Capacity configuration method and system considering delivery curve optimization, and equipment medium

Abstract

The invention discloses a capacity configuration method and system considering delivery curve optimization, and an equipment medium. The method comprises the following steps: S1, establishing a delivery channel curve model; s2, adopting opportunity constraint to convert the standby capacity constraint of the delivery system into standby capacity probabilistic constraint; adopting distributed robust optimization to convert the standby capacity probabilistic constraint into a deterministic constraint; s3, taking the minimization of the total cost of the delivery system as a target, and establishing a delivery system capacity configuration model; and S4, substituting the delivery channel curve model and the reserve capacity determinacy constraint into the delivery system capacity configuration model to obtain a power curve of the delivery channel and the optimal energy storage capacity of the delivery end and the receiving end. More electric power can be provided, new energy resources at the sending end are reasonably utilized, the wind and light abandoning phenomenon is reduced, and the economical efficiency of the system is improved.

Description

Technical Field

[0001] The present invention belongs to the field of DC channel power transmission, and relates to a capacity configuration method, system and equipment medium considering the optimization of transmission curve. Background Art

[0002] Large-scale development and utilization of new energy sources such as wind and solar can reduce carbon emissions and help alleviate climate change. my country's solar and wind energy resources are mainly concentrated in the northwest region. Building new energy bases such as wind and solar in the northwest region can not only achieve green energy transformation and upgrading, but also protect the environment, which has multiple meanings. However, the northwest region is sparsely populated and the electricity demand is far lower than that of the economically developed eastern regions. Therefore, external transmission has become the main way to absorb new energy. At present, large-scale new energy bases mainly use ultra-high voltage direct current to deliver clean electricity to the load center. In order to alleviate the impact of the randomness and volatility of new energy output on the external transmission system, the use of new energy and conventional power bundled transmission has become an effective way to ensure system reliability and absorb new energy.

[0003] At present, the research on energy storage configuration is not comprehensive enough. Most studies only consider the configuration of energy storage and new energy in the same area, that is, only configure energy storage at the sending end. In fact, configuring energy storage at the receiving end of the transmission system can improve the flexibility and peak capacity of transmission. However, the synergistic effect of configuring energy storage at the receiving end cannot be ignored. If too much energy storage is configured at the receiving end, the economy of the system will be reduced. If there is too little energy storage, it will not be able to meet the load demand at the receiving end when the transmission channel is limited. In addition, current research rarely involves the optimization of the transmission curve. Generally, a constant power transmission mode is adopted, which cannot adjust the channel transmission power in time according to load changes. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a capacity configuration method, system and equipment medium that considers the optimization of the transmission curve, which can provide more electricity, rationally utilize the new energy resources at the sending end, reduce the phenomenon of wind and solar power abandonment, and improve the economy of the system.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A capacity configuration method considering delivery curve optimization includes the following processes:

[0007] S1, establish the delivery channel curve model;

[0008] S2, using chance constraints to convert the backup capacity constraints of the transmission system into backup capacity probabilistic constraints; using distributed robust optimization to convert backup capacity probabilistic constraints into deterministic constraints;

[0009] S3, with the goal of minimizing the total cost of the delivery system, establish a delivery system capacity configuration model;

[0010] S4, the transmission channel curve model and the deterministic constraints of the reserve capacity are introduced into the transmission system capacity configuration model to obtain the power curve of the transmission channel and the optimal capacity of the energy storage at both ends of the transmission and the receiving end.

[0011] Preferably, in S1, the transmission channel curve model is established by using the upper and lower limit constraints of the DC transmission channel transmission power, the step-by-step transmission power constraint of the DC transmission channel, the number of DC transmission channel adjustments throughout the day, the minimum duration constraint of the DC transmission channel stable power, the annual utilization hours constraint of the DC transmission channel, the minimum input power constraint during the peak period of the receiving end load, and the seasonal distribution constraint of the input power.

