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

Abstract

The invention discloses a source load scheduling method, device, equipment and system of an electric power system and a medium, and belongs to the technical field of electric power. The method comprises the steps that a first scheduling model is constructed according to output parameters of a clean energy system, load parameters of a load system and first constraint conditions of the clean energy system and the load system, and the first scheduling model is used for representing similarity between output power of the clean energy system and the load parameters of the load system; resource loss generated by scheduling of the load system; solving the first scheduling model to obtain optimized load parameters of the load system by taking the similarity and resource loss generated by scheduling meeting a preset first optimization condition as a target; and scheduling the load system according to the optimized load parameters. According to the embodiment of the invention, the stability of the power system can be improved.

Description

Technical Field

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

[0002] As the traditional power system transforms to a new energy power system, the proportion of non-clean energy power generation devices in the power system decreases, and correspondingly, the proportion of clean energy power generation devices in the power system continues to increase. However, clean energy such as wind power and photovoltaic power generation will be affected by factors such as seasons and weather, and has greater randomness, volatility and unadjustability, which makes the gap between the power consumption of electrical equipment in the power system and the power generation of clean energy power generation devices, that is, the net load of the power system fluctuates greatly, and it is also very easy to have problems with the difficulty of absorbing clean energy, which reduces 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 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, 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, and 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 shape 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 according to the amplitude similarity parameters and the shape 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; constructing a scheduling resource loss model based on the load regulation parameters within the scheduling period and the load system, 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 condition, the first scheduling model is obtained.

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

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

[0009] In some possible embodiments, the method also includes: constructing a second scheduling model based on resource loss parameters of the non-clean energy system, load parameters of the load system, supply and demand imbalance penalty parameters and a second constraint condition of the power system, the second scheduling model is 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 a preset second optimization condition to obtain a first optimized output parameter of the clean energy system and a second optimized output parameter of the non-clean energy system; and scheduling the clean energy system and the non-clean energy system according to the first optimized output parameter and the second optimized output parameter.

[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 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, and the second constraint.

[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 constraint condition of a non-clean energy system, a supply-demand imbalance penalty constraint condition, and a transmission power constraint condition of a 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 building module, which is used to construct 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, and 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 scheduling the load system; a first calculation module, which is used to solve the first scheduling model with the goal of satisfying the preset first optimization condition with the similarity and the resource loss caused by scheduling, and obtain the optimized load parameters of the load system; a scheduling module, which is used to schedule 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 an electric power system, comprising: a load system; a clean energy system; a non-clean energy system; a source-load scheduling device of the electric 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, and 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. According to 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, a first scheduling model can be constructed 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 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 by the first scheduling model using the optimization target. The load system can be 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, and improve the stability of the net load, thereby improving the stability of the electric power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solution 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 three models provided in an embodiment of the present application;

[0024] Figure 5 A flow chart of a source-load dispatching 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 regulating power of a load system provided in an embodiment of the present application;

[0028] Fig. 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] Fig.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;

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

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

[0032] Fig.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 only to provide a better understanding of the present application by illustrating the examples of the present application.

[0034] As the traditional power system transforms to a new energy power system, the proportion of non-clean energy power generation devices in the power system decreases, and correspondingly, the proportion of clean energy power generation devices in the power system continues to increase. However, clean energy such as wind power and photovoltaic power generation will be affected by factors such as seasons and weather, and has greater randomness, volatility and unadjustability, which makes the gap between the power consumption of electrical equipment in the power system and the power generation of clean energy power generation devices, that is, the net load of the power system fluctuates greatly, and it is also very easy to have problems with the difficulty of absorbing clean energy, which reduces the stability of the power system.

