A method and system for optimizing configuration of energy storage of a multi-energy complementary external delivery system and a storage medium

Abstract

The application discloses a kind of multi-energy complementary external delivery system energy storage optimization configuration method, system and storage medium, belong to electric power system planning involves field, including: acquisition multi-energy complementary external delivery system basic parameter, establish parameter database;Generation sends and receives both ends energy storage configuration capacity seed scheme;Establish the time sequence scheduling optimization model considering sending and receiving both ends collaborative peak shaving, embedding core constraint;Based on seed scheme, carry out all-year time sequence scheduling optimization solution;Establish multidimensional evaluation system, screen optimal energy storage configuration scheme.The application can break through the limitation of traditional single-end energy storage configuration, realize sending and receiving both ends energy storage collaborative optimization, fully consider the rigid operation constraint of extra-high voltage direct current and all-year time sequence working condition change, through multi-scheme iteration and time sequence verification, effectively reduce energy storage configuration redundancy, improve new energy consumption level and sending and receiving both ends power grid collaborative peak shaving capability, guarantee the safe and efficient operation of extra-high voltage external delivery system.

Description

Technical Field

[0001] This invention belongs to the field of power system planning and design, and specifically relates to a method, system and storage medium for optimizing the configuration of energy storage in a multi-energy complementary transmission system. Background Technology

[0002] Multi-energy complementary transmission systems with renewable energy as the mainstay have become an important application scenario for ultra-high voltage direct current (UHVDC) transmission. The randomness, intermittency, and volatility of renewable energy sources such as wind and solar power pose significant challenges to peak shaving and absorption by the power grids at both the sending and receiving ends. Energy storage, as a flexible regulatory resource, can effectively mitigate renewable energy fluctuations and enhance the grid's peak shaving capacity, making it a core configuration element of multi-energy complementary transmission systems.

[0003] In existing technologies, the energy storage configuration of multi-energy complementary transmission systems is mostly concentrated on the sending end. The total energy storage capacity is determined only by considering the demand for new energy consumption at the sending end, without taking into account the differences and synergies in peak-shaving demand between the sending and receiving ends. Although some technologies attempt to configure energy storage at the receiving end, it is only an independent configuration strategy. The coupling optimization relationship between the energy storage capacity ratio and the total capacity at the sending and receiving ends has not been established. Furthermore, the annual time-series scheduling verification has not been carried out in conjunction with the step-like adjustment constraints of the UHVDC transmission curve and the peak-shaving depth constraints of thermal power. This can easily lead to redundant energy storage configuration capacity, low peak-shaving efficiency, or failure to meet the rigid constraints of UHVDC transmission.

[0004] Meanwhile, ultra-high voltage direct current (UHVDC) transmission is subject to maximum transmission capacity limitations, and its power transmission curves are mostly hourly stepped adjustments. Existing energy storage configuration methods do not incorporate this constraint into time-series scheduling optimization, which can easily lead to mismatches between scheduling strategies and actual DC operation requirements. Therefore, there is an urgent need for an energy storage optimization configuration method that considers coordinated peak shaving at both the transmitting and receiving ends. This method involves designing multiple schemes for the energy storage capacity ratio at both ends, combining year-round time-series scheduling optimization with core constraint verification, to determine the optimal total energy storage capacity and the capacity ratio at both ends, thereby achieving coordinated satisfaction of peak shaving needs at both ends and efficient utilization of energy storage resources. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, and storage medium for optimizing the energy storage configuration of a multi-energy complementary transmission system. This invention solves the problems in the prior art where energy storage configuration only focuses on single-end demand, does not consider transmission-receiver coordination and DC operation constraints, and lacks time-series scheduling verification of configuration schemes. It achieves joint optimization of the total energy storage capacity and the ratio of transmission and receiving end capacity in the UHVDC transmission system, thereby improving the utilization efficiency of energy storage resources and the peak-shaving and absorption capacity of the power grids at both the transmission and receiving ends.

[0006] To achieve the above objectives, the present invention adopts the following technical solution;

[0007] On one hand, the present invention provides a method for optimizing the energy storage configuration of a multi-energy complementary power transmission system, comprising:

[0008] Collect the DC power transmission curves at the sending end, the output characteristics of new energy and thermal power, the load characteristics at the receiving end, and the operating constraint parameters of UHVDC, and establish a basic parameter database.

