Multi-regional power grid reserve planning method and device adopting three-stage decomposition coordination acceleration time sequence production simulation

Abstract

The invention discloses a multi-regional power grid reserve planning method and device adopting three-stage decomposition coordination acceleration time sequence production simulation, and relates to the field of power grid reserve planning. Under each planned reserve rate scheme, for each partition, constructing a regional independent optimization model by taking the minimum regional cost as a target, and solving to obtain the total inflow power of the tie line; taking each partition as a node, and obtaining an equivalent node parameter of each partition; constructing a global coordinated optimization model by taking the minimum total load shedding capacity of the system as a target, and solving to obtain the transmission power of the tie line; updating system constraints according to the tie line transmission power; and a regional feedback optimization model is constructed by taking the minimum total cost of the system as a target, the target total cost of the target system of each planning reserve rate scheme is solved under the new constraint, and then the target planning reserve rate scheme is determined. By implementing the method, the problem of long calculation time consumption of an existing centralized optimization method is solved, and high efficiency of large-scale power grid optimization is realized.

Description

Technical Field

[0001] This invention relates to the field of power grid reserve planning, and in particular to a multi-regional power grid reserve planning method and apparatus that employs a three-stage decomposition and coordination to accelerate time-series production simulation. Background Technology

[0002] To address global climate change and promote a clean and low-carbon energy transition, large-scale development and grid connection of renewable energy have become crucial energy development strategies. However, the output of new energy sources, such as wind and solar power, exhibits significant randomness, volatility, and uncertainty. Their large-scale integration poses unprecedented challenges and operational risks to the safe and stable operation of the power system. Ensuring reliable power supply under high renewable energy penetration rates, configuring sufficient reserve capacity, or utilizing the mutual balancing capabilities of large power grids through the construction of inter-regional interconnected grids, has become an urgent requirement for current power system planning and operation.

[0003] Traditional reserve planning practices typically employ deterministic methods, allocating fixed reserve capacity based on preset standards such as a certain percentage of maximum load or maximum single-unit capacity. While simple, this approach struggles to accurately capture the dramatic fluctuations in renewable energy output and load demand over time and across geographical regions. This often results in reserve configurations that are either overly conservative and economical, or unreliable under extreme conditions, and fail to optimize and coordinate reserve resources across different areas. Therefore, introducing time-series production simulation technology, capable of precisely simulating the system's annual operating status, for reserve planning has significant practical implications and application value.

[0004] However, applying time-series production simulation to reserve planning in multi-regional interconnected power grids faces significant technical bottlenecks. Multi-regional interconnected power systems can leverage the spatiotemporal correlation of load curves and renewable energy output to achieve cross-regional reserve sharing, but this requires time-series production simulation models to consider complex tie-line physical transmission constraints and to perform highly coordinated unified optimization of generation and transmission plans across all regions. This centralized optimization approach leads to an explosive increase in model dimensionality and computational complexity, resulting in extremely long computation times. Summary of the Invention

[0005] This invention provides a multi-regional power grid reserve planning method and apparatus that uses a three-stage decomposition and coordination to accelerate time-series production simulation. This method can solve the problem of long computation time in existing centralized optimization methods and achieve high efficiency in large-scale power grid optimization.

[0006] One embodiment of the present invention provides a multi-regional power grid reserve planning method employing a three-stage decomposition and coordination to accelerate time-series production simulation, comprising:

[0007] Obtain the power grid operation parameters for each zone in the power grid and set several planned reserve ratio schemes;

[0008] Under each planned reserve rate scheme, for each zone, based on the power grid operating parameters and the planned reserve rate, a zone-independent optimization model is constructed with the objective of minimizing the sum of the zone operating cost and the tie-line power exchange cost. Zone constraints, cluster unit constraints and system constraints are also constructed. Under the constraints of the zone constraints, cluster unit constraints and system constraints, the zone-independent optimization model is solved to generate the total tie-line inflow power.

[0009] Using each partition as a node, obtain the equivalent node parameters for each partition;

[0010] Based on the total inflow power and equivalent node parameters of each partition's tie lines, a global coordination optimization model is constructed with the goal of minimizing the total load shedding of the system. Power balance constraints, unit output constraints, and system safety constraints are also constructed. Under the constraints of power balance constraints, unit output constraints, and system safety constraints, the global coordination optimization model is solved to generate the tie line transmission power.

[0011] Based on the transmission power of the tie line, update the system constraints to obtain new system constraints;

[0012] For each region, based on the power grid operating parameters and the planned reserve rate, a regional feedback optimization model is constructed with the goal of minimizing the total system cost. Under the constraints of regional constraints, cluster unit constraints, and new system constraints, the regional feedback optimization model is solved to generate the target total system cost.

[0013] The target planned reserve ratio scheme is determined based on the target total system cost of each planned reserve ratio scheme.

[0014] Furthermore, after solving the regional feedback optimization model and generating the total cost of the target system under the constraints of regional constraints, cluster unit constraints, and new system constraints, the process also includes:

[0015] Under the constraints of regional constraints, cluster unit constraints, and new system constraints, the regional feedback optimization model is solved to generate the power generation plan corresponding to the total cost of the target system.

[0016] After determining the target planned reserve rate scheme based on the target total system cost of each planned reserve rate scheme, the following steps are also included:

[0017] Grid dispatch is carried out based on the power generation plan corresponding to the target reserve ratio scheme.

