V2G (Vehicle-to-grid) communication protocols are standards for the interactions between the Electric Vehicles (EVs) and the grids. Open communication standards like OCPP (Open Charge Point Protocol) allow interoperability and are suitable for V2G technology. They use a common framework and allow anyone in the underlying framework to share information. In doing this, they allow the back-end software of the charging management system to get updates on the status of electric vehicle charging going on at the time.
Launched in 2018, OCPP 2.0 is the latest version of OCPP from the open charge alliance – a group of private and public EV infrastructure companies (160 members as of 2020). The previously popular OCPP 1.6 has been improved to meet the new needs of electric vehicle infrastructure. That is, while OCPP 1.6 is great, OCPP 2.0 is better. Its first adoption, OCPP 2.0.1, was in March 2020 and has proved to be the one for the future.
However, before I begin to talk about the new version of OCPP, you should know a bit about how OCPP operates generally.
How Does OCPP Operate?
The Open Charge Point Protocol (OCPP) exists between the charging stations (also known as Electric Vehicle Supply Equipment) and the central system. This central system is a back-end software that receives and controls information regarding charging sessions, reservations, and updates. In addition, OCPP 1.6 allows for smart charging, a highly desirable feature for load balancing and other advantages.
Smart charging involves a system where elements of the electric vehicle network, including the EVs, charging stations and charging operators, share data connections and access specific details. All versions of OCPP also use an open platform to connect EVSEs with the cloud-based back-end system to aid communication.
What you should know about OCPP 2.0 V2G Communication Protocol.
OCPP 2.0 is an improvement to OCPP 1.6 and 1.5, which, in itself, is of highly significant importance. While the features of open communication and smart introductions of OCPP 1.6 are still in place, OCPP 2.0 adds more significant changes that welcome the future. Allow me to walk you through five things you should know about this protocol.
1. OCPP 2.0 supports the ISO/IEC 15118 v2g communication protocol
The IEC 15118 protocol allows for easy two-way communication between Electric Vehicles and the charging stations. It also has a feature that allows for automatic identification. So, as a user, you’re free to decide whether to use external identification means (EIM) by using RFID (Radio-Frequency Identification) cards or by using the automatic identification system to get identified based on your initial data captured.
You may ask, how does OCPP 2.0 come in as a support for IEC 15118 in this case?
With the EVs’ plug and charge and smart charging requirements in place, OCPP 2.0 allows smooth cooperation. This support is simply in place as it works with IEC 15118 efficiently, and together, they both give grounds for smart charging even though they have not yet been fully adopted.
In addition, the central system can set constraints to the amount of power during a charge transaction for smart charging.
2. Better security arrangements come with OCPP 2.0
OCPP 2.0 is more secure, and this is needed in every smart system to avoid cyber attacks. Unlike OCPP 1.6, it does not require VPN or any other third party for a secure connection. This was formerly necessary for encryption of the entire communication channel, and it posed a risk to the security of the EV charging system. However, using IEC 15118, there is easier identification from the known PKIs (Public Key Infrastructures), which are very secure.
OCPP 2.0 can achieve this because of the new security profiles for authentication, security logging, and event notification.
3. Improved functionalities for smart charging
In an EV charging arrangement, OCPP 2.0 allows for a request for the particular amount of power the charging station needs. Meanwhile, OCPP 1.6 does not allow for this kind of data field that OCPP 2.0 now allows.
Instead, it only allows for the vehicle to give a State of Charge (SoC), telling the percentage of battery it has at the time. This limits a lot of things, especially with the introduction of Vehicle-to-grid communication that has to be bidirectional and specific and smart. While the use of State of Charge is vital, it can be more useful when the charging process is better managed using OCPP 2.0.
4. OCPP 2.0 is reliable, even for the sake of finances
Efficiency is what everyone wants. With OCCP, charging stations are normally independent of vendors since there is a central underlying framework, unlike how it was before OCCPs came on board in 2009. This interoperability that comes with it alone is an advantage, but it’s not the only advantage.
With OCCP, no one gets stuck to one vendor, and in cases of a price increase by the vendors, even financial troubles or bankruptcy, there is a freedom to switch vendors even while using the same charging station.
This is good for the Vehicle-to-grid technology because it allows any EV and any Electric Vehicle Service Equipment to communicate. OCPP 2.0 also keeps the market healthily competitive.
5. OCPP 2.0 allows for flexibility and better device management
With OCPP 2.0, charging stations can be monitored, and this is helpful to their operators who have complex multi-vendor charging stations. Even from the end of an EV driver, the display and messaging features reflect all information they need, such as rates and the likes. This way, we can manage EVs and charging stations more effectively.
