Source-load-storage collaborative optimization scheduling method and system based on double-layer optimization model

Abstract

The invention discloses a source-load-storage collaborative optimization scheduling method and system based on a double-layer optimization model, and the method comprises the steps: obtaining corresponding power data of a power system in each time period, the power data comprising power data and load data; inputting the power data into a preset upper-layer optimization model, and performing optimization by taking the lowest total power generation cost as a target to obtain a first output value corresponding to each time period of the resource cluster; and inputting the first output value into a preset lower-layer optimization model, performing optimization by taking the lowest cost of each power generation unit as a target to obtain a second output value corresponding to each power generation unit in each time period, and controlling each power generation unit to generate power based on the second output value. According to the invention, efficient collaborative scheduling of the source-load-storage resources can be realized, and the stability, flexibility and economy of the power system are improved.

Description

Technical Field

[0001] The present application relates to the field of power dispatching automation, and in particular to a source-load-storage collaborative optimization dispatching method and system based on a double-layer optimization model. Background Art

[0002] Traditional units are the main components of the power system, including thermal power units, hydropower units and nuclear power units. These units provide stable power output for the power system by converting energy (such as coal, hydropower, nuclear energy, etc.) into electricity. Resource clusters usually refer to the centralized management and optimized scheduling of multiple energy resources or power generation units. At present, the combination of traditional units and resource clusters can give full play to their respective advantages. Among them, traditional units provide stable power output, while resource clusters improve the flexibility and reliability of the power system through optimized scheduling and complementary utilization. However, with the rapid development and widespread application of new energy technologies, the power system is facing unprecedented challenges of uncertainty and volatility, which puts higher requirements on the real-time scheduling and resource optimization allocation of the power system. New energy power generation, due to its inherent intermittent and unpredictable nature, brings significant volatility to the power supply, and the demand on the power load side is also changing. In this context, energy storage systems, as a key means of regulation, play a vital role in reconciling the contradiction between supply and demand and enhancing system stability.

[0003] However, the current dispatching modes are mostly limited to the optimization management of a single resource, and fail to fully consider and realize the coordinated dispatching among the power generation end (source), the load end (load) and the energy storage system (storage). This limitation restricts the improvement of resource utilization efficiency, weakens the overall adjustment flexibility of the system, and may even pose a threat to the safety and reliability of the power system due to the lack of sufficient response speed and adjustment ability. Therefore, how to comprehensively consider the characteristics and real-time changes of power generation, load and energy storage resources, realize the optimal dispatch of different resources, and then provide guarantees for the stability, flexibility and economy of the power system.

[0004] Application Contents

[0005] The present application provides a source-load-storage collaborative optimization scheduling method based on a two-layer optimization model, which can achieve efficient collaborative scheduling of source-load-storage resources and improve the stability, flexibility and economy of the power system.

[0006] In a first aspect, the present application provides a source-load-storage collaborative optimization scheduling method based on a two-layer optimization model, including:

[0007] Acquiring power data corresponding to the power system in each time period, wherein the power data includes power data and load data;

[0008] Input the power data into a preset upper-level optimization model, optimize with the goal of minimizing the total power generation cost, and obtain the first output value corresponding to the resource cluster in each time period;

[0009] The first output value is input into a preset lower-level optimization model, and optimization is performed with the goal of minimizing the cost of each power generation unit to obtain a second output value corresponding to each power generation unit in each time period, and each power generation unit is controlled to generate electricity based on the second output value.

[0010] By acquiring the power data corresponding to the power system in each time period, the embodiment of the present application can accurately grasp the characteristics and real-time changes of power generation and load, and facilitate the subsequent input of the upper optimization model to obtain the power output value with the lowest total power generation cost in the resource cluster; by inputting the power data into the preset upper optimization model, the power output value with the lowest total power generation cost in the resource cluster can be obtained, which can improve the flexibility and economy of the power system; by inputting the first output value into the preset lower optimization model, the second output value with the lowest cost of each power generation unit can be accurately obtained, and the first output value of the resource cluster can be reasonably allocated to each power generation unit, and each power generation unit can be controlled to generate electricity based on the second output value, which can ensure the efficient use of resources and improve the economy and stability of the power system. Compared with the prior art, the present application can comprehensively consider the characteristics and real-time changes of power generation, load and energy storage resources, realize the optimal scheduling of different resources, and thus provide guarantee for the stability, flexibility and economy of the power system.

[0011] Furthermore, before inputting the power data into a preset upper optimization model, the method further includes:

[0012] Obtain the power generation cost, output upper and lower limits, and ramp efficiency upper and lower limits corresponding to traditional units and resource clusters respectively;

[0013] According to the power data, the power generation cost, the output upper and lower limits, and the climbing efficiency upper and lower limits, an objective function and constraints with the lowest total power generation cost are established, wherein the constraints include: unit output constraints, climbing rate constraints, power constraints, and load constraints.

[0014] In this way, based on the power data, the power generation cost, the upper and lower limits of the output, and the upper and lower limits of the climbing efficiency, an objective function and constraints with the lowest total power generation cost are established, so that an upper-level optimization model that considers the characteristics and real-time changes of power generation, load and energy storage resources can be accurately obtained, which facilitates the subsequent acquisition of the power generation output value with the lowest total power generation cost in the resource cluster, thereby improving the flexibility and economy of the power system.

