A method, system, device and medium for estimating balance margin of a power system

Abstract

The application discloses a kind of balance margin estimation method, system, equipment and medium of power system, comprising: considering the cost of outsourcing power and penalty cost, with the running state and output of each unit in power system at different time as variable, with the lowest total cost of power system as optimization goal, the balance margin optimization model of power system is constructed;Solve the balance margin optimization model of the power system, get the balance margin of power system, the method, system, equipment and medium can accurately calculate the balance margin of power system.

Description

Technical Field

[0001] This invention belongs to the field of power system control technology, and relates to a method, system, equipment and medium for estimating the balance margin of a power system. Background Technology

[0002] Power balance margin assessment is a crucial step in understanding the system's balance status, promptly identifying supply-demand imbalance risks, and making early balance optimization decisions. Currently, the widely used balance margin assessment method is the simplified linear superposition method. Positive balance margin = maximum generating capacity of conventional power sources + reliable generating capacity of renewable energy sources + tie lines - load forecasting - positive reserve; negative balance margin = load forecasting - predicted renewable energy generation - tie lines - minimum generating capacity of all types of power sources - spinning negative reserve. The advantages of this method are its clear concept, simple calculation, and ease of operation. However, its disadvantages are equally apparent: Disadvantage 1: This method can only passively calculate the balance margin index under the condition that the thermal power generation mode is fixed. When the thermal power generation mode and tie line plan are adjustable, it cannot obtain the system's balance margin and cannot actively leverage the guiding role of balance margin in renewable energy consumption and power supply security; Disadvantage 2: When network constraints exist, the maximum generating capacity of conventional power sources may be limited by network constraints, thus making it impossible to obtain an accurate generating capacity value, leading to inaccurate balance margin calculation results.

[0003] Furthermore, to mitigate climate change, accelerate clean and low-carbon development, and achieve carbon neutrality as soon as possible, it has become a global consensus and an inevitable trend, thus proposing the construction of a new power system. A key characteristic of this new power system is the gradual increase in the proportion of renewable energy. As the proportion of renewable energy increases, on the one hand, extreme / process-based weather events such as "extreme cold with no sunlight," "extreme heat with no wind," and "prolonged rainy seasons" will significantly impact the output of renewable energy sources like wind and solar power, leading to a substantial decrease in the system's power supply capacity. Simultaneously, cold waves and high temperatures are usually accompanied by a significant increase in load demand, and the mismatch between power generation capacity and load demand further increases the pressure on power supply. On the other hand, renewable energy generation is characterized by short-term, large fluctuations. To maintain the dynamic balance of the system by adapting to these fluctuations, a large amount of peak-shaving and reserve resources are needed. Due to the insufficient flexibility of my country's regulation resources, the system also faces the challenge of fully absorbing renewable energy during periods of high renewable energy generation.

[0004] To proactively identify risks of supply-demand imbalances such as insufficient system power supply capacity and curtailment of renewable energy, power system dispatching and operation agencies need to conduct balance margin assessments in advance. This clarifies the system's power supply surplus and gaps, guiding subsequent operations. Currently, the main method for conducting balance margin assessments is to use positive balance margin as an example. Positive balance margin = maximum generating capacity of conventional power sources + reliable generating capacity of renewable energy sources + tie lines - load forecasting - safety reserve.

[0005] (1) Calculate the maximum power generation capacity of conventional power sources

[0006] Conventional power generation capacity includes: coal-fired power generation capacity, hydropower generation capacity, gas turbine power generation capacity, and other power generation capacity.

[0007] Power generation capacity of coal-fired units = Total operating capacity of coal-fired units - Capacity shut down due to coal shortage - Capacity hindered by defects in coal-fired power units - Capacity hindered by poor coal quality - Capacity hindered by coal-fired power, heating and gas supply - Capacity hindered by self-owned power plants and small thermal power plants.

