CN110957758B - Comprehensive energy efficiency evaluation and control performance optimization method based on wide-area situation awareness - Google Patents
Comprehensive energy efficiency evaluation and control performance optimization method based on wide-area situation awareness Download PDFInfo
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Abstract
The invention discloses a comprehensive energy efficiency evaluation and control performance optimization method based on wide-area situation perception, which comprises a source network source load wide-area situation perception method based on the combination of a mechanism model and data drive, an edge node calculation method based on situation perception and a comprehensive energy efficiency and control performance evaluation method; a source network source-load wide area situation perception method and an edge node calculation method are based on and provide support for a comprehensive energy efficiency and control performance evaluation method. The invention is suitable for complex network systems including but not limited to complex network theory and visual data mining algorithm of power systems, and aims to realize the technologies of clean energy broadband measurement, wide area perception, power generation capacity prediction and real-time control, establish a smart power grid operation control system with wide interconnection of sources, networks and loads and friendly interaction and establish an intelligent regulation and control system supporting an energy market based on ubiquitous power Internet of things.
Description
Technical Field
The invention belongs to the technical field of energy system management, optimization and control, and particularly relates to a comprehensive energy efficiency evaluation and control performance optimization method based on wide-area situation perception.
Background
Along with large-scale quick access of new energy, the high volatility and randomness of the new energy bring drastic changes to a power generation side, a power utilization side and a power grid side, and a friendly interactive source-grid-load coordinated operation control system and an intelligent regulation and control system for supporting an energy market are key technical means for solving the problems. And with the continuous improvement of the living standard of people, the demand for energy utilization is changed from 'using more, using better' to 'using better'. Therefore, the source network load coordination control needs to improve the level and the energy utilization efficiency, and social green low-carbon economic operation is realized.
An intelligent regulation and control system for a friendly and interactive smart power grid operation control system and a supporting energy market needs to rely on a ubiquitous sensing technology, dynamically monitor multiple parameters such as user energy behaviors, equipment states and energy consumption information, realize interconnection and intercommunication of diversified systems and equipment based on a unified information model of 'model + data drive', promote application of a ubiquitous power internet-of-things technology in energy efficiency analysis scenes of terminal energy internet (such as large industrial and mining enterprises, enterprises and public institutions, public service institutions, commercial complexes and industrial/agricultural industrial parks) and regional energy internet (thermal power, large gas turbines, centralized photovoltaic and wind power) systems, and improve the full service chain capacity of focused energy planning and design, diagnostic analysis, scheme making, project operation and optimized control by using control core algorithms such as multi-energy complementary coupling and flexible resource aggregation and adjustment. The high-efficiency utilization of comprehensive energy is realized, and the balance of supply and demand of a power grid in a source grid load area is promoted.
Therefore, on the basis of deeply understanding the mechanism of the frequency modulation of the conventional power system, a series of theoretical and technical problems of participation of the smart grid system in the grid frequency modulation need to be deeply researched, theoretical basis and technical reference are provided for constructing efficient and economical frequency modulation application of the smart grid system, and technical support is provided for guiding the application and positioning of the smart grid system in the frequency modulation auxiliary service.
Disclosure of Invention
The invention provides a comprehensive energy efficiency evaluation and control performance optimization method based on wide-area situation awareness, which comprises but is not limited to complex network theory and a visual data mining algorithm of a power system, and aims to realize clean energy broadband measurement, wide-area awareness, generating capacity prediction and real-time control technology based on ubiquitous power Internet of things, and establish a smart power grid operation control system and an intelligent regulation and control system supporting an energy market, wherein the smart power grid operation control system is widely interconnected with a source, a network and a load and is friendly to interact.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a comprehensive energy efficiency evaluation and control performance optimization method based on wide-area situation perception comprises a source network source load wide-area situation perception method based on the combination of a mechanism model and data driving, an edge node calculation method based on situation perception and a comprehensive energy efficiency and control performance evaluation method;
a source network source load wide area situation perception method and an edge node calculation method are based on and support is provided for a comprehensive energy efficiency and control performance evaluation method.
As a further improvement of the invention, the source network source-load wide-area situation perception method based on the combination of the mechanism model and the data drive comprises the following steps:
the method comprises the steps of modeling a multi-time-scale electric energy management control architecture facing to an energy internet, considering an external characteristic function model of distributed multi-type energy and industrial and residential loads and considering a comprehensive energy internet model including a power transmission line model, a natural gas network model, a microgrid power electronic model and an industrial and residential load model aiming at the characteristic of high permeability of the distributed energy, and analyzing the influence of a relevant model on the system stability of the energy internet.
The correlation modeling, controllability and quantitative analysis of a thermal power generation system and a gas turbine unit in the comprehensive energy system are used for proposing that the gas turbine comprehensive energy microgrid participates in thermal power generation grid source coordination control frequency modulation control architecture modeling and analyzing the influence of the architecture modeling, and the intelligent microgrid participates in thermal power generation grid source coordination predictive control algorithm research and verification;
situation perception of data-driven modeling, collecting historical data including residential electricity consumption data, heating data, hot events and meteorological data, and fitting objective functions of various factors and demand load of an electricity consumption side; aiming at a controlled object or equipment, establishing an object-oriented information fusion data model; determining a normal data fluctuation interval in a normal state aiming at an object-oriented information fusion data model; and adopting an online identification modeling or big data model to judge the deviation degree of the current data fluctuation interval and the normal data fluctuation interval of the controlled object or equipment in real time or offline, namely situation awareness.
