The landscape of today’s electric grid is fast changing due to the high penetration of distributed renewable energy sources. The soaring number of grid -tied devices has a negative cumulative impact on these grids which makes a centralized system more impractical. This highly distributed energy future can be handled by power electronics based autonomous self-resilient energy systems. The concept and core technology of this grid system makes use of power electronic based players, which mimic the dynamics of a synchronous machine for synchronization with the grid. The concept of an autonomous energy grid uses artificial intelligence, renewable energy and energy storage to optimize the grid. This grid architecture automatically senses and instantly reacts to drop in system frequency power imbalance, system instability, voltage fluctuation etc and takes the maximum benefit from renewable energy sources. Thus this cutting-edge technology paves the path for green energy sources...

Autonomous resilient energy grids (AEGs) are the electric networks of the future as they encompass the power electronic interface to the power system. The growth of AEGs accelerates our progression to clean energy sources. Today’s energy grid faces few challenges in terms of stability, reliability, security and resiliency. The electricity generation in the current power grid is dominated by large centralized power plants that use combustion of non-sustainable fossil fuels and contribute to climate change (emission of greenhouse gases). To overcome this problem renewable Distributed Energy Sources (DERs) are used but integration of large scale renewable DERs raises stability and reliability concerns. As we know, smart grid makes the energy grid more efficient but the information and communication technology (ICT) system, which makes a grid smart also increases the chance of cyber-attack on power systems. Another problem faced by operators is that the grid is functioning under unpredictable supply and demand. And the current grid systems rely on centralized computing platforms for grid control,  which cannot handle this amount of coordination. These problems can be solved by autonomous energy grids.

Autonomous Energy Grids (AEGs) could self-organize and control themselves using advanced machine learning and simulation. This is possible through scalable cellular blocks, which are similar to micro grids. AEGs can self-optimize when islanded, runs at high efficiency, ensures system operation by quickly bouncing back from outages and participates in optimal operation when interconnected to a larger grid. Thus, the application of an autonomous system to the power grid reduces the operation cost, increases the utilization of renewable energy sources and improves system resiliency. This technology ensures the flexibility necessary for the robust control of the power system and facilitates future transactive energy markets.

Future Energy Grids

Based on the current dynamics of the electric grid, there is a need to reevaluate the operational capacity due to the growing penetration of renewable power and aging grid infrastructure. And future energy grids will be more distributed and very complex to control with today’s technologies and techniques. With autonomous and decentralized control, large-scale complicated networks such as power grids can be decomposed into smaller sized networks. This new architecture will relieve the power system from traditional centralized control where a small number of large facilities control system stability to democratized interaction of a large number of relatively small generators and flexible loads. In this technology, the inherent synchronization mechanism of a synchronous machine is used to achieve legal equality among the heterogeneous players. i.e. to maintain voltage and frequency around the rated value, it is important for all individuals to synchronize with each other. This mechanism is achieved by depending on a communication channel through the electrical system. Future energy systems will depend on scalable cellular blocks, which can automatically optimize in real time when disconnected from a larger network and can also participate in optimal operation when it is interconnected to the same large network. These scalable cells can be part of the power grid that can independently work as a microgrid, which are segregated from a control perspective as shown in figure 1. This ensures economic and reliable performance while systematically integrating energy in all forms.

Figure 1: Self optimizing cells of AEG

Role Of Power Electronics In Grid Modernization

Given the present scenario of the electric grid, power electronics have a vital role in the ongoing process of grid modernization. Power electronics-based players already exist in several stages of generation, transmission and distribution. On the supply side, most of the renewable energy sources such as wind, solar etc., are connected to the grid through power electronics converters. These converters are necessary to control the generated power and to make it compatible with the grid. For example power electronic inverters are used in the conversion of DC electricity generated from solar panel to AC electricity. Similarly, electric vehicle and storage systems also require power electronics devices to interact with the grid. In the transmission and distribution network, flexible power transfer is done through power electronic converters such as high voltage DC links (HVDC) and Flexible AC Transmission System (FACTS). These devices improve controllability and help to reduce power losses. Also, if we look at the different load types such as variable speed motor drives, internet devices, lighting loads etc, are also equipped with power electronic rectifiers at the front end, which enhances operational capability. Putting all the above together, the deployment of power electronic equipment in the power system enhances reliability, efficiency, controllability and can respond much faster than conventional synchronous machines. If these electronic-based based players possess the synchronous mechanism of a synchronous machine, then they automatically interact with each other. i.e., to harmonize and unify future energy grids including DERs and loads, all heterogeneous players should be homogenized with a common law of synchronization.