[0009] Based on the basic parameter database, seed schemes for energy storage configurations are generated to cover different total capacities and different sender-receiver capacity ratios.

[0010] Based on the obtained energy storage configuration seed scheme and the pre-established time-series scheduling optimization model that considers the coordinated peak shaving of both the sending and receiving ends, the annual time-series scheduling optimization solution is performed to obtain the optimized index corresponding to each scheme.

[0011] Based on the optimized indicators, a multi-dimensional evaluation system is established to select the optimal energy storage configuration scheme.

[0012] This invention breaks through the limitations of existing single-end energy storage configurations. By generating seed schemes with different total capacities and different ratios between the sending and receiving ends, it establishes a coupled optimization relationship between the energy storage capacities of the sending and receiving ends. This enables the energy storage configuration to simultaneously meet the dual requirements of renewable energy consumption at the sending end and load peak regulation at the receiving end, thereby improving the coordinated peak regulation capability of the power grids at both the sending and receiving ends and realizing the coordinated optimization configuration of energy storage at both ends.

[0013] The aforementioned method for optimizing the energy storage configuration of a multi-energy complementary transmission system, wherein generating seed schemes for energy storage configuration based on a basic parameter database to cover different total capacities and different ratios of sender and receiver capacities, includes:

[0014] Based on the fluctuations in renewable energy output at the sending end and the adjustment requirements of the UHVDC power transmission curve, a reasonable candidate range for the total energy storage capacity is determined using the following formula:

[0015] ;

[0016] in, The minimum candidate value for total energy storage capacity is taken as 30% to 50% of the maximum power output fluctuation of the sending-end renewable energy source. The maximum candidate value for total energy storage capacity is taken as 1.2 to 1.5 times the maximum output fluctuation of the new energy source at the sending end;

[0017] According to the preset step size The following formula is used to generate n different candidate values ​​for the total energy storage capacity using an arithmetic sequence:

[0018] ;

[0019] in, Let i be the candidate value for the total energy storage capacity of the i-th group;

[0020] Introducing the sending-end energy storage capacity ratio coefficient This is used to characterize the proportion of sending-end energy storage capacity to the total capacity;

[0021] According to the preset proportion step size The following formula is used to generate m sets of candidate values ​​for the sending-end energy storage capacity ratio coefficient using an arithmetic sequence:

[0022] ;

[0023] in, This represents a candidate value for the energy storage capacity allocation coefficient of the j-th group of sending-end systems.

[0024] Candidate values ​​based on the sending-end energy storage capacity ratio coefficient The mathematical expressions for the energy storage capacity at the sending and receiving ends are derived separately:

[0025] ;

[0026] ;

[0027] in, Let i be the total energy storage capacity of the i-th group and the energy storage capacity ratio coefficient of the j-th group; To ensure the receiving-end energy storage capacity under corresponding operating conditions. This satisfies the total capacity conservation constraint;

[0028] By combining n different candidate values ​​for total energy storage capacity with m candidate values ​​for the sending-end energy storage capacity ratio coefficient, a full combination is generated. The seed scheme for group energy storage configuration capacity is as follows:

[0029] ;

[0030] in, Configure a seed scheme for the i×j group of energy storage.

[0031] The aforementioned method for optimizing the energy storage configuration of a multi-energy complementary power transmission system includes the following formula for the multi-objective optimization objective function in the time-series scheduling optimization model:

[0032] ;

[0033] in, The total peak-shaving cost for both the sending and receiving ends includes the peak-shaving cost of thermal power and the charging and discharging cost of energy storage; , As a weighting factor, satisfying ; The formula for the curtailment rate of renewable energy at the sending end is as follows:

[0034] ;

[0035] in, These represent the actual wind power and photovoltaic power consumed by the sending end at time t, respectively. The actual output of the wind power at the sending end at time t; The actual output of the photovoltaic system at time t;

[0036] The combined peak-shaving cost at both the sending and receiving ends at time t The calculation formula is as follows:

[0037] ;

[0038] in, To reduce peak-shaving costs for thermal power plants at the sending end, , For the unit peak-shaving cost of thermal power plants, For thermal power benchmark output, take , The timing step size; To reduce the peak-shaving cost of thermal power plants at the receiving end, , The benchmark output for receiving-end thermal power; To reduce the charging and discharging costs of energy storage at the sending end, , The charging and discharging cost of energy storage units; For the cost of receiving-end energy storage charging and discharging, .