As a further improvement of the invention, the process that the comprehensive energy micro-grid such as a gas turbine participates in the modeling of the thermal power generation grid source coordination control frequency modulation control architecture and the influence of the modeling is analyzed as follows: firstly, combining the frequency characteristic and the load characteristic of a power system of a traditional coal-fired thermal generator set coordinated control system, systematically analyzing a region equivalent method in a power grid frequency modulation simulation model, and constructing a region equivalent model of conventional power system frequency modulation; according to the heterogeneous and dispersive characteristics of a communication network architecture and a coordination control system, a looped network communication architecture model of the thermal power generation local control communication equipment containing renewable energy source supplement is constructed, and basic theory verification of data monitoring, economic dispatching plan, power flow analysis, frequency and voltage stability, self power generation plan and real-time monitoring function is completed; the supply and demand balance in the primary and secondary frequency modulation control loops of the interconnected power system consists of three parts, namely a generator, a load and the exchange power of a tie line; the key of constructing a frequency response model required by primary and secondary frequency modulation control of the generator set is the modeling of a speed regulating system and a prime motor; the thermal power generating unit is provided with a primary frequency modulation control loop and a secondary frequency modulation control loop on the structures of a speed regulating system and a prime motor; in addition, the load of the system and the links between control areas need to be considered; respectively modeling the unit, the load, the tie line and the primary and secondary frequency modulation control loops, and combining according to the structural relationship in the actual power system to obtain a frequency modulation dynamic model of the power system;
secondly, considering external characteristic function models of distributed energy sources and loads and three-phase steady-state models of a power distribution system including a thermal power generation model, a power transmission line model, a distribution transformer model, a parallel capacitor model and a load model, and analyzing the influence of related models on the system stability of the self-optimization-seeking power distribution network aiming at the characteristics of high permeability of distributed energy sources such as a gas turbine, photovoltaic and wind power and the like and high permeability of demand side response and the like.
As a further improvement of the invention, the intelligent micro-grid participates in the research and verification process of the thermal power generation grid source coordination predictive control algorithm as follows: providing an intelligent micro-grid participating thermal power generation grid source coordination prediction control system which is of a layered distributed framework structure and comprises an upper-layer regional coordination controller and a lower-layer local frequency modulation controller; introducing into a regional coordination controller, constructing an optimization model taking output error and control increment weighting as objective functions and thermal power generating units, a micro-grid and tie line regulation capacity as constraint conditions by establishing the relation between a battery energy storage power supply, frequency modulation control input quantity and frequency and tie line power prediction quantity on the basis of a system dynamic model for interconnecting a power system and the battery energy storage power supply; the local frequency modulation controller of each area receives the instruction of the upper layer controller, and the frequency and the tie line power deviation of the system can be corrected.
As a further improvement of the invention, situation awareness has the functions of anomaly detection and anomaly sorting through time sequence analysis.
As a further improvement of the invention, the edge node computing method based on situation awareness is an edge computing node mirroring based on big data and distributed computing, a mirroring system is realized by a reproducible environment integrated by various platforms of VMWare + Ubuntu + PaddlePaddle + Mahout + Docker, and the mirroring system is set into distributed computing nodes according to level requirements, and the distributed computing nodes are high-performance chips FGPA or neural network chips or high-performance servers.
As a further improvement of the invention, the situation awareness-based edge node calculation method comprises the following steps:
data verification preprocessing
Establishing a transfer function model, and carrying out performance precision verification on the transfer function to obtain an optimal transfer function model;
determining the source network load coordination control response characteristic as response time according to a control principle;
decomposing the measured signals into two parts of linear signals and disturbance signals through an ESO (electronic stability and optical isolation), wherein the linear signals can be identified to obtain a transfer function model, calculating the robustness performance of the system through a robustness algorithm, sequencing the anomalies through time sequence analysis, tracking the variation trend among dependent items in measurement data, and locking the anomalies of equipment, control and energy utilization networks and the like in the comprehensive energy Internet;
backbone degree time sequence mining algorithm
A new index bone quality is provided, the electric power data concept is redefined, and a visual model of the network concept of the electric power data forest is provided; if the connection is the connection of branches and trees, a community can be regarded as a tree, a power data network can be regarded as a data forest, a strong area included in the network can be regarded as a forest of a power data area, and other weak data areas can be regarded as shrubs; according to the hypothesis, the characteristics of power data network biology, namely power data forest, are endowed;
method for realizing situation awareness of edge computing nodes
According to the electric forest model theory, any data combination can be regarded as an edge node, and then a high-performance computing module or a server is added on the node, and a distributed computing network is deployed by utilizing an edge computing node mirror image; each node realizes a backbone degree time sequence algorithm and an abnormal locking algorithm, the dependency of each edge node data set is calculated in real time, and when the dependency of the data set exceeds a threshold value, the node is determined to have situation change, namely situation perception occurs.
As a further improvement of the invention, the process of the backbone degree time series mining algorithm is as follows: dividing all data in a net source charge comprehensive energy power data forest model according to a relation of contact degrees, and defining the data into an overlapped energy data set and a non-overlapped energy data set, wherein data in the non-overlapped energy data set have more and better link relations than data nodes in other sets in the net source charge comprehensive energy power data forest model, and the link relations among all the data nodes are defined as edges;
defining the expansion degree as the minimum ratio of all outward edges in the network source load integrated energy power data forest model to the total number of internal edges in the power data forest model;
given a power data network undirected graph G (V, E) with | V | vertices and | E | edges, a node list NL is given to store the vertices in V within the power data network undirected graph G (V, f), and the current data set is denoted as C i ，C i The contiguous set of data is
Handle C i The set of boundaries is marked as +>
Giving a backbone list BL to save the backbones in the set E;
setting a set J (W, F), wherein W is the vertex of J, F is a diaphyseal degree boundary, and a target set CF belongs to J; ec _ PRE is defined as the expansion degree in the forest model of the power data, all CF are backbone degree target sets, CF is initially an empty set, and i =0; the BL's are arranged in descending order.
