftable load characteristic modeling of the industrial park includes time-shiftable load adjustment range constraints, time-shiftable load dispatching cost constraints and dispatching cost linearization constraints;

[0012] The distributed photovoltaic characteristic modeling includes light intensity probability density function constraints, distributed photovoltaic active power output constraints, and distributed photovoltaic reactive power constraints;

[0013] The energy storage resource characteristic modeling includes electric vehicle energy storage charging and discharging power constraints and logic constraints, energy storage power station charge state constraints, energy storage power station charging and discharging power constraints, and energy storage power station charging and discharging power balance constraints;

[0014] The controllable distributed power source characteristic modeling includes controllable generator set power limit constraints and controllable generator set power ramp limit constraints.

[0015] As a preferred scheme of the industrial park source-grid-load-storage aggregate optimization and control method described in the present invention, the source-grid-load-storage aggregate control boundary equivalent calculation model includes one of the industrial park power grid network flow constraints, power balance constraints, safe transmission capacity constraints, and voltage safety limit constraints.

[0016] As a preferred solution of the method for optimizing and controlling the source-grid-load-storage aggregate in an industrial park described in the present invention, wherein: the obtaining of the aggregate control boundary includes defining a linearized state space, defining decision variables and state variables corresponding to the constraints contained in the linear state space;

[0017] Select the initial normal vector to calculate the initial boundary point;

[0018] Based on the state space constraint calculation, the new boundary vertices of the industrial park regulation are obtained, and the contribution rate of the new vertices is solved to make choices.

[0019] As a preferred solution of the industrial park source-grid-load-storage aggregate optimization control method described in the present invention, wherein: the optimization control model includes an objective function and constraint conditions;

[0020] The objective function is expressed as,

[0021]

[0022] Where C is the total system regulation cost; K is the set of industrial park source-grid-load-storage aggregates; k is the kth industrial park source-grid-load-storage aggregate, They represent the active power regulation cost and reactive power regulation cost of the kth aggregate in the system respectively.

[0023] As a preferred solution of the industrial park source-grid-load-storage aggregate optimization control method described in the present invention, wherein: the constraint condition is expressed as:

[0024]

[0025] Among them, P k , Q k Respectively represent the active and reactive power output of the kth aggregate, P D , Q D Respectively represent the total active and reactive load demand of the system P loss , Q loss Respectively represent the total active and reactive losses of the system, ΔP k , ΔQ k The active and reactive power adjustment increments of the kth aggregate are respectively, is the aggregate control boundary solved by S3, are the upper and lower limits of the voltage amplitude allowed for grid node i.

[0026] Another object of the present invention is to provide an industrial park source-grid-load-storage aggregate optimization and control system. By constructing an industrial park source-grid-load-storage aggregate optimization and control system, the problems of insufficient data acquisition, inflexible scheduling strategies, and low energy resource utilization efficiency in existing industrial park energy management systems are solved.

[0027] In order to solve the above technical problems, the present invention provides the following technical solutions: an industrial park source-grid-load-storage aggregate optimization control system, comprising: the data modeling module is used to obtain source-grid-load-storage operation data, build the industrial park source-load-storage resource power characteristic model, and embed it into the control boundary solution model as a specific constraint;

[0028] The boundary modeling module is used to construct a source-grid-load-storage aggregate control boundary equivalent calculation model considering network security constraints based on the power characteristic model of the source-load-storage resource in the industrial park;

[0029] The boundary calculation module is used to solve the source-grid-load-storage aggregate control boundary equivalent calculation model based on the high-dimensional aggregate boundary approximate projection method to obtain the aggregate control boundary;

[0030] The optimization control module is used to construct a source-grid-load-storage aggregate optimization control model taking into account network security constraints based on the aggregate control boundary, so as to realize the coordinated control of source-grid-load-storage resources in the industrial park;

[0031] The industrial park source-load-storage resource power characteristic model includes at least one of the industrial park time-shiftable load characteristic modeling, distributed photovoltaic characteristic modeling, energy storage resource characteristic modeling, and controllable distributed power source characteristic modeling.

