Grid-province-tertiary power system collaborative optimization scheduling method, system and device

Abstract

The application provides a grid-province-county three-level power system collaborative optimization scheduling method, system and equipment. Among them, under the background of large-scale distributed and centralized new energy power source access to the grid, the optimization scheduling method considering the collaborative action of grid-province-county three-level power system is proposed. On the basis of fully considering the coordination of flexible resources of county-level power grid, the integration optimization scheduling function of grid-province-level power grid is strengthened, and the uncertainty of source and load prediction results is considered. The renewable energy consumption capacity and system risk resistance capacity of large power grid can be effectively improved.

Description

Technical Field

[0001] This application relates to a method, system, and equipment for coordinated optimization scheduling of a three-level power system (grid, province, and locality), belonging to the field of power system optimization operation technology. Background Technology

[0002] According to the "Guiding Opinions on Accelerating the Construction of a Unified National Electricity Market System," various types of power resources in my country's power system will need to be optimally allocated at the provincial, inter-provincial, and larger regional levels in the future. However, the current traditional planned dispatch model employs a two-tiered grid-province management system, with each grid level only responsible for planning and safety verification within its designated area. Under this traditional model, the grid level first formulates plans for inter-regional interconnection lines and total power exchange between provinces, and then the provinces formulate their own regional power generation plans. This process often involves several iterations between the grid and provinces to determine the final dispatch scheme, and the optimal allocation of resources within a province cannot achieve global (grid-wide) optimization. Furthermore, because each level of the system formulates its plans independently, coordination is limited, resulting in a lack of coordinated optimization of resources at the prefecture level. This leads to insufficient flexibility in addressing power system uncertainties and the challenges of large-scale distributed power source integration. Therefore, with the increasing prevalence of various renewable energy sources, how to leverage the coordinated and optimized dispatch of the grid-province-prefecture three-tiered power system to improve renewable energy absorption capacity and system resilience has become a crucial issue in the field of power system optimization. Summary of the Invention

[0003] In view of the above problems, this invention proposes a three-level (grid, province, and locality) power system collaborative optimization scheduling method, system, and equipment. By innovating the collaborative business system and system optimization operation model of different levels of power grid scheduling plans, it improves the coordinated operation capability of the power system and the overall resource optimization allocation level, thereby promoting the large power grid's capacity to carry and absorb clean energy.

[0004] To achieve the above objectives, this application provides the following technical solution:

[0005] In a first aspect, embodiments of this application provide a method for coordinated optimization scheduling of a three-tiered power system (grid, province, and locality), comprising:

[0006] A collaborative business system for dispatching agencies at the grid, provincial, and municipal levels is constructed, along with a collaborative optimization dispatching model for the power system at these three levels. The collaborative optimization dispatching model is used to support the operation of the collaborative business system for dispatching agencies at the grid, provincial, and municipal levels.

[0007] Acquire system-related information, which includes basic physical information and model parameters of the three-level power system (grid, province, and locality) as well as system equipment operation constraint information;

[0008] Based on the relevant system information, a source load prediction interval is calculated and generated. Based on the source load prediction interval, the node net load is formed, and normal prediction scenarios and extreme scenarios are generated as key conditions for collaborative optimization scheduling.

[0009] Based on the aforementioned key conditions, the power system collaborative optimization scheduling model is solved to obtain the scheduling operation results;

[0010] The aforementioned construction of a three-tiered (network, province, and prefecture) collaborative business system for dispatching agencies includes:

[0011] The prefecture-level dispatching agency aggregates resources upwards, optimizing the feasible region for prefecture-level dispatch based on the local distributed renewable energy output level and uncertainty, the controllable load resource regulation characteristics and adjustable capacity at the prefecture level, and the characteristics of the prefecture-level power grid network. The provincial-level dispatching agency, based on the feasible region at the prefecture level and the power exchange demand between provinces and grids, uses an integrated optimization model to uniformly optimize global resources according to load forecasting adjustments, resulting in the provincial-level dispatching plan. Based on the characteristics of power grid networks at all levels, system operation and maintenance arrangements, and power generation resource costs, the provincial-level dispatching plan is decomposed downwards, forming a three-tiered (prefecture, grid, and provincial) power system collaborative optimization dispatching plan.

[0012] The establishment of a three-tiered (grid, province, and prefecture) power system collaborative optimization scheduling model includes:

[0013] A prefecture-level optimized dispatch model is established with the lowest prefecture-level power grid dispatch cost as the optimization objective. Based on prioritizing the acceptance of new energy power, a provincial-level optimized dispatch model is established with the lowest overall grid generation cost as the optimization objective. The prefecture-level optimized dispatch model and the provincial-level optimized dispatch model are then linked to obtain a three-tiered (prefecture, grid, and provincial) power system collaborative optimized dispatch model. The linking process includes approximating the prefecture-level power grid as an equivalent node and connecting it to the upper-level provincial power grid.

[0014] Based on the above methods, optionally, the objective function of the prefecture-level optimal scheduling model is:

[0015]

[0016] In the formula, F lower,i Let i be the day-ahead total dispatch cost of distribution network i; The total day-ahead scheduling cost of a controllable micro-source k; Let k be the output power of the controllable microsource k at time t; The total day-ahead scheduling cost of the uncontrollable micro-source j; Let J be the output power of the uncontrollable micro-source j at time t; This refers to the total day-ahead dispatch cost of the energy storage device; Let be the charging and discharging power of the energy storage device at time t; To reduce the interaction costs of inter-provincial and inter-regional communication lines; To save power in the inter-regional tie line at time t; To regulate the day-ahead scheduling costs of load control; The adjustable load regulation capacity at time t.