[0035] The present application provides a source-load dispatching method, device, equipment, system and medium of an electric power system, which can construct a first dispatching model that can 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 dispatching the load system according to the output of the clean energy system in the electric power system and the load of the load system in the electric power system. The first dispatching 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. 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, 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 load 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 of the stability of the net load can also stabilize the output of the non-clean energy system, reduce or even avoid the situation where the output of the non-clean energy system fluctuates violently, and can also improve 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 the present application is briefly described here. Figure 1 A schematic diagram of the architecture of a power system provided in an embodiment of the present application is shown in FIG. 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 may be electrically connected to the clean energy system 12 and the non-clean energy system 13. The source-load scheduling device 14 may perform scheduling control on 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, the power consumption system may include but is not limited to the electrolytic aluminum system 112, the cement manufacturing system 113, etc. The power consumption of the power consumption system can be adjusted by scheduling, such as increasing the load power of the power consumption system or reducing the load power of the power consumption system. The energy storage system 111 may include an electrochemical energy storage system or other types of energy storage systems, which are not limited here. In the embodiment of the present application, the charging and discharging of the energy storage system 111 can also be regarded as the increase or decrease of the load power, so the energy storage system 111 can also be regarded as a load system. Correspondingly, the source-load scheduling in the embodiment of the present application can also be regarded as source-load-storage scheduling, and the "source" in "source-load scheduling" and "source-load-storage scheduling" is the power source, the "load" is the load, and the "storage" is the energy storage. As a high-energy-consuming industrial load system and energy storage system of the load system, the controllable resource regulation capacity is large and has good regulation characteristics. The scheduling control of the load system 11 can achieve the complementarity of the random volatility of the output of the clean energy system without increasing the additional power transmission of the power grid, especially in the scenario where the proportion of clean energy systems as power sources is high, it can effectively alleviate the problem of insufficient regulation capacity in the power system.

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

[0039] The non-clean energy system 13 is a system that uses non-clean energy to generate electricity. The non-clean energy system 13 may include but is not limited to a thermal power generation system. In the embodiment of the present application, the electric energy provided by the non-clean energy system 13 can be used to make up for the net load between the load system 11 and the clean energy system 12, to achieve a closed loop of power generation and consumption of the load system 11, the clean energy system 12 and the non-clean energy system 13 in the power system, and to minimize 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 respectively.

[0042] The first aspect of the present application provides a source-load dispatching method for an electric power system, which can be applied to an electric power system. The specific contents of the electric power system can be referred to the relevant descriptions above, which will not be repeated here. The source-load dispatching method for the electric power system can be executed by a source-load dispatching device, equipment, etc. of the electric 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, such as 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 load system device power, 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, and the limit charge state of the energy storage system in the load system.

[0045] The first constraint of the load system corresponds to the type of the load system. The first constraint 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 requirements for the adjustable range of the load power of the power consumption system under normal operation, the requirements for the adjustable range of the number of power consumption devices running in the power consumption system under normal operation, the requirements for the continuous operation time of the power consumption system under normal operation, the requirements for the product quantity of the power consumption system under normal operation, etc., but is not limited thereto. The load regulation model of the power consumption system as the first constraint condition may be set accordingly according to 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 the response time of the power consumption system must be greater than or equal to the time duration that the power consumption system needs to ensure operation 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 may be as shown in the following formula (1):

[0048]

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

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

[0051]

[0052] in, and are the maximum number of crushers that can be reduced in the ith cement manufacturing system at time t and the maximum number of crushers that can be added in the ith 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 capacity of crushed raw materials obtained for the i-th cement manufacturing system; is the storage amount 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 ith cement manufacturing system at the end of the scheduling period, i.e., at time T. The above formula (2) can constrain the number of crushers 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 range of the state of charge under the 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 discharge state and negative when it is in the charge 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; 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 volatility. 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. The similarity between the output power of the clean energy system and the load parameters of the load system can be jointly evaluated from the two aspects of amplitude and morphology, which can further improve the accuracy of the similarity and correspondingly improve the accuracy of the scheduling in the subsequent scheduling process.

[0058] Scheduling the load system will result in resource loss, and in the embodiments of the present application, it is expected 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 satisfying 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 are different 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 an 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 through the first scheduling model, and the load parameters obtained by reversed 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 highest similarity and the minimum resource loss caused by scheduling. 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 here. For example, the multi-objective grey wolf algorithm can be used to solve the first scheduling model in the simulation software to obtain the load parameters after the load system is optimized. The load parameters after the load system is optimized may include load adjustment parameters, and may also include 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. The load parameters of the dispatched load system reach the optimized load parameters, which can improve the stability of the net load of the clean energy system and the load system and reduce the resource loss of the load system dispatch.

[0065] In an embodiment of the present application, 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 and the resource loss caused by scheduling the load system can be constructed according to 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. The optimized load parameters that can stabilize the net load of the clean energy system and the load system can be solved by the first scheduling model using the optimization objective. 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, 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, the load parameters of the load system may include load power and load regulation parameters, and 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 state of the load system, the load system device regulation quantity, the load system device power, the load system product quantity, the load system product quantity range, the energy storage system state of charge, the energy storage system energy storage loss coefficient, the energy conversion efficiency of the energy storage system, the capacity of the energy storage system, the limit state of charge of the energy storage system, etc. 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 the scheduling load system. Figure 3 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 The step S201 in the method can be specifically divided into steps S2011 to S2014.