[0032] The time-shiftable load characteristic modeling of the industrial park includes time-shiftable load adjustment range constraints, time-shiftable load scheduling cost constraints and scheduling cost linearization constraints;

[0033] The distributed photovoltaic characteristic modeling includes light intensity probability density function constraints, distributed photovoltaic active power output constraints, and distributed photovoltaic reactive power constraints;

[0034] The energy storage resource characteristic modeling includes electric vehicle energy storage charging and discharging power constraints and logic constraints, energy storage power station charge state constraints, energy storage power station charging and discharging power constraints, and energy storage power station charging and discharging power balance constraints;

[0035] The controllable distributed power source characteristic modeling includes controllable generator set power limit constraints and controllable generator set power ramp limit constraints.

[0036] The source-grid-load-storage aggregate control boundary equivalent calculation model includes one of the industrial park power grid network flow constraints, power balance constraints, safe transmission capacity constraints, and voltage safety limit constraints.

[0037] The obtaining of the aggregate control boundary includes defining a linearized state space, and defining decision variables and state variables corresponding to the constraints contained in the linear state space;

[0038] Select the initial normal vector to calculate the initial boundary point;

[0039] Based on the state space constraint calculation, the new boundary vertices of the industrial park regulation are obtained, and the contribution rate of the new vertices is solved to make choices.

[0040] The optimization and control model includes an objective function and constraint conditions;

[0041] The objective function is expressed as,

[0042]

[0043] Where C is the total system regulation cost; K is the set of industrial park source-grid-load-storage aggregates; k is the kth industrial park source-grid-load-storage aggregate, They represent the active power regulation cost and reactive power regulation cost of the kth aggregate in the system respectively.

[0044] The constraint condition is expressed as,

[0045]

[0046] Among them, P k , Q k Respectively represent the active and reactive power output of the kth aggregate, P D , Q D Respectively represent the total active and reactive load demand of the system P loss , Q loss Respectively represent the total active and reactive losses of the system, ΔP k , ΔQ k The active and reactive power adjustment increments of the kth aggregate are respectively, is the aggregate control boundary to be solved, are the upper and lower limits of the voltage amplitude allowed for grid node i.

[0047] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned industrial park source-grid-load-storage aggregate optimization and control method are implemented.

[0048] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned industrial park source-grid-load-storage aggregate optimization and control method.

[0049] Beneficial effects of the present invention: The industrial park source-grid-load-storage aggregate optimization control method provided by the present invention constructs a flexible and adjustable aggregate model by uniformly modeling the power regulation characteristics of the source-grid-load-storage resources. This helps to improve the flexibility and adaptability of the power system when facing large-scale new energy access, and can better cope with the challenges brought by the volatility and randomness of new energy output. By constructing an equivalent calculation model of the source-grid-load-storage aggregate control boundary that takes into account network security constraints, this patent can more accurately evaluate the active-reactive output coupling range of the aggregate. Compared with traditional assumption-based empirical methods and sampling-based methods, the high-dimensional aggregate boundary approximate projection calculation method proposed in this patent effectively reduces the computational complexity, while improving the accuracy of solving the control boundary, thereby improving the control efficiency of the entire power system. The control boundary proposed in this patent can be fully characterized by a group of linear inequalities and is fully compatible with existing dispatching systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0051] Figure 1 An example of verifying the electrical topology diagram of an industrial park source-grid-load-storage aggregate optimization control method provided by an embodiment of the present invention.

[0052] Figure 2 A schematic diagram of the initial projection domain and external normal vector of an industrial park source-grid-load-storage aggregate optimization control method provided by an embodiment of the present invention.

[0053] Figure 3 A projection domain and external normal vector diagram of secondary search update of an industrial park source-grid-load-storage aggregate optimization control method provided by an embodiment of the present invention.

[0054] Figure 4 A flow chart for solving the control boundary of an industrial park source-grid-load-storage aggregate of an industrial park source-grid-load-storage aggregate optimization control method provided by an embodiment of the present invention.

[0055] Figure 5 A flow chart of an industrial park source-grid-load-storage aggregate optimization control method for an industrial park provided by an embodiment of the present invention, which takes into account network security constraints. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0058] Example 1

[0059] Reference Figure 1-Figure 5 , which is an embodiment of the present invention, provides an industrial park source-grid-load-storage aggregate optimization control method, comprising:

[0060] S1 obtains source-grid-load-storage operation data, builds a power characteristic model of source-load-storage resources in the industrial park, and embeds it into the control boundary solution model as a specific constraint;

[0061] S2 is based on the power characteristic model of source-load-storage resources in industrial parks, and builds a source-grid-load-storage aggregate control boundary equivalent calculation model considering network security constraints;

[0062] S3 solves the equivalent calculation model of the source-grid-load-storage aggregate control boundary based on the high-dimensional aggregate boundary approximate projection method to obtain the aggregate control boundary;

[0063] Based on the aggregate control boundary, S4 constructs an optimal control model for source, grid, load and storage aggregates that takes into account network security constraints, and realizes coordinated control of source, grid, load and storage resources within the industrial park.

[0064] Specifically, the source, grid, load and storage operation data are obtained, and the distributed adjustable resources such as source, load and storage in the industrial park are uniformly modeled to facilitate the unified regulation of resource aggregates;

[0065] Obtain the topological data of the industrial park, construct the network flow constraints and network security operation constraints of the industrial park power grid, and construct the equivalent calculation model of the source-grid-load-storage aggregate control boundary based on the unified control model of source-load-storage and the network security operation constraints of the industrial park;

[0066] Based on the high-dimensional aggregate boundary approximate projection calculation method, the equivalent calculation model of the source-grid-load-storage aggregate control boundary is solved to obtain the source-grid-load-storage aggregate control boundary of the industrial park;

[0067] The solved aggregate control boundary is embedded in the control model as a boundary constraint, and a multi-aggregate optimization control model in the industrial park considering network security constraints is constructed.

[0068] In a specific embodiment of the present invention, the S1 industrial park source-load-storage resource power characteristic modeling includes: industrial park time-shiftable load characteristic modeling, distributed photovoltaic characteristic modeling, energy storage resource characteristic modeling, and controllable distributed power supply characteristic modeling;

[0069] (1) Establish a time-shiftable load characteristic model for industrial parks. The time-shiftable load in industrial parks refers to the load whose power consumption time can be adjusted according to the plan. According to the characteristics of the total power consumption of the shiftable load that remains unchanged and the power consumption period can be adjusted, the model includes: time-shiftable load adjustment range constraints, time-shiftable load scheduling cost constraints, and scheduling cost linearization constraints; the specific constraints are constructed as follows:

[0070] (1-1) The time-shiftable load adjustment range constraint is expressed as follows:

[0071]

[0072]

[0073] Among them, D DR Represents the total electricity demand; P DR,t represents the actual dispatching power during period t; and They represent the upper and lower limits of the operating power allowed in time period t, which are related to the user's comfort requirements; N T represents the scheduling period; Δt represents the scheduling step.

[0074] (1-2) The scheduling cost constraint of the time-shiftable load. The scheduling cost of the time-shiftable load per unit time can be specifically expressed as follows:

[0075]

[0076] Among them, C DR,t (·) is the scheduling cost per unit time; K DR To adjust the compensation coefficient of electricity; Indicates the original planned power consumption; N DR Represents a collection of nodes that connect time-shiftable loads.

[0077] (1-3) Dispatch cost linearization constraint. The absolute value term in the constraint represents the deviation between the actual dispatch power and the originally planned power. Since this constraint is a special nonlinear programming problem with an absolute value, two auxiliary variables are introduced to linearize it:

[0078]

[0079] in, and To introduce auxiliary variables, It can be understood as the upward adjustment of the load power based on the planned power consumption. It can be understood as the power reduction of the shiftable load based on the planned power consumption.

[0080] (2) Modeling of distributed photovoltaic characteristics. A photovoltaic generator set is a device that converts light energy into electrical energy. Its output power is affected by the intensity of sunlight. According to the characteristics of distributed photovoltaic output power affected by light, the model constraints include: light intensity probability density function constraint, distributed photovoltaic active output constraint, and distributed photovoltaic reactive power constraint. The specific constraints are constructed as follows:

[0081] (2-1) Light intensity probability density function constraint, light intensity approximately obeys Beta distribution, and the probability density function expression is as follows:

[0082]

[0083] Among them, L and L max is the actual and maximum light intensity under test conditions; μ PV ,σ PV are the mean and standard deviation of light intensity; α, β are shape parameters, which are calculated based on μ PV and σ PV Approximate calculation shows that the Γ(·) function is an abnormal integral defined by an infinite product function with parameters.

[0084] (2-2) Distributed photovoltaic active output constraints. After obtaining the probability density function of light intensity, the active output of the photovoltaic generator set can be approximately expressed as:

[0085]

[0086] Where A is the total area of ​​the photovoltaic unit; η is the photoelectric conversion efficiency; P pv,t is the active power generated by the PV system at time t.

[0087] (2-3) Distributed photovoltaic reactive power constraints, photovoltaic generator sets also use parallel capacitors to ensure that the power factor is basically a constant, then the photovoltaic reactive power at time t is:

[0088]

[0089] in, is the reactive power of the photovoltaic unit p, is the power factor angle of photovoltaic, generally required

[0090] (3) Modeling of energy storage resource characteristics can alleviate the impact of uncertainty in the output of renewable energy sources such as wind and solar to a certain extent. For example, when there is sufficient wind and solar, the energy storage will work in a charging state, and when there is insufficient wind and solar, the energy storage will work in a discharging state. According to the power regulation characteristics of energy storage charging and discharging, the model constraints are constructed, including: electric vehicle energy storage charging and discharging power limit constraints and logical constraints, energy storage charge state constraints, and energy storage power station charging and discharging power balance constraints. The specific constraints are constructed as follows:

[0091] (3-1) Electric vehicle energy storage charging and discharging power constraints and logical constraints. Electric vehicle energy storage can only be charged or discharged at a single moment and must be less than the charging and discharging power limit. The specific expression is as follows:

[0092]

[0093] in, The maximum value of the charging and discharging power of the electric vehicle energy storage; U ess,b and U ess,s is the discharge state and charge state of the electric vehicle energy storage during period t.

[0094] (3-2) Energy storage station charge state constraint. The charge state of the energy storage station at the current moment is determined by the charge state and charge and discharge power at the previous moment. The specific expression is as follows:

[0095]

[0096] Among them, E max and E min is the maximum and minimum state of charge of the energy storage; E(t) is the state of charge of the energy storage power station in time period t; u is the self-discharge rate of the energy storage power station, which can generally be ignored; η abs and η relea are the charging efficiency and discharging efficiency of the energy storage power station respectively; P abs (t) and P relea (t) are the charging power and discharging power of the energy storage station respectively.

[0097] (3-3) Energy storage power station charging and discharging power constraints. The energy storage power station can only charge or discharge at a single moment and the power must be less than the charging and discharging power limit. The specific expression is as follows:

[0098]

[0099] Among them, U abs and U relea P is the charging state and discharging state of the energy storage power station; max It is the maximum charging and discharging power of the energy storage station.

[0100] (3-3) The balance constraint of the energy storage station charging and discharging power. The sum of the charging and discharging power of the energy storage station used by each electric vehicle is the charging and discharging power of the energy storage station. The specific expression is as follows:

[0101]

[0102] Wherein, N represents the electric vehicle energy storage set, and i represents the i-th electric vehicle energy storage.

[0103] (4) Establish a controllable distributed power source characteristic model. The controllable generator sets in industrial parks mainly include micro gas turbines, fuel cells, diesel generators, etc. According to the characteristics of the single-time power limit and regulation limit of the controllable generator set, the model contains the following constraints: controllable generator set power limit constraint, controllable generator set power ramp limit constraint. The specific constraints are constructed as follows:

[0104] (4-1) Power limit constraints of controllable generator sets. During optimal scheduling, the total active output of controllable generator sets must meet certain upper and lower limit constraints:

[0105]

[0106] Among them, P G,t is the active power output of the controllable generator set in period t; and They represent the upper and lower limits of the output power of the controllable generator set respectively; u G,t Indicates the start and stop status of the controllable generator set. It is a 0-1 variable. When the value is 1, it means it is in the on state at time t, otherwise it is in the off state.

[0107] (4-2) The power ramp limit constraint of the controllable generator set. The power adjustment range of the controllable generator set at the previous moment and the current moment cannot be too large. The ramp constraint that needs to be met is:

[0108] P G,t -P G,t-1 ≤R up Δt

[0109] P G,t-1 -P G,t ≤R down Δt (13)

[0110] Among them, R up and R down They represent the upper and lower bounds of the ramp rate respectively; Δt is the scheduling step size.

[0111] In a specific embodiment of the present invention, the S2 constructs an equivalent calculation model for the control boundary of a source-grid-load-storage aggregate taking into account network security constraints. Based on an established controllable resource characteristic model of an industrial park, the network topology of the industrial park is further considered to establish an equivalent calculation model for the control boundary of a source-grid-load-storage aggregate taking into account network security constraints, specifically including: network flow constraints of the industrial park power grid, power balance constraints, safe transmission capacity constraints, and voltage safety limit constraints.

