CN108075468B - Adjustment method, control device and management system of multi-distribution node load - Google Patents
Adjustment method, control device and management system of multi-distribution node load Download PDFInfo
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Abstract
本申请属于配电网控制技术领域，公开了一种多配电节点负荷的调整方法、控制装置及管理系统。所述多配电节点负荷的调整方法包括：设置电价控制模型，并根据所述电价控制模型获得当前时刻每个配电节点的预设控制时域内的最小电价；根据所述预设控制时域内的最小电价获得当前时刻每个配电节点的预设控制时域内的最优经济目标电价；根据当前时刻每个配电节点的预设控制时域内的最优经济目标电价对应的电参数调整每个配电节点预设控制时域内的负荷。这样能够通过调整配电节点负荷，优化配置电网资源，解决电网容易出现局部电力不足或者浪费的问题。
The application belongs to the technical field of distribution network control, and discloses a load adjustment method, a control device and a management system for multiple distribution nodes. The method for adjusting the load of multiple distribution nodes includes: setting an electricity price control model, and obtaining, according to the electricity price control model, the minimum electricity price in the preset control time domain of each distribution node at the current moment; obtain the optimal economic target electricity price in the preset control time domain of each distribution node at the current moment; adjust each electric parameter according to the electric parameters corresponding to the optimal economic target electricity price in the preset control time domain of each distribution node at the current moment Each distribution node is preset to control the load in the time domain. In this way, by adjusting the load of power distribution nodes and optimizing the allocation of power grid resources, the problem that the power grid is prone to local power shortage or waste can be solved.
Description
Technical Field
The application belongs to the technical field of power distribution network control, and particularly relates to a method for adjusting loads of multiple power distribution nodes, a control device and a management system.
Background
For a long time, people are used to meet the requirements of convenient and comfortable life by developing and utilizing electric energy, but the demand of the social life for the electric energy objectively has great difference in time and space.
The traditional power grid structure for transmitting power is easy to have intermittent power fluctuation and load peak-valley difference, so that the problem of local power shortage or waste of the power grid is caused, and the economic benefit and social benefit of a power company are influenced.
Disclosure of Invention
In view of this, embodiments of the present application provide a method, a control device, and a management system for adjusting loads of multiple power distribution nodes, so as to solve the problem of local power shortage or waste of the current power grid.
A first aspect of an embodiment of the present application provides a method for adjusting loads of multiple power distribution nodes, including:
setting a power rate control model, and obtaining the minimum power rate in a preset control time domain of each power distribution node at the current moment according to the power rate control model;
obtaining the optimal economic target electricity price in the preset control time domain of each power distribution node at the current moment according to the minimum electricity price in the preset control time domain;
and adjusting the load in the preset control time domain of each power distribution node according to the electric parameter corresponding to the optimal economic target electricity price in the preset control time domain of each power distribution node at the current moment.
A second aspect of the embodiments of the present application provides a device for adjusting loads of multiple power distribution nodes, including:
the minimum electricity price determining module is used for setting an electricity price control model and obtaining the minimum electricity price in a preset control time domain of each power distribution node at the current moment according to the electricity price control model;
the optimal economic target electricity price determining module is used for obtaining the optimal economic target electricity price in the preset control time domain of each power distribution node at the current moment according to the minimum electricity price in the preset control time domain;
and the power distribution node load adjusting module is used for adjusting the load in the preset control time domain of each power distribution node according to the electric parameter corresponding to the optimal economic target electricity price in the preset control time domain of each power distribution node at the current moment.
A third aspect of an embodiment of the present application provides a power distribution network energy management system, including:
the energy management system comprises a plurality of power distribution node micro-sources, a plurality of power distribution node energy storage devices, a server, a power distribution network centralized energy management system and a computer program which is stored on the server and can run on the server, and is characterized in that the server executes the computer program to realize the steps of the method provided by the first aspect of the embodiment of the present application.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium,
the computer-readable storage medium stores a computer program which, when executed by one or more processors, implements the steps of the method provided by the first aspect of an embodiment of the present application.
