Abstract
Today’s power systems are seeing a paradigm shift under the energy transition, sparkled by the electrification of demand, digitalisation of systems, and an increasing share of decarbonated power generation. Most of these changes have a direct impact on their control centers, forcing them to handle weather-based energy resources, new interconnections with neighbouring transmission networks, more markets, active distribution networks, micro-grids, and greater amounts of available data. Unfortunately, these changes have translated during the past decade to small, incremental changes, mostly centered on hardware, software, and human factors. We assert that more transformative changes are needed, especially regarding human-centered design approaches, to enable control room operators to manage the future power system. This paper discusses the evolution of operators towards continuous operation planners, monitoring complex time horizons thanks to adequate real-time automation. Reviewing upcoming challenges as well as emerging technologies for power systems, we present our vision of a new evolutionary architecture for control centers, both at backend and frontend levels. We propose a unified hypervision scheme based on structured decision-making concepts, providing operators with proactive, collaborative, and effective decision support.
POWER systems continue to evolve to accommodate new demands and challenges, to support the energy transition. Today’s power systems are more interconnected than ever within the cyber and physical spaces. While their evolution was mostly driven in the past by grid infrastructure and capacity expansion, it now becomes a matter of greater grid management and optimization over existing infrastructure. As the central nerve of the power system, control centers have always supported its evolution [
Today, the energy transition is forcing radical changes on the working environment of system operators, at an even faster pace [
This paper reviews why the underlying systems are changing today and the consequences for operating the power system in Section II. It further develops a vision on what needs to change in the control center. An holistic approach for rethinking decision-making that enables operators become “hypervisors” of cyber-physical systems (CPSs) [
To address climate change, governments around the world have set aggressive targets for carbon emission reductions in the coming decades. The paths of the various sectors towards zero emissions are uncertain. There may be unavoidable adaptation to some climate change level with rising temperatures and extreme weather events [
Driven by renewable resource integration, the operation uncertainty of the power system will increase beyond what it was designed for [

Fig. 1 New operational needs under energy transition that is impacting operations along different dimensions.
Control centers may be redesigned to consider several changes to the power and market system that are being imposed externally and are described below.
1) Micro-grids and local energy communities (LECs) are small entities such as towns or villages that are aiming to balance their own energy demands. Driven by the trend towards more DERs, LECs are rapidly increasing across the world [
2) Market participation and new mechanisms will increase, which require the interactions with a larger number of market participants [
3) Sharing of open data and information will likely increase as per current regulatory and policy directions for increased transparency, sensitivity, and privacy [
In the future, the manual process of report generation in control rooms should be more automated. This should free up valuable operator cognitive load to analyze the risk in real time, by studying the system and applying their experience and engineering knowledge. One slight drawback of an open data policy is that the general public with little knowledge and experience of the actual system dynamics may make incorrect interpretations of events, which may have to be repudiated by the TSO in case it spreads as misinformation. Open data should be accompanied [
In parallel to externally forced changes, new technology integration, asset management, or system interconnections are also changing the power system from the inside.
1) New Technologies for Observability and Control
Technological advancements in sensor, information, and communication technologies provide state-of-the-art ones for power system monitoring. Power electronics in high-voltage direct current (HVDC) [
The digitisation of infrastructure has brought the power grid into a new era, which creates many opportunities for greater flexibility by allowing the collection of more data or capability on the edge [
2) New Construction, Outages, and Asset Management
In most countries around the world, the power grid is well established. Investing in the development of new power lines in such grids is becoming increasingly difficult. The historical approach to accompany system transitions is often not viable anymore: existing structural grid topology will not change much. However, most transmission systems around the world are experiencing rapid growth in construction projects to interconnect new renewable energies at the periphery of grids (mountains and near coasts) while most of the existing backbone infrastructure grid is aging. Each new project requires outages of the existing grid, which can further stress the grid. Outages require the coordination and consultation between the responsible TSOs and associated TSOs or market operators [
This development has led to alarm and information overload, where operators are swamped by superfluous information. For the future control center, a streamlined and analytical approach to outage management that optimises cost, duration, and factors in variable resources will be required [
3) Coordination and Interfaces
Control centers now have increased interactions with similar system operators, market operators, or security and reliability coordinators. This trend is likely to continue in North America with multi-state independent system operators (ISOs), in Europe with regional coordination centers (RCCs) [
Similarly, the interaction between TSOs and distribution system operators (DSOs) is likely to intensify and some control approaches for this interaction were proposed [
Control centers are likely to be more connected to collateral aspects of the grid: telecommunication network, supervision of information system or asset monitoring, market variations or even social networks. Models and processes that assess the feasible operation domain should be commonly shared online during real-time operations across all those interfaces. They could possibly be co-designed between stakeholders and regularly re-adapted offline ahead of operations. This web of interactions constitutes an additional workload for the operator. In Europe, ENTSO-E is working with TSOs to achieve this vision for future control centers, enabled by common grid models and data platforms [
Given this context, a number of consequences can be anticipated for the way in which electricity transmission systems are operated as partly outlined in [
1) The dependence of DER on weather conditions, the decommissioning of conventional generation and an aging grid, and the electrification of sectors of society will lead to higher operation uncertainty.