[0012] Preferably, in S2, the process of converting the spare capacity constraint of the outbound transmission system into a probabilistic spare capacity constraint by adopting the chance constraint is as follows:

[0013] The sending end system in the external transmission system reserves a part of thermal power reserve capacity to suppress the volatility of renewable energy output, and obtains the relationship between reserve capacity and output;

[0014] Due to the uncertainty of renewable energy, its output may be 0 in some extreme cases. The sending-end system reserves sufficient spare capacity to make up for the power shortage and converts the relationship between spare capacity and output into a probabilistic constraint.

[0015] Furthermore, the specific process of converting the probabilistic constraint of spare capacity into a deterministic constraint by using distributed robust optimization is as follows:

[0016] Decision variables and random variables are used to represent probabilistic constraints. By adjusting the distance tolerance to change the range of the actual probability distribution density function, the probabilistic constraints become the probability of the probability density function being satisfied, that is, the satisfaction function of the probability density function in the worst case.

[0017] Substitute the KL divergence into the satisfaction function of the worst-case probability density function and convert the satisfaction function into a deterministic constraint.

[0018] Preferably, in S3, installed capacity related constraints, power balance constraints, new energy unit constraints, thermal power unit constraints and energy storage unit constraints at both ends of transmission and reception are used to establish a capacity configuration model of the transmission system.

[0019] Preferably, in S3, the total cost of the delivery system includes investment cost, maintenance cost and operation cost;

[0020] The investment cost includes the investment costs of wind power, photovoltaic power, thermal power and energy storage at the sending end, the transmission channel and the energy storage at the receiving end; the maintenance cost is related to the investment cost; the system operating cost is the sum of the daily operating costs of the transmission system, including the fuel cost of the thermal power units at the sending end, start and shutdown costs, standby costs, energy storage operating costs, penalties for wind and solar power abandonment, transmission channel operating costs, receiving end energy storage operating costs and penalties for insufficient electricity.

[0021] A capacity configuration system considering delivery curve optimization, comprising:

[0022] An external delivery channel curve model building module is used to build an external delivery channel curve model;

[0023] The spare capacity constraint module is used to convert the spare capacity constraint of the transmission system into a probabilistic spare capacity constraint by adopting chance constraint; and convert the probabilistic spare capacity constraint into a deterministic constraint by adopting distributed robust optimization;

[0024] The module for establishing the capacity configuration model of the delivery system is used to establish the capacity configuration model of the delivery system with the goal of minimizing the total cost of the delivery system;

[0025] The optimal capacity acquisition module is used to bring the transmission channel curve model and the deterministic constraints of the reserve capacity into the transmission system capacity configuration model to obtain the power curve of the transmission channel and the optimal capacity of the energy storage at both ends of the transmission and the receiving end.

[0026] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the capacity configuration method considering the optimization of the delivery curve when executing the computer program.

[0027] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the capacity configuration method considering the optimization of a delivery curve.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The capacity configuration method, system and device medium considering the optimization of the transmission curve of the present invention first establish a transmission channel curve model including upper and lower limits of channel transmission power, channel transmission power stepping, channel adjustment times throughout the day and other constraints according to the characteristics of the DC transmission channel, so as to optimize the transmission curve; then, by using the opportunity constraint, the spare capacity constraint of the wind, solar, thermal and storage transmission system is converted into a probabilistic constraint of the spare capacity, and the spare capacity probabilistic constraint is processed by distributed blue stick opportunity constraint, so as to convert the said probabilistic constraint of the spare capacity into a deterministic constraint; finally, with the minimum total cost of the transmission system as the goal, considering the constraints such as the transmission channel and the spare capacity, a transmission system capacity configuration model is established, and the model is solved to obtain the optimal capacity of the energy storage at the sending end and the energy storage at the receiving end and the power curve of the transmission channel, which can not only improve the flexibility and peak capacity of the transmission system, but also configure energy storage of appropriate capacity to provide more electricity during the peak load period of the receiving end power grid; optimizing the power curve of the transmission channel can reasonably utilize the new energy resources at the sending end, reduce the phenomenon of wind and solar abandonment, and improve the economy of the system, and the results can provide a reference for relevant energy authorities to evaluate the planning, construction and operation of the channel. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A flow chart of the capacity configuration method considering the optimization of the delivery curve proposed by the present invention;