[0039] The time-series scheduling optimization model must satisfy the following constraints:

[0040] The power balance constraint at the sending end is given by the following formula:

[0041] ;

[0042] in, , These represent the energy storage discharge and charging power at time t, respectively, satisfying... For the local load power at the sending end;

[0043] Subject to end power balance constraints, the formula is as follows:

[0044] ;

[0045] in, , The receiving-end energy storage discharge and charging power at time t are respectively, satisfying... ; For load power;

[0046] The constraints of ultra-high voltage direct current (UHVDC) operation, considering the hourly step-like adjustment of the DC power transmission curve, and the constant power transmission within each hour, are given by the following formula:

[0047] ;

[0048] ;

[0049] in, The output of ultra-high voltage direct current transmission at time t; This represents the maximum power transmission capacity of ultra-high voltage direct current.

[0050] The power output constraint for thermal power plants is given by the following formula:

[0051] Sending end satisfies ;

[0052] The receiving end satisfies ;

[0053] in, , These represent the minimum and maximum output of thermal power plants at the receiving end, respectively.

[0054] Energy storage operation constraints include: charge and discharge power constraints and state of charge constraints;

[0055] The formula for the charge / discharge power constraint is as follows:

[0056] Sending end: , ;

[0057] Receiving end: , ;

[0058] in, This represents the energy storage power capacity ratio; a negative sign indicates charging, and a positive sign indicates discharging. Let i be the total energy storage capacity of the i-th group and the energy storage capacity ratio coefficient of the j-th group; This refers to the receiving-end energy storage capacity under the corresponding operating conditions;

[0059] The formula for the state of charge constraint is as follows:

[0060] Sending-end energy storage constraints: ;

[0061] ;

[0062] Constraints of end-point energy storage: ;

[0063] ;

[0064] in, , These represent the minimum and maximum states of charge of the sending-end energy storage, respectively. , These represent the minimum and maximum states of charge of the receiving end energy storage, respectively. Self-discharge rate; , These represent the energy storage charge states at the receiving and sending ends, respectively. For time step; For charge and discharge efficiency; These represent the energy storage discharge and charging power at time t, respectively. , The energy storage discharge and charging power at time t;

[0065] The energy storage capacity constraint is given by the following formula:

[0066] ;

[0067] in, , These are the maximum charging and discharging powers of the sending and receiving ends, respectively.

[0068] The constraint on the absorption of new energy sources is given by the following formula:

[0069] .

[0070] This invention embeds the maximum transmission capacity and hourly stepwise adjustment constraints of the UHVDC channel into the time-series scheduling optimization model to ensure that the scheduling strategy matches the actual DC operation requirements and avoid the problem of incompatibility between the scheduling scheme and DC operation.

[0071] The aforementioned method for optimizing the energy storage configuration of a multi-energy complementary transmission system includes solving the year-round time-series scheduling optimization problem by: based on the generated seed scheme for each set of energy storage configuration capacities. The total energy storage capacity of the i-th group and the sending-end energy storage capacity under the energy storage capacity ratio coefficient of the j-th group are given. The receiving-end energy storage capacity under the ratio coefficient of the total energy storage capacity of the i-th group and the energy storage capacity of the j-th group. Substituting the established time-series scheduling optimization model, a convex optimization algorithm is used to solve the model for the entire year's time-series scheduling, obtaining the optimization objective function value corresponding to each scheme. curtailment rate of renewable energy at the sending end Peak shaving costs at both the sending and receiving ends DC power transmission curve satisfaction And energy storage utilization efficiency.

[0072] This invention employs an 8760-hour annual time-series scheduling method to solve and verify each seed scheme, fully considering the time-series characteristics of new energy output and load, making the energy storage configuration scheme more in line with actual engineering operation scenarios, and avoiding redundancy or insufficiency in configuration capacity.

[0073] The aforementioned method for optimizing the energy storage configuration of a multi-energy complementary transmission system, wherein the step of establishing a multi-dimensional evaluation system based on optimized indicators and selecting the optimal energy storage configuration scheme includes the following specific steps:

[0074] Establish multi-dimensional evaluation indicators, including the comprehensive value of the objective function, the curtailment rate of renewable energy at the sending end, the comprehensive peak-shaving cost at both the sending and receiving ends, the DC constraint satisfaction, and the energy storage utilization efficiency.

