Source-load scheduling method, device, equipment, system and medium of power system

Abstract

The application discloses a source-load scheduling method, device, equipment, system and medium of a power system, and belongs to the technical field of electric power. The method comprises the following steps: constructing a first scheduling model according to an output parameter of a clean energy system, a load parameter of a load system and a first constraint condition of the clean energy system and the load system, the first scheduling model being used for representing the similarity between the output power of the clean energy system and the load parameter of the load system and the resource loss generated by scheduling of the load system; solving the first scheduling model with the similarity and the resource loss generated by scheduling satisfying a preset first optimization condition as a target, so as to obtain the optimized load parameter of the load system; and scheduling the load system according to the optimized load parameter. According to the embodiment of the application, the stability of the power system can be improved.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a source-load scheduling method, device, equipment, system, and medium for an electric power system. Background Art

[0002] As traditional power systems transition to new energy systems, the proportion of non-clean energy generation devices in the power system is decreasing, while the proportion of clean energy generation devices in the power system is correspondingly increasing. However, clean energy sources such as wind power and photovoltaic power are affected by factors such as season and weather, and have significant randomness, volatility, and unadjustability. This results in large fluctuations in the net load of the power system, namely the gap between the power consumption of electrical equipment in the power system and the power generation of clean energy generation devices. This also makes it very easy for clean energy to be difficult to absorb, reducing the stability of the power system. Summary of the Invention

[0003] The embodiments of the present application provide a source-load scheduling method, device, equipment, system and medium for an electric power system, which can improve the stability of the electric power system.

[0004] In the first aspect, an embodiment of the present application provides a source-load scheduling method for an electric power system, wherein the electric power system includes a load system, a clean energy system, and a non-clean energy system. The method includes: constructing a first scheduling model based on the output parameters of the clean energy system, the load parameters of the load system, and the first constraints of the clean energy system and the load system, the first scheduling model being used to characterize the similarity between the output power of the clean energy system and the load parameters of the load system, as well as the resource loss caused by scheduling the load system; solving the first scheduling model with the goal of ensuring that the similarity and the resource loss caused by scheduling meet a preset first optimization condition, and obtaining the optimized load parameters of the load system; and scheduling the load system according to the optimized load parameters.

[0005] In some possible embodiments, the output parameter includes output power, and the load parameter includes load power and load regulation parameter;

[0006] According to the output parameters of the clean energy system, the load parameters of the load system, and the constraints of the clean energy system and the load system, a first scheduling model is constructed, including: calculating the amplitude similarity parameters and morphological similarity parameters of the output power of the clean energy system and the load power of the load system within the scheduling period; constructing a source-load matching model based on the amplitude similarity parameters and the morphological similarity parameters, and the source-load matching model is used to characterize the similarity between the output power of the clean energy system and the load parameters of the load system; based on the load regulation parameters within the scheduling period and the load system, constructing a scheduling resource loss model, and the scheduling resource loss model is used to characterize the resource loss caused by scheduling; according to the source-load matching model, the scheduling resource loss model and the first constraint, the first scheduling model is obtained.

[0007] In some possible embodiments, the amplitude similarity parameter includes Euclidean distance, and the morphological similarity parameter includes a load tracking coefficient.

[0008] In some possible embodiments, the first optimization condition includes: highest similarity and minimum resource loss caused by scheduling.

[0009] In some possible embodiments, the method further includes: constructing a second scheduling model based on the resource loss parameters of the non-clean energy system, the load parameters of the load system, the supply and demand imbalance penalty parameters and the second constraint conditions of the power system, the second scheduling model being used to characterize the resource loss of the power system; solving the second scheduling model with the goal of ensuring that the resource loss of the power system meets the preset second optimization conditions to obtain the first optimized output parameters of the clean energy system and the second optimized output parameters of the non-clean energy system; and scheduling the clean energy system and the non-clean energy system according to the first optimized output parameters and the second optimized output parameters.

[0010] In some possible embodiments, a second scheduling model is constructed based on the resource loss parameters of the non-clean energy system, the load parameters of the load system, the supply and demand imbalance penalty parameters, and the second constraint conditions of the power system, including: calculating the resource loss of the non-clean energy system based on the resource loss parameters of the non-clean energy system in the scheduling period; calculating the resource loss caused by scheduling the load system based on the load parameters of the load system in the scheduling period; calculating the penalty resource loss based on the supply and demand imbalance penalty parameters in the scheduling period; constructing the second scheduling model based on the sum of the resource loss of the non-clean energy system, the resource loss caused by scheduling, and the penalty resource loss, as well as the second constraint conditions.

[0011] In some possible embodiments, the second optimization condition includes: minimizing resource consumption of the power system.

[0012] In some possible embodiments, the load system includes a power consumption system and an energy storage system.

[0013] In some possible embodiments, the second constraint condition includes: a power balance constraint condition, a non-clean energy system constraint condition, a supply-demand imbalance penalty constraint condition, and a transmission power constraint condition of the transmission line where the power system is located.

[0014] In the second aspect, an embodiment of the present application provides a source-load scheduling device for an electric power system, wherein the electric power system includes a load system, a clean energy system and a non-clean energy system; the device includes: a first model construction module, for constructing a first scheduling model based on the output parameters of the clean energy system, the load parameters of the load system and the first constraints of the clean energy system and the load system, the first scheduling model being used to characterize the similarity between the output power of the clean energy system and the load parameters of the load system, as well as the resource loss caused by scheduling the load system; a first calculation module, for solving the first scheduling model with the goal of ensuring that the similarity and the resource loss caused by scheduling meet a preset first optimization condition, and obtaining the optimized load parameters of the load system; a scheduling module, for scheduling the load system according to the optimized load parameters.

[0015] In a third aspect, an embodiment of the present application provides a source-load scheduling device for an electric power system, comprising: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the source-load scheduling method for the electric power system of the first aspect is implemented.

[0016] In a fourth aspect, an embodiment of the present application provides a power system, comprising: a load system; a clean energy system; a non-clean energy system; a source-load scheduling device of the power system of the third aspect, used to schedule the load system, the clean energy system and the non-clean energy system; wherein the load system, the clean energy system and the non-clean energy system are electrically connected to each other.

[0017] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the source-load scheduling method of the power system of the first aspect is implemented.

[0018] The embodiments of the present application provide a source-load scheduling method, device, equipment, system and medium for an electric power system. The method can construct a first scheduling model that can characterize the similarity between the output power of the clean energy system and the load parameters of the load system, as well as the resource loss caused by scheduling the load system, based on the output parameters of the clean energy system in the electric power system, the load parameters of the load system in the electric power system and the corresponding constraints. The first scheduling model can be used to solve the optimized load parameters that can stabilize the net load of the clean energy system and the load system by using the optimization target. The load system is scheduled according to the optimized load parameters, which can improve the matching degree between the output of the clean energy system and the load of the load system, reduce the peak-to-valley difference and fluctuation of the net load of the clean energy system and the load system, effectively promote the consumption of clean energy, improve the stability of the net load, and thus improve the stability of the electric power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 A schematic diagram of the architecture of a power system provided in one embodiment of the present application;

[0021] Figure 2 A flow chart of a source-load scheduling method for a power system provided in one embodiment of the present application;

[0022] Figure 3 A flow chart of a source-load scheduling method for a power system provided in another embodiment of the present application;

[0023] Figure 4 A schematic diagram of an example of net load curves corresponding to the three models provided in an embodiment of the present application;

[0024] Figure 5 A flow chart of a source-load scheduling method for a power system provided in another embodiment of the present application;

[0025] Figure 6 A schematic diagram of an example of the predicted output of a clean energy system and the predicted load of a load system provided in an embodiment of the present application;

[0026] Figure 7 A schematic diagram of an example of an output curve of a load system and an output curve of a thermal power generation system provided in an embodiment of the present application;

[0027] Figure 8 A schematic diagram of an example of power regulation of a load system provided in an embodiment of the present application;

[0028] Figure 9 A schematic diagram of an example of a net load curve corresponding to Scheme 1, Scheme 2, and Scheme 3 provided in an embodiment of the present application;

[0029] Figure 10 A logical diagram of an example of a source-load scheduling method for a power system provided in an embodiment of the present application;

[0030] Figure 11 A schematic diagram of the structure of a source-load scheduling device for a power system provided in one embodiment of the present application;

[0031] Figure 12 A schematic structural diagram of a source-load dispatching device for a power system provided in another embodiment of the present application;

[0032] Figure 13 A schematic diagram of the structure of a source-load scheduling device for an electric power system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0033] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0034] As traditional power systems transition to new energy systems, the proportion of non-clean energy generation devices in the power system is decreasing, while the proportion of clean energy generation devices in the power system is correspondingly increasing. However, clean energy sources such as wind power and photovoltaic power are affected by factors such as season and weather, and have significant randomness, volatility, and unadjustability. This results in large fluctuations in the net load of the power system, namely the gap between the power consumption of electrical equipment in the power system and the power generation of clean energy generation devices. This also makes it very easy for clean energy to be difficult to absorb, reducing the stability of the power system.