[0018] Furthermore, region-independent optimization models include:

[0019]

[0020] Where f represents the sum of regional operating costs and tie-line power exchange costs, T represents the total number of time periods within the optimization period, t represents the time period index within the optimization period, N represents the total number of regions, n represents the region index, J represents the total number of cluster unit types, and j represents the cluster unit type index. This represents the fuel cost coefficient for thermal power units. This represents the output of the j-th type of cluster unit in region n during time period t. This represents the load shedding cost coefficient. This represents the load shedding amount in region n during time period t. This represents the power switching penalty cost factor for the tie line. This represents the total inflow power of the tie line in region n during time period t. This represents the cost coefficient for wind curtailment. This represents the maximum available wind power output in region n during time period t. This represents the actual wind power absorption capacity of region n during time period t.

[0021] Furthermore, the global coordination optimization model includes:

[0022]

[0023] Where F represents the total system load shedding, N U This represents the total number of equivalent nodes, where u represents the index of the equivalent node. This represents the load shedding amount at the equivalent node u during time period t.

[0024] Furthermore, the regional feedback optimization model includes:

[0025]

[0026] Here, Obj represents the total system cost.

[0027] Furthermore, system constraints include: reserve capacity constraints, tie-line power transmission constraints, and load shedding constraints;

[0028] Based on the tie-line transmission power, the system constraints are updated to obtain new system constraints, including:

[0029] Based on the tie line transmission power, update the tie line power transmission constraint to obtain the new tie line power transmission constraint.

[0030] Tie-line power transmission constraints include:

[0031]

[0032] New tie-line power transfer constraints include:

[0033]

[0034] in, Let m represent the set of connection lines connected to region n, and m represent the index of the connection line connected to region n. This represents the lower limit of the transmission capacity of the m-th tie line in region n. This represents the upper limit of the transmission capacity of the m-th tie line in region n. This represents the total outflow power of the tie line in region n during time period t.

[0035] Furthermore, regional constraints include: regional power balance constraints; and cluster unit constraints include: cluster unit operation constraints, cluster unit output constraints, cluster unit ramp-up constraints, cluster unit minimum start-up and shutdown time constraints, and renewable energy regional output constraints.

[0036] Furthermore, there are unit output constraints, including thermal power unit output constraints and renewable energy unit output constraints; and system safety constraints, including tie line power flow constraints, reserve capacity coordination constraints, and load shedding coordination constraints.

[0037] Based on the above method embodiments, the present invention provides corresponding device embodiments, including: a partition parameter acquisition module, a region independent optimization module, a node parameter acquisition module, a global coordination optimization module, a system constraint update module, a region feedback optimization module, and a target planning confirmation module;

[0038] The partition parameter acquisition module is used to acquire the power grid operation parameters of each partition in the power grid and set several planned reserve rate schemes.

[0039] The regional independent optimization module is used to construct a regional independent optimization model for each partition under each planned reserve rate scheme, based on the grid operation parameters and the planned reserve rate, with the objective of minimizing the sum of regional operating costs and tie-line power exchange costs. It also constructs regional constraints, cluster unit constraints, and system constraints. Under the constraints of regional constraints, cluster unit constraints, and system constraints, it solves the regional independent optimization model and generates the total tie-line inflow power.

[0040] The node parameter acquisition module is used to obtain the equivalent node parameters for each partition, with each partition as a node.

[0041] The global coordination optimization module is used to construct a global coordination optimization model based on the total inflow power of tie lines and equivalent node parameters of each partition, with the goal of minimizing the total load shedding of the system. It also constructs power balance constraints, unit output constraints and system safety constraints. Under the constraints of power balance constraints, unit output constraints and system safety constraints, it solves the global coordination optimization model and generates tie line transmission power.

[0042] The system constraint update module is used to update the system constraints based on the tie line transmission power to obtain new system constraints;

[0043] The regional feedback optimization module is used to construct a regional feedback optimization model for each region based on the power grid operating parameters and planned reserve rate, with the goal of minimizing the total system cost. Under the constraints of regional constraints, cluster unit constraints and new system constraints, the regional feedback optimization model is solved to generate the target total system cost.

[0044] The target planning confirmation module is used to determine the target planning reserve rate scheme based on the target total system cost of each planning reserve rate scheme.

[0045] Furthermore, after solving the regional feedback optimization model and generating the total cost of the target system under the constraints of regional constraints, cluster unit constraints, and new system constraints, the process also includes:

[0046] Under the constraints of regional constraints, cluster unit constraints, and new system constraints, the regional feedback optimization model is solved to generate the power generation plan corresponding to the total cost of the target system.

[0047] The multi-regional power grid backup planning device, which employs a three-stage decomposition and coordination to accelerate time-series production simulation, also includes: a power dispatch module;

[0048] The power dispatch module is used to perform grid dispatching based on the power generation plan corresponding to the target planned reserve rate scheme.

[0049] Compared with the prior art, the beneficial effects of this embodiment are as follows:

[0050] This invention obtains the power grid operating parameters of each region in the power grid and sets several planned reserve rate schemes. Under each planned reserve rate scheme, for each region, based on the power grid operating parameters and the planned reserve rate, a region-independent optimization model is constructed with the objective of minimizing the sum of regional operating costs and tie-line power exchange costs. Regional constraints, cluster unit constraints, and system constraints are also constructed to decompose the power grid into regional optimization sub-problems. Each region becomes a relatively independent optimization sub-problem. Under the constraints of regional constraints, cluster unit constraints, and system constraints, the region-independent optimization model is solved to generate the total tie-line inflow power that minimizes the sum of regional operating costs and tie-line power exchange costs. By solving the model independently for each region, parallel computation shortens the computation time. Next, taking each zone as a node, the equivalent node parameters of each zone are obtained, simplifying the complex power grid network into a simplified network composed of equivalent nodes. Based on the total inflow power of tie lines and the equivalent node parameters of each zone, a global coordination optimization model is constructed with the goal of minimizing the total system load shedding. Power balance constraints, unit output constraints, and system security constraints are also constructed. Under these constraints, the global coordination optimization model is solved to generate the tie line transmission power when the total system load shedding is minimized. Based on the tie line transmission power, the system constraints are updated to obtain new system constraints, thus feeding the tie line power generated in the global coordination stage back to the regional model as boundary conditions. Finally, for each zone, based on the power grid operating parameters and the planned reserve rate, a regional feedback optimization model is constructed with the goal of minimizing the total system cost. Under the constraints of regional constraints, cluster unit constraints, and the new system constraints, the regional feedback optimization model is solved to generate the minimum total system cost, i.e., the target total system cost. Based on the target total system cost of each planned reserve rate scheme, the target planned reserve rate scheme is determined.

