Method and device for optimizing power transmission-energy storage capacity of power system

Abstract

The invention provides a power transmission-energy storage capacity optimization method and device of a power system. The method comprises the following steps: determining a new energy power abandoning quantity function obtained by a real-time operation optimization model based on new energy power generation; the optimization model is operated in real time, and minimization of the new energy abandoned electric quantity is taken as a target function; the new energy abandoned electricity quantity function takes power transmission-energy storage capacity and new energy generating capacity as variables; determining a new energy power abandoning distribution robust constraint according to the new energy power abandoning quantity function and a preset new energy power generation probability distribution set; determining planned capacity and investment cost obtained by the power transmission-energy storage capacity optimization configuration model based on new energy power generation; the power transmission-energy storage capacity optimization configuration model takes the minimum investment cost as an objective function, and takes the value range of the power transmission-energy storage capacity and the new energy abandoned power distribution robust constraint as constraint conditions. The influence of the power transmission-energy storage capacity on the new energy abandoned electric quantity is considered, the stability and the utilization rate of new energy power generation operation are improved, and the investment cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method and device for optimizing the transmission-energy storage capacity of an electric power system. Background Art

[0002] Energy is the cornerstone of human survival and development, and electricity, as the most critical form of energy, is crucial to the development of the national economy. Clean energy generation represented by wind and solar energy can reduce dependence on fossil fuels and reduce carbon emissions.

[0003] Since the output of photovoltaic and wind power generation is affected by weather conditions and has large fluctuations, their average power generation is usually much lower than the rated power. Taking photovoltaic power stations as an example, if a transmission line with the same capacity as that of a renewable energy power plant is selected, the utilization rate of the transmission line will be very low. In order to reduce the uncertainty of photovoltaic power generation, photovoltaic power stations are usually equipped with energy storage systems to smooth the output power and reduce the capacity of transmission lines. Although the construction of energy storage systems requires a certain investment cost, considering the cost of long-distance power transmission, the unit capacity cost of energy storage systems is usually much lower than the unit capacity cost of transmission lines. Therefore, the capacity of transmission lines and energy storage systems needs to be comprehensively optimized. Summary of the invention

[0004] In order to solve the technical problem of high investment cost caused by excessive energy storage capacity or transmission capacity of photovoltaic power stations, the present invention provides a method and device for optimizing the transmission-energy storage capacity of a power system, taking into account the impact of the transmission-energy storage capacity on the amount of abandoned electricity from renewable energy, improving the stability and utilization rate of renewable energy power generation operation, and reducing investment costs.

[0005] The present invention provides a method for optimizing the transmission-energy storage capacity of an electric power system, wherein the electric power system at least uses renewable energy for power generation; the method comprises: determining a renewable energy power abandonment function obtained by a real-time operation optimization model based on renewable energy power generation; the real-time operation optimization model takes minimizing the renewable energy power abandonment as an objective function; the renewable energy power abandonment function is a function with transmission-energy storage capacity and renewable energy power generation as variables; determining a renewable energy power abandonment distribution robust constraint according to the renewable energy power abandonment function and a preset renewable energy power generation probability distribution set; the preset renewable energy power generation probability distribution set includes multiple renewable energy power generation probability distribution values; the renewable energy power abandonment distribution robust constraint is used to limit the renewable energy power abandonment when the renewable energy power generation probability distribution value is lower than a preset probability value; determining the planned capacity and the investment cost of energy storage and transmission lines obtained by a transmission-energy storage capacity optimization configuration model based on renewable energy power generation; the transmission-energy storage capacity optimization configuration model takes minimizing the investment cost as an objective function and takes the value range of the transmission-energy storage capacity and the renewable energy power abandonment distribution robust constraint as constraints.

[0006] According to a method for optimizing the transmission-energy storage capacity of an electric power system provided by the present invention, the function of determining the amount of abandoned new energy power obtained by a real-time operation optimization model based on new energy power generation includes: obtaining the time parameters of the electric power system, the power from power generation to energy storage, the power from power generation to power transmission, the power from energy storage to power transmission, the power generation of the new energy, the power abandoned by the new energy, the transmission-energy storage capacity and the energy storage coefficient; the transmission-energy storage capacity includes the energy storage power capacity, the energy storage energy capacity and the transmission line capacity; according to the time parameters and the abandoned new energy power, determining the objective function of the real-time operation model; according to the power from power generation to energy storage, the power from power generation to power transmission, the power from energy storage to power transmission and the abandoned new energy power, determining the first constraint condition of the real-time operation model; the first constraint condition is used to characterize the power flow direction and the non-negativity of the variable; according to the power from power generation to energy storage The second constraint of the real-time operation model is determined according to the rate, the power from power generation to power transmission, the power generation of the new energy and the power of power abandonment of the new energy; the second constraint is used to characterize the power balance on the power generation side; the third constraint of the real-time operation model is determined according to the power from power generation to power transmission, the power from energy storage to power transmission and the transmission line capacity; the third constraint is used to characterize the capacity limitation of the transmission line; the fourth constraint of the real-time operation model is determined according to the time parameter, the power from power generation to energy storage, the power from energy storage to power transmission, the energy storage power capacity, the energy storage energy capacity and the energy storage coefficient; the fourth constraint is used to characterize the energy storage storage; the new energy abandonment function is determined according to the objective function of the real-time operation model, the first constraint, the second constraint, the third constraint and the fourth constraint.

