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One consequence of digitalizing power grids is the steadily growing volume of captured data. These vast quantities of data harbor valuable information that describes the state of the grid, enables advanced computational analysis of changes, and can improve the performance and extend the service life of assets. The hardware and software from Siemens needed for this fulfills all the requirements.
Transparency in the grid
Deep insights into what is happening in the grids enable targeted, proactive actions to maintain and optimize power quality.
Owing to the increasingly complex tasks, it's becoming more difficult and at the same time more important to make the right decisions. With thoroughly analyzed data as a basis, you gain greater insights into the processes of your grid, enabling you to make well-founded decisions. This enhances performance and extends the service life of assets, while also simplifying processes. By optimizing maintenance deployments and increasing grid availability, you can also improve the cost-effectiveness of your grid.
Analyses of past projects confirm that integrating IT and OT into the engineering process can save time and money. Much of the effort involved in such system integration was attributed to converting between different protocols. High costs were also incurred when connecting differing systems in the control centers and other systems such as energy resource planning. Consequently, power providers and suppliers worldwide have developed common standards in order to reduce costs and effort. This enhances the security of investments.
The products from Siemens are certified to IEC Standard 61850 requirements. More than 1,000 grid substations and more than 1,400,000 protective devices certified to these qualifications are used in daily operations. All SIPROTEC 4 protective devices fabricated since 1998 can be easily migrated to this standard. The SIPROTEC 5 series as well as devices of the SICAM RTU and SICAM PAS lines already meet IEC 61850 requirements. Communication via a common protocol using the Common Interface Model (CIM) ensures compatibility between power control centers and other software applications such as customer information systems, data evaluation processes, energy management systems, and electricity trading.
The volume of data collected from various locations on grids is steadily growing, particularly due to the expanding feed-in of power from renewable energy sources. As a result, for every distribution network a data model exists, which lists all assets and their connections to other such assets. Added to this are data from meters, including their locations and linked lines and branch takeoffs, as well as from communication modules such as power line carriers and data concentrators in the secondary substations.
Alternatively, a new connectivity model can be generated without the evaluation of the stored model. Based on algorithms, a tree model of the grid can be calculated without knowing what assets it contains and how these are linked to one another. The models generated in this way already correspond in this initial draft by 80 to 90 percent to the conventional connectivity model, and can be improved even more in further design rounds.
This method is important, for example, when a generational change is to be made to a grid control system. In such cases, large data quantities have to be migrated from the old to the new system, which almost unavoidably leads to errors. When the algorithm for review comes with the migration, comparative analyses can detect connection errors, for example. This enables definition of qualified instructions to the data administrators on where in the dataset they need to look for errors. The quantity of data sources is what makes or breaks the quality of the connectivity model based on secondary data.
Managing distributed power producers is necessary in order to avoid grid overload situations, prevent voltage problems, and maintain balance between power generation and consumption, i.e. demand. One possible means of doing so is by regulating the reactive power via access to the distributed producers. Alternatively, capacitor banks can optimize the power factor at the critical point, and thereby optimally utilize existing cables and overhead lines.
In many countries, distributed power producers can even be disconnected from the grid in critical situations. In order to prevent such conditions from arising at all, however, energy storage systems can regulate the balance between power generation and load demand. And consumers themselves can also lend their support for balancing through demand management by coordinating power surplus on the one hand with peak- and high-demand periods on the other.
Support for meeting and mastering these challenging tasks is provided by the Grid Optimizer software of Spectrum Power™, which enables coordinated use of distributed power producers, energy storage systems, capacitor banks, and other components for load compensation and balancing. This includes the remote terminal unit SICAM A8000, which links to distributed producers via standardized inputs and outputs. The Grid Optimizer software can also control the charging and discharging of battery storage systems using real-time information from the grid, supplied via SICAM P50 or P850 units. This software is also capable of controlling industrial electrical load demand, as many processes allow electrical demand to be disconnected or load consumption to be increased when surplus power is available.