[0015] Furthermore, the expression of the upper optimization model is specifically:

[0016] The objective function is:

[0017]

[0018] The unit output constraint is:

[0019]

[0020] The climbing rate constraint is:

[0021]

[0022]

[0023] The power constraint is:

[0024]

[0025] The load constraints are:

[0026]

[0027] Where T is the total power generation cost; N CG and N VPP are the number of traditional units and resource clusters respectively; c m and c n They are the power generation costs corresponding to traditional units and resource clusters respectively; and are the output values ​​of the traditional units and resource cluster at time t respectively; P L t is the load data at time t; and They are the lower and upper output limits of traditional units respectively; and They are the lower and upper output limits of the resource cluster respectively; and are the lower limit of the ramp rate and the lower limit of the ramp rate of the traditional unit respectively; Δt is the length of the scheduling period; and are the upper and lower limits of the ramp rate of the resource cluster respectively; N bus and N branch are the node set and line set in the power grid respectively; D l-j is the injection power transfer factor of node j to line l; is the injected power of node j at time t; Indicates the maximum power of line l.

[0028] Furthermore, the expression of the lower layer optimization model is specifically:

[0029]

[0030] Where T is the cost of each power generation unit; N DER 、N ESS and N IL are DER, energy storage equipment and interruptible load respectively; c DER,i 、c ESS,j and c IL,l They are the power generation cost functions corresponding to DER, energy storage equipment and interruptible loads respectively; and are the corresponding output values ​​of the i-th DER, the j-th energy storage device and the l-th interruptible load at time t; and They are the lower and upper output limits corresponding to DER, energy storage equipment and interruptible load respectively; and is the lower and upper limits of the energy storage device’s power, Represents the cross-time domain constraints of the energy storage device; represents the internal load of the virtual power plant; Indicates the first output value of the nth resource cluster at time t.

[0031] Furthermore, the source-load-storage collaborative optimization scheduling method based on the two-layer optimization model also includes taking a relaxation condition for the equality constraint of the lower layer optimization model. After taking the relaxation condition, the expression of the lower layer optimization model is specifically:

[0032]

[0033] Where T is the cost of each power generation unit; N DER 、N ESS and N IL are DER, energy storage equipment and interruptible load respectively; c DER,i 、c ESS,j and c IL,l They are the power generation cost functions corresponding to DER, energy storage equipment and interruptible loads respectively; and are the corresponding output values ​​of the i-th DER, the j-th energy storage device and the l-th interruptible load at time t; and They represent the power grid raising and lowering the reserve price at time t, respectively, which is determined by the actual dispatching cost of the power grid; and are the introduced slack variables, representing the under-supply power and over-supply power of the resource cluster respectively; and They are the lower and upper output limits corresponding to DER, energy storage equipment and interruptible load respectively; and is the lower and upper limits of the energy storage device’s power, Represents the cross-time domain constraints of the energy storage device; represents the internal load of the virtual power plant; Indicates the first output of the nth resource cluster at time t.

[0034] In a second aspect, the present application provides a source-load-storage collaborative optimization scheduling system based on a two-layer optimization model, comprising: an acquisition module, a first processing module, and a second processing module;

[0035] The acquisition module is used to acquire power data corresponding to the power system in each time period, wherein the power data includes power data and load data;

[0036] The first processing module is used to input the power data into a preset upper-level optimization model, optimize with the goal of minimizing the total power generation cost, and obtain the first output value corresponding to the resource cluster in each time period;

[0037] The second processing module is used to input the first output value into a preset lower-level optimization model, optimize with the lowest cost of each power generation unit as the goal, obtain the second output value corresponding to each power generation unit in each time period, and control each power generation unit to generate electricity based on the second output value.

[0038] By acquiring the power data corresponding to the power system in each time period, the embodiment of the present application can accurately grasp the characteristics and real-time changes of power generation and load, and facilitate the subsequent input of the upper optimization model to obtain the power output value with the lowest total power generation cost in the resource cluster; by inputting the power data into the preset upper optimization model, the power output value with the lowest total power generation cost in the resource cluster can be obtained, which can improve the flexibility and economy of the power system; by inputting the first output value into the preset lower optimization model, the second output value with the lowest cost of each power generation unit can be accurately obtained, and the first output value of the resource cluster can be reasonably allocated to each power generation unit, and each power generation unit can be controlled to generate electricity based on the second output value, which can ensure the efficient use of resources and improve the economy and stability of the power system. Compared with the prior art, the present application can comprehensively consider the characteristics and real-time changes of power generation, load and energy storage resources, realize the optimal scheduling of different resources, and thus provide guarantee for the stability, flexibility and economy of the power system.

[0039] Furthermore, the source-load-storage collaborative optimization scheduling system based on the double-layer optimization model further includes a construction model, the construction model includes: an acquisition unit and a construction unit;

[0040] The acquisition unit is used to respectively acquire the power generation cost, output upper and lower limits, and ramp efficiency upper and lower limits corresponding to the traditional units and the resource cluster;

[0041] The construction unit is used to establish an objective function and constraints with the lowest total power generation cost based on the power data, the power generation cost, the output upper and lower limits, and the climbing efficiency upper and lower limits, wherein the constraints include: unit output constraints, climbing rate constraints, power constraints, and load constraints.