The set G is a power data vector-free network set;
v is a vertex data set of G;
e is the edge of the electric power data vector-free network of G;
NL is a data list of vertices in V for storing calculations;
C i is the current data set;
the list BL is a data list of edges in E that can represent bone quality, for storing calculations.
CF is a backbone target set and belongs to a subset of J (W, F);
i is a variable for calculating process counts.
As a further improvement of the method, firstly, a probability scene model facing a terminal energy internet and a regional energy internet system is constructed, linear random power flow calculation is respectively carried out on different scenes on the basis of scene segmentation, simultaneously, daily output curve prediction is carried out on the power consumption and electricity quantity demand and time range of an industrial and residential control system and a distributed energy control system, and on the basis, constraint conditions such as branch power flow, node voltage, transmission power, network topology and the like are added to realize control situation perception; the multi-target convergence speed is accelerated by applying a multi-target hierarchical control algorithm, the infeasible scheme caused by the randomness of energy internet electric energy management is reduced, a comprehensive energy multi-energy complementary coordination optimization control technology is provided, a global optimization autonomous control strategy is completed, and a comprehensive energy electric energy management cooperative optimization control device is developed.
As a further improvement of the invention, a source/grid/charge power system model comprising a thermal power generation boiler coordination control system, a steam turbine coordination control system, an energy storage system or a pumped storage and load system is established by using power system simulation software, research is started aiming at a standardized comprehensive energy coordination control technology, a high-capacity unit coordination optimization control technology and a primary frequency modulation technology route under the condition of ultra-high voltage power grid operation are developed, online hardware closed-loop simulation technology development of the machine-grid coordination control performance is developed, and a hybrid logic dynamic programming method or an intelligent method is used for simulating a digital source/grid/charge model and the comprehensive energy coordination control optimization control technology according to the overall system architecture and parameters of the thermal power generation boiler system, the steam turbine system, the energy storage system and the load system.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a high-density heterogeneous energy universal type enhanced excitation high-precision external characteristic identification modeling method, which realizes the fusion among multiple time scales, multiple control layers and multiple control platforms through the comparison analysis of heterogeneous energy field data and simulation data, and develops a multi-layer fusion, cross-platform heterogeneous energy coupling electric/magnetic/mechanical/thermal block calculation and partition parallel dynamic simulation experiment system; and further providing a complex network theory and a visual data mining algorithm of the source network charge power system, tracking continuous changes in the measurement data, sequencing the anomalies through time sequence analysis, tracking the change trend among dependence items in the measurement data, locking the anomalies of equipment, control and energy utilization networks and the like in the comprehensive energy internet, providing control evaluation optimization and energy efficiency evaluation optimization decision support, expecting to solve the problem of the network source charge comprehensive energy wide-area situation perception theory, and further realizing the prediction of the power generation capacity of the traditional thermal power/gas and new energy. A source network charge situation perception method and a model system based on ubiquitous situation perception are provided. Developing a network source load model system in a large-scale multi-energy complementary comprehensive energy power complex network, displaying a model algorithm processing result, feeding back visualized cognition to a model design and knowledge discovery process, and realizing a situation perception technology and a model system based on a 'mechanism model + data driving'; an edge computing node implementation algorithm for the active disturbance rejection and offline modeling correction signal is provided.
Drawings
FIG. 1 is an architectural schematic of the present invention;
FIG. 2 is a flow chart of power system frequency modulation;
FIG. 3 is a diagram of a multi-energy complementary user-level microgrid topology;
FIG. 4 is a diagram of a coordination control scheme for a microgrid participating in power frequency modulation;
FIG. 5 is a schematic diagram of an MPC control module;
FIG. 6 is a schematic diagram of an anti-interference algorithm based on an ESO extended observer;
FIG. 7 is a schematic diagram of ESO-based online modeling;
FIG. 8 is a schematic diagram of ESO-based offline modeling;
FIG. 9 is a flow chart of a backbone data analysis algorithm;
FIG. 10 is a source/grid/load system energy coordination optimization management model objective function;
FIG. 11 is a flow chart of energy coordination optimization;
FIG. 12 is a schematic diagram of a model for evaluating the coordinated control performance of a supercritical thermal power generating unit;
FIG. 13 is a schematic diagram of a model for evaluating the coordination control performance of a gas turbine unit;
FIG. 14 is a flow chart of power grid large disturbance primary frequency modulation performance analysis;
FIG. 15 is a flow chart of peak shaving ability prediction with heat fixed power;
FIG. 16 is a flow chart of a comprehensive energy control performance evaluation optimization and energy efficiency evaluation optimization system.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the application, its application, or uses. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
A comprehensive energy efficiency evaluation and control performance optimization method based on wide-area situation perception comprises a source network source load wide-area situation perception method based on the combination of a mechanism model and data driving, an edge node calculation method based on situation perception and a comprehensive energy efficiency and control performance evaluation method;
a source network source load wide area situation perception method and an edge node calculation method are based on and support is provided for a comprehensive energy efficiency and control performance evaluation method.
As shown in fig. 1, the source-network-source-load wide-area situational awareness method based on the combination of the mechanism model and the data drive includes:
the method comprises the steps of establishing a multi-time scale electric energy management control architecture model facing an energy internet, considering an external characteristic function model of distributed multi-type energy and industrial and residential loads and considering a comprehensive energy internet model including a power transmission line model, a natural gas network model, a micro-grid power electronic model and an industrial and residential load model aiming at the characteristic of high permeability of the distributed energy, and analyzing the influence of a relevant model on the system stability of the energy internet; the method provides theory and method research of high-order organization mining in the complex network of the power system of the power grid, establishes a situation perception model, positions the abnormality of equipment, control and energy utilization networks and the like in the integrated energy Internet, provides decision support for control evaluation optimization and energy efficiency evaluation optimization, and further realizes the prediction of the power generation capacity of the traditional thermal power/gas and new energy.