[0035] The present application provides a source-load scheduling method, device, equipment, system, and medium for an electric power system. The method can construct a first scheduling model that can characterize the similarity between the output power of the clean energy system and the load parameters of the load system, as well as the resource loss caused by scheduling the load system, based on the output of the clean energy system and the load conditions of the load system in the electric power system. The first scheduling model can be used to solve and obtain optimized load parameters that can stabilize the net loads of the clean energy system and the load system. Scheduling the load system according to the optimized load parameters can improve the matching between the output of the clean energy system and the load of the load system, reduce the peak-to-valley difference and fluctuation of the net loads of the clean energy system and the load system, effectively promote the consumption of clean energy, improve the stability of the net load, thereby improving the stability of the electric power system and reducing the peak-shaving pressure of the electric power system. The net loads of the clean energy system and the load system in the electric power system can be compensated by the output of the non-clean energy system. The improvement in the stability of the net load can also stabilize the output of the non-clean energy system, reduce or even avoid the occurrence of severe fluctuations in the output of the non-clean energy system, and also increase the life of the non-clean energy system and improve the overall performance of the electric power system.

[0036] For ease of understanding, the power system of this application is briefly described here. Figure 1 This is a schematic diagram of the architecture of a power system provided in one embodiment of the present application, such as Figure 1 As shown, the power system may include a load system 11, a clean energy system 12, a non-clean energy system 13 and a source-load scheduling device 14. The load system 11 can be electrically connected to the clean energy system 12 and the non-clean energy system 13. The source-load scheduling device 14 can schedule and control the load system 11, the clean energy system 12 and the non-clean energy system 13, and control the load of the load system 11, the output of the clean energy system 12 and the output of the non-clean energy system 13.

[0037] The load system 11 is a system that obtains electric energy from the power system. The load system 11 may include a power consumption system and an energy storage system 111. The load system 11 may include multiple power consumption systems and multiple energy storage systems 111. The number of power consumption systems and energy storage systems 111 in the load system 11 is not limited here. Each power consumption system and energy storage system 111 in the load system 11 can be dispatched and controlled separately. The power consumption system can be implemented as a high-energy-consuming industrial load system. The type of industrial load system is not limited here, for example, Figure 1As shown, power-consuming systems may include, but are not limited to, an electrolytic aluminum system 112 and a cement manufacturing system 113. The power consumption of power-consuming systems can be adjusted through scheduling, for example, by increasing or decreasing the load power of the power-consuming systems. The energy storage system 111 may include an electrochemical energy storage system or other types of energy storage systems, without limitation. In the embodiments of the present application, the charging and discharging of the energy storage system 111 can also be considered as increasing or decreasing the load power, and thus the energy storage system 111 can also be considered a load system. Accordingly, the source-load scheduling in the embodiments of the present application can also be considered as source-load-storage scheduling, where "source" refers to the power source, "load" refers to the load, and "storage" refers to the energy storage. High-energy-consuming industrial load systems and energy storage systems, as load systems, have large controllable resource regulation capacity and excellent regulation characteristics. Scheduling and control of the load system 11 can complement the random fluctuations in the output of the clean energy system without increasing additional power transmission from the grid. This can effectively alleviate the problem of insufficient regulation capacity in the power system, especially in scenarios where clean energy systems account for a high proportion of the power source.

[0038] The clean energy system 12 is a system that uses clean energy to generate electricity. The clean energy system 12 may include, but is not limited to, a wind power generation system, a photovoltaic power generation system, etc. The electricity generated by the clean energy system 12 can be used to power the load system 11 for consumption by the load system 11.

[0039] Non-clean energy system 13 is a system that uses non-clean energy to generate electricity. Non-clean energy system 13 may include, but is not limited to, thermal power generation systems. In this embodiment of the present application, the electricity provided by non-clean energy system 13 can be used to offset the net load between load system 11 and clean energy system 12, thus achieving a closed loop of power generation and consumption among load system 11, clean energy system 12, and non-clean energy system 13 in the power system, thereby minimizing the additional impact of the power grid on the power system.

[0040] The source-load scheduling device 14 can execute the source-load scheduling method of the power system in the embodiment of the present application, and schedule the load system 11, the clean energy system 12 and the non-clean energy system 13 to improve the stability of the power system.

[0041] The source-load scheduling method, device, equipment, system and medium of the power system provided in this application are described below.

[0042] In a first aspect, the present application provides a source-load scheduling method for a power system, which can be applied to the power system. The specific content of the power system can be found in the relevant description above and will not be repeated here. The source-load scheduling method for the power system can be executed by a source-load scheduling device, equipment, etc. of the power system, which is not limited here. Figure 2 A flow chart of a source-load scheduling method for a power system provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the source-load scheduling method of the power system may include steps S201 to S203.

[0043] In step S201 , a first scheduling model is constructed according to the output parameters of the clean energy system, the load parameters of the load system, and the first constraints of the clean energy system and the load system.

[0044] The output parameters of the clean energy system can be used to characterize the output of the clean energy system. For example, the output parameters of the clean energy system may include but are not limited to the output power. The load parameters of the load system can characterize the load of the load system. For example, the load parameters of the load system may include but are not limited to the load power of the load system; the load system includes a power consumption system and an energy storage system, and the load parameters of the load system may include the load parameters of the power consumption system and the load parameters of the energy storage system; the load parameters of the power consumption system may include but are not limited to the load power of the load system, the load adjustment parameters of the load system, the maximum adjustment load of the load system, the response time of the load system, the minimum response time of the load system, the operating status of the load system, the number of load system device adjustments, the power of the load system device, the product quantity of the load system, the product quantity range of the load system, and other information; the load parameters of the energy storage system may include but are not limited to the charge state of the energy storage system in the load system, the energy storage loss coefficient of the energy storage system in the load system, the energy conversion efficiency of the energy storage system in the load system, the capacity of the energy storage system in the load system, the limit charge state of the energy storage system in the load system, and other information.

[0045] The first constraint condition of the load system corresponds to the type of the load system. The first constraint condition may include a load regulation model of the load system. The load regulation model of the load system is related to at least part of the load parameters of the load system and is used to characterize the load requirements of the load system under normal operating conditions.

[0046] The load regulation model of the power consumption system in the load system may represent one or more of the following requirements, but is not limited to, requirements for the adjustable range of the load power of the power consumption system under normal operation, the adjustable range of the number of power consumption devices operating in the power consumption system under normal operation, the continuous operation duration requirement of the power consumption system under normal operation, and the product quantity requirement of the power consumption system under normal operation. The load regulation model of the power consumption system as the first constraint condition may be set accordingly based on the type of the power consumption system.

