Energy storage optimal configuration method considering blocking management of new energy delivery channel

Abstract

The invention provides an energy storage optimal configuration method considering new energy delivery channel blocking management, and belongs to the field of energy storage optimal configuration. Aiming at exploring the problem that a delivery channel is blocked in a new scene of delivery of a large new energy base, the invention firstly provides a construction method of a tie line transmission power feasible region based on power grid multi-dimensional security constraints, and secondly, under a feasible region theoretical framework, in combination with regulation and control characteristics of different types of energy storage, a new energy base is established. And constructing a multi-type energy storage optimal configuration model for new energy base delivery. And finally, a multi-type energy storage optimal configuration scheme and a charging and discharging strategy are obtained through mixed integer programming, and a new energy optimal delivery curve and an output combination of the thermal power generating unit are obtained. According to the planning scheme obtained by the model, the new energy consumption capability can be effectively improved, and the blockage of a delivery channel is relieved. And meanwhile, by accurately depicting the real-time transmission power feasible space of the tie line, the transmission power can be prevented from being out-of-limit to influence the safety and stability level of the power grid.

Description

Technical Field

[0001] This invention belongs to the field of energy storage optimization configuration, and in particular relates to an energy storage optimization configuration method that takes into account the congestion management of new energy transmission channels. Background Technology

[0002] The large-scale grid connection of new energy sources, due to the inherent volatility and randomness of their output, further exacerbates the challenges of efficient transmission of new energy. Simultaneously, with the rapid development of the power industry, the scale of receiving-end systems is continuously expanding, and electricity load is growing rapidly. However, insufficient power source construction in receiving-end systems leads to an increasing dependence on external power sources. Furthermore, internal operational safety constraints indirectly affect the transmission capacity of transmission channels, resulting in operational risks such as transmission channel congestion and insufficient new energy consumption at new energy bases.

[0003] To address the insufficient transmission capacity of power transmission channels, expanding inter-regional transmission channels can directly improve the transmission capacity and meet the demand for large-scale renewable energy transmission. However, the construction cycle is long and the cost is high, and it may face obstacles due to geographical and environmental limitations. Current research focuses on the planning level of various energy sources in renewable energy bases, mainly by considering the coordinated operation of wind-thermal-solar, wind-thermal-solar-storage, wind-solar-hydro-storage, hydro-thermal-solar, and wind-solar-solar-thermal to construct optimal capacity configuration models for transmission curve planning. Existing research is mostly based on point-to-point transmission scenarios, and when constructing transmission models, it only considers the upper and lower limits of the transmission capacity of the tie line itself, ignoring the mapping relationship between the safe and stable operating space within the receiving area and the transmission capacity space of the tie line. In the process of alleviating the congestion of renewable energy transmission channels, how to accurately characterize the transmission capacity of transmission channels to achieve optimal resource allocation is an urgent problem to be solved. Summary of the Invention

[0004] To address the aforementioned shortcomings in existing technologies, this invention provides an energy storage optimization configuration method that considers congestion management of new energy transmission channels, solving the problems of severe congestion and low utilization rates in new energy transmission channels due to transmission capacity limitations.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: an energy storage optimization configuration method considering the congestion management of new energy transmission channels, comprising the following steps: A comprehensive analysis of the multidimensional security boundary of the receiving-end power grid is used to construct a safe and stable operation space for the power grid. Based on the direction tracing method, the security constraints of the receiving-end power grid are projected onto the tie-line transmission power space to characterize the two-dimensional feasible region of transmission power. The feasible region is the projection of the convex polyhedron formed by all security constraint sets inside the receiving-end power grid onto the tie-line transmission power space. Within the theoretical framework of the feasible region, and combining the regulation characteristics of different types of energy storage, a multi-type energy storage optimization configuration model for new energy transmission is constructed. With the goal of maximizing the overall economic benefits of the system, and based on the multi-type energy storage optimization configuration model, the optimal configuration scheme of multi-type energy storage is obtained by using mixed integer programming, thus completing the optimal configuration of energy storage.

[0006] Furthermore, the two-dimensional feasible region for describing the transmission power is specifically as follows: Comprehensive analysis of the multi-dimensional security boundary of the power grid in the receiving-end area is used to construct a safe and stable operating space for the power grid; Within the safe and stable operation space of the power grid, the direction tracking method is used for angle... ,definition As the objective function, a mixed-integer linear programming approach is used to solve for the objective function that satisfies all multidimensional safety boundaries. The maximum value of the objective function The point corresponding to the maximum value is the angle. The maximum safety margin of tie-line transmission power is determined by the following search model constructed using the direction-tracing method for the critical tie-line transmission power:

[0007]