OCPP 2.0 is a leap and a significant milestone in the advancement of electric vehicles.
Electric vehicle fleet managers and utilities have been learning about IEC 15118 and moving on to put it to use at a slow pace. However, with the availability and working of OCPP 2.0, the synergy with IEC 15118 for EVs and EVSEs is set to move electric vehicle charging to a better place. IEC 15118 needs OCPP 2.0 to communicate more effectively with the central systems.
Smart grids fuse energy development with technological advancements. Using sensors, IoT, and other computing devices, there is a provision for two-way communication between consumers and utility providers in a smart grid. As an artificially intelligent system, a huge amount of data comes from various sources, e.g. smart meters. All the unstructured data gathered from these sources can only be valuable with smart grid analytics.
Smart grid analytics are systematic computational analyses of the data produced in the grids. With these analytics, one can get a more precise interpretation, communication, and identification of data trends or meaningful patterns from the data that comes in. Thus, it is essential to improve grid operations and predict the next course of action.
A Brief History
From the 1990s, attempts at electronic metering, control and monitoring evolved into smart grids. From automated meter readings in the 1980s to Advanced Metering Infrastructures in the 1990s, attempts have been made to go beyond measuring power usage to maximizing the information.
The concepts of analytics can be traced back to the 19th century with Frederick Winslow Taylor’s time management exercises and Henry Ford’s measurements of assembly lines’ speeds. It would interest you to know that predictive analytics (which is now of high importance in smart grids) started in the 1940s. However, it did not attract any attention until the 1960s, when decision support systems became popular. By 2005, businesses applied analytics to make iterative explorations on past activities and make decisions to plan the future.
Applying analytics to smart grid data is what birthed smart grid analytics. The problem of big data (as Roger Magoulas called it in 2005) has always existed as long as the internet. Around early 2012, big data in smart grid systems initiated collaboration between smart grid integration companies and data analytics start-ups.
As grids became smarter, grid data analytics also developed, using available technologies such as machine learning techniques. Computing techniques like statistics, machine learning (under artificial intelligence), and data analytics are now being applied in various facets, and the power sector is not left out. As we will see, smart grid analytics gives relevant information that helps set the course of upcoming activities for the effective distribution of power.
Three Things You Should Know About The Current Trends In Smart Grid Analytics
For one, the smart grids analytics market in Europe was projected in 2019 to grow at the compounded rate of 11-12.7% by 2025. This growth is based on how advanced grid technologies are embraced on a broader scale. However, I have observed these trends;
1. There is currently rapid growth in investment in smart grids projects and, subsequently, smart grids analytics.
Many countries in the European Union have invested in smart grids projects and are recording successes. Of the projects, up to 59% are demonstration projects, 32% are for deployment, while 9% are research and development projects. A significant highlight is the smart meter roll-out in Italy that takes up to 71% of these projects aforementioned.
Smart meters are installed in all of these projects, and to get relevant information from the data, smart grids analytics have to be employed. These projects result from increased interest and initiatives channelled to the ongoing energy transformation and sustainability goals.
2. Smart grid analytics work with real-time data even with the increased speed and variety of requirements.
This easy adaptation is because they are entirely computerized and are built on the blocks of advanced technologies. Smart grid analytics can now generate information from high-speed data of various forms needed for the grids’ operation and prior knowledge of what to put in as resources.
3. Digital technologies and cloud computing would continue to improve and allow for more data computation.
Digital data, which highest storage used to be terabytes, is now accessible on larger scales like exabytes and zettabytes. Manual methods and previous ways of analyzing this data are becoming redundant. Also, with the inclusion of renewable energy in the conventional grids, the adaptation of intelligent systems is increasing, and the need for grid data analytics will follow this trend.
Challenges Of Smart Grid Analytics
Despite the enormous advantages and improved technologies, there are still a few challenges. Some include:
- Cost implications – the initial costs of setting up smart grids make many grid operators sceptical about using smart grid analytics. For the grid as well, it usually includes the costs of sensors and other components in making it effective. The analytics themselves are part of what makes the smart grid a modern electric system. However, it is worth the investment to foster a low-carbon economy and a greener world.
- Security concerns – the fact that smart grids allow for two-way communication is a concern as the data is prone to cyber-attacks. Despite this, cybersecurity has continued to improve and is developing better solutions using codes and encrypted data.
- Customer demand – the demand needed for effective use of smart grid analytics is higher than what exists now. Not enough grid operators have adopted analytics, and the low-scale usage is not optimal. More large-scale energy supplying and distributing firms need to embrace the new technologies at this time.