[0051] In summary, this invention partitions the power grid and constructs regional independent optimization models, uses equivalent node parameters to construct a global coordinated optimization model, and feeds back key information such as tie-line power generated in the global coordination stage to generate regional feedback optimization models. This reduces model dimensionality and computational load while ensuring the overall system's coordination, thereby solving the problem of long computation time in existing centralized optimization methods and achieving high efficiency in large-scale power grid optimization. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating a multi-regional power grid reserve planning method using a three-stage decomposition and coordination acceleration time-series production simulation, provided in an embodiment of the present invention.

[0053] Figure 2 This is a topology diagram of a power grid partition provided in an embodiment of the present invention;

[0054] Figure 3This is a time-series comparison curve of total thermal power output provided by a centralized optimization and a three-stage progressive optimization according to an embodiment of the present invention;

[0055] Figure 4 This is a comparison curve of wind power output time series between centralized optimization and three-stage progressive optimization provided in an embodiment of the present invention;

[0056] Figure 5 This is a schematic diagram of the structure of a power grid dispatching device based on three-stage progressive optimization provided in an embodiment of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0059] like Figure 1 As shown, in order to solve the problem of extremely long computation time in existing centralized optimization methods, an embodiment of the present invention provides a power grid dispatching method based on three-stage progressive optimization, which includes at least the following steps:

[0060] Step S1: Obtain the regional structure parameters, unit parameters, load parameters, and tie line parameters for each section of the power grid;

[0061] In a preferred embodiment, the regional structure parameters include: planned reserve rate;

[0062] For step S1, in this invention, the above-mentioned regional structure parameters include: the total number of regions N, the total number of time periods T within the optimization period, the total number of cluster unit types J, the total number of thermal power units G, and the planned reserve rate β of region n. n ;

[0063] The above unit parameters include: fuel cost coefficient of thermal power units. Wind curtailment cost coefficient Maximum available wind power output in region n during time period t Total nameplate capacity of the j-th type cluster unit in region n Rated capacity of the i-th thermal power unit in region n Minimum output coefficient of the j-th type of cluster unit in region n, time period t Maximum output coefficient of the j-th type of cluster unit in region n during time period t The ratio of the minimum generating capacity of the i-th thermal power unit in region n to its nameplate capacity. a i (t,n), the ratio of the maximum generating capacity of the i-th thermal power unit in region n to its nameplate capacity. The ramp-up coefficient of the j-th type of cluster unit in region n and downhill slope coefficient Minimum startup time T of cluster unit j in region n j U Minimum downtime of cluster unit j in region n

[0064] The above load parameters include: load shedding cost factor Load of region n during time period t Peak load of region n during time period t

[0065] The aforementioned tie-line parameters include: the set of tie-lines connected to region n. Lower limit of transmission capacity of the m-th tie line in area n The upper limit of transmission capacity of the m-th tie line in region n

[0066] like Figure 2 The diagram shows the topology of the power grid partition. In the power grid model constructed in this embodiment, there are four interconnected regions with load ratios of 0.5, 0.8, 1.2 and 1.5, respectively. The regions are connected by five tie lines to achieve power transmission and interaction. The tie line parameters adopt the CB-1 standard parameters of the IEEE three-region RTS-96 test system. Each region is equipped with a wind farm, and its wind power curve is generated based on the actual operating data of a coastal wind farm.

[0067] It should be noted that all simulation calculations in this invention are implemented on the MATLAB R2024b platform. MATLAB, as a powerful numerical computation and simulation software, provides a wealth of tools and functions for building and calculating power grid models. The optimization solution is completed by calling the Gurobi solver through the YALMIP toolbox, which can quickly and accurately solve complex mathematical problems in the power grid optimization model.

[0068] Step S2: Under each planned reserve rate scheme, for each zone, based on the power grid operating parameters and the planned reserve rate, construct a zone-independent optimization model with the objective of minimizing the sum of the zone operating cost and the tie-line power exchange cost, and construct zone constraints, cluster unit constraints and system constraints. Under the constraints of zone constraints, cluster unit constraints and system constraints, solve the zone-independent optimization model to generate the total tie-line inflow power.

[0069] In a preferred embodiment, the region-independent optimization model includes:

[0070]

[0071] Where f represents the sum of regional operating costs and tie-line power exchange costs, T represents the total number of time periods within the optimization period, t represents the time period index within the optimization period, N represents the total number of regions, n represents the region index, J represents the total number of cluster unit types, and j represents the cluster unit type index. This represents the fuel cost coefficient for thermal power units. This represents the output of the j-th type of cluster unit in region n during time period t. This represents the load shedding cost coefficient. This represents the load shedding amount in region n during time period t. This represents the power switching penalty cost factor for the tie line. This represents the total inflow power of the tie line in region n during time period t. This represents the cost coefficient for wind curtailment. This represents the maximum available wind power output in region n during time period t. This represents the actual wind power absorption capacity of region n during time period t.