[0007] According to a method for optimizing the transmission-storage capacity of an electric power system provided by the present invention, after determining the new energy power abandonment function obtained by the real-time operation optimization model based on new energy power generation, it also includes: using a dual algorithm of linear programming to convert the new energy power abandonment function into a linear programming function; and using an approximate estimation algorithm to approximate the linear programming function.

[0008] According to a method for optimizing the transmission-storage capacity of an electric power system provided by the present invention, the method determines the robust constraints on the distribution of renewable energy abandonment based on the renewable energy abandonment amount function and a preset renewable energy power generation probability distribution set, including: obtaining historical renewable energy power generation probability distribution and abandonment parameters; determining the renewable energy power generation probability distribution set based on Wasserstein distance, the historical renewable energy power generation probability distribution and the abandonment parameters; determining the acceptable constraints on renewable energy abandonment based on the renewable energy abandonment amount function, the time parameters, the renewable energy power generation and the abandonment parameters; determining the robust constraints on the distribution of renewable energy abandonment based on the renewable energy abandonment amount function, the abandonment parameters and the acceptable constraints on renewable energy abandonment.

[0009] According to a method for optimizing the transmission-storage capacity of an electric power system provided by the present invention, after determining the robust constraint on the distribution of renewable energy power abandonment according to the renewable energy power abandonment amount function and a preset renewable energy power generation probability distribution set, the method further includes: using a VaR algorithm to convert the robust constraint on the distribution of renewable energy power abandonment into a VaR constraint; using a CVaR algorithm to convert the VaR constraint into a CVaR constraint; and using a linear reconstruction algorithm to linearly reconstruct the CVaR constraint.

[0010] According to a method for optimizing the transmission-energy storage capacity of an electric power system provided by the present invention, the determination of the planned capacity and the investment cost of energy storage and transmission lines obtained by a transmission-energy storage capacity optimization configuration model based on renewable energy power generation includes: obtaining the unit capacity cost of the transmission-energy storage capacity; the unit capacity cost of the transmission-energy storage capacity includes the unit capacity cost of the energy storage power capacity, the unit capacity cost of the energy storage energy capacity and the unit capacity cost of the transmission line capacity; determining the objective function of the transmission-energy storage capacity optimization configuration model based on the unit capacity cost of the transmission-energy storage capacity and the transmission-energy storage capacity; determining the planned capacity and the investment cost of the energy storage and transmission lines based on the objective function of the transmission-energy storage capacity optimization configuration model, the value range of the transmission-energy storage capacity and the blue-robust constraint of the renewable energy power abandonment distribution.

[0011] The present invention also provides an optimization device for the transmission-energy storage capacity of an electric power system, wherein the electric power system at least uses renewable energy for power generation; the device comprises: a first module, used to determine a renewable energy power abandonment function obtained by a real-time operation optimization model based on renewable energy power generation; the real-time operation optimization model takes minimizing the renewable energy power abandonment as an objective function; the renewable energy power abandonment function is a function with transmission-energy storage capacity and renewable energy power generation as variables; a second module, used to determine a renewable energy power abandonment distribution blue-robust constraint based on the renewable energy power abandonment function and a preset renewable energy power generation probability distribution set; the preset renewable energy power generation probability distribution set includes multiple renewable energy power generation probability distribution values; the renewable energy power abandonment distribution blue-robust constraint is used to limit the renewable energy power abandonment when the renewable energy power generation probability distribution value is lower than the preset probability value; a third module, used to determine the planned capacity and the investment cost of energy storage and transmission lines obtained by a transmission-energy storage capacity optimization configuration model based on renewable energy power generation; the transmission-energy storage capacity optimization configuration model takes minimizing the investment cost as an objective function, and takes the value range of the transmission-energy storage capacity and the renewable energy power abandonment distribution blue-robust constraint as constraints.

[0012] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for optimizing the transmission-energy storage capacity of the power system as described in any one of the above-mentioned methods is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for optimizing the transmission-energy storage capacity of a power system as described in any one of the above.

[0014] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for optimizing the transmission-energy storage capacity of the power system as described in any one of the above-mentioned methods is implemented.

[0015] The present invention provides a method and device for optimizing the transmission-energy storage capacity of an electric power system, the method comprising: determining a new energy curtailment function obtained by a real-time operation optimization model based on new energy power generation; the real-time operation optimization model takes minimizing the new energy curtailment as an objective function; the new energy curtailment function takes the transmission-energy storage capacity and the new energy power generation as variables; according to the new energy curtailment function and a preset new energy power generation probability distribution set, the new energy curtailment distribution robust constraint is determined; the planned capacity and investment cost obtained by the transmission-energy storage capacity optimization configuration model based on new energy power generation are determined; the transmission-energy storage capacity optimization configuration model takes minimizing the investment cost as an objective function, and takes the value range of the transmission-energy storage capacity and the new energy curtailment distribution robust constraint as constraints. The present invention takes into account the influence of the transmission-energy storage capacity on the new energy curtailment, improves the stability and utilization rate of the new energy power generation operation, and reduces the investment cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 It is a flow chart of a method for optimizing the transmission-energy storage capacity of a power system provided by the present invention.

