Power system capacity configuration method and device, electronic equipment and storage medium

Abstract

The invention provides a power system capacity configuration method and device, electronic equipment and a storage medium, and relates to the technical field of power systems. The method comprises the following steps: according to the comprehensive cost and environmental cost of a power supply in a power system, constructing a target function with the minimum total cost; determining constraint conditions according to at least one of a system power constraint, an energy storage battery charge and discharge state constraint, an energy storage battery charge and discharge power constraint, a generator output constraint, a demand response load power constraint and a natural gas related constraint; according to the objective function and the constraint condition, establishing a capacity configuration model corresponding to the power system; converting the capacity configuration model by using the cost parameter set of the power supply, and processing the converted capacity configuration model to obtain a capacity configuration result of the power system; wherein the constraint of the cost parameter set is a linear constraint, and the cost parameter set is a convex hull set. According to the scheme, the capacity configuration of the power system can be optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly to a power system capacity configuration method, device, electronic device, and storage medium. Background Art

[0002] Currently, renewable energy sources represented by wind turbines and photovoltaics are increasingly connected to the power system. The uncertainties such as the uncertainty of the cost parameters of power sources, the randomness, volatility, and correlation of the output of renewable energy sources have been greatly enhanced, which have a great impact on the results of power system capacity configuration. At the same time, factors such as the random failures of power sources during the configuration process seriously affect the reliable and stable operation of the power system, resulting in higher and higher requirements for power system capacity configuration. And the capacity configuration optimization problem containing multiple uncertainties and considering demand affects the configuration cost at the same time. Therefore, the effective solution of the power system capacity configuration optimization problem is an urgent problem to be solved at present. Summary of the Invention

[0003] The purpose of the present invention is to provide a power system capacity configuration method, device, electronic device, and storage medium to solve the problem of power system capacity configuration optimization in the prior art.

[0004] To achieve the above purpose, an embodiment of the present invention provides a power system capacity configuration method, including:

[0005] Construct an objective function for minimizing the total cost according to the comprehensive cost and environmental cost of power sources in the power system; wherein, the power sources include at least one of a wind turbine, a photovoltaic, a storage battery, and a generator; the generator includes a diesel generator and / or a natural gas generator;

[0006] Determine constraint conditions according to at least one of system power constraints, storage battery charge and discharge state constraints, storage battery charge and discharge power constraints, generator output constraints, demand response load power constraints, and natural gas-related constraints;

[0007] Establish a capacity configuration model corresponding to the power system according to the objective function and the constraint conditions;

[0008] Convert the capacity configuration model by using the cost parameter set of the power source, and process the converted capacity configuration model to obtain the capacity configuration result of the power system; wherein, the constraint of the cost parameter set is a linear constraint, and the cost parameter set is a convex hull set.

[0009] Optionally, in the power system capacity configuration method, the constructing an objective function for minimizing the total cost according to the comprehensive cost and environmental cost of power sources in the power system includes:

[0010] Using the first formula, construct an objective function for minimizing the total cost based on the investment cost of the power source, the operation and maintenance cost of the power source, the replacement cost of the energy storage battery, the fuel cost of the generator, the power generation cost of the generator, and the environmental cost.

[0011] Among them, the first formula is:

[0012] M = min(C cap + C OM + C rep,batt + C fuel + C onoff + C e + C en );

[0013] The M represents the objective function for minimizing the total cost;

[0014] The C cap represents the investment cost of the power source, and the C cap is determined based on the total number of power sources, the types of power sources, the number of each type of power source, the unit capacity, and the unit capacity investment cost;

[0015] The C OM represents the operation and maintenance cost of the power source, and the C OM is determined based on the system discount rate, the system operation years, the system operation time, the unit capacity operation and maintenance cost of each type of power source, and the output of each type of power source at each moment;

[0016] The C rep，batt represents the replacement cost of the energy storage battery, and the C rep，batt is determined based on the replacement year of the energy storage battery, the unit capacity replacement cost, the number, and the rated capacity of a single energy storage battery;

[0017] The C fuel represents the fuel cost of the generator, and the C fuel is determined based on the fuel cost per unit volume, the volume of fuel consumed at each moment, the system of the fuel curve, the start-stop state of the generator at each moment, and the output of the generator at each moment;

[0018] C onoff represents the start-stop cost of the power source;

[0019] The C e represents the power generation cost of the generator, which is determined by the power generation cost per unit power of the generator and the power generation power of the generator at time t;

[0020] The C en represents the environmental cost, and the C en is determined based on the environmental cost borne for each unit volume of fuel consumed.

[0021] Optionally, in the power system capacity configuration method, the natural gas-related constraints include at least one of the annual investment cost and operation and maintenance cost of natural gas infrastructure construction, the construction status constraints of natural gas storage equipment and pipelines, natural gas supply constraints, natural gas balance constraints, natural gas transmission constraints, and natural gas storage equipment-related constraints.

[0022] Optionally, in the power system capacity configuration method, establishing the capacity configuration model corresponding to the power system includes:

[0023] The objective function for establishing the capacity configuration model is:

[0024]

[0025] The constraint conditions for establishing the capacity configuration model are:

[0026] G X (X) ≥ 0;

[0027]

[0028] where X represents the set of decision variables, and X = [x; y], x represents the set of continuous variables, and y represents the set of integer variables. NC PV represents the fan capacity; NC Wt represents the photovoltaic capacity; respectively represent the auxiliary variables of the charging state of the energy storage battery and the auxiliary variables of the discharging state of the energy storage battery at the t-th moment; E(t) represents the remaining power of the energy storage battery at the t-th moment; P ch (t), P dis (t) respectively represent the charging power of the energy storage battery and the discharging power of the energy storage battery at the t-th moment; P g (t) represents the output power of the generator at the t-th moment; E DR (t) represents the power consumption of the demand response load at the t-th moment; respectively represent the upward regulation power and downward regulation power of the demand response load at the t-th moment; N batt represents the number of energy storage batteries; respectively represent the charging state and discharging state of the energy storage battery; P g_onoff (t) represents the start-stop state of the generator at the t-th moment;

[0029] The w n represents a cost parameter, W represents the set of cost parameters, n represents the number of cost parameters, and w n = [C cap，unit (i), C OM，unit (i), Crep，batt，unit , C fuel，unit , C g，unit ;

[0030] The F X (X) represents the environmental cost;

[0031] The represents the objective function for minimizing the comprehensive cost of the power supply and the total cost;

[0032] The G X (X) represents the state-of-charge constraint of the energy storage battery, the charge-discharge power constraint of the energy storage battery, the generator output constraint, the demand response load power constraint, and the natural gas-related constraint in the constraint conditions;

[0033] The represents the system power constraint in the constraint conditions.

[0034] Optionally, for the power system capacity configuration method, the conversion of the capacity configuration model using the cost parameter set of the power supply includes:

[0035] Determine the first constraint according to the auxiliary variable function; wherein, the auxiliary variable function is: The first constraint is:

[0036] Obtain the second constraint according to the constraint conditions of the capacity configuration model; wherein, the second constraint is a constraint including cost parameters and decision variables, and the second constraint is:

[0037] Process the first constraint and the second constraint to obtain the converted capacity configuration model, wherein the objective function of the converted capacity configuration model is:

[0038] min X (F X (X) + s(X));

[0039] The constraint conditions of the converted capacity configuration model include:

[0040] G X (X) ≥ 0;

[0041] G robust (X) ≥ 0;

[0042] Wherein, G robust (X) is the robust constraint condition corresponding to the state-of-charge constraint of the energy storage battery, the charge-discharge power constraint of the energy storage battery, the generator output constraint, the demand response load power constraint, and the natural gas-related constraint.

