Power grid planning method and system adapted to multiple uncertainties and multi-objective requirements

Abstract

A power grid planning method and system adapted to multiple uncertainties and multi-objective requirements, which relate to the technical field of power grid planning. The method comprises: constructing a deterministic multi-objective coordinated planning model with the objectives of minimizing economic cost, reliability risks and environmental impact; on the basis of an IGDT, constructing a multi-uncertainty model considering wind power, photovoltaic power and load; converting a model into a second-order cone programming model by using variable substitution, absolute value linearization, second-order cone relaxation and McCormick envelopes relaxation methods; and solving the second-order cone programming model, in order to obtain a power grid planning scheme that adapts to uncertainties in wind and photovoltaic power output and load demand growth and meets the requirements for the economy, reliability and environmental friendliness of power grid operation. Thus, economic benefits of power grid planning are improved, carbon emissions from power grid operation are reduced, the reliability of a power grid is enhanced, and an obtained planning scheme has high robustness within expected objectives of planners.

Description

A power grid planning method and system adapting to multiple uncertainties and multi-objective requirements Technical Field

[0001] The present invention relates to the technical field of power grid planning, and in particular to a power grid planning method and system adapting to multiple uncertainties and multiple objectives. Background Art

[0002] Against the backdrop of the "dual carbon" goals and the emergence of a new power system, the integration of a high proportion of renewable energy and a large number of flexible loads has introduced significant volatility and intermittency into the power grid. Currently, addressing the two uncertainties facing grid planning—short-term renewable energy output and long-term load growth—while also meeting the economic, reliability, and environmental requirements of grid planning and operation, and developing grid planning methods that adapt to multiple uncertainties and multi-objective requirements, is a pressing issue.

[0003] Currently, traditional power grid planning primarily considers the impact of uncertainty on either the source or load side. However, as the coordinated interaction between the source, grid, load, and storage sides of a power grid system becomes increasingly close, grid planning focused solely on uncertainty is no longer adaptable to future grid development requirements. Further consideration of uncertainty on both the source and load sides is necessary. Regarding uncertainty modeling, traditional power grid planning often uses methods such as scenario generation and reduction, stochastic optimization planning, and robust optimization planning. However, these methods require probabilistic distributions of uncertainties, and in actual planning, planning solutions are often constrained by budgetary costs. Information gap decision theory (IGDT), on the other hand, can, based on certain expected planning objectives and with limited information about uncertainties, determine the uncertainty set that defines the maximum acceptable fluctuation range for uncertain parameters during the optimization process. This effectively quantifies multiple uncertainties and allows for flexible solution of planning solutions. Regarding planning objectives, existing planning techniques often prioritize economic efficiency for power grid optimization, lacking comprehensive consideration of economic efficiency, reliability, and environmental performance. The resulting planning solutions can only meet investor budgetary requirements. Moreover, existing planning technologies lack the ability to consider multiple uncertainties and multiple objectives simultaneously. It is necessary to study the multiple uncertainty models of renewable energy output and load under the planning time scale based on IGDT theory, and establish a power grid planning method and system that adapts to multiple uncertainties and multiple objectives. Summary of the Invention

[0004] In view of the two uncertainties faced by existing power grid planning, namely short-term renewable energy output and long-term load growth, and the problems in meeting the economic, reliability and environmental protection requirements of power grid planning and operation, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to consider multiple uncertainties and multiple objective requirements simultaneously.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides a power grid planning method that adapts to multiple uncertainties and multi-objective demands, which includes constructing a deterministic multi-objective coordinated planning model with the goals of minimizing economy, reliability and environmental protection; constructing a multiple uncertainty model considering wind and solar loads based on IGDT theory; constructing an IGDT robust planning model that adapts to multiple uncertainties and multi-objective demands with the goal of maximizing the comprehensive deviation coefficient; converting the model into a second-order cone programming model through variable substitution, absolute value linearization, second-order cone relaxation and McCormick envelopes relaxation method; solving the second-order cone programming model to obtain a power grid planning scheme that adapts to the uncertainty of wind and solar output and load demand growth, and meets the economy, reliability and environmental protection of power grid operation.

[0008] As a preferred solution of the power grid planning method of the present invention that adapts to multiple uncertainties and multiple objectives, the following steps are included with the goal of minimizing economy, reliability and environmental protection: Economy is based on minimizing the system comprehensive cost as the objective function, and the specific formula is as follows: minC = C I +C O

[0009] Among them, C is the comprehensive cost of the system, C I is the investment cost, C O is the operating cost; reliability is based on minimizing carbon emission cost as the objective function, and the specific formula is as follows:

[0010] Among them, ΔU all Indicates the voltage offset, represents the per-unit value of the voltage at node i during period t; environmental protection is based on minimizing carbon emission costs as the objective function, and the specific formula is as follows:

[0011] Among them, C T represents the carbon emission penalty cost, represents the carbon emission cost during the operation of the power grid, represents the carbon emission cost of gas turbines, represents the carbon emission cost of photovoltaic units, represents the carbon emission cost of wind turbines, f T represents the carbon price, represents the system carbon emission conversion factor, represents the carbon emission coefficient of the gas turbine, Represents the carbon emission coefficient of the photovoltaic unit, represents the carbon emission coefficient of the wind turbine, It represents the active power injected into the grid at this level by the interaction node i with other grids at time t, Indicates the network loss active power at time t, represents the active power injected into node i by the gas turbine at time t represents the active power injected into node i by the photovoltaic system at time t, represents the active power injected into node i by the wind turbine at time t.

[0012] As a preferred solution of the power grid planning method of the present invention that adapts to multiple uncertainties and multi-objective needs, the investment cost includes the investment cost of new transmission lines, the investment cost of new substations, the investment cost of power sources, and the investment cost of energy storage devices; the operating cost includes the annual operating cost of power supply equipment, the operating cost of energy storage devices, the cost of purchasing electricity from the superior power grid, the penalty cost for wind and solar power abandonment, the load reduction cost, and the network loss cost; the carbon emission cost includes the carbon emission penalty cost, the carbon emission cost during the operation of the power grid, the carbon emission cost of gas turbines, the carbon emission cost of photovoltaic units, and the carbon emission cost of wind turbines.