[0047] In some examples, the first constraint condition may include that the load regulation power of the power consumption system is within the adjustable range of the load power of the power consumption system under normal operation, and that the response time of the power consumption system must be greater than or equal to the time duration that the power consumption system must be guaranteed to operate under normal operation. For example, the power consumption system includes an electrolytic aluminum system, and the load regulation model of the electrolytic aluminum system as the first constraint condition can be shown as follows (1):

[0048]

[0049] in, is the load regulation power of the i-th electrolytic aluminum system at time t; and are the maximum load increase power that can be adjusted for the i-th electrolytic aluminum system and the maximum load reduction power that can be adjusted for the i-th electrolytic aluminum system respectively; is the operating status of the i-th electrolytic aluminum system at time t-1, is the operating status of the i-th electrolytic aluminum system at time t. The operating status of the electrolytic aluminum system may include running or stopped. If the operating status of the electrolytic aluminum system is running, the operating status value can be set to 1. If the operating status of the electrolytic aluminum system is stopped, the operating status value can be set to 0; is the response time of the i-th electrolytic aluminum system at time t, is the minimum continuous response time of the i-th electrolytic aluminum system. The normal operation of the electrolytic aluminum system requires that the response time be greater than or equal to the minimum continuous response time. The above formula (1) can constrain the load regulation power of the electrolytic aluminum system and the operating time of the electrolytic aluminum system. Among them, the difference between the operating state and the difference between the response time and the minimum response time can constrain the operating time of the electrolytic aluminum system to be greater than the minimum operating requirement, thereby avoiding the electrolytic aluminum system from frequently switching between the start and stop states.

[0050] In some examples, the first constraint condition may include the number of power-consuming devices operating in the power-consuming system being within an adjustable range of the number of power-consuming devices operating in the power-consuming system under normal operation, the relationship between the product quantity of the power-consuming system and the power and number of the power-consuming devices, the product quantity of the power-consuming system being within the product quantity range of the power-consuming system under normal operation, and the product quantity of the power-consuming system at the beginning of the scheduling period being the same as the product quantity of the power-consuming system at the end of the scheduling period. For example, the power-consuming system includes a cement manufacturing system, and the load regulation model of the cement manufacturing system as the first constraint condition may be shown in the following equation (2):

[0051]

[0052] in, and are the maximum number of crushers that can be reduced in the i-th cement manufacturing system at time t and the maximum number of crushers that can be added in the i-th cement manufacturing system at time t respectively; is the number of operating crushers reduced or increased in the i-th cement manufacturing system at time t; is the storage capacity of crushed raw materials obtained by the i-th cement manufacturing system at time t+1; is the storage capacity of crushed raw materials obtained by the i-th cement manufacturing system at time t; is the power of the crusher in the i-th cement manufacturing system; and The minimum and maximum storage capacities of crushed raw materials obtained for the i-th cement manufacturing system; is the storage capacity of crushed raw materials obtained by the i-th cement manufacturing system at the beginning of the scheduling period, i.e., time 0; T is the scheduling period; is the storage capacity of crushed raw materials obtained by the i-th cement manufacturing system at the end of the scheduling period, that is, time T. The above formula (2) can constrain the number of crushers adjusted in the cement manufacturing system and the storage capacity of crushed raw materials obtained by the cement manufacturing system, thereby ensuring the product output of the cement manufacturing system.

[0053] The load regulation model of the energy storage system in the load system can represent but is not limited to the requirements for the state of charge of the energy storage system, the requirements for the power of the energy storage system in the charging state and the discharging state, etc. The load regulation model of the energy storage system as the first constraint condition can be set according to the corresponding energy storage system. In some examples, the first constraint condition may include that the power of the energy storage system is negative in the charging state, the power of the energy storage system is positive in the discharging state, the state of charge of the energy storage system is related to the energy storage loss coefficient and the power and energy conversion efficiency of the energy storage system, and the state of charge of the energy storage system is within the state of charge range under normal operation of the energy storage system. For example, the load regulation model of the energy storage system as the first constraint condition can be shown as follows (3):

[0054]

[0055] in, is the power of the i-th energy storage system at time t, is the discharge power of the i-th energy storage system at time t, is the discharge power of the i-th energy storage system at time t. This constraint indicates that the power of the energy storage system is positive when it is in the discharging state and negative when it is in the charging state; is the state of charge of the i-th energy storage system at time t; τ is the energy storage loss coefficient of the energy storage system; is the state of charge of the i-th energy storage system at time t-1; η ch Energy conversion efficiency for charging the energy storage system; ηdis The energy conversion efficiency of the energy storage system discharge; λ min and λ max are the minimum state of charge and maximum state of charge of the energy storage system respectively; is the capacity of the i-th energy storage system. The power and state of charge of the energy storage system can be constrained by the above formula (3).

[0056] The first scheduling model is used to characterize the similarity between the output power of the clean energy system and the load parameters of the load system, as well as the resource loss caused by the scheduling of the load system.

[0057] The similarity between the output power of the clean energy system and the load parameters of the load system can be reflected by the similarity between the output power relationship of the clean energy system and the load parameter relationship of the load system. The output power relationship of the clean energy system is the relationship between the output power of the clean energy system and time. For example, the output power relationship of the clean energy system can be represented by an output power curve. The load parameter relationship of the load system includes the relationship between the load power of the load system and time, such as a load power curve. The higher the similarity between the output power of the clean energy system and the load power of the load system, the higher the matching degree between the output of the clean energy system and the load of the load system, the smaller the peak-to-valley difference between the net load of the clean energy system and the load system, and the lower the fluctuation. In some examples, the similarity between the output power of the clean energy system and the load parameters of the load system may include amplitude similarity and morphological similarity. Evaluating the similarity between the output power of the clean energy system and the load parameters of the load system from the two aspects of amplitude and morphology can further improve the accuracy of the similarity, and correspondingly improve the accuracy of scheduling in the subsequent scheduling process.

[0058] Scheduling the load system will result in resource loss. In the embodiments of the present application, it is hoped to minimize the resource loss caused by scheduling the load system. The higher the resource loss, the higher the cost required for power system scheduling.

[0059] In step S202, with the goal of similarity and resource loss caused by scheduling meeting a preset first optimization condition, the first scheduling model is solved to obtain optimized load parameters of the load system.

[0060] If the output parameter values ​​of the clean energy system and the load parameter values ​​of the load system are different, the similarity and resource loss represented by the first scheduling model will also be different. In this embodiment of the present application, the similarity and resource loss represented by the first scheduling model can be optimized and limited, and the load parameters of the load system can be reversed using the first scheduling model. The reversed load parameters are used as the optimized load parameters of the load system.

[0061] The first optimization condition includes an optimization condition that can improve the stability of the net load of the clean energy system and the load system, which can be set according to the scenario, demand, experience, etc., and is not limited here. In some examples, the first optimization condition may include: the similarity is higher than the similarity threshold, and the resource loss caused by scheduling is less than the first preset loss. In other examples, the first optimization condition may include: the similarity is the highest, and the resource loss caused by scheduling is the smallest. The highest similarity can make the peak-to-valley difference of the net load of the clean energy system and the load system as small as possible and the volatility as low as possible. Minimizing the resource loss caused by scheduling can make the cost of scheduling the load system as small as possible to save resources.

[0062] The algorithm for solving the first scheduling model is not limited herein. For example, a multi-objective grey wolf algorithm can be used to solve the first scheduling model in simulation software to obtain optimized load parameters for the load system. The optimized load parameters for the load system may include load adjustment parameters and load power adjusted according to the load adjustment parameters.

[0063] In step S203, the load system is scheduled according to the optimized load parameters.

[0064] According to the optimized load parameters, the load system is dispatched so that the load parameters of the dispatched load system can reach the optimized load parameters. When the load parameters of the dispatched load system reach the optimized load parameters, the stability of the net load of the clean energy system and the load system can be improved, and the resource loss of the load system dispatch can be reduced.

