Power system flexible adjustment resource optimization scheduling method and device and medium

Abstract

The invention discloses a flexible adjustment resource optimization scheduling method and device for a power system and a medium. The method comprises the steps that a flexible adjustment resource model of the power system is constructed, and the flexible adjustment resource model comprises a flexible adjustment resource operation model and a calling cost calculation method; according to the flexible adjustment resource model, constructing a flexible adjustment resource optimization scheduling model of the power system; and according to the collected real-time power system data, carrying out linear programming solution on the flexible calling resource optimization scheduling model, and determining an optimization scheduling strategy result of the power system at each moment.

Description

Technical Field

[0001] The present invention relates to the field of renewable energy power generation technology, and more specifically, to a method, device and medium for optimizing the dispatching of flexible regulation resources in an electric power system. Background Art

[0002] Under the goal of clean and low-carbon energy transformation, my country's new energy installed capacity continues to maintain rapid growth. After a high proportion of new energy is connected to the grid, its characteristics of "large installed capacity and small output" have put forward higher requirements for system safety, power supply and clean energy consumption. In addition, the frequent occurrence of extreme weather in recent years has accelerated the changes in the characteristics of both supply and demand. The operation of the power grid faces the dilemma of "difficulty in absorbing large-scale power" and "difficulty in ensuring supply of small-scale power". It is required that the power system can operate in a more flexible form to meet the supply and demand balance and regulation needs in various scenarios, and ensure the safe and stable supply of electricity and the efficient consumption of new energy.

[0003] The operating characteristics of various types of flexible adjustment resources vary greatly, and there are many of them. In addition, when performing optimization calculations, it is necessary to take into account multiple factors such as operating characteristics and economic efficiency, which makes modeling difficult. At the same time, the optimal scheduling of large-scale multi-type flexible adjustment resources is a complex mixed integer programming problem. The amount of calculation increases significantly with the scale of the problem, and the difficulty and time of solving the problem increase significantly. In existing research, the joint operation optimization model of new energy and flexible adjustment resources for short-term scheduling is relatively complex, and it is difficult to fully consider the random volatility of new energy output. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method, device and medium for flexibly adjusting resources and optimizing scheduling in an electric power system.

[0005] According to one aspect of the present invention, a method for optimizing and dispatching flexible regulation resources in a power system is provided, comprising:

[0006] Constructing a flexible regulation resource model of the power system, wherein the flexible regulation resource model includes a flexible regulation resource operation model and a call cost calculation method;

[0007] According to the flexible regulation resource model, a flexible regulation resource optimization dispatching model for the power system is constructed;

[0008] Based on the collected real-time power system data, the linear programming solution of the flexible resource optimization dispatching model is carried out to determine the optimal dispatching strategy results of the power system at each moment.

[0009] Optionally, the flexible adjustment resource model includes: a gas unit model, a load demand response model and an electrochemical energy storage model.

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

[0011] The output constraints are:

[0012]

[0013] Where: is the output of gas generator set i in period t; is the spare capacity of gas generator set i in period t; is the startup variable of gas generator set i in period t; Ω Gas Assemble for gas units; is the minimum / maximum output of gas generator set i;

[0014] The shortest start-stop time constraint is:

[0015]

[0016] Where: T i on / T i off is the minimum start / shutdown time of gas generator set i; is the capacity of the gas generator set i in the start-up state in time period t, which is 1 if it changes from shutdown to start-up, otherwise it is 0; is the shutdown capacity of gas generator set i in period t, which is 1 if the unit changes from startup to shutdown, otherwise it is 0;

[0017] The formula for calculating the call cost is:

[0018] The call cost takes into account the fuel cost and start-up and shutdown costs of the unit:

[0019]

[0020] Where: is the total call cost of all gas units in period t; is the startup / shutdown cost of gas unit i; is the fuel cost function of gas generator set i;

[0021] The segment power constraint is:

[0022]

[0023] Where: is the upper limit of the output of the jth stage of gas generator set i; It is the lower limit of the output of the jth section of gas generator set i.