[0018] Figure 2 It is a schematic diagram of the electric energy flow relationship of the photovoltaic power station provided by the present invention.

[0019] Figure 3 It is a structural schematic diagram of a device for optimizing the transmission-energy storage capacity of a power system provided by the present invention.

[0020] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] my country's power system is vigorously developing new energy power generation represented by wind power and photovoltaic power generation to reduce fossil energy consumption and carbon emissions. In my country, large renewable energy resource centers are usually far away from load centers. For example, northwest China has abundant solar energy resources, but low population density and small electricity demand. Therefore, renewable energy power plants in these remote areas transmit electricity over long distances to load centers in the east.

[0023] The output of photovoltaic power generation and wind power generation is unstable and uncertain, depending on real-time weather conditions, and their average power generation is usually much lower than the rated power. Taking photovoltaic power plants as an example, if the transmission line with the same capacity as the renewable energy power plant is selected, its utilization rate is very low. Photovoltaic power plants are usually equipped with energy storage systems to reduce uncertainty and make the output power smoother, thereby reducing the capacity of the transmission line. Although the construction of energy storage systems also has a certain investment cost, considering the long distance of the transmission corridor, the unit capacity construction cost of the energy storage system is usually much lower than the unit capacity construction cost of the transmission line. Therefore, the capacity of the transmission line and the energy storage system should be jointly optimized. At the same time, in order to improve the utilization rate of new energy, it is necessary to limit the amount of abandoned electricity of new energy, and achieve good economic efficiency after comprehensive optimization.

[0024] Please refer to Figure 1 , Figure 1 A schematic flow chart of a method for optimizing the transmission-energy storage capacity of a power system provided by the present invention.

[0025] Please refer to Figure 2 , Figure 2 A schematic diagram of the relationship between electric energy flows in a photovoltaic power station provided by the present invention.

[0026] The present invention provides a method for optimizing the transmission-energy storage capacity of an electric power system, wherein the electric power system at least uses renewable energy for power generation; the method comprises: 101: Determine the new energy curtailment function obtained by the real-time operation optimization model based on new energy power generation; the real-time operation optimization model takes minimizing the new energy curtailment as the objective function; the new energy curtailment function is a function with transmission-storage capacity and new energy power generation as variables; 102: determining a renewable energy abandonment distribution blue-robust constraint according to a renewable energy abandonment quantity function and a preset renewable energy power generation probability distribution set; the preset renewable energy power generation probability distribution set includes a plurality of renewable energy power generation probability distribution values; the renewable energy abandonment distribution blue-robust constraint is used to limit the renewable energy abandonment quantity when the renewable energy power generation probability distribution value is lower than the preset probability value; 103: Determine the planned capacity and investment cost of energy storage and transmission lines obtained by the transmission-energy storage capacity optimization configuration model based on renewable energy power generation; the transmission-energy storage capacity optimization configuration model takes minimizing investment cost as the objective function and takes the value range of transmission-energy storage capacity and the rigid constraint of renewable energy power abandonment distribution as the constraint conditions.

[0027] Taking into account the high investment in transmission lines of renewable energy power stations, the large volatility of renewable energy power generation output and the inability to guarantee the abandonment rate of renewable energy, the present invention provides a method for optimizing the transmission-energy storage capacity of a power system. The method can configure (plan) a reasonable energy storage capacity, thereby improving the stability of the operation of renewable energy plants; it can also appropriately reduce the transmission capacity of transmission lines and reduce the investment cost during construction; at the same time, the method also considers the impact of transmission capacity and energy storage capacity on the abandonment of renewable energy power fields, and can consider the uncertainty of renewable energy under a given capacity limit, optimize the solution of scheduling and operation problems, and minimize the abandonment of renewable energy.

[0028] Specifically, through the real-time operation optimization model of renewable energy power generation, the transmission-energy storage capacity is used as a model parameter to solve the problem of minimizing the amount of renewable energy abandoned, thereby improving the utilization rate of renewable energy. The renewable energy abandoned amount function is a piecewise affine function with the transmission-energy storage capacity and renewable energy power generation as independent variables. The renewable energy abandoned amount function obtained based on the real-time operation optimization model of renewable energy power generation is used as a constraint condition for the transmission-energy storage capacity optimization configuration model of renewable energy power generation. Considering the inaccuracy of the empirical distribution of renewable energy power generation, a set of renewable energy power generation probability distributions is preset, and a renewable energy abandoned amount distribution blue-robust constraint is determined based on the renewable energy abandoned amount function and the preset renewable energy power generation probability distribution set to describe the photovoltaic abandoned amount requirements. The renewable energy abandoned amount distribution blue-robust constraint limits the renewable energy abandoned amount when the renewable energy power generation probability distribution value is lower than the preset probability value. Considering the influence of transmission-energy storage capacity on renewable energy abandoned amount, the renewable energy abandoned amount distribution blue-robust constraint is used to reduce the renewable energy abandoned amount under each renewable energy power generation probability distribution, thereby improving the economic efficiency. The scale of the energy storage system and transmission line of the renewable energy power station is determined through the transmission-energy storage capacity optimization configuration model of renewable energy power generation. The transmission-energy storage capacity optimization configuration model takes minimizing investment cost as the objective function, and takes the range of transmission-energy storage capacity and the renewable energy power abandonment distribution blue-bar constraint as the constraint conditions, thereby reducing the transmission-energy storage capacity (planned capacity), thereby saving the investment cost of energy storage and transmission lines. Energy storage equipment also reduces the uncertainty on the grid side.