[0013] As a preferred solution of the power grid planning method adapted to multiple uncertainties and multi-objective requirements of the present invention, the constraints of the deterministic multi-objective coordinated planning model include equipment installation capacity constraints, new substation construction capacity constraints, power flow constraints, system security constraints, source node power injection into the grid constraints, new substation operation constraints, gas turbine operation constraints, photovoltaic unit and wind turbine operation constraints, renewable energy penetration constraints, energy storage device operation constraints, demand-side response constraints, and load loss constraints; the specific formula of the power flow constraint is as follows:

[0014] Where, L(j,:) represents the set of AC branch end nodes with j representing the head node, L(:,j) represents the set of AC branch head nodes with j representing the end node, P ij,t 、P jk,t and Q ij,t , Q jk,t They represent the active and reactive power on lines ij and jk at time t, respectively, and X ij represents the reactance of line ij, and They represent the reactive power injected into node j by the external power grid, wind turbine and gas turbine at time t, and They represent the active and reactive power of the original load of node j at time t, U i,t Indicates the voltage of node i at time t, I ij,t represents the current flowing through line ij at time t, R ij represents the resistance of line ij, Xij represents the reactance of line ij, It represents the active power injected into the grid at this level by the interaction node i with other grids at time t, represents the active power injected into node j by the photovoltaic system at time t, represents the active power injected into node j by the wind turbine at time t, represents the active power injected into node j by the gas turbine at time t, represents the active power injected into node j by the energy storage device at time t; the specific formula for the energy storage device operation constraint is as follows:

[0015] in, The maximum ratio of the unit capacity ESS charging and discharging power to the total capacity, is the amount of energy storage device at node i during period t, represents the installed capacity of the energy storage device at node i, Represents the charging and discharging power of the energy storage device at node i at time t. Positive values ​​represent charging power, and negative values ​​represent discharging power. ini represents the set of candidate installation nodes for energy storage devices; the specific formula for the load loss constraint is as follows:

[0016] Among them, F1 represents the economic objective function, F2 represents the reliability objective function, F3 represents the environmental objective function, G all (·) represents the inequality constraint, H all (·) represents an equality constraint.

[0017] As a preferred solution of the power grid planning method of the present invention that adapts to multiple uncertainties and multiple objectives, the specific formula for constructing a multiple uncertainty model considering wind and solar loads is as follows:

[0018] Among them, P t WT represents the actual output value of the wind turbine at time t, represents the predicted output value of the wind turbine at time t, α WT Indicates wind power output P t WT The offset coefficient, P t PV Indicates the actual output value of the photovoltaic unit at time t, represents the predicted output value of the photovoltaic unit at time t, α PV Indicates photovoltaic output P t PV The offset coefficient, P t load represents the actual value of the system load at time t, represents the predicted value of system load at time t, α load Indicates the system load P t load The offset coefficient.

[0019] As a preferred solution of the power grid planning method of the present invention that adapts to multiple uncertainties and multiple objectives, the specific formula for constructing the IGDT robust planning model that adapts to multiple uncertainties and multiple objectives is as follows:

[0020] Among them, F 01 Indicates the system comprehensive cost, F 02 Indicates voltage offset, F 03 represents the benchmark value of carbon emission cost, F1 represents the economic objective function, F2 represents the reliability objective function, F3 represents the environmental objective function, α=0, σ is the robust model deviation factor, represents the predicted value of system load at time t, P t load represents the actual value of the system load at time t, P t WT represents the actual output value of the wind turbine at time t, represents the predicted output value of wind turbine at time t, P t PV Indicates the actual output value of the photovoltaic unit at time t, represents the predicted output value of the photovoltaic unit at time t, G all (·) represents the inequality constraint, H all (·) represents an equality constraint.

[0021] As a preferred solution of the power grid planning method adapted to multiple uncertainties and multi-objective requirements of the present invention, the model is converted into a second-order cone programming model, including variable substitution, absolute value term linearization, second-order cone relaxation of the current equation, and McCormick envelope relaxation.

[0022] The specific formula for McCormick envelope relaxation is as follows:

[0023] Among them, P t PV Indicates the actual output value of the photovoltaic unit at time t, P t WT represents the actual output value of the wind turbine at time t, α PV Indicates photovoltaic output P t PV The offset coefficient, α WT Indicates wind power output P tWT The offset coefficient, represents the installed capacity of the PV system at node i, represents the installed capacity of the wind turbine at node i, represents the active power injected into node i by the photovoltaic system at time t, represents the active power injected into node i by the wind turbine at time t, represents the predicted active power output of the photovoltaic unit installed at node i at time t, represents the predicted active power output of the wind turbine installed at node i at time t, Indicates the active power of the original load of node i at time t; the model stores and The bilinear product terms in the model are linearized using the McCormick envelopes relaxation method. The auxiliary variable z is introduced to replace the bilinear terms in the McCormick envelopes relaxation formula. The specific formula in the form of x·y is as follows:

[0024] Among them, x max 、x min and y max 、y min are the upper and lower bounds of the variable, respectively.

[0025] In the second aspect, an embodiment of the present invention provides a system for a power grid planning method that adapts to multiple uncertainties and multi-objective demands, which includes a model construction module for constructing a deterministic multi-objective coordinated planning model and a multiple uncertainty model considering wind and solar loads; an integration module for integrating the deterministic multi-objective coordinated planning model with the multiple uncertainty model considering wind and solar loads to construct an IGDT robust planning model that adapts to multiple uncertainties and multi-objective demands; a model solving module for obtaining a power grid planning scheme that adapts to the uncertainty of wind and solar output and load demand growth and meets the economy, reliability and environmental protection of power grid operation.

[0026] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the power grid planning method that adapts to multiple uncertainties and multiple objective requirements as described in the first aspect of the present invention are implemented.

