Electric power and electric quantity balancing method considering flexible mutual aid of regions under new energy access

Abstract

The invention relates to the technical field of electric power system operation optimization, and discloses an electric power and electric quantity balancing method considering flexible mutual aid of regions under new energy access, which comprises the following steps: establishing a wind and light output joint distribution model based on a Frank-Copula function, and generating a typical scene set; constructing a comprehensive norm constraint fuzzy set to process scene probability uncertainty, and quantifying source-network-storage flexibility supply capability; establishing a flexible supply and demand balance condition considering region mutual aid; and an energy storage-power transmission joint planning double-layer optimization model is constructed, an outer layer model carries out energy storage and power transmission collaborative planning by taking maximization of a whole-network new energy consumption rate as a target, an inner layer model carries out operation optimization by taking minimization of regional injection power and tie line power fluctuation as a target, and dynamic coupling of investment and operation is realized through iterative solution. Compared with a traditional sequential planning method, the method has the advantages that energy storage investment can be reduced, power transmission extension cost can be reduced, resource redundancy configuration is avoided, and economical efficiency of a planning scheme is improved.

Description

Technical Field

[0001] This invention relates to the field of power system operation optimization technology, and in particular to a power balance method that considers flexible regional mutual assistance under the access of new energy sources. Background Technology

[0002] The penetration rate of new energy sources such as wind power and solar power in the power system continues to rise. By the end of 2024, my country's installed capacity of new energy had exceeded 1400GW, and the penetration rate of new energy is expected to reach 55% by 2025. The high proportion of new energy integration has led to significant uncertainty and volatility in the power system. The traditional "deterministic generation tracking uncertain load" model is gradually transforming into a "two-way matching of uncertain generation and uncertain load" model, posing a severe challenge to the balance of power supply and demand.

[0003] Under extreme weather conditions, the drastic fluctuations in renewable energy output further exacerbate the problem of insufficient system regulation capacity. Existing research mainly focuses on two directions: flexible resource allocation and power balance optimization, but it has three shortcomings:

[0004] First, the methods for quantifying flexibility demand are not precise enough. Most studies use net load fluctuation or ramp rate as indicators of flexibility demand, failing to distinguish the differences in flexibility demand across different time scales, and neglecting the combined impact of uncertainties on both the source and load sides. Second, the assessment of flexibility supply capacity is not comprehensive enough. Traditional methods mainly consider the ramp-up capability of conventional units, lacking systematic modeling of the power-efficiency (PE) characteristics of energy storage, transmission constraints of transmission channels, and the inter-regional flexibility mutual support capability. Third, regional collaborative planning methods need improvement. Existing energy storage-transmission joint planning models mostly adopt a sequential approach, failing to fully explore the dynamic coupling relationship between energy storage configuration and transmission expansion, easily leading to investment redundancy or resource waste. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a power balance method that considers flexible regional mutual assistance under the access of new energy sources.

[0006] The objective of this invention is achieved through the following technical solution: a power balance method considering flexible regional mutual assistance under the access of new energy sources, the method comprising,

[0007] Based on the scenario-interval method, the flexibility requirements are quantified, a joint distribution model of wind and solar power output is established based on the Frank-Copula function, and a typical scenario set is generated. A comprehensive norm-constrained fuzzy set is constructed to handle the scenario probability uncertainty, and the system's up-adjustment and down-adjustment flexibility requirements at different time scales are calculated based on the prediction error.

[0008] Quantifying the flexible supply capacity of the source-grid-storage system includes the ramp-up capacity constraints of conventional units, piecewise linearization modeling of the nonlinear power-efficiency characteristics of energy storage systems, and the transmission capacity and regulation rate constraints of the interconnected power grid.

[0009] Establish flexible supply and demand balance conditions that take into account regional mutual assistance, including: defining constraints on the amount of flexible mutual assistance between regions, introducing flexibility adjustment factors to quantify the contribution of various resources, and constructing indicators of insufficient node adjustment capacity and the ratio of line full load time to identify weak links in the system.

[0010] A two-layer optimization model for energy storage-transmission joint planning is constructed. The outer layer model aims to maximize the renewable energy absorption rate of the entire network for coordinated planning of energy storage and transmission. The inner layer model aims to minimize the fluctuations in regional injected power and tie line power for operational optimization. The dynamic coupling of investment and operation is achieved through iterative solution.

[0011] Specifically, the joint distribution model of wind and solar power output is as follows:

[0012] ;

[0013] In the formula, Powering wind power; Contribute to photovoltaic power; These are time-varying correlation parameters; , These are the edge cumulative distribution functions of wind power and photovoltaic power output, respectively;

[0014] The generated scene was reduced using the K-means clustering algorithm to obtain... A typical scenario and its probability:

[0015] ;

[0016] In the formula, Number the scene.