[0029] In summary, the method of the present invention simultaneously achieves high utilization rate of renewable energy, reduced investment cost and reduced uncertainty.

[0030] As a preferred embodiment, determining the new energy abandonment power function obtained by the real-time operation optimization model based on new energy power generation includes: obtaining the time parameters of the power system, the power from power generation to energy storage, the power from power generation to transmission, the power from energy storage to transmission, the new energy power generation, the new energy abandonment power, the transmission-energy storage capacity and the energy storage coefficient; the transmission-energy storage capacity includes the energy storage power capacity, the energy storage energy capacity and the transmission line capacity; according to the time parameters and the new energy abandonment power, determining the objective function of the real-time operation model; according to the power from power generation to energy storage, the power from power generation to transmission, the power from energy storage to transmission and the new energy abandonment power, determining the first constraint condition of the real-time operation model; the first constraint condition is used to characterize the power flow direction and the non-negativity of the variable; according to the power from power generation to energy storage The second constraint of the real-time operation model is determined according to the power from power generation to transmission, the power from energy storage to transmission and the power of abandoned new energy; the second constraint is used to characterize the power balance on the power generation side; the third constraint of the real-time operation model is determined according to the power from power generation to transmission, the power from energy storage to transmission and the capacity of the transmission line; the third constraint is used to characterize the capacity limitation of the transmission line; the fourth constraint of the real-time operation model is determined according to the time parameter, the power from power generation to energy storage, the power from energy storage to transmission, the energy storage power capacity, the energy storage energy capacity and the energy storage coefficient; the fourth constraint is used to characterize the energy stored in the energy storage; the function of the abandoned new energy quantity is determined according to the objective function of the real-time operation model, the first constraint, the second constraint, the third constraint and the fourth constraint.

[0031] Taking a photovoltaic power station as an example, in this embodiment, the photovoltaic power station is equipped with energy storage and connected to the power grid through a transmission line, and energy flows between the photovoltaic power station, the energy storage system, and the transmission end. In the real-time operation optimization model of photovoltaic power generation, the duration is considered to be of T time period (time parameters of the power system T , ), denoted as The power plant is t The maximum output during , is a random variable, and is expressed as Indicates the photovoltaic power abandonment power (new energy power abandonment power). t The power from power generation to energy storage, from power generation to power transmission, and from energy storage to power transmission during the period is respectively , and express. is the photovoltaic power generation (new energy power generation).

[0032] make , and They represent energy storage power capacity, energy storage energy capacity and transmission line capacity respectively.t The energy storage level at the end of the period is , the initial energy storage level is , then the real-time operation optimization model of renewable energy power generation can be expressed as: (1)

[0033] (2)

[0034] (3)

[0035] (4)

[0036] (5)

[0037] (6)

[0038] (7)

[0039] (8)

[0040] in, is the time subscript set, E T SoC for energy storage at the final moment, s 1 is the lower limit of energy storage SoC, s h It is the upper limit of energy storage SoC.

[0041] The objective function (1) minimizes the total abandoned photovoltaic power; the first constraint (2) stipulates the power flow direction and the non-negativity of the variable; the second constraint (3) is used to characterize the power balance on the power generation side; the third constraint (4) is used to characterize the capacity limitation of the transmission line; the fourth constraints (5)-(8) are used to characterize the energy storage. Among them, the energy storage coefficient and are the charging efficiency and discharging efficiency of energy storage, respectively. Equation (5) describes the dynamic change of energy storage state of charge (SoC); energy storage coefficient and are the lower and upper limits of the energy storage SoC, respectively. Formula (6) limits the operating range of the energy storage SoC and requires that the stored energy be the same at the beginning and end. Formula (7) also has upper limits for charging and discharging power. Formula (8) allows a time period to be divided into a charging sub-interval and a discharging sub-interval, so that charging and discharging powers that may not be strictly complementary are still physically achievable: for example, and Considered as t The average power during Power in time Charge the energy storage, and then Power in time Discharge the energy storage, t During the rest of the period, there is no charging or discharging. Therefore, energy storage does not need to be subject to strict complementarity constraints.

[0042] In formula (1), capacity (transmission-energy storage capacity) and photovoltaic power generation (new energy power generation) is regarded as a parameter. Therefore, the optimal value of equation (1), i.e., the amount of photovoltaic power generation abandoned, can be regarded as and The function, using express.

[0043] As a preferred embodiment, after determining the new energy power abandonment function obtained by the real-time operation optimization model based on new energy power generation, it also includes: using the dual algorithm of linear programming to convert the new energy power abandonment function into a linear programming function; using an approximate estimation algorithm to approximate the linear programming function.

[0044] In order to simplify the calculation, in this embodiment, the dual algorithm of linear programming is used to convert the new energy curtailment function (1) into a linear programming function (9).

[0045] (9)

[0046] in, is the parameter vector; is the decision vector, including and , The decision variables; , and c is a coefficient matrix or vector. Representation parameters The domain of , in the capacity allocation optimization problem: (10)

[0047] in, They are , and The upper bound of . Define the random variable The value range is ,in is the installed capacity of the photovoltaic power station.

