CN113850481B - Power system scheduling service assistant decision method, system, device and storage medium - Google Patents
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Abstract
The invention discloses an auxiliary decision-making method, a system, a device and a storage medium for scheduling service of a power system, wherein the method comprises the following steps: acquiring operation mode data of the power system under multiple sections; constructing a knowledge graph of the power system according to the operation mode data; selecting a scheduling decision based on the knowledge graph; wherein the knowledge-graph is continuously updated based on data at a scheduled time; and acquiring the change of the electrical quantity between the nodes and the change of the electrical quantity on the nodes according to the knowledge graph. The method is based on the constantly updated knowledge graph, the operation mode data of the power system is visually presented in the form of the graph, the operation condition of the whole system under the time section is known through the maximum information acquisition surface, researchers can recognize the dynamic change of the power system from the longitudinal axis time axis, and decision of scheduling decision is assisted. The invention can be widely applied to the technical field of power grid dispatching.
Description
Technical Field
The invention relates to the technical field of power grid dispatching, in particular to an auxiliary decision method, system and device for dispatching business of a power system and a storage medium.
Background
Under the new trend of current energy source revolution and electric power market revolution, along with the continuous increase of resource permeability of renewable energy sources, flexible loads, energy storage and the like, the types and the number of power grid dispatching objects are exponentially increased, and the uncertainty of a power grid operation mode is obviously enhanced. The method is limited by conditions such as prediction errors, boundary conditions, mathematical models and optimization algorithms, the problems that the analysis result has larger difference with the actual power grid condition, the optimization result has no solution or the solving time is too long and the like often occur in the actual scheduling, the power grid scheduling is not simple multi-target optimization calculation any more, but the processes of manual re-analysis, adjustment and verification are performed according to the calculation result of scheduling software, the process of manual decision-making usually takes longer time, the efficiency is lower, and the complexity of the optimal scheduling decision-making of the power system is increased sharply.
Disclosure of Invention
To solve at least one of the technical problems in the prior art to a certain extent, an object of the present invention is to provide a power system scheduling service decision-making assisting method, system, device and storage medium.
The technical scheme adopted by the invention is as follows:
an auxiliary decision-making method for power system scheduling service comprises the following steps:
acquiring operation mode data of the power system under multiple sections;
constructing a knowledge graph of the power system according to the operation mode data;
selecting a scheduling decision based on the knowledge graph;
wherein the knowledge-graph is continuously updated based on data at a scheduled time; and acquiring the change of the electrical quantity between the nodes and the change of the electrical quantity on the nodes according to the knowledge graph.
Furthermore, the operation mode data is high-dimensionality and nonlinear multi-section operation simulation data with the scheduling time as the time granularity, and the operation mode data comprises optimal power flow data of the power system, node load active power requirements and fan output power.
Further, the expression of the operation mode data is as follows:
p＝N day (g (num_gen)×T ,r (num_renew)×T ,f (num_branch)×T ,d (num_bus)×T )
wherein g represents the generator set output of the optimal power flow of the power system; r represents the output power of the accessed fan; f represents the line power flow of the optimal power flow of the force system; d represents the network node load active demand; t represents the number of observed sections per day; n is a radical of hydrogen day And representing the number of simulation data groups for simulating the running days of the system.
Further, the operation mode data is acquired through a multi-section operation mode simulation data acquisition model, and the multi-section operation mode simulation data acquisition model is constructed and acquired in the following mode:
taking an IEEE39 node system as a basis of a simulation model, and accessing industrial load, commercial load, residential load and new wind energy into a 39 node;
determining the objective function and constraint condition of the model, and optimizing the variable of the model.
Further, the mathematical model corresponding to the new wind energy is as follows:
wherein V represents the wind speed at the hub height of the fan, V ci Indicating wind cut-in speed, V, of the fan co Indicating the cut-out wind speed, V, of the fan N Indicating rated wind speed, P N Indicating rated output power, P, of the fan WT Representing the actual output power of the wind turbine.
Further, the objective function is the operation cost of the power system including the output of the generator set, and the objective function needs to be minimized to realize optimal scheduling;
the constraint conditions comprise power balance constraint, load flow equation constraint, operation mode reliability and feasibility constraint;
wherein, the operation mode reliability and feasibility constraints comprise: the method comprises the following steps of generator output upper and lower limit constraint, line transmission power namely section upper and lower limit constraint, node voltage allowable deviation range constraint, node phase angle allowable deviation range constraint and unit climbing output constraint.