Compared with the prior art, the technical scheme of the embodiment of the application has the beneficial effects that:
according to the method and the device, the power price control model is set, and the minimum power price in the preset control time domain of each power distribution node at the current moment is obtained according to the power price control model; obtaining the optimal economic target electricity price in the preset control time domain of each power distribution node at the current moment according to the minimum electricity price in the preset control time domain; and adjusting the load in the preset control time domain of each power distribution node according to the electric parameter corresponding to the optimal economic target electricity price in the preset control time domain of each power distribution node at the current moment. Therefore, the power grid resources can be optimally configured by adjusting the load of the power distribution nodes, and the problem that local power is insufficient or wasted easily in the power grid is solved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart illustrating an implementation of a method for adjusting loads of multiple power distribution nodes according to an embodiment of the present disclosure;
fig. 2 is a block diagram of a power distribution node provided in an embodiment of the present application;
FIG. 3 is a flow chart of an algorithm for optimizing the economic target electricity price provided by an embodiment of the present application;
fig. 4 is a structural diagram of a centralized energy management system of a power distribution network according to an embodiment of the present application;
fig. 5 is a block diagram of an energy management system of a power distribution network according to an embodiment of the present application;
FIG. 6 is a flow chart of a power distribution node load coordination optimization provided by an embodiment of the present application;
fig. 7 is a schematic block diagram of an adjusting apparatus for multiple distribution node loads according to an embodiment of the present application;
fig. 8 is a schematic block diagram of an energy management system of a power distribution network according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.
Referring to fig. 1, a flowchart of an implementation of a method for adjusting loads of multiple power distribution nodes according to an embodiment of the present application is shown. As shown the method may comprise the steps of:
step S101, setting a power rate control model, and obtaining the minimum power rate in the preset control time domain of each power distribution node at the current moment according to the power rate control model.
The method comprises the steps of setting the electricity price control model to achieve the purpose that the maximum income of operation of a power distribution network system is achieved, taking the electricity price control model as a power distribution node of a basic control unit of the power distribution network management system, considering the cost of electricity purchase from a power grid, the income obtained by electricity sale to the power grid, the check electricity price, the micro-source power of the power distribution node, the limiting constraint of the power of an energy storage device of the power distribution node, the maintenance cost and depreciation loss of equipment such as the micro-source power of the power distribution node, the energy storage device of the power distribution node and the like, and comprehensively optimizing and calculating based on a preset control time domain and.
The electricity price control model in the embodiment of the application considers the electricity purchasing cost from the power grid, the income obtained by selling electricity to the power grid, the electricity price after the conversion efficiency of the energy storage device of the power distribution node, the input or output power of the energy storage device of the power distribution node, the node assessment electricity price and the like.
Specifically, the electricity price control model:
wherein x represents the xth power distribution node; said C isdg(x)Representing the electricity charge of the x-th power distribution node at the time t; the T represents a preset control time domain; t represents any time from the beginning to the end in the preset control time domain, t is m 1(m is more than or equal to 0 and m is an integer), and e issell(x)(t) represents the online electricity price of the xth power distribution node at the time t; said p isg(x)(t) represents the power value of the xth power distribution node at the time t, the main network outputs power to the microgrid at a positive value, and the power is input at a negative value; said ebuy(x)(t) represents the electricity purchase price of the x-th power distribution node at the time t; said eess(x)(t) representing the electricity price of the xth power distribution node at the moment t after the conversion efficiency of the energy storage device is considered; said p isess(t) represents the energy storage device input at the x-th distribution node at time t orThe power of the output; said KAP(x) Representing the examination price of the x-th power distribution node at the time t; the delta (t) represents the variation of the electricity charge of the xth power distribution node in time at the moment t; said DMINRepresenting the optimal minimum value of the electric charge of the multiple power distribution nodes; n represents n power distribution nodes, n is more than or equal to 1 and n is an integer.