[0017] Specifically, the construction of a comprehensive norm-constrained fuzzy set to handle probabilistic uncertainties in the scenario includes,

[0018] Construct a comprehensive norm-constrained fuzzy set centered on the initial probability distribution:

[0019] ;

[0020] In the formula, in the formula This is the actual probability distribution vector; This is the initial probability distribution vector; Let be the probability of the s-th scenario; , This is the probability deviation threshold; It is a 1-norm; It is an ∞-norm;

[0021] ;

[0022] ;

[0023] In the formula, and The level of confidence for the probability of uncertainty; For sample size; This represents the total number of scenes.

[0024] Specifically, the flexibility requirements for upward and downward adjustments of the prediction error calculation system at different time scales include:

[0025] calculate Time zone Upper and lower limits of new energy sources and load output:

[0026] ;

[0027] In the formula, These are wind power, solar power, and load, respectively. For resources The maximum prediction error coefficient; For the scene Mid-moment resource The predicted output; and These are the upper and lower bounds of the output after considering prediction errors;

[0028] Given time scale Calculation of the system's flexibility requirements for both upward and downward adjustments:

[0029] ;

[0030] ;

[0031] In the formula, For the scene Central region At any moment The need for increased flexibility; To reduce flexibility requirements; For time scale.

[0032] Specifically, the ramp-up capability constraint of the conventional unit is as follows:

[0033] ;

[0034] ;

[0035] In the formula, , These refer to the upward and downward adjustment flexibility supply capacity of unit g at time t, respectively. To increase the climbing rate; To reduce the climbing rate; , Contribute to both maximum and minimum technical strength; To contribute practically; Time scale;

[0036] The nonlinear power-efficiency characteristics of the energy storage system are modeled as follows:

[0037] ;

[0038] ;

[0039] ;

[0040] In the formula, For energy storage charging and discharging power; The power value at the k-th segment point; The weight coefficient for the k-th segment point; For power The corresponding charge and discharge efficiency; Segmentation point Efficiency value; Let be the binary variable of the k-th interval; n is the total number of segments;

[0041] Energy storage flexibility and supply capacity must simultaneously consider power constraints and energy constraints:

[0042] ;

[0043] ;

[0044] In the formula, and Energy storage time The upward and downward adjustment of flexible supply capacity; This represents the maximum discharge power of the energy storage. This represents the maximum charging power for energy storage. and These represent the maximum and minimum energy storage capacities, respectively. Let be the stored energy quantity at time t; For the first The charging and discharging efficiency of the segment;

[0045] Transmission capacity and regulation rate constraints of interconnected power grids:

[0046] ;

[0047] ;

[0048] In the formula, and They are respectively regions and Inter-line communication at time The upward and downward adjustment of flexible supply capacity; and These are the maximum upward and downward adjustment rates of the tie line, respectively; This represents the maximum transmission power of the tie line; Let be the actual transmission power at time t.

[0049] Specifically, the constraints on the flexibility and mutual assistance between regions are as follows:

[0050] ;

[0051] ;

[0052] ;

[0053] In the formula, For the region To the region The increased flexibility and mutual support provided; The mutual assistance quantity in the opposite direction; For the region The conventional generating units will increase their flexible supply capacity; For the region Energy storage enhances flexibility and supply capacity;

[0054] The flexibility adjustment factor is:

[0055] ;

[0056] ;

[0057] In the formula, and Resources At any moment Upregulation and downregulation of flexibility modulators; These are respectively: generating units, energy storage, and interconnected power grids; and Resources Flexible supply capacity; and These are the total system flexibility requirements;

[0058] Construct an indicator of insufficient node adjustment capability:

[0059] ;

[0060] ;

[0061] ;

[0062] In the formula, The indicator for the inadequacy of the adjustment capability of node i; and These represent the average insufficient upward and downward adjustment capabilities of node i, respectively. For nodes At any moment The load shedding power; For nodes At any moment The amount of wind and solar power curtailed; and These represent the total number of periods where upward and downward adjustment capabilities were insufficient, respectively.

[0063] Construct a line full load time ratio index to identify system weaknesses:

[0064] ;

[0065] In the formula, For the line The ratio of full load time; For the line The total number of time periods when the system is at full capacity; For the line The total number of non-full load periods.

