A power system flexible regulation resource optimal scheduling method and device and medium

Abstract

The application discloses a kind of power system flexible regulation resource optimization scheduling method, device and medium.Therein, method includes: the flexible regulation resource model of power system is constructed, wherein flexible regulation resource model includes flexible regulation resource operation model and calling cost calculation method;According to flexible regulation resource model, the flexible regulation resource optimization scheduling model of power system is constructed;According to the linear programming solution of flexible calling resource optimization scheduling model to the real-time power system data collected, the optimization scheduling strategy result of each time of power system is determined.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, and more specifically, to a method, apparatus and medium for flexible adjustment and optimization scheduling of power system resources. Background Technology

[0002] Under the goal of clean and low-carbon energy transformation, my country's installed capacity of new energy sources has continued to grow rapidly. With a high proportion of new energy connected to the grid, its characteristics of "large installed capacity, small output" have placed higher demands on system security, power supply, and clean energy consumption. Furthermore, the frequent occurrence of extreme weather events in recent years has accelerated changes in both supply and demand, leading to a dilemma for the power grid: "difficulty in absorbing large-scale power generation" and "difficulty in ensuring supply during small-scale power generation." This necessitates that the power system operate in a more flexible manner to meet the supply and demand balance and regulation needs under various scenarios, ensuring a safe and stable power supply and efficient absorption of new energy sources.

[0003] The operational characteristics of various types of flexible adjustment resources differ greatly, and their numbers are numerous. Furthermore, optimization calculations require consideration of multiple factors, including operational characteristics and economic efficiency, making modeling challenging. Simultaneously, the optimal scheduling of large-scale, multi-type flexible adjustment resources is a complex mixed-integer programming problem, with the computational complexity and time increasing significantly with the problem size. Existing research on the joint operation optimization models of new energy sources and flexible adjustment resources for short-term scheduling is quite complex and struggles to fully account for the stochastic fluctuations in new energy output. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method, apparatus, and medium for flexible resource adjustment and optimized scheduling in power systems.

[0005] According to one aspect of the present invention, a method for flexible adjustment and optimized scheduling of power system resources is provided, comprising:

[0006] Construct a flexible regulation resource model for the power system, which includes a flexible regulation resource operation model and a method for calculating call costs.

[0007] Based on the flexible resource adjustment model, construct a flexible resource adjustment optimization scheduling model for the power system.

[0008] Based on the collected real-time power system data, a linear programming solution is performed on the flexible resource allocation optimization scheduling model to determine the optimal scheduling strategy results for the power system at each time point.

[0009] Optionally, flexible resource adjustment models include: gas turbine model, load demand response model, and electrochemical energy storage model.

[0010] Optionally, the gas turbine unit model includes: output constraints, shortest start-up and shutdown time constraints, call-up costs, and segmented power constraints, wherein,

[0011] The output constraint is:

[0012]

[0013] In the formula: The output of gas turbine unit i during time period t; Let i be the standby capacity of gas turbine unit i during time period t; Ω represents the start-up variable of gas turbine unit i during time period t; Gas A collection of gas turbine units; The minimum / maximum output of gas turbine unit i;

[0014] The minimum start-up and shutdown time constraint is:

[0015]

[0016] In the formula: T i on / T i off The minimum start / stop time for gas turbine unit i; This represents the on-state capacity of gas turbine unit i during time period t. It is 1 when the unit changes from off to on, and 0 otherwise. This represents the off-state capacity of gas turbine unit i during time period t. It is 1 when the unit changes from on to off, and 0 otherwise.

[0017] The formula for calculating the cost of calling is:

[0018] The cost of dispatching the unit takes into account its fuel costs and start-up and shutdown costs.

[0019]

[0020] In the formula: The total call cost for all gas turbine units during time period t; The start-up / shutdown cost for gas turbine unit i; Let be the fuel cost function of gas turbine unit i;

[0021] The segmented power constraint is:

[0022]

[0023] In the formula: This represents the upper limit of the output of the j-th stage of the gas turbine unit i. This is the lower limit of the output of the j-th stage of the gas turbine unit i.

[0024] Optionally, the load demand response model includes maximum response capacity constraints, state logic constraints, response time constraints, and response count constraints, and calculates the invocation cost of load demand response.