Specifically, the minimum electricity price in the preset control time domain of each power distribution node at the current moment is the optimized minimum electricity fee D of the multiple power distribution nodesMINAnd the minimum value of the electricity price of the n power distribution nodes in the preset control time domain T is obtained.
Specifically, the electricity charge C of the x-th power distribution node at the time tdg(x)The variation in time delta (T), the time length of the specific function calculation amount is a preset control time domain T, and the time T is an integral multiple of the duration 1 of the rolling time domain (T is also an integral multiple of 1); electric charge Cdg(x)And the electricity price e of the x-th power distribution node at the moment t after the conversion efficiency of the energy storage device is consideredess(x)(t) and the power p input or output by the energy storage device at the time t of the x-th power distribution nodeess(t) product and assessment price K of the x-th power distribution node at the time tAP(x) Positive correlation with the electricity price e of the x-th power distribution node on the internet at the time tsell(x)(t) and the electricity purchase price e of the xth power distribution node at time tbuy(x)(t) and multiplying the difference by the power value p of the xth distribution node at time tg(x)(t) plus its own absolute value | pg(x)The value obtained by dividing (t) l by two is positively correlated, wherein p isg(x)(t) positive represents the output power of the main network to the microgrid of the xth distribution node, pg(x)(t) negative indicates mains input power to the microgrid of the xth power distribution node.
The power distribution node in the embodiment of the application is located at the downstream of the power distribution network centralized energy management system and is a basic control unit of the power distribution network centralized energy management system.
Fig. 2 is a structural diagram of a power distribution node according to an embodiment of the present application.
As shown in the figure, the power distribution node comprises a micro-source and grid-connected frequency converter, an energy storage device, a bidirectional inverter, a plurality of groups of loads and the like.
Specifically, the power distribution node researches a composite virtual impedance based on a dq rotation coordinate system based on a virtual impedance method control principle; and based on the composite virtual impedance, the droop distribution control mode of the micro-source and the grid-connected frequency converter is improved so as to meet the requirement that an energy storage device is matched with the bidirectional inverter for control, and the micro-source power of the micro-grid formed by the power distribution nodes is controlled according to the control instruction of the power distribution network centralized energy management system in a grid-connected state, and the stable operation in an off-grid island operation state and the smooth switching of loads among the power distribution nodes in an off-grid operation state can be realized.
And S102, obtaining the optimal economic target electricity price in the preset control time domain of each power distribution node at the current moment according to the minimum electricity price in the preset control time domain.
The preset control time domain is a time period calculated by the method for adjusting the loads of the multiple power distribution nodes by the power distribution network management system.
The step of calculating and obtaining the optimal economic target electricity price in the preset control time domain of each power distribution node at the current moment according to the minimum electricity price comprises the following steps:
dividing the preset control time domain into a plurality of rolling time domains, and obtaining the optimal economic target electricity price of the next rolling time domain based on each rolling time domain;
and obtaining the optimal economic target electricity price in the preset control time domain according to the optimal economic target electricity price in each rolling time domain.
The rolling time domain is the time length of each power distribution node of the power distribution network management system for rolling sampling calculation of the time-of-use electricity price. Specifically, the preset control time domain may include a plurality of the rolling time domains.
The rolling time domain not only considers the current step, but also lists the possible operation states of the power distribution network management system in a future period of time into a calculation range, so that the optimization process has better dynamic control effect,
the optimal economic target electricity price of the next rolling time domain is obtained based on each rolling time domain:
wherein, D isMINRepresenting the current rolling time domain optimal economic target electricity price of the power distribution node; said t ispRepresenting a preset finite time domain duration; the t represents the starting moment of the preset limited time domain; the s represents the time of the passing rolling time domain; s-1 represents the time length of the rolling time domain, s-m-1 (m is more than or equal to 1 and m is an integer), and s is less than or equal to tp(ii) a Said C isdg(x)Represents the electricity rate at the (t + s) time of the xth distribution node.