[0043] Optionally, for the power system capacity configuration method, processing the converted capacity configuration model to obtain the capacity configuration result of the power system includes:

[0044] Using a mixed-integer linear programming solver and inputting deterministic parameters to process the converted capacity configuration model and output the set of decision variables;

[0045] Among them, the deterministic parameters include system parameters and model parameters;

[0046] The system parameters include T, Δt, YR, and R d ; where T represents the total operating time of the system; Δt represents the time interval; YR represents the operating years of the system; R d represents the system discount rate;

[0047] The model parameters include: η inv 、E rated，batt 、η ch 、η dis 、δ、 repyr、w DR and P load (t); where η inv represents the efficiency of the photovoltaic inverter; E rated，batt represents the rated capacity of the energy storage battery; δ, η ch 、η dis respectively represent the self-discharge rate, charging efficiency, and discharging efficiency of the energy storage battery per hour; represents the upper limit value of the number of charge-discharge state conversions of the energy storage battery; repyr represents the replacement year of the energy storage battery; w DR represents the proportionality coefficient of the total load for the demand response load; P load (t) represents the load power at the t-th moment.

[0048] Optionally, for the power system capacity configuration method, processing the second constraint includes:

[0049] Using the Enumeration Robust Counterpart (ERC) algorithm to transform the second constraint and convert the second constraint into an objective constraint containing only decision variables.

[0050] Optionally, for the power system capacity configuration method, using the Enumeration Robust Counterpart (ERC) algorithm to transform the second constraint and convert the second constraint into an objective constraint containing only decision variables includes:

[0051] Obtain the upper bound constraint and the lower bound constraint of the cost parameter according to the set of cost parameters;

[0052] Add the upper bound constraint and the lower bound constraint to the third constraint; wherein, the third constraint is determined according to the constraint conditions of the capacity configuration model, and the third constraint is expressed as The third constraint is a constraint that only contains cost parameters;

[0053] Construct a polyhedron according to the third constraint as: P = {w n |(AE 0 ) * w n ≤ b - Ax 0}; wherein, P represents the polyhedron; E 0 and x 0 are both determined according to whether the third constraint contains an equality constraint; A represents the opposite of the coefficient matrix in the third constraint; b represents the constant matrix in the third constraint;

[0054] Use the second formula to obtain the vertices of the polyhedron; wherein, the second formula is: w i = [x 0 ,..., x 0 j + E 0 * ep T ; w i represents the vertex; i represents the i-th vertex of the polyhedron; ep represents the extreme point matrix of the polyhedron; j represents the length of the column dimension of the extreme point transpose matrix;

[0055] According to the second constraint, obtain (c + A T x) T w n and (b T X + d); c and d are both constant matrices;

[0056] Use the third formula, and replace the cost parameter with the vertex of the polyhedron to transform the second constraint into the target constraint; wherein, the third formula is:

[0057]

[0058] To achieve the above object, an embodiment of the present invention provides a power system capacity configuration device, including:

[0059] ​A construction module for constructing an objective function that minimizes the total cost based on the comprehensive cost and environmental cost of power sources in a power system; wherein, the power sources include at least one of a wind turbine, a photovoltaic, a storage battery, and a generator; the generator includes a diesel generator and / or a natural gas generator;

[0060] A determination module for determining constraint conditions according to at least one of system power constraints, storage battery charge and discharge state constraints, storage battery charge and discharge power constraints, generator output constraints, demand response load power constraints, and natural gas related constraints;

[0061] An establishment module for establishing a capacity configuration model corresponding to the power system according to the objective function and the constraint conditions;

[0062] A obtaining module for converting the capacity configuration model by using a set of cost parameters of power sources, and processing the converted capacity configuration model to obtain a capacity configuration result of the power system; wherein, the constraint of the set of cost parameters is a linear constraint, and the set of cost parameters is a convex hull set.

[0063] To achieve the above object, an embodiment of the present invention provides an electronic device, including: a transceiver, a processor, a memory, and a program or instruction stored on the memory and executable on the processor; when the processor executes the program or instruction, the power system capacity configuration method as described above is implemented.

[0064] To achieve the above object, an embodiment of the present invention provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the power system capacity configuration method as described above is implemented.

[0065] The beneficial effects of the above technical solutions of the present invention are as follows:

[0066] In the embodiment of the present invention, an objective function that minimizes the total cost is constructed according to the comprehensive cost and environmental cost of power sources in a power system, and constraint conditions are determined according to at least one of system power constraints, storage battery charge and discharge state constraints, storage battery charge and discharge power constraints, generator output constraints, demand response load power constraints, and natural gas related constraints. Then, a capacity configuration model corresponding to the power system is established according to the objective function and the constraint conditions, and the capacity configuration model is converted by using a set of cost parameters of power sources, and the converted capacity configuration model is processed to obtain a capacity configuration result of the power system. The constraint of the set of cost parameters is a linear constraint, and the set of cost parameters is a convex hull set, which transforms the uncertain robust optimization problem into a deterministic problem, is beneficial to reducing the configured capacity of power sources, improving energy utilization efficiency, and improving the economy of the power system. Description of the Drawings

[0067] Figure 1 It is a flowchart of the power system capacity configuration method according to an embodiment of the present invention;

[0068] Figure 2 It is an application flowchart of the power system capacity configuration method according to an embodiment of the present invention;

[0069] Figure 3 It is a schematic structural diagram of the power system capacity configuration device according to an embodiment of the present invention;

[0070] Figure 4 It is a schematic structural diagram of the electronic device according to an embodiment of the present invention. Detailed Embodiments

[0071] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0072] It should be understood that the "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, the appearances of "in one embodiment" or "in an embodiment" throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in any suitable manner in one or more embodiments.

[0073] In various embodiments of the present invention, it should be understood that the magnitudes of the serial numbers of the following processes do not mean the order of execution is prior or subsequent, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0074] In addition, the terms "system" and "network" are often used interchangeably in this article.

[0075] In the embodiments provided in the present application, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0076] As Figure 1 shown, an embodiment of the present invention provides a power system capacity configuration method, including:

[0077] Step 101, constructing an objective function for minimizing the total cost according to the comprehensive cost and environmental cost of the power sources in the power system; wherein, the power sources include at least one of a wind turbine, a photovoltaic, a storage battery, and a generator; the generator includes a diesel generator and / or a natural gas generator;

[0078] It should be noted that the comprehensive cost of the power supply includes:

[0079] The investment cost of the power supply, the operation and maintenance cost of the power supply, the energy storage battery replacement cost, the generator fuel cost, and the generator power generation cost.

[0080] The generator can include a diesel generator and / or a natural gas generator according to the fuel used.

