CN110994790B - Enterprise power grid dispatching knowledge decision analysis system - Google Patents
Enterprise power grid dispatching knowledge decision analysis system Download PDFInfo
- Publication number
- CN110994790B CN110994790B CN201911227400.5A CN201911227400A CN110994790B CN 110994790 B CN110994790 B CN 110994790B CN 201911227400 A CN201911227400 A CN 201911227400A CN 110994790 B CN110994790 B CN 110994790B
- Authority
- CN
- China
- Prior art keywords
- power
- power grid
- load
- control
- analysis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P80/00—Climate change mitigation technologies for sector-wide applications
- Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses an enterprise power grid dispatching knowledge decision analysis system which is established on an enterprise power dispatching centralized control system, fully utilizes software and hardware resources of an original system, adopts a manual system, a calculation experiment and a parallel control and management method of parallel execution, and establishes a power grid dispatching knowledge decision system comprising an actual power grid dispatching system, a manual power grid dispatching system, a calculation experiment layer, a management and control layer and a parallel execution layer; establishing an artificial power grid dispatching system according to 4 links of source-grid-load-control, and completing calculation and experiments on the artificial system by using high-grade power application; in management and control, a comprehensive operation cockpit technology is taken as a core, multi-time collaborative optimization of a parallel execution layer is synthesized, the operation of the whole system is driven, effective knowledge decision information is provided for scheduling personnel, and the scheduling efficiency of a power grid is improved.
Description
Technical Field
The invention belongs to the technical field of power grid dispatching intelligent control, and particularly relates to an enterprise power grid dispatching knowledge decision analysis system.
Background
With continuous promotion of power market reform and development of related researches such as power secondary integration and demand response in the smart grid background, large industrial users actively excavate self potential by combining self production characteristics and regulation and control requirements of a power grid side, and improve controllability and flexibility of an internal power grid of an enterprise by utilizing the existing power grid optimization scheduling technology. According to the requirement of intelligent power dispatching of iron and steel enterprises, based on a power secondary integrated data platform, fusion of various power dispatching data and information is realized, application analysis software meeting the requirements of centralized power acquisition, real-time monitoring and dispatching optimization is developed, professional fusion of a secondary system and comprehensive sharing of whole network information are realized, and a foundation is laid for realizing intelligent dispatching; through research and application of the power demand control system, the power demand side response is actively participated, and by combining two charging detailed rules of electricity price making, application software of power demand optimized scheduling, power demand decision analysis and field real-time control is developed, so that the optimized scheduling and real-time control level of a power grid of a steel enterprise is improved, and cost is reduced and efficiency is improved; from the perspective of real-time control, a power supply network model is established, a plant-level control method is adopted to analyze the control problem of the enterprise power grid, a comprehensive optimization and coordination control system is constructed, the analysis of the trend of the power flow of the power grid is realized in the advanced optimization and scheduling link, the relationship among automatic power generation control, power demand control and automatic voltage control is well solved in the coordination and optimization control link, and the coordination and optimization control is realized; the method aims at the steady-state cooperative control of the enterprise power grid, carries out detailed analysis on isolated operation of the power grid after the power grid is separated from a large power grid, strengthens the effect of steady-state control through cooperative operation among control systems, and avoids large load impact; the method is characterized in that a data driving concept of knowledge automation is combined, three driving engines of a KPI engine, a decision analysis engine and an operation engine are constructed through a cockpit technology, data, calculation, optimization and a process are organically combined, and real-time interaction of front-end monitoring and rear-end optimization is achieved. The research and the application pay attention to the dispatching operation of the enterprise power grid, and a certain application effect is achieved. However, the power grid dispatching is a whole, the power supply, the power grid and the load have high coupling, and how to effectively utilize the internal power of source-source interaction, source-grid coordination, grid-load interaction and source-load interaction establishes an effective source-grid-load flexible interaction mechanism, improves the flexibility and reliability of the power grid operation, and is an important means for coping with the future power grid energy structure change.
The power grid dispatching of the iron and steel enterprises needs to be integrated with new concepts and control methods to carry out systematic design and application function research, verify relevant strategies and control methods in the actual production process, and fully utilize technical means such as on-site power grid real-time monitoring, optimized dispatching, training simulation and the like. The parallel system refers to a common system consisting of a natural real system and one or more corresponding virtual or ideal artificial systems. Starting from the actual requirements of complex Systems, an Artificial intelligence method is combined with a control theory, a method and a theoretical system (ACP) which take Artificial Systems (Artificial Systems), Computational Experiments (Computational Experiments) and Parallel Execution (Parallel Execution) as main processes are provided, a theoretical system is provided for modeling, analyzing, controlling and managing the complex Systems, and the method and the theoretical system are primarily applied to the research of the complex Systems such as traffic, electric power, petrifaction and the like.
The parallel system theory can provide a new research direction for the operation and control of enterprise power grid dispatching, and through establishing an artificial power system, the advanced control strategy and method are subjected to experimental evaluation, an effective feedback mechanism is established, and a new path for establishing an intelligent and knowledge type dispatching decision system is explored. The method, the concept, the main process and the implementation method of the parallel system are combined with the requirements of the iron and steel enterprise power grid in the aspects of coordination optimization, demand response, operation management and the like, the parallel system for the enterprise power grid dispatching is constructed, a foundation is laid for establishing a system architecture of power grid dispatching knowledge decision, then source-network-load-storage resources of the dispatching system are controlled by means of a calculation experiment process in parallel control, the power grid operation state is evaluated and analyzed by an effective means, and finally control, management and operation are implemented by parallel execution.