Smart Grid Analytics Scope
As smart grids involve a more frequent measurement of rates and power usage and newer energy generation technologies like renewable energy is integrated, data is gathered from time to time. This measurement is not done manually but via smart meters and other data collection sources, thereby helping utilities and their customers manage their bills and rates of power usage. To interpret all these forms of data that come into the grid, smart grid analytics works across a broad scope, as illustrated in the table below.
|Smart Grid Analytics Scope||Overview||Aspects|
|Operations analytics||This involves the functions that manage the operational aspects of the smart grid, that is, how the entire system is run.||
|Signal analytics||Signal analytics makes the most of the state of the signals that are gotten from the sensors on the advanced metering infrastructure.||
|State analytics||This involves various analyses and interpretations of the state of the grid, even geographically.||
|Enterprise analytics||This analyzes the business expectations and economic values of the entire grid management system.|
|Customer use analytics||This analytics makes use of the operational data of customers, their demand, and their reactions.||
|Event analytics||In the case of unprecedented events, as well as planned ones, analytics also makes room for organizing energy schedules.||
Opportunities for Smart Grid Analytics
According to a report on the market analysis, trends, and forecasts on smart grid analytics, the dynamics affecting how much smart grid analytics can do, shift from time to time. These factors, asides from macroeconomic factors and internal market forces, include:
1. Customer acceptance and engagement
More electric power consumers have come to accept the smart grids deployed in projects by private firms and public institutions, though not so many have engaged the setups. More of those who engaged in trials have been typical volunteers, according to a report after a survey.
In the same study, 55% of the funding from smart grid projects in Europe was from the EU and other government agencies, while 45% came from private investments. 87% of smart grid projects in Europe have received funding despite the business case uncertainty that has restricted many private firms from investing in the ongoing projects. Meanwhile, only 65 out of 112 customers responded to the survey.
2. Regulatory policies
Regulations change from time to time, and recently, policies supporting the growth of renewable energy and smart grids have been put in place. Notably, the European Union has played a significant role in making smart grids effective by policies like the strategic research agenda road mapped for 2007-2035
3. Innovative structures
The structures available are key factors to how much smart grid analytics would thrive. The more innovative systems are implemented, the more innovations like this collaboration between technological advancements and the power grid would improve.
Thus, there are opportunities for smart grid analytics to thrive when these factors are in favour and all work towards achieving the set goals.
Other contributing factors.
Investments have been made in many projects involving smart grids all around the world. These investments have happened over time because grids enabled with smart technologies ensure reliable, secure, and efficient electricity management, meeting the objectives of any power grid.
While these attractive features are of great importance, I cannot stop at them without mentioning that the only way to sustain that power quality management and proper communication, planning, and management of data derived intelligently by smart meters is by the use of smart grid analytics.
The constant development of smart grids has provided a platform for smart grid analytics. Coupled with the fact that smart grids utilize wired and wireless communication infrastructure, information systems, demand response management system, SCADA (Supervisory Control and Data Acquisition), GIS (Geographical Information System), CIS (Customer Information System), and advanced metering infrastructure. Each platform in a smart grid provides an opportunity for smart grid analytics. This is because of the complexity that comes with data from decentralized energy systems.
More so, these issues of data management are the ones that give smart grid analytics a chance. For example, various standards (like models) have been put in place because of differences between networks and devices in terms of bandwidth constraints, energy constraints, continuous and non-continuous data, and the likes. Also, the need to manage massive data and ensure data privacy has led to more and more opportunities for smart grid analytics.
Future of Smart Grid Analytics
I strongly believe that we can achieve a lot with smart grid analytics. Experts expect the market to grow at a CAGR of 25% between 2019 and 2024, given the increase in IoT and big data analytics. In addition, new information and communication technologies keep penetrating the grid, which is enough to make smart grid analytics grow stronger globally.
Speaking of global locations, it is projected that the Asia-Pacific region is likely to witness the fastest growth, particularly in the two countries that dominate the region – India and China. This growth is predicted because the energy consumption in India since 2000 has doubled, and there is potential for more. Interestingly, the current market for smart grid analytics is also really competitive in Europe and worldwide, and this is a driver for growth.
IoT is one technology that is likely to take the grids to another level, despite the predicted challenges of cyberattacks, impersonation, the need for more IT infrastructure, data tampering, security, and privacy issues. While these challenges are already being combated using cybersecurity techniques and blockchain technology as a secured and distributed database, more researchers and developers are making better ways to handle them. As a result, the future of smart grid analytics would include IoT, more than it is now, in how smart grids operate. However, the existing IT infrastructure has led to the discovery and application of certain technologies used for smart grid analytics.