[0042] Furthermore, the expression of the upper optimization model is specifically:

[0043] The objective function is:

[0044]

[0045] The unit output constraint is:

[0046]

[0047] The climbing rate constraint is:

[0048]

[0049]

[0050] The power constraint is:

[0051]

[0052] The load constraints are:

[0053]

[0054] Where T is the total power generation cost; N CG and N VPP are the number of traditional units and resource clusters respectively; c m and c n They are the power generation costs corresponding to traditional units and resource clusters respectively; and are the output values ​​of traditional units and resource clusters at time t respectively; is the load data at time t; and They are the lower and upper output limits of traditional units respectively; and They are the lower and upper output limits of the resource cluster respectively; and are the lower limit of the ramp rate and the lower limit of the ramp rate of the traditional unit respectively; Δt is the length of the scheduling period; and are the upper and lower limits of the ramp rate of the resource cluster respectively; N bus and N branchare the node set and line set in the power grid respectively; D l-j is the injection power transfer factor of node j to line l; is the injected power of node j at time t; Indicates the maximum power of line l.

[0055] Furthermore, the expression of the lower layer optimization model is specifically:

[0056]

[0057] Where T is the cost of each power generation unit; N DER 、N ESS and N IL are DER, energy storage equipment and interruptible load respectively; c DER,i 、c ESS,j and c IL,l They are the power generation cost functions corresponding to DER, energy storage equipment and interruptible loads respectively; and are the corresponding output values ​​of the i-th DER, the j-th energy storage device and the l-th interruptible load at time t; and They are the lower and upper output limits corresponding to DER, energy storage equipment and interruptible load respectively; and is the lower and upper limits of the energy storage device’s power, Represents the cross-time domain constraints of the energy storage device; represents the internal load of the virtual power plant; Indicates the first output value of the nth resource cluster at time t.

[0058] Furthermore, it also includes taking a relaxation condition for the equality constraint of the lower optimization model, and after taking the relaxation condition, the expression of the lower optimization model is specifically:

[0059]

[0060] Where T is the cost of each power generation unit; N DER 、N ESS and N IL are DER, energy storage equipment and interruptible load respectively; c DER,i 、c ESS,j and c IL,l They are the power generation cost functions corresponding to DER, energy storage equipment and interruptible loads respectively; and are the corresponding output values ​​of the i-th DER, the j-th energy storage device and the l-th interruptible load at time t; and They represent the power grid raising and lowering the reserve price at time t, respectively, which is determined by the actual dispatching cost of the power grid; and are the introduced slack variables, representing the under-supply power and over-supply power of the resource cluster respectively; and They are the lower and upper output limits corresponding to DER, energy storage equipment and interruptible load respectively; and is the lower and upper limits of the energy storage device’s power, Represents the cross-time domain constraints of the energy storage device; represents the internal load of the virtual power plant; Indicates the first output value of the nth resource cluster at time t. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of an embodiment of a source-load-storage collaborative optimization scheduling method based on a double-layer optimization model provided by the present application;

[0062] Figure 2 It is a structural diagram of an embodiment of a source-load-storage collaborative optimization scheduling system based on a two-layer optimization model provided in this application. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0064] It should be understood that the step numbers used in this article are only for the convenience of description and are not intended to limit the order in which the steps are executed.

[0065] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0066] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0067] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0068] Traditional units (thermal power, hydropower, nuclear power, etc.) are the main components of the power system, providing stable power output through energy conversion, while resource clusters centrally manage and optimize the dispatch of multiple energy resources. The combination of the two can give full play to their respective advantages and improve the flexibility and reliability of the system. However, the rapid development of new energy technologies has brought uncertainty and volatility challenges to the power system, and has put forward higher requirements for real-time dispatch and resource optimization. The inherent intermittent and unpredictable nature of new energy power generation and the changes in load demand make energy storage systems crucial in reconciling supply and demand contradictions and enhancing system stability. The current dispatching mode is mostly limited to single resource optimization, and fails to achieve coordinated dispatching of power generation, load and energy storage systems. This limitation limits resource utilization efficiency, weakens system regulation flexibility, and may even threaten the safety and reliability of the power system. Therefore, it is necessary to comprehensively consider the characteristics and real-time changes of all resources to achieve optimal dispatching to ensure the stability, flexibility and economy of the power system.

[0069] Next, the nouns involved in this application are analyzed:

[0070] Artificial intelligence (AI) is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. AI is a branch of computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing and expert systems. AI can simulate the information process of human consciousness and thinking. AI is also a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0071] Based on this, the embodiments of the present application provide a source-load-storage collaborative optimization scheduling method and system based on a two-layer optimization model, which can achieve efficient collaborative scheduling of source-load-storage resources and improve the stability, flexibility and economy of the power system.

[0072] The source-load-storage collaborative optimization scheduling method and system based on a two-layer optimization model provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the source-load-storage collaborative optimization scheduling method based on a two-layer optimization model in the embodiments of the present application is described.

[0073] The source-load-storage collaborative optimization scheduling method based on a two-layer optimization model provided in the embodiment of the present application relates to the field of power dispatching automation. The source-load-storage collaborative optimization scheduling method based on a two-layer optimization model provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server side, and can also be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the source-load-storage collaborative optimization scheduling method based on a two-layer optimization model, etc., but is not limited to the above forms.