[0072] For step S2, since interconnected power grids are typically composed of multiple regions connected by tie lines, the overall optimization problem involves numerous variables and complex constraints, making direct solution computationally time-consuming. This invention employs decoupling techniques to decompose the interconnected power grid into regional optimization sub-problems, enabling independent optimization of each region. First, with the objective of minimizing the sum of regional operating costs and tie line power exchange costs, a regional independent optimization model is constructed for each region, along with regional constraints, cluster unit constraints, and system constraints.

[0073] In a preferred embodiment, the regional constraints include: regional power balance constraints; the cluster unit constraints include: cluster unit operation constraints, cluster unit output constraints, cluster unit ramping constraints, cluster unit minimum start-up and shutdown time constraints, and renewable energy regional output constraints.

[0074] Specifically, regional power balance constraints are used to ensure that each region maintains a balance between power generation and load demand, including:

[0075]

[0076] in, PR represents the total outflow power of the tie line in region n during time period t. t,n This represents the dispatchable reserve power of region n during time period t. This represents the load in region n during time period t.

[0077] Cluster unit operation constraints are approximated by three continuous variables, which are traditional binary variables:

[0078]

[0079] in, This represents the total capacity of the j-th type of cluster operating units in region n during time period t, used for power generation and reserve needs. This represents the total capacity of the units planned to be started in the j-th cluster of region n during time period t, and represents the overall behavior of unit startup. This represents the total capacity of the j-th type of clusters planned to be shut down in region n during time period t. The total nameplate capacity of the j-th type of cluster units in region n is calculated using the following formula:

[0080]

[0081] Among them, S j (n) represents the total nameplate capacity of the j-th type of cluster units in region n, G represents the total number of thermal power units, and i represents the index of the thermal power unit. This represents the rated capacity of the i-th thermal power unit within region n.

[0082] Considering the actual scheduling situation, only one scheduling instruction is issued to the units within the network per day. Therefore, the three continuous variables of the state of the same cluster unit satisfy the following equation:

[0083]

[0084] Cluster unit output constraints include:

[0085]

[0086] in, This represents the minimum output coefficient of the j-th type of cluster unit in region n during time period t. This represents the maximum output coefficient of the j-th type of cluster unit in region n during time period t.

[0087] The specific calculation formulas for the maximum processing coefficient and the minimum output coefficient are as follows:

[0088]

[0089] in, This represents the maximum output coefficient of the j-th type of cluster unit in region n during time period t. This represents the ratio of the maximum generating capacity of the i-th thermal power unit within region n to its nameplate capacity. A j (t,n) represents the minimum output coefficient of the j-th type of cluster unit in region n during time period t. a i (t,n) represents the ratio of the minimum power output of the i-th thermal power unit in region n to its nameplate capacity.

[0090] Cluster unit ramping constraints include:

[0091]

[0092] in, This represents the ramp-up coefficient of the j-th type of cluster unit in region n. The two ramp coefficients represent the downward ramp coefficients of the j-th type of cluster units in region n. These two ramp coefficients are calculated as the weighted average of the ramp power of individual units within the cluster.

[0093] Minimum start-up and shutdown time constraints for cluster units include:

[0094]

[0095] Among them, T j U This represents the minimum startup time of the j-th cluster unit in region n. This represents the minimum downtime of the j-th cluster unit in region n.

[0096] Renewable energy regional output constraints limit the power generation output of renewable energy sources to ensure that it does not exceed the maximum permissible power output, including:

[0097]

[0098] In a preferred embodiment, system constraints include: standby capacity constraints, tie-line power transmission constraints, and load shedding constraints;

[0099] Specifically, standby capacity constraints include:

[0100]

[0101] Tie-line power transmission constraints include:

[0102]

[0103]

[0104] in, Let m represent the set of connection lines connected to region n, and m represent the index of the connection line connected to region n. This represents the lower limit of the transmission capacity of the m-th tie line in region n. This represents the upper limit of the transmission capacity of the m-th tie line in region n. This represents the total outflow power of the tie line in region n during time period t. This represents the peak load of region n during time period t. Let represent the planning reserve rate of region n under the k-th planning reserve rate scheme.

[0105] Shear load constraints include:

[0106]

[0107] Under these constraints, the region-independent optimization model is solved. Parallel computing allows for the simultaneous solution of optimization models for multiple regions, improving computational efficiency. The final step is to determine the total power flowing into the tie lines that minimizes the sum of the region operating cost and the tie line power exchange cost, thus identifying the optimal power value exchanged between regions via tie lines.

[0108] Step S3: Using each partition as a node, obtain the equivalent node parameters for each partition;

[0109] For step S3, since the independent parallel optimization of each region lacks inter-regional coordination, the resulting power generation plan and tie-line transmission scheme only satisfy local supply and demand balance, but are not feasible for the overall interconnected system. In order to achieve global coordinated optimization of tie-line power transmission, each region is equivalent to a node for global optimization.

[0110] The equivalent node parameters mentioned above include: the total number of equivalent nodes N. U Equivalent node number u, number of tie lines I for equivalent node u, minimum output coefficient of the i-th thermal power unit during time period t for equivalent node u. The maximum output coefficient of the i-th thermal power unit in the equivalent node time period t The rated capacity of the i-th thermal power unit at equivalent node u Peak load of equivalent node u during time period t

[0111] Step S4: Based on the total inflow power and equivalent node parameters of each partition's tie lines, construct a global coordination optimization model with the goal of minimizing the total load shedding of the system. Construct power balance constraints, unit output constraints, and system safety constraints. Under the constraints of power balance constraints, unit output constraints, and system safety constraints, solve the global coordination optimization model to generate tie line transmission power.

