Saggob base transmission and storage collaborative planning method considering ultra-high voltage direct current transmission characteristics

Abstract

The invention discloses an extra-high-voltage direct-current transmission characteristic-considered Saggob base transmission and storage collaborative planning method, which comprises the following steps of: generating a model by using a wind and light scene, generating typical and atypical scenes of wind and light output, and constructing a Saggob base matched transmission-storage collaborative planning model which comprises a planning layer and an operation simulation layer; based on the output of the wind and light scene, planning the assorted delivery power transmission starting and falling point of the Saggob base and the electrochemical energy storage capacity and power of the base, and constructing the planning layer; constructing an operation simulation layer by combining a typical extra-high voltage direct current transmission curve set construction method and a system load loss wind quantitative index and taking random-time sequence operation simulation as a basis; introducing a system loss load risk avoidance coefficient, adjusting the preference degree between the power load guarantee supply demand and the investment cost through the coefficient, and optimizing the model by taking the lowest operation simulation cost of the Saggoc base delivery system as a target; and solving each flexibility resource of the system in each time sequence scene and an operation strategy of the extra-high voltage direct current curve through a scene analysis method.

Description

Technical Field

[0001] The present invention belongs to the technical field of ultra-high voltage direct current transmission systems, and in particular relates to a transmission and storage coordinated planning method for a Shagohuang base taking into account ultra-high voltage direct current transmission characteristics. Background Art

[0002] Accelerate the construction of large-scale wind power and photovoltaic bases focusing on deserts, Gobi and wasteland areas (referred to as "Shan Ge Huang") to replace traditional water and fire energy bases. Constructing a safe, stable and reliable UHV DC transmission system and supporting transmission decisions is of great significance to improving the capacity to absorb new energy resources and integrating the development of new energy construction with the protection and restoration of the ecosystem.

[0003] Compared with traditional hydrothermal energy bases, the main energy sources of Shagohuang Base are wind power and solar energy, which are renewable resources significantly affected by nature. In order to achieve the stability, reliability and economy of power transmission from Shagohuang Base, it is necessary to adjust the uncertainty brought by wind power and photovoltaic power through flexible resources such as thermal power and energy storage, and select the appropriate receiving power grid for consumption by bundling transmission. The ultra-high voltage direct current transmission system, due to its long transmission distance and large transmission capacity, can make up for the uneven distribution of energy resources and power load centers in my country, and has now become the main way for large-scale new energy power generation bases to consume electricity; energy storage, as a flexible resource that can respond quickly, can effectively smooth out the fluctuations in wind power and photovoltaic output, and cooperate with the ultra-high voltage direct current transmission system to participate in the power transmission of Shagohuang Base. Therefore, the reasonable planning of the supporting energy storage scale of Shagohuang Base and the starting and landing points of cross-regional ultra-high voltage direct current transmission lines will help to make full use of the wind and solar resources of Shagohuang Base, balance the operating costs of Shagohuang Base and the receiving power grid, and enhance the stability and flexibility of the system. Summary of the invention

[0004] In order to solve the technical problems existing in the background technology, the present invention aims to provide a transmission-storage coordinated planning method for the Shagohuang base taking into account the characteristics of ultra-high voltage direct current transmission, and proposes a Shagohuang base supporting transmission-storage coordinated planning model considering the typical ultra-high voltage direct current curve set construction method. The system operation uncertainty is considered through the scenario analysis method, and the risk of system load loss in extreme or adverse scenarios is quantified through the CVaR theory.

[0005] In order to solve the technical problem, the technical solution of the present invention is:

[0006] A transmission and storage coordination planning method for Shagohuang base taking into account the characteristics of ultra-high voltage direct current transmission, the method comprising:

[0007] Using the wind and solar scenario generation model, taking into account the randomness and temporal volatility of wind and solar energy, typical and atypical scenarios of wind and solar output are generated, and a transmission-storage collaborative planning model for the Shagohuang base is constructed, including the planning layer and the operation simulation layer;

[0008] Based on the output of wind and solar scenarios, the supporting transmission and transmission points of the Shagohuang base and the electrochemical energy storage capacity and power of the base are planned to construct the planning layer; combined with the typical UHV DC transmission curve set construction method and the system load loss wind quantification index, the operation simulation layer is constructed based on random-sequential operation simulation;

[0009] The objective function of the operation simulation layer is constructed, and the system load loss risk avoidance coefficient is introduced. The preference between the power load supply demand and the investment cost is adjusted through this coefficient. The collaborative planning model is optimized with the goal of minimizing the operation simulation cost of the Shagohuang base transmission system. The scenario analysis method is used to solve the operation strategies of the system's flexibility resources and UHV DC curves in each timing scenario.