[0008] in, Describe the objective function. and These represent the transmission power of the first and second tie lines, respectively. Indicates the search angle as The search vector at that time, express , Indicates angle The cosine value, express , Indicates angle The sine value, Indicates the initial search angle. Indicates the search step size. Represents the spatial boundary set for the safe and stable operation of the power grid. , and These represent the generator output power, node phase angle, and line power flow, respectively. , and These represent the upper and lower limits of generator output power constraints, node phase angle constraints, and line power flow constraints, respectively. and These represent the system power flow balance equation and the line DC power flow equation, respectively. and They represent thermal power units Maximum and minimum output, This represents a column vector with the same number of scheduling periods and all elements being 1. express Matrix number Column elements, Indicates thermal power unit exist Constant effort , and These represent the sets of generators, nodes, and tie lines, respectively. and These represent the upper and lower limits of the phase angle at the nodes, respectively. and They represent Time Node and nodes phase angle, This represents the maximum value of the power flow along the line. , , and They represent The column vectors of generator output power, tie-line injected power, nodal load, and line power flow at any given time. Represents the generator node correlation matrix. Represents the node-branch association matrix. Indicates the line The reactance; Based on the maximum safety margin, the two-dimensional feasible region of transmission power is characterized time-by-time according to the time step. The linear constraints corresponding to the boundary of the feasible region are as follows:

[0009] in, and They represent The coefficient matrix and constant term matrix corresponding to the feasible region boundary at each time step. This indicates the number of time periods within the scheduling phase.

[0010] Based on the maximum safety margin, the two-dimensional feasible region of transmission power is characterized time-by-time according to the time step. The linear constraints corresponding to the boundary of the feasible region are as follows:

[0011] in, and They represent The coefficient matrix and constant term matrix corresponding to the feasible region boundary at each time step. This indicates the number of time periods within the scheduling phase.

[0012] Furthermore, the expression for the optimization objective of the multi-type energy storage optimization configuration model is as follows:

[0013]

[0014]

[0015]

[0016] in, Indicates total economic benefits. , and These represent the costs of wind and solar power curtailment, the overall system operating costs, and the revenue from electricity transmission to other regions, respectively. Indicates the number of time periods within the scheduling phase. Indicates the number of sending regions. Indicates the sending area China's new energy units in The timely and effective prediction of output, Indicates the sending area China's new energy units in The timely and effective allocation of resources, This represents the penalty cost per unit of wind and solar power curtailment. and These represent the total operating cost of the conventional generator set and the total cost of the energy storage system, respectively. This represents the cost of generating electricity per unit of power from a thermal power unit. This represents the social cost of carbon emissions per unit of electricity generated by a thermal power unit. Indicates the sending area exist The power generation capacity of thermal power units at any given time. Indicates the type of energy storage. K This indicates the total number of energy storage types. Indicates energy storage Unit capacity investment cost coefficient Indicates the sending area Energy storage Configuration capacity, Indicates energy storage exist The unit power operation and maintenance factor at any given time. Indicates the sending area Energy storage exist Operating power at any given time Indicates the first One connecting line, This indicates the total amount of electricity transmitted. This indicates the revenue generated per unit of electricity transmitted to other entities.

[0017] Furthermore, the constraints of the multi-type energy storage optimization configuration model include: Constraints on new energy output:

[0018] in, Indicates the sending area China's new energy units in The timely and effective allocation of resources, Indicates the sending area China's new energy units in The timely and effective prediction of output, express The column vector One element, This represents a column vector with the same number of scheduling periods and all elements being 0; Power balance constraints:

[0019] in, Indicates the sending area exist Power is transmitted via the constant communication line. Indicates the sending area exist Load power at any given time Indicates the sending area exist Energy storage at all times Operating power Indicates the receiving end region Load power at any given time Indicates the sending area The power generation capacity of thermal power units, K This indicates the total number of energy storage types. Indicates the number of sending end areas; Multiple types of energy storage constraints:

[0020] in, These are 0-1 variables, representing the configuration status of various energy storage types. Indicates energy storage Rated power, and They represent energy storage Minimum and maximum operating power Indicates energy storage exist Operating power during the period Indicates energy storage The ratio of rated capacity to rated power. and These represent the start and end times of the energy storage operation, respectively. , , They represent energy storage exist State of charge at time t, initial time, and final time; Thermal power unit output constraints:

[0021] in, Indicates the sending area thermal power units Power generation during the period Indicates the sending area thermal power units Power generation during the period This is an integer variable representing the sending region. thermal power units in Number of units in operation during a time period Indicates the sending area thermal power units in Number of units in operation during a time period and All are 0-1 variables. Indicates the sending area medium-sized thermal power units Start / stop status during a time period Indicates the sending area medium-sized thermal power units The downtime status during a certain period. and Representing the sending area Maximum and minimum technical output of thermal power units and Representing the sending area The maximum and minimum number of thermal power units in operation. Indicates the maximum number of start-stop operations. Indicates the number of time periods within the scheduling phase. and These represent the maximum climbing and descent power of the thermal power unit, respectively. Tether line transmission capacity constraints:

[0022] in, and They represent The coefficient matrix and constant term matrix corresponding to the feasible region boundary at each time step.