[0066] Specifically, the objective function of the outer model is:

[0067] ;

[0068] In the formula, The outer objective function; For the scene The probability of; For the scene Central region At any moment The amount of new energy consumed; Power can be generated from renewable energy sources;

[0069] The constraints of the outer model include:

[0070] Total capacity constraints for energy storage planning:

[0071] ;

[0072] ;

[0073] In the formula, and They are respectively regions Planned energy storage power and capacity; and These refer to the total planned power and total capacity of the entire network's energy storage system;

[0074] Energy storage power-capacity ratio constraints:

[0075] ;

[0076] In the formula, and These are the minimum and maximum rated charge and discharge times for energy storage, respectively.

[0077] Specifically, the objective function of the inner model is:

[0078] ;

[0079] In the formula, The inner objective function; For the scene Central region At any moment The injection power; For the scene Central region To the area The transmission power of the connecting line; To optimize the total number of time periods;

[0080] The constraints of the inner model include:

[0081] Node power balance constraints:

[0082] ;

[0083] ;

[0084] In the formula, To provide power to conventional generating units; For energy storage charging and discharging power; For load power; For the scene Central region To the area The transmission power of the connecting line;

[0085] Flexibility and supply-demand balance constraints:

[0086] ;

[0087] ;

[0088] In the formula, For the scene Next period Traditional generator sets The increased flexibility offered; For the scene Next period Time-based energy storage devices The increased flexibility offered; For the scene Next period Time demand side resources The increased flexibility offered; For the scene Next period Traditional generator sets Offers flexibility in price reductions; For the scene Next period Time-based energy storage devices Offers flexibility in price reductions; For the scene Next period Time demand side resources Offers flexibility in price reductions;

[0089] Energy storage operation constraints:

[0090] ;

[0091] ;

[0092] ;

[0093] In the formula, This represents the minimum charging power (negative value) for energy storage. This refers to the charging power. This refers to the discharge power. For a moment The energy storage capacity; For time step; and These represent the lower and upper limits of the energy storage state of charge, respectively.

[0094] The present invention has the following advantages:

[0095] 1. This invention integrates the quantification of flexibility requirements, the assessment of multi-type supply capacity, regional coordination and mutual assistance, and the coordinated planning of energy storage and transmission into a unified framework, systematically addressing the power balance problem caused by the "two-sided uncertainty" of new energy sources, and breaking through the limitations of traditional methods in terms of precision, comprehensiveness, and coordination.

[0096] 2. This invention adopts the Copula-scene clustering + comprehensive norm constrained fuzzy set method, which can capture the spatiotemporal correlation and complementary characteristics of wind and solar power output, and handle the uncertainty of scene probability through confidence constraints. This enables the model to reflect typical operating conditions and be robust to rare scenarios such as extreme weather, significantly improving the accuracy of flexibility demand prediction.

[0097] 3. This invention innovatively models the nonlinear power-efficiency (PE) characteristics of energy storage using piecewise linearization, overcoming the evaluation bias caused by traditional fixed-efficiency models. This model can more accurately evaluate the actual regulation capabilities of compressed air energy storage, pumped hydro storage, and electrochemical energy storage at different operating points, providing a reliable basis for the efficient configuration of energy storage.

[0098] 4. By defining a “flexibility adjustment factor” and establishing regional mutual assistance constraints, this invention quantifies the marginal contribution of different resources such as conventional units, energy storage, and interconnection lines to the system flexibility balance. This mechanism enables flexibility resources not only to cope with fluctuations on the local time scale, but also to provide mutual assistance support between regions through the power grid, which greatly improves the utilization efficiency of the entire network’s flexibility resources.

[0099] 5. This invention constructs a two-layer planning model for dynamic coupling of energy storage and power transmission. Through iterative feedback between the outer layer (investment) and the inner layer (operation), it reveals the deep interactive relationship between energy storage configuration and power transmission expansion. Compared with traditional sequential planning, this method can effectively avoid investment redundancy or resource misallocation, and reduce the overall investment and operating costs of the system while ensuring high absorption of new energy.

[0100] 6. This invention proposes two diagnostic indicators: "node regulation capacity insufficiency" and "line full load time ratio," which can accurately locate weak links in the power grid and transmission congestion bottlenecks. The diagnostic results are fed back to the planning model, guiding investment to prioritize the most urgent nodes and lines, realizing a closed-loop optimization from "imbalance source tracing" to "precise reinforcement," significantly improving the effectiveness of investment and the overall safety level of the system. Attached Figure Description

[0101] Figure 1 This is a schematic diagram of the power balancing method of the present invention. Detailed Implementation

[0102] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described embodiments are merely some embodiments of the invention, and not all embodiments. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0103] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0104] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0105] The present invention will be further described below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0106] like Figure 1 As shown, a power balance method considering flexible regional mutual assistance under the access of new energy sources is proposed. This method includes:

[0107] Based on the scenario-interval method, the flexibility requirements are quantified, a joint distribution model of wind and solar power output is established based on the Frank-Copula function, and a typical scenario set is generated. A comprehensive norm-constrained fuzzy set is constructed to handle the scenario probability uncertainty, and the system's up-adjustment and down-adjustment flexibility requirements at different time scales are calculated based on the prediction error.