[0010] Furthermore, the solution goal of the planning layer is to minimize the annualized investment cost of the supporting electrochemical energy storage equipment and the transmission channel of the Shagohuang base transmission system. The objective function is as follows:

[0011] minC inv +C opr (1)

[0012] C inv =C inv,ES +C inv,line (2)

[0013]

[0014] Where: C inv,ES and C inv,line Respectively represent the construction costs of electrochemical energy storage equipment and transmission lines; CPF x represents the capital recovery factor for the construction of transmission lines or electrochemical energy storage equipment, where α x represents the discount rate of energy storage device e in transmission line l or region z, Y χ It represents the operating life of energy storage equipment e in the transmission line l or area z; and They represent the construction cost per unit power and per unit capacity of energy storage equipment e in region z respectively; and They represent the construction power and construction capacity of the energy storage device e in the area z respectively; κ l represents the construction decision of the transmission channel l; Ω ES Represents the set of nodes where the electrochemical energy storage devices are located.

[0015] Furthermore, the objective function of the operation simulation layer includes: unit operation cost, DC transmission revenue and transmission curve deviation penalty, and system load loss risk quantitative index.

[0016] Furthermore, a system load loss risk avoidance coefficient is introduced, and the preference between the power load supply demand and the investment cost is adjusted through the coefficient. The collaborative planning model is optimized with the goal of minimizing the operation simulation cost of the Shagohuang base transmission system. The collaborative planning model is optimized, including:

[0017] minC opr =C unit +C bia -C profit +βC CVaR (6)

[0018]

[0019] Where: C unit Represents the operating and power abandonment costs of each unit in the system; Ω G ,Ω RES ,Ω Z are the collections of nodes where thermal power units and new energy units are located; P zist represents the output of thermal power unit i in region z during period t in scenario s, represents the power generation cost function of thermal power unit i in region z, a zi , b zi 、c zi represents the unit power generation cost of thermal power unit i in region z, SU zist and SD zist They represent the startup and shutdown costs of thermal power unit i in region z respectively; and They represent the power abandonment cost of renewable energy unit j in region z and the power abandonment amount in period t in scenario s respectively; C profit represents the transmission revenue; η ratio =8760 / T period Represents the time conversion coefficient; σ x and s Respectively represent the probabilities of typical and atypical new energy output scenarios; represents the unit transmission price of transmission channel l; P lxt P represents the transmission power of DC transmission channel l in period t in typical scenario x; lxst represents the transmission power of the DC transmission channel l in the non-typical scenario s belonging to the typical scenario x during the period t; C bia and c bia They represent the deviation penalty and unit deviation penalty of the transmission curve respectively; ΔP lst represents the transmission deviation of DC transmission channel l in period t in atypical scenario s; C CVaR Represents the total system load loss risk, represents the system load loss risk cost during period t; ws represents the distance between the atypical scene s and the typical scene to which it belongs, w s ∈[0,1]; represents the value of the system load loss risk value (VaR).

[0020] Furthermore, the constraints of the operation simulation layer are:

[0021] 1) Power balance constraints

[0022]

[0023] Where: P zixt and P zist P represents the actual output of thermal power unit i in region z during period t in typical scenario x and atypical scenario s respectively; zjxt and P zjst They represent the actual power consumption of new energy unit j in region z during period t in typical scenario x and atypical scenario s respectively; and They represent the discharge power of the energy storage device e in the area z during the period t in the typical scenario x and the atypical scenario s respectively; and They represent the charging power of energy storage device e in area z in period t in typical scenario x and atypical scenario s respectively; L+ and L- represent the power supply and receiving areas respectively; and P represents the power loss of DC transmission channel l in the typical scenario x and the atypical scenario s during period t respectively; znxt and P znst They represent the load demand of load n in region z during period t in typical scenario x and atypical scenario s respectively; represents the load demand of load n in area z during period t in atypical scenario s;