Best Technologies for Smart Grid Analytics
Smart grid analytics has evolved over the years and consists of various techniques involving the integration of data from electric power sources, analysis, processing, and visualization.
There are generally four kinds of analytics. In order of complexity, they are
- predictive, and
There is also cognitive analytics though it is a recent advancement that combines many functions. Of the four, the smart grid operators prioritize predictive analytics to find out what could happen at any point in time.
Nevertheless, the best technologies that have also evolved for smart grid analytics include Business Intelligence and Data Analysis (BI&DA), or big data analytics. The two terms were introduced to companies around 2009-2010 and have been unified as they work together to form the most relevant technology for smart grid analytics.
1. Business Intelligence (BI) and Data Analysis
Business intelligence is a broad term that comprises several activities aimed at helping companies make better use of the data at their disposal. Also, data analytics is used to make conclusions from raw data by computational analysis. Business intelligence and data analytics fuse together to maximize the benefits of the smart grid by applying business-centric methodologies to get useful information from the smart grid. BI makes use of tools and software to mine data, process it, and make meaning out of it using tools like spreadsheets, OLAP (Online Analytical Processing), data mining tools, and data reporting and visualization software. In the end, the data that has been collected is evaluated, optimized, and re-evaluated.
BI and DA also perform some processes to manage and create centralized data from various sources continuously. Some of these processes that occur before any form of analysis include pattern mining (that is, identifying patterns and similar arrangements), classification, the association of rule mining, clustering, making of regressions, and detection of outliers.
2. Other technologies that form the framework – Databases like Apache Hadoop, MapReduce, SQL
Asides from the techniques and technologies used to analyze data, a proper database is necessary for smart grid analytics. The currently available databases form software frameworks that are open source and they spread offline data across clusters and nodes for easy processing. They have improved with time, so new ones have more specific functions than the previous ones. For example, though Apache Hadoop is a popular and basic database, there is a need to use MapReduce because of background indexing. More so, for the sake of ad-hoc querying issues, SQL has been introduced.
The NoSQL database allows the newer data management technologies that are easier to scale and perform optimally than its counterparts. Some of the NoSQL open source database types include Cassandra, Elastic Search, MongoDB, and Hbase. Hbase and Cassandra are each column stores based on the concept of BigTable. However, Hbase bases also on Apache Hadoop, while Cassandra bases on DynamoDB.
The Important Role of Big Data in Smart Grid Analytics
Big data refers to the massive amount of data that an institution, unit, or system has to manage to utilize efficiently. The amount of data chunked into the smart grid through the smart meters, weather updates, social media, programmable thermostat, traffic updates, remote terminal units, and so on can be overwhelming and difficult to evaluate using the existing traditional methods. Of course, big data goes beyond the size, but the data format is usually diversified in the power grid.
Big data analytics is of utmost importance in the smart grid as relevant information needs to be extracted from available data.
In a day, there can be 30GB of PMU data and 120GB of smart meter measurements, not to mention 16GB of weather data from satellites, radars, and weather forecast models, and up to 2.7GB of vegetation and topography data.
They all come in at a high velocity and give updates in intervals of about 1-15 minutes. With the variety and complexities involved, big data plays a significant role.
Some ways in which big data analytics play a role in smart grid analytics include:
- power generation analysis
- load management with demand
- performance analysis of energy consumption
- forecasting and scheduling of loads
- evaluation of economic effects and constraints.
Big data analytics plays its role in the smart grid and motivates all that has to do with smart grid analytics.
Market Analysis of Smart Grid Analytics technology in Europe
Without a doubt, there’s so much we can achieve with smart grid analytics. Experts project the global smart grid analytics market to grow at a CAGR of 25% between 2019 and 2024, given the development of the Internet of Things (IoT) and big data analytics. While for Europe, the market growth projected is a CAGR of 10.38% by 2028.
Despite North America and Europe leading the market significantly, experts predict that the Asia-Pacific region is likely to witness the fastest growth, particularly in the two countries that dominate the region – India and China. With India’s energy consumption increase (over 100% in the last 21 years), these predictions are on track.
From observing Europe’s smart grid analytics market, these things are worthy of mention;
- There are key factors propelling the market, like increased investment in research and pilots in the UK, growth of renewable energy in France, Austria, the Netherlands and the likes, and the predominance of the use of the IoT in Italy.
- There have been constant technological advancements in IoT, consequently improving its application to smart grids.
- There is a growing interest in smart grid systems that have led to more funding and investments by the European Union Commission and other key bodies.
- More renewable energy sources are being integrated to meet energy demands from Industrial growth.
As a result of the factors highlighted above, more advanced grid analytics companies in Europe like Hive Power, ERIGrid, GridCure and the likes are on the path to making the grids effective with their solutions.