[0025] The maximum response capability constraint is:

[0026]

[0027] Where: Ω D For load sets; This represents the maximum response capacity of load i; a value of 0 indicates that the load does not participate in demand response. Let i be the annual maximum load power; Let i be the load curve value of load i in time period t; Let be the response power of load i during time period t; This represents the maximum response ratio for urgent needs. For the state variables of loads participating in demand response, if the value is 1, it means that load i participates in demand response during time period t; otherwise, it does not participate. This is the state variable for initiating demand response. If the value is 1, it means that time period t is the start time for load i to participate in demand response. The state variable for ending demand response; if the value is 1, it means that the time period t is the end time for load i to participate in demand response.

[0028] The state logic constraints are:

[0029]

[0030] The response time constraint is:

[0031]

[0032] The response count constraint is:

[0033]

[0034] The load response cost is:

[0035]

[0036] In the formula: The maximum / minimum duration for load i to participate in demand response; c is the maximum number of responses of load i within the computation time scale; IDR The subsidy is per unit response power.

[0037] Optionally, the electrochemical energy storage model includes constraints on energy storage charging and discharging power, energy storage capacity, energy storage charging and discharging state logic, and energy storage charging and discharging call costs, among which,

[0038] The energy storage charging and discharging power constraint is:

[0039]

[0040] Where: Ω ESS For system energy storage collection; Let i be the charging / discharging state variable of energy storage in time period t; This represents the maximum charging / discharging power of energy storage i; The charging / discharging power of energy storage i during time period t;

[0041] The energy storage capacity constraint is:

[0042]

[0043] In the formula: This represents the maximum / minimum energy storage capacity of energy storage i. Since some types of energy storage require a minimum energy storage capacity to ensure the stability of the medium, a minimum energy storage capacity needs to be set. The charging / discharging efficiency of energy storage i; Let i be the amount of energy stored in energy storage i during time period t;

[0044] The logic constraints for energy storage charge / discharge states are as follows:

[0045]

[0046] The cost of energy storage charging and discharging is:

[0047]

[0048] In the formula: The unit cost of charging / discharging energy storage during time period t.

[0049] Optionally, the objective function of the scheduling optimization model is:

[0050]

[0051] In the formula: Adjust the cost of connection line i at time t; To save costs by flexibly adjusting resources within time interval t; To save the power curtailment of new energy source i and the power shedding of load i at time t; β RE,Curt / β L,Curt Costs of curtailment penalties for renewable energy / costs of load shedding penalties;

[0052] The output constraints of coal-fired power plants are:

[0053]

[0054] The power output constraints for new energy sources are:

[0055]

[0056] In the formula: The actual output of the new energy power station i at time t; The available power output of the new energy power station i at time t;

[0057] The power balance constraint is:

[0058]

[0059] In the formula: The load value of load a at time t;

[0060] The alternative constraints are:

[0061]

[0062] In the formula: α RS This is the system's reserve factor;

[0063] The cross-sectional constraints are:

[0064]

[0065] In the formula: This represents the upper limit of the transmission capacity of section i.

[0066] According to another aspect of the present invention, a power system flexible adjustment and resource optimization scheduling device is provided, comprising:

[0067] The first construction module is used to construct a flexible adjustment resource model for the power system, which includes a flexible adjustment resource operation model and a call cost calculation method.

[0068] The second construction module is used to construct a flexible adjustment resource optimization scheduling model for the power system based on the flexible adjustment resource model.

[0069] The determination module is used to perform linear programming to solve the flexible resource allocation optimization scheduling model based on the collected real-time power system data, and determine the optimization scheduling strategy results of the power system at each time point.

[0070] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing a computer program for performing the methods described in any of the above aspects of the present invention.

[0071] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method described in any of the preceding aspects of the present invention.

[0072] Therefore, this invention patent establishes an operation model and a method for calculating the call cost of flexible adjustment resources such as gas turbine units, load demand response, and electrochemical energy storage. Taking into account system constraints and the operation constraints of various types of power sources, it establishes an optimized scheduling model for flexible adjustment resources, realizes accurate simulation of the operation mode of high-proportion new energy power systems, and conforms to the actual scheduling process. Attached Figure Description

[0073] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0074] Figure 1 This is a flowchart illustrating a power system flexible adjustment resource optimization scheduling method provided in an exemplary embodiment of the present invention;

[0075] Figure 2 This is a schematic diagram of the structure of a power system flexible adjustment and resource optimization scheduling device provided in an exemplary embodiment of the present invention;

[0076] Figure 3 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. Detailed Implementation

[0077] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0078] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0079] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of the present invention are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0080] It should also be understood that in the embodiments of the present invention, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0081] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more unless explicitly defined or given contrary instructions in the context.

