Power scheduling method and device based on flexible load and pumped storage and terminal equipment

Abstract

The invention discloses a power scheduling method and device based on a flexible load and pumped storage, and terminal equipment, and the method comprises the steps: taking the maximum flexible load income in a scheduling period as a first target, taking the minimum output fluctuation variance in the scheduling period as a second target, constructing a cooperative scheduling model, and solving the cooperative scheduling model, obtaining a load scheduling result in the scheduling period, a power generation power value of the target pumped storage power station in the scheduling period and a power utilization power value of the target pumped storage power station in the scheduling period; and then, according to a scheduling result, regulating and controlling a reducible load, a transferable load and a transferable load, and according to the power generation power value and the power utilization power value, controlling a target pumped storage power station. According to the method, pumped storage is combined with three types of flexible loads including the transferable load, the transferable load and the reducible load to cooperatively perform power scheduling, so that the power system can keep stable power output under different working conditions.

Description

Technical Field

[0001] The present invention relates to the field of electric power systems, and in particular to a power dispatching method, device and terminal equipment based on flexible load and pumped storage. Background Art

[0002] In existing research, pumped storage can effectively cooperate with wind farms and photovoltaic plants to reduce the power coordination pressure when new energy is connected to the grid, but new energy (such as wind energy and solar energy) itself has significant uncertainty and intermittency. This uncertainty makes it difficult for pumped storage power stations to accurately predict and match their energy storage and release needs, and thus may not be able to achieve the optimal operating state. This mismatch may lead to energy waste, reduced system efficiency or even grid stability problems. Flexible loads (such as adjustable industrial electricity, smart home devices, etc.) have a certain degree of freedom in choosing the time of electricity consumption. This flexibility makes flexible loads a potential scheduling resource. However, in the existing technology, this potential of flexible loads has not been fully recognized and utilized, especially in the coordinated scheduling with pumped storage. There is a lack of effective integration and scheduling of flexible loads, which makes it difficult to achieve the expected scheduling effect in practical applications. Summary of the invention

[0003] The present invention provides a power dispatching method, device and terminal equipment based on flexible load and pumped storage. The method combines pumped storage with three types of flexible loads, namely transferable load, shiftable load and reducible load, to coordinate power dispatching, so that the power system can maintain stable power output under different working conditions.

[0004] An embodiment of the present invention provides a power dispatching method based on flexible load and pumped storage, comprising:

[0005] Obtain the original load, the electricity price of each dispatching period in the dispatching cycle, the total amount of load that can be reduced in the dispatching cycle, the total amount of load that can be shifted in the dispatching cycle, the total amount of load that can be transferred in the dispatching cycle, the power consumption value of the load that can be reduced before reduction in each dispatching period, the power consumption value of the load that can be shifted before shifting in each dispatching period, the power consumption value of the load that can be transferred before transfer in each dispatching period, the power parameters of the target pumped-storage power station, the minimum continuous startup duration of the target pumped-storage power station, the power limit parameters of the transferable load, and the power limit parameters of the load that can be reduced;

[0006] Based on the acquired data, construct pumped storage constraints and flexible load constraints;

[0007] Based on the acquired data, a collaborative scheduling model is constructed with the maximum flexible load benefit within the scheduling period as the first goal and the minimum output fluctuation variance within the scheduling period as the second goal.

[0008] Under the constructed constraints, the collaborative scheduling model is solved to obtain the load scheduling results within the scheduling period, the power generation value of the target pumped storage power station within the scheduling period, and the power consumption value of the target pumped storage power station within the scheduling period; wherein the load scheduling results include the load transfer power value, the load reduction power value, and the load translation power value;

[0009] According to the scheduling result, regulating the load that can be cut, the load that can be translated, and the load that can be transferred;

[0010] The target pumped-storage power station is controlled according to the power generation value of the target pumped-storage power station in the scheduling period and the power consumption value of the target pumped-storage power station in the scheduling period.

[0011] Furthermore, based on the acquired data, the collaborative scheduling model is constructed with the maximum flexible load benefit within the scheduling period as the first goal and the minimum output fluctuation variance within the scheduling period as the second goal, including:

[0012] Taking the maximum flexible load benefit within the dispatching period as the first goal, the first function is constructed;

[0013] Substituting the electricity price of each scheduling period in the scheduling cycle, the power value of the load that can be cut before being cut in each scheduling period, the power value of the load that can be shifted before being shifted in each scheduling period, and the power value of the load that can be shifted before being shifted in each scheduling period into the first function, to obtain a first objective function;

[0014] Taking the minimum output fluctuation variance within the dispatch period as the second objective, the second function is constructed;

[0015] Substituting the original load into the second function to obtain a second objective function;

[0016] A collaborative scheduling model is constructed based on the first objective function and the second objective function.