[0112] In a preferred embodiment, the global coordination optimization model includes:

[0113]

[0114] Where F represents the total system load shedding, N U This represents the total number of equivalent nodes, where u represents the index of the equivalent node. This represents the load shedding amount at the equivalent node u during time period t.

[0115] For step S4, in the process of global optimization, firstly, based on the total inflow power of tie lines and equivalent node parameters of each region, a global coordinated optimization model is constructed with the minimum total load shedding of the system as the optimization objective. This aims to coordinate the gap between the power injection demand of each region obtained from the independent optimization of the region and the actual power supply, thereby driving the optimization solver to find the optimal tie line transmission plan among all physically feasible solutions that best matches the economic intentions of each region in the independent optimization process and best meets its ideal needs, so as to ensure the economy and rationality of the global solution.

[0116] At the same time, key constraints such as power balance constraints, unit output constraints, and system safety constraints are constructed. Power balance constraints are used to ensure the overall balance between power generation and load in the entire system and to ensure the stability of power supply and demand. Unit output constraints are used to regulate the output power range of each unit so that the units can operate under safe and reasonable conditions. System safety constraints are used to maintain the safety and stability of the system as a whole and to prevent safety problems such as voltage exceeding limits and frequency abnormalities.

[0117] Specifically, power balance constraints include:

[0118]

[0119] Among them, PR t,u This represents the schedulable reserve power of the equivalent node u during time period t. This represents the output of the thermal power unit during time period t at the equivalent node u. This represents the actual wind power absorption capacity of the equivalent node u during time period t, where I represents the number of tie lines to the equivalent node u, and v represents the tie line index of the equivalent node u. This represents the transmission power of the v-th tie line during time period t. This represents the load of the equivalent node u during time period t.

[0120] In a preferred embodiment, the unit output constraints include: thermal power unit output constraints and renewable energy unit output constraints; the system safety constraints include: tie line power flow constraints, reserve capacity coordination constraints and load shedding coordination constraints.

[0121] Specifically, the output constraints of thermal power units include:

[0122]

[0123] in, This represents the minimum output coefficient of the i-th thermal power unit during time period t at equivalent node u. This represents the rated capacity of the i-th thermal power unit at the equivalent node u. It represents the maximum output coefficient of the i-th thermal power unit during time period t at the equivalent node u.

[0124] Output constraints for renewable energy units include:

[0125]

[0126] in, This represents the maximum available wind power output of the equivalent node during time period t.

[0127] Connection flow constraints include:

[0128] L v,min ≤L v (t)≤L v,max ;

[0129] Among them, L v (t) represents the transmission power of the v-th tie line during time period t, L v,min L represents the minimum transmission power of the v-th tie line. v,max This represents the maximum transmission power of the v-th tie line.

[0130] Reserve capacity coordination constraints include:

[0131]

[0132] in, This represents the peak load of the equivalent node u during time period t. Let represent the planning reserve rate of the equivalent node u under the k-th planning reserve rate scheme.

[0133] Load shedding coordination constraints include:

[0134]

[0135] After constructing the global coordination optimization model and constraints, the corresponding optimization algorithm is used to solve the global coordination optimization model. The solution can generate the tie-line transmission power when the total system load shedding is minimized, so that each region can exchange power through the tie-line in the way that the total system load shedding is minimized, reducing unnecessary power adjustments and resource waste, and improving the utilization efficiency of power resources.

[0136] Step S5: Update the system constraints based on the tie line transmission power to obtain new system constraints;

[0137] In a preferred embodiment, the system constraints are updated based on the tie-line transmission power to obtain new system constraints, including:

[0138] Based on the tie line transmission power, update the tie line power transmission constraint to obtain the new tie line power transmission constraint.

[0139] Tie-line power transmission constraints include:

[0140]

[0141] New tie-line power transfer constraints include:

[0142]

[0143] in, Let m represent the set of connection lines connected to region n, and m represent the index of the connection line connected to region n. This represents the lower limit of the transmission capacity of the m-th tie line in region n. This represents the upper limit of the transmission capacity of the m-th tie line in region n. This represents the total outflow power of the tie line in region n during time period t.

[0144] For step S5, the tie-line transmission power determined in step S4 is used as a fixed condition within the region and embedded into the tie-line power transmission constraints of each region, setting a benchmark for power transmission in each region based on global optimization. Specifically, the tie-line power transmission constraints are updated with new tie-line power transmission constraints, enabling each region to better exchange and coordinate power with adjacent regions while meeting its own operational needs, further improving the overall operating efficiency and economy of the power grid, and ensuring the stable and reliable operation of the power system.

[0145] Step S6: For each region, based on the power grid operating parameters and the planned reserve rate, construct a regional feedback optimization model with the goal of minimizing the total system cost. Under the constraints of regional constraints, cluster unit constraints, and new system constraints, solve the regional feedback optimization model to generate the target total system cost.

[0146] In a preferred embodiment, after solving the regional feedback optimization model and generating the total cost of the target system under the constraints of regional constraints, cluster unit constraints, and new system constraints, the method further includes:

[0147] Under the constraints of regional constraints, cluster unit constraints, and new system constraints, the regional feedback optimization model is solved to generate the power generation plan corresponding to the total cost of the target system.

[0148] In a preferred embodiment, the regional feedback optimization model includes:

[0149]

[0150]

[0151] Here, Obj represents the total system cost.

[0152] For step S6, a regional feedback optimization model is constructed for each partition, with the goal of minimizing the total system cost. Then, under the combined influence of regional constraints, cluster unit constraints, and new system constraints, the regional feedback optimization model is solved to generate a power generation plan that minimizes the total system cost. At this point, the minimum total system cost is taken as the target total system cost, and the power generation plan corresponds to the output of each unit. The new system constraints are obtained by updating the tie-line transmission power based on the global optimization results in step S5, and accurately reflect the specific requirements of the global optimization for each partition.