[0082] Furthermore, the term "and / or" in this invention is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this invention generally indicates that the preceding and following related objects have an "or" relationship.

[0083] It should also be understood that the description of the various embodiments in this invention emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0084] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0085] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0086] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0087] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0088] The embodiments of this invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose computing system environments or configurations. Well-known examples of terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.

[0089] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0090] Exemplary methods

[0091] Figure 1 This is a flowchart illustrating a power system flexible adjustment and resource optimization scheduling method according to an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as… Figure 1 As shown, the power system flexible adjustment resource optimization scheduling method 100 includes the following steps:

[0092] Step 101: Construct a flexible adjustment resource model for the power system, which includes a flexible adjustment resource operation model and a method for calculating call costs.

[0093] Step 102: Based on the flexible adjustment resource model, construct a flexible adjustment resource optimization scheduling model for the power system;

[0094] Step 103: Based on the collected real-time power system data, perform linear programming to solve the flexible resource allocation optimization scheduling model to determine the optimization scheduling strategy results for the power system at each time point.

[0095] Specifically, this invention patent proposes a flexible adjustment resource optimization scheduling method that considers adjustment costs. It establishes mathematical models for flexible adjustment resources such as gas turbine units, load demand response, and electrochemical energy storage. The optimization objectives are to minimize the adjustment costs of flexible resources within the province and inter-provincial interconnection lines, as well as the curtailment and load shedding of new energy sources. Taking into account system constraints and the operating constraints of various types of power sources, a flexible adjustment resource optimization scheduling model is established to achieve accurate simulation of the operation mode of a high-proportion new energy power system.

[0096] The specific steps are as follows:

[0097] 1. Collect and organize historical data on wind power, photovoltaic power, and loads for a certain time period with a time resolution of 15 or 60 minutes in the actual provincial power grid, as well as unit information for various power sources such as thermal power and hydropower, including unit capacity, unit type, maximum and minimum output of the unit, energy storage capacity, load demand response capacity, power of power transmission lines to the grid, and grid topology.

[0098] 2. Establish a gas turbine unit model, mainly including output constraints, ramp-up constraints, and minimum start-up and shutdown time constraints, and calculate the gas turbine unit's call-up cost.

[0099] (1) Output constraint:

[0100]

[0101] In the formula: The output of gas turbine unit i during time period t; Let i be the standby capacity of gas turbine unit i during time period t; Ω represents the start-up variable of gas turbine unit i during time period t; Gas A collection of gas turbine units; This represents the minimum / maximum output of gas turbine unit i.

[0102] (2) Minimum start-up and shutdown time constraint:

[0103]

[0104] In the formula: T i on / T i off The minimum start / stop time for gas turbine unit i; This represents the on-state capacity of gas turbine unit i during time period t. It is 1 when the unit changes from off to on, and 0 otherwise. This represents the off-state capacity of gas turbine unit i during time period t. It is 1 when the unit changes from on to off, and 0 otherwise.

[0105] (3) Calculate the call cost:

[0106] The cost of dispatching the unit takes into account its fuel costs and start-up and shutdown costs.

[0107]

[0108] In the formula: The total call cost for all gas turbine units during time period t;

[0109] The cost function of a gas turbine unit is a quadratic function, which can be expressed as:

[0110]

[0111] In the formula: For the cost function of generating electricity from fuel (gas) i, there are quadratic, linear, and constant terms.

[0112] The output of gas-fired power units is linearized in segments, The range is divided into n segments, and the power generation cost function can be written as:

[0113]

[0114] In the formula: The start-up / shutdown cost for gas turbine unit i; Let be the fuel cost function of gas turbine unit i; Let be the slope of the power generation cost function for the j-th segment of gas turbine unit i; Let represent the operating state of gas turbine unit i during time period t. It is a 0-1 variable, where 0 represents the off state and 1 represents the on state.