[0075] Each evaluation indicator is normalized to eliminate the influence of dimensions;

[0076] The comprehensive evaluation score for each group of effective seed schemes is calculated using a weighted summation method, as shown in the following formula:

[0077] ;

[0078] in, is the comprehensive evaluation score of the effective seed schemes in the i×j group, with a value range of [0,1]. The weight of the k-th evaluation index is determined by the Analytic Hierarchy Process (AHP) or engineering experience, and satisfies... ; Let be the normalized value of the k-th indicator of the i×j-th scheme;

[0079] The optimal energy storage configuration is selected by choosing the effective seed scheme with the highest comprehensive evaluation score, using the following formula:

[0080] ;

[0081] in, This is the optimal energy storage configuration scheme; This is the index function for the scheme that takes the maximum value.

[0082] The aforementioned method for optimizing the energy storage configuration of a multi-energy complementary transmission system includes the following optimized indicators for each set of schemes:

[0083] For cost-related negative indicators, the larger the normalized value, the better the indicator's performance. The mathematical expression is:

[0084] ;

[0085] in, The original value of the indicator; This is the normalized value, with a range of [0,1]. For this metric in all valid seed schemes ( The maximum value in ); This is the minimum value of the index among all effective seed schemes; if ,but ;

[0086] For positive performance indicators, the larger the normalized value, the better the indicator's performance. The mathematical expression is:

[0087] ;

[0088] Among them, each variable is defined with a negative index, if ,but .

[0089] This invention establishes a multi-dimensional evaluation system that includes comprehensive cost, curtailment rate, constraint satisfaction, and energy storage utilization efficiency. Through normalization and weighting, it achieves quantitative evaluation of the schemes, making the screening results more scientific and objective, and providing a clear basis for energy storage configuration in engineering practice.

[0090] In a second aspect, the present invention provides a computer device / apparatus / system, comprising:

[0091] Memory, used to store computer programs / instructions;

[0092] A processor is configured to execute the computer program / instructions to implement the steps of the multi-energy complementary transmission system energy storage optimization configuration method described in any of the preceding claims.

[0093] Thirdly, the present invention provides a storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the steps of the energy storage optimization configuration method for the multi-energy complementary transmission system described in any of the preceding claims.

[0094] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention provides a method, system and storage medium for optimizing the energy storage configuration of a multi-energy complementary transmission system, which solves the problems in the prior art where energy storage configuration only focuses on single-end demand, does not consider transmission-receiver coordination and DC operation constraints, and lacks time-series scheduling verification of configuration schemes. It realizes the joint optimization of the total energy storage capacity and the ratio of transmission and receiving end capacity in the UHVDC transmission system, and improves the utilization efficiency of energy storage resources and the peak-shaving and absorption capacity of the power grids at both the transmission and receiving ends.

[0095] This invention overcomes the limitations of existing single-end energy storage configurations by generating seed schemes with different total capacities and varying sender-receiver ratios. It establishes a coupled optimization relationship between the energy storage capacities of both ends, enabling the energy storage configuration to simultaneously meet the dual requirements of renewable energy consumption at the sending end and load peak shaving at the receiving end, thereby enhancing the coordinated peak shaving capability of the power grids at both ends. This invention embeds the maximum transmission capacity of the UHVDC channel and hourly stepped adjustment constraints into a time-series scheduling optimization model, ensuring that the scheduling strategy matches the actual DC operation requirements and avoiding incompatibility issues between the scheduling scheme and DC operation. This invention verifies each seed scheme by employing an 8760-hour annual time-series scheduling method, fully considering the time-series characteristics of renewable energy output and load, making the energy storage configuration scheme more aligned with actual engineering operation scenarios and avoiding redundant or insufficient configuration capacity. This invention establishes a multi-dimensional evaluation system including comprehensive cost, curtailment rate, constraint satisfaction, and energy storage utilization efficiency. Through normalization and weighting, it achieves quantitative evaluation of the schemes, resulting in more scientific and objective selection results and providing clear energy storage configuration guidelines for practical engineering projects. This invention is applicable to multi-energy complementary transmission systems of different scales. The model and constraints can be adjusted according to the actual parameters of the sending and receiving ends to adapt to the energy storage configuration requirements of different UHVDC transmission projects, and has broad engineering application value. Attached Figure Description