Further, the constructing a knowledge graph of the power system according to the operation mode data includes:
according to the operation mode data, combining with other power grid dispatching service data, and constructing a dynamic knowledge map of the power system through python software and neo4j software;
the other power grid dispatching service data comprise dispatching regulations, historical cases and experience data.
The other technical scheme adopted by the invention is as follows:
an electric power system scheduling service aid decision system, comprising:
the data acquisition module is used for acquiring the operation mode data of the power system under multiple sections;
the map construction module is used for constructing a knowledge map of the power system according to the operation mode data;
the decision auxiliary module is used for assisting the selection of scheduling decisions according to the knowledge graph;
wherein the knowledge-graph is continuously updated based on data at a scheduled time; and acquiring the change of the electrical quantity between the nodes and the change of the electrical quantity on the nodes according to the knowledge graph.
The other technical scheme adopted by the invention is as follows:
an electric power system dispatching business aid decision-making device comprises:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method described above.
The invention adopts another technical scheme that:
a storage medium having stored therein a processor-executable program for performing the method as described above when executed by a processor.
The invention has the beneficial effects that: the invention is based on the knowledge graph which is updated continuously, the operation mode data of the power system is visually presented in the form of the graph, the operation condition of the whole system under the time section is known by the maximum information acquisition surface, and researchers can axially identify the dynamic change of the power system from the time axis of the longitudinal axis to assist the decision of scheduling decision.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description is made on the drawings of the embodiments of the present invention or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart illustrating steps of a power system scheduling service decision-making aid method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of data acquisition of an operation mode of a multi-section power system according to an embodiment of the present invention;
FIG. 3 is a flow diagram of knowledge graph construction in an embodiment of the invention;
FIG. 4 is a full map of a knowledge graph profile in an embodiment of the present invention;
FIG. 5 is a knowledge-graph of different discontinuities in an embodiment of the present invention;
FIG. 6 is a schematic diagram of an IEEE39 node system in an embodiment of the present invention;
fig. 7 is a schematic diagram of an electric power system scheduling service assistant decision method in an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings only for the convenience of description of the present invention and simplification of the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and larger, smaller, larger, etc. are understood as excluding the number, and larger, smaller, inner, etc. are understood as including the number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise specifically limited, terms such as set, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention by combining the specific contents of the technical solutions.
The artificial intelligence technology can learn and simulate the accumulated massive power grid regulation and control operation experience and knowledge, and can replace a large amount of repetitive manual adjustment work. Multisource data, professional Knowledge and manual experience accumulated throughout the year by a dispatching center are extracted and condensed by means of a Knowledge Graph (KG) technology, and intelligent auxiliary decision making before and after optimization operation is carried out through Knowledge search and reasoning, so that the efficiency and the quality of the optimization process are expected to be improved, and manual tedious work is reduced. The regulation and control field develops the preliminary exploration of knowledge map application and supports the application of electronization, fault handling, switching operation, dialogue question and answer and the like based on operation rules; the above scenarios are based on fixed rules or the repeatability and immobilization operation of the process. Considering that the power system is a high-dimensional and dynamic change system, the data source, the data structure and the data content of the power system change at any time, and accordingly, the optimization decision conditions also change accordingly. Therefore, the method is of great significance for optimizing decision-making service, constructing a dynamic knowledge graph covering scheduling rules and artificial experiences and reflecting dynamic changes of scheduling scenes, and realizing intelligent scheduling aid decision based on the dynamic knowledge graph.
The embodiment provides an assisted decision method for scheduling service of a super-large-scale power system based on a knowledge graph, aiming at the power system scheduling service with data uncertainty, dimensionality and exponential increase and the application of the knowledge graph technology. The method is characterized in that a knowledge graph framework facing fixed rules is designed on the basis of multi-source heterogeneous data related to scheduling optimization decision-making services such as a power grid model, power grid operation data, scheduling rules, historical cases, dispatcher experience and the like, and a dynamic knowledge graph auxiliary scheduling decision-making task of the power system is defined. The dynamic knowledge graph not only comprises entities such as loads, nodes, line tide, generator sets, wind energy sets and the like, but also comprises attribute data such as set parameters, set output and the like, and the graph can update the attribute data in the entities along with different time sections. By means of the advantages of large data storage capacity of the knowledge graph and high data searching and reasoning speed, a dispatcher realizes intelligent retrieval and deep mining of the relationship between nodes through the knowledge graph, provides a new visual structure data source for departments of operation inspection, dispatching and the like, completes the auxiliary decision of dispatching services of the super-large-scale power system, provides a new auxiliary scheme for solving the problem of pain points of the dispatching services under new conditions, and greatly shortens the time required by the decision.