[0115] (4) Segmented power-related constraints:

[0116]

[0117] In the formula: This represents the upper limit of the output of the j-th segment of the gas turbine unit i. This is the lower limit of the output of the j-th stage of the gas turbine unit i.

[0118] 3. Establish a load demand response model, mainly including maximum response capacity constraints, state logic constraints, response time constraints, and response count constraints, and calculate the call cost of load demand response.

[0119] (1) Maximum response capability constraint:

[0120]

[0121] Where: Ω D For load sets; This represents the maximum response capacity of load i. Setting it to 0 indicates that the load does not participate in demand response. Let i be the annual maximum load power; Let i be the load curve value of load i in time period t; Let be the response power of load i during time period t; This represents the maximum response ratio for urgent needs. This is a state variable for loads participating in demand response. If the value is 1, it means that load i participates in demand response during time period t; otherwise, it does not participate. This is the state variable for initiating demand response. If the value is 1, it means that time period t is the start time for load i to participate in demand response. The state variable for ending demand response. If the value is 1, it means that the period t is the end time for load i to participate in demand response.

[0122] (2) State logic constraints:

[0123]

[0124] (3) Response time constraint:

[0125]

[0126] (4) Response count constraint:

[0127]

[0128] (5) Load response cost:

[0129]

[0130] In the formula: The maximum / minimum duration for load i to participate in demand response; c represents the maximum number of responses to load i within the computation timescale. IDR The subsidy is per unit response power.

[0131] 4. Establish an electrochemical energy storage model, which mainly includes constraints on energy storage charging and discharging power, energy storage capacity, and energy storage charging and discharging state logic, and considers the impact of time-of-use electricity pricing to calculate the call cost of electrochemical energy storage.

[0132] (1) Energy storage charging and discharging power constraints:

[0133]

[0134] Where: Ω ESS For system energy storage collection; Let i be the charging / discharging state variable of energy storage in time period t; This represents the maximum charging / discharging power of energy storage i; Let i be the charging / discharging power of energy storage i during time period t.

[0135] (2) Energy storage capacity constraint:

[0136]

[0137] In the formula: This represents the maximum / minimum energy storage capacity of energy storage i. Since some types of energy storage require a minimum energy storage capacity to ensure the stability of the medium, a minimum energy storage capacity needs to be set. The charging / discharging efficiency of energy storage i; Let i be the amount of energy stored in energy storage i during time period t.

[0138] (3) Energy storage charging and discharging state logic constraints:

[0139] To prevent energy storage from simultaneously charging and discharging, mutual exclusion constraints need to be added.

[0140]

[0141] (4) Energy storage charging and discharging costs:

[0142]

[0143] In the formula: The unit cost of energy storage charging / discharging during time period t is calculated by fully considering the impact of time-of-use electricity pricing.

[0144] 5. A flexible resource inter-provincial scheduling optimization model is established, aiming to maximize the absorption of new energy sources across the entire network with minimal adjustment costs while ensuring reliable power supply. Therefore, the objective function is to minimize the adjustment costs of flexible resources within the province and inter-provincial transmission lines, as well as the curtailment and load shedding of new energy. Constraints mainly include coal-fired power output constraints, new energy output constraints, power balance constraints, reserve constraints, and external transmission line constraints. The priority of various flexible resource allocation mechanisms is fully considered.

[0145] (1) Objective function:

[0146]

[0147] In the formula: Adjust the cost of connection line i at time t; To save costs by flexibly adjusting resources within time interval t; To save the power curtailment of new energy source i and the power shedding of load i at time t; β RE，Curt / β L，Curt Costs of curtailment penalties for renewable energy / costs of load shedding penalties.

[0148] (2) Coal-fired power output constraints:

[0149]

[0150] (3) Constraints on new energy output:

[0151]

[0152] In the formula: The actual output of the new energy power station i at time t; The available power output of the new energy power station i at time t.

[0153] (4) Power balance constraints:

[0154] Regarding balancing strategies, various balancing methods can be considered, such as unified balancing across the entire network, unified balancing across the entire network plus inter-provincial balancing, and provincial balancing. This patent adopts provincial balancing as the power balancing constraint. If an imbalance occurs in a certain province, provincial balancing is achieved through flexible resource adjustments within the province and adjustments to inter-provincial tie lines.