Disclosure of Invention
The invention aims to provide an enterprise power grid dispatching knowledge decision analysis system, which is used for establishing an artificial power grid dispatching system according to the power supply dispatching operation and management requirements of an enterprise and the actual power grid dispatching system by combining the power supply characteristics of the enterprise, fully utilizing three factors of calculation experiment, parallel execution, management and control in a parallel control and management method and laying a foundation for establishing a knowledge type dispatching decision system.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
an enterprise power grid dispatching knowledge decision analysis system, comprising: the system comprises an actual power grid dispatching system, an artificial power grid dispatching system, a calculation experiment layer, a management and control layer and a parallel execution layer;
the actual power grid dispatching system is used for power grid acquisition and monitoring, power grid optimized dispatching, electric energy metering and dispatching management;
the artificial power grid dispatching system is established according to 4 dimensions of power supply, power grid, load and control, and reorganizes and classifies power grid equipment;
the calculation experiment layer is used for preparing data and models, generating a power grid calculation analysis scene, and performing experiment evaluation analysis and feedback correction;
the management and control layer is configured with a comprehensive operation cockpit for completing a process control task to realize a power grid operation target; the system is used for collecting multivariate information, identifying scenes, calculating indexes, counting and classifying, and analyzing the power utilization characteristics of a load end;
and the parallel execution layer is used for performing unified coordination control on the automatic power generation control subsystem, the automatic voltage control subsystem, the power demand control subsystem and the load management control subsystem, performing knowledge decision analysis and providing results for scheduling personnel.
Further, the actual power grid dispatching system respectively is from top to bottom: the intelligent power dispatching system comprises a hardware platform layer, an operating system layer, a unified application supporting platform and an intelligent power dispatching system;
the unified application support platform is used for network communication, database management, graphic service, calculation service, historical service, human-computer interface and system management;
the intelligent power dispatching system is used for carrying out centralized acquisition of power grid field data, power grid equipment modeling and network topology, power grid analysis, power grid monitoring, comprehensive intelligent dispatching, protection fault information management, system optimization and control, electric energy metering information management, dispatching management and stable control information management.
Furthermore, in the artificial power grid dispatching system,
the power grid comprises a self-contained power plant, a gas and steam combined generator set, a dry quenching waste heat generating device, a blast furnace gas waste pressure turbine generating device and distributed new energy;
the power grid comprises power supply and distribution networks with different voltage levels;
the load comprises pelletizing, sintering, coking, blasting, a blast furnace, a converter, oxygen generation, cold rolling, hot rolling, an electric furnace, life and office;
the control comprises demand control, power factor assessment, active balance control and voltage reactive power control.
Further, the calculation experiment layer is specifically used for,
acquiring a power grid model, real-time operation data, historical section data, load prediction data, a power generation plan, a maintenance plan and a production plan;
generating a research section of a specific calculation analysis scene based on state estimation and dispatcher load flow calculation;
according to the calculation task, real-time operation data and a power grid model are fused by utilizing state estimation, the power grid is comprehensively sensed through ground state load flow calculation, and auxiliary decision information of the power grid operation situation is given;
and according to the planned information, performing stability judgment on disturbance factors of the power grid by using stability limit out-of-limit analysis and static safety analysis, finding out the adjustment direction of a power supply or a load by using sensitivity analysis, and modifying and perfecting the plan.
Further, the management and control layer is specifically configured to,
integrating the expert rules into the comprehensive operation cockpit, and completing a flow control task in a task guide mode according to a determined control flow;
according to event triggering or manual starting, a series of control tasks triggered in sequence form a flow for achieving the operation target of the power grid.
Furthermore, the comprehensive operation cockpit finishes a flow control task by operating an operation engine;
the implementation of the operation engine needs the support provided by an expert rule base, a management database and a real-time database of a data layer, the information transmission and check provided by a workflow engine, a network check service and an evaluation analysis service of a logic layer, and the maintenance customization, the operation flow control and the operation ticket management of a presentation layer.
Further, the multivariate information collection refers to unified management of power grid monitoring data, power degree data, protection information, video monitoring and power grid equipment models in an actual power grid dispatching system;
the scene recognition refers to the selection of power grid data and a model for research according to different user requirements and event driving according to different links of power supply, power grid, load and control;
the index calculation and statistical classification means that index calculation and statistical classification are carried out longitudinally according to power generation, power transmission, power distribution and power utilization and transversely according to 4 dimensions of safety indexes, high-quality indexes, economic indexes and environment-friendly indexes;
the load end electricity utilization characteristic analysis means that the load of the whole enterprise is divided according to different categories, and load characteristic indexes are analyzed, wherein the analysis indexes comprise: daily maximum load, daily maximum load occurrence time, daily minimum load occurrence time, daily average load, daily load rate, daily minimum load rate, daily peak-valley difference, daily load temperature correlation, typical curve for air conditioning in summer, typical curve for heating in winter, monthly maximum load time, monthly minimum load time, monthly average load, monthly load rate, monthly unbalance factor, annual average monthly load rate, quarterly maximum load time, quarterly minimum load time, quarterly average load, annual maximum load time, annual minimum load time, annual average load time, annual peak-valley difference, annual average peak-difference rate, annual load valley, maximum load utilization hours, seasonal unbalance factor, daily load curve, typical load curve, monthly load curve and annual load curve.
Further, the parallel execution layer is specifically used for performing unified coordination control by adopting multi-time scale collaborative optimization, and comprises advanced optimization scheduling, real-time optimization scheduling and coordination optimization control;
the advanced optimization scheduling is to establish an optimization objective function comprising electric power charge, demand charge, power factor adjustment charge, power generation cost and external power selling; optimizing and scheduling aiming at reducing the demand electric charge, adjusting the power factor of the monthly test core and reducing the power supply cost of enterprises;
the real-time optimization scheduling is to generate set values of system load summation, power generation plans and central bus voltage and provide control instructions for load control, automatic power generation control and automatic voltage control in coordinated optimization control;
the coordination optimization control is to adjust the simulation generator set and the electric arc furnace according to the control command so as to adjust the post-power value, perform load flow calculation, count the changes of voltage and load flow, and calculate the influence of the change power on an external electricity purchasing connection line, a demand monitoring point and a central voltage by adopting active sensitivity; and formulating a coordination control strategy of AVC by monitoring voltage change in real time, considering active power change of a monitoring gateway, directly outputting a control command if the voltage and gateway active constraint conditions are met, and otherwise, alternately calculating until the convergence condition is met.
