Smart grids, software defined grids, and big data

The business models and technology architectures of electric utility grids worldwide are under attack both at the edge and overall. The first FAQ in this series looked at the attack on the edge of the grid in “Nano grids, microgrids and the decentralization of the electric grid.” Virtual power plants, distributed energy resources, and power in the cloud that overlay and challenge the traditional grid were the second article’s focus. But the utilities that operate the existing electric grid are fighting back through developments related to the smart grid, software defined grids, big data analytics, artificial intelligence, and machine learning.

One of the biggest challenges utilities face when deciding to implement a smart grid system is the lack of processing capacity in the legacy SCADA control systems. The existing control systems were designed to serve the needs of the operators in a centralized control room. Compared with the need to make real-time decisions to maximize smart grid benefits, the control room model based on SCADA systems is glacial. In addition, the quantity of data needed to implement a smart grid is far greater, further overburdening the legacy systems.

There is not a single unified approach to the development of smart grid technology by utilities. In most cases, the utilities overlay a “smart layer” of more sensors and some additional data analysis over the top of existing SCADA systems. That has the benefit of minimizing costs and speeding deployments. But to achieve a full smart grid implementation requires significant investments in new control systems, higher resolution and faster sensors, and more complex analytical and control software.

What is a smart grid?

The first official definition of smart grid was provided by the Energy Independence and Security Act of 2007 (EISA-2007). Ten attributes are used in EIAS-2007 to describe a smart grid:

(1) Increased use of digital information and controls technology to improve the electric grid’s reliability, security, and efficiency.

(2) Dynamic optimization of grid operations and resources, with full cyber-security.

(3) Deployment and integration of distributed resources and generation, including renewable resources.

(4) Development and incorporation of demand response, demand-side resources, and energy-efficiency resources.

(5) Deployment of ‘smart’ technologies (real-time, automated, interactive technologies that optimize the physical operation of appliances and consumer devices) for metering, communications concerning grid operations and status, and distribution automation.

(6) Integration of ‘smart’ appliances and consumer devices.

(7) Deployment and integration of advanced electricity storage and peak-shaving technologies, including plug-in electric and hybrid electric vehicles and thermal storage air conditioning.

(8) Provision to consumers of timely information and control options.

(9) Development of standards for communication and interoperability of appliances and equipment connected to the electric grid, including the infrastructure serving the grid.

(10) Identification and lowering of unreasonable or unnecessary barriers to the adoption of smart grid technologies, practices, and services.”

The European Union Commission Task Force for Smart Grids defines a Smart Grid as an electricity network that can cost-efficiently integrate the behavior and actions of all users connected to it, generators, consumers, and those that do both, to ensure an efficient, sustainable power system economically with low losses and high levels of quality and security of supply and safety. A smart grid employs innovative products and services, together with intelligent monitoring, control, communication, and self-healing technologies in order to:

• Better facilitate the connection and operation of generators of all sizes and technologies.
• Allow consumers to play a part in optimizing the operation of the system.
• Provide consumers with greater information and options for how they use their supply.
• Significantly reduce the environmental impact of the whole electricity supply system.
• Maintain or even improve the existing high levels of system reliability, quality, and security of supply.
• Maintain and improve existing services efficiently.

A common element to most definitions is applying digital processing and communications to the power grid, making data flow and information management central to the smart grid. Various capabilities result from the deeply integrated use of digital technology with power grids. Integration of the new grid information is one of the key issues in the design of smart grids. Electric utilities now find themselves making three classes of transformations: improvement of infrastructure, called the strong grid in China; addition of the digital layer, which is the essence of the smart grid; and business process transformation, necessary to capitalize on the investments in smart technology.

Phaser measurement units

Phaser measurement units (PMUs), also known as synchrophasors, are an important part of smart grid systems. PMUs are used to measure voltage and current at various points on the grid and then compute the signals’ magnitude and phase, with each digitized measurement receiving a timestamp accurate to within 1ms. These measurements can be used to identify changes in the status of the network. PMUs can provide measurements 60 times a second, 200-times faster than the sampling rate of SCADA systems used by most utilities.

Because most utilities employ relatively slower SCADA control systems, the capabilities of PMUs are underutilized. Pxise Energy Solutions is working to utilize the full capabilities of PMUs using software for the grid based on the concept of multivariable feedback control, also known as two-by-two decoupling control. This technology is in use in aircraft autopilot systems, oil refineries, and other systems that require the coordination of many different inputs in complex feedback loops.

The fundamental idea is to design a grid-balancing system that’s “designed as a system, as opposed to designed for specific devices.” In field tests, Pxise has shown that its technology can control multiple devices from different vendors, all through a common software platform running on a single hardened server. To get the real-time data it needs, Pxise taps the full capabilities PMUs. These devices measure the magnitude and angle of electrical sine waves at the grid’s speed — 60 cycles per second in North America — and synchronize that data across entire regions and grid networks using global positioning system radio clocks.

The data resolution enables the system to simultaneously control multiple grid disturbances, from relatively simple energy supply-demand imbalances to more complicated reactive power and phase angle-related disruptions. While most PMUs in use today are purpose-built for transmission grid monitoring, devices can be modified. There are new devices under development for use on distribution and local grids.

The software developed by Pxise can automatically control multiple devices, from solar and battery inverters to grid capacitors and voltage regulators, to balance out these disturbances in real-time. The platform also includes a human interface for grid or DER operators to initiate their own commands — but the most challenging balancing tasks require speeds much faster than operators can achieve.

Software defined electricity and even faster data rates

3DFS Technology has embedded intelligence into the power network delivering continuously balanced, controlled power flow. 3DFS’ technology can sample and process electricity data in extremely high fidelity, much faster than the PMUs used by utilities. The technology can derive 26 parameters using current and voltage samples in 24-bit resolution at MHz sampling rates on each phase, neutral and ground, through a precision, software-controlled oversampling methodology.

This proprietary data acquisition process acquires and distills to a near-perfect, near error-free digital mimic of the analog signal nanoseconds after it is sensed, opening up true real-time visibility of electricity flow. This processing method allows the software-defined power controllers to acquire, analyze, and process over 294 million data points every second, roughly 50,000 times the amount of data currently acquired using smart meters.

3DFS’ software-defined electricity corrects for harmonics and reactive power while always balancing the phases as the power flows in real-time for digitally perfect power flow at all times, no matter the upstream fluctuation or downstream consumption.

The electric grid is changing. The centralized grid that was designed to provide inexpensive electricity with a reasonable level of quality and reliability is no longer meeting society’s needs. As seen in this series of FAQs, the grid is being challenged at the edge by nano grids and microgrids and from the top down by virtual power plants. The utilities that operate the existing electric grid are fighting back through developments related to the smart grid, software defined grids, and big data analytics.

Resources:

EU Smart grids task force, European Union
Power Networks with Software-Defined Electricity, 3DFS
Smart Grid, Wikipedia
The Software-Defined Power Grid Is Here, IEEE Spectrum