[0017] Furthermore, the first function is specifically:

[0018]

[0019] Where, C is the flexible load benefit; q t is the electricity price during the dispatch period t; is the power consumption value of the curtailable load i before curtailment in the scheduling period t; is the power consumption value of the transferable load r before the transfer in the scheduling period t; is the power consumption value of the movable load s before being moved in the scheduling period t; is the power consumption value of the reducible load i after being reduced in the scheduling period t; is the power consumption value of the transferable load r after the transfer in the scheduling period t; is the power consumption value of the shiftable load s after shifting in the scheduling period t; T is the length of the scheduling period.

[0020] Furthermore, the second function is specifically:

[0021]

[0022] In the formula, is the power supply of the power grid during the scheduling period t; is the average power supply power during the scheduling period t; f elc is the output fluctuation variance; E0 is the original load; P water-elc (t) is the power generation capacity of the target pumped storage power station in the dispatching period t, P water-use (t) is the power consumption of the target pumped-storage power station in the t scheduling period; T is the length of the scheduling cycle.

[0023] Furthermore, under the constructed constraints, the collaborative scheduling model is solved to obtain the load scheduling result within the scheduling period, the power generation value of the target pumped storage power station within the scheduling period, and the power consumption value of the target pumped storage power station within the scheduling period, including:

[0024] Get the preset maximum iteration value;

[0025] Determine the population quantity according to the total amount of load that can be reduced within the scheduling period, the total amount of load that can be shifted within the scheduling period, and the total amount of load that can be transferred within the scheduling period;

[0026] Determining a population movement range according to the population quantity, the pumped storage constraint and the flexible load constraint;

[0027] Determine a behavior selection coefficient and a decay coefficient according to the population size, the first objective function, the second objective function and the iterative maximum value;

[0028] According to the behavior selection coefficient, the iterative maximum value, the attenuation coefficient, the population size, the population movement range, the first objective function and the second objective function, the collaborative scheduling model is solved to obtain the load scheduling results within the scheduling period, the power generation value of the target pumped-storage power station within the scheduling period, and the power consumption value of the target pumped-storage power station within the scheduling period.

[0029] Furthermore, determining the behavior selection coefficient according to the population size, the first objective function, the second objective function and the iterative maximum value includes:

[0030] The behavior selection coefficient is determined by the following formula:

[0031]

[0032] Where B is the behavior selection coefficient; n is the population size; Max_I ner is the iterative maximum value; k is the total number of objective functions; I ner is the number of iterations.

[0033] Further, determining the attenuation coefficient according to the population size, the first objective function, the second objective function and the iterative maximum value includes:

[0034] The attenuation coefficient is determined by the following formula:

[0035]

[0036] Where A is the attenuation coefficient; A max is the upper limit of the attenuation coefficient; A min is the lower limit of the attenuation coefficient; Max_I ner is the maximum value of iteration; I ner is the number of iterations.

[0037] Further, the pumped storage constraints include: pumped storage power generation constraints, pumped storage power consumption constraints, power amplitude constraints and power station state transition constraints;

[0038] The flexible load constraints include: transferable load constraints, translatable load constraints and reducible load constraints.

[0039] An embodiment of the present invention further provides a power dispatching device based on flexible load and pumped storage, comprising: a data acquisition module, a constraint construction module, a model construction module, a model solving module, a flexible load control module and a target pumped storage power station control module;

[0040] The data acquisition module is used to obtain the original load amount, the electricity price of each scheduling period in the scheduling cycle, the total amount of load that can be reduced in the scheduling cycle, the total amount of load that can be shifted in the scheduling cycle, the total amount of load that can be transferred in the scheduling cycle, the power consumption value of the load that can be reduced before reduction in each scheduling period, the power consumption value of the load that can be shifted before shifting in each scheduling period, the power consumption value of the load that can be shifted before shifting in each scheduling period, the power parameters of the target pumped-storage power station, the minimum continuous startup duration of the target pumped-storage power station, the power limit parameters of the transferable load, and the power limit parameters of the load that can be reduced;

[0041] The constraint building module is used to build pumped storage constraints and flexible load constraints based on the acquired data;

[0042] The model building module is used to build a collaborative scheduling model based on the acquired data and the constructed constraints, with the maximum flexible load benefit within the scheduling period as the first goal and the minimum output fluctuation variance within the scheduling period as the second goal;

[0043] The model solving module is used to solve the collaborative scheduling model under the constructed constraints to obtain the load scheduling result within the scheduling period, the power generation value of the target pumped storage power station within the scheduling period, and the power consumption value of the target pumped storage power station within the scheduling period; wherein the load scheduling result includes the load transfer power value, the load reduction power value, and the load translation power value;

[0044] The flexible load control module is used to control the reducible load, the shiftable load and the transferable load according to the scheduling result;

[0045] The target pumped-storage power station control module is used to control the target pumped-storage power station according to the power generation value of the target pumped-storage power station within the scheduling period and the power consumption value of the target pumped-storage power station within the scheduling period.

[0046] The present application also provides a terminal device, including:

[0047] one or more processors;

[0048] A memory, coupled to the processor, for storing one or more programs;

[0049] When the one or more programs are executed by the one or more processors, the one or more processors implement the power dispatching method based on flexible load and pumped storage as described in the above-mentioned embodiment of the invention.