[0031] Figure 2 Schematic diagram of the delivery system of the present invention. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0033] It should be noted that the words "front", "rear", "left", "right", "up" and "down" used in the following description refer to directions in the drawings, and the words "inside" and "outside" refer to directions toward or away from the geometric center of a specific component, respectively.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0035] See also Figure 1 , Figure 1 The capacity configuration method of the transmission system proposed in the present invention takes into account the optimization of energy storage and channel curves at both ends of transmission and reception. First, the predicted output of wind power and photovoltaic power, the parameters of thermal power and energy storage units, and the relevant parameters of the transmission curve are input. Then, the uncertainty of the output of new energy is processed by chance constraints, and the distributed robust chance constraints are used to convert it into deterministic constraints. Finally, a capacity and curve optimization model of the wind, solar, thermal and storage transmission system is established, and the model is solved to obtain the optimal capacity and transmission curve of wind, solar, thermal and storage.

[0036] The schematic diagram of the delivery system of the present invention is as follows Figure 2 As shown, it consists of a sending power source, an external transmission channel, and a receiving energy storage. The sending power source consists of four types of power sources: wind, solar, thermal, and energy storage. When the external transmission power source at the sending end cannot meet the needs of the receiving end power grid, it is necessary to purchase electricity from the sending end power grid, and then merge it into the DC external transmission channel and send it to the receiving end power grid together with the supporting energy storage at the receiving end to meet the load demand at the receiving end.

[0037] Step 1: Establish the delivery channel curve model.

[0038] Conventional DC transmission lines are generally not suitable for frequent adjustment of operating modes during the day. The power transmission of DC lines is mostly in 3 to 5 steps. Its main characteristic indicators include upper and lower power limits, power adjustment times, minimum duration of stable power, and annual channel utilization hours. The receiving-end power grid mainly focuses on the power and electricity support capacity of the transmission channel. The main characteristic indicators of its injected power include the minimum input power during peak load periods and the seasonal distribution of the received power. The specific constraints are as follows:

[0039] (1) DC transmission channel transmission power

[0040]

[0041] Where: P t L is the transmission power of the DC transmission channel in time period t, and They are respectively the lower and upper limits of the DC transmission channel power.

[0042] (2) Step-by-step transmission power of DC transmission channels

[0043] )

[0044] Where: It is a 0-1 variable. When the DC line power does not change from time t-1 to time t, its value is 0. Otherwise, its value is 1.

[0045] (3) Number of times the DC transmission channel is adjusted throughout the day

[0046]

[0047] Where: T d is the total number of time periods in a day, It is the maximum number of power adjustments of the DC transmission channel in one day.

[0048] (4) Minimum duration of stable power of DC transmission channel

[0049]

[0050] Where: It is the minimum time that the power of the DC transmission channel does not adjust. This constraint achieves stable power output of the DC line by limiting the number of power adjustments in a day.

[0051] (5) Annual utilization hours of DC transmission channels

[0052]

[0053] Where: and are the minimum and maximum annual utilization hours of the DC transmission channel, T y is the total number of time periods in a year. This constraint ensures the utilization rate of the transmission channel by limiting the annual utilization hours of the line.

[0054] (6) Minimum receiving power during peak load period

[0055]

[0056] Where: P rec,t The sum of the DC transmission channel power and the receiving end supporting energy storage power is the minimum input power requirement of the receiving power grid during peak hours, T peak It is the peak period of the receiving power grid.

[0057] (7) Seasonal distribution of power input

[0058]

[0059] Where: The four seasons are spring, summer, autumn and winter. They are the electricity consumption coefficients of the receiving power grid in the four seasons. This constraint achieves a reasonable distribution of electricity by limiting the electricity consumption in the four seasons.

[0060] The transmission channel curve model is established by using the upper and lower limit constraints of the DC transmission channel transmission power, the step-by-step constraints of the DC transmission channel transmission power, the constraints on the number of DC transmission channel adjustments throughout the day, the minimum duration of stable power of the DC transmission channel, the annual utilization hours of the DC transmission channel, the minimum incoming power constraints during the peak load period of the receiving end, and the seasonal distribution constraints of the incoming power.