[0023] This invention takes the renewable energy absorption rate and the overall economic benefits of the system as optimization objectives, and proposes a multi-type energy storage optimization configuration method for renewable energy transmission channel congestion management under the tie-line feasible region theory, which has the following beneficial effects: (1) Due to the limitations of transmission capacity of external transmission channels and multi-dimensional security boundaries within the receiving-end regional power grid, large-scale renewable energy, exhibiting strong randomness and volatility, faces severe challenges from transmission channel congestion. By comprehensively considering the combined effects of various security operation boundaries, a feasible domain for tie-line transmission power can be constructed to ensure the safe and stable operation of the power grid, effectively reflecting the transmission capacity of the tie-line and providing theoretical support for flexible regulation of external transmission power. Given the significant advantages of various types of energy storage in the flexible regulation of the power grid, this invention proposes a multi-type energy storage optimization configuration strategy based on feasible domain theory to alleviate the congestion of renewable energy transmission channels and promote renewable energy consumption. First, a comprehensive analysis of the multi-dimensional security boundary of the receiving-end power grid is conducted to construct a space for safe and stable grid operation. This space is then projected onto the tie-line transmission power space using the boundary pursuit method, characterizing the two-dimensional feasible region of transmission power. Second, under the constraints of the feasible region boundary, a multi-type energy storage optimization configuration model is established with the optimization objectives of renewable energy absorption rate and comprehensive economic benefits. Mixed-integer linear programming (MILP) is used to solve for the optimal configuration of multiple types of energy storage, obtaining the optimal tie-line power transmission curve. Finally, a case study analysis is conducted based on actual data. Compared to cases without energy storage configuration or only considering tie-line threshold constraints, the proposed scheme demonstrates superior performance, economy, and safety.

[0024] (2) Based on the multidimensional safe operation boundary of the receiving-end regional power grid, the safe and stable operation space of the system is mapped to the two-dimensional solution space of the transmission power of the tie line, accurately characterizing the transmission capacity of the tie line and providing a dynamic control benchmark for subsequent energy storage configuration.

[0025] (3) Based on the different control characteristics of lithium-ion battery energy storage, compressed air energy storage and supercapacitor energy storage, with the total economic benefit as the objective function, the configuration type, rated power and rated capacity of multiple types of energy storage are solved by the mixed integer programming method, thus realizing the optimized configuration of multiple types of energy storage.

[0026] (4) By configuring multiple types of energy storage, the present invention can increase the power transmission by 13.4%, reduce wind and solar curtailment by 79.5%, and increase economic benefits by 15.8%. By constructing a feasible domain for the transmission power of tie lines, the safety hazards caused by unreasonable tie line transmission power can be avoided. The proposed strategy has certain advantages in terms of performance, economy, and safety. Attached Figure Description

[0027] Figure 1This is a flowchart of the method of the present invention.

[0028] Figure 2 This is a schematic diagram of the new energy inter-regional transmission system of the present invention.

[0029] Figure 3 This is a schematic diagram illustrating the principle of the direction tracking method.

[0030] Figure 4 This is a schematic diagram of the overall framework of the present invention.

[0031] Figure 5 Example diagram of feasible domain for transmission power of tie line in time period 7.

[0032] Figure 6 This is a schematic diagram of the forecast curves for typical daily renewable energy and load data in the sending-end region.

[0033] Figure 7 The diagram shows the charging and discharging operation of energy storage in the sending-end region 1 of Example 2.

[0034] Figure 8 The diagram shows the charging and discharging operation of energy storage in the sending-end region 2 of Example 2.

[0035] Figure 9 The diagram shows the charging and discharging operation of energy storage in the sending-end region 1 of Example 3.

[0036] Figure 10 The diagram shows the charging and discharging operation of energy storage in the sending-end region 2 of Example 3.

[0037] Figure 11 The diagram shows the power transmission curves for three different calculation methods.

[0038] Figure 12 The diagram illustrates the wind and solar power curtailment scenarios for three different calculation examples.

[0039] Figure 13 This is a schematic diagram of the feasible region constraint boundary and the threshold constraint boundary.

[0040] Figure 14 The diagram shows the tie-line power curves for three different calculation examples.

[0041] Figure 15 The diagram shows the power flow of the lines during time period 10 in three different calculation examples. Detailed Implementation

[0042] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0043] Example like Figure 1 As shown, this invention provides an energy storage optimization configuration method that considers congestion management of new energy transmission channels, and its implementation method is as follows: S1. Comprehensive analysis of the multidimensional security boundary of the receiving-end area power grid to construct the safe and stable operation space of the power grid, and based on the direction tracking method, the security constraints of the receiving-end area power grid are projected onto the tie line transmission power space to characterize the two-dimensional feasible region of transmission power. The feasible region is the projection of the convex polyhedron formed by all security constraint sets inside the receiving-end area power grid onto the tie line transmission power space. In this embodiment, the two-dimensional feasible region of transmission power is characterized as follows: Comprehensive analysis of the multi-dimensional security boundary of the power grid in the receiving-end area is used to construct a safe and stable operating space for the power grid; Using the direction tracking method, for angle ,definition As the objective function, a mixed-integer linear programming approach is used to solve for the objective function that satisfies all multidimensional safety boundaries. The maximum value of the objective function The point corresponding to the maximum value is the angle. The maximum safety margin of the transmission power of the tie line; Based on the maximum safety margin, the two-dimensional feasible region of transmission power is characterized in stages according to the time step; S2. Within the theoretical framework of the feasible region, and combining the regulation characteristics of different types of energy storage, construct a multi-type energy storage optimization configuration model for new energy transmission. S3. Based on the multi-type energy storage optimization configuration model, use mixed integer programming to obtain multi-type energy storage optimization configuration schemes and charging and discharging strategies, so as to obtain the optimal new energy transmission curve and the output combination of thermal power units, and complete the optimization configuration of energy storage.