[0096] Figure 1 This is a schematic diagram of the process steps of an energy storage optimization configuration method for a multi-energy complementary power transmission system according to the present invention. Detailed Implementation

[0097] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0098] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0099] Example 1:

[0100] like Figure 1 As shown, this embodiment provides a method for optimizing the energy storage configuration of a multi-energy complementary power transmission system, including:

[0101] Collect the DC power transmission curves at the sending end, the output characteristics of new energy and thermal power, the load characteristics at the receiving end, and the core operating constraint parameters of UHVDC, and establish a basic parameter database.

[0102] Based on the basic parameter database, seed schemes for energy storage configurations are generated to cover different total capacities and different sender-receiver capacity ratios.

[0103] Establish a time-series scheduling optimization model that considers coordinated peak shaving at both the sending and receiving ends;

[0104] Based on the obtained energy storage configuration seed scheme and time-series scheduling optimization model, the annual time-series scheduling optimization solution is performed to obtain the optimized index for each scheme.

[0105] Based on the optimized indicators, a multi-dimensional evaluation system is established to select the optimal energy storage configuration scheme.

[0106] This embodiment uses an ultra-high voltage direct current wind-solar-thermal-storage multi-energy complementary transmission system as an application scenario to practically verify the energy storage optimization configuration method of the present invention.

[0107] I. This embodiment uses 8760 hours of time-series data from the entire year as the basis for calculation. The core basic parameters are as follows; parameters without units are dimensionless:

[0108] Sending end design: DC power transmission curve with hourly stepped adjustment, maximum transmission capacity of the channel. Average annual output of wind power Average annual output of photovoltaic power Maximum positive fluctuation of new energy Thermal power unit capacity Peak shaving depth , minimum output Reference output Unit peak-shaving cost of thermal power .

[0109] Receiving end side: Maximum load in the near-field zone of the receiving end minimum load Minimum output of thermal power at the receiving end , maximum output Reference output .

[0110] Energy storage side: charge and discharge efficiency Power capacity ratio Self-discharge rate State of charge range Unit charge / discharge cost Yuan / MWh, initial state of charge .

[0111] Algorithm and weights: Particle swarm optimization is used to solve the problem; population size... Maximum number of iterations ; Optimize target weights , Multi-dimensional evaluation index weights , , , , DC constraint satisfaction threshold .

[0112] II. Energy Storage Configuration Capacity Seed Scheme Generation Calculation

[0113] The mathematical calculation of the seed scheme is completed based on the basic parameters. The core parameters are set as follows: total capacity step size. Proportional step size .

[0114] 1. Calculation of candidate range and candidate value for total energy storage capacity

[0115] The upper and lower limits of total capacity are determined based on the maximum positive fluctuation of new energy sources:

[0116] ;

[0117] ;

[0118] By step size The formula for generating candidate values ​​for total capacity is as follows: The total number of candidate capacity values ​​was calculated. Groups, intervals This covers the reasonable configuration range of the project.

[0119] 2. Calculation of the ratio and capacity of the sending and receiving ends.

[0120] According to the proportion step size Generate proportion coefficient ,have to Group coefficients: .

[0121] Based on total capacity For example, according to the formula , The calculated core ratios for the sending and receiving ends are as follows:

[0122]

[0123] 3. Seed scheme combination

[0124] Total capacity Group × Proportion Coefficient Group, Generate Group seed scheme, denoted as ,For example This represents a total capacity of 1800MWh, with 60% (1080MWh) at the sending end and 40% (720MWh) at the receiving end.

[0125] III. Calculation of Time-Series Scheduling Optimization Solution for the Whole Year

[0126] The 36 seed schemes were successively substituted into the time-series scheduling optimization model, and the particle swarm optimization algorithm was used to complete the solution in 8760 hours. For example, this demonstrates the calculation process of core indicators and the time series step size. .

[0127] 1. Model initialization and solving the instantaneous optimization subproblem

[0128] Initialized upper limit of energy storage charging and discharging power: Sending end , receiving end ;

[0129] Select typical moments (Noon, peak photovoltaic output), basic parameters at this time: , , , ;

[0130] Solve the instantaneous optimization subproblem at this moment. The optimization results are as follows:

[0131] , (Charge), , (Charge);

[0132] , (No wasted electricity).