Distributing various measuring devices throughout the entire distribution grid is a question of time and money. Strategically critical and important local distribution substations are the first locations to be equipped in order to optimize such deployment cost effectively. Real-time status estimation plays an important role in this process, enabling that portion of grid substations that are not equipped with measuring equipment to be mapped. Real-time status estimation can be calculated either centrally or on a distributed basis, performed by the Spectrum Power™ Advanced Distribution Management System (ADMS). This enables advanced detection of critical grid situations. This also provides grid planning engineers with detailed insight into recurring load demand histories and up-to-date load flow diagrams as well as into voltage level monitoring.
Phase asymmetries in grids can be detected and located based on captured data. These asymmetries often negatively impact equipment and electrical loads (i.e. electrical consumers) such as motors. As a growing volume of sensor and meter measuring data are available, this data can be used for analysis purposes, thereby greatly facilitating the difficult and time-intensive search for asymmetries among loads as well as sensors that have been cross-phase connected.
Parallel data processing via MapReduce functions can be introduced when dealing with large data quantities for the purpose of identifying sensor information that displays significant deviation from other condition histories. In addition, interactions and correlations with other processes can be evaluated and visualized. The more often events occur simultaneously at meters, the stronger the connection between them. Hence, this allows detection of asymmetries that a group of meters must verify independently of one another.
For example, such an investigation may reveal that these asymmetries occur in single-phase switched-mode power supply units of a residential building complex. The centralized telecontrol signal causes increased asymmetries when actuated because all loads are erroneously connected to the same phase. What is termed the power snapshot analysis method delivers a synchronous replica of the grid condition based on smart meter data. A method developed especially for this purpose enables phase assignment in the low-voltage grid by means of electronic meters. The voltage histograms yielded by the power snapshots deliver information on asymmetric loads. One analysis method involves interactive visualization of defined events such as sum totals or overvoltage and undervoltage.
Distribution grid monitoring makes it possible to optimize grid maintenance. It enables prioritization of maintenance deployments and early detection of faults in load switches and circuit breakers. Systematic monitoring can identify potential faults and failures before they occur. Based on this information, it's possible to perform maintenance on disconnect switches, circuit breakers, reclosers, and distribution grid transformers before faults and failures occur. Owing to the high reliability of transformers and switches in distribution grids, failure monitoring starts with aging equipment in critical grid substations. This also helps proactively and efficiently plan maintenance cycles based on the state and history of the grid. A long-term database is helpful in assessing when the service life of operating equipment will come to an end. Equipment monitoring also gives insight into the condition of gas-insulated switchgear (GIS) and shortening expensive cyclical maintenance downtime.
With its grid control platform Spectrum Power™ 7 and ISCM platform, Siemens offers the appropriate solution package for condition monitoring of all installed components. This integrated system enables forecasting of condition changes in the assets of distribution grids, rather than monitoring of individual components, which is what conventional systems do. The alternative software package RCAM Dynamic enables online condition data to be combined with inventory data for the purpose of calculating the current state as well as the future state. Based on various parameters and a self-developing aging model, an Asset Health Index is generated for the various categories of operating equipment. RCAM Dynamic also lends support for introducing condition-based maintenance concepts as well as strategic investment planning.
The products and solutions from Siemens help maintain the right balance of power generation and consumption load and enable cost-effective operation of microgrids. Listed below are just some of the reference projects.
Optimization of the control of power producers and battery storage systems as well as demand response
Prevention of voltage fluctuations that exceed the required voltage level range that arise from integrating renewable energy sources
Rapid implementation ensures optimum grid operation and cost reduction
Photovoltaic technology replaces diesel generators
Lower CO₂ emissions
Lithium-ion battery storage systems optimize use of PV installations
Cost-effective operation of a real network as a microgrid
Hybrid structure with wind power, PV, heat and power cogeneration unit, and plant-oil-fired generator
Supply of power to the superimposed grid