2) The decentralisation or market participants and lack of new infrastructure will lead to a reduction in operating margins of the power system, and the operation of the system will be closer to its limits.
3) Increased interconnection between transmission systems will require coordination and oversight. Increased interaction between transmission and distribution systems will require more active monitoring and control.
4) The dynamics of evolution (market rules, behaviours of actors, technologies on the grid) in the power system will be faster, requiring rapidly deployed new process, tools, and monitoring capability.
5) The power system will become cyber-physical but less predictable while relying on extended delegation or sharing of aspects of control, the splitting of areas of responsibility and functions.
6) Operations and decisions will become more complex and require more anticipation, coordination, and automation in real time.
Faced with these changes, the traditional decision-making process, which is mostly based on the operators’ knowledge and real-time awareness, will not be feasible anymore: it will have to be adaptive and well-structured.
As operations and decisions become more complex, there is now a requirement to rethink the operator’s decision-making environment through human-centered design through: ①renewed definition of operator’s role, functions, processes, and tasks; ② integrating structured decision-making frameworks; and ③ greater integration of the working environment ecosystem with a simplified, adaptive, and modular smart interface.
More consistent and structured decision-making processes will allow for improved coordination and automation integration.

Fig. 2 Evolution of operator’s decision-making environment over decades with increasing number of tasks.
In recent decades, control centers have automated some of these manual processes [
Today, operators in most control centers still manually switch on the system, study the network for outages and do planned switching and intervene for unplanned outages and emergencies. They still manually perform ex-post reporting and workforce management, but unplanned outages that do not reclose (or transformer or cable outages) are likely to become the only process that is managed in real time. This can be considered as very rare events and manual interventions when automation fails–in a way similar to manually dispatching generation if AGC fails.
In the future control center, the aim should be to continue the trend of automation of manual processes that do not increase operators’ situational awareness, but are time-consuming, tedious, and repetitive tasks. This is especially relevant when considering manual administrative processes such as logging and reporting on incidents, logging and dispatching asset health anomalies, and managing workforce.
Due to the trend of automation, the operators’ time horizon can be considered to be moving further away from real time, where they monitor the system and assess risks associated with real-time market operations, system peaks, and renewable energy ramps. This is because decisions are getting more numerous, complex, and inter-dependant under greater uncertainty [
As an inspirational analogy, aeroplane pilots moved away from continuously steering the plane, based on real-time perception and indicators. They eventually became navigators by planning most of the flight trajectory ahead of time with forecasts, relying on an autopilot to follow this trajectory. Occasionally they would adjust the trajectory in or close to real time. Similarly, in the future, it can be expected that grid operators become grid navigators, planning and defining expected future trajectories supported by forecasts with an assistant that assesses risks, makes recommendations, and helps plan and execute tasks and reporting.
Within a range from a defined trajectory, an autopilot could help handle local or global fast system dynamics with proper reactivity. Large-scale automatic frequency regulations or local simple automatons are the examples of automatic control that have been deployed. However, to develop a more integrated autopilot that operators can rely on, coordinate with, and reconfigure, a supporting and unified cyber architecture beyond individual task automaton needs to be deployed. This would come as hierarchical modular and configurable cyber layers. At the top, the operators must manage an “optimize” layer, from which they have a global view of the system and can receive aggregated information and send macro orders to the underlying layers (voltage setpoint, automaton configuration, etc.). Zonal distributed “control” layers, a new type of layer, would monitor local areas covering several substations and provide advanced control with automatic remedial action schemes around configured setpoints or delimited operational domains. The “protect” layer, located at the substation, eventually ensures that material limits are respected at all times.
When thinking further about the process of automation in decision-making, it is important to consider which processes are carried out and in what time horizon, how much the process contributes to situational awareness of the operator [
An automated process may still require manual confirmation by the operator, in particular to select or validate and confirm non-trivial decision-making. Keeping the human in the loop should increase situational awareness. However, the operator confirmation might be omitted where fast response is required, possibly during some emergencies, and when an automated process continuously produces the expected outcome.
Ultimately, the level of process automation depends on the process being automated. For example, some straightforward processes can be fully automated and executed autonomously, while some can be automated only in parts or not at all. Nevertheless, the first step for any kind of process automation is to standardize the execution sequence and associated information exchange between the process steps. Moreover, the application that executes the process should be able to detect any inconsistencies in the process execution and process step failures with related reasons, and communicate to operators.
