Impact of Cyber-physical Systems on the Energy Transition
Read on to learn more about the impact of cyber-physical systems on the energy transition.
The energy transition has resulted in a major shift referred to as decentralization. Instead of central bulk generation of electricity, renewable energy resources allow for local micro-generation, reducing the dependence on a centralized energy system. However, the resulting widespread integration of renewable and distributed energy sources makes control of the energy supply chain increasingly difficult.
The combination of such intermittent and heterogeneous clusters is only possible with the use of cyber-physical systems (CPSes). The interplay between demand, generation and storage are extremely important in the context of energy transition [1].
Cyber-physical Systems
Gathering insights from big data through machine learning is a core competency in many CPS applications and computational intelligence plays a key role during such processes [2].
Recent advances in machine learning techniques, especially deep learning, opened new possibilities or data-driven approaches in many energy system contexts. However, most of the machine learning based models are black box and interpretability is very low. Also, the existing energy management systems are largely rule- or logic-based. A combination of machine intelligence with traditional physical systems poses another challenge along with the possible computational cost during the integration.
It is to be noted that the intelligent models are computationally expensive and might be slower relatively in some cases which might impose barriers for real-time applications like parameter updating and model predictive control. Thus, from a futuristic perspective, it is expected that the computational engine in CPS-based energy systems could balance the domain knowledge along with the machine intelligence in a delicate manner so the best performance can be achieved with a modest computational cost.
These ideas are illustrated using a typical procedure of knowledge extraction from data through machine learning as shown in Figure 1.
Figure 1. A typical procedure of knowledge extraction from data through machine learning. Image property of EETech
It is evident that CPSes are critical for the transformation of centralized, high carbon energy systems to decentralized low carbon energy provision. Along with the benefits from this association, there are significant costs to bear as the significant investments in sensing and computational models precede the benefits arising from more efficient resource use and lean operations [2]. The three critical areas to be discussed in this regard include economics, security concerns and policy.
Digital Transformation in Electricity
The electricity sector is undergoing a significant digital transformation as the traditional boundaries between the various branches of energy supply sectors like heating, cooling and transport begin to blur. The established conceptions of the energy markets, business models and consumption patterns are being turned upside down and new providers are entering the market.
In addition to current transformation challenges, new technologies are impacting internal business culture, strategies and general management of energy companies at an even faster rate. The key study in this regard is with respect to the economic and environmental impact assessment. The main contributors in this area include smart demand response by preserving energy consumption and massive investment in new installed electricity supply capacity, integration of intermittent renewables, advanced charging technologies for electric vehicles and promotion of distributed energy resources [2]. This further demands the study of electricity generation, distribution costs along with energy infrastructure and maintenance.
Implications of Energy Security
The implications of energy security are multifold. The enormous benefits of CPS highlighted throughout comes with its own distinct downsides like cyber threats to energy and industrial security. CPS is proven to enhance the operations of critically important sectors such as energy and industry, but it will simultaneously make them more vulnerable to cyber attacks, thereby making them cyber dependent. This could severely impact the national economy by disrupting the strategically important supply of energy at any point across the energy value chain [2].
Due to the inherent nature of the CPS that connects cyberspace with the physical world, attacks can easily penetrate from one to the other, creating significant risk in the real world. After all this, the economic impact due to cybercrime cannot be neglected. This makes it inevitable to focus on cyber security and resilience by investing in critical assets like strong foundation, pressure testing and investing in next-generation technology.
All said and done, digital technology developments need efficient policy and market design to help steer the transformation to a secure and sustainable path. Governments now understand the economic competitiveness associated with these technological advancements and the policy implications that govern these to enable efficient implementation at multiple levels.
It is also important to monitor the environmental processes associated with the CPS ranging from water supply to fire detection to establish clear environmental policy, along with focusing on environmental concerns, security policy and governing rules around artificial intelligence.
Key References
1. Gerwin Hoogsteen, A cyber-physical systems perspective on decentralized energy management, 2017