[0133] 2. Instantaneous index calculation

[0134] (1) Instantaneous peak shaving cost (unit: yuan)

[0135] ;

[0136] ;

[0137] ;

[0138] ;

[0139] ;

[0140] (2) Instantaneous power curtailment rate

[0141] ;

[0142] (3) Instantaneous objective function value

[0143] (Cost items normalized to) (Scale, ensuring dimensional consistency)

[0144] 3. Calculation of core indicators for the whole year

[0145] Integrate the instantaneous results of 8760 time series points, and calculate the core indicators for the whole year according to the formula derived in step four:

[0146] (1) Optimize the objective function value throughout the year

[0147]

[0148] (2) Overall curtailment rate of new energy sources at the sending end

[0149]

[0150] (3) Annual comprehensive peak-shaving cost

[0151]

[0152] (4) DC constraint satisfaction

[0153] (Fully meets DC operation constraints)

[0154] (5) Energy storage utilization efficiency

[0155] ;

[0156] 4. Screening of the solution results for the entire scheme

[0157] After solving the 36 seed schemes, the following schemes were discarded. The three invalid solutions were identified, leaving 33 valid solutions. The key performance indicators for these solutions are excerpted below:

[0158]

[0159] IV. Multi-dimensional evaluation and optimal solution selection calculation

[0160] The process of normalizing indicators, calculating comprehensive scores, and selecting the optimal solution for 33 effective solutions is as follows. The mathematical calculations for the entire process are shown below, with the four core solutions in the table as examples for verification.

[0161] 1. Determination of Extreme Values ​​of Evaluation Indicators

[0162] Extract the extreme values ​​of each index from 33 effective schemes ( / ):

[0163]

[0164] 2. Indicator Normalization Calculation

[0165] Calculate according to the normalization formula in step five, using the scheme For example:

[0166] (1) Negative indicators ( )

[0167] ;

[0168] ;

[0169] ;

[0170] (2) Positive indicators ( )

[0171] ;

[0172] ;

[0173] The normalized results for the four core schemes are as follows:

[0174]

[0175] 3. Calculation of overall evaluation score

[0176] According to the formula Calculation, weight ,

[0177] Comparison of overall scores for the four core solutions:

[0178]

[0179] 4. Optimal Solution Selection

[0180] Based on the comprehensive score calculation of all 33 schemes, the scheme His overall score of 0.9558 was the highest in the game, according to the formula. The optimal solution has the following core configuration parameters:

[0181] ;

[0182] ;

[0183] ;

[0184] ;

[0185] V. Verification of Results through Examples

[0186] The optimized energy storage configuration scheme obtained by this invention, compared with the traditional 1800MWh single-end configuration scheme at the sending end, shows the following improvements in key indicators:

[0187] The curtailment rate of renewable energy at the sending end decreased from 4.8% to 3.72%, a reduction of 22.5%.

[0188] The combined peak-shaving cost at both the sending and receiving ends decreased from 208 million yuan to 185 million yuan, a reduction of 11.1%.

[0189] Energy storage utilization efficiency improved from 0.251 to 0.328, an increase of 30.7%;

[0190] The DC constraint satisfaction rate remains at 100%, fully meeting the requirements for UHVDC operation.

[0191] The verification results show that the configuration method of the present invention achieves coordinated optimization of energy storage at both the sending and receiving ends. Under the premise of meeting the rigid constraints of UHVDC, it effectively improves the level of new energy consumption, reduces peak-shaving costs, and improves the utilization efficiency of energy storage resources, and has significant engineering application value.

[0192] Example 2:

[0193] This embodiment provides a system, including:

[0194] Memory, used to store computer programs / instructions;

[0195] A processor is configured to execute the computer program / instructions to implement the steps of the multi-energy complementary transmission system energy storage optimization configuration method for coordinated peak shaving at both the sending and receiving ends as described in any of the preceding claims.

[0196] Example 3:

[0197] This embodiment provides a storage medium storing a computer program / instruction. When the computer program / instruction is executed by a processor, it implements the steps of the energy storage optimization configuration method for a multi-energy complementary transmission system with coordinated peak shaving at both the transmitting and receiving ends as described in any of the preceding embodiments.