The correlation modeling, controllability and quantitative analysis of a thermal power generation system and a gas turbine unit in the comprehensive energy system are used for proposing that the gas turbine comprehensive energy microgrid participates in thermal power generation grid source coordination control frequency modulation control architecture modeling and analyzing the influence of the architecture modeling, and the intelligent microgrid participates in thermal power generation grid source coordination predictive control algorithm research and verification;
situation perception of data-driven modeling, collecting historical data including residential electricity consumption data, heating data, hot events and meteorological data, and fitting objective functions of various factors and demand load of an electricity consumption side; aiming at a controlled object or equipment, establishing an object-oriented information fusion data model; determining a normal data fluctuation interval in a normal state aiming at an object-oriented information fusion data model; and adopting an online identification modeling or big data model to judge the deviation degree of the current data fluctuation interval and the normal data fluctuation interval of the controlled object or equipment in real time or offline, namely situation perception.
As shown in fig. 2, the process of the gas turbine and other comprehensive energy micro-grids participating in the modeling of the thermal power generation grid source coordination control frequency modulation control architecture and analyzing the influence thereof is as follows: firstly, combining the frequency characteristic and the load characteristic of a power system of a traditional coal-fired thermal generator set coordinated control system, systematically analyzing a region equivalent method in a power grid frequency modulation simulation model, and constructing a region equivalent model of conventional power system frequency modulation; according to the heterogeneous and dispersive characteristics of a communication network architecture and a coordination control system, a looped network communication architecture model of the thermal power generation local control communication equipment containing renewable energy source supplement is constructed, and basic theory verification of data monitoring, economic dispatching plan, power flow analysis, frequency and voltage stability, self power generation plan and real-time monitoring function is completed; the supply and demand balance in the primary and secondary frequency modulation control loops of the interconnected power system consists of three parts, namely a generator, a load and the exchange power of a tie line; the key of constructing a frequency response model required by primary and secondary frequency modulation control of the generator set is the modeling of a speed regulating system and a prime motor; the thermal power generating unit is provided with a primary frequency modulation control loop and a secondary frequency modulation control loop on the structures of a speed regulating system and a prime motor; in addition, the load of the system and the links between control areas need to be considered; respectively modeling the unit, the load, the tie line and the primary and secondary frequency modulation control loops, and combining according to the structural relationship in the actual power system to obtain a frequency modulation dynamic model of the power system;
secondly, considering external characteristic function models of distributed energy sources and loads and three-phase steady-state models of a power distribution system including a thermal power generation model, a power transmission line model, a distribution transformer model, a parallel capacitor model and a load model according to the characteristics of high permeability of distributed energy sources such as a gas turbine, photovoltaic and wind power and demand side response, and analyzing the influence of relevant models on system stability of the self-optimization-approaching power distribution network.
As shown in fig. 3, when the power system is operating, the control of the frequency and tie line exchange power deviation is mainly accomplished by the second order modulation. Under the conditions of comprehensive energy micro-grid and frequency modulation of a gas turbine and the like, reasonably coordinating each frequency modulation power supply to control and adjust the output power of each generator and the intelligent micro-grid containing renewable energy sources so that the system frequency meets the requirement of the grid. The strategy of using a gas turbine and other comprehensive energy micro-grids to participate in primary frequency modulation is as follows: and frequency conversion adjustment effect coefficient strategies.
The method for determining the fixed frequency regulation effect coefficient comprises the following steps: primary frequency modulation in a power grid is a regulation mode for preventing the frequency of the system from deviating from a standard by utilizing the inherent load frequency characteristic of the system and the action of a speed regulator of a generator set. Therefore, the intelligent microgrid with renewable energy can participate in primary frequency modulation of the power grid in a mode of simulating a generator set to adjust self output according to the frequency deviation and the frequency adjustment effect coefficient. Namely, the relationship between the microgrid current increment delta ib of the comprehensive energy sources such as the gas turbine and the like and the primary frequency modulation control signal delta f is shown as the following formula: the subject is to
Wherein, K b And R b The frequency regulation effect coefficient (or unit regulation current) and the static droop coefficient (or difference regulation coefficient) of the battery energy storage power supply are respectively.
Frequency conversion adjustment effect coefficient strategy: if the comprehensive energy micro-grid such as the gas turbine has a large amount of energy surplus, the frequency of the power grid is reduced, and on the premise of not exceeding the rated power of the comprehensive energy micro-grid such as the gas turbine, the frequency regulation effect coefficient of the micro-grid is selected to be a value larger than that determined by the traditional method; if the grid frequency is higher than 50Hz, the micro-grid should be selected to discharge small current with a certain reasonable frequency regulation effect coefficient from the viewpoint of protecting the micro-grid. This is beneficial to the energy management of the micro-grid and can influence the frequency modulation of the grid as little as possible.
As a further improvement of the invention, the intelligent micro-grid participates in the research and verification process of the thermal power generation grid source coordination predictive control algorithm as follows: providing an intelligent micro-grid participating thermal power generation grid source coordination prediction control system which is of a layered distributed framework structure and comprises an upper-layer area coordination controller and a lower-layer local frequency modulation controller; introducing into a regional coordination controller, constructing an optimization model taking output error and control increment weighting as objective functions and thermal power generating units, a micro-grid and tie line regulation capacity as constraint conditions by establishing the relation between a battery energy storage power supply, frequency modulation control input quantity and frequency and tie line power prediction quantity on the basis of a system dynamic model for interconnecting a power system and the battery energy storage power supply; and the local frequency modulation controller of each area receives the instruction of the upper layer controller, and can correct the frequency and the tie line power deviation of the system.