The preset limited time domain duration is a time period for the power distribution network management system to calculate the optimal economic target electricity price of the power distribution nodes in the rolling time domain according to a limited rolling time domain algorithm, and generally comprises a plurality of rolling time domains.
Therefore D isMINCalculating the optimal economic target electricity price at [ t t + t by using a rolling time domain algorithm for calculating the power distribution nodep]Within a finite time domain, a minimization is obtained within the finite time domain.
In particular, at the end of the last rolling period, i.e. the finite time domain tpAt the end, t + tpCalculating to obtain an optimal control sequence C at the momentdg(x)(t)＝{Cdg(x)(t|t),(t+1|t),…,Cdg(x)(t+tp-1| t) } and take the first control variable Cdg(x)(t | t) wherein the optimal target economic price C is obtaineddg(x)Comprising the power value p of the x-th distribution node at time tg(x)(t) input and output power p of energy storage device at t moment of the x distribution nodeess(t) is the x-th power distribution node in the finite time domain [ t t + tp]Optimal economic target electricity price D obtained by internally utilizing rolling time domain algorithmMIN。
Referring to fig. 3, it is a flowchart of an algorithm for optimizing the economic target electricity price according to an embodiment of the present application.
As shown in the figure, in the embodiment of the present application, a rolling time domain global evolution method is adopted for the optimal economic target electricity price, the finite time domain is the preset control time domain, a specific implementation process is to predict, at the current time T, that the electricity price control model solves an optimization problem in the preset control time domain [ T T + T ], and by calculating an optimal control sequence in the rolling time domain [ T T + s ], the optimal economic target electricity price in the rolling time domain is minimized. The duration of the rolling time domain may be assumed to be s ═ 1, for example, the optimal economic target electricity prices of each power distribution node in the power distribution network management system from T to a finite time domain T + n × 1 may be first rolled to form an optimal control sequence, and the first control variable is taken as a new electricity price control model, and then rolling update in the next rolling time domain [ T + n × 1T + (n +1) × 1] is continued until before the last rolling time domain [ T + T-1T + T ], that is, the rolling update optimization in the preset control time domain [ T T + T ] is finished. Wherein T is an integral multiple of the duration 1 of the rolling time domain, n is more than or equal to 1 and n is an integer.
And S103, adjusting the load in the preset control time domain of each power distribution node according to the electric parameter corresponding to the optimal economic target electricity price in the preset control time domain of each power distribution node at the current moment.
The optimal economic target electricity price in the preset control time domain of the power distribution nodes is in a plurality of rolling time domains in the preset control time domain, a plurality of power distribution nodes in each rolling time domain obtain the minimum electricity price of the power distribution nodes based on the electricity price control model rolling optimization calculation, the minimum electricity price obtained by the last rolling optimization calculation is the optimal economic target electricity price in the preset control time domain, and the electric parameters set in the corresponding electricity price control model at the moment are the electric parameters corresponding to the optimal economic target electricity price in the preset control time domain of the power distribution nodes.
Specifically, the electrical parameters corresponding to the optimal economic target electricity price include: the power distribution node micro-source power, the power of the power distribution node energy storage device, the conversion electricity price of the power distribution node energy storage device, the current-time on-line electricity price of the power distribution node, the current-time electricity purchasing price of the power distribution node and the current-time electricity price assessment of the power distribution node.
The power distribution node micro-source power represents the working power of a micro-source controlled by the power distribution node through droop distribution of a grid-connected frequency converter;
the power of the energy storage device of the power distribution node represents the working power of the energy storage device controlled by the droop distribution of the bidirectional inverter by the power distribution node;
the conversion electricity price of the energy storage device of the power distribution node represents the electricity price obtained by considering the maintenance cost and depreciation loss of equipment such as a micro source and the energy storage device of the power distribution node, the conversion efficiency of the energy storage device and other factors;
the power price of the power distribution node on the internet at the current moment represents the power price of the power distribution node for supplying power to the user side, and the power price is the selling power price of the power distribution node and is generally determined by authoritative organizations such as governments and the like;
the electricity purchasing price of the power distribution node at the current moment represents the cost electricity price of electricity purchasing of the power distribution node from an upstream large power grid;
the examination electricity price at the current moment of the power distribution node represents the electricity price obtained by an execution management method, a total power factor and other related factors of the power distribution node and the power distribution network centralized energy management system;
all of the above electrical parameters are variables that vary with time.