[0065] In an embodiment of the present application, a first scheduling model can be constructed based on the output parameters of the clean energy system in the power system, the load parameters of the load system in the power system, and the corresponding constraints, which can characterize the similarity between the output power of the clean energy system and the load parameters of the load system, as well as the resource loss caused by scheduling the load system. The optimized load parameters that can stabilize the net load of the clean energy system and the load system can be solved through the first scheduling model using the optimization objective. The load system is dispatched according to the optimized load parameters, which can improve the matching degree between the output of the clean energy system and the load of the load system, reduce the peak-to-valley difference and fluctuation of the net load of the clean energy system and the load system, effectively promote the consumption of clean energy, and improve the stability of the net load, thereby improving the stability of the power system.

[0066] In some embodiments, the output parameters of the clean energy system may include output power, and the load parameters of the load system may include load power and load regulation parameters. The load regulation parameters may include, but are not limited to, the load regulation parameters of the load system, the maximum regulated load of the load system, the response time of the load system, the minimum response time of the load system, the operating status of the load system, the number of load system device adjustments, the power of the load system device, the product quantity of the load system, the product quantity range of the load system, the state of charge of the energy storage system, the energy storage loss coefficient of the energy storage system, the energy conversion efficiency of the energy storage system, the capacity of the energy storage system, and the ultimate state of charge of the energy storage system. The first scheduling model may include a source-load matching model that can characterize the similarity between the output power of the clean energy system and the load parameters of the load system, and a scheduling resource loss model that can characterize the resource loss generated by scheduling the load system. Figure 3 This is a flow chart of a source-load scheduling method for a power system provided in another embodiment of the present application. Figure 3 and Figure 2 The difference is that Figure 2 Step S201 in the process can be further divided into steps S2011 to S2014.

[0067] In step S2011 , the amplitude similarity parameter and the shape similarity parameter between the output power of the clean energy system and the load power of the load system within the scheduling period are calculated.

[0068] The scheduling period can be set according to the scenario, demand, etc., and is not limited here. For example, the scheduling period can be 24 hours. The amplitude similarity parameter can characterize the amplitude similarity between the output power of the clean energy system and the load power of the load system. For example, the amplitude similarity parameter may include Euclidean distance or other parameters that can reflect the similarity in amplitude. The morphological similarity parameter can characterize the morphological similarity between the output power of the clean energy system and the load power of the load system, that is, it can reflect the morphological similarity between the output power curve of the clean energy system and the load power curve of the load system. For example, the morphological similarity parameter may include a load tracking coefficient or other parameters that can quantitatively evaluate the characteristics of the power source tracking the load, that is, the source-load morphological similarity.

[0069] In order to further improve the accuracy of the amplitude similarity parameter and the morphological similarity parameter, the output power of the clean energy system and the load power of the load system can be normalized first, and the amplitude similarity parameter and the morphological similarity parameter can be calculated using the normalized output power and load power. For example, if the amplitude similarity parameter is the Euclidean distance and the morphological similarity parameter is the load tracking coefficient, the Euclidean distance and the load tracking coefficient can be obtained based on the following equations (4) to (6):

[0070]

[0071]

[0072] Among them, P′ new is the output power of the clean energy system after normalization; P new is the output power of the clean energy system before normalization; min(P new ) and max(P new ) are the minimum and maximum output power of the clean energy system before normalization; P′ L is the load power of the load system after normalization; P L is the load power of the load system before normalization; min(P L ) and max(P L ) is the minimum and maximum value of the load power of the load system before normalization; D1 is the Euclidean distance between the output power of the clean energy system and the load power of the load system; D2 is the load tracking coefficient between the output power of the clean energy system and the load power of the load system; T is the scheduling period; α n n ew is the rate of change of the output power of the clean energy system before normalization; α L n is the rate of change of load power of the load system before normalization; P ′ new (t+1) is the output power of the clean energy system after normalization at time t+1; P ′ new (t) is the normalized output power of the clean energy system at time t; P ′ L (t+1) is the load power of the load system after normalization at time t+1; P ′ L (t) is the normalized load power of the load system at time t.

[0073] In step S2012, a source-charge matching model is constructed based on the amplitude similarity parameter and the morphology similarity parameter.

[0074] The source-load matching model is used to characterize the similarity between the output power of the clean energy system and the load parameters of the load system. The source-load matching model can be constructed using a weighted algorithm based on the amplitude similarity parameter, the morphological similarity parameter, and their respective weight values. For example, if the amplitude similarity parameter is the Euclidean distance and the morphological similarity parameter is the load tracking coefficient, the source-load matching model can be obtained according to the following formula (7):

[0075] F1=αD1+βD2 (7)

[0076] Here, F1 represents the source-load matching model; D1 represents the Euclidean distance between the clean energy system's output power and the load system's load power; α represents the weight corresponding to the Euclidean distance; D2 represents the load tracking coefficient between the clean energy system's output power and the load system's load power; and β represents the weight corresponding to the load tracking coefficient. A smaller F1 value indicates a stronger match between the clean energy system's output power and the load system's load power, and a smaller peak-to-valley difference and volatility in the net load.

[0077] In step S2013, a scheduling resource loss model is constructed based on the load regulation parameters within the scheduling period and the load system.

[0078] The scheduling resource loss model is used to characterize the resource loss caused by scheduling. The resource loss caused by load system scheduling can be related to information such as the power of the energy storage system, the resource loss of the energy storage system's charging and discharging, the number of power consumption systems, the resource compensation for load regulation of the power consumption system, the load regulation power of the power consumption system, the operating status of the power consumption system, the number of regulated power consumption devices operating in the power consumption system, and the power of the power consumption devices in the power consumption system. The load regulation parameters of the load system can be used to determine the resource loss caused by the scheduling of each load system. The sum of the resource losses caused by the scheduling of each load system is the resource loss caused by the scheduling of the entire load system.

[0079] For example, if the load system includes the electrolytic aluminum system, the cement manufacturing system, and the energy storage system, the scheduling resource loss model can be obtained according to the following formula (8):

[0080]

[0081] Among them, F2 is the scheduling resource loss model, which can represent the total resource loss of the load system; The unit resource compensation for load adjustment of the i-th electrolytic aluminum system; is the operating state of the i-th electrolytic aluminum system at time t, the operating state is running, The value can be 1, the running state is stopped, The value of can be 0; is the load regulation power of the i-th electrolytic aluminum system at time t; N A is the number of electrolytic aluminum systems; The unit resource compensation for regulating load of the i-th cement manufacturing system; is the power of each crusher in the i-th cement manufacturing system; is the adjustment number of the crusher running in the i-th cement manufacturing system at time t; k ES The charging and discharging resource loss of the energy storage system; is the power of the i-th energy storage system at time t.

[0082] In step S2014, a first scheduling model is obtained according to the source-load matching model, the scheduling resource loss model and the first constraint condition.

[0083] The first scheduling model may include the source-load matching model, the scheduling resource loss model and the first constraint condition in the above embodiment.

[0084] In order to verify the effect of the first scheduling model in the embodiment of the present application, the first scheduling model in the embodiment of the present application is compared and analyzed with the other two scheduling models. The three scheduling models are as follows:

[0085] Model 1: A model with dual optimization objectives of minimizing the Euclidean distance and minimizing the resource loss of the dispatching load system;

[0086] Model 2: A model with dual optimization objectives of maximizing the correlation coefficient and minimizing the resource loss of the dispatching load system;

[0087] Model 3: A model with dual optimization objectives of minimizing the similarity including amplitude similarity and morphological similarity and minimizing the resource loss of the scheduling load system, that is, the first scheduling model in the embodiment of the present application.

[0088] The calculation of the Euclidean distance of model 1 can be referred to formula (5) in the above embodiment, which will not be repeated here.

[0089] The correlation coefficient of model 2 can be obtained according to the following formula (9):

[0090]

[0091] Among them, R(P new ,P L ) is the correlation coefficient between the output power of the clean energy system and the load power of the load system; cov(P new ,P L ) is the covariance between the output power of the clean energy system and the load power of the load system; T is the scheduling period; is the output power of the clean energy system at time t; is the average output power of the clean energy system; is the load power of the load system at time t; is the average value of the load power of the load system; V var [P new ] is the variance of the output power of the clean energy system; V var [P L ] is the variance of the load power of the load system.