[0048] The dual problem of formula (9) is: (11)

[0049] According to the physical meaning of renewable energy power restriction, formula (9) The parameters in must have a finite optimal value, so from the duality theory, we can get that equation (11) and equation (9) have the same optimal value: (12)

[0050] The dual feasible set It is a polyhedron.

[0051] in, is the first dual variable, is the second dual variable.

[0052] because is finite, so the maximum value of the dual problem will be A vertex of is reached. express The vertex set of , then: (13)

[0053] in, , is the pointwise maximum of a finite number of affine functions; For the k The first dual variable, For the k The second dual variable. Therefore, is convex and piecewise affine. The number of constraints in (1) determines the dual variables Since energy storage requires long-term coupling operations, The dimension of must be very high, which leads to The vertices of are large. Therefore, enumerating However, only a small number of vertices are needed to generate the piecewise affine formula in (13).

[0054] The approximate estimation algorithm is used to approximate the linear programming function. Specifically, for any subset ,have: (14)

[0055] in, yes The approximate value of The larger is, the more accurate the approximation is. is piecewise affine, so the analysis is done in its affine region. Obviously, this region includes the points where a vertex reaches the maximum value among all vertices, that is: (15)

[0056] in, l For collection Any element subscript in .

[0057] when At that time, k vertices reach the maximum value, and .because and are all affine functions, so is a polyhedron. If Come closer , The maximum error can be calculated by the following formula: (16)

[0058] in: (17)

[0059] It is obtained from the dual problem (11).

[0060] Although the feasible region of equation (16) is a polyhedron, the objective function is a bilinear function , including the product term and For its solution, a linear iterative procedure can be used to solve equation (16), so the problems that need to be solved are all linear equations.

[0061] As a preferred embodiment, the robust constraints on the distribution of renewable energy power abandonment are determined according to the renewable energy power abandonment amount function and the preset renewable energy power generation probability distribution set, including: obtaining the historical renewable energy power generation probability distribution and power abandonment parameters; determining the renewable energy power generation probability distribution set based on the Wasserstein distance, the historical renewable energy power generation probability distribution and power abandonment parameters; determining the acceptable constraints on renewable energy power abandonment according to the renewable energy power abandonment amount function, time parameters, renewable energy power generation and power abandonment parameters; determining the robust constraints on renewable energy power abandonment according to the renewable energy power abandonment amount function, power abandonment parameters and the acceptable constraints on renewable energy power abandonment.

[0062] Considering the photovoltaic power generation is uncertain, and the random variable has been defined The value range of . for The distribution set supported on , satisfy The distribution set .in, is the 1-norm, For distribution The power curtailment parameters include , and .

[0063] Assumptions The true distribution of , which uses existing data for approximation. N Based on historical meteorological data When the sample , we can get the historical probability distribution of renewable energy generation: (18)

[0064] in, express The probability of is 1, so for For example, the probability of each sample is .

[0065] generally, ,but Usually in Therefore, the definition The fuzzy set is used to consider All possible probability distributions nearby. The distance between two probability distributions is defined using the Wassertein distance.

[0066] (19)

[0067] in, is a joint distribution with marginal values ​​of and .

[0068] According to this Wassertein definition, the probability distribution set (fuzzy set) of renewable energy generation is: (20)

[0069] Fuzzy Sets Depend on Zhongyu The distance is no greater than All probability distributions of Composition, among which, is a parameter that controls the radius of the fuzzy set.

[0070] In order to establish a strict constraint on the distribution of renewable energy power, represents the acceptable photovoltaic power curtailment rate. Then, the acceptable constraint of photovoltaic power curtailment (acceptable constraint of renewable energy power curtailment) is expressed by the formula Represented by fuzzy set The probability distribution under the definition is obtained, and the PV power curtailment distribution constraint is obtained: (twenty one)

[0071] in, is the minimum acceptable probability; Indicates that the distribution Down The probability of an event occurring. Then, constraint (21) means that for For all distributions in , including the worst case distribution, the probability of acceptable power abandonment is always no less than .

[0072] As a preferred embodiment, after determining the robust constraint on the distribution of renewable energy power abandonment according to the renewable energy power abandonment amount function and the preset renewable energy power generation probability distribution set, it also includes: using the VaR algorithm to convert the robust constraint on the distribution of renewable energy power abandonment into a VaR constraint; using the CVaR algorithm to convert the VaR constraint into a CVaR constraint; and using a linear reconstruction algorithm to linearly reconstruct the CVaR constraint.

[0073] In this embodiment, constraint (21) can be regarded as the worst-case risk constraint. In fact, by using the loss function and the VaR concept, the VaR algorithm is used to transform the renewable energy curtailment distribution constraint (21) into the VaR constraint (25).

[0074] The loss function is defined as: (twenty two)

[0075] Constraint (21) can be refactored as: (twenty three)

[0076] In distribution Next, the loss function of Defined as: (twenty four)

[0077] in, yis the minimum threshold such that At least The probability of being established. Therefore, constraint (23) is equivalent to: (25)

[0078] The CVaR algorithm is used to transform the VaR constraint (25) into the CVaR constraint (28).