As shown in fig. 4, the task of the coordination controller is mainly as follows:
(1) Monitoring state
The regional coordination controller receives system real-time state information monitored by each region, and the system real-time state information comprises inter-region tie line exchange power deviation, output power change of generators in each region, output power change of battery energy storage power supplies in each region, discharge depth of the battery energy storage power supplies, load power change in each region, frequency deviation of systems in each region and the like.
(2) Predicting the future
The regional coordination controller predicts the future dynamic track on the basis of the system dynamic model, establishes the relation between the controlled variable and the predicted variable and lays a foundation for the optimal strategy.
(3) Optimal control decision
And (3) on the basis of the result of the step (2), solving by the regional coordination controller according to the set objective function and the constraint of each frequency modulation device. After comparing the predicted values with the actual values, errors can be derived to further correct the prediction model.
The corresponding state equation of the system predictive control is basically formed as the formula:
y(t)＝Cx(t)+D v v(t)+D d d(t)
wherein x (t) is a state variable of the system; u (t) is a control input quantity; v (t) is the measurable disturbance quantity; d (t) is the amount of unmeasurable disturbance; y (t) is the output quantity of the system; A. bu, bv, bd, C, D and Dd are coefficient matrixes corresponding to the system quantities. The MPC control module principle is shown in FIG. 5.
The correlation modeling, controllability and quantitative analysis of the thermal power generation system with the integrated energy supplement of the gas turbine and the like are the basis for realizing the coordinated control operation of the thermal power generation system with the integrated energy supplement of the gas turbine and the like, and the main innovation achievement comprises that a traditional frequency response model is researched on the basis of researching the primary and secondary frequency modulation mechanism and performance characteristics of a conventional power grid and a traditional thermal power generation frequency modulation power supply and on the basis of a region equivalent method. Performing a large amount of simulation based on actual machine-to-grid coordination (CCS) data, summarizing and analyzing frequency modulation defects and requirements of a conventional power grid and traditional thermal power generation; on the basis of deeply understanding the structure, the working principle and the self limiting factors of an intelligent power grid system consisting of renewable energy sources, the dominant characteristic parameters and models of the intelligent power grid system in frequency modulation application are extracted. Meanwhile, aiming at the characteristics of thermal power generation and future distributed new energy fusion, an external characteristic function model of a schedulable micro source (a diesel generator and the like), an energy storage device and an elastic load (an electric automobile and the like) is considered, a three-phase steady state model of a power distribution system including a thermal power generation model, a power transmission line model, a power distribution transformer model, a parallel capacitor model and a load model is considered, and the influence of a relevant model on the stability of the power system is analyzed. Providing mechanical, electrical and magnetic dynamic characteristics of renewable energy sources connected to thermal power generation under different working conditions such as a normal state (grid-connected operation and off-grid independent operation) and an emergency state (voltage offset and frequency offset); a characteristic description method during grid connection, grid disconnection and fault operation; and (3) an optimization control method of comprehensive energy and thermal power generation considering load characteristic matching.
The comprehensive energy participation thermal power generation grid source coordinated control frequency modulation control technology is a support for realizing overall optimization control of a thermal power generation system supplemented by a comprehensive energy microgrid comprising a gas turbine and the like, and the main innovation achievement comprises that the overall optimization and the regional cooperative operation control among primary control devices are carried out on the basis of the comprehensive energy microgrid comprising the thermal power generation system, the gas turbine and the like; active power and reactive power control and cooperative interaction control of a thermal power generation system, a gas turbine and other comprehensive energy micro-grids; the reactive power optimization adjustment under the action of comprehensive energy micro-grids such as a thermal power generation system, a gas turbine and the like is considered. The electric energy quality of the system is ensured, and the real-time performance, stability and robustness of the control of the comprehensive energy micro-grid such as a thermal power generation system, a gas turbine and the like are realized.
As a further improvement of the invention, situation awareness has the functions of anomaly detection and anomaly sorting through time sequence analysis.
The abnormality detection flow is as follows: collecting metric data for a plurality of time points; capturing hidden correlations between different metric data; tracking persistent changes in the metrology data; and (4) positioning the abnormality of equipment, networks and the like in the power grid and providing decision support.
The process of sorting the anomalies by time series analysis is as follows: resolving pairwise dependencies between metric data; tracking a trend of change between dependent items in the metric data; locking abnormity, realizing situation perception, and realizing power generation capacity prediction modules such as a plant-level peak regulation capacity prediction module, a generator set equipment state early warning and fault analysis system, a cogeneration unit peak regulation capacity big data analysis module and the like.
The situation awareness-based edge node calculation method comprises the following steps:
data verification preprocessing
Establishing a transfer function model, and performing performance precision verification on the transfer function (based on an authorized patent, namely 'a general enhanced excitation simulation data verification processing method', ZL 201510883385.5) to obtain an optimal transfer function model (based on an authorized patent, namely 'an enhanced excitation simulation genetic optimization method', ZL 201510883621.3);
determining the source network load coordination control response characteristic as response time according to a control principle;
as shown in fig. 6-8, the measured signal is decomposed into two parts, namely a linear signal and a disturbance signal, by the ESO, wherein the linear signal can be identified to obtain a transfer function model, the robustness of the system is calculated by a robustness algorithm, and meanwhile, the anomalies are sequenced by time sequence analysis, the variation trend among dependent items in the measurement data is tracked, and the anomalies of equipment, control, energy utilization networks and the like in the integrated energy internet are locked;
backbone degree time sequence mining algorithm
A new index bone quality is provided, the electric power data concept is redefined, and a visual model of the network concept of the electric power data forest is provided; if the connection is the connection of branches and trees, a community can be regarded as a tree, a power data network can be regarded as a data forest, a strong area included in the network can be regarded as a forest of a power data area, and other weak data areas can be regarded as shrubs; according to the hypothesis, the characteristics of power data network biology, namely power data forest, are endowed;
method for realizing situation awareness of edge computing nodes
According to the electric forest model theory, any data combination can be regarded as an edge node, and then a high-performance computing module or a server is added on the node, and a distributed computing network is deployed by utilizing an edge computing node mirror image; each node realizes a backbone degree time sequence algorithm and an abnormal locking algorithm, the dependency of each edge node data set is calculated in real time, and when the dependency of the data set exceeds a threshold value, the node is determined to have situation change, namely situation perception occurs.