The load in the preset control time domain of each power distribution node can be adjusted according to the electrical parameters of the power distribution node corresponding to the optimal economic target electricity price in the preset control time domain of the current time of each power distribution node by adjusting the micro-source power and the energy storage device power in the power distribution node, and the load among different power distribution nodes can be translated according to the difference of the electrical parameters of different power distribution nodes in the same power distribution network energy management system, so that the effects of optimizing the energy management in the power distribution network energy management system and increasing the energy storage are achieved; in addition, load among different power distribution nodes is translated, so that the single power distribution node can quickly meet the setting requirement of the electrical parameter corresponding to the optimal economic target electricity price at the corresponding moment.
The power grid structure and the control method for transmitting power are two important aspects for determining the resource allocation capacity of a power grid, specifically, the power distribution network centralized energy management system completes the calculation of the multi-power distribution node translation load control method through a server to obtain the optimal economic target electricity price in the preset control time domain at the current moment, and the optimal economic target electricity price is located at the upstream of the power distribution network management system; the power distribution network energy management system is connected with the power distribution network centralized energy management system through a power distribution network central controller, is located in the midstream of the power distribution network management system, responds to a decision instruction of the power distribution network centralized energy management system, and further controls each corresponding power distribution node located in the downstream of the power distribution network management system to adjust the load according to the decision instruction.
Fig. 4 is a structural diagram of a centralized energy management system of a power distribution network according to an embodiment of the present application.
As shown in the figure, the hardware basis of the power distribution network centralized energy management system for implementing the multi-power distribution node translation load control method includes a power distribution network centralized energy management system, a server, a protocol converter, a power distribution network energy management system, and the like.
Specifically, the power distribution network energy management system converts collected electrical parameters and control data into an IEC61850 standard protocol through a protocol converter through optical fiber communication and communicates the IEC61850 standard protocol to the power distribution network centralized energy management system, and the power distribution network centralized energy management system coordinates energy running conditions of all power distribution nodes through the optimal economic target electricity price and stores related electrical parameters and historical data into a server.
Referring to fig. 5, a diagram of an energy management system of a power distribution network according to an embodiment of the present application is shown.
As shown, the power distribution network energy management system includes a Micro power source, an energy storage device, a power distribution network central Controller (MC), a plurality of loads and a Load Controller (LC).
Specifically, the distribution network central Controller is responsible for optimizing the flow of energy in the distribution network management system and controlling a Micro power Controller (MC) and a Load Controller (LC) according to real-time electricity prices, Micro source prediction data and the running state of the Load through a decision made by the distribution network energy management system, so as to stably and reliably provide the required electric energy to the Load at the optimal economic cost. The power distribution network energy management system converts collected information and control data into an IEC61850 standard protocol through a protocol converter through optical fiber communication and communicates the information and the control data to the power distribution network centralized energy management system, and the power distribution network centralized energy management system coordinates energy running conditions of all nodes and stores basic data and historical data into a server. The server provides method support for the power distribution network centralized energy management system.
Referring to fig. 6, a flow chart of load coordination and optimization of a power distribution node according to an embodiment of the present application is shown.
The server in the power distribution network centralized energy management system is responsible for realizing the multi-distribution node translation load control method, the power distribution network central controller of the power distribution network energy management system responds to the optimal economic target electricity price obtained by the server of the power distribution network centralized energy management system through calculation according to the multi-distribution node load adjustment method, the flow of load energy of each distribution node in the power distribution network energy management system and at the downstream is optimized, the micro-power controller and the load controller are controlled, loads among translation distribution nodes are optimized, required electric energy is stably and reliably provided for the loads at the optimal economic cost, and the loads of the distribution nodes are optimized.