[0024] 2) Output constraints of thermal power units

[0025]

[0026]

[0027] y zist -z zist =I zist -I zis(t-1) (twenty two)

[0028] y zist +z zist ≤1 (23)

[0029]

[0030] Where: and They represent the upper and lower limits of the output of thermal power unit i in region z respectively; UR zi and DR zi They represent the maximum climbing and descending rates of thermal power unit i in area z respectively; y zist 、z zist and I zist They represent the start, stop and operation status of thermal power unit i in region z during period t in scenario s respectively; and They represent the minimum startup and shutdown time of thermal power unit i in area z respectively; UT zis and DT zis They represent the minimum remaining startup and shutdown time of thermal power unit i in region z in scenario s respectively;

[0031] 3) Constraints on new energy output

[0032]

[0033] Where: represents the actual output power of renewable energy unit j in region z during period t in scenario s;

[0034] 4) Spinning reserve constraints

[0035]

[0036] Where: SR zist and SR zest They represent the reserve power of thermal power unit i and electrochemical energy storage device e in region z during period t in scenario s; SR zst represents the basic reserve power of area z in period t in scenario s; r d and r res denote the load and new energy reserve demand coefficients respectively;

[0037] 5) Electrochemical energy storage operation constraints

[0038]

[0039] Where: and They represent the charging and discharging power efficiencies of the electrochemical energy storage device e in region z, respectively; and They represent the minimum and maximum states of charge of the electrochemical energy storage device e in the region z respectively; and They represent the energy storage capacity and power upper limit of the electrochemical energy storage device e in region z respectively; E zestT represents the energy storage capacity of the electrochemical energy storage device e in area z during period t in scene s; ze represents the energy storage period of the electrochemical energy storage device e in the region z; η dc Indicates the natural loss rate of the amount of electricity stored in an electrochemical energy storage device;

[0040] 6) UHVDC transmission constraints

[0041]

[0042] -x lst P l C ≤P lst -P ls(t-1) ≤x lst P l C (38)

[0043]

[0044]

[0045] |P lst |≤κ l P l C (42)

[0046] L lst =TLC l κ l P l C +TOLC l P lst (43)

[0047] Where: x lst represents the change state of the transmission curve of transmission line l in the scenario s during the period t; T l represents the minimum change time interval of the transmission curve of the transmission line l; λ represents the time that the transmission curve has been in the current state; P represents the maximum number of daily changes in the transmission curve of transmission line l; l C represents the construction capacity of the transmission channel l; H l Indicates the annual utilization hours of transmission line l; ΔP lst represents the transmission deviation of the DC transmission channel l in the non-typical scenario s during period t, and and Represent positive and negative transmission deviations respectively; P lst represents the transmission power of DC transmission channel l in scenario s during period t; Formula (43) is the transmission loss coefficient, where TLC l and TOLCl They represent the unit fixed loss coefficient and unit variable loss coefficient of the transmission line l respectively;

[0048] 7) Linear transformation constraints

[0049] Since formula (11) is a convex function, the value of VaR needs to be solved before calculating CVaR, which will make the solution difficult. Therefore, formula (11) is transformed through formula (44):

[0050]

[0051] Where: x + represents max 0,x, in order to avoid the nonlinear term x in equation (44) + Difficult to solve, introduce auxiliary variables Convert nonlinear terms to linear inequalities:

[0052]

[0053] Compared with the prior art, the advantages of the present invention are:

[0054] 1. Comprehensively consider system uncertainty and risk

[0055] Advantages: By generating models based on typical and atypical scenarios of wind and solar resources, the uncertainty of wind and solar energy is fully considered, and various complex situations that may occur in actual operation can be simulated. This random-sequential operation simulation method enables the system to make more accurate and reliable planning and scheduling in the face of uncertainty and extreme scenarios.

[0056] 2. Realize coordinated optimization of transmission and storage

[0057] Advantages: This solution optimizes the power transmission, energy storage configuration and transmission line operation strategy of the Shagohuang base through the combination of planning layer and operation simulation layer, achieving the synergy between power transmission and energy storage. In the case of fluctuations in power demand and supply, it can effectively mobilize flexible resources such as energy storage systems, thermal power units and new energy units to ensure a stable supply of electricity.

