Cross-cycle connection calculation method and device for medium-and-long-term electric power and electric quantity balance, and storage medium

Abstract

The invention discloses a cross-period connection calculation method and device for medium-and-long-term electric power and electric quantity balance and a storage medium, and belongs to the field of electric power system planning and dispatching. According to the method, an electric power and electric quantity balance rolling optimization framework crossing four periods of year, quarter, month and week is constructed, and ordered connection among multi-period balance plans is realized. The long-period model determines a macroscopic boundary of system power balance and resource configuration, and the short-period model determines a detailed scheme of system power balance under refined temporal-spatial resolution. By defining decision variables and boundary conditions of balance plans of all periods, a cross-period balance plan time resolution connection method and a key factor refining decomposition method from macroscopic to microscopic are provided, and cross-period ordered connection of medium-and-long-term electric power and electric quantity balance is achieved. The method can improve the continuity, coordination and rolling adaptability of the power and electric quantity balance plan of the power grid under the condition of high proportion of new energy, and guarantees the safety and economical efficiency of system operation.

Description

Technical Field

[0001] This invention relates to the field of power system planning and dispatching, and in particular to a method, apparatus, and storage medium for cross-cycle connection calculation of medium- and long-term power balance. Background Technology

[0002] Power balance analysis is fundamental to optimizing system resource allocation and ensuring the safe and stable operation of the power system. Power balance analysis spans multiple cycles, including long-term (annual, quarterly), medium-term (monthly, weekly), and short-term (day-ahead, intraday). The focus of balance analysis differs across cycles. Long-term analysis examines the system's power balance from a macro perspective, allocating generation plans, inter-provincial power transmission and reception plans, and unit maintenance plans at a coarse-grained time scale. Short-term analysis examines the system's power balance from a micro perspective, determining unit start-up and shutdown, and unit output plans at a fine-grained time scale. Through the connection and coordination of power balance analysis across multiple cycles, comprehensive optimization at both macro and micro scales can be achieved, promoting the consumption of new energy sources and ensuring the safe and stable operation of the power system. Therefore, clarifying the cross-cycle connection mechanism for medium- and long-term power balance is of great significance. Summary of the Invention

[0003] The present invention aims to at least partially solve one of the technical problems in the related art.

[0004] Therefore, one objective of this invention is to propose a method, device, and storage medium for cross-cycle connection calculation of medium- and long-term power balance, and to study a method for refining key factors from macroscopic to microscopic levels, thereby achieving orderly cross-cycle connection of medium- and long-term power balance. This is beneficial for improving the continuity, coordination, and rolling adaptability of power grid balance plans under conditions of high renewable energy proportion, and ensuring the safety and economy of system operation.

[0005] To achieve the above objectives, in a first aspect, this invention proposes a cross-cycle calculation method for medium- and long-term power balance, comprising the following steps: An annual power balance model uses annual forecast data as boundary conditions, and decision variables are passed to a quarterly balance model with a monthly time resolution; a quarterly power balance model uses quarterly forecast data and annual model decision variables as boundary conditions, and decision variables are passed to a monthly balance model with a weekly time resolution; a monthly power balance model uses monthly forecast data and quarterly model decision variables as boundary conditions, and decision variables are passed to a weekly balance model with a daily time resolution; a weekly power balance model uses weekly forecast data and monthly model decision variables as boundary conditions, and decision variables are passed to a hourly time resolution.

[0006] This invention presents a cross-cycle calculation method for long-term power balance. In the long cycle, it simplifies the spatial scale by introducing a balance sub-region, simplifies the cycle by introducing a typical day, and simplifies the maintenance plan by introducing unit availability. In the medium cycle, it restores the simplified factors from the long-cycle model, achieving coordination between macro-level planning and micro-level detail.

[0007] In addition, the cross-cycle connection calculation method for medium- and long-term power balance in this invention may also have the following additional technical features: Furthermore, the annual forecast data includes: annual load forecast curves for each balancing sub-region. New energy prediction curves for each balance sub-region Hydropower inflow prediction curves for each balanced sub-region Balance Zone 1 j Annual power transmission and reception volume of the inter-regional connection lines Each balanced sub-region i Annual maintenance requirements for similar units ,in t The resolution is in months. b Indicates the first b Each equilibrium sub-region. The decision variables described in S1 include: each equilibrium sub-region. b No. i Monthly power generation target for this type of unit , No. j Monthly power supply and reception plan for the inter-regional connection line Reflecting each balanced sub-region b No. i Monthly unit availability rate of the unit maintenance plan .

[0008] Furthermore, the balanced sub-region is a subset of the balanced region, the balanced sub-regions are non-overlapping, and the union of all balanced sub-regions constitutes the balanced region. The concept of the balanced sub-region is a method for simplifying the spatial scale of the model. In long-term (annual, quarterly) power balance analysis, the balanced region is divided into a few balanced sub-regions, and units of the same type within a balanced sub-region are aggregated into one unit for analysis. Network constraints only consider the cross-sectional constraints between balanced sub-regions.

[0009] Furthermore, the quarterly forecast data includes quarterly load forecast curves for each balancing sub-region. Hydropower inflow prediction curves for each balanced sub-region Quarterly renewable energy forecast curves for each balanced sub-region .in t Resolution is week. b Indicates the first b Each balancing sub-region. The annual model decision variables described in S2 are the boundary conditions (including those from the annual electricity balance model output at a monthly resolution)... , , The decision variables described in S2 include: each equilibrium sub-region. b No. i Weekly power generation target for this type of unit , No. j Weekly power supply and reception plan for the external connection line of the balanced area Reflecting each balanced sub-region b No. i Weekly unit availability rate of the unit maintenance plan .

[0010] Furthermore, in the long-term (annual, quarterly) power balance analysis, a simplified method using typical days is adopted on the time scale. The optimization time resolution of the long-term power balance model is hourly. The annual model sets 3 typical days per month, for a total of 12 months, optimizing 864 time periods using a proportionality coefficient. This indicates the weight of a typical day in the monthly timeframe; the quarterly model sets one typical day per week for a total of 13 weeks, optimizing a total of 312 time periods.