[0153] Solving the model under the aforementioned constraints essentially integrates global optimization into the regional optimization process. This allows regions to make reasonable resource allocation and power generation plan optimization adjustments without being limited to their own local interests. Instead, they can maintain a high degree of synergy with global optimization, enabling efficient utilization of resources within the region, reducing power generation costs, and, due to the adherence to system constraints based on global optimization updates, better coordinating with the operation of adjacent regions and the entire power grid, thereby improving the overall power grid's operational efficiency and stability.

[0154] To verify the effectiveness of the three-stage progressive optimization of this invention, a planned reserve rate of 2% was set, with total thermal power output as the first evaluation indicator. Figure 3 The figure shows a time-series comparison curve of total thermal power output obtained by centralized optimization and three-stage progressive optimization. The horizontal axis of the curve represents time, and the vertical axis represents total thermal power output. The total thermal power output is calculated using the multi-regional power grid reserve planning method of this invention, which employs three-stage decomposition coordination to accelerate time-series production simulation, corresponding to the total thermal power output of the improved model. The total thermal power output is obtained through traditional centralized optimization, corresponding to the total thermal power output of the baseline model. By comparing the fit between the two curves, the relative error is used to evaluate the output accuracy between the output results of this invention based on three-stage decomposition optimization and the output results of traditional centralized optimization. The specific formula is as follows:

[0155]

[0156] Where RE represents the relative error. The evaluation index is represented by 'y', which is based on three-stage decomposition optimization. The traditional centralized optimization evaluation index is represented by 'y'. Calculations show that the relative error between the two methods in terms of the total annual thermal power generation is only 1.24%.

[0157] Further, wind power output is used as a second evaluation indicator, such as Figure 4The figure shows a comparison curve of wind power output time series using centralized optimization and three-stage progressive optimization. The horizontal axis of the curve represents time, and the vertical axis represents wind power output. The wind power output is calculated using the multi-regional power grid reserve planning method of the present invention, which employs three-stage decomposition and coordination to accelerate time series production simulation, corresponding to the wind power output of the improved model. The wind power output is obtained through traditional centralized optimization, corresponding to the wind power output of the baseline model. By comparing the fit of the two curves, the difference in the cumulative wind power generation throughout the year is 0.00%.

[0158] The verification by the two indicators fully demonstrates that the three-stage decomposition and coordination strategy of this invention can effectively approximate the global optimal solution of centralized optimization in terms of running results, ensuring high solution accuracy.

[0159] Regarding computational efficiency, as shown in Table 1, there is a significant difference in computation time between the two methods. The benchmark method takes as long as 1762.98 seconds, while the three-stage progressive optimization method proposed in this invention can complete the same simulation task in only 35.87 seconds, achieving a computational speedup of approximately 49.2 times and significantly reducing computation time.

[0160] Table 1: Comparison of computation time between the baseline model and the improved model

[0161] Model Calculation time (s) benchmark model 1762.98 Improved Model 35.87

[0162] Step S7: Determine the target planned reserve ratio scheme based on the target total system cost of each planned reserve ratio scheme.

[0163] For step S7, in a preferred embodiment, after determining the target planned reserve rate scheme based on the target total system cost of each planned reserve rate scheme, the method further includes:

[0164] Grid dispatch is carried out based on the power generation plan corresponding to the target reserve ratio scheme.

[0165] In one embodiment of the present invention, each planned reserve ratio scheme is obtained through steps S1 to S6. The corresponding total cost of the target system, where k = 1, 2, ..., K, K represents the total number of planned reserve rate schemes, β (k) This represents the k-th planned reserve ratio scheme. Let represent the planning reserve rate of region n under the k-th planning reserve rate scheme. This is determined by comparing the total cost Obj of all target systems. (k) The minimum planned reserve ratio among all target system total costs is selected as the target planned reserve ratio scheme. This ensures both the reliability of the power system and the optimal cost-effectiveness. The specific formula is as follows:

[0166] Obj (r) =min(Obj(1) Obj (2) ,…,Obj (κ) ,…,Obj (K+1) ),κ=1,2,…,K+1;

[0167] Among them, Obj (r) Obj represents the total target system cost corresponding to the target planning reserve ratio scheme. (k) Let κ represent the total target system cost corresponding to the planned reserve ratio scheme.

[0168] Furthermore, to investigate the impact of reserve capacity configuration on the operating characteristics of multi-regional interconnected power systems, this invention conducted experiments under six planned reserve rate scenarios ranging from 2% to 15%. The system operating costs under different planned reserve rates are detailed in Table 2.

[0169] Table 2 System operating costs under different planned reserve rates

[0170]

[0171] As shown in Table 2, the total system operating cost shows a significant downward trend as the planned reserve rate gradually increases. When the planned reserve rate increases from 2% to 15%, the total system cost decreases from RMB 24.328 billion to RMB 18.925 billion, a reduction of 22.2%. This result indicates that the strategic allocation of reserve resources is an effective means to optimize system operation and reduce overall operating costs.

[0172] The decrease in system costs is mainly due to two factors. First, high-cost thermal power generation has been effectively replaced. As shown in Table 2, with the increase in planned reserve ratio, the cost of thermal power decreased from RMB 23.394 billion to RMB 18.489 billion. This indicates that in production simulation, reserve resources meet part of the load demand by replacing high-cost thermal power units, thereby significantly reducing system fuel costs.