[0058] 3. Reduce system operating costs

[0059] Advantages: By minimizing the operating cost of the system, the configuration of unit dispatching, transmission lines and energy storage systems is optimized, maximizing the economic benefits of the system. In the optimization process, not only the unit operating cost is considered, but also factors such as transmission revenue, transmission curve deviation penalty and load loss risk are integrated, which can effectively reduce unnecessary waste and costs.

[0060] 4. Enhance load loss risk management

[0061] Advantages: The introduction of the system load loss risk avoidance coefficient enables the system to flexibly adjust the balance between the reliability of power supply and investment cost according to the risk tolerance of different decision makers. This design not only optimizes costs, but also effectively avoids the risk of load loss and enhances the safety and reliability of the system.

[0062] 5. Accurate optimization decision support

[0063] Advantages: By solving different wind and solar power scenarios through scenario analysis, the optimal operation strategy for each time sequence scenario can be obtained, including the scheduling of flexible resources and the transmission of power by DC transmission lines. This precise decision support can help the system operate efficiently in complex environments and ensure the stable supply of power demand.

[0064] 6. Strong adaptability and flexibility

[0065] Advantages: The solution takes into account a variety of possible power scenarios and different operating conditions, and can adjust the planning scheme according to actual needs. Therefore, the solution has strong adaptability and can flexibly respond to challenges brought about by different time periods, changes in power demand and uncertain factors in the future.

[0066] 7. Improve overall system efficiency

[0067] Advantages: Through comprehensive scheduling and optimization of all aspects of the system (such as energy storage, power generation, transmission, etc.), the utilization efficiency of resources is maximized, and over-investment and waste of resources are avoided. At the same time, through reasonable planning and intelligent scheduling, the overall operating efficiency of the system is effectively improved, supporting more efficient power transmission and more reasonable use of energy storage.

[0068] 8. Strong feasibility and application value

[0069] Advantages: The model framework and optimization method of this solution are highly practical and suitable for the transmission and energy storage system planning of the Shagohuang base and other similar wind and solar power bases. In addition, the solution has good scalability and can be further optimized and adjusted according to specific needs, with high application value.

[0070] The present invention comprehensively considers the uncertainty, risk management and economic benefits of the power system, and adopts methods such as wind and solar scene generation, transmission-storage coordinated optimization and random-sequence operation simulation to solve the scheduling and planning problems of the Shagohuang base transmission system when facing complex power demand fluctuations and unstable supply. Its advantages are to reduce system costs, improve system stability, reduce the risk of load loss, and have strong adaptability and feasibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1, the framework diagram of the transmission-storage coordinated planning model of the Shagohuang base;

[0072] Figure 2 , the structure diagram of the Shagohuang base delivery system. DETAILED DESCRIPTION

[0073] The specific implementation mode of the present invention is described below in conjunction with embodiments:

[0074] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to match the contents disclosed in the specification so that people familiar with this technology can understand and read them, and are not used to limit the conditions under which the present invention can be implemented. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0075] At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" cited in this specification are only for the convenience of description and are not used to limit the scope of implementation of the present invention. Changes or adjustments to their relative relationships should be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0076] Transmission curve: The transmission curve can reflect the changes in the transmission power of the transmission system in different time periods. It is usually used to plan and optimize the operation and scheduling of the power system. By formulating a reasonable transmission curve, the power transmission strategy can be optimized according to the power generation characteristics of renewable energy in different time periods to ensure that the base power can be stably and efficiently transmitted to the receiving power grid.

[0077] Embodiment 1:

[0078] The present invention considers the characteristics of ultra-high voltage direct current transmission, proposes a method for constructing a typical ultra-high voltage direct current transmission curve set for different renewable energy output scenarios, and combines this method to establish a transmission-storage coordinated planning model for the Shagohuang base. The typical annual output scenario of 8760h wind power and photovoltaic power generation is divided into (365×24)h daily output scenarios according to the daily time scale, and then the center point clustering (K-Mediods) algorithm is used to collaboratively cluster the daily output scenarios in the Shagohuang base transmission system into a typical daily output scenario set with discrimination and representativeness, and the typical scenario probability and the scenario distance between the atypical scenario and the typical scenario are calculated; combined with the scenario probability and the scenario distance, the conditional value at risk (CVaR) theory is used to construct a function to quantify the system load loss risk.