Further, the optimization objective function is:
J＝(Pkwh_pfp+Pkwh_fff+Pkwh_vfv+(Pkwh_p+Pkwh_f+Pkwh_v)fextra)wkwh
+Ppdcfpdcwpdc+(0.9-Pfactor)ffactorwfactor+Pkwhgfgwg-Psellfsws
wherein J is the cost of electricity, Pkwh_p、Pkwh_f、Pkwh_vRespectively measuring peak, average and valley electric power of monthly electric power charge; f. ofp、ff、fvIs the peak, flat and valley electricity price fextraAn additional fee charged based on the amount of electricity used by the enterprise; p ispdcIs the maximum monthly demand value, fpdcA unit price is charged for the demand; pfactorPower factor, f, for monthly assessment pointsfactorIs a power factor compensation coefficient;
for monthly power generation, fgThe cost of electricity generation per degree of electricity; psellPower sold for the rest of the enterprise on the Internet, fsThe price is the price of electricity selling; w is akwh、wpdc、wfactor、wg、wsAnd the weighting coefficients in the objective function are respectively the electricity degree electricity charge, the basic electricity charge, the power factor adjustment electricity charge, the power generation cost and the power selling benefit.
Further, in the above-mentioned case,
the result of the short-term load prediction is corrected in real time by using the ultra-short-term load prediction, and optimization is performed by combining trend analysis of various loads to generate an optimal load total addition set value;
according to the current running state and the load change trend of the unit, the stability of the voltage and the frequency of a power grid is combined to carry out real-time checking, and a plan fixed value meeting the requirement is used as a power generation plan set value;
and the set value of the central bus voltage is obtained by adjusting within a certain range according to the result of the reactive power optimization.
According to the production requirements and power grid dispatching operation requirements of iron and steel enterprises, software and hardware resources of an original system are fully utilized, and a power grid dispatching knowledge decision-making system comprising an actual power grid dispatching system, an artificial power grid dispatching system, a calculation experiment layer, a management and control layer and a parallel execution layer is constructed by adopting a parallel control and management method of an artificial system, a calculation experiment and parallel execution; establishing an artificial power grid dispatching system according to 4 links of source-grid-load-control, and completing calculation and experiments on the artificial system by using high-grade power application; in the management and control, the comprehensive operation cockpit technology is taken as a core, and the multi-time collaborative optimization of the parallel execution layer is synthesized to drive the operation of the whole system.
Drawings
FIG. 1 is a schematic diagram of the composition and implementation of a parallel system;
FIG. 2 is a power grid dispatching knowledge decision analysis system architecture diagram provided by the present invention;
FIG. 3 is an implementation of the present invention running an operation engine;
fig. 4 is a command execution flow chart between the intelligent command platform and the power grid dispatching knowledge decision analysis system of the invention.
Detailed Description
The invention is further described below. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
FIG. 1 is a diagram of the components and implementation of a parallel system. The parallel system refers to a common system consisting of a natural real system and one or more corresponding virtual or ideal artificial systems. From the actual requirements of a complex system, an Artificial intelligence method is combined with a control theory, a method and a theoretical system (ACP) which take Artificial Systems (Artificial Systems), Computational Experiments (Computational Experiments) and Parallel Execution (Parallel Execution) as main processes are provided, and the theoretical system is provided for modeling, analyzing, controlling and managing the complex system.
The parallel system theory provides a new research direction for the operation and control of enterprise power grid dispatching, and an artificial power system is established, so that advanced control strategies and methods are experimentally evaluated, an effective feedback mechanism is established, and a new path for establishing an intelligent and knowledge type dispatching decision system is explored. The method, the concept, the main process and the implementation method of the parallel system are combined with the requirements of the iron and steel enterprise power grid in aspects of coordination optimization, demand response, operation management and the like to construct the parallel system for enterprise power grid dispatching, a foundation is laid for establishing a system architecture for power grid dispatching knowledge decision, then source-grid-load-storage resources of the dispatching system are controlled by means of a calculation experiment process in parallel control, the power grid operation state is evaluated and analyzed by an effective means, and finally control, management and operation are implemented by parallel execution.
The ACP method defines the concrete steps of solving the problems of the actual system by using a parallel system: firstly, an artificial system which is equivalent to an actual system needs to be established; secondly, analyzing and evaluating the complex problem in a manual system by utilizing a calculation experiment; and thirdly, completing the management and control of the actual system through the interaction of the actual system and the manual system, performing experiments and evaluation on related behaviors and decisions, learning and training related personnel and systems, completing the reference and estimation of the future conditions of the actual system and the manual system, correspondingly adjusting the management and control modes of the actual system and the manual system, and achieving the purposes of implementing an effective solution and learning and training. The parallel system mainly comprises a manual system, a calculation experiment and a parallel execution 3 part. The main process is as follows:
(1) and (5) learning and training. The artificial system is mainly used as a center for learning and training the management and control of the complex system, and by combining the actual system and the artificial system in a proper connection manner, the personnel related to the management and control of the actual complex system can quickly master various conditions of the complex system and respond to disturbance, and under the condition of permission, the artificial system is operated by the actual management and control method so as to obtain better real effect.
(2) And (4) performing experiment and evaluation. Manual systems are mainly used for computational experiments, analyzing and understanding the behavior and reactions of various complex systems, and evaluating the effects of different solutions as a basis for selection and support of management and control decisions.
(3) And (4) managing and controlling. The artificial system tries to simulate the actual system as much as possible and predicts the behavior of the actual system, so that a basis is provided for searching a solution effective for the actual system and improving the improvement of the current scheme; by establishing an actual system and a manual system evaluation feedback mechanism, the evaluation mode or parameters of the manual system are corrected, and the rules and parameters of the test and analysis are optimized.
The interference source of the parallel system can be from external disturbance of an actual system, and also can be from hypothetical faults and experiments, and the final action result can be transmitted to the control and manager through a feedback mechanism to carry out further management and control, experiments and analysis, learning and training until a satisfactory execution result is obtained.
FIG. 2 is a power grid dispatching knowledge decision system architecture diagram constructed according to a parallel control and management method, a power grid dispatching parallel system of an iron and steel enterprise is constructed, a power supply and production characteristics of the enterprise are combined, an artificial power grid dispatching system is established according to power supply dispatching operation and management requirements of the enterprise, an actual power grid dispatching system is adopted, three major factors of calculation experiment, parallel execution, management and control in an ACP method are fully utilized, and a foundation is laid for establishing a knowledge type dispatching decision system. The power grid dispatching knowledge decision making system based on the ACP method comprises 5 main parts, namely an actual power grid dispatching system, a manual power grid dispatching system, a calculation experiment layer, a management and control layer and a parallel execution layer.