[0044] In this embodiment, the multi-type energy storage configuration for managing transmission congestion from large-scale new energy bases is crucial for alleviating line congestion and promoting the consumption of new energy. A reasonable configuration and operation scheme can effectively achieve peak shaving and valley filling of new energy output under the constraint of tie-line transmission capacity, further realizing the efficient and stable transmission of new energy. The new energy inter-regional transmission system model constructed in this invention is as follows: Figure 2 As shown, on the sending-end system side, high-penetration renewable energy units exhibit typical output counter-peak characteristics. When the power transmitted through the tie line exceeds the transmission capacity limit, energy time-shifting needs to be achieved by configuring multiple types of energy storage. On the receiving-end system side, its load support mainly relies on the coordination between the regulation of thermal power units and the power transmission. In addition, accurately characterizing the impact of regional power grid security constraints on the transmission capacity of transmission channels is crucial for the rational allocation of energy storage.

[0045] In this embodiment, the geometric meaning of the feasible region refers to the spatial range formed by all feasible solutions that satisfy given constraints in a mathematical programming or optimization problem; that is, the set of all solutions that meet the conditions in geometric space. This invention visualizes the security constraints by projecting them onto the transmission power space of the tie line, thereby accurately characterizing the feasible region of tie line power. For tie line transmission power, its feasible region refers to the projection of the convex polyhedron formed by all constraints within the receiving-end power grid, including all nodes, lines, loads, and thermal power units, onto the tie line transmission power space. In specific scenarios, when there are two tie lines, the feasible region appears as an irregular convex polygon in a two-dimensional plane. Within the feasible region of tie line transmission power, for any point, i.e., a specific set of tie line transmission powers, there always exists at least one adjustment scheme that can be executed and satisfies all security boundaries of the power grid. These adjustment schemes mainly include thermal power unit output and controllable loads.

[0046] In this embodiment, the feasible domain space of tie-line transmission power is defined by the power grid operation safety boundary of the receiving end area. For the power grid operation at a single moment, the safety boundary mainly includes the system power flow balance equation, generator output safety constraints, etc., and its expression is: (1) in, Represents the boundary set for safe operation of the power grid. , and These represent the generator output power, node phase angle, and line power flow, respectively. , and These are the upper and lower limits of generator output power constraints, node phase angle constraints, and line power flow constraints. and Let represent the system power flow balance equation and the line DC power flow equation, respectively, which can be expressed in detail as follows: (2) (3) in, , and These represent the sets of generators, nodes, and tie lines, respectively. and They represent thermal power units Maximum and minimum output, This represents a column vector with the same number of scheduling periods and all elements being 1. express Matrix number Column elements, Indicates thermal power unit exist Constant effort and They represent Time Node and nodes phase angle, and These represent the upper and lower limits of the phase angle at the nodes, respectively. Indicates the receiving-end regional power grid Timetable The trend This represents the maximum value of the power flow along the line. , , and They represent The column vectors of generator output power, tie-line injected power, nodal load, and line power flow at any given time. Represents the generator node correlation matrix. This represents the node-branch association matrix.

[0047] In this embodiment, the feasible region boundary of the tie line is the set of all critical operating points when the tie line transmission power reaches its limit. Searching for the feasible region boundary is equivalent to searching for the critical points within the feasible region. The search for the critical points of the feasible region can be described as: for a given power growth direction, searching for the maximum safety margin of the tie line transmission power when the system reaches the line transmission limit from the current operating point, while satisfying various safety boundaries. Based on these characteristics, using the direction-tracking method, the following search model can be constructed for the critical points of the tie line transmission power: (4) in, and These represent the transmission power of the first and second tie lines, respectively. express , Indicates angle The cosine value, express , Indicates angle The sine value, Indicates the search angle as The search vector at that time, Indicates the initial search angle. Indicates the search step size.

[0048] Figure 3 The basic principle of using the direction-following method proposed in equation (4) to optimize the model for searching the feasible region boundary is described: O Point is the initial point of the proposed optimization model, with the starting angle... Based on the pre-set fixed step size, Generate search angle sequences in a counter-clockwise direction. , , ...along the power growth direction corresponding to each angle. , , …Search for the system security margin that satisfies all security constraints, and obtain the boundary points 1, 2, 3… of the feasible region for tie-line transmission power. The two-dimensional planar region enclosed by all boundary points is the desired feasible region space for tie-line transmission power. Further, take the intersection of this space and the tie-line threshold constraint space to finally obtain the feasible space for tie-line transmission power.