[0050] The following beneficial effects are achieved by implementing the present invention:

[0051] The present invention provides a power dispatching method, device and terminal equipment based on flexible load and pumped storage. The method combines pumped storage with three types of flexible loads, namely transferable load, shiftable load and reducible load, to coordinate power dispatching. On the one hand, with the goal of maximizing the benefit of flexible load within the dispatching cycle, by reasonably arranging the power dispatching time periods of transferable and shiftable loads, and accurately controlling the reducible load, combined with the energy conversion and storage characteristics of pumped storage, cost reduction and benefit improvement can be effectively achieved; on the other hand, with the goal of minimizing the output fluctuation variance within the dispatching cycle, power fluctuations can be effectively smoothed. Since pumped storage can quickly adjust the power generation and power consumption, combined with the flexible adjustment of flexible loads, the entire power system can maintain stable power output under different working conditions, reduce the impact on the power grid, enhance the reliability and safety of the power system operation, and improve the overall operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the implementation manner will be briefly introduced below. Obviously, the drawings described below are only some implementation manners of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 It is a flowchart of a power dispatching method based on flexible load and pumped storage provided in a certain embodiment of the present application;

[0054] Figure 2 It is a structural schematic diagram of a power dispatching device based on flexible load and pumped storage provided in a certain embodiment of the present application;

[0055] Figure 3 It is a structural diagram of a terminal device provided in a certain embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0058] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0059] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0060] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0061] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0062] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0063] The following are the professional terms and known technical contents involved in the embodiments of the present invention:

[0064] (1) Flexible load: Flexible loads are divided into three categories: transferable loads, shiftable loads, and curtailable loads. Transferable loads refer to loads whose total power consumption remains unchanged throughout the entire scheduling cycle, but whose power consumption can be adjusted in each time period. This type of flexible load can flexibly adjust the load in different time periods according to actual needs; shiftable loads refer to loads that can adjust the energy supply time within a certain time period, but must be shifted as a whole. This type of flexible load has a certain degree of flexibility in time, but needs to be shifted as a whole to ensure that the shape and peak value of the load curve remain unchanged. Curtailable loads can be reduced according to scheduling requirements by reducing power, reducing working hours, or interrupting operation. This type of flexible load has extremely high flexibility and can be flexibly reduced according to system requirements.

[0065] (2) Pumped storage: Pumped storage is an energy facility used to store and dispatch electric energy. It can effectively meet the energy balance and load regulation needs of the power grid. It converts electric energy into water energy and stores the water energy in a storage pool. During peak hours when power is needed, pumped storage will release the stored water energy to supply power to the power grid. This characteristic of storing and releasing electric energy enables pumped storage to balance the supply and demand differences in the power grid and provide stable and reliable electricity. It plays a very important role in ensuring the safe and stable operation of the power grid, optimizing the power grid energy structure and improving the power grid's response capabilities.

[0066] (3) Whale Optimization Algorithm: The Whale Optimization Algorithm is an algorithm based on the behavior of whales encircling their prey. In the Whale Optimization Algorithm, the position of each whale represents a feasible solution. During the hunting process of a group of whales, each whale has two behaviors: one is to surround the prey, and all whales move towards other whales; the other is to use a bubble net, where the whales swim in a circle and spray bubbles to drive the prey. In each generation of swimming, the whales will randomly choose between these two behaviors to hunt. The whales will randomly choose whether to swim towards the whale in the best position or randomly choose a whale as their target and approach it. The bubble net simulates the collective behavior of whales to search and optimize targets. The formation and contraction process of the bubble net can be used to find the optimal solution, cluster analysis, and other problems. By simulating the behavior of the bubble net, global search and local optimization of the problem space can be achieved, improving the search efficiency and accuracy of the algorithm.

[0067] See also Figure 1 , is a flow chart of a power dispatching method based on flexible load and pumped storage provided by an embodiment of the present invention, including:

[0068] S1. Obtain the original load, the electricity price of each dispatching period in the dispatching cycle, the total amount of load that can be reduced in the dispatching cycle, the total amount of load that can be shifted in the dispatching cycle, the total amount of load that can be transferred in the dispatching cycle, the power consumption value of the load that can be reduced before reduction in each dispatching period, the power consumption value of the load that can be shifted before shifting in each dispatching period, the power consumption value of the load that can be transferred before transfer in each dispatching period, the power parameters of the target pumped-storage power station, the minimum continuous startup time of the target pumped-storage power station, the power limit parameters of the transferable load, and the power limit parameters of the load that can be reduced;

[0069] Specifically, the power parameters of the target pumped-storage power station include: the upper limit of the pumped-storage power consumption of the target energy storage pool, the lower limit of the pumped-storage power consumption of the target energy storage pool, the upper limit of the pumped-storage power generation of the target energy storage pool, the lower limit of the pumped-storage power generation of the target energy storage pool, and the power amplitude limit;

[0070] Specifically, the power limit parameter of the transferable load includes a load transfer power value lower limit of the transferable load and a load transfer power value upper limit of the transferable load;

[0071] Specifically, the power limit parameter of the load that can be reduced includes a lower limit of the load reduction power value of the load that can be reduced and an upper limit of the load reduction power value of the load that can be reduced.