[0011] Furthermore, the monthly forecast data includes monthly load forecast curves for each node. Water inflow forecast curves for each hydropower station Monthly output forecast curves for each new energy power station .in t Resolution is day, n Indicates the first n 1 node i Indicates the first i One hydroelectric power station, r Indicates the first r Each new energy power station. The decision variables for the quarterly model described in S3 are the boundary conditions (including those from the quarterly power balance model output at a weekly resolution)... , , The decision variables described in S3 include: i Taiwan's daily power generation target , No. j Daily power supply and reception plan for the external connection line of the balance zone , No. i Maintenance schedule for the Taiwanese unit ,in t The resolution is in Japanese.

[0012] Furthermore, the weekly forecast data includes weekly load forecast curves for each node. Water inflow forecast curves for each hydropower station Weekly power output forecast curves for each new energy power station . tThe resolution is in hours. The monthly model decision variables described in S4 are the boundary conditions (including those from the monthly power balance model output with daily resolution) at the daily resolution. , , The decision variables described in S4 include: i Taiwan unit hourly output plan , No. i Taiwan unit start-stop status , No. es Taiwan's energy storage charging and discharging power , No. r Power curtailment at new energy power plants , No. j Power of the external tie line in the balanced zone ,in t The resolution is in hours.

[0013] Furthermore, in the aforementioned medium-cycle (monthly and weekly) power balance analysis, the analysis object at the spatial scale changes from the balance sub-region to key nodes above 220kV, and from aggregated units to actual units. The monthly network constraint analysis only considers the system's critical section constraints, while the weekly analysis considers both line power flow and critical section constraints.

[0014] Furthermore, the optimization time resolution of the medium-cycle power balance model is in the hour. The total number of optimization periods for the monthly model is approximately 720 hours, and the total number of optimization periods for the weekly model is 168 hours.

[0015] Furthermore, the optimization time resolution of each power balance model is in hours. The optimization results with hourly resolution are then summed and aggregated to a preset decision variable resolution, thereby achieving the connection of the boundary condition time resolution from month to week.

[0016] Furthermore, the objective function of each power balance model includes six items: load gap, renewable energy curtailment, hydropower curtailment, reserve gap, power flow relaxation penalty, and system operating cost, with each item assigned a weight.

[0017] Furthermore, in the decomposition of the maintenance plan, the long-term balance analysis focuses on determining the maintenance capacity, which is a continuous quantity. In the monthly balance analysis, the unit maintenance plan focuses on the specific unit and maintenance time, which is a discrete quantity.

[0018] Furthermore, the cross-cycle connection calculation method for medium- and long-term power balance in this invention specifically includes: 1) Annual power balance model 1-1) Objective function (1) In the formula The total number of optimization periods is 864. For time period The weight of a typical day represents the actual number of days that typical day represents in the corresponding month. In the formula... This represents the system load constraint relaxation penalty factor. These are the positive and negative slack variables of the system load, representing the load gap. The total number of balanced sub-regions in the system. They are respectively balanced sub-regions Domestic new energy and hydropower during the period The amount of abandoned electricity / water. As a penalty factor for the curtailment of renewable energy, The penalty factor for water wastage power at hydropower plants. The system's spare capacity relaxation penalty factor. These are the positive and negative slack variables for the system's reserve capacity, respectively. This represents the penalty factor for relaxing the power flow constraints in the balanced sub-region cross-section network. These are the forward and reverse current relaxation variables for the cross-section, respectively. This represents the system operating cost coefficient, simplifying the consideration of unit operating costs as linear. This represents the unit cost coefficient.

[0019] 1-2) Constraints 1-2-1) System load balance constraints (2) In the formula For the output of the b-th balancing sub-region thermal power unit at time t, the corresponding For hydroelectric power units, It is a new energy power station. Let j be the power of the external tie line of the j-th balancing zone. This is the load in the balanced zone.

[0020] 1-2-2) System positive / negative reserve capacity constraints (3) In the formula The unit availability rate of the equivalent thermal power unit in the balanced sub-region b during time period t, and the monthly availability rate in the annual model. Since the value remains unchanged, it is represented as , where is the availability rate of the i-th type of equivalent unit in the m-th month zone b. This represents the maximum and minimum output of the generator unit.

[0021] 1-2-3) Upper and lower limits of unit output constraints (4) 1-2-4) Energy storage constraints (5) (6) (7) In the formula To balance the discharge and charging power of the equivalent energy stored in sub-region b, The energy state for energy storage. These are the upper and lower limits of the energy state of stored energy. The charging and discharging efficiency of energy storage in sub-region b.

[0022] 1-2-5) Current Constraints Line power flow constraints are simplified to cross-sectional transmission capacity constraints between balanced sub-regions: (8) In the formula For section index, For balanced sub-regions Net injection power for cross section The distribution factor of tidal current transfer cross-section The limits of current transmission.

[0023] 1-2-6) Maintenance plan constraints The annual balance analysis shows that the maintenance plan meets the unit availability rate. reflect: (9) (10) (11) In the formula The upper and lower limits of availability are determined by factors such as the unit maintenance time window. The maximum synchronous maintenance coefficient is determined by maintenance resources. For the hour in month m, Let B be the maintenance time of the i-th unit, B be the number of thermal and hydropower units included in the balance sub-region b, and BZ be the number of thermal and hydropower units in the balance sub-region b that need to be maintained.

[0024] 1-2-7) Power transmission and reception plan outside the region Annual power transmission and reception contract volume constraints: (12) In the formula This represents the total annual power transmission and reception contract volume.

[0025] Power constraints of inter-regional connecting lines: (13) 1-2-8) New Energy Output (14) in To balance the predicted output of the new energy units in sub-region b during a typical day period t.

[0026] 1-3) Calculate decision variables The annual power balance model is optimized at an hourly time resolution. The optimization results at the hourly resolution are summed and aggregated to a monthly resolution to obtain the monthly power generation target for the i-th type of unit in each balance sub-region b. Monthly power supply and reception plan for the j-th inter-regional connection line Monthly availability of the i-th type of unit in the balanced sub-region b .

[0027] 2) Quarterly power balance model 2-1) Objective function (15) In the formula The total number of optimization periods is 312.