[0049] In this embodiment, a feasible region for tie-line transmission power is constructed based on multi-dimensional boundaries such as power flow balance constraints, upper and lower limits of power flow, thermal power unit output constraints, and phase angle constraints within the receiving-end area power grid. The tie-line power and thermal power unit output are decision variables, while the node load is a known quantity. The tie-line transmission power and thermal power unit output together meet the load demand. For the power system operation scenario studied in this invention, the feasible region refers to the projection of a convex polyhedron formed by all constraints within a power grid, including power flow, phase angle, and thermal power units, onto the tie-line node transmission power space. Within the tie-line transmission power space, for a certain operating point... x (Tie line transmission power combination), there is always at least one adjustment scheme. y (The output combination of thermal power units) enables its execution and satisfies all grid security constraints; the set of these points is called the feasible region of tie-line power transmission. That is, satisfying... , ,in, Represents the feasible region. This represents the empty set. In practice, exploring each feasible tie-line power combination one by one is not feasible. Therefore, the research shifts to determining the feasible region range by determining the boundary of the feasible region, i.e., the maximum safety margin of the tie-line transmission power. The direction-tracing method is used for a specific angle... ,definition As the objective function, mixed-integer linear programming is used to find the solution that satisfies all safety boundaries. The maximum value.

[0050] Input: Nodal load data at a specific moment, upper and lower limits of power flow constraints, thermal power unit output constraints, phase angle constraints, etc., and search angle. And the tidal current balance equation.

[0051] Output: The maximum value, corresponding to , The value of and the output of the thermal power unit are used to plot the feasible region at a certain moment using the above method. The feasible region is then solved for each time step according to the time increment. After plotting the feasible region, all linear constraints corresponding to the boundaries of the feasible region can be obtained. .

[0052] In this embodiment, as Figure 4 As shown, to alleviate congestion in the transmission channels of large-scale new energy bases, promote the consumption of new energy, and fully leverage the regulation potential of various types of energy storage, this invention constructs an optimized configuration model for managing congestion in the transmission channels of new energy bases. Under conditions such as feasible region constraints and system power balance constraints, with the goal of maximizing the overall economic benefits of the system, the optimal configuration scheme for various types of energy storage is obtained using a mixed-integer linear programming approach.

[0053] In this embodiment, the sending-end area includes new energy sources such as wind and solar power, thermal power units, various types of energy storage, and loads. After meeting the load demand, the surplus electricity is used for external transmission. Different types of energy storage have different technical and economic parameters, and there are priority issues in terms of configuration capacity and charging / discharging strategies.

[0054] Input: (1) 24h new energy output data and load data, technical and economic parameters of various types of energy storage, and various constraints of the sending-end area (including the obtained linear constraints of the feasible region of the tie line). Output: (2) Power transmission curves, power output combinations of thermal power units, actual active power dispatch curves of new energy sources, configuration schemes and charging and discharging strategies for various types of energy storage.

[0055] Constraints: For each sending-end region, the power balance equation must be satisfied: ,in, Indicates the sending area exist Always powered by new energy sources Indicates the sending area exist Thermal power unit output at all times Indicates the sending area exist Power is transmitted via the constant communication line. Indicates the sending area exist Load power at any given time k Indicates the type of energy storage. K Indicates the total number of energy storage types. Indicates the sending area exist Energy storage at all times Operating power.

[0056] In addition to satisfying the power balance equation: (1) The output of both new energy sources and thermal power should meet the upper and lower limits at a certain moment; (2) The output of the thermal power unit meets the time-domain coupling constraints such as start-stop constraints and ramping constraints; (3) Multiple types of energy storage meet various operational constraints; (4) For the power transmitted by the tie line, the feasible region constraint of the tie line and the threshold constraint of the tie line itself must be satisfied at the same time.

[0057] Objective function: Maximize total economic benefits.

[0058] Solution method: The output combination of thermal power units in the sending-end region, the amount of wind and solar curtailment, and the optimal power combination of the connecting line can be obtained by solving the mathematical model using mixed integer linear programming.

[0059] In this embodiment, the key to optimized congestion management operation lies in maximizing the absorption of new energy sources at the lowest cost during the solution process for optimizing the configuration of multiple types of energy storage. This invention establishes an optimization model with the goal of maximizing the overall economic benefits of the system: (5) in, Indicates total economic benefits. , and These represent the costs of wind and solar power curtailment, the overall system operating costs, and the revenue from power transmission to other regions, respectively.

[0060] Costs of curtailing wind and solar power: (6) in, Indicates the number of time periods within the scheduling phase. Indicates the number of sending regions. Indicates the sending area China's new energy units in The timely and effective prediction of output, Indicates the sending area China's new energy units in The timely and effective allocation of resources, This represents the penalty cost per unit of wind and solar power curtailment.

[0061] Overall system operating cost: (7) in, and These represent the total operating cost of the conventional generator set and the total cost of the energy storage system, respectively. This represents the cost of generating electricity per unit of power produced by a generator set. This represents the social cost of carbon emissions per unit of electricity generated. Indicates the sending area exist The power generation capacity of thermal power units at any given time. Indicates the type of energy storage. K This indicates the total number of energy storage types. Indicates energy storage Unit capacity investment cost coefficient Indicates the sending area Energy storage Configuration capacity, Indicates energy storage In time The unit power operation and maintenance coefficient, Indicates the sending area Energy storage exist Operating power at any given time; Revenue from electricity transmission: (8) in, Indicates the first One connecting line, This indicates the total amount of electricity transmitted. This indicates the revenue generated per unit of electricity transmitted to other entities.