[0108] To address the spatiotemporal correlation and uncertainty of wind and solar power output, a method combining Copula-scene clustering and interval optimization is adopted to quantify flexibility requirements.

[0109] The joint distribution model of wind and solar power output is constructed as follows:

[0110] ;

[0111] In the formula, Powering wind power; Contribute to photovoltaic power; These are time-varying correlation parameters; , These are the edge cumulative distribution functions of wind power and photovoltaic power output, respectively;

[0112] The generated scene was reduced using the K-means clustering algorithm to obtain... A typical scenario and its probability:

[0113] ;

[0114] In the formula, The scene is numbered; scene reduction preserves the main statistical features of the original scene set while ensuring computational efficiency.

[0115] The construction of a comprehensive norm-constrained fuzzy set to handle probabilistic uncertainties in the scenario includes...

[0116] Since the actual probability distribution values ​​of each discrete scenario still have uncertainty, in order to make them closer to the real operating conditions and fluctuate within a reasonable range, a comprehensive norm-constrained fuzzy set centered on the initial probability distribution is constructed to handle the uncertainty of scenario probabilities.

[0117] ;

[0118] In the formula, in the formula This is the actual probability distribution vector; This is the initial probability distribution vector; Let be the probability of the s-th scenario; , The probability deviation threshold is determined through confidence constraints; It is the 1-norm, representing the overall deviation of the probability vector; The ∞-norm represents the maximum deviation of the probability of a single scene;

[0119] ;

[0120] ;

[0121] In the formula, and The confidence level for the uncertainty probability is typically set between 0.90 and 0.95. For sample size; The total number of scenarios is defined by this constraint mechanism, which ensures that scenario probabilities fluctuate within a reasonable range, guaranteeing model robustness while avoiding excessive conservatism. This invention describes the spatiotemporal correlation and complementarity of wind and solar power output using a Frank-Copula function, combines K-means clustering to generate typical scenarios, and innovatively introduces a fuzzy set of probability distributions with combined 1-norm and ∞-norm constraints to handle scenario probability uncertainty. This method achieves robust modeling of abnormal scenarios such as extreme weather through confidence constraints, improving flexibility and accuracy of demand quantification compared to traditional single-scenario methods, thus providing a basis for subsequent resource allocation.

[0122] The flexibility requirements for upward and downward adjustments of the prediction error calculation system at different time scales include:

[0123] Considering the flexibility requirements of prediction error calculation, the calculation is performed. Time zone Upper and lower limits of new energy sources and load output:

[0124] ;

[0125] In the formula, These are wind power, solar power, and load, respectively. For resources The maximum prediction error coefficient is typically 0.15-0.20 for wind power, 0.10-0.15 for photovoltaic power, and 0.03-0.05 for load power. For the scene Mid-moment resource The predicted output; and These are the upper and lower bounds of the output after considering prediction errors.

[0126] Given time scale Calculation of the system's flexibility requirements for both upward and downward adjustments:

[0127] ;

[0128] ;

[0129] In the formula, For the scene Central region At any moment The increased flexibility requirement indicates that the system needs to increase power generation or reduce load; Reducing flexibility requirements means the system needs to reduce power generation or increase load; Using a time scale, 15 minutes is typically used for short-term flexibility needs, and 1 hour is used for intraday flexibility needs. This method accurately quantifies the system flexibility requirements caused by source load fluctuations through first-order difference summation.

[0130] Quantifying the flexible supply capacity of the source-grid-storage system includes the ramp-up capacity constraints of conventional units, piecewise linearization modeling of the nonlinear power-efficiency characteristics of energy storage systems, and the transmission capacity and regulation rate constraints of the interconnected power grid.

[0131] The ramp-up capability constraint of the conventional unit is:

[0132] ;

[0133] ;

[0134] In the formula, , These refer to the upward and downward adjustment flexibility supply capacity of unit g at time t, respectively. To increase the climbing rate; To reduce the climbing rate; , Contribute to both maximum and minimum technical strength; To contribute practically; For time scale.