Smart Grid Analytics Use-Cases in Europe
Below are three use cases and projects on smart grid analytics in Europe.
To make customers active participants in their energy savings, Hive Power, in partnership with AEM (Azienda Elettrica di Masagno), created an application that updates customers with information regarding their electricity usage called Drainspotter.
In a preliminary survey of 9,000 homes in Lugano, we analyzed the data obtained from 15-minute sampled load profiles. The meters used to get this data were L+G E450. DrainSpotter allows users to monitor their pattern of energy usage, as well as summaries of customer behaviour. An exciting feature of the DrainSpotter is its ability to notify customers of anomalies in their energy consumption patterns.
For example, Hive Power figured out that if AEM’s residential users eliminated excessive standby power of more than 200W beyond 14 days, 5% of them would reduce their energy consumption by at least 20%. Also, for one and half years, 4.2% of the customers would save up to 500 CHF($546.49) on energy. In all, the DrainSpotter gives a system that supports the inclusion of users in their energy cost management and supports DSOs in providing expert advice on end-users.
Enexis Netbeheer, a grid operator in the Netherlands, started to make an internet of things inspired smart grid in 2016. They had 900,000 smart meters already installed for a start and sought to increase them with time to 2.8 million by 2020. One motivation for this project was that more stakeholders were interested in making grids more flexible, hence the installation of smart meters in their numbers.
To get to the desired number of meters, 7,700 meters were installed weekly and gradually increased to 10,000 meters per week. With such a large number of smart meters, it calls for proper data management.
Also, a futuristic step they put in place was the plan to install sensors in all their 50,000 substations to know the details of their activities. In the same way, the meters that were already installed and only had 2G were upgraded by creating sim cards that helped them function with 4G enabled devices.
Using an in-home display to show the energy use by customers in real-time, Enexis and the partner companies were able to achieve the awareness customers needed to understand the cost implications of their use of one electronic device or the other.
This is an ongoing horizon 2020 project by BRIDGE, and this is the project’s final year. According to EU-SysFlex, their name stands for “Pan-European system with efficient, coordinated use of flexibilities to integrate a large share of RES.” The aim is to provide new services to support systems with more than 50% renewable energy sources and find problems associated with renewable energy integration and solutions.
With 34 partners, including TSOs, DSOs, researchers, and €20 million from the European Union horizon 2020 funds, this project is globally recognized as a very innovative one. Demonstrations for this project exist in Germany, Finland, Italy, Estonia, France, and Portugal. EU-SysFlex applies Smart Grid Analytics in the aspect of grid data management, flexibility management, and forecasting.
The demonstration in Brandenburg, Saxony, and Saxony-Anhalt in Germany resumed live operation earlier this year (2021). Currently, up to 100 solar photovoltaic systems and wind power plants are being managed there. In addition, this project has innovatively provided flexible solutions to real-time data management for similar energy systems.
A lot of research goes into developing analytical models fit for smart power grids, especially when renewable energy is involved, which hints that the future holds more.
From all I have discussed in this series, it is evident that smart grid analytics is of great advantage to the power sector in Europe and worldwide. The best way to keep it growing is for more grid operators and energy suppliers to explore it more and apply it to various aspects of their operations.
Vehicle-to-grid (V2G) technology is a means to a greater end for the world of sustainable energy. Even though V2G is not yet prevalent, the structures necessary for communication between grids and electric vehicles have already started growing with advanced technology. It is essential to note that communication protocols that serve as guidelines in their various applications have to be flexible enough to accommodate change constantly.
Communication protocols guide the interactions between two digitally connected entities. In this case, electric vehicles and grids are the entities. Without standards, there is always a gap and disorderliness. Such chaos is not needed in the exchange of data and the facilitation of communication in the application of V2G (Vehicle-to-grid) technology. The IEC 15118 protocol steps in to solve this problem.
V2G technology can only be implemented swiftly and much more if the points of interaction between the two elements, the vehicle, and the grid, recognize each other. You would agree with me that adaptability makes any product or technology, like the advent of electric vehicle usage, more feasible and desirable. The IEC 15118 protocol is one of the other communication protocols but paves the way for a smooth transition in vehicle-grid integration.
The Focus of V2G Communication Protocols
Many concerns come up when it comes to any kind of data exchange. There is a need for the details (like the specifications & unique identity) of a vehicle to be communicated in V2G. Asides from the fact that details may easily be tracked and need a high level of security, the flexibility of the interactions between EVs, charging systems, and grids are highly required for V2G to thrive.