[0062] In this embodiment, the constraints include: Constraints on new energy output: (9) in, Indicates the sending area China's new energy units in The timely and effective allocation of resources, Indicates the sending area China's new energy units in The timely and effective prediction of output, This represents a column vector with the same number of scheduling periods and all elements being 0. for The column vector One element; Power balance constraints: (10) in, Indicates the sending area exist Power is transmitted via the constant communication line. Indicates the sending area exist Load power at any given time Indicates the sending area exist Energy storage at all times Operating power Indicates the receiving end region Load power at any given time Indicates the sending area The power generation capacity of thermal power units, K This indicates the total number of energy storage types. Indicates the number of sending end areas; Multiple types of energy storage constraints: (11) in, These are 0-1 variables, representing the configuration status of various energy storage types. Indicates energy storage Rated power, and They represent energy storage Minimum and maximum operating power Indicates energy storage exist Operating power during the period For energy storage The ratio of rated capacity to rated power. and These represent the start and end times of the energy storage operation, respectively. , , They represent energy storage exist State of charge at time t, initial time, and final time; Thermal power unit output constraints: (12) in, Indicates the sending area thermal power units Power generation during the period Indicates the sending area thermal power units Power generation during the period This is an integer variable representing the sending region. thermal power units in Number of units in operation during a time period Indicates the sending area thermal power units in Number of units in operation during a time period and All are 0-1 variables. Indicates the sending area medium-sized thermal power units Start / stop status during a time period Indicates the sending area medium-sized thermal power units The downtime status during a specific period; and Representing the sending area Maximum and minimum technical output of thermal power units; and Representing the sending area The maximum and minimum number of thermal power units in operation. Indicates the maximum number of start-stop operations. and These represent the maximum climbing and descent power of the thermal power unit, respectively. Tether line transmission capacity constraints: The transmission capacity constraint of the tie line is determined by the feasible region boundary during the scheduling period. In the context of the feasible region boundary, dynamic changes occur, and its transmission capacity constraint can be described by equation (13): (13) in, and They represent The coefficient matrix and constant term matrix corresponding to the feasible region boundary at each time step. This indicates the number of time periods within the scheduling phase.

[0063] The present invention will be further described below.

[0064] This embodiment uses the two-dimensional tie-line transmission power as a variable and takes a power grid in a region of northern my country as an example to verify the proposed method. The above optimization model is established using a two-sending-end regional power grid as an example, setting the scheduling cycle to 24 hours and the scheduling step size to 1 hour. The safe and stable operation space of this power grid is mapped to the two-dimensional space of tie-line transmission power corresponding to the tie-line nodes. Taking time period 7 as an example, its feasible region is as follows: Figure 5 As shown in the figure, the irregular geometry of the feasible region reveals a coupling relationship between the power transmission of the two transmission channels. This phenomenon is essentially the result of the combined effect of multi-dimensional security constraints within the regional power grid. Specifically, the multi-dimensional constraints required for the safe operation of the regional power grid do not act independently on a single transmission channel, but rather form a coordinated constraint on multiple transmission channels through the characteristics of power flow distribution and energy flow. Furthermore, due to the time-varying nature of the load data at power grid nodes at different times, the boundaries of the feasible region also change dynamically at different times. The predicted curves of typical daily renewable energy output and load data for the two regions are shown in the figure. Figure 6 As shown.

[0065] To compare and analyze the feasibility and superiority of the various energy storage optimization configuration schemes proposed in this invention, the following three classic calculation examples are set up for comparative analysis and explanation in the scenario of blocked new energy transmission channels: Example 1: Energy storage configuration is not introduced, and tie-line transmission power feasible region constraints and tie-line threshold constraints are taken into account; Example 2: Introducing multiple types of energy storage configurations, only considering tie-line threshold constraints; Example 3: Introduce multiple types of energy storage configurations and take into account the feasible domain constraints of tie line transmission power and tie line threshold constraints.

[0066] In Examples 2 and 3, thermal power units and multiple types of energy storage collaboratively participate in system regulation. Based on the differentiated technical and economic characteristics of lithium-ion battery energy storage, compressed air energy storage, and supercapacitor energy storage, mixed integer programming is used to achieve optimal configuration of multiple types of energy storage. The optimal configuration scheme for Example 2 is shown in Table 1, which is the result of the multi-type energy storage optimization configuration in Example 2. The energy storage charge and discharge operation diagram is shown below. Figure 7 and Figure 8 As shown.

[0067] Table 1

[0068] Table 1 shows the optimal configuration results of various types of energy storage. Figure 7 and Figure 8 The results of the energy storage charging and discharging operations are mutually supportive. Since the operating cost of lithium-ion battery energy storage is higher than that of compressed air energy storage, compressed air energy storage is prioritized when the system calls upon energy storage, and its charge-discharge cycles are significantly greater than those of lithium-ion battery energy storage. Both types of energy storage store electrical energy during peak renewable energy output periods and release it during other periods. Similarly, the optimized energy storage configuration scheme for Example 3 is shown in Table 2, which presents the results of the multi-type energy storage optimization configuration for Example 3. The energy storage charging and discharging operation diagram is shown below. Figure 9 and Figure 10 As shown.