[0024] Optionally, the load demand response model includes a maximum response capacity constraint, a state logic constraint, a response time constraint, a response number constraint, and calculates the call cost of the load demand response, wherein:

[0025] The maximum responsiveness constraint is:

[0026]

[0027] Where: Ω D is the load collection; is the maximum response capacity of load i, and 0 means that the load does not participate in demand response; is the annual maximum load power of load i; is the load curve value of load i in time period t; is the response power of load i in time period t; The maximum response ratio for emergency demand response; is the state variable of the load participating in demand response. If the value is 1, it means that load i participates in demand response in time period t, otherwise it does not participate; is the state variable for starting demand response. If the value is 1, it means that the load i starts participating in demand response in time period t; is the state variable for ending demand response. If the value is 1, it means that load i ends participating in demand response in time period t.

[0028] The state logic constraints are:

[0029]

[0030] The response time constraints are:

[0031]

[0032] The response times are constrained as:

[0033]

[0034] The load response cost is:

[0035]

[0036] Where: is the maximum / minimum duration for load i to participate in demand response; is the maximum response number of load i within the calculation time scale; c IDR Subsidy per unit response power.

[0037] Optionally, the electrochemical energy storage model includes energy storage charging and discharging power constraints, energy storage quantity constraints, energy storage charging and discharging state logic constraints, and energy storage charging and discharging call costs, wherein:

[0038] The energy storage charging and discharging power constraints are:

[0039]

[0040] Where: Ω ESS Energy storage collection for the system; is the charge / discharge state variable of energy storage i in time period t; is the maximum charge / discharge power of energy storage i; is the charging / discharging power of energy storage i in period t;

[0041] The energy storage capacity constraint is:

[0042]

[0043] Where: is the maximum / minimum storage capacity of energy storage i. Since some types of energy storage require a minimum storage capacity to ensure the stability of the medium, it is necessary to set a minimum storage capacity; is the charge / discharge efficiency of energy storage i; is the storage capacity of energy storage i in time period t;

[0044] The logical constraints of energy storage charging and discharging states are:

[0045]

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

[0047]

[0048] Where: is the unit cost of energy storage charging / discharging during period t.

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

[0050]

[0051] Where: is the adjustment cost of tie line i at time t; To save the adjustment cost of flexible resource i at time t; To save the power abandoned by renewable energy i and the power cut off by load i at time t; β RE,Curt / β L,Curt It is the penalty cost of abandoning electricity of new energy / the penalty cost of shedding load;

[0052] The coal power output constraints are:

[0053]

[0054] The output constraints of new energy are:

[0055]

[0056] Where: is the actual output of the renewable energy station i at time t; is the available output of renewable energy station i at time t;

[0057] The power balance constraint is:

[0058]

[0059] Where: is the load value of load i at time t in province a;

[0060] The alternative constraints are:

[0061]

[0062] Where: α RS is the system's reserve factor;

[0063] The section constraints are:

[0064]

[0065] Where: is the upper limit of the transmission capacity of section i.

[0066] According to another aspect of the present invention, there is provided a device for optimizing and dispatching power system flexible regulation resources, comprising:

[0067] A first building module is used to build 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;

[0068] The second building module is used to build a flexible adjustment resource optimization dispatching model of the power system according to the flexible adjustment resource model;

[0069] The determination module is used to perform linear programming solution on the flexible resource optimization dispatching model according to the collected real-time power system data, and determine the optimization dispatching strategy results of the power system at each moment.

[0070] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above aspects of the present invention.

[0071] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; 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 the above aspects of the present invention.

[0072] Therefore, the patent of this invention establishes the operation model and call cost calculation method of flexible adjustment resources such as gas units, load demand response, and electrochemical energy storage, takes into account system constraints and 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 a high-proportion new energy power system and conform to the actual scheduling process. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0074] Figure 1 It is a flow chart of a method for optimizing and dispatching power system flexible adjustment resources provided by an exemplary embodiment of the present invention;

[0075] Figure 2 It is a structural schematic diagram of a power system flexible regulation resource optimization scheduling device provided by an exemplary embodiment of the present invention;

[0076] Figure 3 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0077] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

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

[0079] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0080] It should also be understood that, in the embodiments of the present invention, “plurality” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[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 limited or otherwise indicated in the context.

[0082] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0083] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0084] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

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

[0086] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

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

[0088] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. 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 personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0089] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, 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 a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0090] Exemplary Methods

[0091] Figure 1 1 is a flow chart of a method for optimizing the dispatching of power system flexible adjustment resources provided by 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 regulation resource optimization scheduling method 100 includes the following steps:

[0092] Step 101, constructing 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;

[0093] Step 102, constructing a flexible regulation resource optimization dispatching model for the power system according to the flexible regulation resource model;

[0094] Step 103 , performing linear programming solution on the flexible resource optimization dispatch model according to the collected real-time power system data, and determining the optimization dispatch strategy results of the power system at each moment.