As shown in fig. 9, the process of the skeleton degree time series mining algorithm is as follows: dividing all data in a net source charge comprehensive energy power data forest model according to a relation of contact degrees, and defining the data into an overlapped energy data set and a non-overlapped energy data set, wherein data in the non-overlapped energy data set have more and better link relations than data nodes in other sets in the net source charge comprehensive energy power data forest model, and the link relations among all the data nodes are defined as edges;
defining the expansion degree as the minimum ratio of all outward-pointing edges in the network source load comprehensive energy power data forest model to the total number of internal edges in the power data forest model;
given a power data network undirected graph G (V, E) with | V | vertices and | E | edges, a node list NL is given to store the vertices in V within the power data network undirected graph G (V, E), and the current data set is denoted as C i ，C i The contiguous set of data is
Handle C i The set of boundaries is marked as +>
Giving a backbone list BL to save the backbones in the set E;
setting a set J (W, F), wherein W is the vertex of J, F is a diaphyseal degree boundary, and a target set CF belongs to J; ec _ PRE is defined as the expansion degree in the forest model of the power data, all CF are backbone degree target sets, CF is initially an empty set, and i =0; the BL's are arranged in descending order.
The set G is a power data vector-free network set;
v is a vertex data set of G;
e is the edge of the electric power data vector-free network of G;
NL is a data list of vertices in V for storing calculations;
C i is the current data set;
the list BL is a data list of edges in E that can represent bone quality, for storing calculations.
CF is a backbone target set and belongs to a subset of J (W, F);
i is a variable used to calculate the process count.
As a further improvement of the method, firstly, a probability scene model facing a terminal energy internet and a regional energy internet system is constructed, linear random power flow calculation is respectively carried out on different scenes on the basis of scene segmentation, simultaneously, daily output curve prediction is carried out on the power consumption and electricity quantity demand and time range of an industrial and residential control system and a distributed energy control system, and on the basis, constraint conditions such as branch power flow, node voltage, transmission power, network topology and the like are added to realize control situation perception; the multi-target hierarchical control algorithm is applied to accelerate the multi-target convergence speed, the infeasible scheme caused by the randomness of energy internet electric energy management is reduced, the comprehensive energy multi-energy complementary coordination optimization control technology is provided, the global optimization autonomous control strategy is completed, and the comprehensive energy electric energy management cooperative optimization control device is developed.
Furthermore, a source/grid/charge power system model containing a thermal power generation boiler coordination control system, a steam turbine coordination control system and an energy storage system or a pumped storage and load system is established by using power system simulation software, research is started aiming at a standardized comprehensive energy coordination control technology, a high-capacity unit coordination optimization control technology and a primary frequency modulation technology route under the condition of ultra-high voltage power grid operation are developed, online hardware closed-loop simulation technology development of the machine-grid coordination control performance is developed, a digital source/grid/charge model and the comprehensive energy coordination optimization control technology are simulated by using a hybrid logic dynamic programming method or an intelligent method according to the overall system architecture and parameters of the thermal power generation boiler system, the steam turbine system, the energy storage system and the load system, and the feasibility of the standardized comprehensive energy coordination control technology research and development management simulation system for improving the stability of a large power grid is verified.
As shown in FIG. 11, an objective function of the energy coordination optimization management model of the source/grid/load system is established
On the basis of ensuring the power supply of the local load, the aim is to minimize the operation cost of the source/grid/load, wherein the operation cost comprises the cost of purchasing electricity from the power grid, the income obtained by selling the electricity to the power grid, and the maintenance cost and depreciation loss of the storage battery.
In the formula, eShell (t) is the real-time electricity purchasing price of a power grid; eBuy (t) is the real-time electricity selling price of the power grid; ebat (t) is the operation management cost of the storage battery; pgBuy (t) is electric power absorbed by a large power grid at the t moment, and the sign is negative; pgSell (t) is the electric power generated by the large power grid at the t moment, and the sign is positive; pbat (t) is the active power of the storage battery at the t moment; delta t is the running time interval of the system, and the value is 1 hour;
the objective function includes the cost of purchasing electricity from the power grid and the income obtained by selling electricity to the power grid, and how to use Pg (t) to represent the main grid to source/grid/load output power when the value is positive, and the input power when the value is negative is specifically represented by the following formula.
In the formula, when Pg (t) is a positive value, the electricity purchase cost is expressed as eSell (t) Pg (t); when Pg (t) is negative, the electricity selling cost is expressed as-eBuy (t) · Pg (t).
The project adopts a prediction control framework based on rolling time domain optimization, at the time t, a model solves the optimization problem of minimum consumption cost from an external power grid in the rolling time domain [ t t + tp ] when a power source/grid/load operates, and the target function in the rolling time domain is minimum by calculating the optimal control sequence in [ t t + tp ]. Therefore, on the basis of the above formula, a rolling optimization range is added, the optimization is performed by rolling in a period of one hour, the step length of the rolling range is assumed to be tp, and the consumption cost in the time from t to t + tp is calculated and is expressed as a new objective function:
under the new objective function, the rolling time domain not only considers the current step, but also puts the system operation state in the future period into the calculation range, thus leading the optimization process to have better optimization effect.