[0069] Table 2

[0070] In this embodiment, the comparison between the three methods of mitigating transmission channel congestion and the consumption of new energy sources is as follows: Figure 11The paper presents the power transmission optimization curves of the tie-line in three examples. Examples 1 and 3 both consider both feasible region constraints and threshold constraints on tie-line transmission power. From 6h to 17h, renewable energy output is sufficient, but compared to Example 2, the tie-line in Examples 1 and 3 cannot transmit some power due to feasible region constraints. From 3h to 6h and 17h to 18h, renewable energy output is at its lowest point. In Example 1, there is insufficient power for transmission, resulting in low utilization of the transmission channel. Examples 2 and 3 both consider configuring multiple types of energy storage, enabling time-shifting of power, thus maintaining a high utilization rate of the transmission channel even during periods of low renewable energy output. Figure 12 The study presents three examples of wind and solar curtailment. Analysis of the renewable energy output and load data curves for the two sending regions reveals that from 11:00 to 15:00, both sending regions experience peak renewable energy output, with a relatively abundant surplus of electricity beyond local load. However, due to congestion in the transmission channels, effective external transmission is difficult, resulting in significant wind and solar curtailment. In sending region 1, renewable energy output significantly exceeds load between 18:00 and 21:00, thus also experiencing some wind and solar curtailment during this period. Examples two and three utilize various types of energy storage for charging during this time to absorb excess electricity, mitigating the wind and solar curtailment phenomenon to some extent.

[0071] In this embodiment, the power flow analysis of the receiving-end area power grid lines in the three examples is as follows: By comparing and analyzing the power flow of the receiving-end area power grid lines in the three examples, the necessity of feasible region constraints is verified. Taking time period 16 as an example, the comparison of tie-line transmission power space under feasible region constraints and threshold constraints is considered. Figure 13 As shown, the dashed area represents the risk zone. The combined tie-line transmission power within this risk zone poses a security risk to the receiving end. Examples 1 and 3, based on tie-line threshold constraints, further consider the feasible transmission power region taking into account security constraints; the transmission power space is the intersection of these two. Example 2 only considers threshold constraints, and the final optimization results will show tie-line transmission power combinations within the risk zone, such as... Figure 14 As shown. The power flow of the receiving-end area lines is analyzed based on the optimized transmission power results of the tie lines obtained from each example. Taking time period 10 as an example, in example two, the power flow of lines 123, 126, 127, and 183 in the receiving-end area exceeds the limit, posing a potential safety hazard. Figure 15 As shown.

[0072] In this embodiment, the simulation data of the three examples are compared and analyzed as follows: Taking the overall economic benefit of the system as the objective function, the comparison of power transmission, wind and solar curtailment, and economic benefits under the optimization of the three examples is shown in Table 3. Table 3 is a comparison table of the optimization results of the three examples.

[0073] Table 3

[0074] Comparative analysis of the optimization results obtained in Table 3 shows that, compared to Example 1, Example 3 improves power transmission by 13.4%, reduces wind and solar curtailment by 79.5%, and increases revenue by 15.8%. This verifies that using multiple types of energy storage in conjunction with thermal power units for system regulation can effectively alleviate the blockage of new energy transmission channels and the phenomenon of wind and solar curtailment, while also improving the overall economic benefits of the system. Example 2 shows a slight improvement in the above indicators compared to Example 3, but the power transmitted through the tie line sometimes exceeds the feasible domain boundary, which may introduce safety risks.

[0075] In summary, the multi-type energy storage optimization configuration scheme that takes into account the feasible region constraint has certain advantages in terms of performance, economy and safety. The simulation results verify the feasibility and superiority of the proposed optimization model.

Claims

1. A method for optimizing energy storage configuration considering congestion management of new energy transmission channels, characterized in that, Includes the following steps: A comprehensive analysis of the multidimensional security boundary of the receiving-end power grid is used to construct a safe and stable operation space for the power grid. Based on the direction tracing method, the security constraints of the receiving-end power grid are projected onto the tie-line transmission power space to characterize the two-dimensional feasible region of transmission power. The feasible region is the projection of the convex polyhedron formed by all security constraint sets inside the receiving-end power grid onto the tie-line transmission power space. Within the theoretical framework of the feasible region, and combining the regulation characteristics of different types of energy storage, a multi-type energy storage optimization configuration model for new energy transmission is constructed. With the goal of maximizing the overall economic benefits of the system, and based on the multi-type energy storage optimization configuration model, the optimal configuration scheme of multi-type energy storage is obtained by using mixed integer programming, thus completing the optimal configuration of energy storage.