[0092] Using the above models 1, 2 and 3 to dispatch the load system, we can get the following: Figure 4The net load curve shown in Table 1 below shows the peak-to-valley difference and fluctuation of the net load obtained by using Model 1, Model 2, and Model 3 to dispatch the load system, as well as the peak-to-valley difference and fluctuation corresponding to the original net load obtained without dispatching the load system.

[0093] Table 1

[0094]

[0095] Depend on Figure 4 As shown in Table 1 above, the net load curve obtained by using Model 1 to dispatch the load system mainly reduces the amplitude of the net load compared with the original net load curve, which reduces the peak-to-valley difference and fluctuation of the net load compared with the original net load. However, the effect is not obvious, and Figure 4 In the time period 1 to 5, the net load corresponding to Model 1 is far lower than the minimum output of 400MW (megawatts) of the non-clean energy system, which will result in a large amount of electricity generated by the clean energy system being unable to be consumed. Compared with the original net load curve, the net load curve obtained by using Model 2 to dispatch the load system has a significant reduction in the peak-to-valley difference and fluctuation of the net load, but Figure 4 In the time periods 6 to 9, 12, and 18 to 23 shown, the net load curve corresponding to model 2 still has large fluctuations. Figure 4 In the time period 1 to 5, the net load corresponding to model 2 is only slightly less than the minimum output of 400MW of the non-clean energy system, and most of the electricity generated by the clean energy system can be consumed. The peak-to-valley difference and fluctuation of the net load curve obtained by using model 3 to dispatch the load system are the smallest compared with the net load curves corresponding to model 1 and model 2. Figure 4 During time periods 1 to 5, the net load corresponding to model 3 is only slightly less than the minimum output of 400MW of the non-clean energy system. Most of the electricity generated by the clean energy system can be absorbed. Compared with the original net load curve, the net load curve corresponding to model 3 has a peak-to-valley difference reduced by 226MW and a volatility reduced by 66.11%. Therefore, using the first scheduling model in the embodiment of the present application to schedule the load system can optimize the scheduling of the load system, better make the load power curve of the optimized load system follow the output power curve fluctuations of the clean energy system, improve the source-load matching, and thus make the net load tend to be stable, effectively promoting the absorption of the electricity generated by the clean energy system.

[0096] In some embodiments, based on the first scheduling model, a second scheduling model that can characterize the resource loss of the power system can be constructed in combination with non-clean energy systems to further optimize the scheduling of the entire power system. Figure 5 This is a flow chart of a source-load scheduling method for a power system provided in another embodiment of the present application. Figure 5 and Figure 2The difference is that Figure 5 The source-load scheduling method of the power system shown may further include steps S204 to S206 .

[0097] In step S204 , a second scheduling model is constructed according to the resource loss parameter of the non-clean energy system, the load parameter of the load system, the supply-demand imbalance penalty parameter, and the second constraint condition of the power system.

[0098] The resource loss parameters of the non-clean energy system may include parameters related to the resource loss of the non-clean energy system. In some examples, the resource loss parameters of the non-clean energy system may include, but are not limited to, the number of power generation devices in the non-clean energy system, the power generation of the power generation devices in the non-clean energy system, the raw material consumption coefficient of the power generation devices in the non-clean energy system, the start-stop resource loss coefficient of the power generation devices in the non-clean energy system, the start-stop status of the power generation devices in the non-clean energy system, etc. In some cases, if the power generation devices in the non-clean energy system need to climb, the resource loss parameters of the non-clean energy system may also include, but are not limited to, the climbing resource loss of the power generation devices in the non-clean energy system, the climbing factor of the power generation devices in the non-clean energy system, etc.

[0099] The specific contents of the load parameters of the load system can be found in the relevant descriptions in the above embodiments, which will not be repeated here.

[0100] The supply-demand imbalance penalty parameters include penalty parameters for oversupply and undersupply. In some examples, the oversupply penalty parameters may include, but are not limited to, penalty losses for discarding clean energy and the power corresponding to discarded clean energy. The undersupply penalty parameters may include, but are not limited to, penalty losses for failing to meet load demand and the power that fails to meet load demand.

[0101] The second scheduling model is used to characterize the resource loss of the power system, which can be realized as the sum of the resource loss of the non-clean energy system, the penalty resource loss of supply and demand imbalance, and the resource loss of the load system scheduling, and is subject to the second constraint condition.

[0102] In some examples, the resource loss of the non-clean energy system can be calculated based on the resource loss parameters of the non-clean energy system in the scheduling period; the resource loss caused by the scheduling of the load system can be calculated based on the load parameters of the load system in the scheduling period; the penalty resource loss can be calculated based on the supply and demand imbalance penalty parameters in the scheduling period; and a second scheduling model is constructed based on the sum of the resource loss of the non-clean energy system, the resource loss caused by scheduling, and the penalty resource loss, as well as the second constraint condition.

[0103] For example, if the non-clean energy system includes a thermal power generation system, the power generation device in the thermal power generation system is a thermal power unit, the load system includes an electrolytic aluminum system, a cement manufacturing system, and an energy storage system, and the clean energy system includes a wind power generation system and a photovoltaic power generation system, then the second scheduling model can be obtained according to the following equations (10) to (14):

[0104]

[0105] f cut,t =k Wout P Wout +k PVcut P PVcut +k Lcut P Lcut (13)

[0106]

[0107] Among them, F3 is the sum of the resource losses in the second scheduling model; T is the scheduling period; f G,t is the operating resource loss of the thermal power generation system; g G,t is the ramp resource loss of thermal power units in the thermal power generation system; f cut,t Penalty resource loss for wind power abandonment, solar power abandonment and load loss; f FCL,t Resource loss caused by scheduling the load system; N G is the number of thermal power units in the thermal power generation system; a i 、b i and c i are the raw material loss coefficients of the i-th thermal power unit in the thermal power generation system; P G,i,t is the power generation of the i-th thermal power unit in the thermal power generation system at time t; S i is the start-stop resource loss coefficient of the i-th thermal power unit in the thermal power generation system; u G,i,t-1 is the start and stop status of the i-th thermal power unit in the thermal power generation system at time t-1, u G,i,t is the start / stop state of the i-th thermal power unit in the thermal power generation system at time t. If the start / stop state is started, the value of the start / stop state is 1; if the start / stop state is stopped, the value of the start / stop state is 0; γ is the thermal power unit climbing factor; P G,i,t is the power of the i-th thermal power unit in the thermal power generation system at time t; k Wout 、k PVcut and k Lcut are the penalty resource losses for wind abandonment, solar abandonment, and load loss; P Wout 、P PVcut and P Lcut are the powers of wind curtailment, solar curtailment, and load loss, respectively. The physical meanings of the various parameters in formula (14) can be found in the parameter description of formula (8) above and will not be repeated here.

[0108] The second constraints of the power system are constraints for the entire power system and may include constraints for the normal operation of the power system. In some examples, the second constraints may include, but are not limited to, power balance constraints, constraints on the non-clean energy system, supply-demand imbalance penalty constraints, and transmission power constraints on the transmission lines where the power system is located. The constraints of the non-clean energy system can be implemented as a regulation model for the non-clean energy system. The constraints of the non-clean energy system may include, but are not limited to, upper and lower power limits for the non-clean energy system, ramp constraints for the non-clean energy system, and start-up and shutdown time constraints for power generation devices in the non-clean energy system.