Based on the system architecture and characteristics of the actual power grid dispatching system, establishing an artificial power grid dispatching system according to 4 links of 'source-grid-load-control', and completing calculation and experiments of the artificial system by high-level application of electric power; the comprehensive operation cockpit technology is used as a core in the management and control layer, and the multi-time scale collaborative optimization of the parallel execution layer is combined to drive the operation of the whole system, so that effective knowledge decision information is provided for scheduling personnel, and the scheduling efficiency of the power grid is improved.
(1) The actual power grid dispatching system: the power grid dispatching system of the iron and steel enterprise adopts an electric power secondary integrated platform, and the system is respectively as follows from top to bottom: the system comprises a hardware platform layer, an operating system layer, a unified application support platform and an intelligent power dispatching system. The unified application support platform has the functions of network communication, database management, graphic service, calculation service, history service, human-computer interface, system management and the like; the intelligent power dispatching system comprises the functions of centralized acquisition of power grid field data (power grid monitoring data, watt-hour data, protection information and video monitoring), power grid equipment modeling and network topology, power grid analysis, power grid monitoring, comprehensive intelligent dispatching, protection fault information management, system optimization and control, electric energy metering information management, dispatching management, stable control information management and the like.
(2) The artificial power grid dispatching system comprises: an artificial power grid dispatching system is established, and a reasonable construction method is selected by combining the production flow of the iron and steel enterprise so as to meet the requirements of operation and intelligent dispatching. Primary and secondary equipment (generators, circuits, buses, switches, transformers and loads), personnel control modes, automatic control systems, upper and lower-level management of the power grid and the like in the power grid are all manual components. The artificial power grid dispatching system is built according to 4 dimensions of power supply, power grid, load and control (source-grid-load-control), and reorganizes and classifies the equipment of the power grid. The power supply comprises a self-contained power plant, a gas-steam combined generator set (CCPP), a coke dry quenching waste heat power generation device (CDQ), a blast furnace gas residual pressure turbine power generation device (TRT) and distributed new energy sources (wind power generation, photovoltaic power generation and battery energy storage); the power grid comprises power supply and distribution networks with different voltage grades, and the control requirements of high voltage and low voltage are greatly different; the load is divided according to the process electricity consumption, and comprises pelletizing, sintering, coking, blasting, a blast furnace, a converter, oxygen generation, cold rolling, hot rolling, an electric furnace, life, office work and the like; the control part mainly focuses on demand control, power factor assessment, active balance control and voltage reactive power control at a gateway.
The information interaction is carried out between the actual power grid dispatching system and the artificial power grid dispatching system through a management and control layer, which is mainly embodied as follows:
1) the artificial power grid dispatching system establishes a KPI index system for power grid dispatching according to the specific realization target of 'source-grid-load-control', and optimizes the evaluation standard of the index system through continuous calculation and statistical analysis;
2) the actual power grid dispatching system provides data, models and technical analysis means for the artificial power grid dispatching system, the load end energy consumption characteristic analysis result is sent to the artificial power grid dispatching system through the management and control layer, and a data basis is provided for process load analysis.
(3) Calculating the experimental layer: the calculation experiment layer fully utilizes mature application functions of dispatcher load flow, sensitivity calculation, short-circuit current calculation, static safety analysis and the like in power advanced application, provides an intelligent effective analysis means for dispatching operation and accident handling of a power grid, and achieves the purpose of experiment evaluation. The specific implementation process comprises 4 links of data and model preparation, power grid calculation and analysis scene generation, experimental evaluation and analysis and feedback correction.
a) Data and model preparation: the power grid model and the real-time operation data are the basis for constructing the power grid operation, and meanwhile, historical section data, load prediction data, a power generation plan, a maintenance plan and a production plan are needed.
b) Generating a power grid calculation analysis scene: after various data are obtained, a research section of a specific calculation scene is generated by utilizing state estimation and dispatcher load flow calculation, and the research section is used as the basis of experimental evaluation analysis.
c) And (3) experimental evaluation and analysis: and according to the calculation task, fusing the data and the model by using state estimation, and performing comprehensive perception on the power grid through ground state load flow calculation to give auxiliary decision information of the power grid operation situation.
d) And (3) feedback correction: inputting planning information (power generation plan and maintenance plan), carrying out stability judgment on disturbance factors of the power grid by using stability limit out-of-limit analysis and static safety analysis, and finding out the adjustment direction of a power supply or a load by using sensitivity analysis so as to modify and perfect the plan.
The calculation experiment layer evaluates the safety stability margin of the power grid operation through dispatcher load flow calculation, sensitivity calculation, short circuit current calculation and static safety analysis, performs disturbance resistance experiment by combining with the planning information, finds out the optimized margin and direction, and sends the calculation analysis results of the optimization target and the constraint of the parallel execution layer to the parallel execution layer.
(4) A management and control layer: the management and control layer is the central pivot of the parallel system and is a bridge connecting the actual power grid dispatching system and the artificial power grid dispatching system. The comprehensive operation cockpit is taken as a core, and the analysis of the power utilization characteristics of the load end is realized through multivariate information collection, scene recognition, index calculation and statistical classification.
The multivariate information collection refers to unified management of power grid monitoring data, electric power data, protection information, video monitoring and power grid equipment models in an actual power grid dispatching system.
The scene identification is to select a proper data set and a model as research support according to different links of 'source-network-load-control', different user requirements and event driving.
Index calculation and statistics are carried out longitudinally according to 4 links of power generation, power transmission, power distribution and power consumption, and index calculation and statistical classification are carried out transversely according to 4 dimensions of safety indexes, high-quality indexes, economic indexes and environment-friendly indexes, and the index calculation and statistical classification are specifically shown in the following table 1.
TABLE 1 index calculation
The analysis of the electrical characteristics of the load end refers to dividing the load of the whole enterprise according to different categories, and analyzing the load characteristic indexes of the load, wherein the analysis indexes are shown in the following table 2.