2. The energy storage optimization configuration method considering congestion management of new energy transmission channels according to claim 1, characterized in that, The two-dimensional feasible region for describing the transmission power is specifically as follows: Comprehensive analysis of the multi-dimensional security boundary of the power grid in the receiving-end area is used to construct a safe and stable operating space for the power grid; Within the safe and stable operation space of the power grid, the direction tracking method is used for angle... ,definition As the objective function, a mixed-integer linear programming approach is used to solve for the objective function that satisfies all multidimensional safety boundaries. The maximum value of the objective function The point corresponding to the maximum value is the angle. The maximum safety margin of tie-line transmission power is determined by the following search model constructed using the direction-tracing method for the critical tie-line transmission power: in, Describe the objective function. and These represent the transmission power of the first and second tie lines, respectively. Indicates the search angle as The search vector at that time, express , Indicates angle cosine value, express , Indicates angle The sine value, Indicates the initial search angle. Indicates the search step size. Represents the spatial boundary set for the safe and stable operation of the power grid. , and These represent the generator output power, node phase angle, and line power flow, respectively. , and These represent the upper and lower limits of generator output power constraints, node phase angle constraints, and line power flow constraints, respectively. and These represent the system power flow balance equation and the line DC power flow equation, respectively. and They represent thermal power units Maximum and minimum output, This represents a column vector with the same number of scheduling periods and all elements being 1. express Matrix number Column elements, Indicates thermal power unit exist Constant effort , and These represent the sets of generators, nodes, and tie lines, respectively. and These represent the upper and lower limits of the phase angle at the nodes, respectively. and They represent Time Node and nodes phase angle, This represents the maximum value of the power flow along the line. , , and They represent The column vectors of generator output power, tie-line injected power, nodal load, and line power flow at any given time. Represents the generator node correlation matrix. Represents the node-branch association matrix. Indicates the line The reactance; Based on the maximum safety margin, the two-dimensional feasible region of transmission power is characterized time-by-time according to the time step. The linear constraints corresponding to the boundary of the feasible region are as follows: in, and They represent The coefficient matrix and constant term matrix corresponding to the feasible region boundary at each time step. This indicates the number of time periods within the scheduling phase.

3. The energy storage optimization configuration method considering congestion management of new energy transmission channels according to claim 1, characterized in that, The expression for the optimization objective is as follows: in, Indicates total economic benefits. , and These represent the costs of wind and solar power curtailment, the overall system operating costs, and the revenue from electricity transmission to other regions, respectively. Indicates the number of time periods within the scheduling phase. Indicates the number of sending regions. Indicates the sending area China's new energy units in The timely and effective prediction of output, Indicates the sending area China's new energy units in The timely and effective allocation of resources, This represents the penalty cost per unit of wind and solar power curtailment. and These represent the total operating cost of the conventional generator set and the total cost of the energy storage system, respectively. This represents the cost of generating electricity per unit of power from a thermal power unit. This represents the social cost of carbon emissions per unit of electricity generated by a thermal power unit. Indicates the sending area exist The power generation capacity of thermal power units at any given time. Indicates the type of energy storage. K This indicates the total number of energy storage types. Indicates energy storage Unit capacity investment cost coefficient Indicates the sending area Energy storage Configuration capacity, Indicates energy storage exist The unit power operation and maintenance factor at any given time. Indicates the sending area Energy storage exist Operating power at any given time Indicates the first One connecting line, This indicates the total amount of electricity transmitted. This indicates the revenue generated per unit of electricity transmitted to other entities.

4. The energy storage optimization configuration method considering congestion management of new energy transmission channels according to claim 1, characterized in that, The constraints of the multi-type energy storage optimization configuration model include: Constraints on new energy output: in, Indicates the sending area China's new energy units in The timely and effective allocation of resources, Indicates the sending area China's new energy units in The timely and effective prediction of output, express The column vector One element, This represents a column vector with the same number of scheduling periods and all elements being 0; Power balance constraints: in, Indicates the sending area exist Power is transmitted via the constant communication line. Indicates the sending area exist Load power at any given time Indicates the sending area exist Energy storage at all times Operating power Indicates the receiving end region Load power at any given time Indicates the sending area The power generation capacity of thermal power units, K This indicates the total number of energy storage types. Indicates the number of sending end areas; Multiple types of energy storage constraints: in, These are 0-1 variables, representing the configuration status of various energy storage types. Indicates energy storage Rated power, and They represent energy storage Minimum and maximum operating power Indicates energy storage exist Operating power during the period Indicates energy storage The ratio of rated capacity to rated power. and These represent the start and end times of the energy storage operation, respectively. , , They represent energy storage exist State of charge at time t, initial time, and final time; Thermal power unit output constraints: in, Indicates the sending area thermal power units Power generation during the period Indicates the sending area thermal power units Power generation during the period This is an integer variable representing the sending region. thermal power units in Number of units in operation during a time period Indicates the sending area thermal power units in Number of units in operation during a time period and All are 0-1 variables. Indicates the sending area medium-sized thermal power units Startup status of the time period Indicates the sending area medium-sized thermal power units The downtime status during a specific period; and Representing the sending area Maximum and minimum technical output of thermal power units and Representing the sending area The maximum and minimum number of thermal power units in operation. Indicates the maximum number of start-stop operations. Indicates the number of time periods within the scheduling phase. and These represent the maximum climbing and descent power of the thermal power unit, respectively. Tether line transmission capacity constraints: in, and They represent The coefficient matrix and constant term matrix corresponding to the feasible region boundary at each time step.

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