[0061] Step 2: Use opportunity constraints to deal with the uncertainty of renewable energy output.

[0062] The sending end system in the external transmission system needs to reserve a part of spare capacity to suppress the volatility of renewable energy output. The relationship between spare capacity and output is shown in equations (8)-(9):

[0063] P g,i,t +R g,i,t ≤y g,i,t P g,i,max (8)

[0064] P s,e,q,d,t +R s,e,q,t ≤P e,max (9)

[0065] In the formula, R g,i,t , R s,e,q,t are the reserve capacities of thermal power unit g and energy storage power station q at the sending end at time t, P g,i,t is the output of the i-th thermal power unit at time t, y g,i,t is the start / stop state of thermal power unit i at time t. When its value is 1, it means that the unit is on, otherwise it is off. g,i,max and P e,max is the maximum output of thermal power unit g and sending-end energy storage power station q.

[0066] Due to the uncertainty of renewable energy, its output may be 0 in some extreme cases. The sending-end system needs to reserve sufficient spare capacity to make up for the power shortage. Therefore, the above equation can be converted into the probabilistic constraint shown in equation (10).

[0067]

[0068] Where: P r {·} is the probability that the constraint is established; E(P co,t ) is the expected value of the combined output of wind power and photovoltaic power at time t; α is the set confidence level.

[0069] Normal distribution is usually used to describe the wind and solar power output prediction error. However, in practical applications, it is difficult to obtain accurate probability distribution coefficients. The robust optimization combines the advantages of robust optimization and stochastic optimization to find the worst probability distribution of uncertain parameters, which can take into account both economy and conservatism. The KL divergence is the distance between the actual probability distribution f and the empirical probability distribution f0, which can be described by formula (11). By adjusting the KL divergence value to obtain the range of the actual probability density function, the model has a certain degree of conservatism, making the results more reliable.

[0070]

[0071] Where: D KL Represents the KL divergence between the actual probability distribution density function f and the empirical probability distribution density function f0.

[0072] Furthermore, the probability distribution fuzzy set based on KL divergence can be:

[0073]

[0074] Where: d KL is the distance tolerance, which ranges from [0,∞] and reflects the degree of confidence of the decision maker in the reference distribution; d KL The larger the value, the more distribution functions are included in the fuzzy set D, and the larger the search range is, which means that the decision maker has a lower trust in the reference distribution and a stronger attitude to avoid distribution risks. On the contrary, when d KL The smaller it is, the smaller the KL distance between the actual distribution f and the reference distribution f0 is, and the fewer distribution functions are included in the fuzzy set D, indicating that the decision maker has a higher degree of trust in the reference distribution.

[0075] The probabilistic constraint expressed in equation (10) is expressed in equation (13).

[0076] Pr[C(x,ξ)≤0]≥1-α (13)

[0077] Where x is the decision variable and ξ is the random variable.

[0078] By adjusting d KL The parameters can change the range of the actual probability distribution density function. The probability of the chance constraint being true becomes the probability of the probability density function being true. That is, the probability density function in the worst case must also satisfy formula (14):

[0079]

[0080] Based on KL divergence, the distributed robust chance constraint expressed in formula (14) can be equivalently transformed into a traditional deterministic constraint, expressed by formula (15):

[0081]

[0082] Where: Pr 0 Represents the probability of occurrence in the empirical probability distribution function; α 1+ is the reliability correction value, which is related to d KL related.

[0083] Step three: Establish a capacity configuration model for the wind, solar, thermal and energy storage transmission system.

[0084] The capacity configuration model of the transmission system takes the minimum total system cost as the objective function, including the investment cost C sys, Maintenance cost C maint and operating cost C op As shown in formula (17), the investment cost includes the investment cost of wind power, photovoltaic power, thermal power and energy storage at the sending end, the investment cost of the transmission channel and the energy storage at the receiving end; the maintenance cost is related to the investment cost, as shown in formula (18); the system operation cost is the sum of the daily operation cost, which can be calculated by formula (20), including the fuel cost of the thermal power unit at the sending end Start-Stop Cost and Backup cost Energy storage operating costs Punishment for abandoning wind and light and Delivery channel operation cost Receiving end energy storage operating cost and low battery penalty As shown in formulas (21)-(30) respectively.