[0079] Specifically, the present invention comprehensively considers the operational uncertainty of the Shagohuang base transmission system and the operational risks in extreme or unfavorable scenarios, and proposes a transmission-storage collaborative planning model based on the random-sequential operation simulation of the Shagohuang base transmission system. The model is based on the wind-solar scenario generation model, and uses an integrated planning layer and operation simulation layer to perform associated modeling. The planning layer studies the Shagohuang base supporting transmission transmission take-off and landing points and the base's electrochemical energy storage capacity / power, and the operation simulation layer constructs a typical DC curve set and quantifies the system load loss risk. In order to simplify the complexity of the model, if there is no formula that needs to be specially distinguished in the subsequent constraints, the scene is represented by the superscript s, and no distinction is made between the typical scene x and the atypical scene s. The model framework is as follows Figure 1 shown.

[0080] The Shagohuang base transmission system uses ultra-high voltage direct current transmission lines to carry out "stepped" power transmission, bundling the wind, solar, thermal and storage output in the Shagohuang base to be transmitted to the receiving power grid for consumption. The Shagohuang base transmission system studied in this invention includes m Shagohuang bases, u ultra-high voltage direct current transmission lines to be built and n receiving power grids. The specific structure is as follows: Figure 2 As shown, in order to simplify the subsequent model, the Shagohuang base B in the present invention m With the receiving power grid S n All belong to region Z.

[0081] First, combining the typical UHV DC transmission curve set construction method and the system load loss wind quantification index, based on the random-sequential operation simulation, the operation simulation layer of the Shagohuang base supporting transmission-storage coordinated planning model is constructed. The objective function of the operation simulation layer includes the unit operation cost, DC transmission income and transmission curve deviation penalty, and the system load loss risk quantification index. The system load loss risk aversion coefficient β is introduced. The decision maker's preference for the power load supply demand and investment cost is simulated by β. The operation simulation cost of the Shagohuang base transmission system is minimized. The scenario analysis method is used to solve the system's various flexibility resources and UHV DC curve operation strategies in each time sequence scenario. The optimization model is shown as follows:

[0082] min C opr =C unit +C bia -C profit +βC CVaR (1)

[0083]

[0084]

[0085] Where: C unit Represents the operating and power abandonment costs of each unit in the system; Ω G ,ΩRES ,Ω Z are the collections of nodes where thermal power units and new energy units are located; P zist represents the output of thermal power unit i in region z during period t in scenario s, represents the power generation cost function of thermal power unit i in region z, a zi , b zi 、c zi represents the unit power generation cost of thermal power unit i in region z, SU zist and SD zist They represent the startup and shutdown costs of thermal power unit i in region z respectively; and They represent the power abandonment cost of renewable energy unit j in region z and the power abandonment amount in period t in scenario s respectively; C profit represents the transmission revenue; η ratio =8760 / T period Represents the time conversion coefficient; σ x and σ s Respectively represent the probabilities of typical and atypical new energy output scenarios; represents the unit transmission price of transmission channel l; P lxt P represents the transmission power of DC transmission channel l in period t in typical scenario x; lxst represents the transmission power of the DC transmission channel l in the non-typical scenario s belonging to the typical scenario x during the period t; C bia and c bia They represent the deviation penalty and unit deviation penalty of the transmission curve respectively; ΔP lst represents the transmission deviation of DC transmission channel l in period t in atypical scenario s; C CVaR Represents the total system load loss risk, represents the system load loss risk cost during period t; w s represents the distance between the atypical scene s and the typical scene to which it belongs, w s ∈[0,1]; δ represents the value of the system load loss risk value (VaR).

[0086] The constraints for running the simulation layer are:

[0087] 1) Power balance constraints

[0088]

[0089] Where: P zixt and P zist P represents the actual output of thermal power unit i in region z during period t in typical scenario x and atypical scenario s respectively; zjxt and P zjstThey represent the actual power consumption of new energy unit j in region z during period t in typical scenario x and atypical scenario s respectively; and They represent the discharge power of the energy storage device e in the area z during the period t in the typical scenario x and the atypical scenario s respectively; and They represent the charging power of energy storage device e in area z in period t in typical scenario x and atypical scenario s respectively; L+ and L- represent the power supply and receiving areas respectively; and P represents the power loss of DC transmission channel l in the typical scenario x and the atypical scenario s during period t respectively; znxt and P znst They represent the load demand of load n in region z during period t in typical scenario x and atypical scenario s respectively; It represents the load demand of load n in area z during period t in atypical scenario s.