[0074] The present application can also be used in numerous general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0075] Embodiment 1

[0076] Please refer to Figure 1 , Figure 1 It is a flow chart of an embodiment of a source-load-storage collaborative optimization scheduling method based on a two-layer optimization model provided by the present application, including steps S101 to S103;

[0077] Step S101, acquiring power data corresponding to the power system in each time period, wherein the power data includes power data and load data;

[0078] It is understandable that the power data corresponding to each time period can be obtained through a data acquisition system and at a preset sampling frequency, but is not limited to, wherein the power data includes but is not limited to power data and load data, wherein the sampling frequency can be daily, weekly, monthly, and every three months, etc. After the power data and load data are obtained, the power data needs to be cleaned and verified to remove abnormal values ​​and erroneous data to ensure the accuracy of the data, and the cleaned power data needs to be formatted for subsequent analysis and optimization model input.

[0079] Step S102, inputting the power data into a preset upper-level optimization model, optimizing with the goal of minimizing the total power generation cost, and obtaining the first output value corresponding to the resource cluster in each time period;

[0080] It is understandable that before the power data is input into the preset upper optimization model, an upper optimization model needs to be constructed. Specifically, first, it is necessary to clarify that the optimization goal is to minimize the total power generation cost, where the total power generation cost includes the power generation cost of traditional units and the power generation cost of resource clusters; second, obtain the power generation cost, output upper and lower limits, and climbing efficiency upper and lower limits corresponding to traditional units and resource clusters respectively; third, according to the power data, the power generation cost, the output upper and lower limits, and the climbing efficiency upper and lower limits, select a suitable optimization algorithm to establish the objective function and constraints with the lowest total power generation cost, where the constraints include: unit output constraints, climbing rate constraints, power constraints, and load constraints.

[0081] In this way, based on the power data, the power generation cost, the upper and lower limits of the output, and the upper and lower limits of the climbing efficiency, an objective function and constraints with the lowest total power generation cost are established, so that an upper-level optimization model that considers the characteristics and real-time changes of power generation, load and energy storage resources can be accurately obtained, which facilitates the subsequent acquisition of the power generation output value with the lowest total power generation cost in the resource cluster, thereby improving the flexibility and economy of the power system.

[0082] It should be noted that the power generation costs corresponding to traditional units and resource clusters can be obtained by, but not limited to, consulting the public information of the power company or obtaining them through cost estimation; the corresponding upper and lower limits of output can be obtained by, but not limited to, consulting the technical specifications or analyzing the actual operating data of traditional units and resource clusters; the upper and lower limits of climbing efficiency corresponding to traditional units and resource clusters can be obtained by, but not limited to, consulting the performance parameter tables of traditional units and resource clusters or obtaining them through actual operation tests. These acquisition methods are not the focus of this application and will not be expanded here.

[0083] After the upper-level optimization model is constructed, the power data, i.e., power data and load data, need to be input as input parameters into the upper-level optimization model, and then a suitable solver (such as the optimization library in MATLAB, Python, etc.) is selected to solve the upper-level optimization model to obtain the minimum total power generation cost that meets the constraints, and determine the corresponding output value of the corresponding traditional unit based on the objective function. and the first output value of the resource cluster The expression of the upper optimization model is specifically:

[0084] The objective function is:

[0085]

[0086] The unit output constraint is:

[0087]

[0088] The climbing rate constraint is:

[0089]

[0090]

[0091] The power constraint is:

[0092]

[0093] The load constraints are:

[0094]

[0095] Where T is the total power generation cost; N CG and N VPP are the number of traditional units and resource clusters respectively; c m and c n They are the power generation costs corresponding to traditional units and resource clusters respectively; and are the output values ​​of traditional units and resource clusters at time t respectively; is the load data at time t; and They are the lower and upper output limits of traditional units respectively; and They are the lower and upper output limits of the resource cluster respectively; and are the lower limit of the ramp rate and the lower limit of the ramp rate of the traditional unit respectively; Δt is the length of the scheduling period; and are the upper and lower limits of the ramp rate of the resource cluster respectively; Nbus and N branch are the node set and line set in the power grid respectively; D l-j is the injection power transfer factor of node j to line l; is the injected power of node j at time t; Indicates the maximum power of line l.

[0096] Step S103, inputting the first output value into a preset lower-level optimization model, optimizing with the goal of minimizing the cost of each power generation unit, obtaining the second output value corresponding to each power generation unit in each time period, and controlling each power generation unit to generate electricity based on the second output value.