[0085] minC=C sys +C maint +C op (16)

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100] Where: are the investment and construction costs of a single unit of wind power, photovoltaic power, thermal power, energy storage and energy storage at the receiving end, respectively. w 、N pv 、N g 、N s,e 、N r,e are the number of wind power, photovoltaic, thermal power, energy storage and energy storage units at the receiving end, respectively. d is the discount rate; Y is the planning period; is the investment cost per unit capacity of the outbound channel; is the maintenance factor of generator g; r maint is the growth rate of maintenance cost; It is the daily operating cost of the system. is the fuel cost of thermal power units; are the start-up and shutdown costs of thermal power units; are the startup and shutdown costs of thermal power unit i at time t respectively; P is the thermal power reserve cost. s,e,q,d,t , P s,e,q,c,t are the discharge and charging power of the energy storage station q at the sending end at time t, P r,e,q,d,t , P r,e,q,c,t are the discharge and charging power of the receiving end energy storage station q at time t, are the operating costs per unit power of energy storage at the sending end and the receiving end respectively. g,i,t is the output of the i-th thermal power unit at time t; a i 、b i 、c i are the characteristic parameters of the i-th thermal power unit respectively; They are the penalty costs for wind power abandonment, solar power abandonment and insufficient electricity; is the unit reserve capacity price at time t; They are the penalty coefficients for wind power abandonment, solar power abandonment and insufficient electricity; is the operating cost per unit power of the transmission channel. P L,r,t are the predicted demand and actual demand of the receiving power grid at time t; R g,i,t is the spare capacity of the i-th thermal power unit at time t.

[0101] Installed capacity related constraints

[0102] Formulas (31)-(33) represent the upper and lower limits of the planned capacity of new energy. When the output of new energy is 0, the thermal power units and energy storage at the sending end must meet the requirements of the transmission agreement, as shown in formula (34). In order to cope with the randomness of the output of new energy, the regulation capacity of the energy storage at the sending end must be greater than the variation of new energy, as shown in formula (35). At the same time, when the energy storage cannot be discharged, the compensation capacity of the thermal power units must be greater than the installed capacity of the new energy to meet the transmission demand, as shown in formula (36).

[0103] c w,min ≤N w c w ≤c w,max (31)

[0104] c pv,min ≤N pv c pv ≤c pv,max (32)

[0105] c new,min ≤N w c w +N pv c pv ≤c new,max (33)

[0106] N g c g +N e c e ≥P L (34)

[0107] γ e N e c e ≥γ w N w c w +γ pv N pv c pv (35)

[0108] (1-ρ)N g c g ≥N w c w +N pv c pv (36)

[0109] Where: c w,min 、c w,max are the lower and upper limits of wind power installed capacity respectively; c pv,min 、c pv,max are the lower and upper limits of photovoltaic installed capacity respectively; c new,min 、c new,max are the lower and upper limits of the planned new energy capacity respectively; cg 、c e 、c w 、c pv , are the capacities of thermal power, energy storage at the sending end, wind power and photovoltaic power generation units respectively; ρ is the minimum output rate of thermal power; γ w and γ pv are the output change rates of wind power and photovoltaic units respectively; γ e is the regulation rate of energy storage at the sending end.

[0110] Power balance constraints

[0111] The sum of the power injected by the sending-end supporting power source and the sending-end power grid is equal to the transmission power of the external transmission channel, as shown in formula (37); the transmission power of the external transmission channel and the energy storage power of the receiving-end power grid are equal to the power required by the receiving-end power grid, as shown in formula (38).

[0112]

[0113]

[0114]

[0115] Where: P co,t It is the combined output of wind power and photovoltaic units at time t.

[0116] Constraints on new energy units

[0117] Equations (40)-(41) represent the upper and lower limit constraints of wind power and photovoltaic output.