[0072] S2. Constructing pumped storage constraints and flexible load constraints based on the acquired data;

[0073] In a preferred embodiment, the pumped storage constraints include: pumped storage power generation constraints, pumped storage power consumption constraints, power amplitude constraints, and power station state transition constraints;

[0074] The flexible load constraints include: transferable load constraints, translatable load constraints and reducible load constraints;

[0075] Specifically, the pumped storage power generation power constraint is:

[0076]

[0077] Where P fa_min is the lower limit of pumped storage power generation; P fa_max This is the upper limit of pumped storage power generation capacity.

[0078] Specifically, the pumped storage power consumption constraint is:

[0079] P use_min ≤P water_use (t)≤P use_max ; (2)

[0080] Where P use_min P is the lower limit of power consumption for pumped storage; use_max It is the upper limit of electricity consumption for pumped storage.

[0081] Specifically, the power amplitude constraint is:

[0082] |P water (t)-P water (t-1)|≤ΔP k ; (3)

[0083] In the formula, ΔP k It is the power fluctuation limit of the target pumped storage power station in the dispatching period t.

[0084] Specifically, the frequent switching between opening and closing of pumped storage is not conducive to the long-term operation of the power station. The operation duration of the pumped storage power station under power generation conditions shall not be less than the minimum start-up duration. Therefore, the state transition constraint of the storage station is:

[0085] [S ge (t-1)-S ge (t)][T on (t-1)-T k min ]≥0; (4)

[0086] In the formula, S ge (t) is a 0-1 variable, S ge When (t) = 0, it means that the target pumped storage power station is in a shutdown state during the t scheduling period, otherwise, it is in a power generation state; Ton(t-1) is the continuous power generation duration of the target pumped storage power station during the t-1 scheduling period; The minimum continuous operation duration of the target pumped storage power station;

[0087] Specifically, the power station state transition constraint is a built-in condition for the operation of the target pumped-storage power station, requiring that the target pumped-storage power station's operating time each time be greater than its minimum operating time each time; for example: the pumped-storage power station is required to operate for at least 2 hours each time it is started, and can only be shut down again after running for 2 hours.

[0088] Specifically, the transferable load constraints include upper and lower limit constraints and transfer-in and transfer-out constraints, wherein the upper and lower limit constraints are:

[0089]

[0090] In the formula, is the lower limit of the load transfer power value of the transferable load r; is the upper limit of the load transfer power value of the transferable load r;

[0091] The transfer-in and transfer-out constraints are:

[0092]

[0093] In the formula, is the state where the transferable load r is transferred to the scheduling period t, is the state of the transferable load r being transferred out of the scheduling period t, which is a 0-1 variable, 1 means that the transferable load r has been transferred, and 0 means that it has not been transferred;

[0094] Specifically, the translatable load has only two states, namely, untranslated and translated. Therefore, the translatable load constraint is:

[0095]

[0096] In the formula, μ s,τ is the state of the subset τ of the translatable load s in the dispatchable period, 1 means starting to work, and 0 means not starting to work;

[0097] Specifically, the load that can be reduced is constrained as follows:

[0098]

[0099] In the formula, is the lower limit of the load reduction power value of load i that can be reduced; is the upper limit of the load reduction power value that can reduce load i.

[0100] S3. Based on the acquired data, a collaborative scheduling model is constructed with the maximum flexible load benefit within the scheduling period as the first goal and the minimum output fluctuation variance within the scheduling period as the second goal;

[0101] In a preferred embodiment, the collaborative scheduling model is constructed based on the acquired data, with the maximum flexible load benefit within the scheduling period as the first goal and the minimum output fluctuation variance within the scheduling period as the second goal, including:

[0102] Taking the maximum flexible load benefit within the dispatching period as the first goal, the first function is constructed;

[0103] Substituting the electricity price of each scheduling period in the scheduling cycle, the power value of the load that can be cut before being cut in each scheduling period, the power value of the load that can be shifted before being shifted in each scheduling period, and the power value of the load that can be shifted before being shifted in each scheduling period into the first function, to obtain a first objective function;

[0104] Taking the minimum output fluctuation variance within the dispatch period as the second objective, the second function is constructed;

[0105] Substituting the original load into the second function to obtain a second objective function;

[0106] A collaborative scheduling model is constructed based on the first objective function and the second objective function.

[0107] In a preferred embodiment, the first function is specifically:

[0108]

[0109] Where, C is the flexible load benefit; q t is the electricity price during the dispatch period t; is the power consumption value of the curtailable load i before curtailment in the scheduling period t; is the power consumption value of the transferable load r before the transfer in the scheduling period t; is the power consumption value of the movable load s before being moved in the scheduling period t; is the power consumption value of the reducible load i after being reduced in the scheduling period t; is the power consumption value of the transferable load r after the transfer in the scheduling period t; is the power consumption value of the shiftable load s after shifting in the scheduling period t; T is the length of the scheduling period;

[0110] Specifically, the power consumption value of load i before reduction in the scheduling period t can be reduced by The power consumption value of the transferable load r before the transfer in the scheduling period t and the power consumption value of the shiftable load s before shifting during the scheduling period t All are known values.