[0135] To address the nonlinear power-efficiency (PE) characteristics of compressed air energy storage, pumped hydro storage, and electrochemical energy storage, this invention employs a piecewise linearization method for modeling, introducing continuous weight variables. and binary variables This method constructs constraints to ensure the mutual exclusion of weights at adjacent segment points, achieving linearity of the efficiency function. It expresses the flexible supply capacity of energy storage as a constraint of power and electricity, improving evaluation accuracy compared to the traditional fixed-efficiency model. When an energy storage system deviates from its rated power, its charging and discharging efficiency drops significantly; using a fixed-efficiency model would lead to biases in the assessment of flexible supply capacity. Taking compressed air energy storage as an example, the charging and discharging power-efficiency curve is divided into... Section, usually Choose 5-10 to balance accuracy and computational complexity:

[0136] ;

[0137] ;

[0138] ;

[0139] In the formula, For energy storage charging and discharging power; The power value at the k-th segment point; The weight coefficient for the k-th segment point; For power The corresponding charge and discharge efficiency; Segmentation point Efficiency value; is a binary variable for the k-th interval, used to ensure the mutual exclusion of the weights of adjacent segment points; n is the total number of segments; through this piecewise linearization method, any charge / discharge power point and its corresponding efficiency can be represented by a convex combination of no more than two adjacent segment points.

[0140] Energy storage flexibility and supply capacity must simultaneously consider power constraints and energy constraints:

[0141] ;

[0142] ;

[0143] In the formula, and Energy storage time The upward and downward adjustment of flexible supply capacity; This represents the maximum discharge power of the energy storage. This represents the maximum charging power for energy storage. and These represent the maximum and minimum energy storage capacities, respectively. Let be the stored energy quantity at time t; For the first The charging and discharging efficiency of the segment; the first term represents the power constraint, and the second term represents the limitation of the power constraint on flexibility within a given time scale.

[0144] Transmission capacity and regulation rate constraints of interconnected power grids:

[0145] ;

[0146] ;

[0147] In the formula, and They are respectively regions and Inter-line communication at time The upward and downward adjustment of flexible supply capacity; and These are the maximum upward and downward adjustment rates of the tie line, respectively; This represents the maximum transmission power of the tie line; The actual transmission power at time t; the interconnected power grid realizes the transfer of flexibility demand through spatial distribution characteristics. When the flexibility demand in a certain area increases, flexibility support can be obtained from the neighboring area by adjusting the cross-regional power transmission power.

[0148] Establish flexible supply and demand balance conditions that take into account regional mutual assistance, including: defining constraints on the amount of flexible mutual assistance between regions, introducing flexibility adjustment factors to quantify the contribution of various resources, and constructing indicators of insufficient node adjustment capacity and the ratio of line full load time to identify weak links in the system.

[0149] Set up a region To the region The flexibility of mutual assistance is and The constraints on the flexibility and mutual assistance between regions are:

[0150] ;

[0151] ;

[0152] ;

[0153] In the formula, For the region To the region The increased flexibility and mutual support provided; The mutual assistance quantity in the opposite direction; For the region The conventional generating units will increase their flexible supply capacity; For the region The energy storage system enhances the flexibility of supply capacity; the first equation indicates that for the same interconnected power grid, the sum of the flexibility allocation values ​​in different directions at a certain moment is zero; the second equation indicates the regional... The flexibility of power transmission is limited by the flexibility margin of the region; the third equation indicates that the allocation of flexibility is limited by the transmission capacity of the interconnected power grid.

[0154] The flexibility adjustment factor is:

[0155] ;

[0156] ;

[0157] In the formula, and Resources At any moment Upregulation and downregulation of flexibility modulators; These are respectively: generating units, energy storage, and interconnected power grids; and Resources Flexible supply capacity; and These represent the total system flexibility requirements; when At this point, the system's flexibility supply and demand reach a precise balance, with neither flexibility deficit nor redundancy. This indicator reflects the contribution of different resources to achieving flexibility supply and demand balance, providing a quantitative basis for optimal resource allocation. The system defines flexibility adjustment factors and quantifies the marginal contributions of various resources (source, grid, and storage) to flexibility balance, as well as inter-regional flexibility mutual assistance constraints and tie-line transmission capacity constraints, establishing supply and demand balance conditions. This enables the optimization of flexible resources in both the time dimension (different time scales) and the spatial dimension (different regions), providing a theoretical foundation for flexible mutual assistance in multi-regional power grids.