[0017] Based on the above method, optionally, the constraints of the prefecture-level optimal scheduling model include power balance constraints and parameter constraints of each power device in the prefecture-level power grid.

[0018] Based on the above methods, optionally, the objective function of the provincial-level network optimization scheduling model is:

[0019]

[0020] In the formula, c g,t c represents the power generation cost of a conventional thermal power unit g during time period t; h,t c is the power generation cost of the hydropower unit h during time period t; m,t To reduce the cost of flexible load m during time period t; c n,t Δ represents the load transfer cost of the transferable flexible load n during time period t; w,t The penalty cost for power reduction of new energy generating units during period t; Let $t$ be the optimal scheduling cost of the prefecture-level power grid k at time $t$.

[0021] Based on the above methods, optionally, the constraints of the provincial-level optimized scheduling model include the power generation and consumption balance constraints of the entire network, the load balance constraints of each province, and the power and electricity exchange constraints of AC and DC transmission lines.

[0022] Optionally, based on the above method, solving the power system collaborative optimization scheduling model based on the key conditions to obtain the scheduling operation results includes:

[0023] Based on the probability of source load in the city and the interval prediction results, we obtain the normal prediction scenario and extreme scenario, as well as the probability of different scenarios. According to the external input system operation boundary information and operation constraints, we use uncertainty optimization methods to take into account different scenarios and multiple optimization objectives. Under the premise of ensuring the safe operation of the distribution network, we optimize the net load information of each node reported by the distributed power generation plan.

[0024] Based on the provincial source-load probability and interval prediction results, conventional and extreme prediction scenarios are obtained, as well as the probability of different scenarios. According to the externally input system operation boundary information and operation constraints, taking into account the equivalent model of the prefecture-level power system, based on robust optimization considering different scenarios and multiple optimization objectives, the next day's multi-level scheduling plan and resource combination status are output, including AC / DC tie line power exchange plan, pumped storage and energy storage control plan, power generation intervals in various provinces and regions, cascade hydropower unit combination plan, water-wind-solar coordinated operation plan, and orderly power consumption plan.

[0025] Based on the above methods, optionally, the Ward equivalent method can be used to perform equivalent calculations on the prefecture-level power grid.

[0026] Secondly, embodiments of this application also provide a three-tiered (grid, province, and locality) power system collaborative optimization dispatching system, which includes:

[0027] The module is used to construct a collaborative business system for dispatching agencies at the grid, provincial, and municipal levels, and to establish a collaborative optimization dispatching model for the power system at the grid, provincial, and municipal levels; the collaborative optimization dispatching model for the power system is used to support the operation of the collaborative business system for dispatching agencies at the grid, provincial, and municipal levels.

[0028] The acquisition module is used to acquire system-related information, which includes basic physical information and model parameters of the three-level power system (grid, province, and locality) and system equipment operation constraint information.

[0029] The calculation module is used to calculate and generate source load prediction intervals based on the relevant information of the system, and to form node net loads based on the source load prediction intervals, generating normal prediction scenarios and extreme scenarios as key conditions for collaborative optimization scheduling.

[0030] The scheduling module is used to solve the power system collaborative optimization scheduling model based on the key conditions to obtain the scheduling operation results;

[0031] The aforementioned construction of a three-tiered (network, province, and prefecture) collaborative business system for dispatching agencies includes:

[0032] The prefecture-level dispatching agency aggregates resources upwards, optimizing the feasible region for prefecture-level dispatch based on the local distributed renewable energy output level and uncertainty, the controllable load resource regulation characteristics and adjustable capacity at the prefecture level, and the characteristics of the prefecture-level power grid network. The provincial-level dispatching agency, based on the feasible region at the prefecture level and the power exchange demand between provinces and grids, uses an integrated optimization model to uniformly optimize global resources according to load forecasting adjustments, resulting in the provincial-level dispatching plan. Based on the characteristics of power grid networks at all levels, system operation and maintenance arrangements, and power generation resource costs, the provincial-level dispatching plan is decomposed downwards, forming a three-tiered (prefecture, grid, and provincial) power system collaborative optimization dispatching plan.

[0033] The establishment of a three-tiered (grid, province, and prefecture) power system collaborative optimization scheduling model includes:

[0034] A prefecture-level optimized dispatch model is established with the lowest prefecture-level power grid dispatch cost as the optimization objective. Based on prioritizing the acceptance of new energy power, a provincial-level optimized dispatch model is established with the lowest overall grid generation cost as the optimization objective. The prefecture-level optimized dispatch model and the provincial-level optimized dispatch model are then linked to obtain a three-tiered (prefecture, grid, and provincial) power system collaborative optimized dispatch model. The linking process includes approximating the prefecture-level power grid as an equivalent node and connecting it to the upper-level provincial power grid.

[0035] Thirdly, embodiments of this application also provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor calls and executes the computer program, it implements the three-level coordinated optimization scheduling method for the power grid, province, and locality as described in any one of the first aspects.