[0079] CVaR is a conservative approximation of VaR and has convexity. Defined as: (26)

[0080] It is exist The conditional expected value under the condition. Therefore: (27)

[0081] Always holds. The reduced worst-case CVaR constraint is: (28)

[0082] According to the above analysis, formula (28) is a sufficient condition for formula (21).

[0083] in, is based on expectations under current conditions.

[0084] The linear reconstruction algorithm is used to linearly reconstruct the CVaR constraints.

[0085] Due to the worst-case CVaR constraint (28), the capacity configuration optimization problem (40) cannot be solved directly. With the help of the CVaR characteristic, equation (28) is transformed into the worst-case expectation constraint and then transformed into a linear constraint through the approximate function expression (16). It can be equivalently converted by the following formula .

[0086] (29)

[0087] Will and Interchange, we get: (30)

[0088] Therefore, constraint (28) can be equivalently transformed into the following constraint on the expectation of the worst probability distribution: (31)

[0089] According to Approximate estimate, which is approximated by the maximum value of some affine function, so for the approximate function have the same form, namely

[0090] (32)

[0091] in, and are all constants.

[0092] (33)

[0093] is the maximum of some affine function.

[0094] For a constant matrix M and vector m , you can Written . The expectation of the worst probability distribution The values ​​are: (34)

[0095] (35)

[0096] (36)

[0097] (37)

[0098] (38)

[0099] (39)

[0100] in, yes Guidelines, is the first decision variable, is the second decision variable, n is the number of days of historical data, k Subscripting the running time t The function of is the third decision variable, Powering new energy The subscript set of the sampling set , is the set in formula (14) W The subscript set of .

[0101] As a preferred embodiment, determining the planned capacity and the investment cost of energy storage and transmission lines obtained by the transmission-energy storage capacity optimization configuration model based on photovoltaic power generation includes: obtaining the unit capacity cost of the transmission-energy storage capacity; the unit capacity cost of the transmission-energy storage capacity includes the unit capacity cost of the energy storage power capacity, the unit capacity cost of the energy storage energy capacity and the unit capacity cost of the transmission line capacity; determining the objective function of the transmission-energy storage capacity optimization configuration model according to the unit capacity cost of the transmission-energy storage capacity and the transmission-energy storage capacity; determining the planned capacity and the investment cost of energy storage and transmission lines according to the objective function of the transmission-energy storage capacity optimization configuration model, the value range of the transmission-energy storage capacity and the blue-robust constraint of the new energy curtailment distribution.

[0102] In this embodiment, the transmission-storage capacity optimization configuration model for renewable energy power generation is: (40)

[0103] (41)

[0104] (42)

[0105] in, is the unit capacity cost of energy storage power capacity, is the unit capacity cost of energy storage capacity, is the unit capacity cost of the transmission line capacity.

[0106] The objective function (40) minimizes the total investment cost of energy storage and transmission lines; Equation (41) is the non-negative capacity constraint; and Equation (42) is the robust power restriction constraint.

[0107] According to equations (31)-(39), the linear programming of the capacity problem (40) can be expressed as follows: (43)

[0108] in, is a T-dimensional all-one vector, and the value ranges of n and k are consistent with those in equations (34)-(39).

[0109] Equation (43) is an approximation of Equation (40). The optimal scale strategy provided by Equation (43) ensures that the power curtailment rate is still below the threshold even in the worst case of PV power generation distribution. The probability is greater than .

[0110] The following is a description of the power transmission-energy storage capacity optimization device of the power system provided by the present invention. The power transmission-energy storage capacity optimization device of the power system described below and the power transmission-energy storage capacity optimization method of the power system described above can be referred to each other.

[0111] Please refer to Figure 3 , Figure 3 A schematic structural diagram of a device for optimizing the transmission-energy storage capacity of an electric power system provided by the present invention.

[0112] The present invention also provides an optimization device for the transmission-energy storage capacity of an electric power system, wherein the electric power system at least uses renewable energy for power generation; the device comprises: a first module 301, which is used to determine a new energy abandonment amount function obtained by a real-time operation optimization model based on renewable energy power generation; the real-time operation optimization model takes minimizing the new energy abandonment amount as an objective function; the new energy abandonment amount function is a function with transmission-energy storage capacity and renewable energy power generation as variables; a second module 302, which is used to determine a renewable energy abandonment distribution blue-robust constraint based on the new energy abandonment amount function and a preset renewable energy power generation probability distribution set; the preset renewable energy power generation probability distribution set includes multiple renewable energy power generation probability distribution values; the renewable energy abandonment distribution blue-robust constraint is used to limit the renewable energy abandonment amount when the renewable energy power generation probability distribution value is lower than the preset probability value; a third module 303, which is used to determine the planned capacity and the investment cost of energy storage and transmission lines obtained by a transmission-energy storage capacity optimization configuration model based on renewable energy power generation; the transmission-energy storage capacity optimization configuration model takes minimizing the investment cost as an objective function, and takes the value range of the transmission-energy storage capacity and the renewable energy abandonment distribution blue-robust constraint as constraints.