[0067] In step S2011, 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 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 the 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 change rate 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 according to 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 by a weighted algorithm based on the amplitude similarity parameter, the morphology similarity parameter and their respective weight values. For example, if the amplitude similarity parameter is the Euclidean distance and the morphology 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] Among them, F1 is the source-load matching model; D1 is the Euclidean distance between the output power of the clean energy system and the load power of the load system; α is the weight value corresponding to the Euclidean distance; D2 is the load tracking coefficient between the output power of the clean energy system and the load power of the load system; β is the weight value corresponding to the load tracking coefficient. The smaller the value of F1, the stronger the matching between the output power of the clean energy system and the load power of the load system, and the smaller the peak-to-valley difference and volatility of 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 dispatch resource loss model is used to characterize the resource loss caused by dispatch. The resource loss caused by the dispatch of the load system can be related to the power of the energy storage system, the resource loss of the charging and discharging of the energy storage system, the number of power consumption systems, the resource compensation of the 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 power consumption devices running in the power consumption system, and the power of the power consumption devices in the power consumption system. Through the load regulation parameters of the load system, the resource loss caused by the dispatch of each load system can be obtained, and the sum of the resource loss caused by the dispatch of each load system is the resource loss caused by the dispatch 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 dispatch 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 characterize the total resource loss of the load system; The unit resource compensation for load adjustment of the i-th aluminum electrolytic 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 aluminum electrolysis 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 operating 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 refer 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 is shown in Table 1 below. The peak-to-valley difference and fluctuation of the net load obtained by dispatching the load system using Model 1, Model 2, and Model 3 respectively, 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, 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, but the effect is not obvious. Figure 4 In the time period 1 to 5, the net load corresponding to model 1 is much 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 peak-to-valley difference and volatility of the net load are greatly reduced by using model 2 to dispatch the load system. Figure 4 In the time periods 6 to 9, 12, and 18 to 23 shown in FIG, 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 In the time period 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, and most of the electricity generated by the clean energy system can be consumed. 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%. It can be obtained that the first scheduling model in the embodiment of the present application is used to schedule the load system, which can optimize the scheduling of the load system, better make the load power curve of the optimized load system follow the output power curve fluctuation of the clean energy system, improve the source-load matching, so that the net load tends to be stable, and effectively promote the consumption of the electric energy 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 the non-clean energy system to further optimize the scheduling of the entire power system. Figure 5 A flow chart of a source-load dispatching method for a power system provided in another embodiment of the present application is shown in FIG. Figure 5 and Figure 2The difference is that Figure 5 The source-load dispatching 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 parameters of the non-clean energy system, the load parameters of the load system, the supply-demand imbalance penalty parameters 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 state 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 penalty parameters for oversupply may include but are not limited to penalty losses for discarding clean energy, power corresponding to discarding clean energy, etc., and the penalty parameters for undersupply may include but are not limited to penalty losses for failing to meet load demand, power for failing to meet load demand, etc.

[0101] The second dispatch 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 dispatch, 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.

[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 climbing 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; 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 / stop state 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 power abandonment, solar power abandonment 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, which will not be repeated here.

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

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

[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 participating in the 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 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 start-up 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 respectively the predicted value of the output power of the wind power generation system, the predicted value of the output power of the photovoltaic power generation system and the predicted value of the load power of the load system at time t; 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 refer to the physical meanings of the same parameters in other formulas in the above embodiments, which 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, the resource loss of the power system represented by the second scheduling model is also 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, and the output parameters of the optimized clean energy system can be determined as the first optimized output parameters, and the output parameters of the optimized non-clean energy system can be determined as the second optimized output parameters.

[0115] The second optimization condition includes an optimization condition that can minimize the resource loss of the power system, which can be set according to scenarios, requirements, experience, etc., and is not limited here. In some examples, the second optimization condition may include that the resource loss of the power system is less than the second preset loss. In other examples, the second optimization condition includes that the resource loss of the power system is minimized. Minimizing the resource loss of the power system can minimize the cost of scheduling power resources to save resources.

[0116] The algorithm for solving the second scheduling model is not limited here. For example, a 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] According to the first optimized output parameter and the second optimized output parameter, the clean energy system and the non-clean energy system are scheduled 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. 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, which can reduce the overall resource loss of the power system, 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 respectively, 1 photovoltaic power station with a capacity of 300MW, an energy storage system and a power consumption system. The wind and solar power abandonment penalty resource loss of the power system 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, and 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 in FIG. 1 , by optimizing and solving 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 in 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 in 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 regulation capacity is utilized to increase the load. Figure 8 The total regulation power in these periods is increased; during periods 5 to 8, 10 to 16, and 23, the output of the clean energy system is insufficient, and the load system's regulation capacity 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 unit, that is, the output of the non-clean energy system, is more stable than the output of the thermal power unit before optimization, that is, the output of the non-clean energy system, which can effectively alleviate the peak load pressure of the power system.