[0036] The method, system, and equipment for coordinated optimization scheduling of the power grid, province, and prefecture levels provided in this application propose an optimized scheduling method that considers the coordinated effect of the power grid, province, and prefecture levels in the context of large-scale distributed and centralized new energy power sources accessing the power grid. This method strengthens the integrated optimization scheduling effect of the power grid and provincial power grid while fully considering the flexibility and resource coordination of the prefecture-level power grid. It also takes into account the uncertainty of the source and load two-end prediction results, which can effectively improve the renewable energy absorption capacity and system risk resistance capacity of the large power grid. Attached Figure Description

[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. Furthermore, these drawings and textual descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to specific embodiments.

[0038] Figure 1 A flowchart illustrating a three-tiered (grid, province, and locality) power system collaborative optimization scheduling method provided in one embodiment of this application;

[0039] Figure 2 A schematic diagram of the collaborative business system of the three-level dispatching agencies at the network, province, and local levels provided in one embodiment of this application;

[0040] Figure 3 An equivalent model diagram of a three-tiered power grid (province, city, and county) provided in one embodiment of this application;

[0041] Figure 4 A flowchart of a three-tiered (grid, province, and locality) power system coordinated optimization scheduling operation model provided in one embodiment of this application;

[0042] Figure 5 A schematic diagram of the structure of a three-level (grid, province, and locality) power system collaborative optimization dispatching system provided in one embodiment of this application;

[0043] Figure 6 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the embodiments of this application. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0045] To leverage the collaborative optimization and dispatching capabilities of the grid-province-regional power system to enhance renewable energy absorption capacity and system resilience, this invention provides a collaborative optimization and dispatching scheme for the grid-province-regional power system. While fully considering the flexibility and resource coordination of the regional power grid, it strengthens the integrated optimization and dispatching role of the grid and provincial power grids, and also takes into account the uncertainties of source-load dual-end forecasting results. This effectively improves the renewable energy absorption capacity and system resilience of the large power grid. The following examples and embodiments provide non-limiting descriptions of the specific implementation scheme.

[0046] Some embodiments of this application provide a method for coordinated optimization scheduling of a three-tiered power system (grid, province, and locality), referring to... Figure 1 , Figure 1 This is a flowchart illustrating a three-tiered (grid, province, and locality) power system collaborative optimization scheduling method according to an embodiment of this application. In specific implementation, the solution in this embodiment can be a processing system configured in an electronic device such as a computer or server. That is, the solution in this embodiment can be implemented through a software system in an electronic device.

[0047] like Figure 1 As shown, the three-level (grid, province, and local) power system collaborative optimization scheduling method of this embodiment includes the following steps:

[0048] Step S101: Construct a collaborative business system for dispatching agencies at the grid, provincial, and municipal levels, and establish a collaborative optimization dispatching model for the power system at these three levels. The collaborative optimization dispatching model for the power system is used to support the operation of the collaborative business system for dispatching agencies at the grid, provincial, and municipal levels.

[0049] In constructing a collaborative business system for dispatching agencies at the grid, provincial, and prefecture levels, prefecture-level dispatching agencies aggregate resources upwards. Based on the local distributed renewable energy output level and uncertainty, the controllable load resource regulation characteristics and adjustable capacity at the prefecture level, and the characteristics of the prefecture-level power grid network, they optimize to obtain the feasible domain for prefecture-level dispatching. Provincial-level dispatching agencies, based on the feasible domain for prefecture-level dispatching and the power exchange demand between grids and provinces, adopt an integrated optimization model and uniformly optimize global resources according to the load forecast results to obtain the provincial-level dispatching plan. Then, based on the characteristics of power grid networks at all levels, system operation and maintenance arrangements, and power generation resource costs, the provincial-level dispatching plan is decomposed downwards to form a collaborative optimization dispatching plan for the power system at the grid, provincial, and prefecture levels.

[0050] Specifically, refer to Figure 2 , Figure 2 A schematic diagram of the collaborative business system of the three-level dispatching agencies at the network, province, and local levels constructed for the purpose of this invention. Figure 2 In this context, ① indicates the preliminary plan for power transmission and reception via inter-provincial interconnection lines and direct dispatch power plants reported by the grid dispatch center; ② indicates the proposed adjustments to the power transmission and reception and direct dispatch power plant generation plans submitted by the provincial dispatch center; ③ indicates the adjusted and published inter-provincial power transmission and reception and direct dispatch power plant generation plans; ④ indicates the feasible region for the prefecture-level dispatch plan reported by the regional dispatch center; and ⑤ indicates the adjusted and published prefecture-level distributed power generation plans and net load information for each node. Figure 2 It is evident that, vertically, the traditional scheduling planning business model of issuing scheduling instructions at the provincial and network levels can be transformed by constructing an integrated hierarchical coordination and scheduling model that connects scheduling agencies at different levels in a two-way manner, whereas previously only one-way connections were achieved. Horizontally, the previously isolated scheduling centers are now interconnected through cloud-edge fusion information exchange to compensate for the lack of local information, enabling the previously isolated scheduling agencies to interact. By achieving effective interaction among scheduling agencies at different levels and in different regions, the optimal allocation of global (network-wide) resources is ensured.

[0051] The collaborative business system of dispatching agencies at the provincial, municipal, and regional levels specifically includes:

[0052] (1) Optimization of provincial and local collaborative scheduling

[0053] Since provincial dispatch centers generally cannot obtain detailed power grid models corresponding to regional dispatch centers, they cannot directly achieve unified optimization calculations and solutions for the entire provincial and regional power grids. Furthermore, regional dispatch centers typically handle distributed power resources such as distributed generation sources and decentralized loads, which are highly discrete and have weak controllability, and rarely have large-capacity directly controlled generator units, making them unsuitable for direct participation in the active power optimization dispatch of provincial dispatch centers. Therefore, regional dispatching agencies comprehensively consider the output level and uncertainty of local distributed generation sources, the regulation characteristics and adjustable capacity of regional controllable load resources, and the characteristics of regional power grid networks. They aggregate distributed power resources upwards to obtain the regional dispatch feasible region, enabling regional power grids to participate in the power dispatch optimization of the provincial and regional grids in the form of virtual equivalent access.