The IEC 15118 started in 2009 for the Vehicle-to-grid Communication Interface to promote autonomous usage. Interestingly, this protocol is still under development, yet it already gives a platform that allows for a broader scope. As V2G communication is needed to be in place for automatic billing and access to the internet, the IEC 15118 protocol gives a form of global compatibility that applies just as well.
IEC 15118 Protocol: What you should know
Of the two main kinds of community protocols (the front-end protocol and the back-end protocol), I would spotlight the IEC 15118 protocol (which is a front-end protocol. That is as a result of its relevance in V2G technology and its application. Also known as the ISO 15118 protocol, it is one of the International Electrotechnical Commission (IEC) standards for electric vehicles (including trucks). It has some interesting sides to it, as I would explain below.
1. More Advanced Communication with IEC 15118
Compared to a similar protocol, like the IEC 61851, the IEC 15118 communication protocol is more advanced. For example, ISO 15118 gives the requirements for charging load management, billing and metering. It thus promotes bi-directional digital communication, which is the basis for V2G communication.
IEC 61851 can only do basic signalling, like indicating readiness for charging and connection status. However, IEC 15118 is applicable for high-level communication, which is an advancement. This places it at the core of EV charging and even V2G interactions. This way, there is better communication and information transfer between the Electric Vehicle and the Electric Vehicle Supply Equipment (EVSE).
2. Versatile Application of IEC 15118 to Wired and Wireless Charging
In its implementation for charging electric vehicles, you can apply IEC 15118 to both wired (AC and DC) and wireless charging. Since V2G applies to various kinds of electric vehicles, this protocol suits it appropriately.
With the current update on part 8 of the IEC 15118 protocol, you would notice an improvement that would allow for wireless connection. Part 8, which is the Physical layer and data link layer requirements for wireless communication, informs the protocol’s versatility.
3. Security via Digital Certification in IEC 1158
The communication between vehicles and grids (via V2G) with the IEC 15118 protocol is more secure. This is a result of the use of digital certificates. In addition, public key infrastructures issue and manage digital certificates. These certificates link people, systems, and keys.
Like passcodes (but more complex), encrypted data is used in IEC 15118 to keep information safe. This way, the limit of insecurities in V2G communication is eliminated. Even digital signatures can be created and used as and when due. If, at any time, for any reason, a digital certificate is no longer trusted, the public key can be reversed. Also, these security features have time limits and make it harder to cheat on the system.
4. Automated Authorization
Using IEC 15118, there is no need to do any other thing at the point of shedding excess power from an electric vehicle to the grid asides from doing the necessary plugging. The automated system allows the system to authenticate the identity of the two sides in communication. It uses different authentication schemes like the Plug and Charge technology, enabling the vehicle to authenticate and identify itself on behalf of the driver.
The use of RFIDs (Radio Frequency Identification) can be aptly applied in the use of IEC 15118 as a means of external identification. Low power radio waves are used in this application to identify the vehicle and automatically carry out authentication.
5. Standard Nature of the IEC 15118 Protocol
ISO/IEC 15118 is a protocol that forms part of the Combined Charging System (CCS) – a group of standards for hardware and software in charging systems. The CCS agrees to use this to enhance charging that can be operated with various specifications.
The International Organization for Standardization (ISO) also recognizes the IEC 15118 protocol for V2G communication. Being an international body made of different national standards organizations that set standards, the ISO is globally recognized.
With Hive Power’s Flexibility Manager Module, anywhere V2G would be implemented, charging and discharging can be coordinated easily. This is done by maximizing devices that can be remotely controlled under this module. The Hive platform also provides a means of improving the accuracy of energy data and enhancing smart grids.
Generally, the interoperability and openness of IEC 15118 make it fit in as a V2G communication protocol well. Yet, it is not at the level it should be in the market. Moreover, due to the nature of the V2G technology as one which is still under development, the entire structure needs to keep improving to aid more advanced communication between the digitally communicating elements.
Smart grids are the future innovations when it comes to sustainable energy distribution. This also involves a huge amount of data that needs processing at a constant rate. Data management here is essential to the proper running and stability of smart grids and their functionality.
What Is Data Management?
The term ‘Data Management’ refers to the process or practice of collecting, compiling and using information securely and efficiently while saving costs. This activity aims to enable the analysis of information when needed to make sense of the very vast quantities of data at our disposal today. However, data management is streamlined to just the information required to run the grids effectively when it has to do with intelligent grid systems.