[0113] Figure 4 An example of a structural diagram of an electronic device is shown in FIG. Figure 4As shown, the electronic device may include: a processor (processor) 401, a communication interface (Communications Interface) 402, a memory (memory) 403 and a communication bus 404, wherein the processor 401, the communication interface 402, and the memory 403 communicate with each other through the communication bus 404. The processor 401 can call the logic instructions in the memory 403 to execute the optimization method of the transmission-energy storage capacity of the power system, and the power system adopts at least renewable energy power generation; the method includes: determining the renewable energy power abandonment function obtained by the real-time operation optimization model based on renewable energy power generation; the real-time operation optimization model takes minimizing the renewable energy power abandonment as the objective function; the renewable energy power abandonment function is a function with the transmission-energy storage capacity and the renewable energy power generation as variables; determining the renewable energy power abandonment distribution blue-robust constraint according to the renewable energy power abandonment function and the preset renewable energy power generation probability distribution set; the preset renewable energy power generation probability distribution set includes multiple renewable energy power generation probability distribution values; the renewable energy power abandonment distribution blue-robust constraint is used to limit the renewable energy power abandonment when the renewable energy power generation probability distribution value is lower than the preset probability value; determining the planned capacity and the investment cost of energy storage and transmission lines obtained by the transmission-energy storage capacity optimization configuration model based on renewable energy power generation; the transmission-energy storage capacity optimization configuration model takes minimizing the investment cost as the objective function, and takes the value range of the transmission-energy storage capacity and the renewable energy power abandonment distribution blue-robust constraint as the constraint conditions.

[0114] In addition, the logic instructions in the above-mentioned memory 403 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0115] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the optimization method of the transmission-energy storage capacity of the power system provided by the above-mentioned methods, and the power system at least adopts renewable energy power generation; the method includes: determining a new energy abandonment function obtained by a real-time operation optimization model based on renewable energy power generation; the real-time operation optimization model takes minimizing the new energy abandonment as the objective function; the new energy abandonment function is a function with transmission-energy storage capacity and renewable energy power generation as variables; determining a renewable energy abandonment distribution blue-robust constraint according to the new energy abandonment function and a preset renewable energy power generation probability distribution set; the preset renewable energy power generation probability distribution set includes multiple renewable energy power generation probability distribution values; the renewable energy abandonment distribution blue-robust constraint is used to limit the renewable energy abandonment when the renewable energy power generation probability distribution value is lower than the preset probability value; determining the planned capacity and the investment cost of energy storage and transmission lines obtained by the transmission-energy storage capacity optimization configuration model based on renewable energy power generation; the transmission-energy storage capacity optimization configuration model takes minimizing the investment cost as the objective function, and takes the value range of the transmission-energy storage capacity and the renewable energy abandonment distribution blue-robust constraint as constraints.

[0116] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the transmission-energy storage capacity optimization method of the power system provided by the above-mentioned methods, wherein the power system at least uses renewable energy for power generation; the method comprises: determining a new energy abandonment amount function obtained by a real-time operation optimization model based on renewable energy power generation; the real-time operation optimization model takes minimizing the new energy abandonment amount as the objective function; the new energy abandonment amount function is a function with transmission-energy storage capacity and renewable energy power generation as variables; determining a renewable energy abandonment distribution blue-robust constraint according to the new energy abandonment amount function and a preset renewable energy power generation probability distribution set; the preset renewable energy power generation probability distribution set includes multiple renewable energy power generation probability distribution values; the renewable energy abandonment distribution blue-robust constraint is used to limit the renewable energy abandonment amount when the renewable energy power generation probability distribution value is lower than the preset probability value; determining the planned capacity and the investment cost of energy storage and transmission lines obtained by the transmission-energy storage capacity optimization configuration model based on renewable energy power generation; the transmission-energy storage capacity optimization configuration model takes minimizing the investment cost as the objective function, and takes the value range of the transmission-energy storage capacity and the renewable energy abandonment distribution blue-robust constraint as the constraint conditions.

[0117] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0118] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the transmission-storage capacity of a power system, characterized in that: The power system at least uses new energy to generate electricity; the method comprises: Determine a new energy curtailment function obtained by a real-time operation optimization model based on new energy power generation; the real-time operation optimization model takes minimizing the new energy curtailment as an objective function; the new energy curtailment function is a function with transmission-energy storage capacity and new energy power generation as variables; Determine the renewable energy abandonment distribution blue-robust constraint according to the renewable energy abandonment quantity function and the preset renewable energy power generation probability distribution set; the preset renewable energy power generation probability distribution set includes a plurality of renewable energy power generation probability distribution values; the renewable energy abandonment distribution blue-robust constraint is used to limit the renewable energy abandonment quantity when the renewable energy power generation probability distribution value is lower than the preset probability value; Determine the planned capacity and the investment cost of energy storage and transmission lines obtained by the transmission-energy storage capacity optimization configuration model based on renewable energy power generation; the transmission-energy storage capacity optimization configuration model takes minimizing the investment cost as the objective function and takes the value range of the transmission-energy storage capacity and the renewable energy power abandonment distribution rigid constraint as the constraint conditions.