[0129] In order to verify the effectiveness of the scheme of 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: The scheduling solution does not introduce 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 scheduling of the load system;

[0131] Scheme 2: The similarity between the output power of the clean energy system and the load parameters of the load system is not introduced, but the resource loss caused by the scheduling of the load system is considered 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 is adopted 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 fluctuation degree of net load of clean energy system and load system and the peak-to-valley difference index of net load corresponding to the three schemes are obtained to evaluate and compare the dispatching effects of the three schemes.

[0134] Among them, 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-to-valley difference indicators of the above three dispatching schemes can be shown in Table 5 and Fig. 9 As shown in Table 5, the net load fluctuation and the net load peak-to-valley difference index values ​​obtained by using the above schemes 1, 2 and 3 are shown. Fig. 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 Fig. 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 is relatively flatter as a whole. 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 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] Moreover, the resource loss of Solution 3, i.e., the scheduling solution of the embodiment of the present application, is also significantly reduced compared with Solution 1 and Solution 2. Table 6 shows the resource loss of Solution 1, Solution 2, and Solution 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 abandoned clean energy resource loss of Scheme 2 and Scheme 3 is significantly reduced compared with Scheme 1, resulting in a reduction in the total resource loss of Scheme 2 and Scheme 3; compared with Scheme 2, although the abandoned clean energy resource loss and load system regulation resource loss increased by 1,316 yuan and 2,419 yuan respectively, the thermal power unit operation resource loss and ramp resource loss in Scheme 3 were reduced by 2,417 yuan and 8,451 yuan respectively, making the total resource loss of Scheme 3 reduced by 7,135 yuan compared with the total resource loss of Scheme 2. It can be seen that Scheme 3 can reduce the resource loss of power system operation while promoting the local consumption of clean energy.

[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. Fig.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, such as Fig.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 the upper-level scheduling, it is necessary to establish a source-load matching model and a load regulation model of the load system. The load system may include an electrochemical energy storage system, an electrolytic aluminum system, and a cement manufacturing system. The first scheduling model is jointly constructed based on the source-load matching model, the prediction data of the clean energy system, the prediction data of the load system, and the load system and the load regulation model of the load system. The first scheduling model can be regarded as an upper-level optimization scheduling model. The optimization goal of the first scheduling model is to maximize the source-load matching and minimize the resource loss of the load system scheduling. According to the optimization goal 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 the lower-level scheduling, the regulation model of the thermal power unit can be used as at least part of the constraints in the second constraint. The second scheduling model can be constructed based on the predicted data of the clean energy system, the regulation model of the thermal power unit, and the load after the load system is optimized after the upper-level scheduling. The second scheduling model can be regarded as the lower-level optimization scheduling model. The optimization goal of the second scheduling model is to minimize the resource loss of the power system. According to the optimization goal of the second scheduling model, the scheduling plan of the thermal power generation system and the scheduling plan of 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. Fig.11A schematic diagram of the structure of a source-load dispatching device for a power system provided in an embodiment of the present application is shown in FIG. Fig.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 load parameters after the load system is optimized.

[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 building 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 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, construct 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 condition, the first scheduling model is obtained.

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

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

[0158] Fig.12 A schematic diagram of the structure of a source-load dispatching device for a power system provided in another embodiment of the present application is shown in FIG. Fig.12 and Fig.11 The difference is that Fig.12The source-load dispatching 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 dispatch model according to resource loss parameters of the non-clean energy system, load parameters of the load system, supply-demand imbalance penalty parameters, and second constraints 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 satisfying the preset second optimization condition for the resource loss of the power system, 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 used 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 building 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 parameters in the scheduling period; 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 constraint on a non-clean energy system, a supply-demand imbalance penalty constraint, and a transmission power constraint on 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 an electric power system. Fig.13 A schematic diagram of the structure of a source-load dispatching device for a power system provided in an embodiment of the present application is shown in FIG. Fig.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 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 in 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-mentioned 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. Fig.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 realize the communication between the modules, devices, units and / or equipment in the embodiment of the present application. The input device and / or output device can also be accessed through the communication interface 403.