[0008] Hydropower generating capacity = Total installed capacity of hydropower units - Capacity obstructed due to insufficient water head - Maintenance capacity of hydropower units.

[0009] Gas turbine generator capacity = Total installed capacity of gas turbine generators - Capacity of gas supply and heating obstructed - Maintenance capacity of gas turbine generators.

[0010] Power generation capacity of other types of generating units = installed capacity of other types of generating units - blocked capacity - maintenance capacity.

[0011] (2) Calculate the power generation capacity of new energy sources

[0012] Limited by factors such as numerical weather prediction and power prediction model algorithms, renewable energy power prediction has certain errors and significant spatiotemporal uncertainties. Furthermore, the regional renewable energy absorption capacity is limited, influenced by factors such as the regional power grid structure, power source composition, load characteristics, system reserve capacity, and regulation capabilities. Renewable energy included in the power balance refers to the value of renewable energy participating in the power balance after comprehensively considering the confidence level of renewable energy power prediction in the balance zone, as well as factors such as the characteristics of system sources, grids, and loads, and correcting the regional renewable energy prediction results.

[0013] The output of renewable energy sources included in the power balancing calculation is calculated as follows: (Wind power forecast result in the balancing area * k1) + (Photovoltaic power forecast result in the balancing area * k2), where k1 and k2 are correction coefficients for the wind power and photovoltaic power forecast results, respectively. These correction coefficients vary across different regions and time periods and need to be determined by statistically analyzing the forecast accuracy of renewable energy sources in different seasons in the local area.

[0014] (3) Calculate the positive balance margin

[0015] Taking a certain day in Shanxi as an example, the load forecast at 18:30 is 33700MW. The adjustable upper limit of coal-fired power units is 38190MW, the adjustable upper limit of hydropower units is 900MW, the adjustable upper limit of gas-fired power units is 800MW, the adjustable upper limit of other types of units is 1300MW, and the grid obstruction is 0MW. The calculated adjustable upper limit of conventional power sources in the system is 41690MW. The output of new energy sources included in the balance is 4170MW, the power received is -6450MW, the load reserve demand is 1060MW, and the emergency reserve demand is 3877MW. The calculated balance margin is 273MW. This shows that, under the premise of ensuring the safe operation of the system, based on the current load forecast, the Shanxi power grid can bear a load increase of 273MW at 18:30.

[0016] The above is a detailed description of the current calculation method. This method can only passively calculate the balance margin index when the thermal power plant start-up mode is fixed. When the thermal power plant start-up mode and tie line plan are adjustable, the balance margin of the system cannot be known, and the balance margin cannot actively play a guiding role in the consumption of new energy and power supply. When there are network constraints, the maximum power generation capacity of conventional power sources may be limited by network constraints, so it is impossible to obtain an accurate power generation capacity value, which leads to inaccurate balance margin calculation results. Summary of the Invention

[0017] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, system, device and medium for estimating the balance margin of a power system. This method, system, device and medium can accurately calculate the balance margin of a power system.

[0018] To achieve the above objectives, the power system balance margin estimation method of the present invention includes:

[0019] Based on the cost of purchased electricity and penalty costs, and taking the operating status and output of each unit in the power system at different times as variables, and taking the minimum total cost of the power system as the optimization objective, a balance margin optimization model of the power system is constructed.

[0020] Solve the balance margin optimization model of the power system to obtain the balance margin of the power system.

[0021] A further improvement of the power system balance margin estimation method described in this invention is that:

[0022] Furthermore, the objective function of the power system balance margin optimization model is:

[0023]

[0024] Among them, S i,t p represents the operating state of unit i at time t. i,t F(p) represents the output of unit i at time t.i,t ) indicates the unit output p i,t The corresponding operating cost, U(p) i,t Let f(t) represent the start-up cost of unit i at time t, C(Tie,t) represent the power system's electricity purchase cost at time t, f(·) represent the power system's penalty cost at time t, T is the total number of time periods to be calculated, and I is the total number of units in the power system.