[0090] 2) Output constraints of thermal power units

[0091]

[0092] y zist -z zist =I zist -I zis(t-1) (17)

[0093] y zist +z zist ≤1 (18)

[0094]

[0095]

[0096] Where: and They represent the upper and lower limits of the output of thermal power unit i in region z respectively; UR zi and DR zi They represent the maximum climbing and descending rates of thermal power unit i in area z respectively; y zist 、z zist and I zist They represent the start, stop and operation status of thermal power unit i in region z during period t in scenario s respectively; and They represent the minimum startup and shutdown time of thermal power unit i in area z respectively; UT zis and DT zis They represent the minimum remaining startup and shutdown time of thermal power unit i in region z in scenario s respectively.

[0097] 3) Constraints on new energy output

[0098]

[0099] Where: Represents the actual output power of renewable energy unit j in region z during period t in scenario s.

[0100] 4) Spinning reserve constraints

[0101]

[0102] Where: SR zist and SR zest They represent the reserve power of thermal power unit i and electrochemical energy storage device e in region z during period t in scenario s; SR zst represents the basic reserve power of area z in period t in scenario s; r d and r res Represent the load and new energy reserve demand coefficients respectively.

[0103] 5) Electrochemical energy storage operation constraints

[0104]

[0105]

[0106] Where: and They represent the charging and discharging power efficiencies of the electrochemical energy storage device e in region z, respectively; and They represent the minimum and maximum states of charge of the electrochemical energy storage device e in the region z respectively; and They represent the energy storage capacity and power upper limit of the electrochemical energy storage device e in region z respectively; E zest T represents the energy storage capacity of the electrochemical energy storage device e in area z during period t in scene s; ze represents the energy storage period of the electrochemical energy storage device e in the region z; η dc Indicates the natural loss rate of the storage capacity of electrochemical energy storage devices.

[0107] 6) UHVDC transmission constraints

[0108]

[0109] -x lst P l C ≤P lst -P ls(t-1) ≤x lst P lC (33)

[0110]

[0111] |P lst |≤κ l P l C (37)

[0112] L lst =TLC l κlP l C +TOLC l P lst (38)

[0113] Where: x lst represents the change state of the transmission curve of transmission line l in the scenario s during the period t; T l represents the minimum change time interval of the transmission curve of the transmission line l; λ represents the time that the transmission curve has been in the current state; P represents the maximum number of daily changes in the transmission curve of transmission line l; l C represents the construction capacity of the transmission channel l; H l represents the annual utilization hours of transmission line l; and They represent the positive and negative transmission deviations of the DC transmission channel l in the atypical scenario s during period t; P lst represents the transmission power of DC transmission channel l in time period t in scenario s; Formula (38) is the transmission loss coefficient, where TLC l and TOLC l They represent the unit fixed loss coefficient and unit variable loss coefficient of the transmission line l respectively.

[0114] 7) Linear transformation constraints

[0115] Since formula (6) is a convex function, the value of VaR needs to be solved before calculating CVaR, which will make the solution difficult. Therefore, formula (6) is transformed through formula (39):

[0116]

[0117] Where: x + represents max 0,x, in order to avoid the nonlinear term x in equation (39) + Difficult to solve, introduce auxiliary variables Convert nonlinear terms to linear inequalities:

[0118]

[0119] The planning layer solution goal is to minimize the annual investment cost of the supporting electrochemical energy storage equipment and transmission channels of the Shagohuang base transmission system. The objective function is as follows:

[0120] minC inv +C opr (41)

[0121] C inv =C inv,ES +C inv,line (42)

[0122]

[0123] Where: C inv,ES and C inv,line Represent the construction costs of electrochemical energy storage equipment and transmission lines respectively; CPF represents the capital recovery factor of transmission line or electrochemical energy storage equipment construction, where α x represents the discount rate of energy storage device e in transmission line l or region z, Y x It represents the operating life of energy storage equipment e in the transmission line l or area z; and They represent the construction cost per unit power and per unit capacity of energy storage equipment e in region z respectively; and They represent the construction power and construction capacity of the energy storage device e in the area z respectively; κ l represents the construction decision of the transmission channel l; Ω ES Represents the set of nodes where the electrochemical energy storage devices are located.