[0155]

[0156] In the formula: The load value of load a at time t is to save load i.

[0157] (5) Alternative constraints:

[0158] The province-specific reserve system is adopted, which assumes that the province's thermal power, hydropower, and gas power plants will jointly meet the province's reserve requirements.

[0159]

[0160] In the formula: α RS This is the system's reserve coefficient.

[0161] (6) Sectional constraints:

[0162]

[0163] In the formula: This represents the upper limit of the transmission capacity of section i.

[0164] 6. Optimize the model solution. The above-mentioned flexible adjustment resource optimization scheduling model is a mixed integer linear programming problem, which can be solved by calling the Cplex solver. It can obtain the results of the optimized scheduling strategy of the power grid at each time, including the power supply shortage and power curtailment, as well as the call status of inter-provincial tie lines and intra-provincial flexible adjustment resources.

[0165] Therefore, this invention patent establishes an operation model and a method for calculating the call cost of flexible adjustment resources such as gas turbine units, load demand response, and electrochemical energy storage. It takes into account the adjustment cost of flexible resources within the province and inter-provincial interconnection lines, as well as the minimum curtailment of new energy and load shedding, as optimization objectives. It also takes into account system constraints and the operation constraints of various types of power sources, and establishes a flexible adjustment resource optimization scheduling model to achieve accurate simulation of the operation mode of high-proportion new energy power systems and conform to the actual scheduling process.

[0166] Exemplary device

[0167] Figure 2 This is a schematic diagram of the structure of a power system flexible adjustment and resource optimization scheduling device provided in an exemplary embodiment of the present invention. Figure 2 As shown, the device 200 includes:

[0168] The first construction module 210 is used to construct a flexible adjustment resource model of the power system, wherein the flexible adjustment resource model includes a flexible adjustment resource operation model and a call cost calculation method.

[0169] The second construction module 220 is used to construct a flexible adjustment resource optimization scheduling model for the power system based on the flexible adjustment resource model.

[0170] The determination module 230 is used to perform linear programming to solve the flexible resource allocation optimization scheduling model based on the collected real-time power system data, and to determine the optimization scheduling strategy results of the power system at each time point.

[0171] Optionally, flexible resource adjustment models include: gas turbine model, load demand response model, and electrochemical energy storage model.

[0172] Optionally, the gas turbine unit model includes: output constraints, shortest start-up and shutdown time constraints, call-up costs, and segmented power constraints, wherein,

[0173] The output constraint is:

[0174]

[0175] In the formula: The output of gas turbine unit i during time period t; Let i be the standby capacity of gas turbine unit i during time period t; Ω represents the start-up variable of gas turbine unit i during time period t; Gas A collection of gas turbine units; The minimum / maximum output of gas turbine unit i;

[0176] The minimum start-up and shutdown time constraint is:

[0177]

[0178] In the formula: T i on / T i off The minimum start / stop time for gas turbine unit i; This represents the on-state capacity of gas turbine unit i during time period t. It is 1 when the unit changes from off to on, and 0 otherwise. This represents the off-state capacity of gas turbine unit i during time period t. It is 1 when the unit changes from on to off, and 0 otherwise.

[0179] The formula for calculating the cost of calling is:

[0180] The cost of dispatching the unit takes into account its fuel costs and start-up and shutdown costs.

[0181]

[0182] In the formula: The total call cost for all gas turbine units during time period t; The start-up / shutdown cost for gas turbine unit i; Let be the fuel cost function of gas turbine unit i;

[0183] The segmented power constraint is:

[0184]

[0185] In the formula: This represents the upper limit of the output of the j-th stage of the gas turbine unit i. This is the lower limit of the output of the j-th stage of the gas turbine unit i.

[0186] Optionally, the load demand response model includes maximum response capacity constraints, state logic constraints, response time constraints, and response count constraints, and calculates the invocation cost of load demand response.

[0187] The maximum response capability constraint is:

[0188]

[0189] Where: Ω D For load sets; This represents the maximum response capacity of load i; a value of 0 indicates that the load does not participate in demand response. Let i be the annual maximum load power; Let i be the load curve value of load i in time period t; Let be the response power of load i during time period t; This represents the maximum response ratio for urgent needs. For the state variables of loads participating in demand response, if the value is 1, it means that load i participates in demand response during time period t; otherwise, it does not participate. This is the state variable for initiating demand response. If the value is 1, it means that time period t is the start time for load i to participate in demand response. The state variable for ending demand response; if the value is 1, it means that the time period t is the end time for load i to participate in demand response.