[0025] Furthermore, the balance margin optimization model of the power system is expressed as follows:

[0026]

[0027] Where, p i,py,t Let p be the positive reserve variable for unit i during time period t. i,ny,t p represents the reverse balance margin of unit i at time t. i,max p is the maximum output of unit i. nr,t p is a positive backup variable for time period t. i,min p is the minimum output of unit i. py,t p represents the total positive balance margin of the power system at time t. ny,t This represents the total reverse balance margin of the power system at time t.

[0028] Furthermore, the constraints of the power system balance margin optimization model include system balance constraints, unit operation constraints, and grid transmission capacity constraints.

[0029] Furthermore, the system balance constraint is:

[0030]

[0031] Where, p l,t Let p be the load demand of node l at time t, L be the total number of nodes, and p be the load demand of node l at time t. i,t Δp represents the output of unit i at time t. l,t Tie represents the load reduction at node l at time t. o,t For the initial tie line quantity, Tie t Electricity is purchased from external sources.

[0032] Furthermore, the power grid transmission capacity constraint is as follows:

[0033]

[0034] Where, ΔP i,t p represents the additional power generated by unit i at time t. i,t ΔP represents the output of unit i at time t. load,j,t Let L be the load increase of node j at time t. l,t β represents the power flow of branch l at time t.l,i,t β represents the active power sensitivity of unit i to branch l. l,j,t The active power sensitivity of load j to branch l.

[0035] The power system balance margin estimation system of the present invention is characterized by comprising:

[0036] The module is used to construct a balance margin optimization model for the power system based on the cost of purchased electricity and penalty costs, with the operating status and output of each unit in the power system at different times as variables, and with the goal of minimizing the total cost of the power system.

[0037] The solution module is used to solve the balance margin optimization model of the power system and obtain the balance margin of the power system.

[0038] A further improvement of the power system balance margin estimation system described in this invention lies in:

[0039] Furthermore, the objective function of the power system balance margin optimization model is:

[0040]

[0041] Among them, S i,t p represents the operating state of unit i at time t. i,t F(p) represents the output of unit i at time t. i,t ) indicates the unit output p i,t The corresponding operating cost, U(p) i,t Let f(t) represent the start-up cost of unit i at time t, C(Tie,t) represent the power system's electricity purchase cost at time t, f(·) represent the power system's penalty cost at time t, T is the total number of time periods to be calculated, and I is the total number of units in the power system.

[0042] Furthermore, the balance margin optimization model of the power system is expressed as follows:

[0043]

[0044] in, pi,py,t Let be the positive reserve variable for unit i during time period t. pi,ny,t Let i be the reverse balance margin of unit i at time t. pi,max This represents the maximum output of unit i. pnr,t Let be the positive backup variable for time period t. pi,min Let i be the minimum output of unit i. ppy,t The total positive balance margin of the power system at time t. pny,t This represents the total reverse balance margin of the power system at time t.

[0045] Furthermore, the constraints of the power system balance margin optimization model include system balance constraints, unit operation constraints, and grid transmission capacity constraints.

[0046] Furthermore, the system balance constraint is:

[0047]

[0048] Where, p l,t Let p be the load demand of node l at time t, L be the total number of nodes, and p be the load demand of node l at time t. i,t Δp represents the output of unit i at time t. l,t Tie represents the load reduction at node l at time t. o,t For the initial tie line quantity, Tie t Electricity is purchased from external sources.

[0049] Furthermore, the power grid transmission capacity constraint is as follows:

[0050]

[0051] Where, ΔP i,t p represents the additional power generated by unit i at time t. i,t ΔP represents the output of unit i at time t. load,j,t Let L be the load increase of node j at time t. l,t β represents the power flow of branch l at time t. l,i,t β represents the active power sensitivity of unit i to branch l. l,j,t The active power sensitivity of load j to branch l.

[0052] The computer device of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the balance margin estimation method for the power system.