[0124] The preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

[0125] Many other changes and modifications may be made without departing from the concept and scope of the present invention.It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A transmission and storage coordination planning method for the Shagohuang base taking into account the characteristics of ultra-high voltage direct current transmission, characterized in that: The method comprises: Using the wind and solar scenario generation model, taking into account the randomness and temporal volatility of wind and solar energy, typical and atypical scenarios of wind and solar output are generated, and a transmission-storage collaborative planning model for the Shagohuang base is constructed, including the planning layer and the operation simulation layer; Based on the output of wind and solar scenarios, the supporting transmission and transmission points of the Shagohuang base and the electrochemical energy storage capacity and power of the base are planned to construct the planning layer; combined with the typical UHV DC transmission curve set construction method and the system load loss wind quantification index, the operation simulation layer is constructed based on random-sequential operation simulation; The objective function of the operation simulation layer is constructed, and the system load loss risk avoidance coefficient is introduced. The preference between the power load supply demand and the investment cost is adjusted through this coefficient. The collaborative planning model is optimized with the goal of minimizing the operation simulation cost of the Shagohuang base transmission system. The scenario analysis method is used to solve the operation strategies of the system's flexibility resources and UHV DC curves in each timing scenario.

2. The transmission and storage coordinated planning method for the Shagohuang base taking into account the characteristics of ultra-high voltage direct current transmission according to claim 1 is characterized in that: The solution goal of the planning layer is to minimize the annualized investment cost of the supporting electrochemical energy storage equipment and the transmission channel of the Shagohuang base transmission system. The objective function is as follows: my C inv +C opr (1) C inv =C inv,ES +C inv,line (2) Where: C inv,ES and C inv,line Represent the construction costs of electrochemical energy storage equipment and transmission lines respectively; CPF represents the capital recovery factor of transmission line or electrochemical energy storage equipment construction, where α x represents the discount rate of energy storage device e in transmission line l or region z, Y x It represents the operating life of energy storage equipment e in the transmission line l or area z; and They represent the construction cost per unit power and per unit capacity of energy storage equipment e in region z respectively; and They represent the construction power and construction capacity of the energy storage device e in the area z respectively; κ l represents the construction decision of the transmission channel l; Ω ES Represents the set of nodes where the electrochemical energy storage devices are located.

3. The transmission and storage coordinated planning method for the Shagohuang base taking into account the characteristics of ultra-high voltage direct current transmission according to claim 1 is characterized in that: The objective functions of the operation simulation layer include: unit operation cost, DC transmission revenue and transmission curve deviation penalty, and system load loss risk quantitative index.

4. The transmission and storage coordinated planning method for the Shagohuang base taking into account the characteristics of ultra-high voltage direct current transmission according to claim 1 is characterized in that: The system load loss risk avoidance coefficient is introduced to adjust the preference between the power load supply demand and the investment cost through the coefficient. The collaborative planning model is optimized with the goal of minimizing the operation simulation cost of the Shagohuang base transmission system. The collaborative planning model is optimized, including: my C opr =C unit +C bia -C profit +βC CVaR (6) Where: C unit Represents the operating and power abandonment costs of each unit in the system; Ω G ,Ω RES ,Ω Z are the collections of nodes where thermal power units and new energy units are located; P zist represents the output of thermal power unit i in region z during period t in scenario s, represents the power generation cost function of thermal power unit i in region z, a zi , b zi 、c zi represents the unit power generation cost of thermal power unit i in region z, SU zist and SD zist They represent the startup and shutdown costs of thermal power unit i in region z respectively; and They represent the power abandonment cost of renewable energy unit j in region z and the power abandonment amount in period t in scenario s respectively; C pro fi t represents the transmission revenue; η ratio =8760 / T period Represents the time conversion coefficient; σ x and σ s Respectively represent the probabilities of typical and atypical new energy output scenarios; represents the unit transmission price of transmission channel l; P lxt P represents the transmission power of DC transmission channel l in period t in typical scenario x; lxst represents the transmission power of the DC transmission channel l in the non-typical scenario s belonging to the typical scenario x during the period t; C bia and c bia denote the deviation penalty and unit deviation penalty of the transmission curve respectively; ΔP lst represents the transmission deviation of DC transmission channel l in period t in atypical scenario s; C CVaR Represents the total system load loss risk, represents the system load loss risk cost during period t; w s represents the distance between the atypical scene s and the typical scene to which it belongs, w s ∈[0,1]; δ represents the value of the system load loss risk value (VaR).