[0198] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0199] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0200] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0201] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0202] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for optimizing the energy storage configuration of a multi-energy complementary power transmission system, characterized in that, include: Collect the DC power transmission curves at the sending end, the output characteristics of new energy and thermal power, the load characteristics at the receiving end, and the operating constraint parameters of UHVDC, and establish a basic parameter database. Based on the basic parameter database, seed schemes for energy storage configurations are generated to cover different total capacities and different sender-receiver capacity ratios. Based on the obtained energy storage configuration seed scheme and the pre-established time-series scheduling optimization model that considers the coordinated peak shaving of both the sending and receiving ends, the annual time-series scheduling optimization solution is performed to obtain the optimized index corresponding to each scheme. Based on the optimized indicators, a multi-dimensional evaluation system is established to select the optimal energy storage configuration scheme.

2. The method according to claim 1, characterized in that, The seed scheme for energy storage configuration, generated based on the basic parameter database to cover different total capacities and different sender-receiver capacity ratios, includes: Based on the fluctuations in renewable energy output at the sending end and the adjustment requirements of the UHVDC power transmission curve, a reasonable candidate range for the total energy storage capacity is determined using the following formula: ； in, The minimum candidate value for total energy storage capacity is taken as 30% to 50% of the maximum power output fluctuation of the sending-end renewable energy source. The maximum candidate value for total energy storage capacity is taken as 1.2 to 1.5 times the maximum output fluctuation of the new energy source at the sending end; According to the preset step size The following formula is used to generate n different candidate values ​​for the total energy storage capacity using an arithmetic sequence: ； in, Let i be the candidate value for the total energy storage capacity of the i-th group; According to the preset proportion step size The following formula is used to generate m sets of candidate values ​​for the sending-end energy storage capacity ratio coefficient using an arithmetic sequence: ； in, This represents a candidate value for the energy storage capacity allocation coefficient of the j-th group of sending-end systems. Candidate values ​​based on the sending-end energy storage capacity ratio coefficient The mathematical expressions for the energy storage capacity at the sending and receiving ends are derived separately: ； ； in, Let be the sending-end energy storage capacity under the energy storage capacity allocation coefficient of the j-th group; For the receiving-end energy storage capacity under the j-th group of energy storage capacity ratio coefficients, ensure This satisfies the total capacity conservation constraint; By combining n different candidate values ​​for total energy storage capacity with m candidate values ​​for the sending-end energy storage capacity ratio coefficient, a full combination is generated. The seed scheme for group energy storage configuration capacity is as follows: ； in, Configure a seed scheme for the i×j group of energy storage.