As shown in fig. 1 and fig. 7, the present embodiment provides an auxiliary decision method for scheduling service of an electric power system, including the following steps:
s1, constructing a multi-section operation mode simulation data acquisition model.
In this embodiment, the specific steps of the multi-section operation mode simulation data acquisition model are as follows:
1) Establishing a simulation model, accessing a daily load curve and accessing new wind power energy:
referring to fig. 2 and 6, an IEEE39 node system is selected as a simulation basis, and different industrial loads, commercial loads, residential loads, and new wind energy are all connected to 21 load nodes of a 39-node case on the premise of ensuring that the magnitude of the sum of total loads matches the power generation capacity of 39 nodes in order to fit the situation that different power characteristics and the permeability of renewable energy are continuously improved for different load nodes in an actual power system.
The wind power field output depends on wind energy resources and is restrained by natural conditions, and the wind power of China has a reverse regulation characteristic, namely the wind power output is small due to low wind speed or no wind when the load demand is high in the daytime; when the load demand is reduced at night, the wind power output is large but the wind power output is difficult to be timely consumed due to large wind speed. Thus, using the Weibull distribution to describe the random variation of wind speed, the wind speed probability density function is as follows:
in the formula, c represents a proportional parameter and represents the annual average wind speed of statistical data, k represents a shape parameter, the value is generally between 1.8 and 2.3, and the change of the probability function is greatly influenced. Under the condition of keeping c unchanged, along with the increase of k, the trend of the function probability curve in the direction of the longitudinal axis is gradually steeper, and the peak value of the curve is also increased, namely the wind speed distribution is more concentrated near the peak value; and the coordinate value of the horizontal axis corresponding to the peak value of the function probability curve in the horizontal axis direction is increased, namely the wind speed corresponding to the maximum probability is increased continuously.
The wind power output is directly linked with the wind speed, the wind speed distribution rule of the load weibull distribution is obtained by the formula, and the obvious nonlinearity is presented. The mathematical model for establishing the wind power output is as follows:
in the formula, V represents the wind speed at the height of the hub of the fan; v ci Indicating the wind speed, V, cut into the fan co Indicating the cut-out wind speed, V, of the fan N Indicating rated wind speed, P N Indicating rated output power, P, of the fan WT Representing the actual output power of the fan.
According to the wind power output model, wind power generation of wind speed under each time section is obtained and normalized, and the reference of the cut-in wind speed, the cut-out wind speed and the rated wind speed of the fan is shown in table 1.
TABLE 1
2) Objective function and optimization variable
Initially, the economic cost of the generator set is taken as an objective function for determining the operation mode and the optimal power flow of the power system, a power flow equation, a power balance equation and after each approximation, the minimum objective function is taken as a solving target, and the line transmission power, the node voltage, the phase angle and the output of the generator set under the corresponding optimal power flow distribution are obtained and taken as the optimal power flow data of the power system. In this embodiment, the consumption-power characteristic of the generator set is used as an economic cost function, and is expressed as follows:
C k (t)＝a k_2 P gen_k (t) 2 +a k_1 P gen_k (t)+a k_0
wherein, C k The power generation cost of the kth generator is represented and is the sum of the output costs of the 96 time-section generators in the same day; p gen_k Representing the actual output active power of the kth generator set; a is k_2 、a k_1 、a k_0 Representing a cost factor for the kth genset;
optimizing variables including active output P of generator set of power system after power flow gen (busm, T), line current P line (bram, T), which are variables for the number of nodes bumm and the time granularity T and variables for the number of branches bram and the time granularity T, respectively.
3) Power balance constraint
In order to ensure the safety and reliability principles of the changed operation mode load operation, a power balance constraint needs to be set, that is, the active power required by the load on the node in the network keeps balanced with the active power output by the generator set, as follows:
in the formula, P load (t) represents the active demand of the load under each time section, P gen And (t) the active power output of the generator set under each time section. In order to ensure the active power balance of the power system and maintain the frequency stability of the whole system, all the devices must be setThe active load demand of the node is consistent with the active output of the generator; in addition, in order to ensure the stability and reliability of the operation of the power system, it is necessary to set the active load requirements of all the nodes to be less than or at least equal to the sum of the upper active output limits of the generator set.