[0118]

[0119]

[0120] Formula (42) represents the requirement for the proportion of new energy in the transmission channel.

[0121]

[0122] Where: P w,m,t are the predicted output and actual output of the mth wind turbine at time t respectively; P pv,n,t They are the predicted output and actual output of the nth photovoltaic unit at time t respectively.

[0123] Thermal power unit constraints

[0124] Formula (43) represents the upper and lower limit constraints of the output of thermal power units.

[0125] y g,i,t P g,min ≤P g,i,t ≤yg,i,t P g,max (43)

[0126] Where: P g,min , P g,max are the lower and upper limits of the output of thermal power units respectively; y g,i,t It is the start / stop status of thermal power unit i at time t. When its value is 1, it means that the unit is on, otherwise it is off.

[0127] Equations (44) and (45) represent the start-up and shutdown cost constraints of thermal power units.

[0128]

[0129]

[0130] In the formula, They are the costs of starting and stopping a thermal power unit once.

[0131] Formula (46) represents the ramp constraint of the thermal power unit.

[0132] -R g ≤P g,i,t -P g,i,t-1 ≤R g (46)

[0133] In the formula, R g is the maximum ramp rate of the thermal power unit.

[0134] Constraints of energy storage units at both ends

[0135] Equations (47)-(50) represent the upper and lower power constraints during energy storage charging and discharging.

[0136] y s,e,q,c,t P s,e,min ≤P s,e,q,c,t ≤y s,e,q,c,t P s,e,max (47)

[0137] y s,e,q,d,t P s,e,min ≤P s,e,q,d,t ≤y s,e,q,d,t P s,e,max (48)

[0138] y r,e,q,c,t P e,min ≤P r,e,q,c,t ≤y r,e,q,c,t P e,max (49)

[0139] y r,e,q,d,t P e,min ≤P r,e,q,d,t ≤yr,e,q,d,t P e,max (50)

[0140] Where: y s,e,q,c,t ,y r,e,q,c,t are the charging states of the pumped storage power station q at the sending end and the battery energy storage power station q at the receiving end at time t, and the value of 1 indicates that the energy storage power station is charging; s,e,q,d,t ,y r,e,q,d,t are the discharge states of the pumped storage power station q at the sending end and the battery energy storage power station q at the receiving end at time t, and a value of 1 indicates that the energy storage power station is discharging; P e,min , P e,max are the lower and upper limits of the charging and discharging power of the receiving battery energy storage station, respectively. s,e,min , P s,e,max They are respectively the lower and upper limits of the charging and discharging power of the pumped storage power station at the sending end.

[0141] For the receiving battery energy storage, its charge state S OC,r,q,t The calculation formula is

[0142]

[0143] Where: η r,e,q,c , η r,e,q,d are the charging and discharging efficiency of the receiving end energy storage power station q; c s,e,q is the rated capacity of the receiving-end energy storage power station q; Δt is the time interval.

[0144] Formula (52) represents the upper and lower limit constraints of the terminal battery energy storage SOC:

[0145] S OC,q,min ≤S OC,r,q,t ≤S OC,q,max (52)

[0146] Where: S OC,r,q,t are the charge states of the receiving battery energy storage station q at time t; S OC,q,min , S OC,q,max They are respectively the lower limit and the upper limit of the state of charge of the receiving battery energy storage power station q.

[0147] The charging state of the receiving end energy storage power station should be the same at the beginning and end of the dispatching cycle:

[0148] S OC，r,q,1 =S OC，r,q,T (53)

[0149] For the pumped storage power station at the sending end, there is a storage capacity constraint:

[0150] E s,u,q,min ≤E s,u,q,t ≤E s,u,q,max (54)

[0151] E s,d,q,min ≤E s,d,q,t ≤E s,d,q,max (55)

[0152] Where: E s,u,q,t 、E s,d,q,t are the water storage capacity of the upper reservoir and the lower reservoir of the pumped storage power station q during period t; E s,u,q,max 、E s,u,q,min are the upper and lower limits of the reservoir capacity of the pumped storage power station q; E s,d,q,max 、E s,d,q,min They are the upper and lower limits of the reservoir capacity of the pumped storage power station q respectively.