[0081] Step 102, determine the constraint conditions according to at least one of the system power constraint, the energy storage battery charge and discharge state constraint, the energy storage battery charge and discharge power constraint, the generator output constraint, the demand response load power constraint, and the natural gas related constraint;

[0082] Next, the following explanations are given for each constraint.

[0083] Constraint 1, the system power constraint represented by Equation (1):

[0084]

[0085] Among them, considering the correlation between the wind turbine and the photovoltaic, α wt (t) and α pv (t) should satisfy the convex hull set constraint composed of the per-unit values of the historical wind and light data; without considering the uncertainty between the wind turbine and the photovoltaic, α wt (t) and α pv (t) respectively represent the per-unit values of the historical wind and light data; NC wt and NC pv respectively represent the wind turbine capacity and the photovoltaic capacity; N wt and N pv respectively represent the number of wind turbines and the number of photovoltaics; P ch (t), P dis (t) respectively represent the energy storage battery charging power and the energy storage battery discharging power; P g (t) represents the generator output at the t-th moment, with the unit of kW; respectively represent the upward regulation power and the downward regulation power of the demand response load at the t-th moment; P load (t) represents the load power at the t-th moment; η inv represents the photovoltaic inverter efficiency; w DR represents the proportionality coefficient of the total load for the demand response load.

[0086] Specifically, the convex hull set constraint represented by Equation (2) consists of multiple linear inequalities for wind and light processing:

[0087] [A wt (t) A pv (t)]*[α wt (t) αpv (t)] T ≤b(t) (2);

[0088] Among them, A wt (t), A pv (t), b(t) respectively represent the wind power output coefficient matrix and the constant matrix of the convex hull linear inequality.

[0089] Constraint two: To avoid the situation where the energy storage battery is charging and discharging simultaneously, as shown in Equation (3), the charging and discharging states of the energy storage battery are constrained:

[0090]

[0091] Among them, respectively represent the charging state and the discharging state of the energy storage battery. When the energy storage battery is charging, When the energy storage battery is discharging,

[0092] Since too many charging and discharging state conversion times of the energy storage battery will cause great damage to the life of the energy storage battery, in order to improve the economy of the energy storage battery, as shown in Equation (4), it is necessary to constrain the charging state conversion times and the discharging state conversion times of the energy storage battery:

[0093]

[0094] represents the upper limit value of the charging and discharging state conversion times of the energy storage battery;

[0095] Furthermore, Equation (4) is transformed by introducing auxiliary variables characterizing the charging state of the energy storage battery at the t-th moment and the auxiliary variables of the discharging state of the energy storage battery, which is expressed as Equation (5):

[0096]

[0097] Furthermore, Equation (4) is transformed into:

[0098]

[0099] Since Equation (5) is a problem of the absolute value of the subtraction of two binary variables, Equation (5) is transformed into Equation (7):

[0100]

[0101] Constraint three: The charging and discharging power constraints of the energy storage battery as shown in Equations (8) to (11):

[0102] E(t) = E(t - 1)(1 - δΔt)+P ch (t)ηch Δt - P dis (t) / η dis Δt (8);

[0103] SOC min N batt E rated ≤E(t)≤SOC max N batt E rated (9);

[0104]

[0105]

[0106] Among them, E(t) represents the remaining power of the energy storage battery at the t-th moment, with the unit of kWh; Δt represents the time interval, with the unit of h; δ, η ch 、η dis respectively represent the self-discharge rate, charging efficiency, and discharging efficiency of the energy storage battery per hour; N batt 、SOC min 、SOC max respectively represent the number of energy storage batteries, the minimum value of the remaining power percentage, and the maximum value of the remaining power percentage.

[0107] It should be noted that there is a problem of variable multiplication in Equation (10) and Equation (11). A variable can be introduced. Taking Equation (10) as an example, the large M method is used to illustrate the linearization process; and a variable The linearization process of Equation (11) is the same as that of Equation (10), as shown in Equation (12) specifically:

[0108]

[0109] Among them, M is a very large value, which can be set to 10000.

[0110] Constraint Four, generator output constraint. Taking the output of the diesel generator should satisfy Equation (13) as an example:

[0111] 0≤P g (t)≤P g，onoff (t)P gmax (13);

[0112] Among them, P gmax represents the maximum output of the diesel generator; P g_onoff (t) represents the start-stop state of the diesel generator at the t-th moment. Generally, it is 1 when the generator is on and 0 when it is off.

[0113] Here, similar to Equation (12), the large M method is used to process Equation (13).

[0114] Constraint Five, the demand response load power constraint expressed by formulas (14) to (17):

[0115] Adding demand response to the power system can better achieve the interaction between the power source and the load. When fluctuations occur on the power source side, the power on the load side can be adjusted up and down, enhancing the power system's ability to cope with the uncertainty of renewable energy and improving the economic efficiency of the power system operation:

[0116]

[0117] 0 ≤ E DR (t) ≤ w DR max(P load (t))Δt (15);

[0118]

[0119]

[0120] Among them, E DR (t) represents the electricity quantity of the demand response load at the t-th moment.

[0121] Step 103, according to the objective function and the constraint conditions, establish the capacity configuration model corresponding to the power system;

[0122] In this step, according to the objective function of minimizing the total cost and the above constraint conditions, the capacity configuration model corresponding to the power system can be established. This capacity configuration model is the robust optimization model.

[0123] Step 104, use the cost parameter set of the power source to transform the capacity configuration model, and process the transformed capacity configuration model to obtain the capacity configuration result of the power system; among them, the constraint of the cost parameter set is a linear constraint, and the cost parameter set is a convex hull set.

[0124] In this step, the cost parameters include: the investment cost per unit capacity of the wind turbine, the operation and maintenance cost per unit capacity of the wind turbine, the investment cost per unit capacity of the photovoltaic, the operation and maintenance cost per unit capacity of the photovoltaic, the investment cost per unit capacity of the energy storage battery, the operation and maintenance cost per unit capacity of the energy storage battery, the replacement cost per unit capacity of the energy storage battery, the investment cost per unit capacity of the generator, the operation and maintenance cost per unit capacity of the generator, the fuel price of the generator, and the power generation cost per unit capacity of the generator. Here, the wind turbines can be divided into onshore wind turbines and offshore wind turbines; the photovoltaics can be divided into mountain photovoltaics, flat ground photovoltaics, and distributed photovoltaics; the energy storage batteries can be divided into lead-acid batteries, lithium batteries, and vanadium batteries.

[0125] It should be noted that, considering the correlation between the wind turbine and the photovoltaic, the cost parameter set of the power source is a convex hull set.

[0126] In an embodiment of the present invention, according to the comprehensive cost and environmental cost of the power source in the power system, an objective function for minimizing the total cost is constructed, and according to at least one of the system power constraint, the charge and discharge state constraint of the energy storage battery, the charge and discharge power constraint of the energy storage battery, the generator output constraint, the demand response load power constraint, and the natural gas related constraint, the constraint conditions are determined. Thus, according to the objective function and the constraint conditions, a capacity configuration model corresponding to the power system is established, and the capacity configuration model is transformed by using the cost parameter set of the power source, and the transformed capacity configuration model is processed to obtain the capacity configuration result of the power system. The constraint of the cost parameter set is a linear constraint, and the cost parameter set is a convex hull set, which transforms the uncertain robust optimization problem into a deterministic problem, is beneficial to reducing the configuration capacity of the power source, improving the energy utilization rate, and improving the economy of the power system.