The concrete steps
The project adopts the idea of rolling optimization in a finite time domain, and according to the description of the model and the optimization theory, the specific flow is as follows:
step1: the source/grid/charge model setting comprises a boiler model, a steam turbine, a storage battery model and a real-time electricity price model; and (5) initializing and setting parameters.
Step2: and according to the actual source/network/load energy coordination optimization requirements, establishing a constrained economic optimal objective function.
Step3: in an optimization range [ t t + tp ], solving a minimization problem in a finite time domain, obtaining an optimal control sequence U (t) = { U (t | t), … and U (t + tp-1|t) } through each rolling optimization, and taking out control variables U (t | t) of a first step at the current moment, namely Pg (t) and Pb (t) of the first step, as energy exchange variables between a power grid and a power generation unit and a load and charge and discharge energy variables of a storage battery.
Step4: the optimized time domain continues to scroll t = t +1, returning to Step3.
Step5: and sequentially calculating to obtain the control variable of the first step of each time period, thereby realizing rolling optimization.
Step6: after the rolling optimization is finished, the optimal result in the finite time domain [ t t + tn ] is calculated and output.
The energy coordination optimization flow chart is shown in fig. 11 below.
The comprehensive energy efficiency regulation and control performance evaluation method adopts a comprehensive energy efficiency characteristic evaluation formula as follows:
so as to establish a schematic diagram and a schematic diagram of a supercritical thermal power generating unit coordinated control performance evaluation model shown in figure 12
A gas turbine unit coordinated control performance evaluation model shown in fig. 12; therefore, the large-disturbance primary frequency modulation performance analysis of the power grid is carried out, and the peak regulation capability prediction, the comprehensive energy control performance evaluation optimization and the energy efficiency evaluation optimization are carried out according to the heat fixed power, as shown in the figures 14 to 16.
Claims (7)
1. A comprehensive energy efficiency evaluation and control performance optimization method based on wide-area situation perception comprises a source network load storage wide-area situation perception method based on combination of a mechanism model and data driving, an edge node calculation method based on situation perception and a comprehensive energy efficiency and control performance evaluation method;
providing support for a comprehensive energy efficiency and control performance evaluation method based on a source network load storage wide area situation perception method and an edge node calculation method;
the source network load storage wide-area situation perception method based on the combination of the mechanism model and the data drive comprises the following steps:
the method comprises the steps of establishing a multi-time scale electric energy management control architecture model facing an energy internet, considering an external characteristic function model of distributed multi-type energy and industrial and residential loads and considering a comprehensive energy internet model including a power transmission line model, a natural gas network model, a micro-grid power electronic model and an industrial and residential load model aiming at the characteristic of high permeability of the distributed energy, and analyzing the influence of a relevant model on the system stability of the energy internet;
the correlation modeling, controllability and quantitative analysis of a thermal power generation system and a gas turbine unit in the comprehensive energy system are used for proposing that the gas turbine comprehensive energy microgrid participates in thermal power generation grid source coordination control frequency modulation control architecture modeling and analyzing the influence of the architecture modeling, and the intelligent microgrid participates in thermal power generation grid source coordination predictive control algorithm research and verification;
situation perception of data-driven modeling, collecting historical data including residential electricity consumption data, heating data, hot events and meteorological data, and fitting objective functions of various factors and demand load of an electricity consumption side; aiming at a controlled object or equipment, establishing an object-oriented information fusion data model; determining a normal data fluctuation interval in a normal state aiming at an object-oriented information fusion data model; adopting an online identification modeling or big data model to judge the deviation degree of the current data fluctuation interval and the normal data fluctuation interval of the controlled object or equipment in real time or offline;
the situation awareness-based edge node calculation method comprises the following steps:
data verification preprocessing;
establishing a transfer function model, and performing performance precision verification on the transfer function to obtain an optimal transfer function model;
determining the response characteristic of source network load coordination control as response time;
decomposing the measured signal into a linear signal and a disturbance signal through an extended observer, wherein the linear signal can be identified to obtain a transfer function model, calculating the robustness of the system through a robustness algorithm, sequencing the anomalies through time sequence analysis, tracking the variation trend among dependent items in measurement data, and locking the anomalies of equipment, control and energy utilization networks in the comprehensive energy Internet;
a backbone degree time sequence mining algorithm;
providing a visual model of a network concept of the electric power data forest; endowing the power data network with biological characteristics, namely a power data forest;
an edge computing node situation perception implementation method;
according to the electric forest model theory, any data combination can be regarded as an edge node, and then a high-performance computing module or a server is added on the node, and a distributed computing network is deployed by utilizing an edge computing node mirror image; each node realizes a backbone time sequence algorithm and an abnormal locking algorithm, calculates the dependency of each edge node data set in real time, and determines that the node has situation change when the dependency of the data set exceeds a threshold value, namely situation perception occurs;
the comprehensive energy efficiency and control performance evaluation method comprises the following steps: firstly, a probability scene model facing a terminal energy internet and a regional energy internet system is constructed, linear random power flow calculation is respectively carried out on different scenes on the basis of scene segmentation, simultaneously, daily output curve prediction is carried out on the demand and time range of the electric quantity of the industrial and residential control systems and the distributed energy control system, and on the basis, branch power flow, node voltage, transmission power and network topology are added as constraint conditions to realize control situation perception; the multi-target convergence speed is accelerated by applying a multi-target hierarchical control algorithm, the infeasible scheme caused by the randomness of energy internet electric energy management is reduced, a comprehensive energy multi-energy complementary coordination optimization control technology is provided, a global optimization autonomous control strategy is completed, and a comprehensive energy electric energy management cooperative optimization control device is developed.