[0028] 2-2) Constraints The constraints of the quarterly power balance model are basically the same as those of the annual power balance model.

[0029] 2-2-1) System load balance constraints are the same as 1-2-1).

[0030] 2-2-2) The system's positive / negative standby capacity constraints are the same as those in 1-2-2).

[0031] 2-2-3) The upper and lower limits of unit output are the same as those in 1-2-3).

[0032] Supplementing the monthly power generation target constraints of generating units: (16) In the formula This refers to the permissible deviation range of the unit's monthly power generation from the annual planned target.

[0033] 2-2-4) Energy storage constraints are the same as 1-2-4).

[0034] 2-2-5) Current flow constraint, same as 1-2-5).

[0035] 2-2-6) Maintenance plan constraints (17) (18) (19) In the formula For the first The total number of hours per week takes into account both complete and incomplete weeks within a month. This represents the total number of hours in month m.

[0036] 2-2-7) Power transmission and reception plan outside the region The annual power transmission and reception contract total volume constraint has been changed to a monthly power target constraint for tie lines. (20) 2-3) Calculate decision variables The optimization time resolution of the quarterly power balance model is hourly. The optimization results at hourly resolution are summed and aggregated to weekly resolution. The weekly power generation target of unit i in each balance sub-region b is then determined. Weekly power supply and reception plan for the j-th inter-regional connection line Weekly availability of the i-th type of unit in the balanced sub-region b .

[0037] 3) Monthly power balance model 3-1) Objective Function (twenty one) In the formula The total number of optimization periods is approximately 720. These are the number of renewable energy power plants in the balance zone, the number of hydropower stations, the number of key network sections, and the total number of generating units. In the monthly power balance analysis, the spatial analysis object changes from the balance sub-region to key nodes above 220kV, and from aggregated generating units to actual generating units.

[0038] 3-2) Constraints 3-2-1) System load balance constraints (twenty two) In the formula NT represents the output of the i-th thermal power unit, hydropower unit, and new energy power station at time t, and NT represents the total number of external connection lines.

[0039] 3-2-2) System positive / negative reserve capacity constraints (twenty three) In the formula This represents the maintenance status of the i-th thermal power and hydropower unit during time period t.

[0040] 3-2-3) Unit Constraints Unit output upper and lower limit constraints: (twenty four) Weekly power generation target constraints for generating units: (25) 3-2-4) Energy storage constraints (26) (27) (28) In the formula Let i be the discharge and charging power of the i-th energy storage unit. Let i be the energy state of the i-th energy storage unit. Let be the upper and lower limits of the energy state of the i-th energy storage unit. Let be the charging and discharging efficiency of the i-th energy storage unit.

[0041] 3-2-5) Network Flow Constraints Considering critical section constraints: (29) In the formula These are the transfer distribution factors of key section s and unit i (including energy storage), key section s and external connecting line j, and key section s and load k, respectively.

[0042] 3-2-6) Maintenance plan constraints Unit availability transitive constraints: (30) In the formula To allow for deviations, ensure that the monthly maintenance plan is basically consistent with the quarterly plan boundary conditions.

[0043] Maintenance continuous time constraints: (31) (32) In the formula Let be the continuous maintenance time for the i-th unit.

[0044] The maintenance window is the earliest and latest start time for unit maintenance. The constraints of the maintenance window are: (33) In the formula The definition indicates whether unit i switches to maintenance mode during time period t.

[0045] (34) Inspection window constraints: Maintenance resource constraints indicate that the available maintenance personnel, tools, etc., are limited within the same time period, and can be represented as: (35) in This represents the resources required for the maintenance of the i-th generating unit. M represents the total number of units under maintenance, and M represents the total maintenance resources within the balance zone.

[0046] 3-2-7) Power transmission and reception plan outside the region The power transmission constraints of the external connection lines need to be considered: (36) And the weekly power consumption target constraint for the interconnection line: (37) 3-2-8) New Energy Output (38) in The predicted output of the new energy generating unit r during time period t.

[0047] 3-3) Calculate decision variables The monthly power balance model is optimized with an hourly time resolution. The optimization results at the hourly resolution are summed and aggregated to the daily resolution. The daily power generation target of the i-th unit is... Daily power supply and reception plan for the j-th balancing zone external connection line Maintenance schedule for the i-th unit .

[0048] 4) Weekly power balance model 4-1) Objective Function (39) In the formula The total number of optimization periods is 168. The operating cost in the objective function is restored to the actual cost function of each generator unit, while also considering the start-up and shutdown costs of the units. In the formula... It is the operating cost function of unit i.

[0049] 4-2) Constraints 4-2-1) System load balance constraints are the same as those in 3-2-1).

[0050] 4-2-2) The system's positive / negative standby capacity constraints are the same as those in 3-2-2).

[0051] 4-2-3) Unit Constraints Unit output upper and lower limit constraints: (40) in These represent the upper and lower limits of the output of unit i, respectively.

[0052] Unit ramp-up constraints: When the unit is climbing uphill or downhill, the climbing rate requirement must be met: (41) (42) in This represents the maximum ramp rate of unit i. This indicates the maximum downhill / climb rate of unit i.

[0053] Minimum continuous start-up and shutdown time constraints for generating units: (43) (44) in This represents the start-up and shutdown status of unit i during time period t; , The minimum continuous start-up time and minimum continuous downtime for a trading unit; , Let the continuous operating time and continuous downtime of unit i during time period t be represented as: (45) (46) Daily power generation constraints of the unit: (47) 4-2-4) Energy storage constraints are the same as 3-2-4).

[0054] 4-2-5) Network Flow Constraints Considering critical section constraints: (48) In the formula These are the transfer distribution factors of key section s and unit i (including energy storage), key section s and external connecting line j, and key section s and load k, respectively.

[0055] Considering critical section constraints: (49) 4-2-6) Power transmission and reception plan outside the region The power transmission constraints of the external connection lines need to be considered: (50) And the weekly power consumption target constraint for the interconnection line: (51) The regulation rate constraint of the external connecting line can be expressed as: (52) In the formula This represents the maximum adjustment rate of the j-th inter-regional connecting line.

[0056] 4-2-7) The constraint on new energy output is the same as that in 3-2-7).