[0095] Specifically, the patent of this invention proposes a method for optimizing the scheduling of flexible regulating resources taking into account the regulation cost, establishes a mathematical model of flexible regulating resources such as gas units, load demand response, and electrochemical energy storage, takes the adjustment cost of flexible resources within the province and inter-provincial interconnection lines, and minimizes the abandonment of new energy and load shedding as the optimization goals, takes into account system constraints and operating constraints of various types of power sources, establishes a flexible regulating resource optimization scheduling model, and realizes 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 of wind power, photovoltaic power, and load in the actual provincial power grid with a certain time length and a time resolution of 15 or 60 minutes, and unit information of various power sources such as thermal power and hydropower, including unit capacity, unit type, maximum and minimum output of the unit, energy storage installed capacity, capacity, load demand response capacity, power of power grid transmission lines, power grid topology, etc.

[0098] 2. Establish a gas unit model, which mainly includes output constraints, ramp constraints and minimum start-up and shutdown time constraints, and calculate the call cost of the gas unit.

[0099] (1) Output constraints:

[0100]

[0101] Where: is the output of gas generator set i in period t; is the spare capacity of gas generator set i in period t; is the startup variable of gas generator set i in period t; Ω Gas Assemble for gas units; is the minimum / maximum output of gas generator set i.

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

[0103]

[0104] Where: T i on / T i off is the minimum start / shutdown time of gas generator set i; is the capacity of the gas generator set i in the start-up state in time period t, which is 1 if it changes from shutdown to start-up, otherwise it is 0; is the shutdown capacity of gas generator set i in period t, which is 1 if the state changes from startup to shutdown, otherwise it is 0.

[0105] (3) Calculate the call cost:

[0106] The call cost takes into account the fuel cost and start-up and shutdown costs of the unit:

[0107]

[0108] Where: is the total call cost of all gas units in period t;

[0109] The cost function of the gas unit is a quadratic function and can be expressed as:

[0110]

[0111] Where: It is the quadratic term / linear term / constant term of the power generation cost function of fuel (gas) i.

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

[0113]

[0114] Where: is the startup / shutdown cost of gas unit i; is the fuel cost function of gas generator set i; is the slope of the j-th generation cost function of gas unit i; is the operating status of gas unit i in period t, which is a 0-1 variable, 0 is the shutdown state, and 1 is the startup state.

[0115] (4) Segment power related constraints:

[0116]

[0117] Where: It is the upper limit of the output of the jth stage of gas generator set i. It is the lower limit of the output of the jth section of gas generator set i.

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

[0119] (1) Maximum response capability constraints:

[0120]

[0121] Where: Ω D is the load collection; is the maximum response capacity of load i. If it is 0, it means that the load does not participate in demand response. is the annual maximum load power of load i; is the load curve value of load i in time period t; is the response power of load i in time period t; The maximum response ratio for emergency demand response; It is the state variable of the load participating in demand response. If the value is 1, it means that load i participates in demand response in period t, otherwise it does not participate. is the state variable for starting demand response. If the value is 1, it means that the load i starts participating in demand response in time period t; It is the state variable for ending demand response. If the value is 1, it means that the load i ends participating in demand response in period t.

[0122] (2) State logic constraints:

[0123]

[0124] (3) Response time constraints:

[0125]

[0126] (4) Response times constraint:

[0127]

[0128] (5) Load response cost:

[0129]

[0130] Where: is the maximum / minimum duration for load i to participate in demand response; is the maximum response number of load i within the calculation time scale. IDR Subsidy per unit response power.

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

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

[0133]

[0134] Where: Ω ESS Energy storage collection for the system; is the charge / discharge state variable of energy storage i in time period t; is the maximum charge / discharge power of energy storage i; is the charging / discharging power of energy storage i in period t.

[0135] (2) Energy storage capacity constraints:

[0136]

[0137] Where: is the maximum / minimum storage capacity of energy storage i. Since some types of energy storage require a minimum storage capacity to ensure the stability of the medium, it is necessary to set a minimum storage capacity; is the charge / discharge efficiency of energy storage i; is the storage capacity of energy storage i in time period t.