4) Flow equation constraints
In order to more quickly and simply a power flow equation scheduled in an operation mode, the power flow equation is used as a constraint condition of an optimal power flow in the operation mode of the power system, and meanwhile, in order to meet the requirements of quick analysis calculation and real-time operation scheduling, a direct current power flow is generally used, but a large deviation may be brought to calculation, so that a linear alternating current power flow is adopted in the embodiment as follows:
wherein,
N branch is the number of branches; in the above formula, P mn 、Q mn 、r mn 、x mn Respectively, active transmission power, reactive transmission power, resistance and reactance between nodes m and n: p m 、Q m 、U m 、δ m Respectively, the active power, the reactive power, the voltage and the phase angle of the node m.
5) And operation mode reliability and feasibility constraints.
1. And (3) restraining the upper and lower output limits of the generator:
P gen.min ≤P gen ≤P gen.max
in the formula, P gen.min The lower limit of the output of the generator is represented and is generally 0; p gen.max The output upper limit of the generator is expressed, and 10 generators in the default case39 node system all participate in the scheduling process, but a specific set combination can be set in the subsequent research.
2. Line transmission power, namely, upper and lower section limits constraint:
-P mn,max ≤P mn ≤P mn,max
in the formula, P mn,max And (3) representing the line section out-of-limit capacity between the nodes m and n, wherein all branches in the default case39 node system participate in the scheduling process, but whether each line has transmission power depends on the running condition of analog simulation, and a specific line can be set not to participate in scheduling in subsequent research.
3. Node voltage allowed offset range constraint:
U m,min ≤U m ≤U m,max
in the formula of U m,min Represents the lower voltage limit, U, of the node m allowed to operate m,max The upper limit of the voltage at which the node m is allowed to operate is generally set to (0.95-1.05) U N . From the viewpoint of ensuring the quality of the supply voltage, all electrical devices of the system must operate near the rated voltage.
4. Node phase angle allowed shift range constraint:
-δ c ≤Δδ m ≤δ c
wherein, delta c The degree of the phase angle of the granularity per time of the same set node can be shifted is represented by 6.28rad.
Considering the temporal coupling of electrical quantities between time granularities:
5. unit climbing output restraint:
wherein R is a proportionality coefficient, U g Refers to the upward climbing capability of the generator, D g Refers to the ability of the generator to climb downward.
And S2, acquiring operation mode data of the power system under the multiple sections.
In this embodiment, firstly, based on a power system model, a power flow equation constraint and a safety and stability constraint, matlab and gams business software are used to design an optimal power flow scheme of a power system, and taking an IEEE39 node system as an example, three load curves of residents, industries and businesses are accessed, wind power generation conforming to weibull distribution is accessed, and power system operation mode data under multiple sections is obtained and used as a data support for subsequent research, wherein the specific flow is as shown in fig. 2.
The scheduling period can be divided according to time granularity, such as one day, one hour, one quarter of a clock, five minutes and the like. The scheduling data of one day is collected and expressed in the form of fine time granularity and multiple time discontinuities, so that the change of the state of the power system can be reflected more clearly. As the design and construction of the dynamic knowledge graph need a power grid topological structure, and multi-source heterogeneous data such as operation mode data, scheduling regulations and the like are supported. However, measurement data of an actual system operation mode is often difficult to collect and is easily influenced by communication, so a multi-section operation mode simulation data acquisition model needs to be constructed to acquire a massive data support construction map.
The data defining the operating mode of the power system for data driving comprises: optimal power flow data (generator set active output, line active power flow), node load active demand and fan output power of the power system. The data is high-dimensional, non-linear multi-section running simulation data with time granularity of time (e.g., 15 minutes).
p＝N day (g (num_gen)×T ,r (num_renew)×T ,f (num_branch)×T ,d (num_bus)×T )
G in the formula represents the output of a generator set of the optimal power flow of the power system; r represents the output power of the accessed fan, and the fan is accessed by a fixed node; f represents the line power flow of the optimal power flow of the force system; d represents the network node load active demand. T represents the number of observed sections per day, the granularity is determined by taking 15 minutes as time granularity, and T is 96; n is a radical of day And representing the number of simulation data sets, which is used for simulating the number of days for operating the system so as to quantize the data available for data-driven processing.
And S3, constructing a knowledge graph of the power system according to the operation mode data.
And (3) replacing actual measurement data which is acquired by a data acquisition and monitoring System (SCADA) and a synchronous phasor measurement system (PMU) in the power system and is easy to be interfered by communication and a network by using the high-dimensional nonlinear simulation data acquired in the step (S2) and combining other power grid dispatching service data. By the steps of knowledge extraction, knowledge representation learning, knowledge mining, knowledge reasoning, running-in and the like, multi-source heterogeneous data such as a power grid topological structure, operation mode data, scheduling rules and the like are used as supports, and a power system knowledge graph is constructed by utilizing python and Neo4j commercial software. The map can be updated according to the scheduling time data in a rolling manner, and the specific flow is shown in fig. 3.
