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Abstract
As massive distributed energy resources (DERs) are integrated into distribution networks (DNs) and the distribution automation facilities are widely deployed, the DNs are evolving to active distribution networks (ADNs). This paper introduces the architecture and main function modules of an integrated distribution management system (IDMS) and its applications in China. This system consists of three subsystems, including the real-time operation and control system (OCS), outage management system (OMS), and operator training simulator (OTS). The OCS has a hierarchical architecture with three levels, including the local controller for DER clusters, the optimization of DNs incorporated with multi-clusters, and the coordination operation of integrated transmission & distribution (T&D) networks. The OMS is developed based on the geographical information system (GIS) and coordinated with OCS. While in the OTS, both the ADN and its host transmission network (TN) are simulated to make the simulation results more credible. The main functions of the three subsystems and their interaction data flows are described and some typical application scenarios are also presented.
RENEWABLE energy sources are being integrated into distribution networks (DNs) at a fast rate, pushed by policies and the environmental pressure [
In the 1980s and 1990s, various functions and architectures of energy management system (EMS) for transmission networks (TNs) have been fully investigated [
There are some research works on developing ADN techniques. A state estimation solution for ADNs using the Hamiltonian cycle theory is presented in [
Some pioneer works have been conducted to design and implement DMSs. The architectural design of Korean Smart Distribution Management System (KSDMS) is introduced in [
In China, more than 80% of the power outages are caused by incidents in the DNs, and over 70% of the network losses also occur in the DNs. Due to the large-scale and complex terrain of the DN, the requirements and workload for fault diagnosis and recovery are different from those of the TN. Compared with the traditional DN, the ADN contains more elements, the structure is more complex, and the interrelationship between its elements is more complicated [
Due to the particularity of DNs, the traditional operation and control system (OCS) meets the following challenges [
1) Massive controllable entities: the large-scale DERs and the controllable loads and networks lead to a sharp rise in the amount of information collected. Due to the bottleneck of communication and information processing, it is impossible to send all information to the control center for centralized decision-making.
2) Model maintenance efforts: ADN has a large number of components and frequent changes, and it is difficult for the control center to maintain the model of DNs in a timely and accurate manner.
3) Agility problems: renewable energy generation has strong fluctuations and rapid changes, and the long time delay in the control process hinders it to meet the requirements of real-time operation.
4) System reliability: the centralized system has the single-point failure issue, i.e., if the central server fails, the whole system will stop working. Therefore, such system is fragile.
5) Information privacy: DERs belong to different stakeholders, and the centralized system may not be able to access the detailed information of various entities.
In addition, the scale of DN is much larger than that of the TN, and is mainly maintained in the geographic information system (GIS) and product management system (PMS). The real-time or quasi-real-time measurements are generated from distribution automation devices, DERs, customers meters, and trouble calls, and thus a tremendous amount of information is involved [
The T&D networks have significant differences in voltage levels, network topologies, and impedance properties, and they are managed by different control centers, but their electrical coupling greatly affects each other. The traditional operator training simulator (OTS) of the TN can no longer be handled in an adequate way due to the increasing interdependencies with the systems such as TNs, DNs, DERs, and EVs. Therefore, a more comprehensive OTS is necessary to simulate the system interdependencies and determine the appropriate control strategies for optimizing power system operation [
The remainder of this paper is organized as follows. Section II introduces the overall architecture of integrated distribution management system (IDMS). The specific functions and key technologies of OCS, OMS, and OTS are described in Sections III, IV, and V, respectively. Section VI presents several pilot projects and some results from real applications of the IDMS. The conclusion and prospects are provided in Section VII.
IDMS is a distribution management platform for ADN that is composed of multiple subsystems. The system follows a layered software design and development strategy. Combining the intelligent analysis and decision-making technology based on full-phase model and the visual development of advanced analysis based on real-time GIS platform, an intelligent integrated application system of DN is formed. As shown in
Fig. 1 Architecture of IDMS.
The OCS is a multi-level, autonomous, and coordinated active management system. Its core is “cluster schedule and control”, which means each cluster governs its own state separately, and the superior coordination layers take clusters as the control object for regulation. Its main functions consist of dynamic autonomous control of DER clusters, optimization of the ADNs incorporated with multi-clusters, and coordinated optimization of integrated T&D networks.
The OMS is a fault repair and recovery system, which efficiently manages planned and unplanned power outages in ADNs. Its functions mainly include fault analysis and judgment, power outage analysis, work order management, visual comprehensive display, statistical analysis, mobile application (APP), etc.
The OTS simulates various normal and fault conditions of the ADN considering the coordination of T&D networks. Its main functions involve the ADN model, network analysis, joint anti-accident exercise, operator research, and monitoring simulation, etc.
The three subsystems do not exist in the isolated mode, but coordinate with each other in an integrated manner. As shown in
Fig. 2 Interaction among OCS, OMS, and OTS.