[0027] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the power grid planning method that adapts to multiple uncertainties and multiple objective requirements as described in the first aspect of the present invention are implemented.

[0028] The beneficial effects of the present invention are as follows: a multi-objective optimization system is constructed with system comprehensive cost, voltage deviation and carbon emission cost as indicators, and a multi-objective planning modeling method that adapts to the economic, reliability and environmental protection requirements of power grid operation is proposed, which is beneficial to improving the economic benefits of power grid planning, reducing carbon emissions from power grid operation, and improving power grid reliability; multiple constraints are introduced to effectively realize the coordinated operation of the source, grid, load and storage sides during power grid planning and operation, and optimize the power grid architecture; based on IGDT robust planning, a multi-objective coordinated planning model and a multiple uncertainty model are integrated to propose a power grid planning method that adapts to multiple uncertainties and multiple objective requirements, obtain a robust power grid planning scheme and power grid operation strategy, and improve the ability of the obtained planning scheme to avoid uncertainty risks; based on variable substitution, absolute value linearization, second-order cone relaxation and McCormick envelopes relaxation method, the model is converted into a second-order cone programming model, which simplifies the model and improves the calculation speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] FIG1 is a flow chart of the steps of a power grid planning method that adapts to multiple uncertainties and multiple objectives.

[0031] FIG2 is a flowchart of a power grid planning method that adapts to multiple uncertainties and multiple objectives, and a power grid planning system that adapts to multiple uncertainties and multiple objectives.

[0032] Figure 3 is a comparison of the system renewable energy penetration and absorption rate results before and after technical optimization of the power grid planning method that adapts to multiple uncertainties and multi-objective requirements.

[0033] Figure 4 is a comparison of the system power purchase results before and after technical optimization of the power grid planning method that adapts to multiple uncertainties and multi-objective requirements. DETAILED DESCRIPTION

[0034] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0035] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. 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.

[0036] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive of other embodiments.

[0037] Example 1

[0038] 1 and 2 , which are the first embodiment of the present invention, provide a power grid planning method adapted to multiple uncertainties and multiple objectives, including:

[0039] S1: Construct a deterministic multi-objective coordinated planning model with the goal of minimizing economy, reliability and environmental protection.

[0040] Preferably, in order to reflect the adaptability of power grid planning to the three requirements of economy, environmental protection and reliability during power grid operation, with the goal of minimizing comprehensive cost, carbon emission cost and voltage offset, the investment constraints of source, network, storage and other equipment, the operation constraints of power grid and various equipment, demand response constraints, new energy penetration constraints and load loss constraints are comprehensively considered to establish a deterministic multi-objective coordinated planning model.

[0041] Specifically, a deterministic multi-objective coordinated planning model: the model aims to minimize the comprehensive cost (economic efficiency), voltage offset (reliability), and carbon emission cost (environmental protection), taking into account the investment and construction planning of wind turbines, photovoltaic units, gas turbines, substations, transmission lines, and energy storage devices, the load that can be reduced, and the operation control of various types of equipment. The constraints are equipment investment and construction constraints, flow constraints, renewable energy penetration constraints, load loss constraints, and other grid operation constraints and equipment operation constraints, effectively realizing the coordinated optimization planning of the power system's source, network, load, and storage.

[0042] Preferably, the economic efficiency is based on the specific formula of minimizing the overall cost of the system as follows: minC=C I +C O

[0043] Among them, C is the comprehensive cost of the system; C I is the investment cost; C O For running costs.

[0044] Specifically, the investment cost includes the annual investment cost of new transmission lines. Investment cost of new substation Power supply investment cost Energy storage device investment cost Operating costs include the annual operating costs of power equipment Energy storage device operating costs Cost of purchasing electricity from the upper-level power grid Penalty costs for curtailing wind and solar power Load reduction costs Network loss costs

[0045] Specifically, the formula for the investment cost of a new transmission line is as follows:

[0046] Where b is the discount rate, y line Represents the full life cycle of the line, Ω nline Represents the set of newly built transmission lines in the system, L ij represents the length of line ij, represents the investment cost of new transmission lines, c line It represents the investment cost of building a new transmission line per unit length.

[0047] Specifically, the specific formula for the investment cost of a new substation is as follows:

[0048] Where b is the discount rate, represents the investment cost of a new substation, y sub Represents the full life cycle of the substation, Ω sub Indicates the set of newly built substations in the system, N sub represents the number of newly built substations, f sub represents the inherent investment cost of a new single substation, c sub represents the unit capacity investment cost of a new substation, It represents the construction capacity of the new substation i.

[0049] Specifically, the specific formula for the investment cost of power supply equipment is as follows:

[0050] Where b is the discount rate, represents the power supply investment cost, y PV 、y WT 、y MT Respectively represent the full life cycle of photovoltaic units, wind turbine units, and gas turbines, Ω ini represents the candidate installation node set of photovoltaic units, wind turbine units, gas turbines and energy storage devices, c PV 、c WT 、c MT Represent the investment cost per unit capacity of photovoltaic units, wind turbine units, and gas turbines, respectively. They represent the installed capacity of photovoltaic units, wind turbine units and gas turbines in node i respectively.

[0051] Specifically, the specific formula for the investment cost of energy storage devices is as follows:

[0052] in, represents the investment cost of energy storage device, b represents the discount rate, y ESS is the full life cycle of the energy storage device, c ESS is the investment cost per unit capacity of the energy storage device, is the installed capacity of the energy storage device at node i.

[0053] Furthermore, the specific formula for the operating cost of power supply equipment is as follows:

[0054] in, Indicates the operating cost of the power supply equipment, are the operating costs per unit of electricity generated by photovoltaic units, wind turbines, and gas turbines, respectively. are the active powers injected into node i by the photovoltaic unit, wind turbine unit, and gas turbine at time t; Δt is the duration of time period t, which is 1 hour.