[0109] For example, the sum of the resource losses in the second scheduling model is shown in equations (10) to (14) above, and the corresponding second constraint conditions can be obtained by equations (15) to (20) below:

[0110]

[0111]

[0112] in, is the net load of the power system at time t; is the system active load without considering the load system’s participation in regulation at time t; is the output power of the wind power generation system at time t; is the output power of the photovoltaic power generation system at time t; and are the maximum and minimum power values ​​of the i-th thermal power unit in the thermal power generation system; R i is the ramp rate of the i-th thermal power unit in the thermal power generation system; is the continuous startup time of the i-th thermal power unit in the thermal power generation system at time t-1; is the minimum continuous startup time of the i-th thermal power unit in the thermal power generation system; is the continuous shutdown time of the i-th thermal power unit in the thermal power generation system at time t-1; is the minimum continuous shutdown time of the i-th thermal power unit in the thermal power generation system; and are the predicted values ​​of the output power of the wind power generation system, the output power of the photovoltaic power generation system, and the load power of the load system at time t respectively; is the maximum transmission power of the transmission line between node i and node j; B ij is the susceptance between node i and node j; θ i,t is the phase angle of node i at time t; θ j,tis the phase angle of node j at time t; the physical meanings of other parameters can be found in the physical meanings of the same parameters in other formulas in the above embodiments, and will not be repeated here.

[0113] In step S205 , with the goal of ensuring that the resource loss of the power system meets a preset second optimization condition, the second scheduling model is solved to obtain the first optimized output parameters of the clean energy system and the second optimized output parameters of the non-clean energy system.

[0114] If the output parameters of the clean energy system are different and the output parameters of the non-clean energy system are different, then the resource loss of the power system represented by the second scheduling model will also be different. In an embodiment of the present application, the resource loss of the power system represented by the second scheduling model can be optimized and limited, and the output parameters of the clean energy system and the non-clean energy system can be reversed through the second scheduling model. The output parameters of the optimized clean energy system are determined as the first optimized output parameters, and the output parameters of the optimized non-clean energy system are determined as the second optimized output parameters.

[0115] The second optimization condition includes an optimization condition that minimizes power system resource loss. This condition can be set based on scenarios, requirements, experience, and the like, and is not limited herein. In some examples, the second optimization condition may include the power system resource loss being less than a second preset loss. In other examples, the second optimization condition includes minimizing power system resource loss. Minimizing power system resource loss can minimize the cost of scheduling power resources, thereby conserving resources.

[0116] The algorithm for solving the second scheduling model is not limited here. For example, the differential evolution algorithm can be used to solve the second scheduling model in simulation software to obtain the first optimized output parameter and the second optimized output parameter, thereby determining the scheduling strategy for the clean energy system and the scheduling strategy for the non-clean energy system.

[0117] In step S206 , the clean energy system and the non-clean energy system are scheduled according to the first optimized output parameter and the second optimized output parameter.

[0118] The clean energy system and the non-clean energy system are scheduled according to the first optimized output parameter and the second optimized output parameter so that the output parameters of the clean energy system and the non-clean energy system after scheduling can reach the first optimized output parameter and the second optimized output parameter. When the output parameters of the clean energy system and the non-clean energy system after scheduling reach the first optimized output parameter and the second optimized output parameter, the overall resource loss of the power system can be reduced, thereby saving resources.

[0119] In order to verify the effect of scheduling the power system using the first scheduling model and the second scheduling model in the embodiment of the present application, the present application provides a specific example. In this example, the power system includes 2 thermal power units, 3 wind farms with a capacity of 300MW each, 1 photovoltaic power station with a capacity of 300MW, an energy storage system and a power consumption system. The power system's wind and solar power abandonment penalty resource loss is 300 yuan / (MWh), and the load loss penalty resource loss is 8000 yuan / (MWh). The parameters of the thermal power units are shown in Table 2, the parameters of the energy storage system are shown in Table 3, the parameters of the power consumption system are shown in Table 4, the predicted output of the wind power generation system, the predicted output of the photovoltaic power generation system, and the predicted load of the load system are shown in Table 4. Figure 6 shown.

[0120] Table 2

[0121]

[0122]

[0123] Table 3

[0124]

[0125] Table 4

[0126]

[0127] In the above Tables 2 to 4 and Figure 6 Under the conditions shown, by optimizing the first dispatch model and the second dispatch model, the load power curve of the optimized load system, i.e., the output curve, and the output power curve of the optimized thermal power generation system, i.e., the output curve, are obtained as shown in FIG. Figure 7 As shown, Figure 7 The new energy output is the output of the clean energy system. Figure 8 The regulation power of the cement manufacturing system, the electrolytic aluminum system, the energy storage system and the total regulation power of the load system are shown. The total regulation power of the load system is the sum of the regulation powers of the cement manufacturing system, the electrolytic aluminum system and the energy storage system.

[0128] Depend on Figure 7 and Figure 4 It can be seen that the output of the clean energy system is sufficient during time periods 1 to 5, time 9, and time 19 to 22. During these periods, the load system's regulatory capacity is utilized to increase the load. Figure 8 The total regulation power in these periods is increased; in time periods 5 to 8, time periods 10 to 16, and time 23, the output of the clean energy system is insufficient, and the regulation capacity of the load system is utilized to reduce the load during these periods. Figure 8The total regulated power in these periods decreases. Through the source-load scheduling method in the embodiment of the present application, the load power curve of the load system and the output power curve of the clean energy system are highly similar in shape and timing, which improves the matching degree between the output of the clean energy system and the load of the load system, and makes the net load of the clean energy system and the load system tend to be stable. The power demand of the net load is provided by the non-clean energy system, from Figure 7 It can also be seen that the output of the optimized thermal power units, that is, the output of the non-clean energy system, is more stable than the output of the thermal power units before optimization, that is, the output of the non-clean energy system, which can effectively alleviate the peak load regulation pressure of the power system.

[0129] In order to verify the effectiveness of the scheme for scheduling the power system using the first scheduling model and the second scheduling model in the embodiment of the present application, the scheduling scheme of the embodiment of the present application is compared and analyzed with the other two scheduling schemes. The three scheduling schemes are as follows:

[0130] Solution 1: This scheduling solution does not consider the similarity between the output power of the clean energy system and the load parameters of the load system, nor does it consider the resource loss caused by the load system scheduling.

[0131] Option 2: This does not introduce the similarity between the output power of the clean energy system and the load parameters of the load system, but considers the resource loss caused by the load system scheduling to be as small as possible;

[0132] Solution 3: Introducing the similarity between the output power of the clean energy system and the load parameters of the load system, and considering the solution of minimizing the resource loss caused by the scheduling of the load system, that is, the source-load scheduling solution of the first scheduling model and the second scheduling model in the embodiment of the present application.

[0133] The above three schemes are respectively used for system dispatch in the power system under the same conditions. According to the actual situation after dispatch, the net load fluctuation and peak-to-valley difference index of the clean energy system and load system corresponding to the three schemes are obtained to evaluate and compare the dispatch effects of the three schemes.

[0134] The net load fluctuation and the net load peak-to-valley difference index can be calculated according to the following formulas (21) and (22):

[0135]

[0136] P m =P max -P min (twenty two)

[0137] Among them, I s is the fluctuation of net load; T is the dispatching period; is the net load power at the next moment; is the net load power at the current moment; P m is the peak-to-valley difference index of the net load; P max is the peak value of the net load; P min is the valley value of the net load.

[0138] The net load fluctuation and net load peak-valley difference indicators of the above three dispatching schemes can be shown in Table 5 and Figure 9 As shown in Table 5, the net load fluctuation and net load peak-to-valley difference index values ​​obtained by using the above schemes 1, 2 and 3 are shown. Figure 9 The net load curves obtained by using the above-mentioned schemes 1, 2 and 3 are shown.

[0139] Table 5

[0140]

[0141] Depend on Figure 9 It can be seen that the net load curves of Scheme 1 and Scheme 2 have more fluctuations and larger fluctuation amplitudes, while the net load curve of Scheme 3 has a smaller fluctuation amplitude and the curve as a whole is relatively flatter. As can be seen from Table 1, the peak-to-valley difference and fluctuation of the net load of Scheme 3 are reduced by 226MW (i.e., megawatts) and 66.06% respectively compared with Scheme 1, and are reduced by 41MW and 46.73% respectively compared with Scheme 2. It can be seen that Scheme 3, which adopts the first scheduling model and the second scheduling model in the embodiment of the present application for scheduling, can effectively reduce the peak-to-valley difference and fluctuation of the net load, smooth the net load curve, and improve the stability of the net load, thereby improving the stability of the power system.