TABLE 2 load end electricity characteristic analysis index
Meanwhile, expert rules are integrated into the comprehensive operation cockpit, control tasks such as normal plan execution type and abnormal handling type are performed according to a determined control flow by following the power grid control operation experience, and a task guiding mode is used for completing a flow control task.
According to event triggering or manual starting, a series of tasks triggered in sequence form a flow for achieving the operation target of the power grid.
The specific tasks are related to operation and operation instructions issued by a dispatcher to a subordinate duty dispatcher or a dispatching administration plant station duty dispatcher; the dispatcher defines operation tasks, requirements and all operation steps, operation sequences, operation equipment and confirmation of corresponding equipment states in the process from the beginning to the end of an operation object according to operation contents.
Providing a picture to guide a user to gradually realize a task according to a control event executed by a trigger flow; the daily work tasks and various abnormal processing operation tasks generated by the decision analysis engine provide operation interfaces, send operation commands, monitor task processes and related KPIs, form task completion conclusions and generate related event records.
(5) Parallel execution layer: a multi-time scale collaborative optimization method is adopted in a parallel execution layer, economic indexes are fully considered, the problems that contact and information interaction are lacked among different control subsystems of Automatic Generation Control (AGC), Automatic Voltage Control (AVC), Power Demand Control (PDC) and Load Management Control (LMC), control targets are not unified, excessive control or repeated adjustment is easy to occur are solved, unified coordination is carried out on different time dimensions, and active and reactive coordinated control of a power grid of a steel enterprise is achieved. The advanced optimization scheduling of the parallel execution layer generates a total electricity utilization prediction curve and gateway load prediction results of each branch plant or process by adopting a plurality of prediction algorithms according to production operation plans, process maintenance plans and special process trend analysis results and combining the electricity utilization characteristics of various processes of the iron and steel enterprise; and the system load summation, the power generation plan and the set values of the central bus voltage are generated by optimizing and scheduling in real time, and reliable control instructions are provided for load control, automatic power generation control and automatic voltage control in coordinated optimization control. The multi-time scale collaborative optimization mainly comprises advanced optimization scheduling, real-time optimization scheduling and coordinated optimization control.
a) Advanced optimization scheduling: the power grid situation perception process is divided into 3 stages of situation element data collection, real-time situation recognition and future situation prediction, and the method aims to better perform decision analysis on the power grid and provide a basis for accurate closed-loop control of the power grid. Establishing an optimized objective function comprising electricity consumption, demand electricity, power factor adjustment electricity, power generation cost and external electricity selling,
J＝(Pkwh_pfp+Pkwh_fff+Pkwh_vfv+(Pkwh_p+Pkwh_f+Pkwh_v)fextra)wkwh
+Ppdcfpdcwpdc+(0.9-Pfactor)ffactorwfactor+Pkwhgfgwg-Psellfsws
wherein: pkwh_p、Pkwh_f、Pkwh_vRespectively measuring the peak, average and valley electric charges of the monthly electric power charge; f. ofp、ff、fvPeak, flat, valley electricity prices, fextraAn additional fee to be charged based on the electricity usage of the enterprise; p ispdcIs the maximum monthly demand value, fpdcCharging a unit price for the demand; p isfactorPower factor, f, of monthly check pointsfactorA power factor compensation coefficient;
monthly power generation amount fgThe cost of electricity generation per degree of electricity; psellPower sold for the rest of the enterprise on the Internet, fsThe price is the price of electricity selling; w is akwh、wpdc、wfactor、wg、wsAnd the weighting coefficients in the objective function are respectively the electricity degree charge, the basic charge, the power factor adjustment charge, the power generation cost and the power sale benefit.
On one hand, the electricity consumption is related to the total monthly electricity consumption, on the other hand, due to the existence of the time-of-use electricity price, the electricity consumption cost can be reduced by adjusting the electricity consumption at different time periods, so that the electricity generation plan needs to be corrected through a peak-to-valley economy evaluation module, and the electricity purchasing quantity at the peak time period is reduced; as for the units of the self-contained power plants of the iron and steel enterprises, the units are generally coal gas co-combustion units, so the most direct means for reducing the power generation cost is to reasonably utilize coal gas resources, reduce the coal gas diffusion loss and improve the co-combustion proportion of the coal gas. Therefore, the aim of the coordinated optimization control is to reduce the required electricity fee, reasonably adjust monthly assessment power factor and reduce the power supply cost of enterprises.
b) And (3) real-time optimization scheduling: generating set values of system load total sum, power generation plan and central bus voltage, and providing reliable control instructions for load control, automatic power generation control and automatic voltage control in coordinated optimization control; the result of the short-term load prediction is corrected in real time by using the ultra-short-term load prediction, and is optimized by combining trend analysis of various loads to generate an optimal load total addition set value; the set value of the power generation plan needs to be checked in real time according to the current operation state and the load change trend of the unit and in combination with the stability of the voltage and the frequency of the power grid, and the plan set value meeting the requirements is sent to a next-stage control system; and the set value of the central bus voltage is adjusted in a small range in a certain range according to the result of the reactive power optimization.
c) And (3) coordination and optimization control: simulating the generator set and the electric arc furnace to be adjusted according to the control command, carrying out load flow calculation according to the adjusted power value, counting the changes of voltage and load flow, and calculating the influence of the changed power on an external electricity purchasing connection line, a demand monitoring point and a central voltage by adopting active sensitivity; and formulating an AVC coordination control strategy by monitoring voltage change in real time, considering active power change of a monitoring gateway, directly outputting a control command if the voltage and gateway active constraint conditions are met, and otherwise, performing alternate calculation until the convergence condition is met.
Fig. 3 shows an implementation process of the operation engine, which is one of the engines of the integrated operation cockpit, and follows the power grid control operation experience to complete the flow control task in a task-oriented manner according to a determined control flow, such as a normal plan execution type and an exception handling type; a series of sequentially triggered tasks form a flow to reach the grid operating goal, either triggered by an event or manually initiated. Providing pictures to gradually guide a user to gradually realize tasks according to a control event executed by a trigger flow; the daily work tasks and various abnormal processing operation tasks generated by the decision analysis engine provide operation interfaces, send operation commands, monitor task processes and related KPIs, form task completion conclusions and generate related event records.