[0111] Specifically, the mathematical model of the transferable load is:

[0112]

[0113] In the formula, is the power consumption value of the transferable load r after the transfer in the scheduling period t; is the power consumption value of the transferable load r after the transfer in the scheduling period t; is the state where the transferable load r is transferred to the scheduling period t, is the state of the transferable load r being transferred out of the scheduling period t, which is a 0-1 variable, 1 means that the transferable load r has been transferred, and 0 means that it has not been transferred; is the load transfer power value of the transferable load r.

[0114] Specifically, the mathematical model of the translatable load is:

[0115]

[0116] In the formula, is the power consumption value of the shiftable load s after shifting in the scheduling period t; is the dispatchable period of the shiftable load s in the dispatching period t, is the starting time of the shiftable load s in the dispatchable period, is the end time of the shiftable load s in the dispatchable period; μ s,τ is the state of the subset τ of the translatable load s in the dispatchable period, 1 means starting to work, and 0 means not starting to work; is s,τ The corresponding power distribution vector, is the power consumption value of the shiftable load s in the scheduling period k and the scheduling period k+1 in the subset τ of the dispatchable period, and so on.

[0117] Specifically, the mathematical model of the load reduction is:

[0118]

[0119] In the formula, is the power consumption value of the reducible load i after being reduced in the scheduling period t; is the power consumption value of the curtailable load i before curtailment in the scheduling period t; is the load reduction power value of the curtailable load i in the scheduling period t;

[0120] Specifically, the relationship between the power consumption values ​​before and after the reduction of the reducible load is determined by the mathematical model of the reducible load, the relationship between the power consumption values ​​before and after the translation of the shiftable load is determined by the mathematical model of the shiftable load, and the relationship between the power consumption values ​​before and after the translation of the shiftable load is determined by the mathematical model of the transferable load.

[0121] In a preferred embodiment, the second function is specifically:

[0122]

[0123] In the formula, is the power supply of the power grid during the scheduling period t; is the average power supply power during the scheduling period t; f elc is the output fluctuation variance; E0 is the original load; P water-elc (t) is the power generation capacity of the target pumped storage power station in the dispatching period t, P water-use (t) is the power consumption of the target pumped storage power station during the t dispatch period; T is the length of the dispatch period;

[0124] Specifically, and They are all sequences. As the scheduling period increases, the sequence length will also increase. It should be noted that The initial value of is 0, and The sequence length is longer than the grid power One less length;

[0125] For example: Grid power supply There are currently two dispatch periods, the first dispatch period is 100MW and the second period is 200MW. is 100MW; when the third dispatch period arrives It is (100+200) / 2MW.

[0126] S4. Under the constructed constraints, the collaborative scheduling model is solved to obtain the load scheduling result within the scheduling period, the power generation value of the target pumped storage power station within the scheduling period, and the power consumption value of the target pumped storage power station within the scheduling period; wherein the load scheduling result includes the load transfer power value, the load reduction power value, and the load translation power value;

[0127] In a preferred embodiment, under the constructed constraints, the collaborative scheduling model is solved to obtain the load scheduling result within the scheduling period, the power generation value of the target pumped storage power station within the scheduling period, and the power consumption value of the target pumped storage power station within the scheduling period, including:

[0128] Get the preset maximum iteration value;

[0129] Determine the number of populations according to the total amount of load that can be reduced within the scheduling period, the total amount of load that can be shifted within the scheduling period, and the total amount of load that can be transferred within the scheduling period;

[0130] Determining a population movement range according to the population quantity, the pumped storage constraint and the flexible load constraint;

[0131] Determine a behavior selection coefficient and a decay coefficient according to the population size, the first objective function, the second objective function and the iterative maximum value;

[0132] According to the behavior selection coefficient, the iterative maximum value, the attenuation coefficient, the population size, the population movement range, the first objective function and the second objective function, the collaborative scheduling model is solved to obtain the load scheduling result within the scheduling period, the power generation value of the target pumped-storage power station within the scheduling period, and the power consumption value of the target pumped-storage power station within the scheduling period;

[0133] Indicatively, when solving the collaborative scheduling model, the whale algorithm is introduced to solve the collaborative scheduling model;

[0134] Specifically, the attenuation coefficient A is a quantity that changes linearly with the number of iterations. The value of the attenuation coefficient A determines whether the whale algorithm seeks the best solution around the optimal solution or at a random position. If the whale algorithm does not jump out of the local optimal solution in the early stage, it will only iterate within the local optimal solution in the later stage, and the calculation accuracy will decrease. Therefore, the present application improves the attenuation coefficient A by changing the attenuation coefficient A from linearly decaying with the iteration coefficient to nonlinear attenuation of a concave function. The concave function decays more slowly in the early stage, which can increase the optimization area and ensure the global optimality of the optimal solution.