[0053] The computer-readable storage medium of the present invention stores a computer program, which, when executed by a processor, implements the steps of the power system balance margin estimation method.

[0054] The present invention has the following beneficial effects:

[0055] The power system balance margin estimation method, system, equipment, and medium described in this invention, in specific operation, consider the cost of purchased electricity and penalty cost, take the operating status and output of each unit in the power system at different times as variables, and take the minimum total cost of the power system as the optimization objective to construct a power system balance margin optimization model. This invention adds the cost of purchased electricity and the penalty cost for not meeting the balance margin to the traditional optimization objective to improve the accuracy of balance margin calculation, and is convenient to operate and highly practical.

[0056] Furthermore, by considering network power flow within the power grid transmission capacity constraints, a more realistic balance margin value can be calculated. Attached Figure Description

[0057] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0058] Figure 1 This is a system structure diagram of the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0061] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0062] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0063] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0064] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present 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 present invention without inventive effort are within the scope of protection of the present invention.

[0066] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0067] Example 1

[0068] The power system balance margin estimation method of the present invention includes the following steps:

[0069] Considering the cost of purchased electricity and penalty costs, with the goal of minimizing total cost, the optimization objective is constructed as follows:

[0070]

[0071] Among them, S i,t p represents the operating state of unit i at time t, where 1 indicates startup and 0 indicates shutdown, and is a variable for optimization decision; i,t F(p) represents the output of unit i at time t, and is an optimization decision variable. i,t U(p) represents the operating cost corresponding to the unit's output, as shown in equation (2). i,t ) represents the start-up cost of unit i at time t, as shown in equation (3), C(Tie,t) represents the power purchase cost of the system at time t, as shown in equation (4), f(·) represents the penalty cost of the system at time t, T is the total number of calculation time periods, and I is the total number of units.

[0072] Among them, the operating cost F(p) corresponding to the unit output i,t )for:

[0073]

[0074] Among them, a i ,b i ,c i The quadratic, linear, and constant terms of the cost curve for unit i can be obtained experimentally and are known values.

[0075] The start-up cost U(p) of unit i at time t i,t )for:

[0076]

[0077] Among them, B i,0 K is the cost required to start up the boiler of unit i from a cooled state, which is a constant; g τ is the turbine start-up cost, which is a constant; τ is the boiler cooling time constant, t s This refers to the downtime.

[0078] The system's electricity purchase cost C(Tie,t) at time t is:

[0079]

[0080] Among them, Tie t For the purchased electricity at time t, For price.

[0081] The constraints involved in this embodiment include system balance constraints, unit operation constraints, and grid transmission capacity constraints.

[0082] The system equilibrium constraint is:

[0083]

[0084] Where, p l,t Let L be the load demand of node l at time t, and L be the total number of nodes. pi,t Δp represents the output of unit i at time t; l,t The load reduction at node l at time t; Tie o,t This is the initial tie line quantity; t Regarding the use of purchased electricity, it should be noted that this invention incorporates load reduction and purchased electricity into the traditional balance constraints.

[0085] The unit balance margin optimization model is constructed as follows:

[0086]

[0087] in, pi,py,t Let be the positive backup variable for time period t. pi,ny,t Let be the reverse balance margin at time t. pi,max This represents the maximum output of unit i; pnr,t Let be the positive backup variable for time period t. pny,t The negative balance margin at time t. pi,min This represents the minimum output of unit i. In other words, the unit's current output + reserved positive reserve + positive balance margin = the unit's maximum generating capacity. ppy,t This represents the total positive balance margin of the power system at time t. pny,t This represents the total reverse balance margin of the power system at time t.

[0088] The unit's operating constraints are:

[0089]

[0090] Where, p i,min p i,max p represents the minimum and maximum output of unit i; i,up p represents the maximum lift output of unit i. i,down S represents the maximum output power of unit i; N,i This represents the maximum number of times unit i can be started within the optimization period. B i,off B represents the minimum downtime period of unit i. i,on This indicates the minimum operating time of unit i; This represents the cumulative downtime up to time period t. This represents the cumulative number of times the system has been running up to time period t.