[0190] The state logic constraints are:

[0191]

[0192] The response time constraint is:

[0193]

[0194] The response count constraint is:

[0195]

[0196] The load response cost is:

[0197]

[0198] In the formula: The maximum / minimum duration for load i to participate in demand response; c is the maximum number of responses of load i within the computation time scale; IDR The subsidy is per unit response power.

[0199] Optionally, the electrochemical energy storage model includes constraints on energy storage charging and discharging power, energy storage capacity, energy storage charging and discharging state logic, and energy storage charging and discharging call costs, among which,

[0200] The energy storage charging and discharging power constraint is:

[0201]

[0202] Where: Ω ESS For system energy storage collection; Let i be the charging / discharging state variable of energy storage in time period t; This represents the maximum charging / discharging power of energy storage i; The charging / discharging power of energy storage i during time period t;

[0203] The energy storage capacity constraint is:

[0204]

[0205] In the formula: This represents the maximum / minimum energy storage capacity of energy storage i. Since some types of energy storage require a minimum energy storage capacity to ensure the stability of the medium, a minimum energy storage capacity needs to be set. The charging / discharging efficiency of energy storage i; Let i be the amount of energy stored in energy storage i during time period t;

[0206] The logic constraints for energy storage charge / discharge states are as follows:

[0207]

[0208] The cost of energy storage charging and discharging is:

[0209]

[0210] In the formula: The unit cost of charging / discharging energy storage during time period t.

[0211] Optionally, the objective function of the scheduling optimization model is:

[0212]

[0213] In the formula: Adjust the cost of connection line i at time t; To save costs by flexibly adjusting resources within time interval t; To save the power curtailment of new energy source i and the power shedding of load i at time t; β RE,Curt / βL,Curt Costs of curtailment penalties for renewable energy / costs of load shedding penalties;

[0214] The output constraints of coal-fired power plants are:

[0215]

[0216] The power output constraints for new energy sources are:

[0217]

[0218] In the formula: The actual output of the new energy power station i at time t; The available power output of the new energy power station i at time t;

[0219] The power balance constraint is:

[0220]

[0221] In the formula: The load value of load a at time t;

[0222] The alternative constraints are:

[0223]

[0224] In the formula: α RS This is the system's reserve factor;

[0225] The cross-sectional constraints are:

[0226]

[0227] In the formula: This represents the upper limit of the transmission capacity of section i.

[0228] Exemplary electronic devices

[0229] Figure 3 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. For example... Figure 3 As shown, the electronic device 30 includes one or more processors 31 and memory 32.

[0230] The processor 31 may be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0231] The memory 32 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 31 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above, and / or other desired functions. In one example, the electronic device may also include an input device 33 and an output device 34, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0232] In addition, the input device 33 may also include, for example, a keyboard, a mouse, etc.

[0233] The output device 34 can output various information to the outside. The output device 34 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0234] Of course, for the sake of simplicity, Figure 3 Only some of the components of this electronic device relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0235] Exemplary computer program products and computer-readable storage media

[0236] In addition to the methods and apparatus described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0237] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0238] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0239] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0240] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0241] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0242] The block diagrams of devices, systems, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0243] The methods and systems of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the present invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers recording media storing programs for performing the methods according to the present invention.