[0097] It is understandable that after obtaining the first output value of the resource cluster Afterwards, since the resource cluster is a centralized management of multiple energy resources or power generation units, it is necessary to calculate the first output value based on the resource cluster. To determine the output value corresponding to each power generation unit, that is, the first output value of the resource cluster Reasonably allocate to each power generation unit to ensure efficient use of resources, where each power generation unit includes but is not limited to DER, energy storage equipment and interruptible loads. Specifically, first, it is necessary to obtain the power generation cost, output upper and lower limits and power upper and lower limits corresponding to each power generation unit, and select a suitable optimization algorithm based on the power generation cost, output upper and lower limits and power upper and lower limits corresponding to each power generation unit to establish the objective function and constraints with the lowest power generation cost of each power generation unit, where the constraints include: power generation unit output constraints, power constraints and load constraints, and the expression of the lower optimization model is specifically:

[0098]

[0099] Where T is the cost of each power generation unit; N DER 、N ESS and N IL are DER, energy storage equipment and interruptible load respectively; c DER,i 、c ESS,j and c IL,l They are the power generation cost functions corresponding to DER, energy storage equipment and interruptible loads respectively; and are the corresponding output values ​​of the i-th DER, the j-th energy storage device and the l-th interruptible load at time t; and They are the lower and upper output limits corresponding to DER, energy storage equipment and interruptible load respectively; and is the lower and upper limits of the energy storage device’s power, Represents the cross-time domain constraints of the energy storage device; represents the internal load of the virtual power plant; Indicates the first output value of the nth resource cluster at time t.

[0100] Considering that the distributed resource cluster cannot complete the output according to the planned power, the equality constraints in the lower model can be relaxed. After the relaxation conditions are taken, the expression of the lower optimization model is specifically as follows:

[0101]

[0102] Where T is the cost of each power generation unit; N DER 、N ESS and N IL are DER, energy storage equipment and interruptible load respectively; c DER,i 、c ESS,j and c IL,l They are the power generation cost functions corresponding to DER, energy storage equipment and interruptible loads respectively; and are the corresponding output values ​​of the i-th DER, the j-th energy storage device and the l-th interruptible load at time t; and They represent the power grid raising and lowering the reserve price at time t, respectively, which is determined by the actual dispatching cost of the power grid; and are the introduced slack variables, representing the under-supply power and over-supply power of the resource cluster respectively; and They are the lower and upper output limits corresponding to DER, energy storage equipment and interruptible load respectively; and is the lower and upper limits of the energy storage device’s power, Represents the cross-time domain constraints of the energy storage device; represents the internal load of the virtual power plant; Indicates the first output value of the nth resource cluster at time t.

[0103] After the lower-level optimization model is constructed, the first output value of the resource cluster needs to be used as an input parameter and input into the lower-level optimization model. Then, a suitable solver (such as the optimization library in MATLAB or Python, etc.) is selected to solve the lower-level optimization model to obtain the minimum power generation cost of each power generation unit under the constraint conditions, and determine the second output value of each corresponding distributed power generation unit in each time period based on the objective function. and The second output value is converted into a control instruction and sent to each power generation unit to control each power generation unit to generate electricity.

[0104] By acquiring the power data corresponding to the power system in each time period, the embodiment of the present application can accurately grasp the characteristics and real-time changes of power generation and load, and facilitate the subsequent input of the upper optimization model to obtain the power output value with the lowest total power generation cost in the resource cluster; by inputting the power data into the preset upper optimization model, the power output value with the lowest total power generation cost in the resource cluster can be obtained, which can improve the flexibility and economy of the power system; by inputting the first output value into the preset lower optimization model, the second output value with the lowest cost of each power generation unit can be accurately obtained, and the first output value of the resource cluster can be reasonably allocated to each power generation unit, and each power generation unit can be controlled to generate electricity based on the second output value, which can ensure the efficient use of resources and improve the economy and stability of the power system. Compared with the prior art, the present application can comprehensively consider the characteristics and real-time changes of power generation, load and energy storage resources, realize the optimal scheduling of different resources, and thus provide guarantee for the stability, flexibility and economy of the power system.

[0105] Embodiment 2

[0106] Please refer to Figure 2 , Figure 2 It is a structural schematic diagram of an embodiment of a source-load-storage collaborative optimization scheduling system based on a double-layer optimization model provided by the present application, including an acquisition module 100, a first processing module 200 and a second processing module 300;

[0107] The acquisition module 100 is used to acquire power data corresponding to the power system in each time period, wherein the power data includes power data and load data;

[0108] It is understandable that the power data corresponding to each time period can be obtained through a data acquisition system and at a preset sampling frequency, but is not limited to, wherein the power data includes but is not limited to power data and load data, wherein the sampling frequency can be daily, weekly, monthly, and every three months, etc. After the power data and load data are obtained, the power data needs to be cleaned and verified to remove abnormal values ​​and erroneous data to ensure the accuracy of the data, and the cleaned power data needs to be formatted for subsequent analysis and optimization model input.

[0109] The first processing module 200 is used to input the power data into a preset upper-level optimization model, optimize with the goal of minimizing the total power generation cost, and obtain the first output value corresponding to the resource cluster in each time period;

[0110] Among them, the source-load-storage collaborative optimization scheduling system based on the two-layer optimization model also includes a construction model, and the construction model includes: an acquisition unit and a construction unit; specifically, the acquisition unit is used to respectively obtain the power generation cost, output upper and lower limits, and climbing efficiency upper and lower limits corresponding to the traditional units and resource clusters; specifically, the construction unit is used to establish an objective function and constraint conditions with the lowest total power generation cost based on the power data, the power generation cost, the output upper and lower limits, and the climbing efficiency upper and lower limits, wherein the constraint conditions include: unit output constraints, climbing rate constraints, power constraints, and load constraints.