[0054] (2) Optimization of network-province collaborative scheduling

[0055] Since the grid-level central dispatch center obtains the model of all 220kV and above power grids in the province through large-scale model splicing, it can perform optimization calculations for the entire provincial power grid by combining the power transmission and reception information sent by the provincial dispatch center and the power generation plan adjustment suggestions of directly dispatched power plants. The grid-province collaborative optimization dispatch achieves "two-way interaction" between the provincial dispatch center and the grid, incorporating the power of inter-provincial tie lines as a decision variable, and jointly optimizing them with each grid-level, provincial directly controlled generating unit, and flexible load. This enables tie lines to coordinate and interact with resources within the province, achieving unified optimization of the entire provincial power grid resources.

[0056] To support the operation of the collaborative business system of dispatching agencies at the grid, provincial, and prefecture levels, it is necessary to further establish a collaborative optimization dispatching model for the power system at these three levels. The establishment process of this model includes: establishing a prefecture-level optimized dispatching model with the lowest dispatching cost of the prefecture-level power grid as the optimization objective; establishing a provincial-level optimized dispatching model with the lowest overall power generation cost of the grid, based on prioritizing the acceptance of new energy power; and connecting the prefecture-level optimized dispatching model with the provincial-level optimized dispatching model to obtain a collaborative optimization dispatching model for the power system at the grid, provincial, and prefecture levels. The connection process includes: approximating the prefecture-level power grid as an equivalent node and connecting it to the upper-level provincial power grid.

[0057] More specifically, the prefecture-level optimal scheduling model is as follows:

[0058] 1) Objective function of the prefecture-level optimal scheduling model

[0059] Under the premise of prioritizing local consumption of distributed energy resources, the prefecture-level dispatching agency aims to minimize the dispatching cost of the prefecture-level power grid and achieve economical coordination of distribution network power. This is achieved by optimizing the power of distributed power sources, controllable micro-sources, energy storage devices, and the power of interconnections with the upper-level power grid and controllable loads. Its objective function is:

[0060]

[0061] In the formula, F lower,i Let i be the day-ahead total dispatch cost of distribution network i; The total day-ahead scheduling cost of a controllable micro-source k; Let k be the output power of the controllable microsource k at time t; The total day-ahead scheduling cost of the uncontrollable micro-source j; Let J be the output power of the uncontrollable micro-source j at time t; This refers to the total day-ahead dispatch cost of the energy storage device; Let be the charging and discharging power of the energy storage device at time t; To reduce the interaction costs of inter-provincial and inter-regional communication lines; To save power in the inter-regional tie line at time t; To regulate the day-ahead scheduling costs of load control; The adjustable load regulation capacity at time t

[0062] 2) Constraints of the prefecture-level optimal scheduling model

[0063] In this embodiment, the dispatching constraints of the prefecture-level low-voltage distribution network are mainly divided into power balance constraints and parameter constraints of each power device within the prefecture-level power network, as detailed below:

[0064]

[0065] In the formula, Let i be the total power supply load of the prefecture-level power grid at time t; These represent the upper and lower limits of the output power of the corresponding power source k (min in the superscript represents the lower limit, and max represents the upper limit, the same below); E represents the charge of the energy storage device at time t; min E max These are the upper and lower limits of the state of charge of the energy storage device, respectively. These are the upper and lower limits of the charging and discharging power of the energy storage device (in superscript, charge indicates charging and discharge indicates discharging); These are the upper and lower limits of transmission power for provincial-local connection lines; These are the upper and lower limits of the controllable load capacity, respectively.

[0066] In addition, the provincial-level optimized scheduling model is as follows:

[0067] 1) Objective function of the provincial-level network optimization scheduling model

[0068] The objective function of the provincial-level optimized dispatch model is to minimize the overall power generation cost of the grid while prioritizing the acceptance of renewable energy power. Its objective function is:

[0069]

[0070] In the formula, c g,t c represents the power generation cost of a conventional thermal power unit g during time period t; h,t c is the power generation cost of the hydropower unit h during time period t; m,t To reduce the cost of flexible load m during time period t; c n,t Δ represents the load transfer cost of the transferable flexible load n during time period t; w,t The penalty cost for power reduction of new energy generating units during period t; Let $t$ be the optimal scheduling cost of the prefecture-level power grid k at time $t$.

[0071] 2) Constraints of the provincial-level network optimization scheduling model

[0072] The constraints of the provincial-level optimized dispatch model include overall grid power generation and consumption balance constraints, provincial load balance constraints, and power and energy exchange constraints between AC and DC transmission lines. Specifically:

[0073] 1. Power generation and consumption balance constraints across the entire grid:

[0074]

[0075] In the formula: p i,t L represents the power output of generator unit i during time period t. Generator unit i includes conventional thermal power unit g, hydropower unit h, and wind and solar new energy units w. z,t The system load forecast for control area z after deducting flexible loads during time period t; l m,t To reduce the load value of flexible load m during time period t; l n,t Δp represents the load value of the transferable flexible load n during time period t; k,t Let t be the power loss of AC / DC tie line k during time period t.

