Parallel Cyber-Physical-Social Systems Based Smart Energy Robotic Dispatcher and Knowledge Automation: Concepts, Architectures, and Challenges

Summary The new generation of artificial intelligence (AI), called AI 2.0, has recently become a research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and electric power system (Smart EEPS). In AI 2.0, machine learning (ML) forms a typical representative algorithm category used to achieve predictions and judgments by analyzing and learning from massive amounts of historical and synthetic data to help people make optimal decisions. ML has preliminarily been applied to the Smart Grid (SG) and Energy Internet (EI) fields, which are important Smart EEPS representatives. AI 2.0, especially ML, is undergoing a critical period of rapid development worldwide and will play an essential role in Smart EEPS. In this context, this study, combined with the emerging SG and EI technologies, takes the typical representative of AI 2.0—ML—as the research objective and reviews its research status in the operation, optimization, control, dispatching, and management of SG and EI. The paper focuses on introducing and summarizing the mainstream uses of seven representative ML methods, including reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, and ensemble learning, in the SG and EI fields. In this survey, we begin with an introduction to these seven types of ML methods and then systematically review their applications in Smart EEPS. Finally, we discuss ML development under the big data thinking and offer a prospect for the future development of AI 2.0 and ML in Smart EEPS. We conduct this survey intended to arouse the interest and excitement of experts and scholars in the EEPS industry and to look ahead to efforts that jointly promote the rapid development of AI 2.0 in the Smart EEPS field.

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“…In the field of SG, the AGC of interconnected grids is a dynamic multilevel decision issue . This control process can be regarded as an MDP.…”

Section: Reinforcement Learningmentioning

confidence: 99%

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

Cheng

2019

Int J Energy Res

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189

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“…From the perspective of industrial automation, mechanical automation, electrical automation, and electronical automation, in essence, are all treated as automation of physical processes. On the contrast, the “Internet +” era is represented as the information automation and intelligence automation in the cyberspace, ie, the KA Key technologies involved in KA …”

Section: Several Important Concepts Supporting the Parallel Dispatch mentioning

confidence: 99%

“…Arguably, energy is an important material basis for economic and social development . Moreover, human civilization development is closely related to the utilization of various energy resources …”

Section: Introductionmentioning

confidence: 99%

“…Compared with conventional energy systems, Energy 4.0 systems have a high integration of both information and energy at the physical level, in which various types of high‐penetration renewables such as wind power and solar energy are largely coupled and complementary . Cyber‐physical system (CPS) is a crucial component of Energy 4.0; thus, CPS has become an investigation highlight in the industry, energy, and electric power sectors …”

Section: Introductionmentioning

confidence: 99%

“…There is a parallel control layer between VPAS and actual system, through which we can achieve the integration of existing CPSs and human society systems, so as to realize a revolutionary transformation of controlled objects from control paradigm to guidance paradigm. For a small‐size and isolated industrial production system, such as a chemical plant or power plant, it is technically feasible to develop its internal VPAS . However, how to build a parallel system for the huge, open, and ever‐growing EI will be very challenging.…”

Section: Introductionmentioning

confidence: 99%

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Smart dispatching for energy internet with complex cyber‐physical‐social systems: A parallel dispatch perspective

Cheng

2019

Int J Energy Res

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Summary Energy internet (EI) is a complex coupled multienergy system; it is essential to investigate its multienergy dispatching optimization issues. To this end, this paper first proposes a novel conception of smart dispatching for EI with a complex cyber‐physical‐social system (CPSS) network from the perspective of parallel dispatch, called parallel dispatching robot (PDR), and investigates the implementations of PDR based on smart artificial society (SAS) modeling. First, we introduce EI and describe the dispatching issues of EI. Second, we discuss several important concepts supporting the parallel dispatch conception of EI, including knowledge automation (KA), CPSS, and parallel machine learning (PML). On the basis of these, we elaborate the concept of parallel dispatch. Moreover, we construct a large closed‐loop feedback control framework of parallel dispatch for EI integrating a CPSS network based on KA and PML. Third, we establish an experimental platform for PDR research based on the proposed parallel dispatch framework. Fourth, we develop the PML‐based SAS models of a single PDR in centralized dispatching modes and group PDRs in decentralized dispatching modes to achieve crowd wisdom emergence and performance improvement in current cyber‐physical system frameworks of EI. Moreover, we design an external global closed loop for PDR to evaluate its operation stability. Lastly, we conduct a detailed discussion on PDR and offer some prospects for its engineering implementations. The biggest innovation of this paper lies in systematically proposing the smart dispatching concept and framework for complex CPSS‐based EI from the perspective of parallel dispatch and thoroughly investigating how to use SAS modeling to implement parallel dispatching and control for EI considering human and social factors, which is a major extension and theoretical improvement to existing single smart wide area robot concept and a preliminary attempt in investigating a shift from Energy 4.0 to Energy 5.0 in China.

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Cyber–Physical–Social Systems for Smart City

Xiong

Anwar

et al. 2023

Cyber–Physical–Human Systems

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Parallel Cyber-Physical-Social Systems Based Smart Energy Robotic Dispatcher and Knowledge Automation: Concepts, Architectures, and Challenges

Cited by 30 publications

References 6 publications

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

Smart dispatching for energy internet with complex cyber‐physical‐social systems: A parallel dispatch perspective

Cyber–Physical–Social Systems for Smart City

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