[0111] It is understandable that before the power data is input into the preset upper optimization model, an upper optimization model needs to be constructed. Specifically, first, it is necessary to clarify that the optimization goal is to minimize the total power generation cost, where the total power generation cost includes the power generation cost of traditional units and the power generation cost of resource clusters; second, obtain the power generation cost, output upper and lower limits, and climbing efficiency upper and lower limits corresponding to traditional units and resource clusters respectively; third, according to the power data, the power generation cost, the output upper and lower limits, and the climbing efficiency upper and lower limits, select a suitable optimization algorithm to establish the objective function and constraints with the lowest total power generation cost, where the constraints include: unit output constraints, climbing rate constraints, power constraints, and load constraints.

[0112] In this way, based on the power data, the power generation cost, the upper and lower limits of the output, and the upper and lower limits of the climbing efficiency, an objective function and constraints with the lowest total power generation cost are established, so that an upper-level optimization model that considers the characteristics and real-time changes of power generation, load and energy storage resources can be accurately obtained, which facilitates the subsequent acquisition of the power generation output value with the lowest total power generation cost in the resource cluster, thereby improving the flexibility and economy of the power system.

[0113] It should be noted that the power generation costs corresponding to traditional units and resource clusters can be obtained by, but not limited to, consulting the public information of the power company or obtaining them through cost estimation; the corresponding upper and lower limits of output can be obtained by, but not limited to, consulting the technical specifications or analyzing the actual operating data of traditional units and resource clusters; the upper and lower limits of climbing efficiency corresponding to traditional units and resource clusters can be obtained by, but not limited to, consulting the performance parameter tables of traditional units and resource clusters or obtaining them through actual operation tests. These acquisition methods are not the focus of this application and will not be expanded here.

[0114] After the upper-level optimization model is constructed, the power data, i.e., power data and load data, need to be input as input parameters into the upper-level optimization model, and then a suitable solver (such as the optimization library in MATLAB, Python, etc.) is selected to solve the upper-level optimization model to obtain the minimum total power generation cost that meets the constraints, and determine the corresponding output value of the corresponding traditional unit based on the objective function. and the first output value of the resource cluster The expression of the upper optimization model is specifically:

[0115] The objective function is:

[0116]

[0117] The unit output constraint is:

[0118]

[0119] The climbing rate constraint is:

[0120]

[0121]

[0122] The power constraint is:

[0123]

[0124] The load constraints are:

[0125]

[0126] Where T is the total power generation cost; N CG and N VPP are the number of traditional units and resource clusters respectively; c m and c n They are the power generation costs corresponding to traditional units and resource clusters respectively; and are the output values ​​of traditional units and resource clusters at time t respectively; is the load data at time t; and They are the lower and upper output limits of traditional units respectively; and They are the lower and upper output limits of the resource cluster respectively; and are the lower limit of the ramp rate and the lower limit of the ramp rate of the traditional unit respectively; Δt is the length of the scheduling period; and are the upper and lower limits of the ramp rate of the resource cluster respectively; N bus and N branch are the node set and line set in the power grid respectively; D l-j is the injection power transfer factor of node j to line l; is the injected power of node j at time t; Indicates the maximum power of line l.

[0127] The second processing module 300 is used to input the first output value into a preset lower-level optimization model, optimize with the lowest cost of each power generation unit as the goal, obtain the second output value corresponding to each power generation unit in each time period, and control each power generation unit to generate electricity based on the second output value.

[0128] It is understandable that after obtaining the first output value of the resource cluster Afterwards, since the resource cluster is a centralized management of multiple energy resources or power generation units, it is necessary to calculate the first output value based on the resource cluster. To determine the output value corresponding to each power generation unit, that is, the first output value of the resource cluster Reasonably allocate to each power generation unit to ensure efficient use of resources, where each power generation unit includes but is not limited to DER, energy storage equipment and interruptible loads. Specifically, first, it is necessary to obtain the power generation cost, output upper and lower limits and power upper and lower limits corresponding to each power generation unit, and select a suitable optimization algorithm based on the power generation cost, output upper and lower limits and power upper and lower limits corresponding to each power generation unit to establish the objective function and constraints with the lowest power generation cost of each power generation unit, where the constraints include: power generation unit output constraints, power constraints and load constraints, and the expression of the lower optimization model is specifically:

[0129]

[0130] Where T is the cost of each power generation unit; N DER 、N ESS and N IL are DER, energy storage equipment and interruptible load respectively; c DER,i 、c ESS,j and c IL,l They are the power generation cost functions corresponding to DER, energy storage equipment and interruptible loads respectively; and are the corresponding output values ​​of the i-th DER, the j-th energy storage device and the l-th interruptible load at time t; and They are the lower and upper output limits corresponding to DER, energy storage equipment and interruptible load respectively; and is the lower and upper limits of the energy storage device’s power, Represents the cross-time domain constraints of the energy storage device; represents the internal load of the virtual power plant; Indicates the first output value of the nth resource cluster at time t.