3. The method according to claim 1, characterized in that, The multi-objective optimization objective function formula in the time-series scheduling optimization model is as follows: ； in, The total peak-shaving cost for both the sending and receiving ends includes the peak-shaving cost of thermal power and the charging and discharging cost of energy storage; , As a weighting factor, satisfying ; The formula for the curtailment rate of renewable energy at the sending end is as follows: ； in, These represent the actual wind power and photovoltaic power consumed by the sending end at time t, respectively. The actual output of the wind power at the sending end at time t; The actual output of the photovoltaic system at time t; The combined peak-shaving cost at both the sending and receiving ends at time t The calculation formula is as follows: ； in, To reduce peak-shaving costs for thermal power plants at the sending end, , For the unit peak-shaving cost of thermal power plants, For thermal power benchmark output, take , The timing step size; To reduce the peak-shaving cost of thermal power plants at the receiving end, , The benchmark output for receiving-end thermal power; To reduce the charging and discharging costs of energy storage at the sending end, , The charging and discharging cost of energy storage units; For the cost of receiving-end energy storage charging and discharging, ; The time-series scheduling optimization model must satisfy the following constraints: The power balance constraint at the sending end is given by the following formula: ； in, , These represent the energy storage discharge and charging power at time t, respectively, satisfying... For the local load power at the sending end; Subject to end power balance constraints, the formula is as follows: ； in, , The receiving-end energy storage discharge and charging power at time t are respectively, satisfying... ; For load power; The constraints of ultra-high voltage direct current (UHVDC) operation, considering the hourly step-like adjustment of the DC power transmission curve, and the constant power transmission within each hour, are given by the following formula: ； ； in, The output of ultra-high voltage direct current transmission at time t; This represents the maximum power transmission capacity of ultra-high voltage direct current. The power output constraint for thermal power plants is given by the following formula: Sending end satisfies ; The receiving end satisfies ; in, , These represent the minimum and maximum output of thermal power plants at the receiving end, respectively. Energy storage operation constraints include: charge and discharge power constraints and state of charge constraints; The formula for the charge / discharge power constraint is as follows: Sending end: , ; Receiving end: , ; in, This represents the energy storage power capacity ratio; a negative sign indicates charging, and a positive sign indicates discharging. Let be the sending-end energy storage capacity under the energy storage capacity allocation coefficient of the j-th group; Let be the receiving-end energy storage capacity under the energy storage capacity allocation coefficient of the j-th group; The formula for the state of charge constraint is as follows: Sending-end energy storage constraints: ; ； Constraints of end-point energy storage: ; ； in, , These represent the minimum and maximum states of charge of the sending-end energy storage, respectively. , These represent the minimum and maximum states of charge of the receiving end energy storage, respectively. Self-discharge rate; , These represent the energy storage charge states at the receiving and sending ends, respectively. For time step; For charge and discharge efficiency; These represent the energy storage discharge and charging power at time t, respectively. , The energy storage discharge and charging power at time t; The energy storage capacity constraint is given by the following formula: ； in, , These are the maximum charging and discharging powers of the sending and receiving ends, respectively. The constraint on the absorption of new energy sources is given by the following formula: 。 4. The method according to claim 1, characterized in that, The annual time-series scheduling optimization solution includes: based on the generated energy storage configuration capacity seed scheme for each group. The total energy storage capacity of the i-th group and the sending-end energy storage capacity under the energy storage capacity ratio coefficient of the j-th group are given. The receiving-end energy storage capacity under the ratio coefficient of the total energy storage capacity of the i-th group and the energy storage capacity of the j-th group. Substituting the established time-series scheduling optimization model, a convex optimization algorithm is used to solve the model for the entire year's time-series scheduling, obtaining the optimization objective function value corresponding to each scheme. curtailment rate of renewable energy at the sending end Peak shaving costs at both the sending and receiving ends DC power transmission curve satisfaction And energy storage utilization efficiency.

5. The method according to claim 1, characterized in that, The optimized metrics for each set of solutions include: For cost-related negative indicators, the larger the normalized value, the better the indicator's performance. The mathematical expression is: ； in, The original value of the indicator; This is the normalized value, with a range of [0,1]. This is the maximum value of the metric across all valid seed schemes; This is the minimum value of the index among all effective seed schemes; if ,but ; For positive performance indicators, the larger the normalized value, the better the indicator's performance. The mathematical expression is: ； Among them, each variable is defined with a negative index, if ,but .

6. The method according to claim 5, characterized in that, The cost-related negative indicators include: the comprehensive value of the objective function, the curtailment rate of renewable energy at the sending end, and the comprehensive peak-shaving cost at both the sending and receiving ends; the benefit-related positive indicators include: DC constraint satisfaction and energy storage utilization efficiency. Based on the optimized cost-related negative indicators and benefit-related positive indicators, a multi-dimensional evaluation system is established to screen the optimal energy storage configuration scheme. Specific steps include: Establish a multi-dimensional evaluation index based on cost-related negative indicators and benefit-related positive indicators; Each evaluation indicator is normalized to eliminate the influence of dimensions; The comprehensive evaluation score for each group of effective seed schemes is calculated using a weighted summation method, as shown in the following formula: ； in, is the comprehensive evaluation score of the effective seed schemes in the i×j group, with a value range of [0,1]. The weight of the k-th evaluation index is determined by the Analytic Hierarchy Process (AHP) or engineering experience, and satisfies... ; Let be the normalized value of the k-th indicator of the i×j-th scheme; The optimal energy storage configuration is selected by choosing the effective seed scheme with the highest comprehensive evaluation score, using the following formula: ； in, This is the optimal energy storage configuration scheme; This is the index function for the scheme that takes the maximum value.

7. A computer device / equipment / system, characterized in that, include: Memory, used to store computer programs / instructions; A processor is configured to execute the computer program / instructions to implement the steps of the energy storage optimization configuration method for a multi-energy complementary transmission system as described in any one of claims 1 to 6.

8. A storage medium having a computer program / instruction stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the energy storage optimization configuration method for the multi-energy complementary transmission system as described in any one of claims 1 to 6.

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