The power distribution network centralized energy management system ensures the realization of the control method by executing the multi-power distribution node translation load control method, can reduce the peak-valley difference of the power system, smoothens the intermittent power supply power fluctuation, increases the standby capacity of the power system load, and improves the safety stability and the power supply quality of the power grid.
According to the method and the device, the power price control model is set, and the minimum power price in the preset control time domain of each power distribution node at the current moment is obtained according to the power price control model; obtaining the optimal economic target electricity price in the preset control time domain of each power distribution node at the current moment according to the minimum electricity price in the preset control time domain; and adjusting the load in the preset control time domain of each power distribution node according to the electric parameter corresponding to the optimal economic target electricity price in the preset control time domain of each power distribution node at the current moment. Therefore, the power grid resources can be optimally configured by adjusting the load of the power distribution nodes, and the problem that local power is insufficient or wasted easily in the power grid is solved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 7 is a schematic block diagram of an adjusting apparatus for multiple distribution node loads according to an embodiment of the present application, and only the portions related to the embodiment of the present application are shown for convenience of description.
The adjusting device 7 for multi-distribution node load may be a software unit, a hardware unit or a combination of software and hardware unit built in a terminal device (e.g. a tablet computer, a notebook computer, a server, etc.), or may be integrated into the terminal device as a separate pendant.
The adjusting device 7 for the loads of the multiple distribution nodes comprises:
the minimum electricity price determining module 71 is configured to set an electricity price control model, and obtain a minimum electricity price in a preset control time domain of each power distribution node at a current time according to the electricity price control model;
the optimal economic target electricity price determining module 72 is configured to obtain an optimal economic target electricity price in the preset control time domain of each power distribution node at the current time according to the minimum electricity price in the preset control time domain;
and the power distribution node load adjusting module 73 is configured to adjust a load in the preset control time domain of each power distribution node according to an electrical parameter corresponding to the optimal economic target electricity price in the preset control time domain of each power distribution node at the current time.
Optionally, the minimum electricity price determining module 71 includes:
wherein x represents the xth power distribution node; said C isdg(x)Representing the electricity charge of the x-th power distribution node at the time t; the T represents a preset control time domain; said t represents said preLet t be m 1(m is not less than 0 and m is an integer) at any time from the beginning to the end in the control time domain, and e issell(x)(t) represents the online electricity price of the xth power distribution node at the time t; said p isg(x)(t) represents the power value of the xth power distribution node at the time t, the main network outputs power to the microgrid at a positive value, and the power is input at a negative value; said ebuy(x)(t) represents the electricity purchase price of the x-th power distribution node at the time t; said eess(x)(t) representing the electricity price of the xth power distribution node at the moment t after the conversion efficiency of the energy storage device is considered; said p isess(t) represents the power input or output by the energy storage device at the x-th power distribution node at the time t; said KAP(x) Representing the examination price of the x-th power distribution node at the time t; the delta (t) represents the variation of the electricity charge of the xth power distribution node in time at the moment t; said DMINRepresenting the optimal minimum value of the electric charge of the multiple power distribution nodes; n represents n power distribution nodes, n is more than or equal to 1 and n is an integer.
Optionally, the optimal economic target electricity price determining module 72 includes:
a rolling time domain optimal economic target electricity price calculation unit 721 that divides the preset control time domain into a plurality of rolling time domains, and obtains an optimal economic target electricity price of a next rolling time domain based on each rolling time domain;
the optimal economic target electricity price determining unit 722 in the preset control time domain is configured to determine the optimal economic target electricity price in the preset control time domain according to the optimal economic target electricity price obtained before the last rolling time domain in the preset control time domain.