The realization of the operation engine needs the support of an expert rule base, a management database and a real-time database of a data layer, the information transmission and check are provided by a workflow engine, a network check service and an evaluation analysis service of a logic layer, the maintenance customization, the operation flow control and the operation ticket management functions of a presentation layer are interacted with a dispatcher in real time, and a convenient control scene is provided for the dispatcher.
The expert rule base comprises an operation rule base and an anti-error rule base in a data layer, the management database comprises workflow data, form data, authority data and operation order records, and the real-time database comprises a power grid topology base and real-time data.
A workflow engine module, a network verification service and an evaluation analysis service are designed in a logic layer, and safety guarantee is provided for specific operation of a presentation layer.
The functions of maintenance customization, operation flow control and operation ticket management are provided on the presentation layer, so that maintenance personnel can conveniently carry out expert planning, workflow, form sample and authority customization and configuration through a special tool, and the functions of operation flow control, operation ticket management and the like are realized; the scheduling personnel operate and control the real-time monitoring system through the graphic system, and the operation engine can automatically check important operation.
The command interaction between the intelligent command platform and the power grid dispatching knowledge decision making system is realized by an automatic command robot, and the command interaction is realized by referring to fig. 4, which comprises the following steps:
(1) information and command interaction: and the intelligent command platform performs command interaction with the power grid dispatching knowledge decision making system, and determines whether to issue a command or not through total value monitoring.
(2) Network verification service: the system interacts with a power grid monitoring system in real time through network verification service and state evaluation analysis verification, and has accurate data, strong real-time performance and high response speed.
(3) Programmed control: the depth integration of programmed control and ordering (network ordering) can be adapted to equipment with operation conditions of a transport party, and can also be adapted to programmed ordering of equipment without remote operation conditions, and manual intervention is not needed for a dispatcher, so that the dispatching is released from mechanical operation.
(4) Safety checking: in the operation rule base and the error prevention rule base, state, five-prevention and tide safety check is added to equipment without remote operation conditions, and the control safety is greatly improved.
In summary, the invention provides an enterprise power grid dispatching knowledge decision system, which is built on an enterprise power dispatching centralized control system, fully utilizes software and hardware resources of the original system, adopts a manual system, a calculation experiment and a parallel control and management method of parallel execution, and builds a power grid dispatching knowledge decision system comprising an actual power grid dispatching system, a manual power grid dispatching system, a calculation experiment layer, a management and control layer and a parallel execution layer; establishing an artificial power grid dispatching system according to 4 links of source-grid-load-control, and completing calculation and experiments on the artificial system by high-level application of electric power; in management and control, a comprehensive operation cockpit technology is taken as a core, multi-time collaborative optimization of a parallel execution layer is synthesized, the operation of the whole system is driven, effective knowledge decision information is provided for scheduling personnel, and the scheduling efficiency of a power grid is improved.
The terms to which the present invention relates are defined as follows:
parallel control and management: starting from the actual requirements of a complex system, an Artificial intelligence method is combined with a control theory, a method and a theoretical system (ACP) which take Artificial Systems (Artificial Systems), Computational Experiments (Computational Experiments) and Parallel Execution (Parallel Execution) as main processes are provided, and the theoretical system is provided for modeling, analyzing, controlling and managing the complex system.
And (3) state estimation: the state estimation is a basic function module of high-level application of a dispatching automation system, can carry out analysis and calculation according to SCADA real-time remote signaling and telemetry data to obtain a relatively accurate and complete operation mode, and can calculate the output of all generators, the bus voltage and all loads. And simultaneously, the SCADA remote signaling and remote measuring are verified, remote measuring points which are possibly abnormal are provided, and the calculation result and the measuring quality mark are returned to the SCADA. The calculation result of the state estimation can be used by other application software as a real-time mode to further analyze the power grid.
Short-term load prediction: the method comprises day-ahead system load prediction and in-day system load prediction, wherein the day-ahead system load prediction refers to prediction of system loads in each period from the next day to multiple days in the future, and the prediction content is 96-point (00:15-24:00, one point every 15 minutes) system loads of the predicted day. Intra-day system load prediction refers to the prediction of system load from 1 hour into the future over multiple periods of time (one point every 15 minutes).
Static security analysis: the main research is to preset electrical components such as: the influence on the safe operation of the power grid is generated under the conditions of faults of lines, transformers, generators, loads, buses, switches and the like and combined faults of the lines, the transformers, the generators, the loads, the buses and the switches. For example, for the analysis of the line N-1, it is assumed that a fault occurs in a certain line, and after the line is disconnected, the line is calculated by using a factor table correction or a full load flow calculation method, so as to determine whether the system is disconnected after the line is disconnected, whether the system is out of order, whether the system is out of limit or equipment is out of limit, whether the system is out of limit, whether the generator, the load and the number of the loss are lost, and the like. The static safety analysis module is mainly used for judging the risk degree of the system to the fault, providing an overload branch, a voltage abnormal node and a reactive out-of-limit generator under the expected fault and the out-of-limit degree, and providing a sufficiently powerful basis for operators to maintain the safe and reliable operation of the power system.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (7)
1. An enterprise power grid dispatching knowledge decision analysis system is characterized by comprising: the system comprises an actual power grid dispatching system, an artificial power grid dispatching system, a calculation experiment layer, a management and control layer and a parallel execution layer;
the actual power grid dispatching system is used for power grid acquisition and monitoring, power grid optimized dispatching, electric energy metering and dispatching management; the actual power grid dispatching system respectively comprises from top to bottom: the intelligent power dispatching system comprises a hardware platform layer, an operating system layer, a unified application supporting platform and an intelligent power dispatching system;
the artificial power grid dispatching system is established according to 4 dimensions of power supply, power grid, load and control, and reorganizes and classifies power grid equipment; the artificial power grid dispatching system establishes a KPI index system for power grid dispatching according to the specific realization target of 'source-grid-load-control', and optimizes the assessment standard of the index system through continuous calculation and statistical analysis;
the artificial power grid dispatching system and the actual power grid dispatching system carry out information interaction through a management and control layer;
the power grid comprises a self-contained power plant, a gas and steam combined generator set, a dry quenching waste heat power generation device, a blast furnace gas waste pressure turbine power generation device and distributed new energy;