[0112] (1) Flow constraint: The specific expression of the steady-state flow equation of the power system is as follows:

[0113]

[0114] Among them, U i and U j Respectively represent the voltage amplitude between node i and node j, θ i and θ j Respectively represent the phase angle between node i and node j, and the phase angle difference between the two nodes is θ ij =θ i -θ j .

[0115] (2) Power balance constraint: The specific expression of the node power balance constraint of the power network is as follows:

[0116]

[0117] in, and They represent the sum of active power and reactive power flowing out of bus h respectively; and They represent the sum of active power and reactive power flowing into bus j respectively; P Gi and Q Gi Respectively represent the active power and reactive power of distributed generation at node i; P load,i and Q load,i They represent the active load and reactive load at node i respectively.

[0118] (3) Safe transmission capacity constraints. For the internal lines and interconnection lines of the power system, the transmission capacity constraints of the corresponding voltage levels are expressed as follows:

[0119]

[0120] Among them, S ij is the power flow limit of the line ij inside the power grid, P0, Q0, and S0 are the active power flow, reactive power flow, and power flow limit of the tie line between the aggregate and the distribution network, respectively.

[0121] (4) Voltage safety constraint: The bus voltage safety constraint needs to be met during the power system optimization and dispatching process. The specific expression is as follows:

[0122]

[0123] in, are the upper and lower limits of the voltage amplitude allowed for grid node i.

[0124] In a specific embodiment of the present invention, the S3 source-grid-load-storage aggregate optimization control model considering network security constraints is a computational solution to the equivalent calculation model of the source-grid-load-storage aggregate control boundary considering network security constraints. The distributed resources of the source-grid-load-storage in the industrial park are aggregated into a whole, connected to the distribution network through interconnection lines, and the safe operation constraints of the power grid in the aggregate can be abstracted into a high-dimensional state space, and its projection on the PQ coupling plane is the control boundary, which is expressed as the feasible domain of interconnection line power. The specific solution steps include: definition of aggregate solution parameters, calculation of initial boundary points, search for control boundary vertices, and case analysis and verification;

[0125] (1) Aggregate solution parameter definition: define the linearized state space as Φ, and define the decision variables and state variables corresponding to the constraints contained in the linear state space Φ as The specific steps are as follows:

[0126] The aggregate control boundary is denoted as x = [P0, Q0] T ,The internal decision variables and state variables include node voltage amplitude U, phase angle δ, distributed photovoltaic active power P pv,t and reactive power Load active power P d and reactive power Q d etc., unified as: All variables are vector values. The constraints composed of all variables can be abstracted into a nonlinear high-dimensional state space, denoted as Where Ω represents the true projection of the state space Φ on the PQ coupling plane.

[0127] (2) Calculation of initial boundary points: Select appropriate initial normal vectors to calculate the initial boundary points. The specific implementation steps are as follows:

[0128] Along the selected normal vector Direction search satisfies the linear state space Constrained 2D polygon The vertex set is denoted as V0. The initial normal vector is {[1, 1], [-1, 1], [-1, -1], [1, -1]}. Substitute the following formula and solve the linear programming problem to obtain the initial vertex set.

[0129]

[0130] (3) Control boundary vertex search: obtain new boundary vertices for industrial park control based on state space constraint calculation, and solve the contribution rate of new vertices for selection. The specific steps are as follows:

[0131] By translating the boundary outward to search for the boundary points of the feasible region Φ of the aggregate output, for k boundaries, there is a boundary equation: The vertices at both ends of the boundary are The new vertex can be obtained by calculating the external normal vector using the following formula and substituting it into formula (19).

[0132]

[0133] Introducing vertex improvement rate Δf j Quantify the contribution of new vertices to the expansion of the current 2D polygon boundary:

[0134]

[0135] in, is the optimal solution for vertex search corresponding to the normal vector of boundary line j, i.e., the solution of equation (19). j is the constant value of the boundary j equation, which is 1 here. Select a reasonable error limit of the feasible domain of the aggregate ε=0.01, when the vertex improvement rate satisfies Δf j When ≥ε, the new vertex can be included in the vertex set and the feasible domain of the aggregate can be reconstructed. Otherwise, it is discarded and a new vertex is found again.

[0136] (4) Example analysis and verification, in order to verify the effectiveness and feasibility of the algorithm in this paper, the following Figure 1 In the grid topology shown, the power reference direction of the tie line between the aggregate and the distribution network is from the distribution network to the aggregate, the grid voltage level is 10kV, the reference voltage VB = 10kV, and the reference capacity SB = 100MVA. The example test is based on the python-3.10.13 platform, AMD Ryzen7 4800H and IPOPT solver. For details of the aggregate topology connection diagram, see Figure 1 .