2. The method for optimizing the transmission-storage capacity of a power system according to claim 1, characterized in that: The step of determining the new energy curtailment function obtained by the real-time operation optimization model based on new energy power generation includes: Obtaining time parameters of the power system, power from power generation to energy storage, power from power generation to power transmission, power from energy storage to power transmission, the power generation of the new energy, the power abandoned by the new energy, the power transmission-energy storage capacity and the energy storage coefficient; the power transmission-energy storage capacity includes energy storage power capacity, energy storage energy capacity and transmission line capacity; Determining the objective function of the real-time operation model according to the time parameter and the abandoned power of the new energy; Determine a first constraint condition of the real-time operation model according to the power from power generation to energy storage, the power from power generation to power transmission, the power from energy storage to power transmission and the power of abandoned new energy; the first constraint condition is used to characterize the power flow direction and the non-negativity of the variable; Determine a second constraint of the real-time operation model according to the power from power generation to energy storage, the power from power generation to power transmission, the power generation of the new energy source and the power abandoned by the new energy source; the second constraint is used to characterize the power balance on the power generation side; Determining a third constraint condition of the real-time operation model according to the power from power generation to power transmission, the power from energy storage to power transmission, and the transmission line capacity; the third constraint condition is used to characterize the transmission line capacity limitation; Determine a fourth constraint of the real-time operation model according to the time parameter, the power from power generation to energy storage, the power from energy storage to power transmission, the energy storage power capacity, the energy storage energy capacity and the energy storage coefficient; the fourth constraint is used to characterize the energy stored in the energy storage; The new energy curtailment amount function is determined according to the objective function of the real-time operation model, the first constraint condition, the second constraint condition, the third constraint condition and the fourth constraint condition.

3. The method for optimizing the transmission-storage capacity of the power system according to claim 2, characterized in that: After the determination of the new energy abandoned power function obtained by the real-time operation optimization model based on new energy power generation, the method further includes: The renewable energy power abandonment function is converted into a linear programming function by using a dual algorithm of linear programming; An approximate estimation algorithm is used to perform an approximate estimation on the linear programming function.

4. The method for optimizing the transmission-storage capacity of a power system according to claim 2, characterized in that: The step of determining the renewable energy abandonment distribution constraint according to the renewable energy abandonment quantity function and a preset renewable energy power generation probability distribution set includes: Obtain historical renewable energy generation probability distribution and power abandonment parameters; Determining the renewable energy generation probability distribution set based on the Wasserstein distance, the historical renewable energy generation probability distribution and the power abandonment parameter; Determining acceptable constraints on renewable energy power abandonment according to the renewable energy power abandonment function, the time parameter, the renewable energy power generation and the power abandonment parameter; The renewable energy power abandonment distribution strict constraint is determined according to the renewable energy power abandonment amount function, the power abandonment parameter and the acceptable constraint of the renewable energy power abandonment.

5. The method for optimizing the transmission-storage capacity of a power system according to claim 2, characterized in that: After determining the renewable energy abandonment distribution constraint according to the renewable energy abandonment amount function and the preset renewable energy generation probability distribution set, the method further includes: The VaR algorithm is used to convert the renewable energy curtailment distribution rigid constraint into a VaR constraint; Using a CVaR algorithm to convert the VaR constraint into a CVaR constraint; A linear reconstruction algorithm is used to linearly reconstruct the CVaR constraint.

6. The method for optimizing the transmission-storage capacity of a power system according to any one of claims 2 to 5, characterized in that: The determination of the planned capacity and the investment cost of energy storage and transmission lines obtained by the transmission-energy storage capacity optimization configuration model based on renewable energy generation includes: Obtaining the unit capacity cost of the power transmission-energy storage capacity; the unit capacity cost of the power transmission-energy storage capacity includes the unit capacity cost of the energy storage power capacity, the unit capacity cost of the energy storage energy capacity and the unit capacity cost of the transmission line capacity; Determining the objective function of the power transmission-energy storage capacity optimization configuration model according to the unit capacity cost of the power transmission-energy storage capacity and the power transmission-energy storage capacity; The planned capacity and the investment costs of the energy storage and transmission lines are determined based on the objective function of the transmission-energy storage capacity optimization configuration model, the value range of the transmission-energy storage capacity and the rigid constraints on the new energy power abandonment distribution.

7. A device for optimizing the transmission-storage capacity of a power system, characterized in that: The power system at least uses new energy to generate electricity; the device includes: The first module is used to determine a new energy curtailment function obtained by a real-time operation optimization model based on new energy power generation; the real-time operation optimization model takes minimizing the new energy curtailment as an objective function; the new energy curtailment function is a function with transmission-energy storage capacity and new energy power generation as variables; The second module is used to determine the renewable energy abandonment distribution blue-robust constraint according to the renewable energy abandonment quantity function and the preset renewable energy power generation probability distribution set; the preset renewable energy power generation probability distribution set includes a plurality of renewable energy power generation probability distribution values; the renewable energy abandonment distribution blue-robust constraint is used to limit the renewable energy abandonment quantity when the renewable energy power generation probability distribution value is lower than the preset probability value; The third module is used to determine the planned capacity and the investment cost of energy storage and transmission lines obtained by the transmission-energy storage capacity optimization configuration model based on renewable energy power generation; the transmission-energy storage capacity optimization configuration model takes minimizing the investment cost as the objective function, and takes the value range of the transmission-energy storage capacity and the renewable energy power abandonment distribution rigid constraint as the constraint conditions.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for optimizing the transmission-storage capacity of the power system according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing the transmission-storage capacity of the power system according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for optimizing the transmission-storage capacity of the power system according to any one of claims 1 to 6 is implemented.

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