[0153] The water storage capacity of the reservoir at the next moment is the difference between the water storage capacity and the water consumption for pumping power generation at the previous moment, so we have:

[0154]

[0155]

[0156] Where η s,e,q,c , η s,e,q,d are the charging and discharging efficiencies of the energy storage power station q at the sending end;

[0157] Energy storage power stations cannot charge and discharge at the same time:

[0158] y s,e,q,c,t +y s,e,q,d,t ≤1 (58)

[0159] y r,e,q,c,t +y r,e,q,d,t ≤1 (59)

[0160] The following are device embodiments of the present invention, which can be used to perform method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0161] In yet another embodiment of the present invention, a capacity configuration system considering the optimization of an outbound delivery curve is provided. The capacity configuration system considering the optimization of an outbound delivery curve can be used to implement the above-mentioned capacity configuration method considering the optimization of an outbound delivery curve. Specifically, the capacity configuration system considering the optimization of an outbound delivery curve includes an outbound delivery channel curve model establishment module, a spare capacity constraint module, an outbound delivery system capacity configuration model establishment module, and an optimal capacity acquisition module.

[0162] Among them, the outbound channel curve model building module is used to build the outbound channel curve model.

[0163] The spare capacity constraint module is used to convert the spare capacity constraint of the transmission system into a probabilistic spare capacity constraint by adopting chance constraint; and to convert the probabilistic spare capacity constraint into a deterministic constraint by adopting distributed robust optimization.

[0164] The module for establishing the capacity configuration model of the delivery system is used to establish the capacity configuration model of the delivery system with the goal of minimizing the total cost of the delivery system.

[0165] The optimal capacity acquisition module is used to bring the transmission channel curve model and the deterministic constraints of the reserve capacity into the transmission system capacity configuration model to obtain the power curve of the transmission channel and the optimal capacity of the energy storage at both ends of the transmission and the receiving end.

[0166] In another embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGAs), or other processors. GateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the capacity configuration method considering the optimization of the transmission curve, including: S1, establishing a transmission channel curve model; S2, using opportunity constraints to convert the spare capacity constraints of the transmission system into probabilistic constraints of spare capacity; using distributed robust optimization to convert the probabilistic constraints of spare capacity into deterministic constraints; S3, with the goal of minimizing the total cost of the transmission system, establishing a transmission system capacity configuration model; S4, bringing the transmission channel curve model and the deterministic constraints of spare capacity into the transmission system capacity configuration model to obtain the power curve of the transmission channel and the optimal capacity of energy storage at both ends of the transmission and the receiving end.

[0167] In another embodiment, the present invention further provides a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory.

[0168] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the capacity configuration method considering the optimization of the transmission curve in the above-mentioned embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and the following steps are executed: S1, establishing a transmission channel curve model; S2, using chance constraints to convert the spare capacity constraints of the transmission system into probabilistic constraints of spare capacity; using distributed robust optimization to convert the probabilistic constraints of spare capacity into deterministic constraints; S3, establishing a capacity configuration model of the transmission system with the goal of minimizing the total cost of the transmission system; S4, bringing the transmission channel curve model and the deterministic constraints of spare capacity into the capacity configuration model of the transmission system to obtain the power curve of the transmission channel and the optimal capacity of energy storage at both ends of the transmission and the receiving end.

[0169] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0170] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0171] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0172] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0173] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0174] It should be understood that the above description is for illustration and not for limitation. Many embodiments and many applications beyond the examples provided will be apparent to those skilled in the art upon reading the above description. Therefore, the scope of the present teachings should not be determined with reference to the above description, but rather with reference to the foregoing claims and the full scope of equivalents to which such claims are entitled. For the purpose of comprehensiveness, all articles and references, including disclosures of patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the foregoing claims is not intended to be a waiver of such subject matter, nor should it be considered that the applicant has not considered such subject matter to be part of the disclosed inventive subject matter.

