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By Anish Jindal and Neeraj Kumar
The focus of this article is on the demand response application aspect of the smart grid by leveraging the data analytics techniques. This would throw light on how these techniques can help in solving the various long-term problems in the smart grid and how the information gained can help the utilities to cut their costs. Smart grid comprises of many entities which are connected to the Internet for transferring the energy-related information, thereby forming a large network of Internet-of-energy. This network can be benefited from various cutting-edge technologies like data analytics to process a large amount of gathered data. The need and impact of performing data analytics on such a network is also described along with the various challenges and constraints that restrict the use of data analytics in the present scenario.
The Role of Artificial Intelligence in the Transition from Conventional Power Systems to Modernized Smart Grids
By Soheil Mohseni, Alan C. Brent, and Daniel Burmester
Over the past three decades, research in artificial intelligence (AI) has advanced a wide range of techniques and approaches that can be adapted or employed to solve complex electric power system problems (e.g. power system planning, operation, as well as transient stability and control), previously thought to be unsolvable without making several simplified assumptions. On the other hand, the “smart grid” concept emerged in the early 21st century to take advantage of improvements in information and communication technologies in the electricity industry, in order to address security issues in the centralized power systems, as well as to provide for increasing the penetration of renewable energy by deploying the advanced metering infrastructure to establish a win-win situation for both the electricity consumers and suppliers. This has raised problems in diverse fields of power system analysis and design, which are not amenable to analytical treatment, and need to be solved using metaheuristic/learning algorithms within the field of AI.
By Andreas T. Procopiou, Kyriacos Petrou, Luis (Nando) Ochoa
As residential battery energy storage (BES) systems become more affordable, more and more households will be able to store excess solar photovoltaic (PV) generation during the day and use it later at night, reducing electricity bills even further. This, however, can mistakenly create the belief or even the expectation that the widespread adoption of BES systems will inherently help reducing the reverse power flows and, hence, mitigate the associated impacts (such as over-voltages and asset congestion) on the electricity distribution network. However, existing controllers embedded in commercially available BES systems are ineffective in this matter, providing little to no benefits to the electricity network. Nonetheless, given the flexible controllability of BES systems, there is an opportunity to adopt advanced battery management strategies that not only provide benefits to their owners (lowering electricity bills) but also to electricity distribution companies, reducing power exports from households with solar PV and, thus, mitigating network impacts. These new BES management strategies could become an alternative to otherwise required costly network reinforcements, saving billions of dollars in investments.
By Mike Zhou, XueWei Shang, Lin Zhao, DongHao Feng, JianFeng Yan, DongYu Shi, and Ying Chen
A new online analysis platform has been developed to support the next generation online analysis system development. The goal is to achieve response speeds in the order of seconds to help the grid operator perform online analysis in real-time. In a pilot project, two new online analysis application functions have been developed based on the platform to create a new online analysis system to augment the existing online analysis system. The new online analysis system has been deployed and is running in a provincial dispatching center in China. The preliminary testing data indicates that the new online analysis system can achieve sub-second response speed.
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