For example, when the OMS generates control strategies to deal with faults, it is based on the relevant real-time calculation results of OCS, and the operation simulations under the normal and fault conditions are carried out in the OTS according to the relevant data and models of OCS and OMS.
The traditional centralized control and decision-making systems face technical challenges such as control agility, system reliability, massive communication, and information privacy etc., which promotes the transformation of the centralized DMS architecture to hierarchical distributed architecture.
The OCS has a hierarchical architecture including local cluster control, ADN optimization, and coordination of T&D networks. The cluster control can facilitate the integration of DERs into the grid and realize “cluster self-discipline”. The optimization layer of DN aims to coordinate the clusters for accommodating the uncertainties of DERs. The coordination of layers in T&D networks fully exploits the regulation capability of the whole networks to realize the efficient and safe operation of DNs.
As shown in
Fig. 3 Framework of OCS.
Fig. 4 Coordination and regulation framework of cluster based on dynamic equivalence.
Fig. 5 Multi-agent-based distributed control architecture for cluster.
The middle layer is the ADN optimization, which realizes the complementary and coordinated optimization of the regional clusters and other flexible resources.
Therefore, it plays a critical role in the OCS. It needs to respond to the control instructions of the top layer as well as calculate and report the regional regulation capability. Moreover, it optimizes the operation of ADNs incorporated with multi-clusters. This layer is on a minute time scale, and it can improve the efficiency and security of the ADNs through coordinating clusters.
The top layer of OCS is the coordination of T&D networks. It is on a 10-min time scale. The joint regulation of the T&D networks is realized through a decomposition and coordination algorithm, on the premise of ensuring the independence operation of the TN and ADN. Besides, the distributed scheme solves the issues of numerical stability and regulation dependence in the conventional centralized solutions.
This hierarchical OCS has a flexible and scalable framework. It can realize the fast control of DERs through local cluster controllers and global optimization by adopting the decomposition and coordination solution. Owing to the hierarchical structure, the information privacy is preserved in the IDMS. The dynamic control of DERs is achieved inside the cluster layer, and the cluster controller provides its equivalent aggregation flexibility to the upper layers, masking the detailed model information. In addition, the computation burden of OCS is not very heavy since the DERs are aggregated into several equivalent cluster models, and the fast control is realized at low cluster level.
This operation and control framework is also applicable for DERs participating in electricity market. In the market setting, the cluster is evolved to virtual power plant (VPP), in which all the DERs can be aggregated as a whole and participate in market bidding. Therefore, the cluster controller takes the responsibility of de-aggregating the market clearing results for the cluster and optimizing the operation of DERs [
The key technologies and detailed models of OCS mainly involve: ① ADN analysis technology [
Since DN has a huge number of components, the computation burden of OMS is very heavy. The OMS in IDMS adopts distributed computation platform that distributes tasks to computer clusters. The distributed SCADA (DSCADA) provides real-time data access and distributed real-time data processing for multiple computers, realizes the localization of calculations and data, and better supports different calculation modes such as distributed batches and memory calculations, as shown in
Fig. 6 Architecture of DSCADA.
The information fusion technology is used for fault analysis in the OMS. After the fusion analysis and processing, it can figure out whether there is a fault and find the fault location. Since the data from different sources are adopted, the creditability of fault analysis results can be improved. In addition, a real-time GIS platform is developed. It supports thousands of concurrent service processing per second, and supports hundreds of power grid model updates every day. It realizes the dynamic display of different states of ADNs as well as that of network analysis results.
A typical fault handling process is shown in
Fig. 7 Flowchart of fault handling procedures.
Based on the above technology and the data sources of IDMS, the OMS uses the topology analysis and system simulation to automatically determine the fault type, location, and outage area, and correct them according to trouble calls. After the fault diagnosis, the repair and recovery process is automatically triggered, and the work order is formed and dispatched. Then, the inspection and repair will be carried out. The fault judgment results, outage information, repair, and recovery progress, etc. are all displayed synchronously in real time, so that service staffs can quickly respond to users.
The OMS preliminarily realizes the visualization of operation state of a large number of ADN equipment, operation process, and decision-making ideas [
In a multi-level scheduling system, the OMS can be deployed in a hybrid centralized and distributed manner.
As shown in
Fig. 8 Multi-level coordination structure of OMS.
The provincial control center makes the maintenance plan and fault repair schedule, which needs to interact with 6 systems. In the district control center, the intelligent fault diagnosis module is locally deployed for real-time decision-making. And the local control centers synchronize the diagnosis results to the provincial control center.
The key technologies of OMS mainly include: ① automatic fault diagnosis and location; ② optimal scheduling of repair resources and path; ③ multi-service integration and application integration; and ④ visualization techniques.
The OTS provides an application and operation environment consistent with the real-time ADN. The conceptual diagram of OTS is shown in
Fig. 9 Conceptual diagram of OTS.
The main functions of OTS include [
1) Power grid simulation: it includes steady state and fault simulation of primary and secondary systems.