Claims

1. A capacity configuration method considering the optimization of the delivery curve, characterized in that: The process includes: S1, establish the delivery channel curve model; S2, using chance constraints to convert the backup capacity constraints of the transmission system into backup capacity probabilistic constraints; using distributed robust optimization to convert backup capacity probabilistic constraints into deterministic constraints; S3, with the goal of minimizing the total cost of the delivery system, establish a delivery system capacity configuration model; S4, the transmission channel curve model and the deterministic constraints of the reserve capacity are introduced into the transmission system capacity configuration model to obtain the power curve of the transmission channel and the optimal capacity of the energy storage at both ends of the transmission and the receiving end.

2. The capacity configuration method considering the optimization of the delivery curve according to claim 1 is characterized in that: In S1, the transmission channel curve model is established by using the upper and lower limit constraints of the DC transmission channel transmission power, the step-by-step constraints of the DC transmission channel transmission power, the constraints on the number of DC transmission channel adjustments throughout the day, the minimum duration of stable power of the DC transmission channel, the annual utilization hours of the DC transmission channel, the minimum input power during the peak period of the receiving load, and the seasonal distribution of the input power.

3. The capacity configuration method considering the optimization of the delivery curve according to claim 1 is characterized in that: In S2, the process of converting the reserve capacity constraint of the transmission system into the probability constraint of reserve capacity by using the chance constraint is as follows: The sending end system in the external transmission system reserves a part of thermal power reserve capacity to suppress the volatility of renewable energy output, and obtains the relationship between reserve capacity and output; Due to the uncertainty of renewable energy, its output may be 0 in some extreme cases. The sending-end system reserves sufficient spare capacity to make up for the power shortage and converts the relationship between spare capacity and output into a probabilistic constraint.

4. The capacity configuration method considering the optimization of the delivery curve according to claim 3 is characterized in that: The specific process of converting the probabilistic constraint of spare capacity into a deterministic constraint by using distributed robust optimization is as follows: Decision variables and random variables are used to represent probabilistic constraints. By adjusting the distance tolerance to change the range of the actual probability distribution density function, the probabilistic constraints become the probability of the probability density function being satisfied, that is, the satisfaction function of the probability density function in the worst case. Substitute the KL divergence into the satisfaction function of the worst-case probability density function and convert the satisfaction function into a deterministic constraint.

5. The capacity configuration method considering the optimization of the delivery curve according to claim 1 is characterized in that: In S3, the capacity configuration model of the transmission system is established by using installed capacity related constraints, power balance constraints, new energy unit constraints, thermal power unit constraints and energy storage unit constraints at both ends of transmission and reception.

6. The capacity configuration method considering the optimization of the delivery curve according to claim 1 is characterized in that: In S3, the total cost of the delivery system includes investment cost, maintenance cost, and operation cost; The investment cost includes the investment costs of wind power, photovoltaic power, thermal power and energy storage at the sending end, the transmission channel and the energy storage at the receiving end; the maintenance cost is related to the investment cost; the system operating cost is the sum of the daily operating costs of the transmission system, including the fuel cost of the thermal power units at the sending end, start and shutdown costs, standby costs, energy storage operating costs, penalties for wind and solar power abandonment, transmission channel operating costs, receiving end energy storage operating costs and penalties for insufficient electricity.

7. A capacity configuration system considering the optimization of the delivery curve, characterized in that: include: An external delivery channel curve model building module is used to build an external delivery channel curve model; The spare capacity constraint module is used to convert the spare capacity constraint of the transmission system into a probabilistic spare capacity constraint by adopting chance constraint; and convert the probabilistic spare capacity constraint into a deterministic constraint by adopting distributed robust optimization; The module for establishing the capacity configuration model of the delivery system is used to establish the capacity configuration model of the delivery system with the goal of minimizing the total cost of the delivery system; The optimal capacity acquisition module is used to bring the transmission channel curve model and the deterministic constraints of the reserve capacity into the transmission system capacity configuration model to obtain the power curve of the transmission channel and the optimal capacity of the energy storage at both ends of the transmission and the receiving end.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the capacity configuration method considering the optimization of the delivery curve as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the capacity configuration method considering the optimization of the delivery curve as described in any one of claims 1 to 6 are implemented.

Images