5. The transmission and storage coordinated planning method for the Shagohuang base taking into account the characteristics of ultra-high voltage direct current transmission according to claim 1 is characterized in that: The constraints of the operation simulation layer are: 1) Power balance constraints Where: P zixt and P zist P represents the actual output of thermal power unit i in region z during period t in typical scenario x and atypical scenario s respectively; zjxt and P zjst They represent the actual power consumption of new energy unit j in region z during period t in typical scenario x and atypical scenario s respectively; and They represent the discharge power of the energy storage device e in the area z during the period t in the typical scenario x and the atypical scenario s respectively; and They represent the charging power of energy storage device e in area z in period t in typical scenario x and atypical scenario s respectively; L+ and L- represent the power supply and receiving areas respectively; and P represents the power loss of DC transmission channel l in the typical scenario x and the atypical scenario s during period t respectively; znxt and P znst They represent the load demand of load n in region z during period t in typical scenario x and atypical scenario s respectively; represents the load demand of load n in area z during period t in atypical scenario s; 2) Output constraints of thermal power units y zist -z zist =I zist -I zis(t-1) (22) y zist +z zist ≤1 (23) Where: and They represent the upper and lower limits of the output of thermal power unit i in region z respectively; UR zi and DR zi They represent the maximum climbing and descending rates of thermal power unit i in area z respectively; y zist 、z zist and I zist They represent the start, stop and operation status of thermal power unit i in region z during period t in scenario s respectively; and They represent the minimum startup and shutdown time of thermal power unit i in area z respectively; UT zis and DT zis They represent the minimum remaining startup and shutdown time of thermal power unit i in region z in scenario s respectively; 3) Constraints on new energy output Where: represents the actual output power of renewable energy unit j in region z during period t in scenario s; 4) Spinning reserve constraints Where: SR zist and SR zest They represent the reserve power of thermal power unit i and electrochemical energy storage device e in region z during period t in scenario s; SR zst represents the basic reserve power of area z in period t in scenario s; r d and r res denote the load and new energy reserve demand coefficients respectively; 5) Electrochemical energy storage operation constraints Where: and They represent the charging and discharging power efficiencies of the electrochemical energy storage device e in region z, respectively; and They represent the minimum and maximum states of charge of the electrochemical energy storage device e in the region z respectively; and They represent the energy storage capacity and power upper limit of the electrochemical energy storage device e in region z respectively; E zest T represents the energy storage capacity of the electrochemical energy storage device e in area z during period t in scene s; ze represents the energy storage period of the electrochemical energy storage device e in the region z; η dc Indicates the natural loss rate of the amount of electricity stored in an electrochemical energy storage device; 6) UHVDC transmission constraints -x lst P l C ≤P lst -P ls(t-1) ≤x lst P l C (38) |P lst |≤κ l P l C (42) Llst=TLC l κ l P l C +TOLC l P lst (43) Where: x lst represents the change state of the transmission curve of transmission line l in the scenario s during the period t; T l represents the minimum change time interval of the transmission curve of the transmission line l; λ represents the time that the transmission curve has been in the current state; P represents the maximum number of daily changes in the transmission curve of transmission line l; l C represents the construction capacity of the transmission channel l; H l Indicates the annual utilization hours of transmission line l; ΔP lst represents the transmission deviation of the DC transmission channel l in the non-typical scenario s during period t, and and Represent positive and negative transmission deviations respectively; P lst represents the transmission power of DC transmission channel l in scenario s during period t; Formula (43) is the transmission loss coefficient, where TLC l and TOLC l They represent the unit fixed loss coefficient and unit variable loss coefficient of the transmission line l respectively; 7) Linear transformation constraints Since formula (11) is a convex function, the value of VaR needs to be solved before calculating CVaR, which will make the solution difficult. Therefore, formula (11) is transformed through formula (44): Where: x + represents max 0,x, in order to avoid the nonlinear term x in equation (44) + Difficult to solve, introduce auxiliary variables Convert nonlinear terms to linear inequalities:

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