[0244] It should also be noted that in the systems, apparatus, and methods of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered equivalents of the present invention. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0245] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for flexible resource optimization scheduling in a power system, characterized in that, include: A flexible adjustment resource model for a power system is constructed, wherein the flexible adjustment resource model includes a flexible adjustment resource operation model and a call cost calculation method. The flexible adjustment resource model includes: a gas turbine unit model, a load demand response model, and an electrochemical energy storage model. Based on the flexible adjustment resource model, construct the flexible adjustment resource optimization scheduling model for the power system; Based on the collected real-time power system data, the flexible resource allocation optimization scheduling model is solved by linear programming to determine the optimization scheduling strategy results of the power system at each time point. The gas turbine unit model includes: output constraints, shortest start-up and shutdown time constraints, dispatch costs, and segmented power constraints. The output constraint is: , In the formula: For gas turbine units i exist t Efforts during a specific time period; For gas turbine units i exist t Reserve capacity for a given time period; For gas turbine units i exist t Startup variables for different time periods; A collection of gas turbine units; / For gas turbine units i Minimum / maximum output; The minimum start-up and shutdown time constraint is: In the formula: For gas turbine units Minimum start / stop time; For gas turbine units i During the period t The power-on state capacity is 1 when the power-off state changes to power-on, and 0 otherwise. For gas turbine units i During the period t The shutdown capacity is 1 when the system changes from power-on to power-off, and 0 otherwise. The formula for calculating the call cost is as follows: The cost of dispatching the unit takes into account its fuel cost and start-up and shutdown costs. In the formula: For all gas turbine units during the time period t Total call cost; For gas turbine units Start-up / shutdown costs; For gas turbine units i The fuel cost function; The segmented power constraint is: In the formula: For gas turbine units i No. j Upper limit of segment output; The load demand response model includes maximum response capacity constraints, state logic constraints, response time constraints, and response count constraints, and calculates the invocation cost of load demand response. The maximum response capability constraint is: In the formula: For load sets; For load i The annual maximum load power; For load i During the period t The load curve value; For load i During the period t The response power; This represents the maximum response ratio for urgent demands. For the state variables of the load participating in demand response, a value of 1 indicates that during the time period t load i Participate in the demand response; otherwise, do not participate. This is the state variable for initiating demand response. If the value is 1, it indicates that during the time period... t It is a load i The start time of participating in the demand response; For the state variable that marks the end of the demand response, a value of 1 indicates that the time period... t It is a load i The end time of participation in the demand response; The state logic constraints are as follows: The response time constraint is: The constraint on the number of responses is: The load response cost is: In the formula: For load i Maximum / minimum duration of participation in demand response; For load i The maximum number of responses within the computation timescale; Subsidies per unit response power; The electrochemical energy storage model includes constraints on energy storage charge / discharge power, energy storage capacity, energy storage charge / discharge state logic, and energy storage charge / discharge call costs. The energy storage charging and discharging power constraint is: In the formula: For system energy storage collection; For energy storage i During the period t Charge / discharge state variables; For energy storage i Maximum charge / discharge power; For energy storage i exist t Charge / discharge power during the time period; The energy storage capacity constraint is: In the formula: For energy storage i The maximum / minimum storage capacity: Since some types of energy storage require a minimum storage capacity to ensure the stability of the medium, a minimum storage capacity needs to be set. For energy storage i Charge / discharge efficiency; For energy storage i During the period t The amount of stored energy; The logic constraints for the energy storage charging and discharging states are as follows: The energy storage charging and discharging call cost is: In the formula: In order to be in t Unit cost of time-based energy storage charging / discharging.

2. The method according to claim 1, characterized in that, The objective function of the scheduling optimization model is: In the formula: For connecting lines i At any moment t Adjust costs; For the province a Inner Moments t Flexible resources i Adjust costs; / For the province a At any moment t New energy i abandoned power and load i The load shedding power; / Costs of curtailment penalties for renewable energy / costs of load shedding penalties; The output constraints of coal-fired power plants are: The power output constraints for new energy sources are: In the formula: For new energy power stations i At any moment t Actual output; For new energy power stations i At any moment t Available output; The power balance constraint is: In the formula: For the province a load i exist t Load value at any given time; The alternative constraints are: In the formula: This is the system's reserve factor; The cross-sectional constraints are: In the formula: cross-section i The upper limit of transmission capacity.

3. A power system flexible adjustment and resource optimization scheduling device, used to implement the method described in claims 1-2, characterized in that, include: The first construction module is used to construct a flexible adjustment resource model for the power system, wherein the flexible adjustment resource model includes a flexible adjustment resource operation model and a call cost calculation method. The second construction module is used to construct the flexible adjustment resource optimization scheduling model of the power system based on the flexible adjustment resource model. The determination module is used to perform linear programming to solve the flexible resource allocation optimization scheduling model based on the collected real-time power system data, and determine the optimization scheduling strategy results of the power system at each time point.

4. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-2.

5. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-2.

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