Dynamic Energy Storage
A unified energy management scheme offers many advantages in terms of power quality improvement, energy time frame shifting, intermittency handling etc in renewable energy integrated power systems. The performance of a distributed energy source integrated energy system depends on the regulation of DC link voltage, power quality, real power transfer such as reactive power support, current harmonic compensation, unity power factor operation etc. When DC-link voltages are subjected to transient conditions, it results in Slow-acting of the same which leads to issues like degradation of compensation performance, maximum power point shifting from operating point on renewable source side, failure in effective power management at DC link. That is why it is important to have an effective energy storage system which supports system operators and renewable energy producers.
Keeping in view the above perspective and issues, battery-supercapacitor units emerge as a solution for dynamic energy storage devices. In this simple energy management scheme, renewable energy source is connected with high-gain DC-DC converter. As energy storage device batteries and supercapacitors are used. They are also responsible for the required power flow in the system. And the power flow control between the utility grid and the energy storage device is done by a bidirectional buck-boost DC-DC converter. The voltage source converter is responsible for the real power exchange from renewable energy sources to the utility grid and to achieve the main function of real power transfer.
This proposed scheme has several advantages including fast DC link voltage regulation, effective energy management at DC link, inherent current limits for both supercapacitor and battery life etc. Another main feature is extended life span, less intensive and less effect of current stress on battery units.
Advanced data analytics, complex system theory, optimization algorithms, and nonlinear controls are some of the technical areas to achieve an autonomous energy grid as shown in Figure 8.
The data analytics based renewable energy forecasting methods have a vital role in the modern industrial system. Large volumes of heterogeneous data on the electric grid are collected, stored and analyzed with the help of smart meters, sensors, smart home energy management systems etc using data management techniques. Big data analytics have the ability to collect, store, manage and efficiently process these data using data mining algorithms or machine learning algorithms. The four main steps in this process are data collection, data communication, data analysis and finally data processing. Fault detection and location, output forecasting, protection, operation and maintenance are some of the major applications of data analytics. Neural network, k-means, and support vector machine are the typical data analytics methods which are widely used.
Optimization methods of the autonomous energy grid can be divided into planning, operations and interactions with ICT infrastructure. Control, optimization and monitoring have to be done in real time and decentralized manner using advanced mathematical models and algorithms. The combined operation of distributed optimization methods and information infrastructures are needed for the exchange of performance between algorithm and data volume.
From a control point of view, in AEG, decentralized control algorithms are used to control the system. But due to communication delays, losses, and synchronous control actions, these algorithms are inherently asynchronous. This leads to challenges in optimal operation and stability analysis. This can be solved by developing scalable-control strategies which can be applied for both large and small scale grids.
Model and simulation of different energy domains are needed in AEG for real-time optimization, system and subsystem control. This is where the role of a complex control system comes in. Big data analytics provides the required information for forecasting. That information can be used as a parameter in optimization algorithms.
Implementation Of Virtual Synchronous Machine
Among the different models of implementation, synchronverter approach and robust droop control offers promising technical routes for the implementation of power electronics based autonomous energy systems.
Synchronverters or virtual synchronous generators are DC-AC converters in which the mathematical model of a synchronous machine is directly embedded into the controller. i.e. these inverters can be operated to mimic the behaviour of a synchronous generator. It can be controlled either by controlling the DC bus voltage or by directly controlling the power exchanged with the grid. The real and reactive power delivered by a synchronous converter connected in parallel can be controlled as well. Thus, there are two control strategies for synchronverter. Among all available options today, the synchronverter model is the simplest. It consists of two parts: one is a power part (Fig 9) which is a basic inverter (converts DC power into three phase AC) and another one is an electronic part which is responsible for controlling the switches in the electronic controller via running code in the processor with lowest number of control parameters. The electronic part consists of voltage sensors, current sensors, analog to digital converters. The power part transforms the energy through three phase inverter legs operated with Pulse Width Modulation (PWM) and three phase LC filters. These filter inductors and capacitors reduce voltage ripples, current ripples and hence suppress the switching noise. In addition to this, inductors and circuit breakers can be adopted to couple with the grid. The electronic part includes sensors and Digital Signal Processor (DSP) and acts as the pattern of a synchronous generator.
A synchronverter, when connected to the grid, helps in voltage and frequency regulation without even measuring the grid frequency through voltage and frequency drooping. This method also helps to achieve load sharing and power regulation. It can be easily operated in island mode or grid connected mode and hence provide epitome solutions for microgrids and smartgrids. Another added advantage is its autonomous and self-organizable property: it can automatically disconnect, reconnect and resynchronize with the grid. Grid connection of renewable energy sources, HVDC application, STATCOM, PV inverter, parallel operation of UPS, isolated or distributed power supplies etc are some of the potential applications of synchronverters.
Robust Droop Control
The robust droop control, which is based on improved droop control strategy, is another technical route to implement inverter based smart power systems. The synchronization mechanism of a synchronous machine inherently exists in drop control strategy and can be equipped with a self-synchronization mechanism which eliminates the need of Phase Locked Loop (PLL) even though the controller structurally resembles an enhanced PLL or the sinusoid-tracking algorithm for synchronization. Thus, establishing a link between droop control and PLL, this control strategy helps in voltage regulation, accurate power sharing, and is applicable to inverters with varying impedances. It should be noted that, in this technology, the inverter attains synchronization by a droop controller, thus eliminating the need for a dedicated synchronization unit. This mitigates concerns caused by PLL such as performance degradation, increased competition, PLL tuning issues etc. Whenever voltage is higher than the rated value, the reactive power automatically gets reduced. Similarly, when the frequency is lower than the rated value, the real power gets increased. Thus, this technique helps in the automatic regulation of real power and reactive power corresponding to the variation of frequency and voltage, while controlling the output voltage within the specified range (even when it is subjected to noise, computational error, component mismatches etc).
Conventional droop control idea takes different forms for different types of impedances such as resistive impedances, capacitive impedance and inductive impedance, which makes it impossible to operate in one system. Also, conventional droop control strategy addresses the problem of parameter shifts, numerical error, disturbances and noise, inaccurate real and reactive power sharing etc, thus leading to problems for large scale renewable energy utilization. Robust universal droop controller is an ideal solution for these problems. This control can be applied to inverters having phase angle varying from -Pi/2 rad to Pi/2 rad, which is called droop control of R-inverters. This method is highly efficient for parallel operation of inverters with varying output impedances and removes all issues raised by the traditional method.
Autonomous energy systems facilitate the future transactive energy market. It offers solutions for many problems faced by traditional power systems. The fundamental concept behind this technology is by interfacing power electronic devices with the grid through a common law of synchronization. The mechanism and dynamics of synchronous machines are embedded in these power electronic based players, so that all individuals attain legal equality and behave as one system. These kinds of power electronic converters are termed as virtual synchronous machines. The control mechanism of VSM is done through synchronverter control strategy and robust droop control strategy which offers potential solutions for the implementation of VSM. Since all individuals in the grid are homogenized they can interact with each other by exchanging power through a dedicated communication channel. This leads to autonomous operation of the power system. Thus this technology makes the power grid more efficient, selforganizable and resilient by utilizing maximum benefit from green energy sources.
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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.