[0127] In a specific embodiment of the present invention, step 101 includes:

[0128] Using the first formula, according to the investment cost of the power source, the operation and maintenance cost of the power source, the replacement cost of the energy storage battery, the fuel cost of the generator, the power generation cost of the generator, and the environmental cost, an objective function for minimizing the total cost is constructed;

[0129] Wherein, the first formula is:

[0130] M = min(C cap + C OM + C rep,batt + C fuel + C onoff + C e + C en );

[0131] The M represents the objective function for minimizing the total cost;

[0132] The C cap represents the investment cost of the power source, and the C cap is determined according to the total number of power sources, the types of power sources, the number of each type of power source, the unit capacity, and the unit capacity investment cost;

[0133] The C OM represents the operation and maintenance cost of the power source, and the C OM is determined according to the system discount rate, the system operation life, the system operation time, the unit capacity operation and maintenance cost of each type of power source, and the output of each type of power source at each moment;

[0134] The Crep，batt represents the replacement cost of the energy storage battery, where C rep，batt is determined based on the replacement year of the energy storage battery, the replacement cost per unit capacity, the number, and the rated capacity of a single energy storage battery;

[0135] where C fuel represents the fuel cost of the generator, where C fuel is determined based on the fuel cost per unit volume, the volume of fuel consumed at each moment, the system of the fuel curve, the start-stop state of the generator at each moment, and the output of the generator at each moment;

[0136] C onoff represents the start-stop cost of the power source;

[0137] where C e represents the power generation cost of the generator, which is determined by the power generation cost per unit power of the generator and the power generation power of the generator at time t;

[0138] where C en represents the environmental cost, where C en is determined based on the environmental cost borne per unit volume of fuel consumed.

[0139] Next, the calculation formulas for each cost will be explained.

[0140] Investment cost of the power source: Among them, G represents the total number of power sources in the power system; i represents the type of power source; N(i), NC unit (i), C cap，unit (i)C cap，unit (i) respectively represent the number, unit capacity, and unit capacity investment cost of the i-th type of power source.

[0141] Operation and maintenance cost of the power source: Among them, R d represents the system discount rate; YR represents the system operation years; T represents the total system operation time; C OM，unit (i) represents the unit capacity operation and maintenance cost of the i-th type of power source; P(i,t) represents the output of the i-th type of power source at time t.

[0142] Replacement cost of the energy storage battery: Among them, repyr, C rep，batt，unit , N batt , E rated，batt respectively represent the replacement year of the energy storage battery, the replacement cost per unit capacity, the number, and the rated capacity of a single energy storage battery.

[0143] Fuel cost of the generator, Among them, C fuel，unit represents the fuel cost per unit volume; Vfuel (t) represents the volume of fuel consumed at the t-th moment, V fuel (t) = aP g，onoff (t) + bP g (t), where a and b represent the coefficients of the fuel curve, and P g，onoff (t) represents the start-stop state of the generator at the t-th moment, and P g (t) represents the output of the generator at the t-th moment.

[0144] The power generation cost of the generator, where C g，unit represents the power generation cost per unit power of the generator.

[0145] Environmental cost: where C en，unit represents the environmental cost borne for each unit volume of fuel consumed.

[0146] In an embodiment of the present invention, the natural gas-related constraints include at least one of the annual investment cost and operation and maintenance cost of natural gas infrastructure construction, the construction status constraints of natural gas storage equipment and pipelines, natural gas supply constraints, natural gas balance constraints, natural gas transmission constraints, and natural gas storage equipment-related constraints.

[0147] Among them, the annual investment cost and operation and maintenance cost of natural gas infrastructure construction represented by Equation (18):

[0148]

[0149] Among them, natural gas infrastructure construction includes infrastructure construction such as natural gas supply, storage, and transmission. S, T, and I respectively represent the number of load modules, planning periods, and subsystems; represents the set composed of NG (Natural Gas) / LNG (Liquefied Natural Gas) in subsystem i; represents the set of pipelines (natural gas) connected to region i; represents the set of NG / LNG gas storage reservoirs in region i; represents the annual investment cost of (NG / LNG) storage / supply equipment in subsystem i during stage t; represents the annual investment cost of the natural gas pipeline connecting subsystems during stage t; represents the operating cost of natural gas production in subsystem i during the planning period t; represents the NG loss cost in subsystem i when the load module is s during the planning period t; Respectively represent the cost of injecting / extracting NG / LNG in subsystem \(i\) at load module \(s\) during the planning period \(t\); \(\sigma_g\) i,j Represents the loss rate (%) of natural gas transmitted from subsystem \(i\) to subsystem \(j\) during the planning period \(t\); Represents the duration of load module \(s\) in subsystem \(i\) during the planning period \(t\); Represents the natural gas demand in subsystem \(i\) at load module \(s\); Represents whether a new natural gas supply or storage project is built in subsystem \(i\) during the planning period \(t\), a binary variable. Represents whether a new pipeline is built to connect subsystem \(i\) and \(k\) during the planning period \(t\), a binary variable; Represents the natural gas produced by gas well \(j\) in subsystem \(i\) at load module \(s\) during the planning period \(t\); Respectively represent the volume of NG / LNG injected / extracted in subsystem \(i\) at load module \(s\) during the planning period \(t\); Represents the loss rate of NG in subsystem \(i\) at load module \(s\) during the planning period \(t\); Represents the NG transmitted between subsystem \(i\) and subsystem \(j\) during the planning period \(t\).

[0150] Constraints on the construction status of natural gas storage equipment and pipelines as shown in Equation (19):

[0151]

[0152] Constraints on natural gas supply under the load module as shown in Equation (20):

[0153]

[0154] Constraints on natural gas balance as shown in Equation (21):

[0155]

[0156] Among them, Represents the volume of the natural gas station; Represents the remaining natural gas in the natural gas station after the end of the previous stage; Represents the injection volume of natural gas during the planning period \(t\); Represents the extraction volume of natural gas during the planning period \(t\).

[0157] Constraints on natural gas transmission as shown in Equation (22):

[0158]

[0159] Constraints related to natural gas storage equipment as shown in Equation (23):

[0160]

[0161] In one embodiment of the present invention, step 103 includes:

[0162] The objective function for establishing the capacity configuration model is:

[0163]

[0164] The constraint conditions for establishing the capacity configuration model are:

[0165] G X (X) ≥ 0;

[0166]

[0167] where X represents the set of decision variables, and X = [x; y], x represents the set of continuous variables, and y represents the set of integer variables. NC PV represents the fan capacity; NC Wt represents the photovoltaic capacity; respectively represent the auxiliary variables of the charging state and the discharging state of the energy storage battery at the t-th moment; E(t) represents the remaining power of the energy storage battery at the t-th moment; P ch (t), P dis (t) respectively represent the charging power and the discharging power of the energy storage battery at the t-th moment; P g (t) represents the output power of the generator at the t-th moment; E DR (t) represents the power consumption of the demand response load at the t-th moment; respectively represent the upward regulation power and the downward regulation power of the demand response load at the t-th moment; N batt represents the number of energy storage batteries; respectively represent the charging state and the discharging state of the energy storage battery; P g_onoff (t) represents the start-stop state of the generator at the t-th moment;

[0168] The w n represents the cost parameter, W represents the set of cost parameters, n represents the number of cost parameters, and w n = [C cap，unit (i), C OM，unit (i), C rep，batt，unit , C fuel，unit , C g，unit ;

[0169] The F X (X) represents the environmental cost;

[0170] The An objective function that represents the comprehensive cost of the power supply and minimizes the total cost;

[0171] The G X (X) represents the constraints on the charge and discharge state of the energy storage battery, the charge and discharge power constraints of the energy storage battery, the generator output constraints, the demand response load power constraints, and the natural gas related constraints in the constraint conditions;

[0172] The Represents the system power constraint in the constraint conditions.