[0055] Furthermore, the specific formula for the operating cost of the energy storage device is as follows:

[0056] in, represents the operating cost of the energy storage device, is the operating cost of the energy storage device per unit charge and discharge capacity, is the charging and discharging power of the energy storage device at node i at time t, Δt is the duration of time period t, positive values ​​represent charging power, and negative values ​​represent discharging power.

[0057] Furthermore, the specific formula for the electricity purchase cost of other power grids is as follows:

[0058] in, Cost of purchasing electricity from the upper grid, f t OUT is the time-of-use electricity price during period t, Ω pur is the set of interaction nodes between the system and other power grids, The active power injected into the local grid by the interaction node i with other grids at time t. The specific formula for the penalty cost of wind and solar power curtailment is as follows:

[0059] in, Penalty costs for curtailing wind and solar power, and They are the penalty costs for curtailing wind and solar power per unit of electricity, and are the predicted active power outputs of the photovoltaic unit and wind turbine installed at node i at time t, represents the active power injected into node i by the photovoltaic system at time t, represents the active power injected into node i by the wind turbine at time t.

[0060] Furthermore, the specific formula for load reduction cost is as follows:

[0061] in, represents the load reduction cost, Λ load,cut The set of nodes that can reduce load, Compensation for load unit power reduction, It is the active load reduction after demand response.

[0062] Furthermore, the specific formula for network loss cost is as follows:

[0063] in, represents the network loss cost, f t OUT It is expressed as the time-of-use electricity price in period t, is the network loss during period t, R ij is the resistance of the transmission line ij; is the square of the current in line ij at time t. Reliability: Minimize voltage offset.

[0064] Furthermore, the reliability objective function considers minimizing the voltage offset ΔU all The specific formula is as follows:

[0065] Among them, ΔU all Indicates the voltage offset, is the per-unit value of the voltage at node i during period t. Environmental protection: Minimize carbon emission costs; considering the carbon emissions in the operation of the power grid and the emission reduction effect of the grid-connected wind and solar power units, the environmental protection objective function takes into account the carbon emission penalty cost C T , including the carbon emission costs during grid operation Gas Turbine Carbon Emission Costs Photovoltaic unit carbon emissions Cost of carbon emissions from wind turbines

[0066] Preferably, environmental protection is based on minimizing carbon emission costs as the objective function, and the specific formula is as follows:

[0067] Among them, C Trepresents the carbon emission penalty cost, represents the carbon emission cost during the operation of the power grid, represents the carbon emission cost of gas turbines, represents the carbon emission cost of photovoltaic units, represents the carbon emission cost of wind turbines, f T represents the carbon price, represents the system carbon emission conversion factor, represents the carbon emission coefficient of the gas turbine, represents the carbon emission coefficient of the photovoltaic unit, represents the carbon emission coefficient of the wind turbine, It represents the active power injected into the grid at this level by the interaction node i with other grids at time t, represents the network loss active power at time t, represents the active power injected into node i by the gas turbine at time t represents the active power injected into node i by the photovoltaic system at time t, represents the active power injected into node i by the wind turbine at time t.

[0068] Specifically, the specific formula for equipment installation capacity constraint is as follows:

[0069] Among them, V i PV,max 、V i WT,max 、V i MT,max and V i ESS,max are the maximum installed capacities of photovoltaic units, wind turbines, gas turbines and energy storage devices at node i respectively.

[0070] Specifically, the specific formula for the capacity constraint of a new substation is as follows:

[0071] in, is the maximum investment capacity of the new substation i. The specific formula of the power flow constraint is as follows:

[0072] Where, L(j,:) represents the set of AC branch end nodes with j representing the head node, L(:,j) represents the set of AC branch head nodes with j representing the end node, P ij,t 、P jk,t and Q ij,t , Q jk,t They represent the active and reactive power on the line and jk at time t, respectively, X ij represents the reactance of line ij, and They represent the reactive power injected into node j by the external power grid, wind turbine and gas turbine at time t, and They represent the active and reactive power of the original load of node j at time t, U i,t Indicates the voltage of node i at time t, I ij,t represents the current flowing through line ij at time t, R ij represents the resistance of line ij, X ij represents the reactance of line ij, It represents the active power injected into the grid at this level by the interaction node i with other grids at time t, represents the active power injected into node j by the photovoltaic system at time t, represents the active power injected into node j by the wind turbine at time t, represents the active power injected into node j by the gas turbine at time t, represents the active power injected into node j by the energy storage device at time t.

[0073] Specifically, the system security constraint formula is as follows:

[0074] Among them, Ω N Represents all nodes in the system, U i,max and U i,min Represent the upper and lower limits of the voltage at node i, I ij,max represents the safe current of the existing transmission line ij. The specific formula for the power constraint injected into the grid by the source node is as follows:

[0075] in, P is the reactive power injected into the grid by the interaction node i with other grids at time t, i pur,max and P i pur,min are the upper and lower limits of the active power injected into the grid by the interaction node i with other grids, and are the upper and lower limits of reactive power injected into the grid for the interaction node i with other grids, respectively.

[0076] Preferably, the specific formula for the operation constraints of the newly built substation is as follows:

[0077] in, represents the construction capacity of the new substation i, Ω sub Represents the set of newly built substations in the system, J i represents the set of load nodes supplied by the newly built substation i, P jrepresents the active load of the newly built substation i at load node j, represents the maximum load rate of the newly built substation i, Represents the power factor of the newly built substation i.

[0078] Preferably, the specific formula of the gas turbine operation constraint is as follows:

[0079] in, represents the active power injected into node i by the gas turbine at time t, represents the installed capacity of the gas turbine at node i, represents the reactive power injected into node i by the gas turbine at time t, and They represent the upper and lower limits of the gas turbine power factor angle respectively, and λ is the upper limit of the gas turbine ramp rate.

[0080] Preferably, the photovoltaic and wind turbines are constrained in operation, and both photovoltaic and wind turbines adopt a constant power factor control strategy. The power factor of the photovoltaic unit is set to 1, that is, only active power is injected into the grid. The specific formula is as follows:

[0081] in, Indicates the power factor angle of the wind turbine, P t PV,max It represents the predicted active power per unit value of the photovoltaic unit at time t, P t WT,max It represents the per-unit active power of the wind turbine predicted at time t.