[0138] (3) Logical constraints on energy storage charging and discharging states:

[0139] In order to prevent the energy storage from being charged and discharged at the same time, it is necessary to add a mutual exclusion constraint.

[0140]

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

[0142]

[0143] Where: is the unit cost of energy storage charging / discharging during period t, taking into full account the impact of time-of-use electricity prices.

[0144] 5. Establish an optimization model for cross-provincial dispatching of flexible resources, hoping to maximize the consumption of new energy with the smallest possible adjustment cost within the entire network while ensuring reliable power supply. Therefore, the objective function is to minimize the adjustment cost of flexible resources within the province and inter-provincial interconnection lines, and to minimize the abandonment of new energy and load shedding. The constraints mainly include coal-fired power output constraints, new energy output constraints, power balance constraints, standby constraints, and transmission interconnection line constraints. The priority of calling various flexible adjustment resources is fully considered.

[0145] (1) Objective function:

[0146]

[0147] Where: is the adjustment cost of tie line i at time t; To save the adjustment cost of flexible resource i at time t; To save the power abandoned by renewable energy i and the power cut off by load i at time t; β RE，Curt / β L，Curt It is the penalty cost for abandoning electricity from renewable energy sources / the penalty cost for cutting loads.

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

[0149]

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

[0151]

[0152] Where: is the actual output of the renewable energy station i at time t; is the available output of renewable energy station i at time t.

[0153] (4) Power balance constraints:

[0154] In terms of balancing strategies, there are many balancing methods that can be considered, such as unified balancing of the entire network, unified balancing of the entire network plus inter-provincial balancing, and balancing by province. The power balancing constraint adopted by this patent is balancing by province. If an imbalance occurs in a province, the balancing by province can be achieved through the adjustment of flexible resources within the province and the adjustment of inter-provincial contact lines.

[0155]

[0156] Where: is the load value of load i at time t.

[0157] (5) Backup constraints:

[0158] The form of provincial reserve is adopted, and it is believed that the thermal power, hydropower and gas power in the province will jointly bear the reserve requirements of the province.

[0159]

[0160] Where: α RS is the system's reserve factor.

[0161] (6) Section constraints:

[0162]

[0163] Where: is the upper limit of the transmission capacity of section i.

[0164] 6. Solving the optimization model. 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. The optimized scheduling strategy results of the power grid at all times can be obtained, including power supply shortages and power abandonment, as well as the calling of inter-provincial interconnection lines and flexible adjustment resources within the province.

[0165] Therefore, the patent of this invention establishes the operation model and call cost calculation method of flexible adjustment resources such as gas units, load demand response, electrochemical energy storage, etc., takes the adjustment cost of flexible resources within the province and inter-provincial interconnection lines, and minimizes the abandonment of new energy and load shedding as the optimization goals, takes into account the system constraints and operation constraints of various types of power sources, and establishes a flexible adjustment resource optimization scheduling model, which realizes the accurate simulation of the operation mode of high-proportion new energy power system and conforms to the actual scheduling process.

[0166] Exemplary Devices

[0167] Figure 2 Schematic diagram of the structure of a power system flexible adjustment resource optimization dispatching device provided by an exemplary embodiment of the present invention. Figure 2 As shown, the device 200 includes:

[0168] A 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 according to the flexible adjustment resource model;

[0170] The determination module 230 is used to perform linear programming solution on the flexible resource optimization dispatch model according to the collected real-time power system data, and determine the optimization dispatch strategy results of the power system at each moment.

[0171] Optionally, the flexible adjustment resource model includes: a gas unit model, a load demand response model and an electrochemical energy storage model.

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

[0173] The output constraints are:

[0174]

[0175] Where: is the output of gas generator set i in period t; is the spare capacity of gas generator set i in period t; is the startup variable of gas generator set i in period t; Ω Gas Assemble for gas units; is the minimum / maximum output of gas generator set i;

[0176] The shortest start-stop time constraint is:

[0177]

[0178] Where: T i on / T i off is the minimum start / shutdown time of gas generator set i; is the capacity of the gas generator set i in the start-up state in time period t, which is 1 if it changes from shutdown to start-up, otherwise it is 0; is the shutdown capacity of gas generator set i in period t, which is 1 if the unit changes from startup to shutdown, otherwise it is 0;

[0179] The formula for calculating the call cost is:

[0180] The call cost takes into account the fuel cost and start-up and shutdown costs of the unit:

[0181]

[0182] Where: is the total call cost of all gas units in period t; is the startup / shutdown cost of gas unit i; is the fuel cost function of gas generator set i;

[0183] The segment power constraint is:

[0184]

[0185] Where: is the upper limit of the output of the jth stage of gas generator set i; It is the lower limit of the output of the jth section of gas generator set i.