[0057] 4-3) Calculate decision variables The weekly power balance model is optimized with a time resolution of hours, yielding the hourly output plan for the i-th generating unit. Start-up and shutdown status of the i-th unit The charging and discharging power of the first energy storage unit The power curtailment of the rth renewable energy power station The power of the external tie line in the j-th balanced zone .

[0058] Secondly, a cross-cycle connection calculation device for medium- and long-term power balance is provided, including: The acquisition unit acquires system parameters and prediction data; The first analysis unit is used for annual-scale power balance analysis; The second analysis unit is used for annual-scale power balance analysis; The third analysis unit is used for annual-scale power balance analysis; The fourth analysis unit is used for annual-scale power balance analysis.

[0059] Thirdly, an electronic device is provided, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the cross-cycle linkage calculation method for medium- and long-term power balance as described in any of the preceding claims.

[0060] Fourthly, a storage medium is provided, the storage medium storing a computer program, wherein the computer program is configured to execute the cross-cycle connection calculation method for medium- and long-term power balance as described above when running.

[0061] Fifthly, a computer program product is provided, including a computer program configured to execute the cross-cycle connection calculation method for medium- and long-term power balance as described in any of the preceding claims when running. Attached Figure Description

[0062] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1A flowchart of a method for cross-cycle connection calculation of medium- and long-term power balance according to an embodiment of the present invention; Figure 2 A flowchart of a method for cross-cycle connection calculation of medium- and long-term power balance according to an embodiment of the present invention; Figure 3 A structural diagram of the cross-cycle connection calculation device for medium- and long-term power balance provided in an embodiment of this application is shown. Detailed Implementation

[0063] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0064] One objective of this invention is to propose a cross-cycle calculation method for medium- and long-term power balance. By clarifying the boundary conditions and decision variables for annual, quarterly, monthly, and weekly power balance analysis, cross-cycle coordination of the balance plan is achieved. The introduction of balance sub-regions and typical days simplifies long-cycle balance analysis from spatial and temporal scales, respectively. Furthermore, a decomposition method for maintenance plans is proposed to achieve the connection between continuous and discrete maintenance quantities. This is beneficial for improving the continuity, coordination, and rolling adaptability of the power grid balance plan under conditions of high renewable energy proportions, ensuring the safety and economy of system operation.

[0065] The following describes, with reference to the accompanying drawings, a method for cross-cycle connection calculation of medium- and long-term power balance according to an embodiment of the present invention.

[0066] Figure 1 This is a flowchart of a method for cross-cycle connection calculation of medium- and long-term power balance according to an embodiment of the present invention.

[0067] like Figure 1 As shown, the cross-cycle transition calculation method for medium- and long-term power balance includes the following steps: In step S1, the annual power balance model uses annual forecast data as boundary conditions and the decision variables have a monthly time resolution.

[0068] It is understood that the annual power balance model in this embodiment of the invention is a long-term balance analysis, which requires the introduction of balance sub-regions and typical days for simplification. Annual system forecast data is obtained from a macro perspective, and decision variables with a monthly time resolution are obtained through annual power balance analysis, serving as boundary conditions for the quarterly balance model.

[0069] In step S2, the quarterly power balance model uses quarterly forecast data as boundary conditions and the decision variables have a weekly time resolution.

[0070] It is understood that the quarterly power balance model in this embodiment of the invention is a long-term balance analysis, which requires the introduction of balance sub-regions and typical days for simplification. Quarterly forecast data of the system is obtained from a macro perspective, and decision variables with a weekly time resolution are obtained through quarterly power balance analysis, serving as boundary conditions for the monthly balance model.

[0071] In step S3, the monthly power balance model uses monthly forecast data as boundary conditions and the decision variables have a daily time resolution.

[0072] It is understood that the monthly power balance model in this embodiment of the invention is analyzed at a specific, refined spatiotemporal resolution. Monthly forecast data of the system is obtained, and decision variables with a daily time resolution are derived through monthly power balance analysis, serving as the boundary conditions for the weekly balance model.

[0073] In step S4, the weekly power balance model uses weekly forecast data as boundary conditions and hourly time resolution as the decision variables.

[0074] It is understood that the weekly power balance model in this embodiment of the invention is analyzed at a specific, refined spatiotemporal resolution. Weekly system forecast data is obtained, and detailed unit output and start-up / shutdown plans are derived through weekly power balance analysis.

[0075] In summary, the method of this invention achieves the orderly connection of multi-cycle balancing plans, which is conducive to the overall coordination of macro and micro levels, promotes the consumption of new energy sources, and ensures the safe and stable operation of the system.

[0076] The following will combine Figure 2 The calculation method for the cross-cycle transition of medium- and long-term power balance is explained in detail below: 1) Annual power balance model 1-1) Objective function (1) In the formula The total number of optimization periods is 864. For time period The weight of a typical day represents the actual number of days that typical day represents in the corresponding month. In the formula... This represents the system load constraint relaxation penalty factor. These are the positive and negative slack variables of the system load, representing the load gap. The total number of balanced sub-regions in the system. They are respectively balanced sub-regions Domestic new energy and hydropower during the period The amount of abandoned electricity / water. As a penalty factor for the curtailment of renewable energy, The penalty factor for water wastage power at hydropower plants. The system's spare capacity relaxation penalty factor. These are the positive and negative slack variables for the system's reserve capacity, respectively. This represents the penalty factor for relaxing the power flow constraints in the balanced sub-region cross-section network. These are the forward and reverse current relaxation variables for the cross-section, respectively. This represents the system operating cost coefficient, simplifying the consideration of unit operating costs as linear. This represents the unit cost coefficient.

[0077] 1-2) Constraints 1-2-1) System load balance constraints (2) In the formula For the first b Each balanced sub-region thermal power unit is in t The effort put in at all times, and the corresponding For hydroelectric power units, It is a new energy power station. For the first j Power of the external tie line in the balanced zone, This is the load in the balanced zone.

[0078] 1-2-2) System positive / negative reserve capacity constraints (3) In the formula Balanced sub-region b The equivalent thermal power unit in the time period t The unit availability rate, for each month in the annual model. Since the value remains unchanged, it is represented as , for the first m Confinement area b The i Availability of equivalent units. This represents the maximum and minimum output of the generator unit.