[0173] Secondly, system reliability has significantly improved. Load shedding costs directly reflect the system's power shortage status. When the planned reserve ratio increased from 2% to 15%, the load shedding cost plummeted from 933 million yuan to 437 million yuan, a decrease of 53.2%. This confirms the crucial role of additional reserve capacity in ensuring power supply reliability and coping with uncertainties (such as peak load periods and fluctuations in renewable energy output). It is worth noting that when the planned reserve ratio exceeds 10%, the rate of decrease in load shedding costs begins to level off, indicating that the reliability benefits of further increasing reserve capacity at this point exhibit diminishing marginal returns.

[0174] Comprehensive analysis shows that the rational allocation of backup resources not only achieves fuel substitution for thermal power units and reduces system operating costs, but also improves system reliability and the level of renewable energy consumption through its regulation capabilities.

[0175] Ultimately, grid dispatch is carried out according to the power generation plan corresponding to the target planned reserve ratio scheme to ensure that the power system operates at the optimal cost and with the most suitable allocation strategy, thereby achieving efficient utilization of power generation resources.

[0176] like Figure 5 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided;

[0177] One embodiment of the present invention provides a multi-regional power grid backup planning device that employs a three-stage decomposition and coordination to accelerate time-series production simulation, comprising: a partition parameter acquisition module, a regional independent optimization module, a node parameter acquisition module, a global coordination optimization module, a system constraint update module, a regional feedback optimization module, and a target planning confirmation module;

[0178] The partition parameter acquisition module is used to acquire the power grid operation parameters of each partition in the power grid and set several planned reserve rate schemes.

[0179] The regional independent optimization module is used to construct a regional independent optimization model for each partition under each planned reserve rate scheme, based on the grid operation parameters and the planned reserve rate, with the objective of minimizing the sum of regional operating costs and tie-line power exchange costs. It also constructs regional constraints, cluster unit constraints, and system constraints. Under the constraints of regional constraints, cluster unit constraints, and system constraints, it solves the regional independent optimization model and generates the total tie-line inflow power.

[0180] The node parameter acquisition module is used to obtain the equivalent node parameters for each partition, with each partition as a node.

[0181] The global coordination optimization module is used to construct a global coordination optimization model based on the total inflow power of tie lines and equivalent node parameters of each partition, with the goal of minimizing the total load shedding of the system. It also constructs power balance constraints, unit output constraints and system safety constraints. Under the constraints of power balance constraints, unit output constraints and system safety constraints, it solves the global coordination optimization model and generates tie line transmission power.

[0182] The system constraint update module is used to update the system constraints based on the tie line transmission power to obtain new system constraints;

[0183] The regional feedback optimization module is used to construct a regional feedback optimization model for each region based on the power grid operating parameters and planned reserve rate, with the goal of minimizing the total system cost. Under the constraints of regional constraints, cluster unit constraints and new system constraints, the regional feedback optimization model is solved to generate the target total system cost.

[0184] The target planning confirmation module is used to determine the target planning reserve rate scheme based on the target total system cost of each planning reserve rate scheme.

[0185] In a preferred embodiment, after solving the regional feedback optimization model and generating the total cost of the target system under the constraints of regional constraints, cluster unit constraints, and new system constraints, the method further includes:

[0186] Under the constraints of regional constraints, cluster unit constraints, and new system constraints, the regional feedback optimization model is solved to generate the power generation plan corresponding to the total cost of the target system.

[0187] The multi-regional power grid backup planning device, which employs a three-stage decomposition and coordination to accelerate time-series production simulation, also includes: a power dispatch module;

[0188] The power dispatch module is used to perform grid dispatching based on the power generation plan corresponding to the target planned reserve rate scheme.

[0189] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the multi-regional power grid reserve planning method using three-stage decomposition, coordination, and accelerated time-series production simulation provided by any of the above-described method embodiments of the present invention.

[0190] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0191] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A multi-regional power grid reserve planning method employing a three-stage decomposition and coordination approach to accelerate time-series production simulation, characterized in that, include: Obtain the power grid operation parameters for each zone in the power grid and set several planned reserve ratio schemes; Under each planned reserve rate scheme, for each zone, based on the power grid operating parameters and the planned reserve rate, a zone-independent optimization model is constructed with the objective of minimizing the sum of the zone operating cost and the tie-line power exchange cost. Zone constraints, cluster unit constraints and system constraints are also constructed. Under the constraints of the zone constraints, cluster unit constraints and system constraints, the zone-independent optimization model is solved to generate the total tie-line inflow power. Using each partition as a node, obtain the equivalent node parameters for each partition; Based on the total inflow power and equivalent node parameters of each partition's tie lines, a global coordination optimization model is constructed with the goal of minimizing the total load shedding of the system. Power balance constraints, unit output constraints, and system safety constraints are also constructed. Under the constraints of power balance constraints, unit output constraints, and system safety constraints, the global coordination optimization model is solved to generate the tie line transmission power. Based on the transmission power of the tie line, update the system constraints to obtain new system constraints; For each region, based on the power grid operating parameters and the planned reserve rate, a regional feedback optimization model is constructed with the goal of minimizing the total system cost. Under the constraints of regional constraints, cluster unit constraints, and new system constraints, the regional feedback optimization model is solved to generate the target total system cost. The target planned reserve ratio scheme is determined based on the target total system cost of each planned reserve ratio scheme.

2. The multi-regional power grid reserve planning method using three-stage decomposition, coordination, and accelerated time-series production simulation as described in claim 1, is characterized in that... After solving the regional feedback optimization model and generating the total cost of the target system under the constraints of regional constraints, cluster unit constraints, and new system constraints, the following steps are also included: Under the constraints of regional constraints, cluster unit constraints, and new system constraints, the regional feedback optimization model is solved to generate the power generation plan corresponding to the total cost of the target system. After determining the target planned reserve rate scheme based on the target total system cost of each planned reserve rate scheme, the method further includes: Grid dispatch is carried out based on the power generation plan corresponding to the target reserve ratio scheme.