[0135] In a preferred embodiment, determining the attenuation coefficient according to the population size, the first objective function, the second objective function and the iterative maximum value includes:

[0136] The attenuation coefficient is determined by the following formula:

[0137]

[0138] Where A is the attenuation coefficient; A max is the upper limit of the attenuation coefficient; Amin is the lower limit of the attenuation coefficient; Max_I ner is the maximum value of iteration; I ner is the number of iterations;

[0139] Specifically, the attenuation coefficient upper limit A max and the lower limit of the attenuation coefficient A min is a fixed value; in a preferred embodiment, A max Set to 2, A min Set to 0.

[0140] Specifically, the nonlinear attenuation of the concave function has both advantages and disadvantages for the algorithm. Although it can increase the optimization area and improve the convergence accuracy, the convergence speed of the algorithm will be slowed down. To solve this problem, the behavior selection coefficient B is introduced to speed up the calculation speed. The behavior selection is the whale's choice between the surrounding behavior and the spitting bubble driving behavior. In the original expression, the whale first chooses from the surrounding prey and driving away the prey behavior, and the probability of executing any behavior is 0.5. Observing the bubble net expression, it can be seen that the main purpose of this behavior is to shrink the optimization area and ensure that the solution is the optimal solution. For the sake of calculation speed, this behavior should be better in the middle and late stages of the algorithm. The improvement of the attenuation coefficient A in this application enhances the whale's swimming behavior to the optimal position and ensures global optimality. The bubble net as the second guarantee of optimality should be placed in the late stage of the algorithm. Therefore, this application uses the behavior selection coefficient B to control the whale's surrounding prey and spitting bubble net behavior;

[0141] In a preferred embodiment, determining the behavior selection coefficient according to the population size, the first objective function, the second objective function and the iterative maximum value includes:

[0142] The behavior selection coefficient is determined by the following formula:

[0143]

[0144] Where B is the behavior selection coefficient; n is the population size; Max_I ner is the iterative maximum value; k is the total number of objective functions; I ner is the number of iterations;

[0145] Specifically, in the present application, k=2.

[0146] Specifically, the solution process of the present application is as follows: first, the number of whale populations is entered according to the total amount of load that can be reduced within the degree cycle, the total amount of load that can be translated within the scheduling cycle, and the total amount of load that can be transferred within the scheduling cycle, and the movement range of the population is defined according to the total amount of load that can be reduced, the total amount of load that can be translated, the total amount of load that can be transferred, the pumped storage constraint, and the flexible load constraint, and then the first objective function and the second objective function are entered. After the above conditions are entered into the simulation software, the iteration position of each whale operator represents the iteration result of a load; the iteration area of ​​each whale operator is determined by the load constraint condition it represents; the position of each whale in the D-dimensional solution space is:

[0147] X=(X1,X2…X D ); (16)

[0148] The behavior selection coefficient B is added to control the behavior selection of the whale, and a vector Z = (1, 1 ..., 1, 1) with the same dimension as equation (16) is established (with a total of D columns), in which each element is multiplied by B. The Z matrix corresponds to the X matrix one by one, representing the behavior selection of each whale. If the element is less than 0.5, the prey encirclement behavior is executed; otherwise, the bubble net is executed to drive away the prey.

[0149] Specifically, the maximum iteration value is set to 70 times (generally, 30 iterations have converged), the behavior selection of the whale population is controlled, and the number of iterations of the current population is recorded. In the behavior of surrounding prey, A is a coefficient matrix. When the absolute value of A is less than 1, the whale swims towards the optimal individual; when A is greater than 1, the whale swims towards the random individual. After adding the improved attenuation coefficient A of this application, the improved two behavior expressions are as follows:

[0150] (1) When the absolute value of A is less than 1, the behavior of surrounding the prey is:

[0151]

[0152] (2) When the absolute value of A is greater than 1, the behavior of surrounding the prey is:

[0153]

[0154] Each iteration returns the first and second objective functions. If the final value converges (the whale position no longer moves), the iteration is considered complete and the calculation is completed. Finally, based on the whale position information, the load scheduling results within the scheduling period, the power generation value of the target pumped storage power station within the scheduling period, and the power consumption value of the target pumped storage power station within the scheduling period are output.

[0155] S5. According to the scheduling result, regulating the load that can be cut, the load that can be translated, and the load that can be transferred;

[0156] S6. Controlling the target pumped-storage power station according to the power generation value of the target pumped-storage power station within the scheduling period and the power consumption value of the target pumped-storage power station within the scheduling period.