[0186] Optionally, the load demand response model includes a maximum response capacity constraint, a state logic constraint, a response time constraint, a response number constraint, and calculates the call cost of the load demand response, wherein:

[0187] The maximum responsiveness constraint is:

[0188]

[0189] Where: Ω D is the load collection; is the maximum response capacity of load i, and 0 means that the load does not participate in demand response; is the annual maximum load power of load i; is the load curve value of load i in time period t; is the response power of load i in time period t; The maximum response ratio for emergency demand response; is the state variable of the load participating in demand response. If the value is 1, it means that load i participates in demand response in time period t, otherwise it does not participate; is the state variable for starting demand response. If the value is 1, it means that the load i starts participating in demand response in time period t; is the state variable for ending demand response. If the value is 1, it means that load i ends participating in demand response in time period t.

[0190] The state logic constraints are:

[0191]

[0192] The response time constraints are:

[0193]

[0194] The response times are constrained as:

[0195]

[0196] The load response cost is:

[0197]

[0198] Where: is the maximum / minimum duration for load i to participate in demand response; is the maximum response number of load i within the calculation time scale; c IDR Subsidy per unit response power.

[0199] Optionally, the electrochemical energy storage model includes energy storage charging and discharging power constraints, energy storage quantity constraints, energy storage charging and discharging state logic constraints, and energy storage charging and discharging call costs, wherein:

[0200] The energy storage charging and discharging power constraints are:

[0201]

[0202] Where: Ω ESS Energy storage collection for the system; is the charge / discharge state variable of energy storage i in time period t; is the maximum charge / discharge power of energy storage i; is the charging / discharging power of energy storage i in period t;

[0203] The energy storage capacity constraint is:

[0204]

[0205] Where: is the maximum / minimum storage capacity of energy storage i. Since some types of energy storage require a minimum storage capacity to ensure the stability of the medium, it is necessary to set a minimum storage capacity; is the charge / discharge efficiency of energy storage i; is the storage capacity of energy storage i in time period t;

[0206] The logical constraints of energy storage charging and discharging states are:

[0207]

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

[0209]

[0210] Where: is the unit cost of energy storage charging / discharging during period t.

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

[0212]

[0213] Where: is the adjustment cost of tie line i at time t; To save the adjustment cost of flexible resource i at time t; To save the power abandoned by renewable energy i and the power cut off by load i at time t; β RE,Curt / βL,Curt It is the penalty cost of abandoning electricity of new energy / the penalty cost of shedding load;

[0214] The coal power output constraints are:

[0215]

[0216] The output constraints of new energy are:

[0217]

[0218] Where: is the actual output of the renewable energy station i at time t; is the available output of renewable energy station i at time t;

[0219] The power balance constraint is:

[0220]

[0221] Where: is the load value of load i at time t in province a;

[0222] The alternative constraints are:

[0223]

[0224] Where: α RS is the system's reserve factor;

[0225] The section constraints are:

[0226]

[0227] Where: is the upper limit of the transmission capacity of section i.

[0228] Exemplary Electronic Devices

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

[0230] The processor 31 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities 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, and the computer program product 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 (cache), etc. 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 run 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, which are 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, and the like.

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

[0234] Of course, to simplify, Figure 3 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

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

[0236] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above-mentioned "Exemplary Method" section of this specification.

[0237] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0238] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0239] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0240] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details disclosed above are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0241] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0242] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0243] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present invention are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0244] It should also be noted that in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but in accordance with the widest range consistent with the principles and novel features disclosed here.

[0245] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for optimizing the dispatching of flexible adjustment resources in a power system, characterized in that: include: Constructing a flexible regulation resource model for the power system, wherein the flexible regulation resource model includes a flexible regulation resource operation model and a call cost calculation method; According to the flexible regulation resource model, a flexible regulation resource optimization scheduling model of the power system is constructed; The linear programming solution is performed on the flexible resource optimization dispatching model according to the collected real-time power system data to determine the optimization dispatching strategy results of the power system at each moment.

2. The method according to claim 1, characterized in that The flexible adjustment resource model includes: gas unit model, load demand response model and electrochemical energy storage model.