[0091] The power grid transmission capacity constraint is:

[0092]

[0093] Among them, P i n P represents the injected power at node i, which is directly related to the unit's output. l i,j V represents the active power of branch l, with its two endpoints at nodes i and j; i V j G represents the voltage magnitudes at nodes i and j; i,j B i,j θ i,j P represents the conductance, susceptance, and phase angle difference between node i and node j; l,max Indicates the thermal stability limit of branch l; M E and M E,max This represents the active power and stability limit of transmission section E, where l∈E indicates that branch l is a component of section E, and γ l,E This indicates the relationship between the positive direction definition of section E and the positive direction definition of branch flow.

[0094] For the AC power flow in the above equation, in a power transmission network, since the node voltage is basically near the rated voltage and the branch resistance is much smaller than the branch reactance, linearization can be achieved by neglecting the line resistance and node voltage drop, i.e.:

[0095]

[0096] Where θ is the node phase angle column vector; Z is the node impedance matrix, which can be obtained by inverting the node susceptance matrix; and p is the node injected power column vector.

[0097] Example 2

[0098] Typically, considering the error in new energy forecasting, in supply guarantee scenarios, new energy is generally included in the balance according to a percentage of the forecast value. For example, if the forecast value of new energy is 100MW, and it is included in the balance at 70%, then the output of new energy is considered to be 70MW in the balance calculation. The reason for this treatment is that the forecast value of 100MW of new energy for the task is unreliable.

[0099] When a positive balance margin is introduced, the same effect can be achieved by reserving a balance margin. Furthermore, the balance margin can be reserved in different levels, thus achieving better and more economical results.

[0100]

[0101] Where f(·) represents the penalty cost of the system at time t, which is included in the objective function (1); P py,t The values ​​reserved for the positive balance margin of the system are the variables to be optimized; P1 to P m To reserve tiered values ​​for balance margin, these are known setpoints. The penalty amount for each level is set at five demand margins: 100, 200, 300, 400, and 500. The penalty coefficient for each level is 10000, 1000, 100, 10, and 0, respectively, i.e., P1 = 100, P5 = 500. At this point, if the balance margin is greater than 500, no penalty will be incurred; if the positive balance margin is less than 100, a large penalty with a penalty coefficient of 10,000 will be incurred. This segmentation can be combined with the segmentation of new energy prediction errors to achieve refined management of new energy inclusion in the balance.

[0102] It should be noted that this invention incorporates the cost of purchased electricity into the traditional optimization objective, as shown in equation (4). In the optimization calculation, it is required that the total positive balance margin of the system is greater than 0 at any time. If the value of purchased electricity is still greater than 0 in the optimization result, it means that the positive balance margin does not meet the supply guarantee requirements and electricity must be purchased from outside.

[0103] In addition, the balance margin of each unit is shown in equation (6). The sum of all units yields the balance margin of the power system. However, when considering network power flow constraints, the balance margin of the units is subject to power flow constraints. The unit output cannot always reach the upper limit of the output. That is, when considering power flow constraints, the actual upper limit of the unit output will be less than or equal to p in equation (6). i,max How to calculate the actual value? pi,max This is another key point of the present invention.

[0104] When calculating the balance margin, since the load and unit output will further increase, the power flow calculation formulas based on the current output in equations (8) and (9) cannot be used to consider power flow constraints in the balance margin, and need to be adjusted as follows:

[0105]

[0106] Where, ΔP i,t Let ΔP be the additional power generated by unit i at time t. load,j,t Let L be the load increase at node j at time t. The load increase should equal the increase in unit output; l,t β represents the power flow of branch l at time t. l,i,t β represents the active power sensitivity of unit i to branch l. l,j,t The active power sensitivity of load j to branch l should be equal to the power flow calculation results after the unit and load increase.