[0137] Considering the coupling constraints of the safe operation of the power grid within the aggregate, the method (3) of this section is used to solve the control boundary of the aggregate. The specific steps are as follows:

[0138] (4-1) Calculation of initial vertex of control boundary approximate projection:

[0139] Search for the initial projection domain vertex along the PQ coupling plane coordinate axis direction: take the initial normal vector n 01 =[1,1], n 01=[1,-1], n 01 =[-1,-1], n 01 =[-1,1], substituting into formula (19), solving the model, we can get the initial vertices A0(0.707, 0.707), B0(-0.707, 0.707), C0(-0.707, -0.707), D0(0.707, -0.707), and calculate the normal vectors n11, n12, n13, n14 on the boundary of the initial projection domain. See the attached figure for details. Figure 2 .

[0140] (4-2) Control the continuous search of the boundary approximate projection vertex:

[0141] Set the error limit of the control boundary approximate projection vertex search iteration process to determine whether the vertex improvement rate of each new vertex satisfies Δf j ≥ε, where ε=0.01.

[0142] Substitute the normal vectors n11, n12, n13 and n14 into equation (19) to solve the new boundary vertices. At the same time, calculate the improvement rate of each new vertex and determine whether it meets the limit requirements. Decide whether to update to the approximate projection domain. The specific coordinates and improvement rates of the four new vertices A1, B1, C1, and D1 of the approximate projection are shown in the following table:

[0143] Table 1 Vertex coordinates and improvement rates

[0144]

[0145] Calculate the normal vectors n21, n22, n23, n24, n25, n26, n27, n28 on the boundary of the new projection domain. See the attached Figure 3 .

[0146] Substitute the normal vectors n21-n28 into equation (19) respectively, repeat the above solution process, obtain the new vertex updated to the projection domain, calculate the external normal vector on the new boundary generated by the vertex update, repeat the above solution process until no new vertex meets the error limit, and end the solution process.

[0147] In a specific embodiment of the present invention, the S4 is a source-grid-load-storage aggregate optimization and control model that takes into account network security constraints. After the source-grid-load-storage resources in the original industrial park are aggregated, the original industrial park distribution network optimization is transformed into multi-aggregate collaborative optimization. Considering that the adjustment costs of each aggregate are different, the multi-aggregate collaborative optimization problem is an economic optimization problem. To this end, the following industrial park source-grid-load-storage multi-aggregate collaborative optimization operation model is constructed, which specifically includes objective functions and constraints.

[0148] (1) Objective function. The goal of the collaborative optimization of the industrial park source-grid-load-storage multi-aggregator is to minimize the total system regulation cost, which specifically includes the active power regulation cost and the reactive power regulation cost. The specific expression is as follows:

[0149]

[0150] Among them, C is the total system regulation cost; K is the set of source-grid-load-storage aggregates in the industrial park; k is the source-grid-load-storage aggregate of the kth industrial park; They represent the active power regulation cost and reactive power regulation cost of the kth aggregate in the system respectively.

[0151] (2) Constraints. The constraints of the industrial park source-grid-load-storage multi-aggregate collaborative optimization operation model include power conservation constraints, aggregate regulation capacity constraints, and voltage safety constraints. The specific function expressions are as follows:

[0152] (2-1) Power balance constraint: the sum of the total load demand and network loss of the system is the total output of the system. The specific expression is as follows:

[0153]

[0154] Among them, P k , Q k Respectively represent the active and reactive power output of the kth aggregate; P D , Q D Respectively represent the total active and reactive load demand of the system P loss , Q loss Respectively represent the total active and reactive losses of the system.

[0155] (2-2) The aggregate regulation capacity is limited. The power regulation range of the aggregate must meet the aggregate regulation boundary solved by S3. The specific expression is as follows:

[0156]

[0157] Where ΔP k , ΔQ k The active and reactive power adjustment increments of the kth aggregate are respectively, is the aggregate control boundary solved by S3.

[0158] (2-3) Voltage safety limit constraint. The specific expression of the bus voltage safety constraint that needs to be met during the power system optimization scheduling process is shown in formula (18).