[0158] To accurately pinpoint weak links, an indicator of insufficient node adjustment capacity is constructed:

[0159] ;

[0160] ;

[0161] ;

[0162] In the formula, The indicator for the inadequacy of the adjustment capability of node i; and These represent the average insufficient upward and downward adjustment capabilities of node i, respectively. For nodes At any moment The load shedding power; For nodes At any moment The amount of wind and solar power curtailed; and These represent the total number of time periods where upward and downward regulation capacity is insufficient, respectively. A high node regulation capacity insufficiency index indicates a significant regulation capacity gap at that node, necessitating the allocation of flexibility resources such as energy storage. This invention proposes a node regulation capacity insufficiency index and a line full-load duration ratio index to pinpoint weak links in grid flexibility. The weak link identification results are incorporated into the constraints of a bi-level programming model, forming a closed-loop optimization mechanism of imbalance source tracing, weak link identification, and precise allocation. This mechanism, by setting upper and lower limits for energy storage allocation, enables high... Value nodes are given priority in obtaining energy storage configurations, and high-voltage lines are prioritized for expansion based on line expansion priority. Compared to the indiscriminate configuration method, the bottleneck line can reduce the inadequacy of the adjustment capability of weak nodes, reduce the full load time of weak lines, and improve the overall flexibility of the system.

[0163] Construct a line full load time ratio index to identify system weaknesses:

[0164] ;

[0165] In the formula, For the line The ratio of full load time; For the line The total number of time periods when the system is at full capacity; For the line The total number of non-full-load periods; The higher the value, the more severe the transmission congestion on that line, and the greater the need for expansion. These two types of indicators allow for precise identification of weak links in the power grid's flexibility, providing guidance for subsequent targeted planning.

[0166] A two-layer optimization model for energy storage-transmission joint planning is constructed. The outer layer model aims to maximize the renewable energy absorption rate of the entire network for coordinated planning of energy storage and transmission. The inner layer model aims to minimize the fluctuations in regional injected power and tie line power for operational optimization. The dynamic coupling of investment and operation is achieved through iterative solution.

[0167] The objective function of the outer model is:

[0168] ;

[0169] In the formula, The outer objective function; For the scene The probability of; For the scene Central region At any moment The amount of new energy consumed; The goal is to maximize the utilization rate of new energy sources by minimizing the objective function.

[0170] The constraints of the outer model include:

[0171] Total capacity constraints for energy storage planning:

[0172] ;

[0173] ;

[0174] In the formula, and They are respectively regions Planned energy storage power and capacity; and These represent the total planned power and total capacity of the entire network's energy storage system; this set of parameters is determined by the regional energy storage system development plan.

[0175] Energy storage power-capacity ratio constraints:

[0176] ;

[0177] In the formula, and These are the minimum and maximum rated charge and discharge times for energy storage, typically 2-4 hours, to ensure that the energy storage has a reasonable energy type and power type ratio.

[0178] The inner layer aims to minimize regional injected power fluctuations and tie-line transmitted power fluctuations. The objective function of the inner layer model is:

[0179] ;

[0180] In the formula, The inner objective function; For the scene Central region At any moment The injection power; For the scene Central region To the area The transmission power of the connecting line; To optimize the total number of time periods, the first optimization is to improve the stability of injected power in each region, and the second optimization is to improve the stability of power transmitted through tie lines. By minimizing power fluctuations, the pressure on system frequency regulation and transmission losses can be reduced.

[0181] The constraints of the inner model include:

[0182] Node power balance constraints:

[0183] ;

[0184] ;

[0185] In the formula, To provide power to conventional generating units; For energy storage charging and discharging power; For load power; For the scene Central region To the area The transmission power of the connecting line;

[0186] Flexibility and supply-demand balance constraints:

[0187] ;

[0188] ;

[0189] In the formula, For the scene Next period Traditional generator sets The increased flexibility offered; For the scene Next period Time-based energy storage devices The increased flexibility offered; For the scene Next period Time demand side resources The increased flexibility offered; For the scene Next period Traditional generator sets Offers flexibility in price reductions; For the scene Next period Time-based energy storage devices Offers flexibility in price reductions; For the scene Next period Time demand side resources The reduced flexibility provided; this constraint ensures that the flexible supply capacity of multiple types of resources from source to grid to storage can meet the system's flexibility requirements, and is the core constraint to ensure the safe operation of the system.

[0190] Energy storage operation constraints:

[0191] ;

[0192] ;

[0193] ;

[0194] In the formula, This represents the minimum charging power (negative value) for energy storage. This represents the maximum discharge power. This refers to the charging power. Indicates discharge. Indicates charging; This refers to the discharge power. For a moment The energy storage capacity; For time step; and These represent the lower and upper limits of the energy storage state of charge, typically set to 0.1 and 0.9 to prevent overcharging and over-discharging. The first equation is the power constraint, the second is the energy balance equation, which considers the impact of charging and discharging efficiency on energy changes, and the third is the state of charge constraint.