When the knowledge graph is constructed by connecting Neo4j through python, the detailed steps can be roughly divided into data acquisition and classification, node type and connection relation determination, assignment of the node and edge attributes and graph visualization.
1) Data input into the python end is standardized, so that the data can be automatically classified when the python reads in the data, classification labels are automatically marked, and nodes and relation connecting edges of corresponding types can be created conveniently when a subsequent graph is constructed. After data is acquired, preprocessing the data to delete some redundant and repeated data and interference noise items; and then classifying the preprocessed data, wherein the large classification aspects comprise node types, connection relation types, relation edge types, attribute types and the like, and the small classification aspects comprise industrial, residential and commercial load node types, renewable energy access node types, light storage and wind storage node types and the like, so that the subsequent map construction is facilitated.
2) And establishing a node for the data belonging to the node type according to the classified data, wherein the node comprises a node name and a node id, and establishing a relationship edge for the relationship edge type node, and the relationship edge comprises a relationship edge name and a relationship edge id. And then, the corresponding nodes and the corresponding relation edges are correspondingly connected one by one through the connection relation data to form a triple directional connection relation of the nodes → the edges → the nodes, and finally, all the nodes and the directional relation edges are connected to construct a preliminary knowledge graph, as shown in fig. 4.
3) And correspondingly adding attributes to the nodes and the directed relationship edges according to attribute type data, wherein the node attributes comprise seasonal load characteristics of different types, probability distribution of renewable energy sources such as wind power generation and photovoltaic power generation, energy storage charge state and the like. And the directed relation side represents the active power and the reactive power of the line tide, and the power flow direction is judged according to the set positive direction. In the process, the direction of the original directed relationship edge can be changed or the direction of the unrefined relationship edge can be added according to the attribute type data. For example, the line tide between two nodes is changed along with the time sequence change of load, new energy and the like, and the A-B relation in the map can be automatically corrected according to the attribute type data to correct and update the map.
4) And connecting python and Neo4j, so that the knowledge map established in python is visualized in Neo4 j. In Neo4j, the constructed full-map information of the knowledge graph, including node information, relationship side information and attribute information, can be viewed. Meanwhile, only the information of a certain type or several types of nodes can be checked, and the knowledge graph information can be better read conveniently. Besides, node or relation edge modification and attribute correction and addition can be carried out on the knowledge graph in Neo4j without reconstructing a new graph. The constructed knowledge graph is shown in FIG. 5.
And S4, selecting auxiliary scheduling decisions according to the knowledge graph.
Through the established knowledge graph, the change of the electrical quantity between the nodes and the change of the electrical quantity on the nodes are clearly observed. The scheduling personnel can know the operation condition of the whole system under the time section by the largest information acquisition surface from the visual map data display, and meanwhile, the power system knowledge map at the future scheduling moment is continuously generated, so that researchers can better identify the dynamic change of the power system under the time sequence, the decision of the scheduling decision is assisted, the blind operation scheduling is effectively avoided, the distance between the scheduling decision and the optimal decision is greatly shortened, and the time required by the decision is shortened. In addition, the knowledge graph can visually display abnormal data such as faults of section out-of-limit, unit output out-of-limit, line short circuit and the like, and real-time troubleshooting is facilitated. Knowledge graph-based aided decision generation has the following steps:
1) Firstly, according to the established knowledge graph, the relationship between the nodes, namely the change of the electrical quantity of the edge, and the change of the electrical quantity on the nodes are clearly observed. Secondly, researchers can get the operation condition of the whole system under the time section through the largest information acquisition surface in the visual map data display, and meanwhile, a knowledge map of the power system under a new time section is continuously generated, so that the researchers can recognize the dynamic change of the power system from the time axis of the longitudinal axis, and the decision of scheduling decision is assisted.
2) And determining a decision for a certain operation mode, guiding the system to make an adjustment action, obtaining a power system operation mode p' after the decision, and obtaining a knowledge graph after the decision again. The researchers compare and mark the change of the knowledge graph before and after the decision, construct a state action pair in the form of 'operation mode-knowledge graph-decision', record different state action pairs, know which parameter or electric quantity has the greatest influence on the decision or has the greatest influence on which nodes, branches and buses of the system after the decision, and complete the feedback of the auxiliary decision. Under the condition of continuous experience accumulation, each state action pair is the update of the previous state action pair, and a dispatcher can gradually form a corresponding 'operation mode-knowledge graph-optimal decision pair' aiming at different operation modes to adapt to the requirement of the power system dispatching service under the new situation, thereby effectively avoiding the blind operation dispatching, greatly shortening the 'distance' between the dispatching decision and the optimal decision and reducing the time required by the decision.