Optionally, the rolling time domain optimal economic target electricity price calculating unit 721 is specifically configured to:
wherein, D isMINRepresenting the current rolling time domain optimal economic target electricity price of the power distribution node; said t ispRepresenting a preset finite time domain duration; the t represents the starting moment of the preset limited time domain; the s represents the time of the passing rolling time domain; s ═ s1 represents the time length of the rolling time domain, s ═ m × 1(m is more than or equal to 1 and m is an integer), and s is less than or equal to tp(ii) a Said C isdg(x)Represents the electricity rate at the (t + s) time of the xth distribution node.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing functional units and modules are merely illustrated in terms of division, and in practical applications, the foregoing functional allocation may be performed by different functional units and modules as needed, that is, the internal structure of the system is divided into different functional units or modules to perform all or part of the above described functions. Each functional unit or module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated units or modules may be implemented in a form of hardware, or in a form of software functional units. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes of the foregoing method embodiments, and are not described herein again.
Referring to fig. 8, a schematic block diagram of an energy management system of a power distribution network according to an embodiment of the present application is shown. The power distribution network energy management system 8 of this embodiment includes: one or more servers 80, a memory 81, and a computer program 82 stored in the memory 81 and operable on the servers 80. The server 80 executes the computer program 82 to implement the steps in the above-mentioned embodiments of the method for adjusting loads of multiple distribution nodes, such as steps S101 to S103 shown in fig. 1. Alternatively, the server 80, when executing the computer program 82, implements the functions of the modules in the above-described embodiment of the distribution network energy management system 8, such as the functions of the modules 71 to 73 shown in fig. 7.
Illustratively, the computer program 82 may be partitioned into one or more modules that are stored in the memory 81 and executed by the server 80 to accomplish the present application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions that describe the execution of the computer program 82 in the power distribution grid energy management system 8. For example, the computer program 81 may be partitioned into a minimum electricity price determining module, an optimal economic target electricity price determining module, and a distribution node load adjusting module.
The minimum electricity price determining module is used for setting an electricity price control model and obtaining the minimum electricity price in a preset control time domain of each power distribution node at the current moment according to the electricity price control model;
the optimal economic target electricity price determining module is used for obtaining the optimal economic target electricity price in the preset control time domain of each power distribution node at the current moment according to the minimum electricity price in the preset control time domain;
and the power distribution node load adjusting module is used for adjusting the load in the preset control time domain of each power distribution node according to the electric parameter corresponding to the optimal economic target electricity price in the preset control time domain of each power distribution node at the current moment. Other units or modules can be referred to the description of the embodiment shown in fig. 7, and are not described again here.
The distribution network energy management system 8 includes, but is not limited to, a server 80, a storage 81, and may further include: a plurality of distribution node micro-sources, a plurality of distribution node energy storage devices, and the like. Those skilled in the art will appreciate that fig. 8 is only one example of a distribution grid energy management system 8 and does not constitute a limitation of the distribution grid energy management system 8, and may include more or fewer components than shown, or some components in combination, or different components.
The server 80 may be a Central Processing Unit (CPU), other general purpose server, a Digital Signal server (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The general-purpose server may be a microserver or the server may be any conventional server or the like.
The storage 81 may be an internal storage unit of the distribution network energy management system 8, such as a hard disk or a memory of the distribution network energy management system 8. The memory 81 may also be an external storage device of the power distribution network energy management system 8, for example, a Smart Media Card (SMC), a Secure Digital Card (SD), a Flash memory Card (FC), or the like is provided on the power distribution network energy management system 8. Further, the memory 81 may also comprise both an internal storage unit of the distribution network energy management system 8 and an external storage device. The memory 81 is used for storing the computer programs and other programs and data required by the distribution network energy management system 8. The memory 81 may also be used to temporarily store data that has been output or is to be output.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated module, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above may be implemented by a computer program, which may be stored in a computer readable storage medium and used by a processor to implement the steps of the embodiments of the methods described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (5)
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| CN107392395A (en) * | 2017-08-23 | 2017-11-24 | 天津大学 | A kind of power distribution network and micro electric network coordination optimization method based on price competition mechanism |
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