[0195] The original problem is transformed into a mixed-integer linear programming (MILP) problem by piecewise linearization, and then solved iteratively using the CPLEX solver. The convergence condition is:

[0196] ;

[0197] In the formula, This represents the number of iterations. This is the convergence threshold.

[0198] This invention constructs a two-layer planning model with the optimal overall renewable energy absorption rate as the outer objective and the minimum regional power fluctuation as the inner objective. Through iterative outer-layer investment decisions and inner-layer operational optimization, it explores the coupling relationship between energy storage configuration and transmission expansion. Energy storage influences tie-line transmission demand through node-injected power regulation, while tie-line capacity constrains the charging and discharging space of energy storage in each region. Compared to traditional sequential planning methods, this approach reduces energy storage investment, lowers transmission expansion costs, avoids redundant resource allocation, and improves the economic efficiency of the planning scheme.

[0199] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any person skilled in the art can make many possible variations and modifications to the technical solution of the present invention, or modify it into equivalent embodiments, without departing from the scope of the present invention. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technology of the present invention without departing from the scope of the present invention are within the protection scope of the present invention.

Claims

1. A power balance method considering flexible regional mutual assistance under the access of new energy sources, characterized by: The method includes, Based on the scenario-interval method, the flexibility requirements are quantified, a joint distribution model of wind and solar power output is established based on the Frank-Copula function, and a typical scenario set is generated. A comprehensive norm-constrained fuzzy set is constructed to handle the scenario probability uncertainty, and the system's up-adjustment and down-adjustment flexibility requirements at different time scales are calculated based on the prediction error. Quantifying the flexible supply capacity of the source-grid-storage system includes the ramp-up capacity constraints of conventional units, piecewise linearization modeling of the nonlinear power-efficiency characteristics of energy storage systems, and the transmission capacity and regulation rate constraints of the interconnected power grid. Establish flexible supply and demand balance conditions that take into account regional mutual assistance, including: defining constraints on the amount of flexible mutual assistance between regions, introducing flexibility adjustment factors to quantify the contribution of various resources, and constructing indicators of insufficient node adjustment capacity and the ratio of line full load time to identify weak links in the system. A two-layer optimization model for energy storage-transmission joint planning is constructed. The outer layer model aims to maximize the renewable energy absorption rate of the entire network for coordinated planning of energy storage and transmission. The inner layer model aims to minimize the fluctuations in regional injected power and tie line power for operational optimization. The dynamic coupling of investment and operation is achieved through iterative solution.

2. The power balance method considering regional flexible mutual assistance under the access of new energy sources according to claim 1, characterized in that: The joint distribution model of wind and solar power output is as follows: ； In the formula, Powering wind power; Contribute to photovoltaic power; These are time-varying correlation parameters; , These are the edge cumulative distribution functions of wind power and photovoltaic power output, respectively; The generated scene was reduced using the K-means clustering algorithm to obtain... A typical scenario and its probability: ； In the formula, Number the scene.

3. The power balance method considering regional flexible mutual assistance under the access of new energy sources according to claim 1, characterized in that: The construction of a comprehensive norm-constrained fuzzy set to handle probabilistic uncertainties in the scenario includes... Construct a comprehensive norm-constrained fuzzy set centered on the initial probability distribution: ； In the formula This is the actual probability distribution vector; This is the initial probability distribution vector; Let be the probability of the s-th scenario; , This is the probability deviation threshold; It is a 1-norm; It is an ∞-norm; ； ； In the formula, and The level of confidence for the probability of uncertainty; For sample size; This represents the total number of scenes.

4. The power balance method considering regional flexible mutual assistance under the access of new energy sources according to claim 1, characterized in that: The flexibility requirements for upward and downward adjustments of the prediction error calculation system at different time scales include: calculate Time zone Upper and lower limits of new energy sources and load output: ； In the formula, These are wind power, solar power, and load, respectively. For resources The maximum prediction error coefficient; For the scene Mid-moment resource The predicted output; and These are the upper and lower bounds of the output after considering prediction errors; Given time scale Calculation of the system's flexibility requirements for both upward and downward adjustments: ； ； In the formula, For the scene Central region At any moment The need for increased flexibility; To reduce flexibility requirements; For time scale.