[0076] 2. Provincial load balancing constraints:

[0077]

[0078] In the formula: and , respectively, represent the power values ​​of the equivalent generator and equivalent load of AC / DC tie line k during time period t; i∈z indicates that generator equipment i is within control zone z.

[0079] 3. Constraints on power and energy exchange in AC / DC transmission lines:

[0080] In practice, the power of DC tie lines is controllable and is a decision variable. However, for AC tie lines, the sending-end power is related to the grid topology and the power injected into the nodes, i.e.:

[0081]

[0082] In the formula: S represents the sending-end power of AC tie line a during time period t; i,a,t and S l,a,t The sensitivities of unit i and load node l to AC tie line a during time period t are calculated based on the network topology model of the entire network; l l,t Let be the load value of load node l at time t.

[0083] For the equivalent equipment on both sides of the AC / DC tie line, we have:

[0084]

[0085]

[0086] In the formula: α k,t Let k be the loss coefficient of the AC / DC tie line.

[0087] The upper and lower limits of AC / DC tie-line power are constrained as follows:

[0088]

[0089] In the formula: P k，min With P k，max These represent the lower and upper limits of the transmission power of the AC / DC tie line k, respectively.

[0090] The AC / DC tie-line power regulation limit constraint is as follows:

[0091]

[0092] In the formula: P k，up With P k，down These are the power rise and fall rate limits for AC / DC tie line k, respectively.

[0093] The power transmission constraints for AC / DC tie lines are:

[0094]

[0095] In the formula: λ is the duration in minutes of a single time period; Q k，min With Q k，max These represent the minimum and maximum power transmission capacity of AC / DC tie line k during the scheduling cycle.

[0096]

[0097] In the formula: Q s-r，min With Q s-r，max These represent the minimum and maximum values ​​of the electrical quantity exchanged between the sending province s and the receiving province r, respectively; k∈(s,r) indicates that the connecting line k is between provinces s and r.

[0098] Furthermore, the connection principle and process between the prefecture-level optimal scheduling model and the provincial-level optimal scheduling model are as follows:

[0099] In normal operation of the power grid, the electrical power flows from the upper provincial transmission network to the lower prefecture-level low-voltage distribution network through the 220kV main transformer. There is a coupling relationship between the transmission and distribution networks in terms of power flow: for the upper provincial transmission network, the prefecture-level distribution network with adjustable output can be regarded as a virtual load with a certain adjustment capability; while for the lower prefecture-level distribution network, the upper provincial transmission network can be regarded as an injected power.

[0100] Besides the direct coupling in power and power flow within the grid, observing the prefecture-level optimal dispatch model and the integrated grid-province-prefecture optimal dispatch model reveals that the objective function of the prefecture-level optimal dispatch model is part of the optimization objective of the integrated grid-province-prefecture optimal dispatch model. Exploring the day-ahead coordinated optimal dispatch problem of the three-tiered power system (grid, province, and prefecture) aims to better leverage the distributed energy characteristics of the prefecture-level grid in centralized dispatch, reduce power losses in the provincial transmission network, and improve operational status. However, currently, provincial dispatch agencies cannot directly control controllable power sources within the prefecture-level grid; adjustments must be made by the regional dispatch center based on the hierarchical structure of the provincial and prefecture-level grids.

[0101] In order to establish a system collaborative optimization operation model to support the collaborative business system of the three-level dispatching agencies of the grid, province, and prefecture, and to avoid the problem of dimensional explosion in the operation optimization process caused by too much grid line information, an approximation of the prefecture-level grid is performed under a certain section of the grid, and equivalent nodes are connected to the upper-level provincial grid, thereby achieving decoupling of the provincial and prefecture-level grids in terms of power flow and power.

[0102] Prefectural-level power grids typically operate in an open-loop state with a closed-loop structure. Many prefectural-level power grids exhibit clustered or radial network structures, which are typical examples of this type of structure. By using "virtual nodes" to represent prefectural-level power grids, and minimizing the impact of power flow changes and power transfers within the prefectural-level power grid on the power flow of the connected upper-level provincial power grids, decoupling in terms of power flow can be achieved.

[0103] In this embodiment, the Ward equivalent method is used for equivalent calculation, and the steps are as follows:

[0104]

[0105] In the formula: B represents the set of boundary nodes of the prefecture-level power grid, E represents the set of nodes within the prefecture-level power grid that need to be virtually equivalent, and P... E P B The corresponding injection power for each node, θ E θ B Each corresponds to a phase angle at a node, and Y is the admittance matrix of the corresponding node.

[0106] Eliminating the equivalent nodes within the prefecture-level power grid, the power injected into the boundary nodes becomes:

[0107]

[0108] A power transfer factor is defined to measure the degree of power correlation between the node to be equivalent and the boundary node, and it is stored in matrix form as shown in the following equation:

[0109] R trans =-Y BE Y EE (15)

[0110]

[0111] Equation (17) characterizes the relationship between the injected power of the virtual boundary point k and the original injected power of each equivalent node inside it after equivalence. k P is the equivalent power value injected at point k. node,j This represents the power of node j within the equivalent region.