[0157] See also Figure 2 , is a power dispatching device based on flexible load and pumped storage provided by an embodiment of the present invention, comprising: a data acquisition module, a constraint construction module, a model construction module, a model solving module, a flexible load control module and a target pumped storage power station control module;

[0158] The data acquisition module is used to obtain the original load amount, the electricity price of each scheduling period in the scheduling cycle, the total amount of load that can be reduced in the scheduling cycle, the total amount of load that can be shifted in the scheduling cycle, the total amount of load that can be transferred in the scheduling cycle, the power consumption value of the load that can be reduced before reduction in each scheduling period, the power consumption value of the load that can be shifted before shifting in each scheduling period, the power consumption value of the load that can be shifted before shifting in each scheduling period, the power parameters of the target pumped-storage power station, the minimum continuous startup duration of the target pumped-storage power station, the power limit parameters of the transferable load, and the power limit parameters of the load that can be reduced;

[0159] The constraint building module is used to build pumped storage constraints and flexible load constraints based on the acquired data;

[0160] The model building module is used to build a collaborative scheduling model based on the acquired data and the constructed constraints, with the maximum flexible load benefit within the scheduling period as the first goal and the minimum output fluctuation variance within the scheduling period as the second goal;

[0161] The model solving module is used to solve the collaborative scheduling model under the constructed constraints to obtain the load scheduling result within the scheduling period, the power generation value of the target pumped storage power station within the scheduling period, and the power consumption value of the target pumped storage power station within the scheduling period; wherein the load scheduling result includes the load transfer power value, the load reduction power value, and the load translation power value;

[0162] The flexible load control module is used to control the reducible load, the shiftable load and the transferable load according to the scheduling result;

[0163] The target pumped-storage power station control module is used to control the target pumped-storage power station according to the power generation value of the target pumped-storage power station within the scheduling period and the power consumption value of the target pumped-storage power station within the scheduling period.

[0164] See also Figure 3 , a certain embodiment of the present application further provides a terminal device, including:

[0165] one or more processors;

[0166] A memory, coupled to the processor, for storing one or more programs;

[0167] When the one or more programs are executed by the one or more processors, the one or more processors implement the power dispatching method based on flexible load and pumped storage as described above.

[0168] The processor is used to control the overall operation of the terminal device to complete all or part of the steps of the above-mentioned power dispatching method based on flexible load and pumped storage. The memory is used to store various types of data to support the operation of the terminal device, and these data may include, for example, instructions for any application or method used to operate on the terminal device, and data related to the application. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk.

[0169] In an exemplary embodiment, the terminal device can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the power scheduling method based on flexible load and pumped storage as described in any of the above embodiments, and achieve the same technical effect as the above method.

[0170] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A power dispatching method based on flexible load and pumped storage, characterized in that: include: Obtain the original load, the electricity price of each dispatching period in the dispatching cycle, the total amount of load that can be reduced in the dispatching cycle, the total amount of load that can be shifted in the dispatching cycle, the total amount of load that can be transferred in the dispatching cycle, the power consumption value of the load that can be reduced before reduction in each dispatching period, the power consumption value of the load that can be shifted before shifting in each dispatching period, the power consumption value of the load that can be transferred before transfer in each dispatching period, the power parameters of the target pumped-storage power station, the minimum continuous startup duration of the target pumped-storage power station, the power limit parameters of the transferable load, and the power limit parameters of the load that can be reduced; Based on the acquired data, construct pumped storage constraints and flexible load constraints; Based on the acquired data, a collaborative scheduling model is constructed with the maximum flexible load benefit within the scheduling period as the first goal and the minimum output fluctuation variance within the scheduling period as the second goal. Under the constructed constraints, the collaborative scheduling model is solved to obtain the load scheduling results within the scheduling period, the power generation value of the target pumped storage power station within the scheduling period, and the power consumption value of the target pumped storage power station within the scheduling period; wherein the load scheduling results include the load transfer power value, the load reduction power value, and the load translation power value; According to the scheduling result, regulating the load that can be cut, the load that can be translated, and the load that can be transferred; The target pumped-storage power station is controlled according to the power generation value of the target pumped-storage power station in the scheduling period and the power consumption value of the target pumped-storage power station in the scheduling period.

2. The power dispatching method based on flexible load and pumped storage as claimed in claim 1, characterized in that: Based on the acquired data, a collaborative scheduling model is constructed with the maximum flexible load benefit within the scheduling period as the first goal and the minimum output fluctuation variance within the scheduling period as the second goal, including: Taking the maximum flexible load benefit within the dispatching period as the first goal, the first function is constructed; Substituting the electricity price of each scheduling period in the scheduling cycle, the power value of the load that can be cut before being cut in each scheduling period, the power value of the load that can be shifted before being shifted in each scheduling period, and the power value of the load that can be shifted before being shifted in each scheduling period into the first function, to obtain a first objective function; Taking the minimum output fluctuation variance within the dispatch period as the second objective, the second function is constructed; Substituting the original load into the second function to obtain a second objective function; A collaborative scheduling model is constructed based on the first objective function and the second objective function.