[0159] The method of the present invention first obtains the basic data of source-grid-load-storage operation, further uniformly models the power regulation characteristics of source-grid-load-storage resources, constructs a source-grid-load-storage aggregate control boundary equivalent calculation model considering network security constraints, and proposes a high-dimensional aggregate boundary approximate projection calculation method to evaluate the active-reactive output coupling range of the aggregate. Finally, a source-grid-load-storage aggregate optimization control model considering network security constraints is constructed. This method proposes a source-grid-load-storage aggregate optimization control method considering network security constraints, including unified power regulation characteristic modeling for sources (distributed power sources such as photovoltaics, gas turbines), grids (grid structures), loads (time-shiftable loads), and storage (energy storage equipment) in industrial parks; the construction of the control boundary equivalent calculation model: not only the physical constraints of the power grid (such as line transmission capacity, voltage limits, etc.) are considered, but also the active-reactive output coupling relationship within the distributed resource aggregate is considered; an efficient approximate projection control boundary solution method is proposed; and a source-grid-load-storage aggregate optimization control model considering network security constraints is constructed.

[0160] Example 2

[0161] As one embodiment of the present invention, a source-grid-load-storage aggregate optimization control system for an industrial park is provided, comprising:

[0162] Data modeling module, boundary modeling module, boundary calculation module and optimization and control module;

[0163] The data modeling module is used to obtain source-grid-load-storage operation data and build a power characteristic model of source-grid-load-storage resources in the industrial park;

[0164] The boundary modeling module is used to construct a source-grid-load-storage aggregate control boundary equivalent calculation model considering network security constraints;

[0165] The boundary calculation module is used to solve the control boundary of the source-grid-load-storage aggregate in the industrial park;

[0166] The optimization and control module is used to construct an optimization and control model for source-grid-load-storage aggregation that takes network security constraints into consideration.

[0167] The industrial park source-load-storage resource power characteristic model includes at least one of the industrial park time-shiftable load characteristic modeling, distributed photovoltaic characteristic modeling, energy storage resource characteristic modeling, and controllable distributed power source characteristic modeling.

[0168] The time-shiftable load characteristic modeling of the industrial park includes time-shiftable load adjustment range constraints, time-shiftable load scheduling cost constraints and scheduling cost linearization constraints;

[0169] The distributed photovoltaic characteristic modeling includes light intensity probability density function constraints, distributed photovoltaic active power output constraints, and distributed photovoltaic reactive power constraints;

[0170] The energy storage resource characteristic modeling includes electric vehicle energy storage charging and discharging power constraints and logic constraints, energy storage power station charge state constraints, energy storage power station charging and discharging power constraints, and energy storage power station charging and discharging power balance constraints;

[0171] The controllable distributed power source characteristic modeling includes controllable generator set power limit constraints and controllable generator set power ramp limit constraints.

[0172] The source-grid-load-storage aggregate control boundary equivalent calculation model includes one of the industrial park power grid network flow constraints, power balance constraints, safe transmission capacity constraints, and voltage safety limit constraints.

[0173] The obtaining of the aggregate control boundary includes defining a linearized state space, and defining decision variables and state variables corresponding to the constraints contained in the linear state space;

[0174] Select the initial normal vector to calculate the initial boundary point;

[0175] Based on the state space constraint calculation, the new boundary vertices of the industrial park regulation are obtained, and the contribution rate of the new vertices is solved to make choices.

[0176] The optimization and control model includes an objective function and constraint conditions;

[0177] The objective function is expressed as,

[0178]

[0179] Where C is the total system regulation cost; K is the set of industrial park source-grid-load-storage aggregates; k is the kth industrial park source-grid-load-storage aggregate, They represent the active power regulation cost and reactive power regulation cost of the kth aggregate in the system respectively.

[0180] The constraint condition is expressed as,

[0181]

[0182] Among them, P k , Q k Respectively represent the active and reactive power output of the kth aggregate, P D , Q D Respectively represent the total active and reactive load demand of the system P loss , Q loss Respectively represent the total active and reactive losses of the system, ΔP k , ΔQ k The active and reactive power adjustment increments of the kth aggregate are respectively, is the aggregate control boundary to be solved, are the upper and lower limits of the voltage amplitude allowed for grid node i.