[0107] By incorporating equation (10) into the optimization model, the accurate balance margin considering power flow constraints can be calculated.

[0108] Example 3

[0109] refer to Figure 1 The power system balance margin estimation system of the present invention includes:

[0110] The module is designed to consider the cost of purchased electricity and penalty costs. It uses the operating status and output of each unit in the power system at different times as variables and the goal of minimizing the total cost of the power system to build a balance margin optimization model for the power system.

[0111] The solution module is used to solve the balance margin optimization model of the power system and obtain the balance margin of the power system.

[0112] A further improvement of the power system balance margin estimation system described in this invention lies in:

[0113] As one embodiment of the present invention, the objective function of the power system balance margin optimization model is:

[0114]

[0115] Among them, S i,t p represents the operating state of unit i at time t. i,t F(p) represents the output of unit i at time t. i,t ) indicates the unit output p i,t The corresponding operating cost, U(p) i,t Let f(t) represent the start-up cost of unit i at time t, C(Tie,t) represent the power system's electricity purchase cost at time t, f(·) represent the power system's penalty cost at time t, T is the total number of time periods to be calculated, and I is the total number of units in the power system.

[0116] As one embodiment of the present invention, the balance margin optimization model of the power system is expressed as follows:

[0117]

[0118] in, pi,py,t Let be the positive reserve variable for unit i during time period t. pi,ny,t Let i be the reverse balance margin of unit i at time t. pi,max This represents the maximum output of unit i. pnr,t Let be the positive backup variable for time period t. pi,min Let i be the minimum output of unit i. ppy,t The total positive balance margin of the power system at time t. pny,tThis represents the total reverse balance margin of the power system at time t.

[0119] As one embodiment of the present invention, the constraints of the power system balance margin optimization model include system balance constraints, unit operation constraints, and grid transmission capacity constraints.

[0120] As one embodiment of the present invention, the system balance constraint is:

[0121]

[0122] in, pl,t Let L be the load demand of node l at time t, and L be the total number of nodes. pi,t Δp represents the output of unit i at time t. l,t Tie represents the load reduction at node l at time t. o,t For the initial tie line quantity, Tie t Electricity is purchased from external sources.

[0123] In one embodiment of the present invention, the power grid transmission capacity constraint is as follows:

[0124]

[0125] Where, ΔP i,t p represents the additional power generated by unit i at time t. i,t ΔP represents the output of unit i at time t. load,j,t Let L be the load increase of node j at time t. l,t β represents the power flow of branch l at time t. l,i,t β represents the active power sensitivity of unit i to branch l. l,j,t The active power sensitivity of load j to branch l.

[0126] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0127] Example 4

[0128] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a power system balance margin estimation method. For example, the steps include: considering the cost of purchased electricity and penalty costs, using the operating state and output of each unit in the power system at different times as variables, and taking the minimum total cost of the power system as the optimization objective, constructing a power system balance margin optimization model; and solving the power system balance margin optimization model to obtain the power system balance margin. The memory may include main memory, such as high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which can be an industry standard architecture bus, a peripheral component interconnection standard bus, an extended industry standard architecture bus, etc. The bus can be divided into address bus, data bus, control bus, etc. The memory is used to store the program; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0129] Example 5

[0130] A computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of a power system balance margin estimation method. For example, the method includes: considering the cost of purchased electricity and penalty costs, using the operating state and output of each unit in the power system at different times as variables, and taking the minimum total cost of the power system as the optimization objective, constructing a power system balance margin optimization model; and solving the power system balance margin optimization model to obtain the power system balance margin. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0131] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0132] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0133] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0134] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0135] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0136] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