[0131] Considering that the distributed resource cluster cannot complete the output according to the planned power, the equality constraints in the lower model can be relaxed. After the relaxation conditions are taken, the expression of the lower optimization model is specifically as follows:

[0132]

[0133] Where T is the cost of each power generation unit; N DER 、N ESS and N IL are DER, energy storage equipment and interruptible load respectively; c DER,i 、c ESS,j and c IL,l They are the power generation cost functions corresponding to DER, energy storage equipment and interruptible loads respectively; and are the corresponding output values ​​of the i-th DER, the j-th energy storage device and the l-th interruptible load at time t; and They represent the power grid raising and lowering the reserve price at time t, respectively, which is determined by the actual dispatching cost of the power grid; and are the introduced slack variables, representing the under-supply power and over-supply power of the resource cluster respectively; and They are the lower and upper output limits corresponding to DER, energy storage equipment and interruptible load respectively; and is the lower and upper limits of the energy storage device’s power, Represents the cross-time domain constraints of the energy storage device; represents the internal load of the virtual power plant; Indicates the first output value of the nth resource cluster at time t.

[0134] After the lower-level optimization model is constructed, the first output value of the resource cluster needs to be used as an input parameter and input into the lower-level optimization model. Then, a suitable solver (such as the optimization library in MATLAB or Python, etc.) is selected to solve the lower-level optimization model to obtain the minimum power generation cost of each power generation unit under the constraint conditions, and determine the second output value of each corresponding distributed power generation unit in each time period based on the objective function. and The second output value is converted into a control instruction and sent to each power generation unit to control each power generation unit to generate electricity.

[0135] By acquiring the power data corresponding to the power system in each time period, the embodiment of the present application can accurately grasp the characteristics and real-time changes of power generation and load, and facilitate the subsequent input of the upper optimization model to obtain the power output value with the lowest total power generation cost in the resource cluster; by inputting the power data into the preset upper optimization model, the power output value with the lowest total power generation cost in the resource cluster can be obtained, which can improve the flexibility and economy of the power system; by inputting the first output value into the preset lower optimization model, the second output value with the lowest cost of each power generation unit can be accurately obtained, and the first output value of the resource cluster can be reasonably allocated to each power generation unit, and each power generation unit can be controlled to generate electricity based on the second output value, which can ensure the efficient use of resources and improve the economy and stability of the power system. Compared with the prior art, the present application can comprehensively consider the characteristics and real-time changes of power generation, load and energy storage resources, realize the optimal scheduling of different resources, and thus provide guarantee for the stability, flexibility and economy of the power system.

[0136] The device embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separated, i.e., may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the method of this embodiment.

[0137] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-monitorable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0138] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application.

[0139] It is particularly pointed out that for those skilled in the art, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A source-load-storage collaborative optimization scheduling method based on a two-layer optimization model, characterized in that: include: Acquiring power data corresponding to the power system in each time period, wherein the power data includes power data and load data; Input the power data into a preset upper-level optimization model, optimize with the goal of minimizing the total power generation cost, and obtain the first output value corresponding to the resource cluster in each time period; The first output value is input into a preset lower-level optimization model, and optimization is performed with the goal of minimizing the cost of each power generation unit to obtain a second output value corresponding to each power generation unit in each time period, and each power generation unit is controlled to generate electricity based on the second output value.

2. The source-load-storage collaborative optimization scheduling method based on a two-layer optimization model according to claim 1 is characterized in that: Before inputting the power data into a preset upper-level optimization model, the method further includes: Obtain the power generation cost, output upper and lower limits, and ramp efficiency upper and lower limits corresponding to traditional units and resource clusters respectively; According to the power data, the power generation cost, the output upper and lower limits, and the climbing efficiency upper and lower limits, an objective function and constraints with the lowest total power generation cost are established, wherein the constraints include: unit output constraints, climbing rate constraints, power constraints, and load constraints.

3. The source-load-storage collaborative optimization scheduling method based on a two-layer optimization model according to claim 2 is characterized in that: The expression of the upper optimization model is specifically: The objective function is: The unit output constraint is: The climbing rate constraint is: The power constraint is: The load constraints are: Where T is the total power generation cost; N CG and N VPP are the number of traditional units and resource clusters respectively; c m and c n They are the power generation costs corresponding to traditional units and resource clusters respectively; and are the output values ​​of traditional units and resource clusters at time t respectively; is the load data at time t; and They are the lower and upper output limits of traditional units respectively; and They are the lower and upper output limits of the resource cluster respectively; and are the lower limit of the ramp rate and the lower limit of the ramp rate of the traditional unit respectively; Δt is the length of the scheduling period; and are the upper and lower limits of the ramp rate of the resource cluster respectively; N bus and N branch are the node set and line set in the power grid respectively; D l-j is the injection power transfer factor of node j to line l; is the injected power of node j at time t; Indicates the maximum power of line l.

4. The source-load-storage collaborative optimization scheduling method based on a two-layer optimization model according to claim 1 is characterized in that: The expression of the lower layer optimization model is specifically: Where T is the cost of each power generation unit; N DER 、N ESS and N IL are DER, energy storage equipment and interruptible load respectively; c DER,i 、c ESS,j and c IL,l They are the power generation cost functions corresponding to DER, energy storage equipment and interruptible loads respectively; and are the corresponding output values ​​of the i-th DER, the j-th energy storage device and the l-th interruptible load at time t; and They are the lower and upper output limits corresponding to DER, energy storage equipment and interruptible load respectively; and is the lower and upper limits of the energy storage device’s power, Represents the cross-time domain constraints of the energy storage device; represents the internal load of the virtual power plant; Indicates the first output value of the nth resource cluster at time t.