[0082] Preferably, the specific formula for renewable energy penetration constraint is as follows:

[0083] in, represents the active power injected into node i by the photovoltaic system at time t, represents the active power injected into node i by the wind turbine at time t, represents the active power of the original load of node i at time t, γ max , γ min They represent the upper and lower limits of the system’s renewable energy penetration rate, respectively.

[0084] It should be noted that energy storage devices have two states: charging and discharging. During operation, they must meet constraints on charge and discharge power and charge capacity. To avoid increasing solution complexity by introducing 0-1 variables indicating the charge and discharge states into the operational layer model, a simplified energy storage device charge and discharge model is adopted. This model assumes that the energy storage device is in an ideal, lossless state, and that the upper and lower limits of charge and discharge power are the same.

[0085] Preferably, the specific formula for the energy storage device operation constraint is as follows:

[0086] in, The maximum ratio of the unit capacity ESS charge and discharge power to the total capacity; is the amount of energy storage device at node i during period t, represents the installed capacity of the energy storage device at node i; Represents the charging and discharging power of the energy storage device at node i at time t. A positive value represents charging power, and a negative value represents discharging power. Ω ini Demand-side response constraint: The demand-side response (DR) model takes into account the curtailable load model and provides certain curtailment compensation to users who curtail their load during peak load periods, thus playing a role in "peak shaving".

[0087] Furthermore, this study only considers the specific formula of the active load demand response model as follows:

[0088] in, represents the active power of the original load of node i at time t, is the load reduction coefficient of node i at time t, and They are the upper and lower limits of the load reduction factor respectively.

[0089] Preferably, the specific formula of the load loss constraint is as follows:

[0090] in, Indicates the load active power reduced after demand response. represents the active power of the original load of node i at time t, κ max is the maximum load loss rate of the system, Δt represents the duration of the time period, 1h.

[0091] Furthermore, in order to facilitate the description of the deterministic multi-objective coordinated planning model, it is written in the following form:

[0092] Among them, F1 represents the economic objective function, that is, F1=C I +C O , F2 represents the reliability objective function, that is, F2=ΔU all , F3 represents the environmental protection objective function, that is, F3=C T , G all (·) represents the inequality constraint, H all (·) represents an equality constraint.

[0093] S2: Based on IGDT theory, a multiple uncertainty model considering wind and solar loads is constructed.

[0094] Specifically, based on the IGDT theory, the uncertainty of wind power output, the uncertainty of photovoltaic unit output and the uncertainty of load demand growth are considered, and a multiple uncertainty model considering wind and solar load is established.

[0095] Considering the multiple uncertainty models of wind and solar loads: The IGDT model can construct an uncertainty set with the maximum fluctuation range of each uncertainty while ensuring that the planning cost is within an acceptable range, effectively quantifying the uncertainty. To this end, the multiple uncertainties of wind and solar loads are modeled based on the IGDT theory. In the multi-objective power grid coordinated optimization planning model, the wind power output, photovoltaic unit processing, and load demand within the scheduling cycle are considered accurate for coordinated optimization planning. However, in actual systems, the above parameters have uncertainty and volatility. The IGDT theory is applied to establish a multi-objective power grid coordinated optimization planning model considering multiple uncertainties. First, the uncertainty set model is established.

[0096] Preferably, the specific formula of the uncertainty set model of wind power output, photovoltaic unit output and load demand is as follows:

[0097] Among them, P t WT represents the actual output value of the wind turbine at time t, represents the predicted output value of the wind turbine at time t, α WT Wind power output P t WT The offset coefficient, P t PV Indicates the actual output value of the photovoltaic unit at time t, represents the predicted output value of the photovoltaic unit at time t, α PV Indicates photovoltaic output P t PV The offset coefficient, P t load represents the actual value of the system load at time t, represents the predicted value of system load at time t, α load Indicates the system load P t load The offset coefficient.

[0098] Specifically, IGDTs typically use a single uncertainty deviation coefficient as their objective function, making them inadequate for simultaneously addressing the uncertainties of wind, solar, and load. This patent addresses this issue by assigning different weights to the deviation coefficients. The weight ratio of the wind, solar, and load deviation coefficients has no impact on the model's applicability and can be determined based on actual conditions to reflect the varying levels of uncertainty decision-makers place on the system's wind, solar, and load uncertainties.

[0099] Furthermore, the specific formula for the comprehensive deviation coefficient of the system is obtained through weighted sum form as follows: ψ=μ WT α WT +μ PV α PV +μ load α load

[0100] Among them, μ WT 、μ PV 、μ load Respectively represent the weights of wind power output, photovoltaic unit output and system load deviation coefficient, α WT Indicates wind power output P t WT The offset coefficient, α PV Indicates photovoltaic output P t PV The offset coefficient, α load Indicates the system load P t load The offset coefficient.

[0101] S3: With the goal of maximizing the comprehensive deviation coefficient, an IGDT robust planning model that adapts to multiple uncertainties and multi-objective requirements is constructed.

[0102] Furthermore, with the goal of maximizing the comprehensive deviation coefficient, considering the investment constraints of source, network, storage and other equipment, the operation constraints of the power grid and various types of equipment, the demand response constraints, the new energy penetration constraints, the load loss constraints, and the deviation degree constraints between the expected target value and the optimal solution of the deterministic model, an IGDT robust planning model that adapts to multiple uncertainties and multi-objective needs is established. IGDT robust planning model that adapts to multiple uncertainties and multi-objective needs: IGDT theory includes risk avoidance strategies and risk preference strategies. The former aims to maximize the avoidance of the impact of uncertainty on the solution results, and the robust model is constructed; the latter aims to seek the maximum possible benefit from the uncertainty risk, and the opportunity model is constructed. In order to avoid the uncertainty brought about by the uncertainty of new energy output and load uncertainty to the results of grid coordination optimization planning, this patent uses the IGDT robust model to model uncertainty. The specific formula is as follows:

[0103] Among them, F 01 、F02 、F 03 is the benchmark value of system comprehensive cost, voltage offset and carbon emission cost, and σ is the robust model deviation factor.