[0112] In the coordinated optimization and dispatching of the power system at the provincial, municipal, and grid levels, changes in power under the municipal dispatching level should have a relatively small impact on the power flow of the upper-level power grid. Based on this, for a certain region to be equivalent, if it contains n power generation nodes, then according to equation (17), the power transfer for the equivalent boundary node k is:

[0113]

[0114] When the power transfer factors of each power node in the equivalent region are similar, the average value is used. When replacing it, the injected power of the boundary node k can be further approximated as follows:

[0115]

[0116] When the power transfer factor is transferred, it can be seen that the power distribution of the power source in the equivalent region has little impact on the injected power of the boundary node, because at this time the region can be equivalent to a virtual node and connected to the upper-level power grid.

[0117] In the three-tiered (grid, province, and local) power system collaborative optimization dispatch model, the equivalent values ​​of the grid, province, and local power grid are as follows: Figure 3 As shown.

[0118] Step S102: Obtain system-related information, including basic physical information and model parameters of the three-level power system (grid, province, and locality) and system equipment operation constraint information.

[0119] Specifically, the data required by prefecture-level dispatching agencies includes: unit type and parameters, unit operating costs, unit start-up and shutdown costs, short-term probability and interval forecast results for new energy sources, load probability and interval forecast results, adjustable capacity of flexible loads and control costs. The data required by provincial-level dispatching agencies includes: unit type and parameters, unit operating costs, unit start-up and shutdown costs, transmission line related information and safety constraints, medium- and long-term power balance early warning information, short-term probability and interval forecast results for new energy sources in each province and region, load probability and interval forecast results, adjustable capacity of flexible loads and control costs.

[0120] Step S103: Calculate and generate source load prediction intervals based on system-related information, and form node net loads based on source load prediction intervals, generating regular prediction scenarios and extreme scenarios as key conditions for collaborative optimization scheduling.

[0121] Specifically, source and load refer to renewable energy (new energy) power generation and load, respectively. To address the uncertainty of renewable energy output and load forecasting results, this embodiment first generates node net load interval forecasting results based on output interval forecasts for photovoltaics, wind power, etc., and load interval forecasting results, and generates conventional forecasting scenarios and extreme scenarios. Subsequently, uncertainty optimization modeling methods can be used to extend the three-level power system collaborative optimization scheduling model established in the aforementioned steps into a two-level programming problem that considers the uncertainties at both the source and load ends, so that it can be solved using methods such as particle swarm optimization, dynamic programming algorithms, and genetic algorithms to obtain the day-ahead collaborative optimization scheduling results of the three-level power system.

[0122] Step S104: Solve the power system collaborative optimization scheduling model based on key conditions to obtain the scheduling operation results.

[0123] Specifically, by solving the aforementioned steps and the constructed model using the acquired data, the scheduling operation results can be obtained, which can then guide the efficient operation of the power system at the provincial, regional, and grid levels. The entire scheduling process is as follows: Figure 4 As shown.

[0124] In some embodiments, this step specifically includes:

[0125] Based on the probability of source load in the city and the interval prediction results, we obtain the normal prediction scenario and extreme scenario, as well as the probability of different scenarios. According to the external input system operation boundary information and operation constraints, we use uncertainty optimization methods to take into account different scenarios and multiple optimization objectives. Under the premise of ensuring the safe operation of the distribution network, we optimize the net load information of each node reported by the distributed power generation plan.

[0126] Based on the provincial source-load probability and interval prediction results, conventional and extreme prediction scenarios are obtained, as well as the probability of different scenarios. According to the externally input system operation boundary information and operation constraints, taking into account the equivalent model of the prefecture-level power system, based on robust optimization considering different scenarios and multiple optimization objectives, the next day's multi-level scheduling plan and resource combination status are output, including AC / DC tie line power exchange plan, pumped storage and energy storage control plan, power generation intervals in various provinces and regions, cascade hydropower unit combination plan, water-wind-solar coordinated operation plan, and orderly power consumption plan.

[0127] Therefore, based on the above scheme, an optimized dispatching method considering the coordinated effect of the power grid, province, and prefecture levels is proposed under the background of large-scale distributed and centralized new energy power sources accessing the grid. This method strengthens the integrated optimized dispatching effect of the grid and provincial power grids while fully considering the flexibility and resource coordination of the prefecture-level power grid. It also takes into account the uncertainty of the source and load two-end prediction results, which can effectively improve the renewable energy absorption capacity and system risk resistance capacity of the large power grid.

[0128] Furthermore, embodiments of this application provide a three-tiered (grid, province, and locality) power system collaborative optimization dispatching system.

[0129] like Figure 5 As shown, the device includes:

[0130] Module 51 is used to build a collaborative business system for dispatching agencies at the grid, provincial, and local levels, and to establish a collaborative optimization dispatching model for the power system at the grid, provincial, and local levels. The collaborative optimization dispatching model for the power system is used to support the operation of the collaborative business system for dispatching agencies at the grid, provincial, and local levels.

[0131] Module 52 is used to acquire system-related information, including basic physical information and model parameters of the three-level power system (grid, province, and locality) and system equipment operation constraint information.

[0132] The calculation module 53 is used to calculate and generate the source load prediction interval based on system-related information, and to form the node net load based on the source load prediction interval, generating normal prediction scenarios and extreme scenarios as key conditions for collaborative optimization scheduling.