Another reason for proper data management in grid systems is for corrective actions to be taken when the need presents itself so that grid participators can maximize benefits within the energy sector. The scope of data management is vast but can be understood within the following factors:
- To create, access, and update data across a differing data tier
- Store data across numerous platforms
- Provide high availability and disaster recovery
- Use data in a growing variety of apps, analytics, and algorithms
- Ensure the privacy and security of data
- Archive and destroy data following retention schedules and compliance requirements
To get the most out of data management, organizations and administrators need data management systems that are peculiar to their requirements. The point is to find the necessary information for analysis.
Data Management In Smart Grid Systems
Smart grids come with their peculiar advantages and changes that involve the information and communication technologies systems sector. These new changes include:
- New forms of information flow coming from the electricity grid
- New players like decentralized producers of renewable energies, prosumers and involved consumers
- New uses linked with DERs such as electric vehicles and connected houses
- New communicating equipment such as smart meters, sensors and remote-control points
These changes will bring a huge amount of information to grid operators and administrators due to the many variables involved in energy production, distribution and consumption. Smart grids are seen as a concrete solution to the concurrent changes hitting the electrical energy sector, and they help with the efficient integration of the entire network. So, because smart grids ensure high integration of the electric grid from production to consumption, large amounts of data are expected to pass through.
This data is not sorted as in conventional grids that would, for example, have one meter reading total consumption in a month. With a feature such as a smart meter that could be set to send consumer readings every 15 minutes, smart grids get larger amounts of data per time set, which means more information to sort through, with higher analysis thresholds. This is why data management is required; intelligent grids need to deal with high-velocity data, storage capacity and advanced data analytics.
There are two main data systems linked with smart grids that we will discuss here; Communication systems and Information systems.
Communication systems in smart grid data management
Communication is a crucial factor in any relationship, even between computer components. In smart grids, maintaining that connection so that data can be relayed between components is essential. This system needs to be secure and capable of high bandwidth and speed. Three types of networks fall under this system, Home Area Networks (HANs), Business Area Networks (BANs) and Neighbourhood Area Networks (NANs). These network types can further be classified into two broad categories, which are wired and wireless technologies.
Information systems in smart grid data management
These are components of the smart grids that communicate together for scalability and flexibility of the grid. They control and load data from the field then use it to extract values and understand the condition of the lines, equipment, energy use etc. There are several components within the information system such as:
- Supervisory control and data acquisition (SCADA) is a safe and reliable system of software and hardware elements used for monitoring control within the grid. The system controls energy distribution processes, monitors and collects real-time data, keeps records of events and interacts with devices through a human-machine interface. SCADA can also be applied in industrial sectors like energy, oil and gas, transportation and recycling. These systems are essential because they help to maintain efficiency, process data more intelligent and mitigate downtime with system issues.
- Advanced metering infrastructure (AMI) helps with cost and time efficiency by compiling data about energy consumption and production. AMI creates two-way communication meters between consumers and utility operators that enable high-frequency data collection of energy consumption within intelligent grids. This gives utility operators the ability to modify the different service level parameters for customers and gather data on usage frequencies and fluctuations.
- Outage management system (OMS) is vital in minimizing the effects and diagnosing the causes of power outages, and improving the system’s availability and reliability. This system is capable of restoring network models after an outage has occurred. They are also capable of tracking, displaying and grouping outages.
- Customer information system (CIS) is needed to develop and understand the relationship between the utilities and consumers. It is a complete customer relationship management system that assists in obtaining customer information efficiently. It helps to provide quality services to consumers by utilizing their collected data.
- Geographic information system (GIS) is considered a visualization tool to gather information about the grid, consumers and technologies. It captures, stores, checks and displays seemingly unrelated data concerning positions on Earth’s surface, which helps to solve real-world problems through understanding spatial patterns.
- Demand response management system (DRMS) gives the utilities the ability to create automated, flexible and integrated platforms to manage demand response solutions efficiently and speedily. It is the critical link between the demand response side of the grid and the utility operators. It helps with the integration of the much-needed two-way communication between consumers and grid operators.
Daki, H., El Hannani, A., Aqqal, A. et al. Big Data management in smart grid: concepts, requirements and implementation. J Big Data 4, 13 (2017).
Data management systems maintain the effectiveness of smart grids, lower costs where necessary, increase response time, and reduce the cumbersome nature of data collection by managing them efficiently. Just as the future is catching up with far-reaching innovations, the Hive Power platform makes various technical options available, especially with robust data analytics and management tools.
We have talked about the smart grid in our previous blog posts and its relation to energy storage, grid stability, and future power needs. It is undeniable that smart grid technology is changing the power sector; how these technologies are correctly applied matters, especially in achieving sustainability goals for a better future.
Six Smart Grid Technology Applications Leading the Change.