3. The method according to claim 2, characterized in that The gas unit model includes: output constraint, shortest start-up and shutdown time constraint, call cost and segmented power constraint, among which, The output constraints are: Where: is the output of gas generator set i in period t; is the spare capacity of gas generator set i in period t; is the startup variable of gas generator set i in period t; Ω Gas Assemble for gas units; is the minimum / maximum output of gas generator set i; The shortest start-stop time constraint is: Where: T i on / T i off is the minimum start / shutdown time of gas generator set i; is the capacity of the gas generator set i in the start-up state in time period t, which is 1 if it changes from shutdown to start-up, otherwise it is 0; is the shutdown capacity of gas generator set i in period t, which is 1 if the unit changes from startup to shutdown, otherwise it is 0; The calculation formula for the call cost is: The call cost takes into account the fuel cost and start-up and shutdown costs of the unit: Where: is the total call cost of all gas units in period t; is the startup / shutdown cost of gas unit i; is the fuel cost function of gas generator set i; The segment power constraint is: Where: is the upper limit of the output of the jth stage of gas generator set i; It is the lower limit of the output of the jth section of gas generator set i.

4. The method according to claim 2, characterized in that: The load demand response model includes a maximum response capacity constraint, a state logic constraint, a response time constraint, a response number constraint, and calculates the call cost of the load demand response, wherein: The maximum responsiveness constraint is: Where: Ω D is the load collection; is the maximum response capacity of load i, and 0 means that the load does not participate in demand response; is the annual maximum load power of load i; is the load curve value of load i in time period t; is the response power of load i in time period t; The maximum response ratio for emergency demand response; is the state variable of the load participating in demand response. If the value is 1, it means that load i participates in demand response in time period t, otherwise it does not participate; is the state variable for starting demand response. If the value is 1, it means that the load i starts participating in demand response in time period t; is the state variable for ending demand response. If the value is 1, it means that load i ends participating in demand response in time period t. The state logic constraints are: The response time constraint is: The response number constraint is: The load response cost is: Where: is the maximum / minimum duration for load i to participate in demand response; is the maximum response number of load i within the calculation time scale; c IDR Subsidy per unit response power.

5. The method according to claim 2, characterized in that: The electrochemical energy storage model includes energy storage charging and discharging power constraints, energy storage quantity constraints, energy storage charging and discharging state logic constraints, and energy storage charging and discharging call costs, wherein: The energy storage charging and discharging power constraint is: Where: Ω ESS Energy storage collection for the system; is the charge / discharge state variable of energy storage i in time period t; is the maximum charge / discharge power of energy storage i; is the charging / discharging power of energy storage i in period t; The energy storage capacity constraint is: Where: is the maximum / minimum storage capacity of energy storage i. Since some types of energy storage require a minimum storage capacity to ensure the stability of the medium, it is necessary to set a minimum storage capacity; is the charge / discharge efficiency of energy storage i; is the storage capacity of energy storage i in time period t; The energy storage charging and discharging state logic constraints are: The energy storage charging and discharging call cost is: Where: is the unit cost of energy storage charging / discharging during period t.

6. The method according to claim 1, characterized in that The objective function of the scheduling optimization model is: Where: is the adjustment cost of tie line i at time t; To save the adjustment cost of flexible resource i at time t; To save the power abandoned by renewable energy i and the power cut off by load i at time t; β RE,Curt / β L,Curt It is the penalty cost of abandoning electricity of new energy / the penalty cost of shedding load; The coal power output constraints are: The output constraints of new energy are: Where: is the actual output of the renewable energy station i at time t; is the available output of renewable energy station i at time t; The power balance constraint is: Where: is the load value of load i at time t in province a; The alternative constraints are: Where: α RS is the system's reserve factor; The section constraints are: Where: is the upper limit of the transmission capacity of section i.

7. A power system flexible adjustment resource optimization dispatching device, characterized in that: include: A first construction module 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; A second construction module is used to construct a flexible adjustment resource optimization scheduling model of the power system according to the flexible adjustment resource model; The determination module is used to perform linear programming solution on the flexible resource optimization dispatching model according to the collected real-time power system data, and determine the optimization dispatching strategy results of the power system at each moment.

8. The device according to claim 7, characterized in that The flexible adjustment resource model includes: gas unit model, load demand response model and electrochemical energy storage model.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 6.

10. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1 to 6.

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