3. The power dispatching method based on flexible load and pumped storage as claimed in claim 2, characterized in that: The first function is specifically: Where, C is the flexible load benefit; q t is the electricity price during the dispatch period t; is the power consumption value of the curtailable load i before curtailment in the scheduling period t; is the power consumption value of the transferable load r before the transfer in the scheduling period t; is the power consumption value of the movable load s before being moved in the scheduling period t; is the power consumption value of the reducible load i after being reduced in the scheduling period t; is the power consumption value of the transferable load r after the transfer in the scheduling period t; is the power consumption value of the shiftable load s after shifting in the scheduling period t; T is the length of the scheduling period.

4. The power dispatching method based on flexible load and pumped storage as claimed in claim 2, characterized in that: The second function is specifically: In the formula, is the power supply of the power grid during the scheduling period t; is the average power supply power during the scheduling period t; f elc is the output fluctuation variance; E0 is the original load; P water-elc (t) is the power generation capacity of the target pumped storage power station in the dispatching period t, P water-use (t) is the power consumption of the target pumped-storage power station in the t scheduling period; T is the length of the scheduling cycle.

5. The power dispatching method based on flexible load and pumped storage as claimed in claim 2, characterized in that: Under the constructed constraints, the collaborative scheduling model is solved to obtain the load scheduling result within the scheduling period, the power generation value of the target pumped storage power station within the scheduling period, and the power consumption value of the target pumped storage power station within the scheduling period, including: Get the preset maximum iteration value; Determine the population quantity according to the total amount of load that can be reduced within the scheduling period, the total amount of load that can be shifted within the scheduling period, and the total amount of load that can be transferred within the scheduling period; Determining a population movement range according to the population quantity, the pumped storage constraint and the flexible load constraint; Determine a behavior selection coefficient and a decay coefficient according to the population size, the first objective function, the second objective function and the iterative maximum value; According to the behavior selection coefficient, the iterative maximum value, the attenuation coefficient, the population size, the population movement range, the first objective function and the second objective function, the collaborative scheduling model is solved to obtain the load scheduling results within the scheduling period, the power generation value of the target pumped-storage power station within the scheduling period, and the power consumption value of the target pumped-storage power station within the scheduling period.

6. The power dispatching method based on flexible load and pumped storage as claimed in claim 5, characterized in that: The determining of the behavior selection coefficient according to the population size, the first objective function, the second objective function and the iterative maximum value includes: The behavior selection coefficient is determined by the following formula: Where B is the behavior selection coefficient; n is the population size; Max_I ner is the iterative maximum value; k is the total number of objective functions; I ner is the number of iterations.

7. The power dispatching method based on flexible load and pumped storage as claimed in claim 6, characterized in that: The step of determining the attenuation coefficient according to the population size, the first objective function, the second objective function, and the iterative maximum value includes: The attenuation coefficient is determined by the following formula: Where A is the attenuation coefficient; A max is the upper limit of the attenuation coefficient; A min is the lower limit of the attenuation coefficient; Max_I ner is the maximum value of iteration; I ner is the number of iterations.

8. The power dispatching method based on flexible load and pumped storage as claimed in any one of claims 1 to 7, characterized in that: The pumped storage constraints include: pumped storage power generation constraints, pumped storage power consumption constraints, power amplitude constraints and power station state transition constraints; The flexible load constraints include: transferable load constraints, translatable load constraints and reducible load constraints.

9. A power dispatching device based on flexible load and pumped storage, characterized in that: include: Data acquisition module, constraint construction module, model construction module, model solving module, flexible load regulation module and target pumped storage power station control module; The data acquisition module is used to obtain the original load amount, the electricity price of each scheduling period in the scheduling cycle, the total amount of load that can be reduced in the scheduling cycle, the total amount of load that can be shifted in the scheduling cycle, the total amount of load that can be transferred in the scheduling cycle, the power consumption value of the load that can be reduced before reduction in each scheduling period, the power consumption value of the load that can be shifted before shifting in each scheduling period, the power consumption value of the load that can be shifted before shifting in each scheduling period, the power parameters of the target pumped-storage power station, the minimum continuous startup duration of the target pumped-storage power station, the power limit parameters of the transferable load, and the power limit parameters of the load that can be reduced; The constraint building module is used to build pumped storage constraints and flexible load constraints based on the acquired data; The model building module is used to build a collaborative scheduling model based on the acquired data and the constructed constraints, with the maximum flexible load benefit within the scheduling period as the first goal and the minimum output fluctuation variance within the scheduling period as the second goal; The model solving module is used to solve the collaborative scheduling model under the constructed constraints to obtain the load scheduling result within the scheduling period, the power generation value of the target pumped storage power station within the scheduling period, and the power consumption value of the target pumped storage power station within the scheduling period; wherein the load scheduling result includes the load transfer power value, the load reduction power value, and the load translation power value; The flexible load control module is used to control the reducible load, the shiftable load and the transferable load according to the scheduling result; The target pumped-storage power station control module is used to control the target pumped-storage power station according to the power generation value of the target pumped-storage power station within the scheduling period and the power consumption value of the target pumped-storage power station within the scheduling period.

10. A terminal device, characterized in that: include: one or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the power dispatching method based on flexible load and pumped storage as described in any one of claims 1-8.

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