[0104] Furthermore, assuming α = 0 and substituting the predicted values ​​of wind power output, photovoltaic unit output, and load into the multi-objective grid coordinated optimization planning model for optimization and solution, we obtain the optimal solutions for system comprehensive cost, voltage offset, and carbon emission cost. The greater the deviation of the expected target value from the optimal solution of the deterministic model, the greater the risk aversion and the more robust the scheduling scheme. It can be seen that the above planning model belongs to a two-layer optimization planning model. The lower layer indicates that when wind power output, photovoltaic unit output, and load demand fluctuate within the uncertainty set, the system comprehensive cost cannot exceed the expected cost value. Since the cost of wind power generation and photovoltaic units is much lower than the power generation cost of conventional units, it is easy to conclude that the smaller the wind power output and photovoltaic unit output and the greater the system load demand, the greater the system comprehensive cost.

[0105] Furthermore, in order to improve the solution efficiency, the specific formula for simplifying the uncertainty model is as follows:

[0106] Among them, F 01 Indicates the system comprehensive cost, F 02 Indicates voltage offset, F 03 represents the benchmark value of carbon emission cost, and F1 represents the economic objective function, that is, F1=C I +C O , F2 represents the reliability objective function, that is, F2=ΔU all , F3 represents the environmental protection objective function, that is, F3=C T , α WT Indicates wind power output P t WT The offset coefficient, α PV Indicates photovoltaic output P t PV The offset coefficient, α load Indicates the system load P t load The offset coefficient, α = 0, σ is the robust model deviation factor, represents the predicted value of system load at time t, P t load represents the actual value of the system load at time t, P t WT represents the actual output value of the wind turbine at time t, represents the predicted output value of wind turbine at time t, P t PV Indicates the actual output value of the photovoltaic unit at time t, represents the predicted output value of the photovoltaic unit at time t, G all (·) represents the inequality constraint, H all (·) represents an equality constraint.

[0107] S4: The model is transformed into a second-order cone programming model through variable substitution, absolute value linearization, second-order cone relaxation, and McCormick envelopes relaxation method.

[0108] Preferably, the model is transformed into a second-order cone programming model through methods such as variable substitution, absolute value linearization, second-order cone relaxation, and McCormick envelopes relaxation, and a grid planning scheme that adapts to the uncertainty of wind and solar power output and load demand growth and meets the economic, reliability, and environmental protection requirements of grid operation is directly solved.

[0109] It should be noted that the single-layer model is still a nonlinear programming model with strong nonlinearity and non-convexity. It is difficult to solve directly, and the convergence of the solution cannot be guaranteed. Therefore, it is necessary to replace the nonlinear elements of the model with variables, linearize the absolute values, relax the McCormick envelopes or relax the second-order cone, and transform it into a second-order cone programming model so that it can be solved efficiently using numerical methods. Variable replacement: The specific formula of the power flow constraint contains square terms of current and voltage, which requires variable replacement. The specific formula for variable replacement is as follows:

[0110] in, These are the variable forms after replacing the square terms of current and voltage respectively.

[0111] Preferably, the ESS operating cost calculation formula in the specific formula of the energy storage device operating cost contains an absolute value term and needs to be linearized. The specific formula is as follows:

[0112] in, To add auxiliary variables, Represents the charging and discharging power of the energy storage device at node i at time t. Positive values ​​represent charging power, and negative values ​​represent discharging power.

[0113] Furthermore, the voltage offset calculation formula in the reliability objective function contains an absolute value term and needs to be linearized. The specific formula is as follows:

[0114] in, To add auxiliary variables, The specific formula of the reliability objective function adopts the form after the corresponding terms in the specific formula are replaced by variables. Indicates the square value of the system voltage reference value, Indicates the variable form after voltage square term variable replacement.

[0115] Furthermore, after replacing the relevant variables in the specific formula of the power flow constraint, the current equation is still a non-convex equation and can be processed using the second-order cone relaxation technique. The specific formula is as follows:

[0116] in, are the variable forms after replacing the square terms of current and voltage, P ij,t represents the active power of line ij at time t, Q ij,t Represents the reactive power of line ij at time t.

[0117] Preferably, the specific formula for McCormickenvelopes relaxation is:

[0118] Among them, P t PV Indicates the actual output value of the photovoltaic unit at time t, P t WT represents the actual output value of the wind turbine at time t, α PV Indicates photovoltaic output P t PV The offset coefficient, α WT Indicates wind power output P t WT The offset coefficient, represents the installed capacity of the PV system at node i, represents the installed capacity of the wind turbine at node i, represents the active power injected into node i by the photovoltaic system at time t, represents the active power injected into node i by the wind turbine at time t, represents the predicted active power output of the photovoltaic unit installed at node i at time t, represents the predicted active power output of the wind turbine installed at node i at time t, Indicates the active power of the original load of node i at time t.

[0119] Furthermore, if the model contains and The McCormick envelopes relaxation method is used to linearize the bilinear product terms in the model, and the auxiliary variable z is introduced to replace the McCormick envelopes relaxation formula. The specific formula of the bilinear term in the form of x·y is as follows:

[0120] Among them, x max 、x min and y max 、y min are the upper and lower limits of the variables x and y, respectively.

[0121] Furthermore, this embodiment also provides a system for a power grid planning method that adapts to multiple uncertainties and multi-objective demands, including a model construction module for constructing a deterministic multi-objective coordinated planning model and a multiple uncertainty model considering wind and solar loads; an integration module for integrating the deterministic multi-objective coordinated planning model with the multiple uncertainty model considering wind and solar loads to construct an IGDT robust planning model that adapts to multiple uncertainties and multi-objective demands; and a model solving module for obtaining a power grid planning scheme that adapts to the uncertainty of wind and solar output and load demand growth and meets the economy, reliability and environmental protection of power grid operation.

[0122] This embodiment also provides a computer device, which is suitable for the situation of a power grid planning method that adapts to multiple uncertainties and multiple objective requirements, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the power grid planning method that adapts to multiple uncertainties and multiple objective requirements proposed in the above embodiment.

[0123] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0124] This embodiment further provides a storage medium having a computer program stored thereon. When the program is executed by a processor, the power grid planning method proposed in the above embodiment that adapts to multiple uncertainties and multiple objectives is implemented.

[0125] In summary, a coordinated optimization planning method and strategy for power grids that adapt to the economic, reliability and environmental protection of power grid planning and operation is proposed, which realizes the coordinated optimization planning of the power grid source, network, load and storage side components, effectively optimizes the power grid operation architecture, promotes the consumption of new energy, and provides a reference for actual power grid planning; in view of the strong uncertainty brought about by the high proportion of new energy and a large number of flexible loads, the IGDT theory is used to model the multiple uncertainties of wind, photovoltaic and load. This method does not rely on historical data, can effectively describe the uncertainties of wind power, photovoltaic and load, and effectively solves the problem of multiple uncertainty modeling under the time scale of power grid planning; an IGDT robust planning model that adapts to multiple uncertainties and multi-objective requirements is proposed, which is conducive to improving the economic benefits of power grid planning, reducing carbon emissions from power grid operation, and improving power grid reliability, and the resulting planning scheme has high robustness within the expected goals of planners.

[0126] Example 2

[0127] 3 and 4 , which are the second embodiment of the present invention, provide a power grid planning method that adapts to multiple uncertainties and multi-objective requirements. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through calculation and simulation experiments of economic benefits, safety benefits, and environmental benefits.

[0128] Table 1 Comparison of system operation benefits before and after technical optimization

[0129] Table 2 Comparison of system equipment planning results before and after technical optimization

[0130] According to Tables 1 and 2, Figures 3 and 4 of the Specification, compared to traditional grid planning techniques, the grid planning technology adapted to multiple uncertainties and multi-objective requirements increased system overall costs, decreased the total installed capacity of photovoltaic and wind turbines, decreased the system's renewable energy penetration, and increased total electricity purchases. This indicates that during the planning period, to mitigate the impact of multiple uncertainties in wind, solar, and load, decision makers reduced investment in renewable energy units, incurring higher investment costs. Calculations show that the system's long-term robustness to multiple uncertainties under the resulting planning scheme is 82.12%. After technical optimization, system voltage deviation decreased significantly, system voltage quality improved, and system carbon emission costs decreased by 5.2678 million yuan, reducing total system carbon emissions. This demonstrates that the grid planning technology adapted to multiple uncertainties and multi-objective requirements enhances the reliability and environmental friendliness of the resulting planning scheme.

[0131] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. 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. A power grid planning method adapted to multi-objective requirements with multiple uncertainties, characterized in that: Including: Construct a deterministic multi-objective coordinated planning model with the goal of minimizing economy, reliability, and environmental protection; Based on the IGDT theory, construct a multiple uncertainty model considering wind power, photovoltaic power, and load; Construct an IGDT robust planning model that adapts to the multi-objective requirements of multiple uncertainties with the goal of maximizing the comprehensive deviation coefficient; Transform the model into a second-order cone programming model through variable substitution, absolute value linearization, second-order cone relaxation of current equations, and McCormick envelopes relaxation method; Solve the second-order cone programming model to obtain a power grid planning scheme that adapts to the uncertainties of wind power output and load demand growth and meets the economy, reliability, and environmental protection of power grid operation.

2. The power grid planning method for adapting to multi-objective requirements with multiple uncertainties as described in claim 1, wherein: The goal of minimizing economy, reliability, and environmental protection includes the following steps: The economy is based on minimizing the comprehensive cost of the system as the objective function, and the specific formula is as follows: min C = C I + C O Among them, C is the total system cost, C I is the investment cost, C O is the operating cost; Reliability takes the minimization of carbon emission costs as the objective function, and the specific formula is as follows: Among them, ΔU all represents the voltage offset, Represents the per-unit value of the voltage at node i in period t; The environmental friendliness takes the minimization of carbon emission cost as the objective function, and the specific formula is as follows: Among them, C T represents the carbon emission penalty cost, Represents the carbon emission cost during the operation of the power grid, Indicates the carbon emission cost of a gas turbine, Indicates the carbon emission cost of the photovoltaic unit, Indicates the carbon emission cost of the wind turbine, f T Indicates the carbon price, Indicates the system carbon emission conversion factor, Indicates the carbon emission coefficient of the gas turbine, Indicates the carbon emission factor of the photovoltaic unit, Indicates the carbon emission coefficient of the wind turbine Indicates the active power injected by the interaction node i with other power grids at time t into the local power grid. Indicates the magnitude of the active power of network loss at time t, Indicates the active power injected by the gas turbine into node i at time t Denote the active power injected by the photovoltaic unit into node i at time t. Represents the active power injected by the wind turbine into node i at time t.

3. The power grid planning method for adapting to multi-objective requirements with multiple uncertainties according to claim 2, characterized in that: The investment costs include the investment costs of newly built transmission lines, newly built substations, power sources, and energy storage devices; the operating costs include the annual operating costs of power equipment, energy storage devices, the cost of purchasing electricity from the superior power grid, the penalty costs for wind and light abandonment, the load shedding costs, and the network loss costs; The carbon emission costs include the carbon emission penalty costs, the carbon emission costs during the operation of the power grid, the carbon emission costs of gas turbines, the carbon emission costs of photovoltaic units, and the carbon emission costs of wind turbines.