[0133] The scheduling module 54 is used to solve the power system collaborative optimization scheduling model based on key conditions and obtain the scheduling operation results;

[0134] The construction of a three-tiered (network, province, and prefecture) collaborative business system for dispatching agencies includes:

[0135] The prefecture-level dispatching agency aggregates resources upwards, optimizing the feasible region for prefecture-level dispatch based on the local distributed renewable energy output level and uncertainty, the controllable load resource regulation characteristics and adjustable capacity at the prefecture level, and the characteristics of the prefecture-level power grid network. The provincial-level dispatching agency, based on the feasible region at the prefecture level and the power exchange demand between provinces and grids, uses an integrated optimization model to uniformly optimize global resources according to load forecasting adjustments, resulting in the provincial-level dispatching plan. Based on the characteristics of power grid networks at all levels, system operation and maintenance arrangements, and power generation resource costs, the provincial-level dispatching plan is decomposed downwards, forming a three-tiered (prefecture, grid, and provincial) power system collaborative optimization dispatching plan.

[0136] Establish a three-tiered (grid, province, and prefecture) collaborative optimization scheduling model for the power system, including:

[0137] A prefecture-level optimized dispatch model is established with the lowest dispatch cost of the prefecture-level power grid as the optimization objective. Based on prioritizing the acceptance of new energy power, a provincial-level optimized dispatch model is established with the lowest overall power generation cost of the grid as the optimization objective. The prefecture-level optimized dispatch model and the provincial-level optimized dispatch model are then linked to obtain a three-level (prefecture-level, grid-province-prefecture) power system coordinated optimized dispatch model. The linking process includes approximating the prefecture-level power grid as an equivalent node and connecting it to the upper-level provincial power grid.

[0138] The specific implementation methods of each module of the above-mentioned three-level power system collaborative optimization dispatching system can be found in the corresponding content of the aforementioned method embodiments, and will not be repeated here.

[0139] Furthermore, embodiments of this application provide an electronic device, such as... Figure 6 As shown, the electronic device includes a memory 61 and a processor 62; wherein, the memory 61 stores a computer program, and when the processor 62 calls and executes the computer program, it implements the three-level power system collaborative optimization scheduling method of the grid, province and locality in any of the above embodiments.

[0140] The electronic device can be a desktop computer, a laptop computer, or a server, etc.

[0141] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0142] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.

[0143] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0144] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0145] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0146] Furthermore, the functional units in the various embodiments of this invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc.

[0147] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0148] 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 coordinated optimization scheduling of a three-tiered power system (grid, province, and locality), characterized in that, include: Construct a collaborative business system for dispatching agencies at the grid, provincial, and local levels, and establish a collaborative optimization dispatching model for the power system at the grid, provincial, and local levels; The power system collaborative optimization scheduling model is used to support the operation of the collaborative business system of the three-level dispatching agencies at the grid, province, and local levels. Acquire system-related information, which includes basic physical information and model parameters of the three-level power system (grid, province, and locality) as well as system equipment operation constraint information; Based on the relevant system information, a source load prediction interval is calculated and generated. Based on the source load prediction interval, the node net load is formed, and normal prediction scenarios and extreme scenarios are generated as key conditions for collaborative optimization scheduling. Based on the aforementioned key conditions, the power system collaborative optimization scheduling model is solved to obtain the scheduling operation results; The aforementioned construction of a three-tiered (network, province, and prefecture) collaborative business system for dispatching agencies includes: The prefecture-level dispatching agency aggregates resources upwards, optimizing the feasible region for prefecture-level dispatch based on the local distributed renewable energy output level and uncertainty, the controllable load resource regulation characteristics and adjustable capacity at the prefecture level, and the characteristics of the prefecture-level power grid network. The provincial-level dispatching agency, based on the feasible region at the prefecture level and the power exchange demand between provinces and grids, uses an integrated optimization model to uniformly optimize global resources according to load forecasting adjustments, resulting in the provincial-level dispatching plan. Based on the characteristics of power grid networks at all levels, system operation and maintenance arrangements, and power generation resource costs, the provincial-level dispatching plan is decomposed downwards, forming a three-tiered (prefecture, grid, and provincial) power system collaborative optimization dispatching plan. The establishment of a three-tiered (grid, province, and prefecture) power system collaborative optimization scheduling model includes: A prefecture-level optimized dispatch model is established with the lowest dispatch cost of the prefecture-level power grid as the optimization objective. Based on prioritizing the acceptance of new energy power, a provincial-level optimized dispatch model is established with the lowest overall power generation cost as the optimization objective. The prefecture-level optimized dispatch model and the provincial-level optimized dispatch model are then linked to obtain a three-tiered (prefecture, grid, and provincial) power system collaborative optimized dispatch model. The linking process includes approximating the prefecture-level power grid as an equivalent node and connecting it to the upper-level provincial power grid. The solution to the power system collaborative optimization scheduling model based on the key conditions, to obtain the scheduling operation results, includes: Based on the probability of source load in the city and the interval prediction results, we obtain the normal prediction scenario and extreme scenario, as well as the probability of different scenarios. According to the external input system operation boundary information and operation constraints, we use uncertainty optimization methods to take into account different scenarios and multiple optimization objectives. Under the premise of ensuring the safe operation of the distribution network, we optimize the net load information of each node reported by the distributed power generation plan. Based on the provincial source-load probability and interval prediction results, conventional and extreme prediction scenarios are obtained, as well as the probability of different scenarios. According to the externally input system operation boundary information and operation constraints, taking into account the equivalent model of the prefecture-level power system, based on robust optimization considering different scenarios and multiple optimization objectives, the next day's multi-level scheduling plan and resource combination status are output, including AC / DC tie line power exchange plan, pumped storage and energy storage control plan, power generation intervals in various provinces and regions, cascade hydropower unit combination plan, water-wind-solar coordinated operation plan, and orderly power consumption plan.