[0079] 1-2-3) Upper and lower limits of unit output constraints (4) 1-2-4) Energy storage constraints (5) (6) (7) In the formula For balanced sub-regions b Equivalent energy storage discharge and charging power, The energy state for energy storage. These are the upper and lower limits of the energy state of stored energy. For sub-region b Energy storage charging and discharging efficiency.

[0080] 1-2-5) Current Constraints Line power flow constraints are simplified to cross-sectional transmission capacity constraints between balanced sub-regions: (8) In the formula For section index, For balanced sub-regions Net injection power for cross section The distribution factor of tidal current transfer cross-section The limits of current transmission.

[0081] 1-2-6) Maintenance plan constraints The annual balance analysis shows that the maintenance plan meets the unit availability rate. reflect: (9) (10) (11) In the formula The upper and lower limits of availability are determined by factors such as the unit maintenance time window. The maximum synchronous maintenance coefficient is determined by maintenance resources. For the first m Hours per month For the first i Maintenance time for the Taiwanese unit. B For balanced sub-regions b The number of thermal and hydropower units included. BZ For balanced sub-regions b The number of thermal and hydropower units that need maintenance.

[0082] 1-2-7) Power transmission and reception plan outside the region Annual power transmission and reception contract volume constraints: (12) In the formula This represents the total annual power transmission and reception contract volume.

[0083] Power constraints of inter-regional connecting lines: (13) 1-2-8) New Energy Output (14) in For balanced sub-regions b New energy units during typical daytime periods t The predicted output.

[0084] 1-3) Calculate decision variables The annual power balance model is optimized at an hourly time resolution. The optimization results at the hourly resolution are summed and aggregated to a monthly resolution to obtain the balance sub-regions. b No. i Monthly power generation target for this type of unit , No. j Monthly power supply and reception plan for the inter-regional connection line Balanced subregion b No. i Monthly availability of type of units .

[0085] 2) Quarterly power balance model 2-1) Objective function (15) In the formula The total number of optimization periods is 312.

[0086] 2-2) Constraints The constraints of the quarterly power balance model are basically the same as those of the annual power balance model.

[0087] 2-2-1) System load balance constraints are the same as 1-2-1).

[0088] 2-2-2) The system's positive / negative standby capacity constraints are the same as those in 1-2-2).

[0089] 2-2-3) The upper and lower limits of unit output are the same as those in 1-2-3).

[0090] Supplementing the monthly power generation target constraints of generating units: (16) In the formula This refers to the permissible deviation range of the unit's monthly power generation from the annual planned target.

[0091] 2-2-4) Energy storage constraints are the same as 1-2-4).

[0092] 2-2-5) Current flow constraint, same as 1-2-5).

[0093] 2-2-6) Maintenance plan constraints (17) (18) (19) In the formula For the first The total number of hours per week takes into account both complete and incomplete weeks within a month. For the first m Total number of hours in a month.

[0094] 2-2-7) Power transmission and reception plan outside the region The annual power transmission and reception contract total volume constraint has been changed to a monthly power target constraint for tie lines. (20) 2-3) Calculate decision variables The optimization time resolution of the quarterly power balance model is hourly. The optimization results at the hourly resolution are summed and aggregated to the weekly resolution, with each balance sub-region... b No. i Weekly power generation target for this type of unit , No. j Weekly power supply and reception plan for the inter-regional connection line Balanced subregion b No. i Weekly availability of type of units .

[0095] 3) Monthly power balance model 3-1) Objective Function (twenty one) In the formula The total number of optimization periods is approximately 720. These are the number of renewable energy power plants in the balance zone, the number of hydropower stations, the number of key network sections, and the total number of generating units. In the monthly power balance analysis, the spatial analysis object changes from the balance sub-region to key nodes above 220kV, and from aggregated generating units to actual generating units.

[0096] 3-2) Constraints 3-2-1) System load balance constraints (twenty two) In the formula For the first i Taiwan's thermal power units, hydropower units, and new energy power plants t Constant effort NT This represents the total number of connecting lines outside the district.

[0097] 3-2-2) System positive / negative reserve capacity constraints (twenty three) In the formula For the first iTaiwan's thermal and hydroelectric power units t Maintenance status during a given period.

[0098] 3-2-3) Unit Constraints Unit output upper and lower limit constraints: (twenty four) Weekly power generation target constraints for generating units: (25) 3-2-4) Energy storage constraints (26) (27) (28) In the formula Let i be the discharge and charging power of the i-th energy storage unit. Let i be the energy state of the i-th energy storage unit. Let be the upper and lower limits of the energy state of the i-th energy storage unit. Let be the charging and discharging efficiency of the i-th energy storage unit.

[0099] 3-2-5) Network Flow Constraints Considering critical section constraints: (29) In the formula These are the transfer distribution factors of key section s and unit i (including energy storage), key section s and external connecting line j, and key section s and load k, respectively.

[0100] 3-2-6) Maintenance plan constraints Unit availability transitive constraints: (30) In the formula To allow for deviations, ensure that the monthly maintenance plan is basically consistent with the quarterly plan boundary conditions.

[0101] Maintenance continuous time constraints: (31) (32) In the formula For the first i The continuous maintenance time of the unit.

[0102] The maintenance window is the earliest and latest start time for unit maintenance. The constraints of the maintenance window are: (33) In the formula Definition represents the uniti During the period t Should we switch to maintenance mode?

[0103] (34) Inspection window constraints: Maintenance resource constraints indicate that the available maintenance personnel, tools, etc., are limited within the same time period, and can be represented as: (35) in Indicates the first i Resources required for the overhaul of the generator set. This indicates the total number of units under maintenance. M This indicates the total maintenance resources within the balance zone.

[0104] 3-2-7) Power transmission and reception plan outside the region The power transmission constraints of the external connection lines need to be considered: (36) And the weekly power consumption target constraint for the interconnection line: (37) 3-2-8) New Energy Output (38) in For new energy units r During the period t The predicted output.

[0105] 3-3) Calculate decision variables The monthly power balance model is optimized at an hourly time resolution. The optimization results at the hourly resolution are summed and aggregated to a daily resolution. i Taiwan's daily power generation target , No. j Daily power supply and reception plan for the external connection line of the balance zone , No. i Maintenance schedule for the Taiwanese unit .

[0106] 4) Weekly power balance model 4-1) Objective Function (39) In the formula The total number of optimization periods is 168. The operating cost in the objective function is restored to the actual cost function of each generator unit, while also considering the start-up and shutdown costs of the units. In the formula... It is a generator set i The running cost function.

[0107] 4-2) Constraints 4-2-1) System load balance constraints are the same as those in 3-2-1).

[0108] 4-2-2) The system's positive / negative standby capacity constraints are the same as those in 3-2-2).

[0109] 4-2-3) Unit Constraints Unit output upper and lower limit constraints: (40) in They represent the generating units. i The upper and lower limits of the output.

[0110] Unit ramp-up constraints: When the unit is climbing uphill or downhill, the climbing rate requirement must be met: (41) (42) in Indicates the unit i Maximum uphill speed, Indicates the unit i Maximum downhill / climbing speed.

[0111] Minimum continuous start-up and shutdown time constraints for generating units: (43) (44) in This represents the start-up and shutdown status of unit i during time period t; , The minimum continuous start-up time and minimum continuous downtime for a trading unit; , Let the continuous operating time and continuous downtime of unit i during time period t be represented as: (45) (46) Daily power generation constraints of the unit: (47) 4-2-4) Energy storage constraints are the same as 3-2-4).

[0112] 4-2-5) Network Flow Constraints Considering critical section constraints: (48) In the formula These are the transfer distribution factors of key section s and unit i (including energy storage), key section s and external connecting line j, and key section s and load k, respectively.

[0113] Considering critical section constraints: (49) 4-2-6) Power transmission and reception plan outside the region The power transmission constraints of the external connection lines need to be considered: (50) And the weekly power consumption target constraint for the interconnection line: (51) The regulation rate constraint of the external connecting line can be expressed as: (52) In the formula For the first j Maximum adjustment rate of the connecting lines outside the zone.

[0114] 4-2-7) The constraint on new energy output is the same as that in 3-2-7).

[0115] 4-3) Calculate decision variables The optimization time resolution of the weekly power balance model is hours, yielding the [number]th [unit]. i Taiwan unit hourly output plan , No. i Taiwan unit start-stop status , No. es Taiwan's energy storage charging and discharging power , No. r Power curtailment at new energy power plants , No. j Power of the external tie line in the balanced zone .

[0116] The cross-cycle connection calculation method for medium- and long-term power balance proposed in this invention achieves cross-cycle connection of balance plans by clarifying the boundary conditions and decision variables for annual, quarterly, monthly, and weekly power balance analysis; simplifies long-cycle balance analysis by introducing balance sub-regions and typical days at spatial and temporal scales, respectively; and achieves the connection between continuous and discrete unit maintenance quantities by proposing a decomposition method for maintenance plans. This method is beneficial for improving the continuity, coordination, and rolling adaptability of power grid balance plans under conditions of high renewable energy proportions, ensuring the safety and economy of system operation.

[0117] It should be noted that the sequence numbers of the steps in the above embodiments do not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. In practical applications, all the above possible implementation methods can be arbitrarily combined in a combined manner to form possible embodiments of this application, which will not be described in detail here.

[0118] Based on the cross-cycle connection calculation method for medium- and long-term power balance provided in the above embodiments, and based on the same inventive concept, this application also provides a cross-cycle connection calculation device for medium- and long-term power balance.

[0119] Figure 3 This is a structural diagram of the cross-cycle connection calculation device for medium- and long-term power balance provided in an embodiment of this application. Figure 3 As shown, the cross-cycle connection calculation device for medium- and long-term power balance may specifically include an acquisition unit 310, a first analysis unit 320, a second analysis unit 330, a third analysis unit 340, and a fourth analysis unit 350.

[0120] The acquisition unit 310 is used to acquire system parameters and annual, quarterly, monthly, and weekly forecast data; The first analysis unit 320 is used for annual-scale power balance analysis; The second analysis unit 330 is used for annual-scale power balance analysis; The third analysis unit 340 is used for annual-scale power balance analysis; The fourth analysis unit, 350, is used for annual-scale power balance analysis.

[0121] This application provides a possible implementation method in which the annual power balance model uses annual forecast data as boundary conditions and the decision variables are passed to the quarterly balance model with a monthly time resolution.

[0122] This application provides a possible implementation method in which the quarterly power balance model uses quarterly forecast data and annual model decision variables as boundary conditions, and the decision variables are passed to the monthly balance model with a weekly time resolution.

[0123] This application provides a possible implementation method in which the monthly power balance model uses monthly forecast data and quarterly model decision variables as boundary conditions, and the decision variables are passed to the weekly balance model with a daily time resolution.

[0124] This application provides a possible implementation method in which the weekly power balance model uses weekly forecast data and monthly model decision variables as boundary conditions, and the decision variables have an hourly time resolution.

[0125] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for cross-period connection calculation of medium and long-term power balance, characterized in that, The method comprises the following steps: S1, an annual power balance model takes annual prediction data as boundary conditions, and decision variables are passed to a quarterly balance model with monthly time resolution; S2, a quarterly power balance model takes quarterly prediction data and annual model decision variables as boundary conditions, and decision variables are passed to a monthly balance model with weekly time resolution; S3, a monthly power balance model takes monthly prediction data and quarterly model decision variables as boundary conditions, and decision variables are passed to a weekly balance model with daily time resolution; S4, a weekly power balance model takes weekly prediction data and monthly model decision variables as boundary conditions, and decision variables are with hourly time resolution.

2. The method of claim 1, wherein, The annual prediction data in the step S1 includes: annual load prediction curve of each balance sub-area , new energy prediction curve of each balance sub-area , water inflow prediction curve of each balance sub-area , annual total amount of power transmission and reception of the balance area j , annual total amount of power transmission and reception of the balance area , annual maintenance demand of the unit of each balance sub-area i , wherein , the resolution is month, t , the resolution is month, b , the resolution is month, b , the resolution is month, b , the resolution is month, i , the resolution is month, , the resolution is month, j , the resolution is month, , the resolution is month, b , the resolution is month, i , the resolution is month, ; The balance sub-area is a subset of the balance area, the balance sub-areas are disjoint, and the union of all balance sub-areas is the balance area, the same type of unit in the balance sub-area is aggregated as one unit for analysis, and the network constraint only considers the cross-section constraint between the balance sub-areas.

3. The method of claim 1, wherein, The quarterly forecast data in step S2 includes the quarterly load forecast curves for each balancing sub-region. Hydropower inflow prediction curves for each balanced sub-region Quarterly renewable energy forecast curves for each balanced sub-region ,in t Resolution is week. b Indicates the first b Each balance sub-region, wherein the annual model decision variables in step S2 are the boundary conditions output by the annual power balance model with monthly resolution, and the decision variables in step S2 include: each balance sub-region b No. i Weekly power generation target for this type of unit , No. j Weekly power supply and reception plan for the external connection line of the balanced area Reflecting each balanced sub-region b No. i Weekly unit availability rate of the unit maintenance plan .

4. The method of claim 1, wherein, The monthly prediction data in the step S3 includes a monthly load prediction curve of each node , a prediction curve of inflow of each hydropower station , and a monthly output prediction curve of each new energy field station , wherein t the resolution is day, n the first n node is represented by i , the first i hydropower station is represented by r , and the first r new energy field station is represented by i The quarterly model decision variable in the step S3 is a boundary condition of a weekly resolution output by a quarterly power balance model, and the decision variable includes: a daily generation target of the first unit j , a daily sending and receiving plan of the first balance area external tie line i , and a maintenance arrangement of the first unit, wherein t the resolution is day.

5. The method of claim 1, wherein, The weekly prediction data in the step S4 includes a weekly load prediction curve of each node , a water inflow prediction curve of each hydropower station , and a weekly output prediction curve of each new energy field station , t The resolution of the monthly prediction data in the step S4 is hour, the monthly model decision variable in the step S4 is a boundary condition of the daily resolution output by the monthly power and energy balance model, and the decision variable includes: i An hourly output plan of the first unit i , a start-stop state of the first unit es , a charging and discharging power of the first energy storage r , an abandoned power of the first new energy field station j , and a power of the first balance area external tie line t , wherein the resolution is hour.

6. The method of claim 1, wherein, The optimization time resolution of each power balance model is hour, and the optimization results with hour resolution are aggregated to the preset decision variable resolution.

7. The method of claim 1, wherein, The objective function of each power balance model includes six items, namely load gap, new energy curtailment, water power curtailment, reserve gap, power flow relaxation penalty and system operation cost, and each item is provided with a weight.

8. The method of claim 1, wherein, Specifically, the method comprises the following steps: 1) an annual power balance model 1-1) objective function (1) In the formula is the total number of optimization periods, is the period is the weight of the typical day, representing the actual number of days represented by the typical day in the corresponding month, in the formula represents the system load constraint relaxation penalty factor, is the positive and negative relaxation variable of the system load, representing the load gap, is the total number of system balance subareas, respectively, are the balance subareas are the new energy and hydropower in the balance subarea in the period is the new energy curtailment penalty factor, is the hydropower curtailment penalty factor, is the system reserve capacity relaxation penalty factor, respectively, are the positive and negative system reserve capacity relaxation variables, represents the balance subarea cross-section network power flow constraint relaxation penalty factor; respectively, are the positive and negative cross-section power flow relaxation variables; represents the system operation cost coefficient, which simplifies the unit operation cost as linear, is the unit cost coefficient; 1-2) constraint condition 1-2-1) system load balance constraint (2) In the formula is the output of the bth balancing sub-area thermal power unit at time t, and the corresponding is the water power unit, is the new energy field station, is the jth balancing area out-of-area tie-line power, is the balancing area load; 1-2-2) system positive / negative reserve capacity constraint (3) In the formula The unit availability of the equivalent thermal power unit of the sub-area b in the time period t, the monthly value in the annual model is The value is constant, so it is represented as is the i-th type of equivalent unit availability of the sub-area b in the m-th month, is the maximum and minimum unit output; 1-2-3) unit output upper and lower limit constraint (4) 1-2-4) energy storage constraint (5) (6) (7) wherein is the discharge, charge power for balancing the equivalent energy storage of sub-zone b, is the energy state of the energy storage, is the upper and lower limits of the energy state of the energy storage, is the charge-discharge efficiency of the energy storage of sub-zone b; 1-2-5) power flow constraint The line power flow constraint is simplified as the cross-section transmission capacity constraint between the balance sub-areas: (8) In the formula For section index, For balanced sub-regions Net injection power for cross section The distribution factor of tidal current transfer cross-section The limit of current transmission; 1-2-6) maintenance plan constraint The maintenance plan in the annual balance analysis passes through the unit availability Embodiments: (9) (10) (11) In the formula are the upper and lower limits of the availability, determined by the maintenance time window of the unit, etc.; is the maximum synchronous maintenance coefficient, determined by the maintenance resources; is the number of hours in the mth month, is the maintenance time of the ith unit, B is the number of thermal and hydroelectric units included in the balance sub-area b, and BZ is the number of thermal and hydroelectric units that need to be maintained in the balance sub-area b. 1-2-7) out-of-area sending and receiving plan Annual sending and receiving contract total constraint: (12) In the formula is the total amount of annual power transmission and reception contracts; Out-of-area tie-line transmission power constraint: (13) 1-2-8) new energy output (14) wherein is the predicted output of the new energy units in the sub-area b at the typical day time period t; 1-3) calculation decision variable The optimization time resolution of the annual power energy balance model is hour, and the optimization results with hour resolution are summed and aggregated to month resolution to obtain the monthly power generation target of the i-th type unit in the balance sub-area b , the monthly power transmission and reception plan of the j-th interconnection line outside the area , the monthly availability of the i-th type unit in the balance sub-area b ; 2) quarterly power balance model 2-1) objective function (15) In the formula is the total number of optimization periods; 2-2) constraint condition 2-2-1) system load balance constraint, same as 1-2-1) 2-2-2) system positive / negative reserve capacity constraint, same as 1-2-2) 2-2-3) unit output upper and lower limit constraint, same as 1-2-3) Supplementary unit monthly power generation target constraint: (16) In the formula is the allowed deviation range of the monthly power generation of the unit compared to the annual plan target; 2-2-4) energy storage constraint, same as 1-2-4) 2-2-5) power flow constraint, same as 1-2-5) 2-2-6) maintenance plan constraint (17) (18) (19) In the formula For the first The total number of hours per week takes into account both complete and incomplete weeks within a month. This represents the total number of hours in month m. 2-2-7) out-of-area sending and receiving plan Annual sending and receiving contract total constraint is changed to tie-line monthly power target constraint: (20) 2-3) calculation decision variable The optimization time resolution of the quarterly power balance model is hour, and the optimization results with hour resolution are summed and aggregated to weekly resolution. The weekly generation target of the i-th type unit in the balance sub-area b , the weekly power transmission and reception plan of the j-th interconnection line outside the area , the weekly availability of the i-th type unit in the balance sub-area b ; 3) monthly power balance model 3-1) objective function (21) In the formula is the total number of optimization periods; respectively, the number of new energy stations, the number of hydropower stations, the number of network key sections, and the total number of units in the balance area; in the monthly power balance analysis, the analysis object in the spatial scale changes from the balance sub-area to the 220kV key node, and from the aggregated unit to the actual unit; 3-2) constraint condition 3-2-1) system load balance constraint (22) In the formula is the output of the i th thermal power unit, hydroelectric power unit, or new energy power station at time t, and NT is the total number of external tie lines. 3-2-2) system positive / negative reserve capacity constraint (23) In the formula is the repair state of the ith thermal power or hydroelectric unit at time period t. 3-2-3) unit constraint Unit output upper and lower limit constraint: (24) Unit weekly power generation target constraint: (25) 3-2-4) energy storage constraint (26) (27) (28) In the formula is the discharging and charging power of the i-th energy storage, is the energy state of the i-th energy storage, is the upper and lower limits of the energy state of the i-th energy storage, is the charging and discharging efficiency of the i-th energy storage; 3-2-5) network power flow constraint Key cross-section constraint is considered: (29) In the formula respectively, the key section s and the unit i, the key section s and the out-of-area tie line j access point, the key section s and the transfer distribution factor of the load k; 3-2-6) maintenance plan constraint Unit availability rate transmission constraint: (30) In the formulae tolerance; Maintenance continuous time constraint: (31) (32) In the formula is the maintenance continuous time of the ith unit The maintenance window is the earliest and latest start time of unit maintenance, and the maintenance window constraint is: (33) In the formula defines whether the unit i switches to the repair state at the time period t; (34) The maintenance window constraint: The maintenance resource constraint represents that the available maintenance personnel, tools, etc. in the same time period are limited, and is represented as: (35) wherein represents the resource needed for the i-th unit maintenance, represents the total number of units under maintenance, and M represents the total maintenance resource in the balancing area. 3-2-7) Out-of-area sending and receiving power plan Considering the out-of-area tie-line power transmission constraint: (36) And the tie-line weekly power target constraint: (37) 3-2-8) New energy output (38) wherein is the predicted output of the new energy unit r at time period t; 3-3) Calculation of decision variables The optimization time resolution of the monthly power balance model is hour, and the optimization results with hour resolution are summed and aggregated to daily resolution, the daily generation target of the i th unit , the daily sending and receiving plan of the j th out-of-balance area tie-line , the maintenance arrangement of the i th unit ; 4) Weekly power balance model 4-1) Objective function (39) In the formula is the total number of optimization periods; the operation cost in the objective function is restored to the actual cost function of each generator unit, while considering the start-up and shut-down cost of the unit, in which is the operation cost function of unit i; 4-2) Constraint conditions 4-2-1) System load balance constraint, same as 3-2-1); 4-2-2) System positive / negative reserve capacity constraint, same as 3-2-2); 4-2-3) Unit constraint Unit output upper and lower limit constraint: (40) wherein Pi, Li, and Uj represent the upper and lower bounds of the output of unit i, respectively; Unit ramping constraint: When the unit ramps up or down, it should meet the ramping rate requirement: (41) (42) wherein represents the maximum up-ramp rate of the machine i, represents the maximum down-ramp rate of the machine i, Unit minimum continuous start / stop time constraint: (43) (44) wherein is the start-stop state of the unit i at time period t; , is the minimum continuous on-time and minimum continuous off-time of the transaction unit; , is the time that unit i has been continuously on and continuously off at time period t, expressed as: (45) (46) Unit daily power generation constraint: (47) 4-2-4) Energy storage constraint, same as 3-2-4); 4-2-5) Network power flow constraint Considering the key section constraint: (48) In the formula respectively, the key section s and the unit i, the key section s and the out-of-area tie line j access point, the key section s and the transfer distribution factor of the load k; Considering the key section constraint: (49) 4-2-6) Out-of-area sending and receiving power plan Considering the out-of-area tie-line power transmission constraint: (50) And the tie-line weekly power target constraint: (51) Out-of-area tie-line regulation rate constraint, represented as: (52) In the formula is the maximum regulating rate of the jth zone external tie line 4-2-7) New energy output constraint, same as 3-2-7); 4-3) Calculation of decision variables The optimization time resolution of the weekly power balance model is an hour, and the output plan of the i th unit per hour is obtained The start-stop state of the i th unit The charging and discharging power of the es th energy storage The abandoned power of the r th new energy field station The power of the j th balancing area external tie line .

9. A device for cross-period bridging calculation of medium and long term power balance employing any of the methods of claims 1-8, characterized by, Including: An acquisition unit for acquiring system parameters and annual, quarterly, monthly, and weekly prediction data; A first analysis unit for annual-scale power balance analysis; A second analysis unit for annual-scale power balance analysis; A third analysis unit for annual-scale power balance analysis; A fourth analysis unit for annual-scale power balance analysis.

10. A storage medium, characterized by The storage medium has a computer program stored therein, wherein the computer program is configured to execute the cross-period connection calculation method of the medium and long-term power balance of claim 1-8 when running. The storage medium has a computer program stored therein, wherein the computer program is configured to execute the cross-period connection calculation method of the medium and long-term power balance of claim 1-8 when running.

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