Proposed Solution Approach

As it is well known, the transient grid stability and organic growth of a power system is due to the synchronization mechanism of a synchronous machine, they can independently synchronize with each other or with the power supply, without any external communication. This mechanism could be adopted for the future autonomous power system to unify the interaction and integration of incompatible distributed renewable energy sources that need power electronics converters to interface with the grid. Thus, from an implementation perspective, by operating these power electronic converters to imitate the inherent benefits of a synchronous machine paves the way for autonomous operation of the future power system. But these power electronic-based players do not possess any rotating inertia, damping response or islanding operation, which is inherently feasible with a synchronous machine. And this leads to deviation in grid frequency and rapid power fluctuations due to which these power converters need control strategies to preserve controllability and stability.  As a solution to this, several control strategies such as reactive power control, emulation of rotating inertia with power electronic converter, damping of oscillation etc., are explicitly designed to mimic the behaviour of a synchronous machine for providing virtual inertia and damping to the grid. This concept leads to Virtual Synchronous Machines (VSMs), in which the dynamics and behaviour of conventional synchronous machines are embedded in power electronic converters, thus providing a unified interface for grid-connected, converter-based distributed energy resources. The concept of VSM is an alternative method for electrical grid feeding and a solution towards stability improvement of the energy system.

The Virtual Synchronous Machine (VSM) is a power inverter with a built-in energy storage and proper control mechanism, as shown in Fig 2.This technology implements a control system and provides virtual inertia in order to combine three phase inverter with synchronous generator behaviour. The capacity of the energy storage unit depends on the power imbalance between the DC and AC side. To implement VSMs, different technical routes are available in literature. Among those available methods, the synchronverter based approach and robust droop control technology offers a promising technical route for the implementation of VSMs.

Figure 2: Virtual Synchronous Machine

Overall Grid Architecture

In this grid architecture, as shown in Fig 3, all traditional power stations such as coal fired thermal plants, hydro power plants, nuclear power plants etc., are connected to the grid through synchronous generators, as normally done, whereas the power electronics converters, which are present at the supply side, inside the network and at the demand side of the energy grid can be controlled to behave as virtual synchronous machine. To be more specific, all renewable energy sources and energy storage systems, which are connected to the grid through power electronic converters are controlled to behave as virtual synchronous generators. This is done by setting the mathematical model of synchronous generators for the inverter controller. As for loads that have rectifiers at the front end can be controlled to behave as virtual synchronous motors. And for HVDC links, these power electronic based players can be controlled as virtual synchronous generators as well as virtual synchronous motors at both sides respectively. By homogenizing all grid-connected converters with a common law of synchronism presents a unified, harmonized and scalable architecture for future power systems.

Figure 3: Overall Grid Architecture

In this new technology, since all SM and VSM are equipped with synchronization mechanisms, they achieve legal equality and autonomously interact with each other by exchanging power via the electric network. This mechanism is the core concept behind the autonomous operation – which does not require a physical operator and can self -optimize to ensure economic and reliable performance. System autonomy is enabled when all players in the grid could directly participate in the regulation of system stability while integrating energy in all forms, as shown in Fig 2. Moreover, the deployment of this grid architecture also eliminates the need to depend on an additional external communication infrastructure, at the same time, having the ability to get reference set-points and instructions if needed. This eliminates the concern of cyber attack, which is one of the systemic flaws in the current grid system. In addition to this, the scalable grid architecture provides the foundation to achieve self-organizable grid structure. That means, it allows small scale grids to merge and form large-scale grids and vice-versa. Another advantage is in terms of resiliency – it automatically senses the fault and isolates faulty parts of the grid; once the fault is cleared, it can be reconnected. It is fair enough to say that in future it will be possible to power homes with distributed renewable energy sources ; without relying on the utility grid. From this, it can be further extended to support neighboring grids, and then to grids and regional grids. The interconnection between different regions can be done with the help of circuit breakers, transformers, power electronic converters etc. Thus, this architecture can be applied from a single node system to multiple node systems.

Artificial Intelligence In Energy Management

Artificial Intelligence (AI) techniques are extremely powerful tools which have gone through fast evolution during the last several decades. The current electric power industry faces few challenges due to the global transition to decentralized renewable energy. From a sustainability point of view, distributed renewable generation has transformed the industry, but it does add complexity to the energy grid. AI in sync with IoT and big data could help address the challenges by processing vast amounts of data, which makes an important contribution in the fight against cyberattacks, perform predictive grid modeling, dynamic grid control and thus managing decentralized grids. In the energy industry, typical areas of AI application are virtual power plants, electricity trading, smart grid and for power consumption. When deployed correctly, AI has the ability to control and synchronize the grid with renewable sources and end-user loads in real time. With the help of AI software, any excess electricity produced by renewable sources can be sent to the grid, while utilities efficiently route energy to where it is needed. It can also help with energy storage by optimizing battery performance and battery – inverter system operation using real time load and market data which is an AI based intelligent controller. This optimization helps the control of individual battery energy systems, maintain and extend battery life. To receive and interpret energy storage, sensor data on edge devices are used. When demand is low, this excess energy is stored in industrial facilities, office buildings, homes etc, hence utilizing this power when generation is inadequate. To attain this, a lot of optimization, forecasting and coordination is required. To make accurate predictions, AI methodologies and techniques analyze both real time and historic data to find weak spots on the grid and power fluctuations using machine learning and deep learning algorithms. By analyzing real-time weather data, AI techniques automatically attain optimal economic dispatch. In the energy operation and predictive maintenance sector, AI with thermal drone technology helps to boost solar power system production, alerts the field operators whenever solar panel maintenance is required thus eliminating the traditional trial and error method. Another particular focus of AI in the energy industry is electro mobility. The large-scale deployment of electric vehicles offers opportunities and challenges. AI based solutions facilitate energy arbitrage through vehicle to grid, vehicle to market services. These services include voltage regulation, demand response etc. With specialized algorithms, AI can perform particularly well by constantly forecasting load, grid needs, generation, electricity sale – maximizing customer benefit while meeting load and grid needs. AI power autonomous energy grid is shown in Figure 4. Large sets of datas are collected from weather satellites, edge device receivers, power generation levels, storage capacities, which are sent to the forecaster that analyzes these datas. The optimizer and controller helps in maintaining the grid stability. To ensure more accurate instructions and forecasts, all these data sets are finally fed back into the AI.