The above method is explained in detail with reference to specific examples below.
In this embodiment, an IEEE39 node system is taken as an illustration object, and the system has a complete topology structure, 21 load nodes, 10 generators, 46 branches, and each load node is connected to a different load curve, and 9 fixed load nodes are respectively set to be connected to a new energy source for output. By using the knowledge graph-based scheduling service auxiliary decision method for the super-large-scale power system, scheduling service decisions in different operation modes are researched. Table 1 shows the fan output parameters:
TABLE 1
In the embodiment, the time granularity is set to be 15 minutes, namely, each 15 minutes is a time section, and relevant information such as topology, load active power demand, new energy output and the like is obtained.
The following specifically describes the steps of the decision-making method for assisting scheduling service of a very large-scale power system based on a knowledge graph to optimize an algorithm:
the method comprises the steps of firstly, acquiring and normalizing industrial, commercial and residential life electricity loads of 96 time sections per hour under the time granularity, accessing a normalized load curve based on IEEE39 node current data, and recording constants such as node number, branch number and generator number, a parameter matrix such as active demand of load, fan power generation output and branch impedance data.
And secondly, importing designed matlab and gams solvers to obtain power system operation mode data under the optimal power flow, and enabling the generator set output and the line power flow in the optimal power flow data, the load active power demand and the wind power generation to jointly form high-dimensional and nonlinear high-frequency power system operation mode data.
And thirdly, constructing a dynamic knowledge graph by utilizing python and neo4 j. Standardizing data input into the python end, creating nodes and relationship connecting edges of corresponding types, and forming a triple directional connection relationship of nodes → edges → nodes to obtain a preliminary knowledge graph. And correspondingly adding attributes to the nodes and the directed relationship edges according to attribute type data, and finally visualizing the knowledge graph in neo4 j. In Neo4j, the constructed full-map information of the knowledge graph, including node information, relationship side information and attribute information, can be viewed, and data can be directly updated on the graph without reprogramming.
And fourthly, on the basis of the formed knowledge graph, the dispatcher observes the relationship between the nodes, namely the change of the electrical quantity of the edge and the change of the electrical quantity on the nodes. Researchers can obtain the maximum information acquisition surface from the visual and clear map data display to know the operation condition of the whole system under the time section; meanwhile, the knowledge graph under different discontinuities is continuously updated, researchers can recognize dynamic changes of the power system from the angle of a longitudinal time axis, and decision making of scheduling is assisted. And finally, continuously updating the state action pair of the operation mode, the knowledge graph and the auxiliary decision under the condition of continuous experience accumulation to obtain a distance optimal scheduling solution.
In summary, compared with the prior art, the method of the embodiment has the following beneficial effects:
(1) The method is driven by simulation data, and the decision-making method is assisted by the scheduling service of the super-large scale system based on the knowledge graph, does not need complex mechanism model reasoning, only needs to obtain the operation mode data of the power system, and can adapt to the requirements of the actual power system.
(2) Compared with the load flow calculation in a module library of packaged commercial software, such as a runnopf function in matpower, the optimal load flow self-programming program provided by the embodiment can obtain simulation data, and the self-programming program can determine operation mode data according to actual system requirements by independently designing a target function. In addition, a more complex and more practical trend can be designed by setting safety constraint conditions or considering the climbing capacity of the time-coupled unit.
(3) The dynamic knowledge graph construction method provided by the embodiment with the advantages of large knowledge graph data storage capacity and high data searching and reasoning speed is based on a dynamic knowledge graph under multi-section power system simulation data, can visually present operation mode data of power system topology and fine time granularity in a graph form, can know the operation condition of the whole system under a time section by using the maximum information acquisition surface, and can enable researchers to recognize dynamic changes of the power system from a longitudinal axis time axis direction and assist decision making of scheduling.
The present embodiment further provides an electric power system scheduling service decision-making assisting system, including:
the data acquisition module is used for acquiring the operation mode data of the power system under multiple sections;
the map construction module is used for constructing a knowledge map of the power system according to the operation mode data;
the decision auxiliary module is used for assisting the selection of scheduling decisions according to the knowledge graph;
wherein the knowledge-graph is continuously updated based on data at a scheduling time; and acquiring the change of the electrical quantity between the nodes and the change of the electrical quantity on the nodes according to the knowledge graph.