2) Power grid analysis: based on historical and real-time network models incorporated with DERs, various analysis functions are realized, including power flow analysis, fault analysis, protective relay analysis, contingency with automatic restoration analysis, accident check analysis, etc.
3) Training and joint anti-accident exercise: ① training on power grid operation and accident handling; ② combining superior and subordinate OTSs to realize joint simulation training and anti-accident exercise of T&D networks.
4) Operation schedule and corrective control simulation: all the operation and corrective control actions are simulated under normal and contingency conditions.
5) Monitoring simulation: ① all on-site events should be recorded; ② various alarms, protection actions, and operation information are divided according to different emergency degrees.
OTS can also realize the joint simulation of T&D networks. The corresponding TN and DN simulation modules are maintained and performed separately. The simulation of the integrated T&D networks is realized through the mutual tracking and matching of boundary power flow. In addition, the simulated fault information and the action status of primary and secondary equipment are transmitted to each simulation module, which can realize joint simulation. The data interaction process for joint simulation of T&D networks is shown in
Fig. 10 Data interaction process for co-simulation of T&D networks.
Sections II-V introduce the main architecture and functional modules of IDMS. This section briefly describes some typical application scenarios.
A demonstration project includes eight 110 kV substations, ten 35 kV substations, and 174 feeders subordinate to these substations. According to the network topology and controllable resource allocation, the demonstration network is divided into six clusters, as shown in
Fig. 11 Layout of clusters in demonstration network.
The detailed information of Cluster 1 is shown in
Fig. 12 Detailed information of Cluster 1 in demonstration project.
The function configuration of OCS is shown in
Fig. 13 Function configuration of OCS.
Resorting to this control system, the power loss and voltage deviation of the whole network have been reduced, and the operation efficiency of the ADN is improved significantly.
Considering the participation of DERs in electricity market as VPPs, the OCS is developed to a more complicated system incorporated with the power market support system, in which the bidding and clearing of energy trading and ancillary services are realized, as shown in
Fig. 14 Operation and coordination of VPP with power grid.
The OMS has been widely deployed in China.
Fig. 15 MMIs of failure analysis and location module.
The left interface is customer service fault location. When a fault is detected or a trouble call is received in the control center, the fault diagnosis is figured out automatically, and the fault location is determined based on the Internet geographic maps. Meanwhile, a work order with solutions is generated and dispatched to the repair staffs of the substation near the fault. The above information will be presented on the duty-room in real time, as shown in the right interface. The operations on the left and right are interrelated, and information is sent to customer service center for users’ tracking.
A mobile APP of OMS realizes the information sharing and interaction of troubleshooting. The system operators, repair staffs, and other users can all follow the troubleshooting progress in real time. As shown in Appendix A Figs. A1-A4, with the APP, the real-time location, status, and trajectory of repair staffs are presented to the command center. The reported fault diagnosis and repeated work order filtering can be realized, and the progress of troubleshooting is shown to customer service center for timely reply to users. The overall progress from the command center to on-site work orders is displayed to the managers of all levels. The automatic transmission of work order information can be realized, which is convenient for repair staffs to contact users and navigate faults.
The OMS provides strong technical supports for network reconfiguration and uninterrupted switching operation during load transfer caused by equipment maintenance or contingencies. It can reduce the number of consumers affected by power outages and accelerate the power restoration process.
Currently, the OTS for DNs is mainly deployed in electric power training schools of State Grid Corporation of China.
Appendix A Figures A6-A8 shows some functional interfaces of OTS, including: ① the operation simulation of centralized master-station feeder automation system; ② the operation simulation of local feeder automation facilities; ③ the operation simulation and power flow analysis considering distributed renewable energy; and ④ the fault recovery control simulation.
It can truly reflect the changes in the power flow of the main grid and DN after a hypothetical accident occurs, and improve the emergence disposal ability and knowledges of operators.
This paper discusses the technical challenges of the ADN operation and proposes an integrated EMS consisting of OCS, OMS, and OTS. It introduces the main architecture, data sources, functional framework, and relationship of the three subsystems, and summarizes the key technologies. The demonstration applications show that the IDMS is well adapted to the operation characteristics of DNs with high penetration rate of DERs, and has a good prospect of popularization and application.
The flexibilities of prosumers connected to the end of DN have shown the potential to mitigate the variability of renewable energies [
Appendix
Fig. A1 Mobile APP of OMS for fault repair and recovery: interface for command center.
Fig. A2 Mobile APP of OMS for fault repair and recovery: interface for customer service.
Fig. A3 Mobile APP of OMS for fault repair and recovery: interfaces for managers.
Fig. A4 Mobile APP of OMS for fault repair and recovery: interfaces for repair teams.
Fig. A5 Operation simulation of centralized master-station feeder automation system.
Fig. A6 Operation simulation of local feeder automation facilities.
Fig. A7 Operation simulation of distributed renewable generation.
Fig. A8 Fault recovery control simulation.
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