[0173] That is, determine the objective function of the capacity configuration model according to the objective function of minimizing the total cost, and determine the constraint conditions of the capacity configuration model according to the constraint conditions determined in step 102, so as to determine the capacity configuration model as:

[0174]

[0175] In an embodiment of the present invention, step 104 includes:

[0176] Determine the first constraint according to the auxiliary variable function; wherein, the auxiliary variable function is: The first constraint is:

[0177] Obtain the second constraint according to the constraint conditions of the capacity configuration model; wherein, the second constraint is a constraint including cost parameters and decision variables, and the second constraint is: G Xwn (X, w n );

[0178] Process the first constraint and the second constraint to obtain the converted capacity configuration model, wherein the objective function of the converted capacity configuration model is:

[0179] min X (F X (X) + s(X));

[0180] The constraint conditions of the converted capacity configuration model include:

[0181] G X (X) ≥ 0;

[0182] G robust (X) ≥ 0;

[0183] Wherein, G robust (X) is the robust constraint condition corresponding to the charge and discharge state constraint of the energy storage battery, the charge and discharge power constraint of the energy storage battery, the generator output constraint, the demand response load power constraint, and the natural gas related constraint.

[0184] That is to say, the converted capacity configuration model can be expressed as:

[0185]

[0186] In a specific embodiment of the present invention, step 104 includes:

[0187] Using a mixed-integer linear configuration solver and inputting deterministic parameters, process the converted capacity configuration model, and output the set of decision variables;

[0188] Among them, the deterministic parameters include system parameters and model parameters;

[0189] The system parameters include T, Δt, YR, and R d ; where T represents the total system operation time; Δt represents the time interval; YR represents the system operation years; R d represents the system discount rate;

[0190] The model parameters include: η inv , E rated，batt , η ch , η dis , δ, repyr, w DR and P load (t); where η inv represents the efficiency of the photovoltaic inverter; E rated，batt represents the rated capacity of the energy storage battery; δ, η ch , η dis respectively represent the self-discharge rate, charging efficiency, and discharging efficiency of the energy storage battery per hour; represents the upper limit value of the number of charge and discharge state conversions of the energy storage battery; repyr represents the replacement year of the energy storage battery; w DR represents the proportionality coefficient of the total load for the demand response load; P load (t) represents the load power at the t-th moment.

[0191] It should be noted that since the microgrid capacity configuration optimization problem belongs to the MILP (Mixed Integer Linear Programming) problem, and the cost parameter set belongs to the interval set, the Enumeration Robust Counterpart (ERC) robust optimization algorithm is used to transform the uncertain robust optimization problem into a deterministic problem, and the MILP solver of GUROBI (a mathematical programming optimizer) is called for optimization to obtain the optimized capacity configuration result.

[0192] Furthermore, the processing of the second constraint includes:

[0193] Using the ERC algorithm, transform the second constraint to convert it into an objective constraint that only contains decision variables.

[0194] Here, the objective constraint is a deterministic constraint, which transforms the uncertain second constraint into a deterministic constraint that only contains decision variables.

[0195] It should be noted that since the constraint of the cost parameter set is a linear constraint and the convex hull set of this cost parameter set, the Enumeration Robust Counterpart (ERC) robust optimization algorithm can be used to transform the second constraint to eliminate the uncertainty. Here, the second constraint (this second constraint is a robust constraint) and the general form of the cost parameter set W are as follows:

[0196]

[0197] Among them, A represents a bilinear coefficient matrix; b represents a linear coefficient matrix; c represents a parameter coefficient matrix; d represents a constant matrix; E and F respectively represent a constant matrix and a parameter coefficient matrix; the matrices E and f define a polyhedron set.

[0198] Furthermore, the step of using the Enumeration Robust Counterpart (ERC) algorithm to transform the second constraint to convert it into an objective constraint that only contains decision variables includes:

[0199] According to the cost parameter set, obtain the upper bound constraint and the lower bound constraint of the cost parameter;

[0200] Add the upper bound constraint and the lower bound constraint to the third constraint; among them, the third constraint is determined according to the constraint conditions of the capacity configuration model, and the third constraint is expressed as The third constraint is a constraint that only contains cost parameters;

[0201] According to the third constraint, construct a polyhedron as: P = {w n |(AE 0 ) * w n ≤ b - Ax 0}; where, P represents a polyhedron; E 0 and x 0 are both determined according to whether the third constraint contains an equality constraint; A represents the opposite number of the coefficient matrix in the third constraint; b represents the constant matrix in the third constraint;

[0202] Use the second formula to obtain the vertices of the polyhedron; where, the second formula is: wi = [x 0 ,..., x 0 j + E 0 * ep T ; w i represents the vertex; i represents the i-th vertex of the polyhedron; ep represents the extreme point matrix of the polyhedron; j represents the length of the column dimension of the extreme point transpose matrix;

[0203] According to the second constraint, obtain (c + A T x) T w n and (b T X + d); both c and d are constant matrices;

[0204] Using the third formula and replacing the cost parameter with the vertices of the polyhedron, transform the second constraint into the target constraint; where the third formula is:

[0205]

[0206] Next, combine Figure 2 to explain the application process of using the Enumeration Robust Counterpart (ERC) algorithm to transform the second constraint, turning the second constraint into a target constraint that only contains decision variables, that is, turning the second constraint into a deterministic constraint that only contains decision variables:

[0207] Step 201, add an auxiliary variable function Then there is a first constraint: Eliminate the uncertainty of the first constraint and the second constraint together.

[0208] Step 202, decompose the constraints of the capacity configuration model into a second constraint that simultaneously contains the decision variable set X and the cost parameter w n a constraint G (X) that only contains the decision variable set X, and a third constraint that only contains the cost parameter w X (X) and only contains the cost parameter w n At this time, the third constraint is the cost parameter set W. That is, the cost parameter set W.

[0209] Step 203, eliminate the uncertainty of the second constraint through the following steps according to the characteristics of the robust constraint:

[0210] Extract the upper and lower bound constraints of the cost parameter w n from the cost parameter set W and add them to the third constraint ;​

[0211] Take the third constraint The opposite of the coefficient matrix in the inequality constraint is A and the constant matrix is b;

[0212] Construct the polyhedron as: P = {w n | (AE 0 ) * w n ≤ b - Ax 0}, and determine the third constraint Whether there is an equality constraint; if there is an equality constraint, E 0 is the orthonormal basis of the null space of E, x 0 = f / E is the value of w at this time, f and E respectively represent The opposite of the parameter coefficient matrix in the equality constraint is and the constant matrix; if there is no equality constraint, E 0 is the identity matrix of dimension n×n, x 0 is the zero matrix of dimension n×1;

[0213] Calculate the vertices w of the polyhedron i , i represents the number of vertices of the polyhedron, w i = [x 0 ,..., x 0 j + E 0 * ep T , where ep represents the extreme point matrix of the polyhedron, and j represents the length of the column dimension of the extreme point transpose matrix;

[0214] Replace the cost parameter w of the robust constraint of the capacity allocation model n with the polyhedron vertex w i , and the second constraint becomes the objective constraint containing only the decision variable X in the following formula, that is, the deterministic constraint:

[0215]

[0216] Step 204, through the following model, transform the robust optimization problem of the capacity allocation model into a deterministic problem:

[0217]

[0218] Step 205, call the MILP of the solver GUROBI to solve the model in Step 204 above.

[0219] ​In summary, the power system capacity configuration method according to the embodiments of the present invention can improve energy utilization efficiency. By setting reasonable charge-discharge state conversion times and flexible charge-discharge power upper limits for energy storage batteries, it is beneficial to reduce the maximum output and configured capacity of energy storage batteries, improve the economy of the system, and ensure the convergence and robustness of the capacity configuration results.

[0220] As Figure 3 shown, the embodiments of the present invention further provide a power system capacity configuration device, including:

[0221] A construction module 301, configured to construct an objective function for minimizing the total cost according to the comprehensive cost and environmental cost of power sources in the power system; wherein, the power sources include at least one of a wind turbine, a photovoltaic, an energy storage battery, and a generator; the generator includes a diesel generator and / or a natural gas generator;

[0222] A determination module 302, configured to determine constraint conditions according to at least one of system power constraints, energy storage battery charge-discharge state constraints, energy storage battery charge-discharge power constraints, generator output constraints, demand response load power constraints, and natural gas-related constraints;

[0223] An establishment module 303, configured to establish a capacity configuration model corresponding to the power system according to the objective function and the constraint conditions;

[0224] A obtaining module 304, configured to convert the capacity configuration model by using a cost parameter set of power sources, and process the converted capacity configuration model to obtain a capacity configuration result of the power system; wherein, the constraint of the cost parameter set is a linear constraint, and the cost parameter set is a convex hull set.

[0225] In the embodiments of the present invention, an objective function for minimizing the total cost is constructed according to the comprehensive cost and environmental cost of power sources in the power system, and constraint conditions are determined according to at least one of system power constraints, energy storage battery charge-discharge state constraints, energy storage battery charge-discharge power constraints, generator output constraints, demand response load power constraints, and natural gas-related constraints. Then, a capacity configuration model corresponding to the power system is established according to the objective function and the constraint conditions, and the capacity configuration model is converted by using a cost parameter set of power sources, and the converted capacity configuration model is processed to obtain a capacity configuration result of the power system. The constraint of the cost parameter set is a linear constraint, and the cost parameter set is a convex hull set, which transforms the uncertain robust optimization problem into a deterministic problem, is beneficial to reducing the configured capacity of power sources, improving energy utilization efficiency, and improving the economy of the power system.

[0226] Optionally, for the power system capacity configuration device, wherein the construction module 301 is specifically configured to:

[0227] Using the first formula, construct the objective function for minimizing the total cost based on the investment cost of the power source, the operation and maintenance cost of the power source, the energy storage battery replacement cost, the generator fuel cost, the generator power generation cost, and the environmental cost.

[0228] Among them, the first formula is:

[0229] M = min(C cap + C OM + C rep,batt + C fuel + C onoff + C e + C en );

[0230] The M represents the objective function for minimizing the total cost.

[0231] The C cap represents the investment cost of the power source, and the C cap is determined based on the total number of power sources, the types of power sources, the number of each type of power source, the unit capacity, and the unit capacity investment cost.

[0232] The C OM represents the operation and maintenance cost of the power source, and the C OM is determined based on the system discount rate, the system operation life, the system operation time, the unit capacity operation and maintenance cost of each type of power source, and the output of each type of power source at each moment.

[0233] The C rep，batt represents the energy storage battery replacement cost, and the C rep，batt is determined based on the replacement year of the energy storage battery, the unit capacity replacement cost, the number, and the rated capacity of a single energy storage battery.

[0234] The C fuel represents the generator fuel cost, and the C fuel is determined based on the fuel cost per unit volume, the volume of fuel consumed at each moment, the system of the fuel curve, the start-stop state of the generator at each moment, and the output of the generator at each moment.

[0235] C onoff represents the start-stop cost of the power source.

[0236] The C e represents the generator power generation cost.

[0237] The C en represents the environmental cost, and the C en is determined based on the environmental cost borne per unit volume of fuel consumed.

[0238] Optionally, in the power system capacity configuration device, the natural gas related constraints include at least one of the following: annual investment cost and operation and maintenance cost of natural gas infrastructure construction, construction status constraints of natural gas storage equipment and pipelines, natural gas supply constraints, natural gas balance constraints, natural gas transmission constraints, and natural gas storage equipment related constraints.

[0239] Optionally, in the power system capacity configuration device, the establishing module 303 is specifically configured to:

[0240] Establish the objective function of the capacity configuration model as:

[0241]

[0242] Establish the constraint conditions of the capacity configuration model as:

[0243] G X (X) ≥ 0;

[0244]

[0245] where X represents the set of decision variables, and X = [x; y], x represents the set of continuous variables, and y represents the set of integer variables. NC PV represents the fan capacity; NC Wt represents the photovoltaic capacity; respectively represent the auxiliary variables of the charging state and discharging state of the energy storage battery at the t-th moment; E(t) represents the remaining power of the energy storage battery at the t-th moment; P ch (t), P dis (t) respectively represent the charging power and discharging power of the energy storage battery at the t-th moment; P g (t) represents the output power of the generator at the t-th moment; E DR (t) represents the power consumption of the demand response load at the t-th moment; respectively represent the upward regulation power and downward regulation power of the demand response load at the t-th moment; N batt represents the number of energy storage batteries; respectively represent the charging state and discharging state of the energy storage battery; P g_onoff (t) represents the start-stop state of the generator at the t-th moment;

[0246] The w n represents the cost parameter, W represents the set of cost parameters, n represents the number of cost parameters, and w n = [C cap，unit (i), C OM，unit (i), Crep，batt，unit , C fuel，unit , C g，unit ;

[0247] The F X (X) represents the environmental cost;

[0248] The represents the objective function for minimizing the comprehensive cost of the power supply and the total cost;

[0249] The G X (X) represents the constraints in the constraint conditions, including the charge and discharge state constraint of the energy storage battery, the charge and discharge power constraint of the energy storage battery, the generator output constraint, the demand response load power constraint, and the natural gas related constraint;

[0250] The represents the system power constraint in the constraint conditions.