The power system scheduling service assistant decision system of the embodiment can execute the power system scheduling service assistant decision method provided by the embodiment of the method of the invention, can execute any combination implementation steps of the embodiment of the method, and has corresponding functions and beneficial effects of the method.
The present embodiment further provides an auxiliary decision device for power system scheduling service, including:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of fig. 1.
The electric power system scheduling service assistant decision-making device provided by the embodiment of the invention can execute the electric power system scheduling service assistant decision-making method provided by the embodiment of the invention, can execute any combination of the implementation steps of the embodiment of the method, and has corresponding functions and beneficial effects of the method.
The embodiment of the application also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and executed by the processor to cause the computer device to perform the method illustrated in fig. 1.
The embodiment also provides a storage medium, which stores an instruction or a program capable of executing the power system scheduling service assistant decision method provided by the embodiment of the method of the invention, and when the instruction or the program is run, the method can be executed by any combination of the embodiments of the method, and the method has corresponding functions and beneficial effects.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be understood that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer given the nature, function, and interrelationships of the modules. Accordingly, those of ordinary skill in the art will be able to practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Further, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. An auxiliary decision-making method for scheduling service of an electric power system is characterized by comprising the following steps:
acquiring operation mode data of the power system under multiple sections;
constructing a knowledge graph of the power system according to the operation mode data;
selecting a scheduling decision based on the knowledge graph;
wherein the knowledge-graph is continuously updated based on data at a scheduled time; acquiring the change of the electrical quantity between the nodes and the change of the electrical quantity on the nodes according to the knowledge graph;
the operation mode data is high-dimensional and nonlinear multi-section operation simulation data with the scheduling time as time granularity, and comprises optimal power flow data of a power system, node load active power requirements and fan output power;
the expression of the operation mode data is as follows:
p＝N day (g (num_gen)×T ,r (num_renew)×T ,f (num_branch)×T ,d (num_bus)×T )
wherein g represents the generator set output of the optimal power flow of the power system; r represents the output power of the accessed fan; f represents the line power flow of the optimal power flow of the force system; d represents the network node load active demand; t represents the number of observation sections per day; n is a radical of hydrogen day Representing the number of simulation data groups for simulating the number of days for operating the system;
the operation mode data is acquired through a multi-section operation mode simulation data acquisition model, and the multi-section operation mode simulation data acquisition model is constructed and acquired in the following mode:
taking an IEEE39 node system as a basis of a simulation model, and accessing industrial load, commercial load, residential load and new wind energy into a 39 node;
determining an objective function and a constraint condition of the model, and optimizing variables of the model; the objective function is the running cost of the power system including the output of the generator set, and the objective function needs to be minimized to realize optimized dispatching;
the mathematical model corresponding to the new wind energy is as follows:
wherein V represents the wind speed at the hub height of the fan, V ci Indicating the wind speed, V, cut into the fan co Indicating the cut-out wind speed, V, of the fan N Indicating rated wind speed, P N Indicating rated output power, P, of the fan WT Representing the actual output power of the fan;
wherein the constraint condition comprises:
(1) Constraint of power balance
Setting power balance constraint, namely, keeping the active power required by the load on the nodes in the network balanced with the active power output by the generator set, as follows:
in the formula, P load (t) represents the active demand of the load under each time section, P gen (t) representing the active power output of the generator set under each time section;
(2) Flow equation constraints
The linear ac power flow is used as follows:
wherein,
in the above formula, P mn 、Q mn 、r mn 、x mn Respectively, active transmission power, reactive transmission power, resistance and reactance between nodes m and n: p is m 、Q m 、U m 、δ m Are respectively a sectionActive power, reactive power, voltage, phase angle at point m;
(3) Operational mode reliability, feasibility constraints
1. And (3) restraining the upper and lower output limits of the generator:
P gen.min ≤P gen ≤P gen.max
in the formula, P gen.min Representing a lower output limit of the generator; p gen.max Expressing the upper limit of the output of the generator;
2. line transmission power, namely, upper and lower section limits constraint:
-P mn,max ≤P mn ≤P mn,max
in the formula, P mn,max Representing the line section out-of-limit capability between the nodes m and n;
3. node voltage allowed offset range constraint:
U m,min ≤U m ≤U m,max
in the formula of U m,min Represents the lower voltage limit, U, of the node m allowed to operate m,max Represents the upper voltage limit allowed for node m to operate;
4. node phase angle allowed shift range constraint:
-δ c ≤Δδ m ≤δ c
wherein, delta c Representing the degree of deviation of the set granularity phase angle per time of the same node;
considering the temporal coupling of electrical quantities between time granularities:
5. unit climbing output restraint:
wherein R is a proportionality coefficient, U g Refers to the upward climbing capability of the generator, D g Refers to the ability of the generator to climb the hill.