Conventional grid technologies perform a simple function, the transmission of electrical power generated at a central power plant. This happens with voltage transformers that increase and decrease voltage levels gradually while delivering energy to the end-users. Smart grids, however, perform all the conventional functions with the added ability or advantage of monitoring all the activities remotely for better and quicker responses and performance.
We will discuss six key applications for Smart Grid technology in this blog post. They are advanced metering infrastructure, demand response, electric vehicles, wide-area situational awareness; distributed energy resources and storage; and distribution grid management.
1. Advanced Metering Infrastructure
This is also known as AMI. It’s simply applying technologies like smart meters to help with the two-way flow of information between customers and utility agencies. This information revolves around consumption time, amount and appropriate pricing. It enables smart grids to have a wide range of functions compared to conventional grid technologies.
These functions include but are not limited to:
- Remote consumption control
- Time-based pricing
- Consumption forecast
- Fault and outage detection
- Remote connection and disconnection of users
- Theft detection and loss measurements
- Effective cash collection and debt management
Having these functions means gaining better control over power efficiency and quality in smart grids across the globe. Still, there are a few drawbacks that worry consumers and utility agencies alike, such as privacy and confidentiality issues and cybersecurity issues relating to unauthorised access to the AMI devices.
2. Demand Response
Demand response (DR) programs are recent and emerging applications for demand‐side management (DSM). Examples are applications that improve grids’ reliability by providing services such as frequency control, spinning reserves and operating reserves, and applications that help reduce wholesale energy prices and their volatility.
The development of energy regulatory commissions with open wholesale markets and policy support has enabled demand response applications in grid technology. There are two categories of demand response programs from the customer perspective:
- Price‐based DR where customers adjust their electricity consumption in response to the time-variant prices created by their utility agencies to maximise their electricity usage and save on bills
- Incentive‐based DR where benefits are increased by promoting an incentive to influence customer behaviours to change their demand consumptions
DR provides the opportunity for consumers to reduce or shift their electricity usage during peak periods through the programs mentioned above, giving them a huge role in the operation of electric grids with the hopes of balancing supply and demand needs.
3. Electric Vehicles (EVs)
This may seem like a misplaced application for smart grids, but with the obvious electrification of the transport industry, EVs are a preferred solution to global warming issues. In terms of smart grid technologies, plug-in electric vehicles’ introduction comes with myriad challenges and opportunities to sustain power systems. If EVs are added to the grids as regular loads, then there will be no allowance for flexibility of load variables, which will endanger the grid as a whole.
However, these challenges can be managed successfully with controlled approaches, especially when charging is shifted to low‐load hours. EVs can also promote Smart grid sustainability by operating as distributed storage resources (V2G) that contribute to ancillary services such as frequency regulation, peak‐shaving power for the system or the integration of fluctuating renewable resources.
4. Wide-Area Situational Awareness
This refers to the implementation of a set of technologies designed to improve the monitoring of the power system across large geographic areas — effectively providing grid operators with a broad and dynamic picture of the functioning of the grid.
WASA systems provide operators and engineers with the right information at the right time for efficient operation and analysis of the power system, according to SELinc. The ultimate goal here remains the same: to understand and optimise the smart grid’s reliability through its performance and anticipate where necessary changes need to occur before problems abound.
Smart grids use phasor measurement units as sensors for collecting data over large geographical areas making phasor measurement sensors the bane of wide-area measurement systems. They can be relied upon to relay situational awareness over large interconnected areas through:
- Real-time monitoring
- Prediction of future disturbances
5. Distributed Energy Resources and Storage
Distributed energy resources are also known as DER and are part of Distributed generation; they refer to energy sources or generation units that are smaller and located on the consumer side of the electricity generation meter.
Energy is generated from sources (mostly renewable) near the point of use rather than from a centralised system. Some examples are rooftop solar photovoltaic units and wind generating units.
While DER storage involves systems that store distributed energy for later use. This is done with two components; DC-charged batteries and bi-directional inverters. It helps in balancing energy generation, demand and supply. Some other key features are:
- Peak shaving
- Load shifting
- Voltage regulation
- Renewable integration
- Back-up power
6. Distribution Grid Management
A distribution grid includes all the equipment needed for energy distribution, such as wires, poles, transformers etc. The management of the distribution grid in smart grids has to do with having a system “capable of collecting, organising, displaying and analysing real-time or near real-time electric distribution system information” as needed.
This system can also allow grid operators to plan and place complex tasks to increase efficiency, meet targets, prevent failures and optimise energy flow. It can also work hand in hand with other systems to create a combined outlook of distributed operations.
Smart grid technologies are created to be smart, with the capabilities of predetermining faults that can then be prevented, cut costs where possible, and deliver the best value to consumers when needed.