[0251] Optionally, for the power system capacity configuration device, wherein the obtaining module 304 includes:

[0252] A determination unit, configured to determine a first constraint according to an auxiliary variable function; wherein the auxiliary variable function is: The first constraint is:

[0253] An obtaining unit, configured to obtain a second constraint according to the constraint conditions of the capacity configuration model; wherein the second constraint is a constraint including cost parameters and decision variables, and the second constraint is:

[0254] A processing unit, configured to process the first constraint and the second constraint to obtain the converted capacity configuration model, wherein the objective function of the converted capacity configuration model is:

[0255] min X (F X (X) + s(X));

[0256] The constraint conditions of the converted capacity configuration model include:

[0257] G X (X) ≥ 0;

[0258] G robust (X) ≥ 0;

[0259] Wherein, G robust (X) is the robust constraint condition corresponding to the charge and discharge state constraint of the energy storage battery, the charge and discharge power constraint of the energy storage battery, the generator output constraint, the demand response load power constraint, and the natural gas related constraint.

[0260] Optionally, for the power system capacity configuration device, the obtaining module 304 is specifically configured to:

[0261] Use a mixed-integer linear configuration solver, input deterministic parameters, process the converted capacity configuration model, and output the set of decision variables;

[0262] Wherein, the deterministic parameters include system parameters and model parameters;

[0263] The system parameters include T, Δt, YR, and R d ; where T represents the total system operation time; Δt represents the time interval; YR represents the system operation years; R d represents the system discount rate;

[0264] The model parameters include: η inv 、E rated，batt 、η ch 、η dis 、δ、 repyr、w DR and P load (t); where η inv represents the photovoltaic inverter efficiency; E rated，batt represents the rated capacity of the energy storage battery; δ, η ch 、η dis respectively represent the self-discharge rate, charging efficiency, and discharging efficiency of the energy storage battery per hour; represents the upper limit value of the number of charge and discharge state conversions of the energy storage battery; repyr represents the replacement year of the energy storage battery; w DR represents the proportionality coefficient of the total load for the demand response load; P load (t) represents the load power at the t-th moment.

[0265] Optionally, for the power system capacity configuration device, the processing unit includes:

[0266] A transformation subunit, configured to use the Enumeration Robust Counterpart (ERC) algorithm to transform the second constraint, and transform the second constraint into an objective constraint that only contains decision variables.

[0267] Optionally, for the power system capacity configuration device, the transformation subunit is specifically configured to:

[0268] Obtain the upper limit constraint and the lower limit constraint of the cost parameters according to the set of cost parameters;

[0269] Add the upper bound constraint and the lower bound constraint to the third constraint; wherein, the third constraint is determined according to the constraint conditions of the capacity configuration model, and the third constraint is expressed as The third constraint is a constraint that only contains cost parameters;

[0270] Construct a polyhedron according to the third constraint as: P = {w n |(AE 0 ) * w n ≤ b - Ax 0}; where P represents the polyhedron; E 0 and x 0 are both determined according to whether the third constraint contains an equality constraint; A represents the opposite of the coefficient matrix in the third constraint; b represents the constant matrix in the third constraint;

[0271] Use the second formula to obtain the vertices of the polyhedron; wherein, the second formula is: w i = [x 0 ,..., x 0 j + E 0 * ep T ; w i represents the vertex; i represents the i-th vertex of the polyhedron; ep represents the extreme point matrix of the polyhedron; j represents the length of the column dimension of the extreme point transpose matrix;

[0272] According to the second constraint, obtain (c + A T x) T w n and (b T X + d); c and d are both constant matrices;

[0273] Use the third formula and replace the cost parameters with the vertices of the polyhedron to transform the second constraint into the target constraint; wherein, the third formula is:

[0274]

[0275] It should be noted that the above device provided by the embodiments of the present invention can implement all the method steps implemented by the embodiments of the above power system capacity configuration method, and can achieve the same technical effects. The same parts and beneficial effects as those in the method embodiments will not be specifically described in this embodiment.

[0276] The embodiments of the present invention also provide an electronic device, such as Figure 4 ​As shown, it includes: a processor 401; and a memory 402 connected to the processor 401 through a bus interface. The memory 402 is used to store the programs and data used by the processor 401 when performing operations. The processor 401 calls and executes the programs and data stored in the memory 402.

[0277] A transceiver 403 is connected to the bus interface and is used to receive and send data under the control of the processor 401.

[0278] Among them, in Figure 4 the bus architecture may include any number of interconnected buses and bridges, specifically various circuits represented by one or more processors represented by the processor 401 and the memory represented by the memory 402 are linked together. The bus architecture can also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, etc. These are well known in the art, so they will not be further described herein. The bus interface provides an interface. The transceiver 403 may be multiple components, that is, including a transmitter and a receiver, and provides a unit for communicating with various other devices on the transmission medium. For different user devices, the user interface 404 may also be an interface capable of externally connecting and internally connecting required devices, and the connected devices include but are not limited to a keypad, a display, a speaker, a microphone, a joystick, etc.

[0279] The processor 401 is responsible for managing the bus architecture and general processing, and the memory 402 can store the data used by the processor 401 when performing operations.

[0280] Those skilled in the art can understand that all or part of the steps to implement the above embodiments can be completed by hardware, or can be completed by a program instructing relevant hardware. The program includes instructions for performing part or all of the steps of the above method; and the program can be stored in a readable storage medium, and the storage medium can be any form of storage medium.

[0281] The embodiment of the present invention also provides a readable storage medium, wherein a program is stored on the readable storage medium, and when the program is executed by a processor, it implements the power system capacity configuration method as described in any one of the above.

[0282] In several embodiments provided in the present application, it should be understood that the disclosed methods and apparatuses can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the apparatuses or units can be in an electrical, mechanical or other form.

[0283] In addition, each functional unit in various embodiments of the present invention can be integrated in a processing unit, or each unit can be physically included separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0284] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit stored in a storage medium includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute some steps of the transceiver methods in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM for short), random access memories (RAM for short), magnetic disks or optical discs that can store program codes.

[0285] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for configuring power system capacity, characterized in that: include: According to the comprehensive cost and environmental cost of the power source in the power system, an objective function of minimizing the total cost is constructed; wherein the power source includes at least one of a wind turbine, a photovoltaic, an energy storage battery and a generator; Determine a constraint condition according to at least one of a system power constraint, an energy storage battery charge and discharge state constraint, an energy storage battery charge and discharge power constraint, a generator output constraint, a demand response load power constraint, and a natural gas-related constraint; Establishing a capacity configuration model corresponding to the power system according to the objective function and the constraint conditions; The capacity configuration model is converted using a cost parameter set of a power source, and the converted capacity configuration model is processed to obtain a capacity configuration result of the power system; wherein the constraints of the cost parameter set are linear constraints, and the cost parameter set is a convex hull set.

2. The method according to claim 1, characterized in that The objective function of minimizing the total cost is constructed based on the comprehensive cost and environmental cost of the power source in the power system, including: Using the first formula, an objective function for minimizing the total cost is constructed according to the investment cost of the power source, the operation and maintenance cost of the power source, the replacement cost of the energy storage battery, the fuel cost of the generator, the power generation cost of the generator, and the environmental cost; Among them, the first formula is: M=min(C cap +C OM +C rep,batt +C fuel +C onoff +C e +C en ); M represents the objective function of minimizing the total cost; C cap Indicates the investment cost of the power supply; C OM Represents the operation and maintenance cost of the power supply; C rep，batt represents the replacement cost of energy storage batteries; C fuel represents the fuel cost of the generator; C onoff represents the start-stop cost of the power supply; C e represents the cost of power generation by the generator; C en Represents the environmental cost.