2. The method for comprehensive energy efficiency evaluation and control performance optimization based on wide-area situation awareness according to claim 1, wherein the process of the gas turbine comprehensive energy microgrid participating in the modeling of the thermal power generation grid source coordinated control frequency modulation control architecture and analyzing the influence of the modeling is as follows: firstly, combining the frequency characteristic and the load characteristic of a power system of a traditional coal-fired thermal generator set coordinated control system, systematically analyzing a region equivalent method in a power grid frequency modulation simulation model, and constructing a region equivalent model of conventional power system frequency modulation; according to the heterogeneous and dispersive characteristics of a communication network architecture and a coordination control system, a looped network communication architecture model of the thermal power generation local control communication equipment containing renewable energy source supplement is constructed, and basic theory verification of data monitoring, economic dispatching plan, power flow analysis, frequency and voltage stability, self power generation plan and real-time monitoring function is completed; the supply and demand balance in the primary and secondary frequency modulation control loops of the interconnected power system consists of three parts, namely a generator, a load and a tie line exchange power; the key of constructing a frequency response model required by primary and secondary frequency modulation control of the generator set is the modeling of a speed regulating system and a prime motor; the thermal power generating unit is provided with a primary frequency modulation control loop and a secondary frequency modulation control loop on the structures of a speed regulating system and a prime motor; in addition, the load of the system and the links between control areas need to be considered; modeling the unit, the load, the tie line, the primary frequency modulation control loop and the secondary frequency modulation control loop respectively, and combining according to the structural relationship in the actual power system to obtain a frequency modulation dynamic model of the power system;
secondly, considering an external characteristic function model of the distributed energy and the load and a three-phase steady state model of the power distribution system including a thermal power generation model, a power transmission line model, a power distribution transformer model, a parallel capacitor model and a load model aiming at the characteristic that the distributed energy and the demand side have high response permeability, and analyzing the influence of the relevant models on the system stability of the self-optimizing power distribution network.
3. The comprehensive energy efficiency evaluation and control performance optimization method based on wide area situation awareness according to claim 2, wherein the intelligent micro-grid participates in the research and verification process of the thermal power generation grid source coordination prediction control algorithm as follows: providing an intelligent micro-grid participating thermal power generation grid source coordination prediction control system which is of a layered distributed framework structure and comprises an upper-layer area coordination controller and a lower-layer local frequency modulation controller; on the basis of a system dynamic model interconnecting a power system and a battery energy storage power supply, by establishing a relation between the battery energy storage power supply and frequency modulation control input quantity and frequency and tie line power prediction quantity, constructing an optimization model taking output error and control increment weighting as objective functions and thermal power generating units, a microgrid and tie line regulation capacity as constraint conditions; the local frequency modulation controller of each area receives the instruction of the upper layer controller, and the frequency and the tie line power deviation of the system can be corrected.
4. The method for comprehensive energy efficiency evaluation and control performance optimization based on wide-area situation awareness according to claim 3, wherein the situation awareness has the functions of anomaly detection and anomaly sorting through time sequence analysis.
5. The method for comprehensive energy efficiency evaluation and control performance optimization based on wide-area situation awareness according to claim 4, wherein the situation awareness-based edge node computing method is edge computing node mirroring based on big data and distributed computing, a mirroring system is implemented by a reproducible environment integrated by multiple platforms of VMWare + Ubuntu + PaddlePaddle + Mahout + Docker, and the mirroring system is set to be distributed computing nodes according to level requirements, and the distributed computing nodes are high-performance FGPA chips or neural network chips or high-performance servers.
6. The comprehensive energy efficiency evaluation and control performance optimization method based on wide-area situation awareness according to claim 5, characterized in that the backbone degree time series mining algorithm process is as follows: firstly, dividing all data in a network source load integrated energy power data forest model according to a relation of contact degrees, defining the data into an overlapped energy data set and a non-overlapped energy data set, wherein the data in the non-overlapped energy data set have more and better link relations than data nodes in other sets in the network source load integrated energy power data forest model, and the link relations among all the data nodes are defined as edges;
defining the expansion degree as the minimum ratio of all outward edges in the network source load integrated energy power data forest model to the total number of internal edges in the power data forest model;
given a power data network undirected graph G (V, E) with | V | vertices and | E | edges, a node list NL is given to store the vertices in V within the power data network undirected graph G (V, E), and the current data set is denoted as C i ，C i The contiguous set of data is
Handle C i The set of boundaries is marked as +>
Giving a backbone list BL to save the backbones in the set E;
setting a set J (W, F), wherein W is the vertex of J, F is a diaphyseal degree boundary, and a target set CF belongs to J; ec _ PRE is defined as the expansion degree in the forest model of the power data, all CF are backbone degree target sets, CF is initially an empty set, and i =0; BL is arranged according to descending order;
the set G is a power data vector-free network set;
v is a vertex data set of G;
e is the edge of the electric power data vector-free network of G;
NL is a data list of vertices in V for storing calculations;
C i is the current data set;
the list BL is a data list of edges in E that can represent bone quality, and is used for storing calculation;
CF is a backbone degree target set and belongs to a subset of J (W, F);
i is a variable for calculating process counts.
7. The method for evaluating and optimizing the energy efficiency and the control performance of the comprehensive energy resources based on the wide-area situation awareness is characterized in that a power system simulation software is used, a digital source/grid/charge power system model comprising a thermal power generation boiler coordination control system, a steam turbine coordination control system, an energy storage system or a pumped storage and load system is built, research is started for a standardized comprehensive energy resource coordination control technology, a large-capacity unit coordination optimization control technology and a primary frequency modulation technology route under the condition of ultra-high voltage power grid operation are developed, online hardware closed-loop simulation technology development of the coordination control performance of the machine grid is developed, and a hybrid logic dynamic programming method or an intelligent method is used for simulating the digital source/grid/charge power system model and the comprehensive energy resource coordination control optimization control technology according to the overall system architecture and parameters of the thermal power generation boiler system, the steam turbine system, the energy storage system and the load system.
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