[0142] Furthermore, Scheme 3, the scheduling scheme of the embodiment of the present application, also significantly reduces resource consumption compared to Scheme 1 and Scheme 2. Table 6 shows the resource consumption of Scheme 1, Scheme 2, and Scheme 3.

[0143] Table 6

[0144]

[0145] As shown in Table 6, due to the participation of more loads in the load system in scheduling, the resource loss of abandoned clean energy in Schemes 2 and 3 is significantly reduced compared to Scheme 1, resulting in a reduction in the total resource loss of Schemes 2 and 3. Compared with Scheme 2, although the resource loss of abandoned clean energy and the resource loss of load system regulation in Scheme 3 increase by 1,316 yuan and 2,419 yuan respectively, the resource loss of thermal power unit operation and ramping in Scheme 3 decrease by 2,417 yuan and 8,451 yuan respectively, resulting in a total resource loss of 7,135 yuan less than that of Scheme 2. This shows that Scheme 3 can promote the local consumption of clean energy while reducing resource losses in power system operation.

[0146] The source-load scheduling method of the power system provided in the embodiment of the present application can be regarded as having two levels of scheduling, namely upper-level scheduling and lower-level scheduling. Figure 10 A logic diagram of an example of a source-load scheduling method for a power system provided in an embodiment of the present application is shown as follows: Figure 10 As shown, the source-load scheduling method of the power system provided in the embodiment of the present application includes upper-level scheduling and lower-level scheduling.

[0147] In upper-level scheduling, it is necessary to establish a source-load matching model and a load regulation model for the load system. The load system may include an electrochemical energy storage system, an electrolytic aluminum system, and a cement manufacturing system. Based on the source-load matching model, the predicted data of the clean energy system, the predicted data of the load system, and the load system and the load regulation model of the load system, a first scheduling model is jointly constructed. The first scheduling model can be regarded as an upper-level optimization scheduling model. The optimization objectives of the first scheduling model are to maximize the source-load matching and minimize the resource loss of the load system scheduling. Based on the optimization objectives of the first scheduling model, the energy storage scheduling plan of the electrochemical energy storage system, the electrolytic aluminum scheduling plan of the electrolytic aluminum system, and the cement scheduling plan of the cement manufacturing system can be obtained. The scheduling plan of the load system obtained after the upper-level optimization can be used as one of the inputs of the lower-level scheduling.

[0148] In lower-level scheduling, the regulation model of the thermal power units can serve as at least part of the second constraint. A second scheduling model can be constructed based on the forecast data of the clean energy system, the regulation model of the thermal power units, and the optimized load of the load system obtained after upper-level scheduling. This second scheduling model can be considered the lower-level optimization scheduling model. The optimization objective of the second scheduling model is to minimize resource loss in the power system. Based on the optimization objective of the second scheduling model, a scheduling plan for the thermal power generation system and a scheduling plan for the clean energy system can be obtained.

[0149] The second aspect of the present application provides a source-load scheduling device for an electric power system. The specific contents of the electric power system can be found in the relevant descriptions in the above embodiments and will not be repeated here. Figure 11A schematic diagram of the structure of a source-load dispatching device for a power system according to an embodiment of the present application is shown in FIG. Figure 11 As shown, the source-load scheduling device 300 of the power system may include a first model building module 301 , a first calculation module 302 and a scheduling module 303 .

[0150] The first model building module 301 may be used to build a first scheduling model according to the output parameters of the clean energy system, the load parameters of the load system, and the first constraints of the clean energy system and the load system.

[0151] The first scheduling model is used to characterize the similarity between the output power of the clean energy system and the load parameters of the load system, as well as the resource loss caused by the scheduling of the load system.

[0152] The first calculation module 302 can be used to solve the first scheduling model with the goal of similarity and resource loss caused by scheduling meeting a preset first optimization condition, and obtain the optimized load parameters of the load system.

[0153] The scheduling module 303 may be used to schedule the load system according to the optimized load parameters.

[0154] In some embodiments, the output parameter includes output power, and the load parameter includes load power and load regulation parameter.

[0155] The first model construction module 301 can be specifically used to: calculate the amplitude similarity parameters and morphological similarity parameters of the output power of the clean energy system and the load power of the load system within the scheduling period; construct a source-load matching model based on the amplitude similarity parameters and morphological similarity parameters, and the source-load matching model is used to characterize the similarity between the output power of the clean energy system and the load parameters of the load system; based on the load regulation parameters within the scheduling period and the load system, construct a scheduling resource loss model, and the scheduling resource loss model is used to characterize the resource loss caused by scheduling; obtain the first scheduling model based on the source-load matching model, the scheduling resource loss model and the first constraint condition.

[0156] In some examples, the magnitude similarity parameter includes Euclidean distance, and the morphological similarity parameter includes a load tracking coefficient.

[0157] In some examples, the first optimization condition includes: highest similarity and minimum resource loss caused by scheduling.

[0158] Figure 12 This is a structural diagram of a source-load scheduling device for a power system provided in another embodiment of the present application. Figure 12 and Figure 11 The difference is that Figure 12The source-load scheduling device 300 of the power system shown may further include a second model building module 304 and a second calculation module 305 .

[0159] The second model building module 304 may be used to build a second scheduling model according to the resource loss parameter of the non-clean energy system, the load parameter of the load system, the supply and demand imbalance penalty parameter, and the second constraint condition of the power system.

[0160] The second dispatch model is used to characterize resource losses in the power system.

[0161] The second calculation module 305 can be used to solve the second scheduling model with the goal of ensuring that the resource loss of the power system meets the preset second optimization condition, and obtain the first optimized output parameters of the clean energy system and the second optimized output parameters of the non-clean energy system.

[0162] The scheduling module 303 may also be configured to schedule the clean energy system and the non-clean energy system according to the first optimized output parameter and the second optimized output parameter.

[0163] In some embodiments, the second model construction module 304 can be specifically used to: calculate the resource loss of the non-clean energy system based on the resource loss parameters of the non-clean energy system in the scheduling period; calculate the resource loss caused by the scheduling of the load system based on the load parameters of the load system in the scheduling period; calculate the penalty resource loss based on the supply and demand imbalance penalty parameter in the scheduling period; and construct a second scheduling model based on the sum of the resource loss of the non-clean energy system, the resource loss caused by scheduling, and the penalty resource loss, as well as the second constraint condition.

[0164] In some examples, the second optimization condition includes minimizing resource consumption of the power system.

[0165] In some examples, the load system includes an electricity consumption system and an energy storage system.

[0166] In some examples, the second constraint includes: a power balance constraint, a non-clean energy system constraint, a supply-demand imbalance penalty constraint, and a transmission power constraint of a transmission line where the power system is located.

[0167] It should be noted that the source-load scheduling device 300 of the power system is a device corresponding to the source-load scheduling method of the above-mentioned power system. All implementation methods in the above-mentioned method embodiments are applicable to the embodiments of the device and can achieve the same technical effects.

[0168] The third aspect of the present application also provides a source-load scheduling device for a power system. Figure 13 A schematic diagram of the structure of a source-load dispatching device for a power system according to an embodiment of the present application is shown in FIG. Figure 13As shown, the source-load scheduling device 400 of the power system includes a memory 401 , a processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0169] In some examples, the processor 402 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0170] The memory 401 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Therefore, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the source-load scheduling method of the power system according to the embodiment of the present application.

[0171] The processor 402 runs a computer program corresponding to the executable program code by reading the executable program code stored in the memory 401 , so as to implement the source-load scheduling method of the power system in the above embodiment.

[0172] In some examples, the source-load dispatching device 400 of the power system may further include a communication interface 403 and a bus 404. Figure 13 As shown, the memory 401 , the processor 402 , and the communication interface 403 are connected via a bus 404 and communicate with each other.