[0183] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0184] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0185] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0186] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0187] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An industrial park source-grid-load-storage aggregate optimization and control method, characterized in that: include: Obtain source-grid-load-storage operation data, build a power characteristic model of source-load-storage resources in the industrial park, and embed it into the control boundary solution model as a specific constraint; Based on the power characteristic model of source-load-storage resources in industrial parks, a boundary equivalent calculation model of source-grid-load-storage aggregate regulation considering network security constraints is constructed; Based on the high-dimensional aggregate boundary approximate projection method, the equivalent calculation model of the source-grid-load-storage aggregate control boundary is solved to obtain the aggregate control boundary; Based on the aggregate control boundary, an optimal control model of source-grid-load-storage aggregate considering network security constraints is constructed to achieve coordinated control of source-grid-load-storage resources within the industrial park.

2. The industrial park source-grid-load-storage aggregate optimization control method according to claim 1, characterized in that: The industrial park source-load-storage resource power characteristic model includes at least one of the industrial park time-shiftable load characteristic modeling, distributed photovoltaic characteristic modeling, energy storage resource characteristic modeling, and controllable distributed power source characteristic modeling.

3. The industrial park source-grid-load-storage aggregate optimization control method according to claim 2, characterized in that: The time-shiftable load characteristic modeling of the industrial park includes time-shiftable load adjustment range constraints, time-shiftable load scheduling cost constraints and scheduling cost linearization constraints; The distributed photovoltaic characteristic modeling includes light intensity probability density function constraints, distributed photovoltaic active power output constraints, and distributed photovoltaic reactive power constraints; The energy storage resource characteristic modeling includes electric vehicle energy storage charging and discharging power constraints and logic constraints, energy storage power station charge state constraints, energy storage power station charging and discharging power constraints, and energy storage power station charging and discharging power balance constraints; The controllable distributed power source characteristic modeling includes controllable generator set power limit constraints and controllable generator set power ramp limit constraints.

4. The industrial park source-grid-load-storage aggregate optimization control method according to claim 3, characterized in that: The source-grid-load-storage aggregate control boundary equivalent calculation model includes one of the industrial park power grid network flow constraints, power balance constraints, safe transmission capacity constraints, and voltage safety limit constraints.

5. The industrial park source-grid-load-storage aggregate optimization control method according to claim 4, characterized in that: The obtaining of the aggregate control boundary includes defining a linearized state space, and defining decision variables and state variables corresponding to the constraints contained in the linear state space; Select the initial normal vector to calculate the initial boundary point; Based on the state space constraint calculation, the new boundary vertices of the industrial park regulation are obtained, and the contribution rate of the new vertices is solved to make choices.

6. The industrial park source-grid-load-storage aggregate optimization control method according to claim 5, characterized in that: The optimization and control model includes an objective function and constraint conditions; The objective function is expressed as, Where C is the total system regulation cost; K is the set of industrial park source-grid-load-storage aggregates; k is the kth industrial park source-grid-load-storage aggregate, They represent the active power regulation cost and reactive power regulation cost of the kth aggregate in the system respectively.

7. The industrial park source-grid-load-storage polymer optimization control method according to claim 6, characterized in that: The constraint condition is expressed as, Among them, P k , Q k Respectively represent the active and reactive power output of the kth aggregate, P D , Q D Respectively represent the total active and reactive load demand of the system P loss , Q loss Respectively represent the total active and reactive losses of the system, ΔP k , ΔQ k The active and reactive power adjustment increments of the kth aggregate are respectively, is the aggregate control boundary to be solved, are the upper and lower limits of the voltage amplitude allowed for grid node i.

8. A system for optimizing and controlling the source-grid-load-storage aggregate in an industrial park, characterized in that: include: The data modeling module is used to obtain source-grid-load-storage operation data, build a source-load-storage resource power characteristic model for the industrial park, and embed it into the control boundary solution model as a specific constraint; The boundary modeling module is used to construct a source-grid-load-storage aggregate control boundary equivalent calculation model considering network security constraints based on the power characteristic model of the source-load-storage resource in the industrial park; The boundary calculation module is used to solve the source-grid-load-storage aggregate control boundary equivalent calculation model based on the high-dimensional aggregate boundary approximate projection method to obtain the aggregate control boundary; The optimization and control module is used to construct a source-grid-load-storage aggregate optimization and control model that takes into account network security constraints based on the aggregate control boundary, so as to realize the coordinated control of source-grid-load-storage resources in the industrial park.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the industrial park source-grid-load-storage aggregate optimization and control method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the industrial park source-grid-load-storage aggregate optimization and control method described in any one of claims 1 to 7 are implemented.

Images