2. The power system dispatching business assistant decision method according to claim 1, wherein the constructing a knowledge graph of a power system according to the operation mode data comprises:
according to the operation mode data, in combination with other power grid scheduling service data, a dynamic knowledge map of the power system is constructed through python software and neo4j software;
the other power grid dispatching service data comprise dispatching regulations, historical cases and experience data.
3. An electric power system dispatching business assistant decision-making system is characterized by comprising:
the data acquisition module is used for acquiring the operation mode data of the power system under multiple sections;
the map construction module is used for constructing a knowledge map of the power system according to the operation mode data;
the decision auxiliary module is used for assisting the selection of scheduling decisions according to the knowledge graph;
wherein the knowledge-graph is continuously updated based on data at a scheduled time; acquiring the change of the electrical quantity between the nodes and the change of the electrical quantity on the nodes according to the knowledge graph;
the operation mode data is high-dimensional and nonlinear multi-section operation simulation data with the scheduling time as time granularity, and comprises optimal power flow data of a power system, node load active power requirements and fan output power;
the expression of the operation mode data is as follows:
p＝N day (g (num_gen)×T ,r (num_renew)×T ,f (num_branch)×T ,d (num_bus)×T )
wherein g represents the generator set output of the optimal power flow of the power system; r represents the output power of the accessed fan; f represents the line power flow of the optimal power flow of the force system; d represents the network node load active demand; t represents the number of observed sections per day; n is a radical of day Representing the number of simulation data groups for simulating the number of days for operating the system; the operation mode data is obtained through a multi-section operation mode simulation data obtaining model, and the multi-section operation modeThe simulation data acquisition model is constructed and obtained in the following way:
taking an IEEE39 node system as a basis of a simulation model, and accessing industrial load, commercial load, residential load and new wind energy into a 39 node;
determining an objective function and a constraint condition of the model, and optimizing variables of the model; the objective function is the running cost of the power system including the output of the generator set, and the objective function needs to be minimized to realize optimized dispatching;
the mathematical model corresponding to the new wind energy is as follows:
wherein V represents the wind speed at the hub height of the fan, V ci Indicating wind cut-in speed, V, of the fan co Indicating the cut-out wind speed, V, of the fan N Indicating rated wind speed, P N Indicating rated output power, P, of the fan WT Representing the actual output power of the fan;
wherein the constraint condition comprises:
(1) Power balance constraint
Setting power balance constraint, namely, keeping the active power required by the load on the nodes in the network balanced with the active power output by the generator set, as follows:
in the formula, P load (t) represents the active demand of the load under each time section, P gen (t) representing the active power output of the generator set in each time section;
(2) Flow equation constraints
The linear ac power flow is used as follows:
wherein,
in the above formula, P mn 、Q mn 、r mn 、x mn Respectively, active transmission power, reactive transmission power, resistance and reactance between nodes m and n: p is m 、Q m 、U m 、δ m The active power, the reactive power, the voltage and the phase angle of the node m are respectively;
(3) Operational mode reliability, feasibility constraints
1. And (3) restraining the upper and lower output limits of the generator:
P gen.min ≤P gen ≤P gen.max
in the formula, P gen.min Representing a lower output limit of the generator; p gen.max Expressing the upper limit of the output of the generator;
2. line transmission power, namely, upper and lower section limits constraint:
-P mn,max ≤P mn ≤P mn,max
in the formula, P mn,max Representing the line section out-of-limit capability between the nodes m and n;
3. node voltage allowed offset range constraint:
U m,min ≤U m ≤U m,max
in the formula of U m,min Represents the lower voltage limit, U, of the node m allowed to operate m,max Represents the upper voltage limit that node m is allowed to operate;
4. node phase angle allowed shift range constraint:
-δ c ≤Δδ m ≤δ c
wherein, delta c Representing the degree of possible deviation of the set granularity phase angle per time of the same node;
considering the temporal coupling of the electrical quantities between the time granularities:
5. unit climbing output restraint:
wherein R is a proportionality coefficient, U g Refers to the upward climbing capability of the generator, D g Refers to the ability of the generator to climb the hill.
4. An electric power system dispatching business assistant decision-making device is characterized by comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of any one of claims 1-2.
5. A storage medium having stored therein a program executable by a processor, wherein the program executable by the processor is adapted to perform the method of any one of claims 1-2 when executed by the processor.
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