3. The multi-regional power grid reserve planning method using three-stage decomposition, coordination, and accelerated time-series production simulation as described in claim 1, is characterized in that... The region-independent optimization model includes: Where f represents the sum of regional operating costs and tie-line power exchange costs, T represents the total number of time periods within the optimization period, t represents the time period index within the optimization period, N represents the total number of regions, n represents the region index, J represents the total number of cluster unit types, and j represents the cluster unit type index. This represents the fuel cost coefficient for thermal power units. This represents the output of the j-th type of cluster unit in region n during time period t. This represents the load shedding cost coefficient. This represents the load shedding amount in region n during time period t. This represents the tie-line power exchange penalty cost factor. This represents the total inflow power of the tie line in region n during time period t. This represents the cost coefficient for wind curtailment. This represents the maximum available wind power output in region n during time period t. This represents the actual wind power absorption capacity of region n during time period t.

4. The multi-regional power grid reserve planning method using three-stage decomposition and coordination to accelerate time-series production simulation as described in claim 3, is characterized in that... The global coordination optimization model includes: Where F represents the total system load shedding, N U This represents the total number of equivalent nodes, where u represents the index of the equivalent node. This represents the load shedding amount at the equivalent node u during time period t.

5. The multi-regional power grid reserve planning method using three-stage decomposition, coordination, and accelerated time-series production simulation as described in claim 4, is characterized in that... The regional feedback optimization model includes: Here, Obj represents the total system cost.

6. The multi-regional power grid reserve planning method using three-stage decomposition, coordination, and accelerated time-series production simulation as described in claim 5, is characterized in that... The system constraints include: standby capacity constraints, tie-line power transmission constraints, and load shedding constraints; The process of updating system constraints based on tie-line transmission power to obtain new system constraints includes: Based on the tie line transmission power, update the tie line power transmission constraint to obtain the new tie line power transmission constraint. The tie-line power transmission constraints include: The new tie-line power transmission constraints include: in, Let m represent the set of connection lines connected to region n, and m represent the index of the connection line connected to region n. This represents the lower limit of the transmission capacity of the m-th tie line in region n. This represents the upper limit of the transmission capacity of the m-th tie line in region n. This represents the total outflow power of the tie line in region n during time period t.

7. The multi-regional power grid reserve planning method using three-stage decomposition, coordination, and accelerated time-series production simulation as described in claim 6, is characterized in that... The regional constraints include: regional power balance constraints; the cluster unit constraints include: cluster unit operation constraints, cluster unit output constraints, cluster unit ramp-up constraints, cluster unit minimum start-up and shutdown time constraints, and renewable energy regional output constraints.

8. The multi-regional power grid reserve planning method using three-stage decomposition, coordination, and accelerated time-series production simulation as described in claim 7, is characterized in that... The unit output constraints include: thermal power unit output constraints and renewable energy unit output constraints; the system safety constraints include: tie line power flow constraints, reserve capacity coordination constraints, and load shedding coordination constraints.

9. A multi-regional power grid reserve planning device employing a three-stage decomposition and coordination acceleration time-series production simulation, characterized in that, include: The system includes a partition parameter acquisition module, a region-independent optimization module, a node parameter acquisition module, a global coordination optimization module, a system constraint update module, a region feedback optimization module, and a target planning confirmation module. The partition parameter acquisition module is used to acquire the power grid operation parameters of each partition in the power grid and set several planned reserve rate schemes. The regional independent optimization module is used to construct a regional independent optimization model for each partition under each planned reserve rate scheme, based on the power grid operating parameters and the planned reserve rate, with the objective of minimizing the sum of regional operating costs and tie-line power exchange costs. It also constructs regional constraints, cluster unit constraints, and system constraints, and solves the regional independent optimization model under the constraints of regional constraints, cluster unit constraints, and system constraints to generate the total tie-line inflow power. The node parameter acquisition module is used to acquire the equivalent node parameters of each partition, with each partition as a node. The global coordination optimization module is used to construct a global coordination optimization model based on the total inflow power of tie lines and equivalent node parameters of each partition, with the goal of minimizing the total load shedding of the system. It also constructs power balance constraints, unit output constraints and system safety constraints, and solves the global coordination optimization model under the constraints of power balance constraints, unit output constraints and system safety constraints to generate tie line transmission power. The system constraint update module is used to update the system constraints according to the tie line transmission power to obtain new system constraints; The regional feedback optimization module is used to construct a regional feedback optimization model for each partition based on the power grid operating parameters and the planned reserve rate, with the goal of minimizing the total system cost. Under the constraints of regional constraints, cluster unit constraints and new system constraints, the regional feedback optimization model is solved to generate the target total system cost. The target planning confirmation module is used to determine the target planning reserve rate scheme based on the target total system cost of each planning reserve rate scheme.

10. The multi-regional power grid reserve planning device employing three-stage decomposition, coordination, and accelerated time-series production simulation as described in claim 9, characterized in that, After solving the regional feedback optimization model and generating the total cost of the target system under the constraints of regional constraints, cluster unit constraints, and new system constraints, the following steps are also included: Under the constraints of regional constraints, cluster unit constraints, and new system constraints, the regional feedback optimization model is solved to generate the power generation plan corresponding to the total cost of the target system. The multi-regional power grid backup planning device, which employs a three-stage decomposition and coordination to accelerate time-series production simulation, also includes: a power dispatch module; The power dispatch module is used to perform power grid dispatching based on the power generation plan corresponding to the target planned reserve ratio scheme.

Images