4. The power grid planning method for adapting to multi-objective requirements with multiple uncertainties as described in claim 1, characterized in that: The constraint conditions of the deterministic multi-objective coordinated planning model include equipment installation capacity constraints, newly built substation construction capacity constraints, power flow constraints, system security constraints, source node power injection into the power grid constraints, newly built substation operation constraints, gas turbine operation constraints, photovoltaic unit and wind turbine operation constraints, renewable energy penetration constraints, energy storage device operation constraints, demand-side response constraints, and load shedding volume constraints; The specific formula for the tidal current constraint is as follows: Among them, L(j, :) represents the set of end nodes of the AC branch with j as the starting node, and L(:, j) represents the set of starting nodes of the AC branch with j as the end node, P ij,t 、P jk,t and Q ij,t 、Q jk,t respectively represent the active and reactive powers on lines ij and jk at time t, and X ij represents the reactance of line ij, And respectively represent the reactive power injected into node j by the external power grid, wind turbine generator, and gas turbine at time t, and respectively represent the active and reactive power magnitudes of the original load of node j at time t, U i,t represents the voltage magnitude of node i at time t, I ij,t represents the current flowing through line ij at time t, R ij represents the resistance of line ij, X ij represents the reactance of line ij, Indicates the active power injected by the interaction node i with other power grids into the local power grid at time t. Indicates the active power injected by the photovoltaic unit into node j at time t, Denote the active power injected by the wind turbine into node j at time t. Indicates the active power injected by the gas turbine into node j at time t, Represents the active power injected by the energy storage device into node j at time t; The specific formulas for the operating constraints of the energy storage device are as follows: Among them, is the maximum ratio of the charge and discharge power of the unit capacity ESS to the total capacity, is the electricity quantity of the energy storage device at node i during period t. Indicates the installed capacity of the energy storage device at node i, Denote the charging and discharging power of the energy storage device at node \(i\) at time \(t\), \(\Omega\) ini Denote the set of candidate installation nodes of the energy storage device; The specific formula for the loss-of-load quantity constraint is as follows: Among them, F1 represents the economic objective function, F2 represents the reliability objective function, F3 represents the environmental protection objective function, and G all (·) represents inequality constraints, and H all (·) represents equality constraints.

5. The power grid planning method for adapting to multi-objective requirements with multiple uncertainties as described in claim 1, characterized in that: The specific formula for constructing the multiple uncertainty model considering wind, light, and load is as follows: Among them, P t WT represents the actual output value of the wind turbine at time t, represents the predicted output value of the wind turbine at time t, α WT represents the wind power output P t WT 's offset coefficient, P t PV represents the actual output value of the photovoltaic unit at time t, Denotes the predicted output value of the photovoltaic unit at time t, α PV Denotes the photovoltaic output P t PV Offset coefficient of, P t load Denotes the actual value of the system load at time t represents the predicted value of the system load at time t, α load represents the system load P t load is the offset coefficient of 6. The power grid planning method for adapting to multi-objective requirements with multiple uncertainties as described in claim 1, characterized in that: The specific formulas included in the IGDT robust planning model for constructing and adapting to multi-objective requirements with multiple uncertainties are as follows: Among them, F 01 represents the system comprehensive cost, F 02 represents the voltage offset, F 03 represents the benchmark value of the carbon emission cost. F1 represents the economic objective function, F2 represents the reliability objective function, F3 represents the environmental protection objective function, α = 0, and σ is the robust model deviation factor. represents the predicted value of the system load at time t, P t load represents the actual value of the system load at time t, P t WT represents the actual output value of the wind turbine at time t Indicates the predicted output value of the wind turbine at time t, P t PV Indicates the actual output value of the photovoltaic unit at time t Denote the predicted output value of the photovoltaic unit at time t, G all (·) denotes the inequality constraint, H all (·) denotes the equality constraint.

7. The power grid planning method for adapting to multi-objective requirements with multiple uncertainties as described in claim 1, characterized in that: The transformation of the model into a second-order cone programming model includes variable substitution, linearization of absolute value terms, second-order cone relaxation of current equations, and McCormick envelopes relaxation, The specific formula for the relaxation of the McCormick envelopes is as follows: Among them, P t PV represents the actual output value of the photovoltaic unit at time t, and P t WT represents the actual output value of the wind turbine unit at time t, and α PV represents the offset coefficient of the photovoltaic output P t PV , and α WT represents the offset coefficient of the wind power output P t WT . Indicates the installed capacity of the photovoltaic unit at node i, Denote the installed capacity of the wind turbine at node i, Denote the active power injected by the photovoltaic unit into node i at time t. Denote the active power injected by the wind turbine into node \(i\) at time \(t\). Indicates the active power magnitude of the load reduced after demand response, Indicates the predicted active power output of the photovoltaic unit installed at node i at time t. Indicates the predicted active power output of the wind turbine installed at node i at time t, Represents the magnitude of the active power of the original load at node i at time t; If there is a form like And For the bilinear terms, the McCormick envelopes relaxation method is used to linearize the bilinear product terms existing in the model. An auxiliary variable z is introduced to replace the bilinear terms in the specific formula of the McCormick envelopes relaxation. The specific formula in the form of x·y is as follows: where x max and x min and y max and y min are the upper and lower limits of the variables, respectively.

8. A system for a power grid planning method that adapts to multi-objective requirements with multiple uncertainties, based on the power grid planning method that adapts to multi-objective requirements with multiple uncertainties according to any one of claims 1 to 7, characterized in that: Also includes, A model construction module for constructing a deterministic multi-objective coordinated planning model and a multiple uncertainty model considering wind power, photovoltaic power, and load; An integration module for integrating the deterministic multi-objective coordinated planning model and the multiple uncertainty model considering wind power, photovoltaic power, and load to construct an IGDT robust planning model that adapts to the multi-objective requirements of multiple uncertainties; A model solving module for obtaining a power grid planning scheme that adapts to the uncertainties of wind power output and load demand growth and meets the economy, reliability, and environmental protection of power grid operation.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the power grid planning method for adapting to the multi-objective requirements of multiple uncertainties according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the power grid planning method for adapting to the multi-objective requirements of multiple uncertainties according to any one of claims 1 to 7.

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