5. The power balance method considering regional flexible mutual assistance under the access of new energy sources according to claim 1, characterized in that: The ramp-up capability constraint of the conventional unit is: ； ； In the formula, , These refer to the upward and downward adjustment flexibility supply capacity of unit g at time t, respectively. To increase the climbing speed; The decision to lower the ramp rate has been carefully considered; , Contribute to both maximum and minimum technical strength; To contribute practically; Time scale; The nonlinear power-efficiency characteristics of the energy storage system are modeled as follows: ； ； ； In the formula, For energy storage charging and discharging power; The power value at the k-th segment point; The weight coefficient for the k-th segment point; For power The corresponding charge and discharge efficiency; Segmentation point Efficiency value; Let be the binary variable of the k-th interval; n is the total number of segments; Energy storage flexibility and supply capacity must simultaneously consider power constraints and energy constraints: ； ； In the formula, and Energy storage time The upward and downward adjustment of flexible supply capacity; This represents the maximum discharge power of the energy storage. This represents the maximum charging power for energy storage. and These represent the maximum and minimum energy storage capacities, respectively. Let be the stored energy quantity at time t; For the first The charging and discharging efficiency of the segment; Transmission capacity and regulation rate constraints of interconnected power grids: ； ； In the formula, and They are respectively regions and Inter-line communication at time The upward and downward adjustment of flexible supply capacity; and These are the maximum upward and downward adjustment rates of the tie line, respectively; This represents the maximum transmission power of the tie line; Let be the actual transmission power at time t.

6. The power balance method considering regional flexible mutual assistance under new energy access as described in claim 5, characterized in that: The constraints on the flexibility and mutual assistance between regions are: ； ； ； In the formula, For the region To the region The increased flexibility and mutual assistance provided; The mutual assistance quantity in the opposite direction; For the region The conventional generating units will increase their flexible supply capacity; For the region Energy storage enhances flexibility and supply capacity; The flexibility adjustment factor is: ； ； In the formula, and Resources At any moment Upregulation and downregulation of flexibility modulators; These are respectively: generating units, energy storage, and interconnected power grids; and Resources Flexible supply capacity; and These are the total system flexibility requirements; Construct an indicator of insufficient node adjustment capability: ； ； In the formula, The indicator for the inadequacy of the adjustment capability of node i; and These represent the average insufficient upward and downward adjustment capabilities of node i, respectively. For nodes At any moment The load shedding power; For nodes At any moment The amount of wind and solar power curtailed; and These represent the total number of periods where upward and downward adjustment capabilities were insufficient, respectively. Construct a line full load time ratio index to identify system weaknesses: ； In the formula, For the line The ratio of full load time; For the line The total number of time periods when the system is at full capacity; For the line The total number of non-full load periods.

7. The power balance method considering regional flexible mutual assistance under new energy access as described in claim 5, characterized in that: The objective function of the outer model is: ； In the formula, The outer objective function; For the scene The probability of; For the scene Central region At any moment The amount of new energy consumed; Power can be generated from renewable energy sources; The constraints of the outer model include: Total capacity constraints for energy storage planning: ； ； In the formula, and They are respectively regions Planned energy storage power and capacity; and These refer to the total planned power and total capacity of the entire network's energy storage system; Energy storage power-capacity ratio constraints: ； In the formula, and These are the minimum and maximum rated charge and discharge times for energy storage, respectively.

8. The power balance method considering regional flexible mutual assistance under the access of new energy sources as described in claim 7, characterized in that: The objective function of the inner model is: ； In the formula, The inner objective function; For the scene Central region At any moment The injection power; For the scene Central region To the area The transmission power of the connecting line; To optimize the total number of time periods; The constraints of the inner model include: Node power balance constraints: ； ； In the formula, To provide power to conventional generating units; For energy storage charging and discharging power; For load power; For the scene Central region To the area The transmission power of the connecting line; Flexibility and supply-demand balance constraints: ； ； In the formula, For the scene Next period Traditional generator sets The increased flexibility offered; For the scene Next period Time-based energy storage devices The increased flexibility offered; For the scene Next period Time demand side resources The increased flexibility offered; For the scene Next period Traditional generator sets Offers flexibility in price reductions; For the scene Next period Time-based energy storage devices Offers flexibility in price reductions; For the scene Next period Time demand side resources Offers flexibility in price reductions; Energy storage operation constraints: ； ； In the formula, This represents the minimum charging power for energy storage. This represents the maximum discharge power. >0 indicates discharge. <0 indicates charging; This refers to the charging power. This refers to the discharge power. For a moment The energy storage capacity; For time step; and These represent the lower and upper limits of the energy storage state of charge, respectively.

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