[0173] The communication interface 403 is mainly used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present application. Input devices and / or output devices can also be connected through the communication interface 403.

[0174] The bus 404 includes hardware, software, or both, and couples the components of the power system's source and load dispatching device 400 to each other. By way of example and not limitation, the bus 404 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-E) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, the bus 404 may include one or more buses. Although embodiments herein describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.

[0175] A fourth aspect of the present application provides an electric power system, which may include a load system, a clean energy system, a non-clean energy system, and a source-load scheduling device of the electric power system.

[0176] The load system, the clean energy system, and the non-clean energy system are electrically connected to each other. The power system's source-load dispatching equipment is used to dispatch the load system, the clean energy system, and the non-clean energy system. The specific details of the power system can be found in the relevant descriptions of the above embodiments, which achieve the same technical effects. To avoid repetition, they are not further described here.

[0177] In a fifth aspect, the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the source-load scheduling method of the power system in the above-mentioned embodiment can be implemented, and the same technical effect can be achieved. To avoid repetition, the above-mentioned computer-readable storage medium may include a non-transitory computer-readable storage medium, such as a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., which is not limited here.

[0178] An embodiment of the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the source-load scheduling method of the power system in the above embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0179] It should be understood that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. For device embodiments, equipment embodiments, system embodiments, computer-readable storage medium embodiments, and computer program product embodiments, the relevant parts can be referred to the description part of the method embodiment. This application is not limited to the specific steps and structures described above and shown in the figures. Those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of this application. In addition, for the sake of brevity, a detailed description of known method technologies is omitted here.

[0180] Aspects of the present application have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed via the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. This processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It is also understood that each box in the block diagram and / or the flowchart and the combination of the boxes in the block diagram and / or the flowchart can also be implemented by the dedicated hardware that performs the specified function or action, or can be implemented by the combination of dedicated hardware and computer instructions.

[0181] Those skilled in the art should understand that the above embodiments are illustrative rather than restrictive. Different technical features appearing in different embodiments can be combined to achieve beneficial effects. Based on a study of the drawings, the specification and the claims, those skilled in the art should be able to understand and implement other variations of the disclosed embodiments. In the claims, the term "comprising" does not exclude other devices or steps; the quantifier "one" does not exclude a plurality; the terms "first" and "second" are used to identify names rather than to indicate any specific order. Any figure marks in the claims should not be understood as limiting the scope of protection. The functions of multiple parts appearing in the claims can be implemented by a separate hardware or software module. The fact that certain technical features appear in different dependent claims does not mean that these technical features cannot be combined to achieve beneficial effects.

Claims

1. A source-load scheduling method for a power system, characterized in that: The power system includes a load system, a clean energy system and a non-clean energy system, and the method includes: Constructing a first scheduling model based on the output parameter of the clean energy system, the load parameter of the load system, and first constraints of the clean energy system and the load system. The first scheduling model is used to characterize the similarity between the output power of the clean energy system and the load parameter of the load system, as well as the resource loss caused by scheduling the load system. The similarity between the output power of the clean energy system and the load parameter of the load system includes amplitude similarity and morphological similarity. With the goal of ensuring that similarity and resource loss caused by scheduling meet a preset first optimization condition, solving the first scheduling model to obtain optimized load parameters of the load system; Dispatching the load system according to the optimized load parameters; The method further comprises: Constructing a second scheduling model based on the resource loss parameter of the non-clean energy system, the load parameter of the load system, the supply-demand imbalance penalty parameter, and the second constraint of the power system, wherein the second scheduling model is used to characterize the resource loss of the power system; With the goal of ensuring that the resource loss of the power system meets a preset second optimization condition, solving the second scheduling model to obtain a first optimized output parameter of the clean energy system and a second optimized output parameter of the non-clean energy system; The clean energy system and the non-clean energy system are scheduled according to the first optimized output parameter and the second optimized output parameter.

2. The method according to claim 1, characterized in that Output parameters include output power, and load parameters include load power and load regulation parameters; The constructing of a first scheduling model according to the output parameters of the clean energy system, the load parameters of the load system, and the constraints of the clean energy system and the load system includes: Calculating the amplitude similarity parameter and the shape similarity parameter of the output power of the clean energy system and the load power of the load system within the scheduling period; Constructing a source-load matching model based on the amplitude similarity parameter and the morphology similarity parameter, wherein the source-load matching model is used to characterize the similarity between the output power of the clean energy system and the load parameters of the load system; Based on the load regulation parameters of the load system within the scheduling period, a scheduling resource loss model is constructed, wherein the scheduling resource loss model is used to characterize resource loss caused by scheduling; The first scheduling model is obtained according to the source-load matching model, the scheduling resource loss model and the first constraint condition.

3. The method according to claim 2, characterized in that The amplitude similarity parameter includes the Euclidean distance, and the morphological similarity parameter includes the load tracking coefficient.

4. The method according to claim 1, wherein The first optimization condition includes: the highest similarity and the minimum resource loss caused by scheduling.

5. The method according to claim 1, wherein The second scheduling model is constructed according to the resource loss parameter of the non-clean energy system, the load parameter of the load system, the supply-demand imbalance penalty parameter, and the second constraint condition of the power system, including: Calculating the resource loss of the non-clean energy system based on the resource loss parameter of the non-clean energy system in the scheduling period; Calculating resource losses caused by scheduling the load system based on load parameters of the load system in the scheduling period; Calculating a penalty resource loss based on the supply-demand imbalance penalty parameter in the scheduling period; The second scheduling model is constructed according to the sum of the resource loss of the non-clean energy system, the resource loss caused by scheduling and the penalty resource loss, and the second constraint condition.

6. The method according to claim 1, characterized in that The second optimization condition includes: the resource consumption of the power system is minimized.

7. The method according to claim 1, characterized in that The load system includes a power consumption system and an energy storage system.

8. The method according to claim 1, characterized in that The second constraint conditions include: power balance constraint conditions, constraint conditions of the non-clean energy system, supply and demand imbalance penalty constraint conditions and transmission power constraint conditions of the transmission line where the power system is located.

9. A source-load dispatching device for a power system, characterized in that: The power system includes a load system, a clean energy system and a non-clean energy system; The device comprises: a first model building module, configured to build a first scheduling model based on the output parameters of the clean energy system, the load parameters of the load system, and first constraints of the clean energy system and the load system, wherein the first scheduling model is configured to characterize the similarity between the output power of the clean energy system and the load parameters of the load system, and the resource loss caused by the scheduling of the load system, wherein the similarity between the output power of the clean energy system and the load parameters of the load system includes amplitude similarity and morphological similarity; a first calculation module, configured to solve the first scheduling model with the goal of similarity and resource loss caused by scheduling satisfying a preset first optimization condition, and obtain optimized load parameters of the load system; A scheduling module, configured to schedule the load system according to the optimized load parameters; a second model building module, configured to build a second scheduling model based on a resource loss parameter of the non-clean energy system, a load parameter of the load system, a supply-demand imbalance penalty parameter, and a second constraint of the power system, wherein the second scheduling model is used to characterize the resource loss of the power system; a second calculation module, configured to solve the second scheduling model with the goal of ensuring that the resource loss of the power system meets a preset second optimization condition, and obtain a first optimized output parameter of the clean energy system and a second optimized output parameter of the non-clean energy system; The scheduling module is further configured to schedule the clean energy system and the non-clean energy system according to the first optimized output parameter and the second optimized output parameter.

10. A source-load dispatching device for a power system, characterized in that: include: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the source-load scheduling method for the power system according to any one of claims 1 to 8 is implemented.

11. A power system, characterized in that: include: Load system; clean energy systems; Non-clean energy systems; The source-load dispatching device of the power system according to claim 10, configured to dispatch the load system, the clean energy system, and the non-clean energy system; Wherein, the load system, the clean energy system and the non-clean energy system are electrically connected to each other.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the source-load scheduling method for the power system according to any one of claims 1 to 8 is implemented.

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