the power grid comprises power supply and distribution networks with different voltage grades, and the control requirements of high voltage and low voltage are different;
the load comprises pelletizing, sintering, coking, blasting, a blast furnace, a converter, oxygen generation, cold rolling, hot rolling, an electric furnace, life and office;
the control comprises demand control, power factor assessment, active balance control and voltage reactive power control;
the calculation experiment layer performs data and model preparation based on an artificial power grid dispatching system, generates a power grid calculation analysis scene, and performs experiment evaluation analysis and feedback correction;
the management and control layer is configured with a comprehensive operation cockpit for completing a process control task to realize a power grid operation target; the system is used for collecting multivariate information, identifying scenes, calculating indexes, counting and classifying, and analyzing the power utilization characteristics of a load end;
the parallel execution layer is used for carrying out unified coordination control on the automatic power generation control subsystem, the automatic voltage control subsystem, the power demand control subsystem and the load management control subsystem, carrying out knowledge decision analysis and providing results to scheduling personnel;
the parallel execution layer adopts multi-time scale collaborative optimization to carry out unified coordination control and consists of advanced optimization scheduling, real-time optimization scheduling and coordination optimization control;
the advanced optimization scheduling is to establish an optimization objective function comprising electric power charge, demand charge, power factor adjustment charge, power generation cost and external power selling; optimizing and scheduling aiming at reducing the demand electric charge, adjusting the power factor of the monthly test core and reducing the power supply cost of enterprises;
the real-time optimization scheduling is to generate set values of system load summation, power generation plans and central bus voltage and provide control instructions for load control, automatic power generation control and automatic voltage control in coordinated optimization control; specifically, the result of the short-term load prediction is corrected in real time by using the ultra-short-term load prediction, and optimization is performed by combining trend analysis of various loads to generate an optimal load sum set value; according to the current operation state and the load change trend of the unit, the stability of the voltage and the frequency of a power grid is combined to carry out real-time check, and a plan fixed value meeting the requirement is used as a power generation plan set value; the set value of the central bus voltage is obtained by adjusting within a certain range according to the result of the reactive power optimization;
the coordination optimization control is that the simulation generator set and the electric arc furnace are adjusted according to the control command, the power value after adjustment is used for carrying out load flow calculation, the changes of voltage and load flow are counted, and the influence of the change power on an external electricity purchasing connection line, a demand monitoring point and a central voltage is calculated by adopting active sensitivity; and formulating a coordination control strategy of AVC by monitoring voltage change in real time, considering active power change of a monitoring gateway, directly outputting a control command if the voltage and gateway active constraint conditions are met, and otherwise, alternately calculating until the convergence condition is met.
2. The enterprise power grid dispatching knowledge decision analysis system of claim 1,
the unified application support platform is used for network communication, database management, graphic service, calculation service, historical service, human-computer interface and system management;
the intelligent power dispatching system is used for carrying out centralized acquisition of power grid field data, power grid equipment modeling and network topology, power grid analysis, power grid monitoring, comprehensive intelligent dispatching, protection fault information management, system optimization and control, electric energy metering information management, dispatching management and stable control information management.
3. The system for enterprise grid dispatching knowledge decision analysis according to claim 1, wherein the computational experiment layer is specifically configured to,
acquiring a power grid model, real-time operation data, historical section data, load prediction data, a power generation plan, a maintenance plan and a production plan;
generating a research section of a specific calculation analysis scene based on state estimation and dispatcher load flow calculation;
according to the calculation task, real-time operation data and a power grid model are fused by utilizing state estimation, the power grid is comprehensively sensed through ground state load flow calculation, and auxiliary decision information of the power grid operation situation is given;
according to the planning information, stability judgment is carried out on disturbance factors of the power grid by using stability limit out-of-limit analysis and static safety analysis, the adjustment direction of a power supply or a load is found by using sensitivity analysis, and the plan is modified and perfected;
evaluating the safety stability margin of the power grid operation through dispatcher load flow calculation, sensitivity calculation, short circuit current calculation and static safety analysis, performing an anti-disturbance experiment by combining with the planning information, finding out the optimized margin and direction, and sending the calculation and analysis results of the optimization target and the constraint of the parallel execution layer to the parallel execution layer.
4. The system according to claim 1, wherein the management and control layer is specifically configured to,
integrating the expert rules into the comprehensive operation cockpit, and completing a flow control task in a task guide mode according to a determined control flow;
according to event triggering or manual starting, a series of control tasks triggered in sequence form a flow for achieving the operation target of the power grid.
5. The system for decision analysis of scheduling knowledge of enterprise grid according to claim 4, wherein said comprehensive operation cockpit performs a flow control task by operating an operation engine;
the implementation of the operation engine needs support provided by an expert rule base, a management database and a real-time database of a data layer, information transmission and check provided by a workflow engine, a network check service and an evaluation analysis service of a logic layer, and maintenance customization, operation flow control and operation ticket management of a presentation layer.
6. The enterprise power grid dispatching knowledge decision analysis system according to claim 1, wherein the multivariate information collection refers to unified management of power grid monitoring data, power degree data, protection information, video monitoring and power grid equipment models in an actual power grid dispatching system;
the scene recognition is to select power grid data and a model for research according to different user requirements and event driving according to different links of power supply, power grid, load and control;
the index calculation and statistical classification means that index calculation and statistical classification are carried out longitudinally according to power generation, power transmission, power distribution and power utilization and transversely according to 4 dimensions of safety indexes, high-quality indexes, economic indexes and environment-friendly indexes;
the load end electricity utilization characteristic analysis means that the load of the whole enterprise is divided according to different categories, and load characteristic indexes are analyzed, wherein the analysis indexes comprise: daily maximum load, daily maximum load occurrence time, daily minimum load occurrence time, daily average load, daily load rate, daily minimum load rate, daily peak-valley difference, daily load temperature correlation, typical curve for air conditioning in summer, typical curve for heating in winter, monthly maximum load time, monthly minimum load time, monthly average load, monthly load rate, monthly unbalance factor, annual average monthly load rate, quarterly maximum load time, quarterly minimum load time, quarterly average load, annual maximum load time, annual minimum load time, annual average load time, annual peak-valley difference, annual average peak-difference rate, annual load valley, maximum load utilization hours, seasonal unbalance factor, daily load curve, typical load curve, monthly load curve and annual load curve.