[0174] The bus 404 includes hardware, software, or both, coupling the components of the source and load dispatching device 400 of the power system to each other. For example, but 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 Hyper Transport (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 of the present application describe and illustrate a particular bus, the present 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 source-load dispatching device of the power system is used to dispatch the load system, the clean energy system and the non-clean energy system. The specific content of the power system can be referred to the relevant description in the above embodiment, and the same technical effect can be achieved. In order to avoid repetition, it will not be repeated here.

[0177] The fifth aspect of the present application provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by the 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, it will not be repeated here. Among them, the above-mentioned computer-readable storage medium may include a non-transitory computer-readable storage medium, such as a read-only memory (Read-Only Memory, referred to as ROM), a random access memory (Random Access Memory, referred to as RAM), a 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 clear that each embodiment in this specification is described in a progressive manner, and the same or similar parts between the 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 refer to the description part of the method embodiment. The present 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 the present application. In addition, for the sake of brevity, a detailed description of known method technologies is omitted here.

[0180] The above reference is according to the method of the embodiment of the present application, the flow chart of the device (system) and the computer program product and / or the block diagram described various aspects of the present application.It should be understood that each square box in the flow chart and / or the block diagram and the combination of each square box in the flow chart and / or the block diagram can be realized 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 by the processor of the computer or other programmable data processing device enable the realization of the function / action specified in one or more square boxes of the flow chart and / or the block diagram.Such a 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 can also be understood that each square box in the block diagram and / or the flow chart and the combination of the square boxes in the block diagram and / or the flow chart can also be realized by the dedicated hardware that performs the specified function or action, or can be realized by the combination of dedicated hardware and computer instructions.

[0181] Those skilled in the art should understand that the above embodiments are exemplary rather than restrictive. Different technical features appearing in different embodiments can be combined to achieve beneficial effects. Based on the 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 multiple; the terms "first" and "second" are used to indicate 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 single hardware or software module. The appearance of certain technical features in different dependent claims does not mean that these technical features cannot be combined to achieve beneficial effects.

Claims

1. A source-load dispatching 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: According to the output parameter of the clean energy system, the load parameter of the load system, and the first constraint conditions of the clean energy system and the load system, a first scheduling model is constructed, wherein 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, and the resource loss caused by the scheduling of the load system; With the goal of similarity and resource loss caused by scheduling satisfying a preset first optimization condition, solving the first scheduling model to obtain optimized load parameters of the load system; The load system is dispatched according to the optimized load parameters.

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 parameter of the clean energy system, the load parameter 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; According to the amplitude similarity parameter and the morphology similarity parameter, a source-load matching model is constructed, wherein the source-load matching model is used to characterize the similarity between the output power of the clean energy system and the load parameter of the load system; Based on the load regulation parameters in the scheduling period and the load system, a scheduling resource loss model is constructed, wherein the scheduling resource loss model is used to characterize the 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, characterized in that: The first optimization condition includes: the highest similarity and the minimum resource loss caused by scheduling.

5. The method according to claim 1, characterized in that The method further comprises: Constructing a second dispatching model 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 of the power system, wherein the second dispatching model is used to characterize the resource loss of the power system; With the goal that the resource loss of the power system meets the preset second optimization condition, the second scheduling model is solved to obtain the first optimized output parameter of the clean energy system and the 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.

6. The method according to claim 5, characterized in that 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; Based on the load parameters of the load system in the scheduling period, calculating the resource loss caused by scheduling the load system; 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.

7. The method according to claim 5, characterized in that The second optimization condition includes: the resource consumption of the power system is minimal.

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

9. The method according to claim 5, characterized in that The second constraint condition includes: a power balance constraint condition, a constraint condition of the non-clean energy system, a supply-demand imbalance penalty constraint condition, and a transmission power constraint condition of the transmission line where the power system is located.

10. A source-load dispatching device for an electric 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, used to build a first scheduling model according to the output parameter of the clean energy system, the load parameter of the load system and the first constraint conditions of the clean energy system and the load system, wherein 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, and the resource loss caused by the scheduling of the load system; A first calculation module is used to solve the first scheduling model with the goal of similarity and resource loss caused by scheduling meeting a preset first optimization condition, so as to obtain the optimized load parameters of the load system; The scheduling module is used to schedule the load system according to the optimized load parameters.

11. A source-load dispatching device for an electric 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 of the power system as described in any one of claims 1 to 9 is implemented.

12. 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 11, used for dispatching 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.

13. 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 of the power system as described in any one of claims 1 to 9 is implemented.

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