Figure 4: Autonomous grid Management using AI

AI solutions offer real-time data processing, real-time control to determine optimal balance of electricity supply and demand to reduce energy wastes and ensure uninterrupted electricity delivery. Machine learning algorithms are used to determine energy supply and demand. Application of AI in AEG will help the operators avoid grid outages, improve operation and reduce cost. By this it ensures secure, reliable and low-carbon electricity. Thus, AI is a key technology for renewable power grid resilience which enables faster grid analytics, better grid asset management, forecasting and control in seconds even after network disruptions.

Service-based, Segmented, 5G Network-based Distributed Energy Storage

The combination of energy grid and 5G networks will reduce the impact of cyberattack and improve performance, availability and reliability of the whole system. 5G networks will bring better bandwidth experience, address cybersecurity concerns, facilitate fine-tuned control for DER, create isolated islands and micro networks that helps provide resilience in the grid. Network slicing provides agile and customizable capabilities and helps in the integration of DER to the grid with 5G. 5G based Internet of Things (IoT) can control demand-side resources and effectively provide regulation services for smart grids. 5G network slicing facilitates wireless communication, which will be required for millisecond-level precise load control, intelligent distributed feeder automation, distributed power supply, information acquisition of low voltage distribution systems. The three defining characteristics of 5G slicing architecture are Extreme Mobile Broadband (eMBB), Massive Machine Communication  (MMC), Ultra-Reliable Low-Latency Communication (URLLC). These classes for the 5G NR network as shown in Figure 5, represent the various types of communication required to enable low latency, high speed data, and mass deployment of connected devices from the network. These three categories together satisfy all the technical needs for the futuristic use cases with billions of connected devices. These generic services can be accommodated by network slicing, which can be isolated from one another. The architecture of software defined networking (SDN) supports the core technology of network slicing. Applying the SDN architecture, an SDN controller for management and orchestration provides individual slices. The controller’s client context provides control logic for constituting a slice, isolated from each other and continuously optimized by global policy functions.

Figure 5: 5G_Connectivity Types

From an operational perspective, 5G network slicing generates virtual network topologies, a series of virtual network functions, for the different functionalities of microgrids such as monitoring , diagnosis, operations etc. This new generation wireless communication technology meets ultra-low latency requirements of industrial control services of the energy grid. 5 G network slicing characteristic in DER domain proactively mitigates the Dos attacks on a DER system. Integration of DER to the grid with 5G network slicing architecture is done through slice alignment strategy as shown in Figure 6. DER monitoring and diagnosis, distributed control for DER, distributed automation, DER application based network configuration are some of the use case scenarios that can be enabled by this technology. Communication Service Management Function (CSMF) is the first step to slice design in the end to end network slicing architecture. It meets service system requirements, converts into end to end network slicing requirements and finally transfers to the network slice management function (NSMF) for network design. NSMF is responsible for generating slice instances and then combining, dividing and transferring the deployment requirement to the Network Slice Subnet Management Function (NSSMF). The NSSMF implements autonomous deployment , manages and designs slices of subnets. The collaboration of CSMF, NSMF AND NSSMF completes the network design and deployment of end to end slice network.

Figure 6: 5G slicing characteristic in DER

Emerging 5G capabilities reduces the capital expenditure, increases the speed of network opening and automatically triggers network redesign to improve network service capabilities. The evolving new age 5G constitutes both licensed and unlicensed frequency bands, which are expected to implement very high service quality. Thus, some of the benefits of a 5G network in an autonomous power grid are reduced power consumption, more connectivity among devices, data transfer at high rate per area, increased scalability, cost effective, high data rate and low latency. From an effective energy management perspective, this effective communication system in smart grid provides two way energy trading and dynamic energy pricing.

                                        …To be continued




K Shanti Swarup is a Professor with the Department of Electrical Engineering, Indian Institute of Technology Madras. His areas of research are Restructuring, Power System Economics, Electricity Markets, Operation, Optimization, Pricing, Forecasting, and Planning.




Drishya J
is a project associate in the Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India. Her areas of research are Machine learning, Swarm Intelligence, Optimization, and Microgrids.

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