3. The method according to claim 1, characterized in that The natural gas-related constraints include: at least one of the annual investment cost and operation and maintenance cost of natural gas infrastructure construction, construction status constraints of natural gas storage equipment and pipelines, natural gas supply constraints, natural gas balance constraints, natural gas transmission constraints, and natural gas storage equipment-related constraints.

4. The method according to claim 1, characterized in that: The establishing of the capacity configuration model corresponding to the power system comprises: The objective function for establishing the capacity configuration model is: The constraints for establishing the capacity configuration model are: G X (X)≥0; Where X represents the set of decision variables, and X = [x; y], x represents the set of continuous variables, y represents the set of integer variables, NC PV Indicates fan capacity; NC Wt represents photovoltaic capacity; They represent the auxiliary variables of the charging state of the energy storage battery and the discharging state of the energy storage battery at the tth moment respectively; E(t) represents the remaining power of the energy storage battery at the tth moment; P ch (t), P dis (t) respectively represent the charging power and discharging power of the energy storage battery at the tth moment; P g (t) represents the generator output at the tth moment; E DR (t) represents the power of the demand response load at time t; They represent the upward and downward power of the demand response load at time t respectively; N batt Indicates the number of energy storage batteries; Respectively represent the charging state and discharging state of the energy storage battery; P g_onoff (t) represents the start / stop status of the generator at time t; The w n represents the cost parameter, W represents the cost parameter set, n represents the number of cost parameters, and w n =[C cap，unit (i) C OM，unit (i) C rep，batt，unit , C fuel，unit , C g，unit ]; C cap，unit (i) represents the unit capacity investment cost of traditional generators, C OM，unit (i) represents the unit capacity replacement cost of traditional generators, C rep，batt，unit represents the replacement cost per unit capacity of energy storage, C fuel，unit represents the fuel price per unit volume of the system, C g，unit It represents the unit power generation cost of the generator; The F X (X) represents the environmental cost; Said An objective function representing the comprehensive cost of the power source and minimization of the total cost; The G X (X) represents the energy storage battery charging and discharging state constraint, energy storage battery charging and discharging power constraint, generator output constraint, demand response load power constraint and natural gas related constraint in the constraint conditions; Said represents the system power constraint in the constraints.

5. The method according to claim 1, characterized in that The converting the capacity configuration model by using the cost parameter set of the power supply includes: According to the auxiliary variable function, a first constraint is determined; wherein the auxiliary variable function is: The first constraint is: According to the constraint condition of the capacity configuration model, a second constraint is obtained; wherein the second constraint is a constraint including a cost parameter and a decision variable, and the second constraint is: The first constraint and the second constraint are processed to obtain the converted capacity configuration model, wherein the objective function of the converted capacity configuration model is: min X (F X (X)+s(X)); The constraints of the converted capacity configuration model include: G X (X)≥0; G robust (X)≥0; Among them, G robust (X) is a robust constraint condition corresponding to the energy storage battery charging and discharging state constraint, the energy storage battery charging and discharging power constraint, the generator output constraint, the demand response load power constraint and the natural gas related constraint.

6. The method according to claim 4, characterized in that The processing of the converted capacity configuration model to obtain a capacity configuration result of the power system includes: Using a mixed integer linear configuration solver and inputting deterministic parameters, processing the converted capacity configuration model and outputting the decision variable set; Wherein, the deterministic parameters include system parameters and model parameters; The system parameters include T, Δt, YR and R d ; Where T represents the total system operation time; Δt represents the time interval; YR represents the system operation years; R d represents the system discount rate; The model parameters include: inv 、E rated，batt , η ch , η dis ,δ, repyr,w DR and P load (t); where η inv Represents the efficiency of photovoltaic inverter; E rated，batt Indicates the rated capacity of the energy storage battery; δ, η ch , η dis Respectively represent the self-discharge rate, charging efficiency and discharge efficiency of the energy storage battery per hour; represents the upper limit of the number of times the energy storage battery is switched between charge and discharge states; repyr represents the replacement year of the energy storage battery; w DR Indicates the proportion coefficient of total load used for demand response load; P load (t) represents the load power at the tth moment.

7. The method according to claim 5, characterized in that The processing of the second constraint includes: The second constraint is transformed by using an enumeration robust equivalence algorithm, and is converted into a target constraint containing only decision variables.

8. The method according to claim 7, characterized in that The method of using an enumeration robust equivalence algorithm to transform the second constraint into a target constraint containing only decision variables includes: According to the cost parameter set, obtaining the upper limit constraint and the lower limit constraint of the cost parameter; The upper limit constraint and the lower limit constraint are added to a third constraint; wherein the third constraint is determined according to the constraint condition of the capacity configuration model, and the third constraint is expressed as The third constraint is a constraint that only includes cost parameters; According to the third constraint, the polyhedron is constructed as follows: P = {w n |(AE0)*w n ≤b-Ax0}; wherein P represents a polyhedron; E0 and x0 are determined according to whether the third constraint contains an equality constraint; A represents the inverse of the coefficient matrix in the third constraint; b represents the constant matrix in the third constraint; using the second formula, the vertices of the polyhedron are obtained; wherein the second formula is: w i =[x0,...,x0] j +E0*ep T ;w i represents the i-th vertex of the polyhedron; E0 is the identity matrix of dimension n×n, x0 is the zero matrix of dimension n×1, and n represents the number of parameters; ep represents the pole matrix of the polyhedron; j represents the length of the column dimension of the pole transposed matrix; According to the second constraint, we get (c+A T x) T w n and (b T X+d); c and d are both constant matrices; The second constraint is transformed into the target constraint by using a third formula and replacing the cost parameter by the vertices of the polyhedron; wherein the third formula is:

9. A power system capacity configuration device, characterized in that: include: A construction module is used to construct an objective function of minimizing the total cost according to the comprehensive cost and environmental cost of the power source in the power system; wherein the power source includes at least one of a wind turbine, a photovoltaic, an energy storage battery and a generator; A determination module, used to determine a constraint condition according to at least one of a system power constraint, an energy storage battery charge and discharge state constraint, an energy storage battery charge and discharge power constraint, a generator output constraint, a demand response load power constraint, and a natural gas related constraint; An establishment module is used to establish a capacity configuration model corresponding to the power system according to the objective function and the constraint conditions; A module is obtained, which is used to convert the capacity configuration model using the cost parameter set of the power source, and process the converted capacity configuration model to obtain the capacity configuration result of the power system; wherein the constraints of the cost parameter set are linear constraints, and the cost parameter set is a convex hull set.

10. An electronic device comprising: A transceiver, a processor, a memory, and a program or instruction stored in the memory and executable on the processor; characterized in that when the processor executes the program or instruction, the power system capacity configuration method as described in any one of claims 1 to 8 is implemented.

11. A readable storage medium having a program or instruction stored thereon, characterized in that: When the program or instruction is executed by a processor, the power system capacity configuration method according to any one of claims 1 to 8 is implemented.

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