2. The method according to claim 1, characterized in that, The objective function of the prefecture-level optimal scheduling model is: In the formula, Let i be the day-ahead total dispatch cost of distribution network i; The total day-ahead scheduling cost of a controllable micro-source k; Let k be the output power of the controllable microsource k at time t; The total day-ahead scheduling cost of the uncontrollable micro-source j; Let J be the output power of the uncontrollable micro-source j at time t; This refers to the total day-ahead dispatch cost of the energy storage device; Let be the charging and discharging power of the energy storage device at time t; To reduce the interaction costs of inter-provincial and inter-regional communication lines; To save power in the inter-regional tie line at time t; To regulate the day-ahead scheduling costs of load control; The adjustable load regulation capacity at time t.

3. The method according to claim 2, characterized in that, The constraints of the prefecture-level optimal scheduling model include power balance constraints and parameter constraints of each power device within the prefecture-level power grid.

4. The method according to claim 1, characterized in that, The objective function of the provincial-level optimized scheduling model is: In the formula, The power generation cost of a conventional thermal power unit g during time period t; Let h be the power generation cost of the hydropower unit during time period t; To reduce the cost of flexible load m during time period t; The load transfer cost of the transferable flexible load n during time period t; The penalty cost for power reduction of new energy generating units during period t; Let $t$ be the optimal scheduling cost of the prefecture-level power grid k at time $t$.

5. The method according to claim 4, characterized in that, The constraints of the provincial-level optimized dispatch model include the overall power generation and consumption balance constraints, the provincial load balance constraints, and the power and electricity exchange constraints of AC and DC transmission lines.

6. The method according to claim 1, characterized in that, The Ward equivalent method was used to perform equivalent calculations on the prefecture-level power grid.

7. A three-tiered (grid, province, and local) power system collaborative optimization dispatching system, characterized in that, include: The module is used to build a collaborative business system for dispatching agencies at the grid, provincial, and local levels, and to establish a collaborative optimization dispatching model for the power system at the grid, provincial, and local levels. The power system collaborative optimization scheduling model is used to support the operation of the collaborative business system of the three-level dispatching agencies at the grid, province, and local levels. The acquisition module is used to acquire system-related information, which includes basic physical information and model parameters of the three-level power system (grid, province, and locality) and system equipment operation constraint information. The calculation module is used to calculate and generate source load prediction intervals based on the relevant information of the system, and to form node net loads based on the source load prediction intervals, generating normal prediction scenarios and extreme scenarios as key conditions for collaborative optimization scheduling. The scheduling module is used to solve the power system collaborative optimization scheduling model based on the key conditions to obtain the scheduling operation results; The aforementioned construction of a three-tiered (network, province, and prefecture) collaborative business system for dispatching agencies includes: The prefecture-level dispatching agency aggregates resources upwards, optimizing the feasible region for prefecture-level dispatch based on the local distributed renewable energy output level and uncertainty, the controllable load resource regulation characteristics and adjustable capacity at the prefecture level, and the characteristics of the prefecture-level power grid network. The provincial-level dispatching agency, based on the feasible region at the prefecture level and the power exchange demand between provinces and grids, uses an integrated optimization model to uniformly optimize global resources according to load forecasting adjustments, resulting in the provincial-level dispatching plan. Based on the characteristics of power grid networks at all levels, system operation and maintenance arrangements, and power generation resource costs, the provincial-level dispatching plan is decomposed downwards, forming a three-tiered (prefecture, grid, and provincial) power system collaborative optimization dispatching plan. The establishment of a three-tiered (grid, province, and prefecture) power system collaborative optimization scheduling model includes: A prefecture-level optimized dispatch model is established with the lowest dispatch cost of the prefecture-level power grid as the optimization objective. Based on prioritizing the acceptance of new energy power, a provincial-level optimized dispatch model is established with the lowest overall power generation cost as the optimization objective. The prefecture-level optimized dispatch model and the provincial-level optimized dispatch model are then linked to obtain a three-tiered (prefecture, grid, and provincial) power system collaborative optimized dispatch model. The linking process includes approximating the prefecture-level power grid as an equivalent node and connecting it to the upper-level provincial power grid. The solution to the power system collaborative optimization scheduling model based on the key conditions, to obtain the scheduling operation results, includes: Based on the probability of source load in the city and the interval prediction results, we obtain the normal prediction scenario and extreme scenario, as well as the probability of different scenarios. According to the external input system operation boundary information and operation constraints, we use uncertainty optimization methods to take into account different scenarios and multiple optimization objectives. Under the premise of ensuring the safe operation of the distribution network, we optimize the net load information of each node reported by the distributed power generation plan. Based on the provincial source-load probability and interval prediction results, conventional and extreme prediction scenarios are obtained, as well as the probability of different scenarios. According to the externally input system operation boundary information and operation constraints, taking into account the equivalent model of the prefecture-level power system, based on robust optimization considering different scenarios and multiple optimization objectives, the next day's multi-level scheduling plan and resource combination status are output, including AC / DC tie line power exchange plan, pumped storage and energy storage control plan, power generation intervals in various provinces and regions, cascade hydropower unit combination plan, water-wind-solar coordinated operation plan, and orderly power consumption plan.

8. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and when the processor calls and executes the computer program, it implements the three-level coordinated optimization scheduling method for power systems at the grid, province, and local levels as described in any one of claims 1 to 6.

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