7. The enterprise power grid dispatching knowledge decision analysis system of claim 1, wherein the optimization objective function is:
J＝(Pkwh_pfp+Pkwh_fff+Pkwh_vfv+(Pkwh_p+Pkwh_f+Pkwh_v)fextra)wkwh+Ppdcfpdcwpdc+(0.9-Pfactor)ffactorwfactor+Pkwhgfgwg-Psellfsws
wherein J is the cost of electricity, Pkwh_p、Pkwh_f、Pkwh_vRespectively measuring the peak, average and valley electric charges of the monthly electric power charge; f. ofp、ff、fvIs the peak, flat and valley electricity price fextraAn additional fee charged based on the amount of electricity used by the enterprise; ppdcIs the maximum monthly demand value, fpdcCharging a unit price for the demand; p isfactorPower factor, f, for monthly assessment pointsfactorA power factor compensation coefficient; pkwhgMonthly power generation amount fgThe cost of electricity generation per degree of electricity; psellPower sold for the rest of the enterprise on the Internet, fsThe price is the price of electricity selling; w is akwh、wpdc、wfactor、wg、wsAnd respectively weighting coefficients of the electric power charge, the basic electric charge, the power factor adjustment electric charge, the power generation cost and the power selling income in an objective function.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911227400.5A CN110994790B (en) | 2019-12-04 | 2019-12-04 | Enterprise power grid dispatching knowledge decision analysis system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911227400.5A CN110994790B (en) | 2019-12-04 | 2019-12-04 | Enterprise power grid dispatching knowledge decision analysis system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110994790A CN110994790A (en) | 2020-04-10 |
| CN110994790B true CN110994790B (en) | 2022-07-22 |
Family
ID=70089953
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911227400.5A Active CN110994790B (en) | 2019-12-04 | 2019-12-04 | Enterprise power grid dispatching knowledge decision analysis system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110994790B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111966663A (en) * | 2020-08-07 | 2020-11-20 | 广东卓维网络有限公司 | Multi-user-side comprehensive energy data service system |
| CN114372595B (en) * | 2021-12-20 | 2024-03-15 | 国网江苏省电力有限公司苏州供电分公司 | Automatic inspection system and method for power grid dispatching control running state |
| CN115178590B (en) * | 2022-09-07 | 2022-12-06 | 承德建龙特殊钢有限公司 | Seamless steel tube production method |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105137756A (en) * | 2015-08-31 | 2015-12-09 | 南京南瑞继保电气有限公司 | Coordination control method and system for power grid of iron and steel enterprise |
-
2019
- 2019-12-04 CN CN201911227400.5A patent/CN110994790B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105137756A (en) * | 2015-08-31 | 2015-12-09 | 南京南瑞继保电气有限公司 | Coordination control method and system for power grid of iron and steel enterprise |
Non-Patent Citations (2)
| Title |
|---|
| 冶金企业电网综合运行驾驶舱的设计与实现;郝飞等;《有色冶金设计与研究》;20180630;第39卷(第3期);第42-46页 * |
| 郝飞等.冶金企业电网综合运行驾驶舱的设计与实现.《有色冶金设计与研究》.2018,第39卷(第3期),第42-46页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110994790A (en) | 2020-04-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zia et al. | Microgrids energy management systems: A critical review on methods, solutions, and prospects | |
| Ghasemi-Marzbali | Fast-charging station for electric vehicles, challenges and issues: A comprehensive review | |
| Bao et al. | A multi time-scale and multi energy-type coordinated microgrid scheduling solution—Part II: Optimization algorithm and case studies | |
| CN111478312A (en) | Comprehensive energy cluster coordination control method for improving power grid stability | |
| Logenthiran et al. | Multiagent system for real-time operation of a microgrid in real-time digital simulator | |
| CN110994790B (en) | Enterprise power grid dispatching knowledge decision analysis system | |
| CN106849109B (en) | A kind of urban distribution network load control method for the access of scale charging pile | |
| CN111030188A (en) | Hierarchical control strategy containing distributed and energy storage | |
| Wang et al. | Transactive energy sharing in a microgrid via an enhanced distributed adaptive robust optimization approach | |
| CN113888132A (en) | Energy management system of large-scale industrial enterprise | |
| Casini et al. | Optimal energy management and control of an industrial microgrid with plug-in electric vehicles | |
| CN116599151A (en) | Source network storage safety management method based on multi-source data | |
| Zheng et al. | Meta-heuristic techniques in microgrid management: A survey | |
| CN117526432B (en) | Distribution network regulation and control system and method for source-load interaction | |
| CN112993985A (en) | Micro-grid multi-target planning method considering uncertainty | |
| Leopold et al. | Simulation-based methodology for optimizing energy community controllers | |
| CN110929968A (en) | Comprehensive regulation and control method and system for multi-energy combined supply of smart city | |
| Kazemi et al. | Coordinated energy management strategy in scheme of flexible grid-connected hubs participating in energy and reserve markets | |
| Nejad et al. | Economic model for coordinating large-scale energy storage power plant with demand response management options in smart grid energy management | |
| Ren et al. | Decision-making approach in charging mode for electric vehicle based on cumulative prospect theory | |
| Cordova et al. | Netload Range Cost Curves for a Transmission-Aware Distribution System Planning under DER Growth Uncertainty | |
| CN112270432B (en) | Energy management method of comprehensive energy system considering multi-subject benefit balance | |
| Dai et al. | Low carbon scheduling strategy of regional integrated energy system with concentrated solar power plant and carbon capture system based on IGDT | |
| Berg et al. | Implementation of Battery Energy Storage Systems in Residential Buildings: A case study of a multifamily building in southern Sweden, exploring profitability, self-sufficiency and environmental performance | |
| Hui et al. | The study of spot electricity trading in view of big dispatching mode and market monitoring measurements |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |