A tie line planning method and system

Abstract

The application provides a tie line planning method and system, and the method comprises the following steps: obtaining data information of new energy and thermal power generating units; and obtaining a tie line plan based on the data information and a pre-constructed tie line planning model; wherein the construction of the tie line planning model considers the operation of the power grids of both the sending side and the receiving side and the prediction error of new energy in a limit scenario. The application adopts the tie line planning model to reasonably plan the tie line plan in consideration of the prediction error of new energy, thereby improving the new energy consumption level.

Description

Technical Field

[0001] This invention relates to tie-line planning for power grid operation, and more particularly to a tie-line planning method and system. Background Technology

[0002] With the construction of large-scale centralized new energy and ultra-high-voltage (UHV) power grids, most UHV inter-regional interconnection lines are tasked with transmitting new energy. Due to the highly random and uncontrollable nature of new energy, its intraday operation often deviates significantly from the previous day's forecasts. However, current interconnection line plans lack the flexibility to consider the actual operating conditions of both the sending and receiving power grids during the actual planning process. This makes it impossible to achieve multi-regional joint absorption of new energy when forecast errors are significant. Therefore, how to arrange more reasonable interconnection line plans and provincial dispatch plans to improve the level of new energy absorption is a key issue currently facing China's power grid. Summary of the Invention

[0003] To address the aforementioned shortcomings in the existing technology, the present invention provides a method for compiling a contact line plan.

[0004] The technical solution provided by this invention is: a method for compiling a tie-line plan, comprising:

[0005] Acquire data and information on new energy sources and thermal power units;

[0006] The tie-line plan is derived based on the data and the pre-built tie-line plan model.

[0007] The construction of the tie-line planning model takes into account the operation of the power grids of both the sending and receiving parties, as well as the prediction error of new energy sources under extreme scenarios.

[0008] Preferably, the construction of the tie-line planning model includes:

[0009] In the extreme scenario based on the prediction error of new energy sources, the objective function is determined with the goal of minimizing the cost of thermal power units.

[0010] And set system balance constraints, unit operation constraints, provincial reserve constraints, and branch and section limit constraints for the optimization objectives.

[0011] Preferably, the objective function is as follows:

[0012]

[0013] In the formula, S represents the number of new energy scenarios; s = 1, s = 2, and s = 3 represent the maximum positive error limit scenario, the maximum negative error limit scenario, and the prediction scenario, respectively; ω s t represents the weighting coefficient for each scenario; t represents the time period number; T represents the total number of time periods; N represents the total number of thermal power units. It refers to the output of unit i at time t in scenario s, a i b i c i A is the cost factor for unit i; i The start-up cost of thermal power unit i; u i,t u i,t-1 These represent the start-up and stop-down states of unit i at times t and t-1, respectively, with 1 for start-up and 0 for stop-down.

[0014] Preferably, the system balance constraint is as follows:

[0015]

[0016] In the formula, N j It is the total number of thermal power units in province j. In scenario s, the new energy power of province j at time t, p Lj,t The power of the connecting line in province j at time t, p dj,t It is the load power of province j at time t.

[0017] Preferably, the unit operating constraints are as follows:

[0018]

[0019] In the formula, p i,min p i,max The upper and lower limits of unit i's output; p i,up p i,down For unit i, the upper and lower limits of the ramp rate; p i,aup p i,adown These are the upper and lower limits of the start-up and shutdown ramp-up rate for unit i.

[0020] Preferably, the provincial reserve constraint is as shown in the following formula:

[0021]

[0022] In the formula, R uj,t R dj,t To save on the load and reserve requirements at time t.

[0023] Preferably, the branch and cross-section limit constraints are as follows:

[0024]

[0025] In the formula, f represents the power flow value of the branch between nodes m and n in scenario s at time t. mn,max f mn,min These are the upper and lower limits of this branch road. For the current flow at time t of section h in scenario s, S h,max ,S h,min These are the upper and lower limits of the cross-section.

[0026] Preferably, the extreme scenarios of the new energy prediction error include: the maximum positive error of new energy prediction and the maximum negative error of new energy prediction.

[0027] A tie-line planning system based on a set of extreme scenarios includes:

[0028] The acquisition module is used to acquire data information from new energy sources and thermal power units;

[0029] The tie-line planning module calculates the tie-line plan based on the data information and the pre-built tie-line planning model;

[0030] The construction of the tie-line planning model takes into account the operation of the power grids of both the sending and receiving parties, as well as the prediction error of new energy sources under extreme scenarios.

[0031] Preferably, the tie-line planning module includes: an interconnected model building submodule and a data input submodule;

[0032] The data input submodule is used to input data information from new energy sources and thermal power units into the model building submodule;

[0033] The model building submodule is used to build the tie-line planning model.

[0034] Preferably, the model building submodule includes: an objective function building unit and a constraint setting unit;

[0035] The objective function construction unit determines the objective function based on the extreme scenario of new energy prediction error, with the minimum cost of thermal power units as the optimization objective;

[0036] The constraint setting unit is used to set system balance constraints, unit operation constraints, provincial reserve constraints, and branch and section limit constraints for the optimization target.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] This invention provides a method for preparing tie-line plans, comprising: acquiring data information on new energy sources and thermal power units; and calculating tie-line plans based on the data information and a pre-constructed tie-line plan model. The tie-line plan model is constructed considering the operating conditions of both the sending and receiving power grids, as well as new energy prediction errors. This invention, by taking into account new energy prediction errors, rationally arranges tie-line plans based on the tie-line plan model, thereby improving the level of new energy absorption. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the tie-line planning method of the present invention;

[0040] Figure 2 This is a schematic diagram of the tie-line planning model construction method of the present invention. Detailed Implementation

[0041] To better understand this invention, the following description, in conjunction with the accompanying drawings and examples, will further illustrate the invention.

[0042] In the planning of power grid interconnection lines, renewable energy transmission plans are mostly treated as known quantities in calculations, without considering forecast deviations. When significant forecast deviations occur, insufficient provincial regulation margins may lead to wind and solar power curtailment. This invention, combining existing interconnection line planning models, rationally constructs an interconnection line planning model. Under constraints such as unit operation and grid security, it introduces an extreme scenario optimization method for renewable energy forecasting to improve the tolerance of provinces to renewable energy forecasting errors, thereby enhancing the level of renewable energy consumption.

[0043] The present invention will be further described in detail below with reference to specific implementation examples, but this is not intended to limit the present invention.

[0044] like Figure 1 As shown, a method for compiling a tie-line plan includes:

[0045] Step 1: Obtain data information on new energy sources and thermal power units;

[0046] Step 2: Calculate the tie-line plan based on the data and the pre-built tie-line plan model;

[0047] The construction of the tie-line planning model takes into account the operation of the power grids of both the sending and receiving parties, as well as the prediction error of new energy sources under extreme scenarios.

[0048] Step 1: Obtain data information from new energy sources and thermal power units, specifically including:

[0049] Acquire the number of new energy scenarios, the total number of thermal power units, the start-up and shutdown status of units at each time in each scenario, the upper and lower limits of thermal power unit output, the upper and lower limits of unit ramp rate, the upper and lower limits of unit start-up and shutdown ramp rate, the upper and lower limits of power flow value of branches between any two nodes in each scenario at the corresponding time, and the upper and lower limits of cross sections in any scenario.

[0050] Step 2: Obtain the tie-line plan based on the data and the pre-built tie-line plan model: The tie-line plan model construction method is as follows... Figure 2 As shown:

[0051] (1) Establish optimization objectives for the connection line plan that take into account extreme scenarios;

[0052] Based on the extreme scenarios of new energy prediction errors, the objective function is determined with the goal of minimizing the cost of thermal power units; wherein, the extreme scenarios of new energy prediction errors include: the maximum positive error of new energy prediction and the maximum negative error of new energy prediction.

[0053] (2) Establish constraints for the tie-line plan considering extreme scenarios of new energy prediction errors;

[0054] And set system balance constraints, unit operation constraints, provincial reserve constraints, and branch and section limit constraints for the optimization objectives.

[0055] (3) Solve using commercial software.

[0056] In step (1), the optimization objective for establishing a connection plan considering extreme scenarios is...

[0057] The optimization objective is to minimize the cost of thermal power units, as shown in the following formula.

[0058]

[0059] In the formula, S represents the number of new energy scenarios, taken as S=3; s=1, s=2, and s=3 represent the maximum positive error limit scenario, the maximum negative error limit scenario, and the prediction scenario, respectively; ω s t represents the weighting coefficient for each scenario; t represents the time period number; T represents the total number of time periods; N represents the total number of thermal power units. It refers to the output of unit i at time t in scenario s, a i b i c i A is the cost factor for unit i; i The start-up cost of thermal power unit i; u i,t u i,t-1 These represent the start-up and stop-down states of unit i at times t and t-1, respectively, with 1 for start-up and 0 for stop-down.

[0060] The determination of this extreme scenario is based on a combination of confidence limits at a certain confidence level. For example, the predicted power of new energy sources in time period t is p. W,t With a confidence level of 0.95, and assuming that the renewable energy power follows a normal distribution F(·) based on the predicted power, for each time period t, let... and Therefore, it is assumed that the actual renewable energy power can be encompassed within this fluctuation range. In the maximum negative error limit scenario, the power of new energy sources is Maximum positive error limit scenario

[0061] In step (2), the constraints of the tie-line plan for the extreme scenario considering the error in new energy prediction are established.

[0062] By considering extreme scenarios in the constraints, it can be ensured that the tie-line plan in extreme scenarios can meet the system's operational requirements and reduce wind and solar power curtailment. The constraints are as follows:

[0063] (1) System equilibrium constraints

[0064]

[0065] In the formula, N j It is the total number of thermal power units in province j. In scenario s, the new energy power of province j at time t, p Lj,t The power of the connecting line in province j at time t, p dj,t It is the load power of province j at time t.

[0066] (2) Unit operation constraints

[0067]

[0068] In the formula, p i,min p i,max The upper and lower limits of unit i's output; p i,up p i,down For unit i, the upper and lower limits of the ramp rate; p i,aup p i,adown These are the upper and lower limits of the start-up and shutdown ramp-up rate for unit i.

[0069] (3) Provincial reserve constraints

[0070]

[0071] In the formula, R uj,t R dj,t To save on the load reserve requirements and down reserve requirements at time t.

[0072] (4) Limits on branch roads and cross-sections

[0073]

[0074] In the formula, f represents the power flow value of the branch between nodes m and n in scenario s at time t. mn,max ,f mn,min These are the upper and lower limits of this branch road. For the current flow at time t of section h in scenario s, S h,max ,S h,min These are the upper and lower limits of the cross-section.

[0075] Based on the same inventive concept, this invention provides a tie-line planning system, comprising:

[0076] The acquisition module is used to acquire data information from new energy sources and thermal power units;

[0077] The tie-line planning module calculates the tie-line plan based on the data information and the pre-built tie-line planning model;

[0078] The construction of the tie-line planning model takes into account the operation of the power grids of both the sending and receiving parties, as well as the prediction error of new energy sources under extreme scenarios.

[0079] The contact line planning module includes: an interconnected model building submodule and a data input submodule;

[0080] The data input submodule is used to input data information from new energy sources and thermal power units into the model building submodule;

[0081] The model building submodule is used to build the tie-line planning model.

[0082] The model construction submodule includes: an objective function construction unit and a constraint setting unit;

[0083] The objective function construction unit determines the objective function based on the extreme scenario of new energy prediction error, with the minimum cost of thermal power units as the optimization objective;

[0084] The constraint setting unit is used to set system balance constraints, unit operation constraints, provincial reserve constraints, and branch and section limit constraints for the optimization target.

[0085] Among them, the extreme scenarios include: maximum positive error in new energy prediction and maximum negative error in new energy prediction.

[0086] The objective function is shown in the following equation:

[0087]

[0088] In the formula, S represents the number of new energy scenarios; s = 1, s = 2, and s = 3 represent the maximum positive error limit scenario, the maximum negative error limit scenario, and the prediction scenario, respectively; ω s t represents the weighting coefficient for each scenario; t represents the time period number; T represents the total number of time periods; N represents the total number of thermal power units. It refers to the output of unit i at time t in scenario s, a i b i c i A is the cost factor for unit i; i The start-up cost of thermal power unit i; u i,t u i,t-1These represent the start-up and stop-down states of unit i at times t and t-1, respectively, with 1 for start-up and 0 for stop-down.

[0089] The constraints set for the optimization objectives include: system balance constraints, unit operation constraints, provincial reserve constraints, and branch line and section limit constraints.

[0090] The system equilibrium constraints are shown in the following equation:

[0091]

[0092] In the formula, N j It is the total number of thermal power units in province j. In scenario s, the new energy power of province j at time t, p Lj,t The power of the connecting line in province j at time t, p dj,t It is the load power of province j at time t.

[0093] The unit's operating constraints are shown in the following formula:

[0094]

[0095] In the formula, p i,min p i,max The upper and lower limits of unit i's output; p i,up p i,down For unit i, the upper and lower limits of the ramp rate; p i,aup p i,adown These are the upper and lower limits of the start-up and shutdown ramp-up rate for unit i.

[0096] The provincial reserve constraint is shown in the following formula:

[0097]

[0098] In the formula, R uj,t R dj,t To save on the load and reserve requirements at time t.

[0099] The branch road and cross-section limit constraints are shown in the following formula:

[0100]

[0101] In the formula, f represents the power flow value of the branch between nodes m and n in scenario s at time t. mn,max f mn,min These are the upper and lower limits of this branch road. For the current flow at time t of section h in scenario s, S h,max ,S h,min These are the upper and lower limits of the cross-section.

[0102] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0104] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0105] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0106] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A method for compiling a tie-line plan, characterized in that, include: Acquire data and information on new energy sources and thermal power units; The tie-line plan is derived based on the data and the pre-built tie-line plan model. The construction of the tie-line planning model takes into account the operation of the power grids of both the sending and receiving parties, as well as the prediction error of new energy sources under extreme scenarios. The construction of the tie-line planning model includes: In the extreme scenario based on the prediction error of new energy sources, the objective function is determined with the goal of minimizing the cost of thermal power units. And set system balance constraints, unit operation constraints, provincial reserve constraints, and branch and section limit constraints for the optimization objectives; The branch road and cross-section limit constraints are shown in the following formula: In the formula, f represents the power flow value of the branch between nodes m and n in scenario s at time t. mn,max f mn,min These are the upper and lower limits of this branch road. For the current flow at time t of section h in scenario s, S h,max ,S h,min These are the upper and lower limits of the cross-section; The extreme scenarios for the new energy prediction error include: the maximum positive error in new energy prediction and the maximum negative error in new energy prediction. The determination of this extreme scenario is based on a combination of confidence limits at a certain confidence level; The objective function is shown in the following equation: In the formula, S represents the number of new energy scenarios; s = 1, s = 2, and s = 3 represent the maximum positive error limit scenario, the maximum negative error limit scenario, and the prediction scenario, respectively; ω s t represents the weighting coefficient for each scenario; t represents the time period number; T represents the total number of time periods; N represents the total number of thermal power units. It refers to the output of unit i at time t in scenario s, a i b i c i A is the cost factor for unit i; i The start-up cost of thermal power unit i; u i,t u i,t-1 These represent the start-up and stop-up states of unit i at times t and t-1, respectively, with 1 for start-up and 0 for stop-up; The provincial reserve constraint is shown in the following formula: In the formula, R uj,t R dj,t To save on the reserve requirements and down reserve requirements of j at time t; p i,min p i,max The upper and lower limits of the output of unit i; In scenario s, the new energy power of province j at time t, p Lj,t The power of the connecting line in province j at time t, p dj,t It is the load power of province j at time t.

2. The method as described in claim 1, characterized in that, The system equilibrium constraints are shown in the following equation: In the formula, N j It is the total number of thermal power units in province j. In scenario s, the new energy power of province j at time t, p Lj,t The power of the connecting line in province j at time t, p dj,t It is the load power of province j at time t.

3. The method as described in claim 2, characterized in that, The unit's operating constraints are shown in the following formula: In the formula, p i,min p i,max The upper and lower limits of unit i's output; p i,up p i,down For unit i, the upper and lower limits of the ramp rate; p i,aup p i,adown These are the upper and lower limits of the start-up and shutdown ramp-up rate for unit i.

4. A system for implementing the tie-line planning method as described in any one of claims 1-3, characterized in that, include: The acquisition module is used to acquire data information from new energy sources and thermal power units; The tie-line planning module calculates the tie-line plan based on the data information and the pre-built tie-line planning model; The construction of the tie-line planning model takes into account the operation of the power grids of both the sending and receiving parties, as well as the prediction error of new energy sources under extreme scenarios.

5. The system as described in claim 4, characterized in that, The link planning module includes: an interconnected model building submodule and a data input submodule; The data input submodule is used to input data information from new energy sources and thermal power units into the model building submodule; The model building submodule is used to build the tie-line planning model.

6. The system as described in claim 5, characterized in that, The model construction submodule includes: an objective function construction unit and a constraint setting unit; The objective function construction unit determines the objective function based on the extreme scenario of new energy prediction error, with the minimum cost of thermal power units as the optimization objective; The constraint setting unit is used to set system balance constraints, unit operation constraints, provincial reserve constraints, and branch and section limit constraints for the optimization target.

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