[0137] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for estimating the balance margin of a power system, characterized in that, include: Based on the cost of purchased electricity and penalty costs, and taking the operating status and output of each unit in the power system at different times as variables, and taking the minimum total cost of the power system as the optimization objective, a balance margin optimization model of the power system is constructed. Solve the balance margin optimization model of the power system to obtain the balance margin of the power system; The objective function of the power system balance margin optimization model is: （1） in, This indicates the operating state of unit i at time t. This represents the output of unit i at time t. Indicates the unit output The corresponding operating costs, This represents the startup cost of unit i at time t. This represents the cost of purchasing electricity for the power system at time t. This represents the penalty cost of the power system at time t, where T is the total number of time periods calculated, and I is the total number of generating units in the power system. The balance margin optimization model of the power system is expressed as follows: （6） in, Let i be the positive balance margin of unit i at time t. Let i be the reverse balance margin of unit i at time t. This represents the maximum output of unit i. For time period t, the reverse backup variable is... Let i be the minimum output of unit i. The total positive balance margin of the power system at time t. The total reverse balance margin of the power system at time t. It is a positive backup variable for time period t.

2. The method for estimating the balance margin of a power system according to claim 1, characterized in that, The constraints of the power system balance margin optimization model include system balance constraints, unit operation constraints, and grid transmission capacity constraints.

3. The method for estimating the balance margin of a power system according to claim 2, characterized in that, The system equilibrium constraint is: （5） in, Let L be the load demand of node l at time t, and L be the total number of nodes. This represents the output of unit i at time t. Let be the load reduction amount at node l at time t. This is the initial tie line quantity. Electricity is purchased from external sources.

4. The method for estimating the balance margin of a power system according to claim 2, characterized in that, The power grid transmission capacity constraint is: （10） in, Let be the additional power generated by unit i at time t. This represents the output of unit i at time t. Let be the load increase of node j at time t. This represents the power flow of branch l at time t. The active power sensitivity of unit i to branch l. The active power sensitivity of load j to branch l.

5. A balance margin estimation system for a power system, characterized in that, include: The module is used to construct a balance margin optimization model for the power system based on the cost of purchased electricity and penalty costs, with the operating status and output of each unit in the power system at different times as variables, and with the goal of minimizing the total cost of the power system. The solution module is used to solve the balance margin optimization model of the power system to obtain the balance margin of the power system. The objective function of the power system balance margin optimization model is: （1） in, This indicates the operating state of unit i at time t. This represents the output of unit i at time t. Indicates the unit output The corresponding operating costs, This represents the startup cost of unit i at time t. This represents the electricity purchase cost of the power system at time t. This represents the penalty cost of the power system at time t, where T is the total number of time periods calculated, and I is the total number of generating units in the power system. The balance margin optimization model of the power system is expressed as follows: （6） in, Let i be the positive balance margin of unit i at time t. Let i be the reverse balance margin of unit i at time t. This represents the maximum output of unit i. For time period t, the reverse backup variable is... Let i be the minimum output of unit i. The total positive balance margin of the power system at time t. The total reverse balance margin of the power system at time t. It is a positive backup variable for time period t.

6. The power system balance margin estimation system according to claim 5, characterized in that, The constraints of the power system balance margin optimization model include system balance constraints, unit operation constraints, and grid transmission capacity constraints.

7. The power system balance margin estimation system according to claim 6, characterized in that, The system equilibrium constraint is: （5） in, Let L be the load demand of node l at time t, and L be the total number of nodes. This represents the output of unit i at time t. Let be the load reduction amount at node l at time t. This is the initial tie line quantity. Electricity is purchased from external sources.

8. The power system balance margin estimation system according to claim 6, characterized in that, The power grid transmission capacity constraint is: （10） in, Let i be the additional power generated by unit i at time t. This represents the output of unit i at time t. Let be the load increase of node j at time t. This represents the power flow of branch l at time t. The active power sensitivity of unit i to branch l. The active power sensitivity of load j to branch l.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the power system balance margin estimation method as described in any one of claims 1-4.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the power system balance margin estimation method as described in any one of claims 1-4.

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