5. The source-load-storage collaborative optimization scheduling method based on a two-layer optimization model according to claim 1 is characterized in that: It also includes taking a relaxation condition for the equality constraint of the lower optimization model. After taking the relaxation condition, the expression of the lower optimization model is specifically: Where T is the cost of each power generation unit; N DER 、N ESS and N IL are DER, energy storage equipment and interruptible load respectively; c DER,i 、c ESS,j and c IL,l They are the power generation cost functions corresponding to DER, energy storage equipment and interruptible loads respectively; and are the corresponding output values ​​of the i-th DER, the j-th energy storage device and the l-th interruptible load at time t; and They represent the power grid raising and lowering the reserve price at time t, respectively, which is determined by the actual dispatching cost of the power grid; and are the introduced slack variables, representing the under-supply power and over-supply power of the resource cluster respectively; and They are the lower and upper output limits corresponding to DER, energy storage equipment and interruptible load respectively; and is the lower and upper limits of the energy storage device’s power, Represents the cross-time domain constraints of the energy storage device; represents the internal load of the virtual power plant; Indicates the first output value of the nth resource cluster at time t.

6. A source-load-storage collaborative optimization scheduling system based on a two-layer optimization model, characterized in that: include: An acquisition module, a first processing module and a second processing module; The acquisition module is used to acquire power data corresponding to the power system in each time period, wherein the power data includes power data and load data; The first processing module is used to input the power data into a preset upper-level optimization model, optimize with the goal of minimizing the total power generation cost, and obtain the first output value corresponding to the resource cluster in each time period; The second processing module is used to input the first output value into a preset lower-level optimization model, optimize with the lowest cost of each power generation unit as the goal, obtain the second output value corresponding to each power generation unit in each time period, and control each power generation unit to generate electricity based on the second output value.

7. The source-load-storage collaborative optimization scheduling system based on a two-layer optimization model according to claim 6 is characterized in that: The invention also includes constructing a model, wherein the constructing model: acquires a unit and constructs a unit; The acquisition unit is used to respectively acquire the power generation cost, output upper and lower limits, and climbing efficiency upper and lower limits corresponding to the traditional units and the resource cluster; The construction unit is used to establish an objective function and constraints with the lowest total power generation cost based on the power data, the power generation cost, the output upper and lower limits, and the climbing efficiency upper and lower limits, wherein the constraints include: unit output constraints, climbing rate constraints, power constraints, and load constraints.

8. The source-load-storage collaborative optimization scheduling system based on a two-layer optimization model according to claim 7 is characterized in that: The expression of the upper optimization model is specifically: The objective function is: The unit output constraint is: The climbing rate constraint is: The power constraint is: The load constraints are: Where T is the total power generation cost; N CG and N VPP are the number of traditional units and resource clusters respectively; c m and c n They are the power generation costs corresponding to traditional units and resource clusters respectively; and are the output values ​​of traditional units and resource clusters at time t respectively; is the load data at time t; and They are the lower and upper output limits of traditional units respectively; and They are the lower and upper output limits of the resource cluster respectively; and are the lower limit of the ramp rate and the lower limit of the ramp rate of the traditional unit respectively; Δt is the length of the scheduling period; and are the upper and lower limits of the ramp rate of the resource cluster respectively; N bus and N branch are the node set and line set in the power grid respectively; D l-j is the injection power transfer factor of node j to line l; P j t is the injected power of node j at time t; P l max Indicates the maximum power of line l.

9. The source-load-storage collaborative optimization scheduling system based on a two-layer optimization model according to claim 6 is characterized in that: The expression of the lower layer optimization model is specifically: Where T is the cost of each power generation unit; N DER 、N ESS and N IL are DER, energy storage equipment and interruptible load respectively; c DER,i 、c ESS,j and c IL,l They are the power generation cost functions corresponding to DER, energy storage equipment and interruptible loads respectively; and are the corresponding output values ​​of the i-th DER, the j-th energy storage device and the l-th interruptible load at time t; and They are the lower and upper output limits corresponding to DER, energy storage equipment and interruptible load respectively; and is the lower and upper limits of the energy storage device’s power, Represents the cross-time domain constraints of the energy storage device; represents the internal load of the virtual power plant; Indicates the first output value of the nth resource cluster at time t.

10. The source-load-storage collaborative optimization scheduling system based on a two-layer optimization model according to claim 6 is characterized in that: It also includes taking a relaxation condition for the equality constraint of the lower optimization model. After taking the relaxation condition, the expression of the lower optimization model is specifically: Where T is the cost of each power generation unit; N DER 、N ESS and N IL are DER, energy storage equipment and interruptible load respectively; c DER,i 、c ESS,j and c IL,l They are the power generation cost functions corresponding to DER, energy storage equipment and interruptible loads respectively; and are the corresponding output values ​​of the i-th DER, the j-th energy storage device and the l-th interruptible load at time t; and They represent the power grid raising and lowering the reserve price at time t, respectively, which is determined by the actual dispatching cost of the power grid; and are the introduced slack variables, representing the under-supply power and over-supply power of the resource cluster respectively; They are the lower and upper output limits corresponding to DER, energy storage equipment and interruptible load respectively; and is the lower and upper limits of the energy storage device’s power, Represents the cross-time domain constraints of the energy storage device; represents the internal load of the virtual power plant; Indicates the first output value of the nth resource cluster at time t.

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