Transmission system operations have changed incrementally over the preceding decades and experienced operators have ingrained mental models for operations. However, if the system operating modes change, as predicted for the coming decades, operators may not be able to rely on existing mental models to solve new challenges. As an example, contingencies are generally slow to emerge, which is predictable, and thus operators typically have ready-made solutions. But with changing resource and demand mix, newer contingencies will emerge faster and unpredictably, meaning solutions may be more complex.
A better approach may be to equip operators with techniques to adjust to new paradigms and operation modes, so that they can think through problems and develop the optimal solution in a standardized manner. Structured decision-making frameworks also have the added benefit of being good proxies for task automation and artificial intelligence (AI) [
The decision ladder was theorized and developed by Jens Rasmussen [

Fig. 3 Rasmussen’s decision ladder with additional possible leaps by expert operators and a refreshed feedback loop when adapting ladder to forecasting for anticipation.
It is a very effective model for how operators in high reliability control center make decisions in critical scenarios. The decision ladder is not intended to describe how the brain works to process information via human physiology, which is a realm of complexity beyond the scope of this paper. It is intended to define the states of knowledge and process activities that occur while an operator is facing a system challenge. The decision ladder starts as a linear process flow, starting with activation on the left and finishing with execution on the right. The innovation with the decision ladder comes with the “ending” of the process flow, to make the process visually more intuitive and to enable leaps between states, as can be observed in
1) Not all processes or tasks will require all steps of the ladder, hence there are in-built leaps from left to right.
2) Experienced operators generally do not move through every stage of the ladder. Their inherent system knowledge and experience allow them to leap between stages or across the ladder, to fast track task execution.
3) The bottom level of the ladder represents “skill-based behaviour”, or automatic response. The middle level of the ladder represents the “rule-based behaviour” or operators following procedures or checklists in response to an event. The top level of the decision ladder is the “knowledge-based behaviour”, which relies on high cognitive workload and experience.
Ideally, operators should spend most of their time in the knowledge-based behaviour loop, diagnosing problems, optioneering, testing hypothesis, and assessing risk. Similarly in an automation scheme, this “simulation” and “validation” part of the algorithm might be the most computationally expensive.
The decision ladder is serial in nature, thus it is a useful proxy for a single and independent manual task decomposition and automation such as voltage control. But in real-time operations, tasks are highly interconnected, so voltage control can be linked to asset monitoring, stability monitoring, contingency management, and generator monitoring. As multiple tasks also need to be completed in varying time periods, task prioritization is not included. These can be improved by a move from a serial supervision structure to hypervision interface, as shown in Sections III-C and V-A. Hypervision would, in theory, takes the outputs of all serial processes in a control center and streamline decision-making and relevant information into one interface. But the decision support activity would be structured by the decision ladder. The system state, target state, option nodes of the decision ladder would take inputs from all processes, not just a single process, and the task, procedure, and execution would be optimized control actions for all the processes, not just a single process.
The original version of the decision ladder does not have a loop, review, or check stage (a review stage is added in the modified version in
Today’s supervision over many screens and applications leaves the user the cognitive load to prioritize, organize, and link disparate displayed information and alarms before considering any decision or action. It can be regarded as a fragmented ecosystem from an operator’s viewpoint. While it has been manageable for up to ten applications, it becomes impractical with more information to process and non-integrated applications under heterogeneous formats. It contributes to the problem of information overload and does not add context to system problems that need to be managed. This fragmented system dilutes the operator’s attention while making tasks often not explicit, eventually leaving the operator connecting the dots. Human-machine interfaces and interactions were mostly disregarded in the past in the control centers, but they now need to be considered more carefully. Sub-optimal design of human-machine interfaces and interactions has been identified as a risk factor to human error in operations [
A single and unified interface should support the decision-making process, and prioritisation of tasks for the operator. A new “hypervision” scheme will likely be required for the future control center, which will define and represent individual tasks with their context providing: ① relevant context and the problem diagnosis associated with the left part of the decision ladder; ② possible recommended decisions associated with the top of the ladder; and ③ related plans, procedures, and execution means to apply the decision associated with the right of the ladder.
All applications would still be running in the background while the hypervision will aggregate information to be represented in a meaningful way for operators to take decisions. It will also prioritize tasks based on the urgency and the time horizon, not just real-time tasks. This will allow the definition of an expected operation trajectory monitored by the hypervision core. If it goes as expected, the operator can continue planning its future trajectories without worrying about real time. Otherwise, if some refreshed information requires adaptation of the defined trajectory, it will ask the operator for reconfiguration and suggest solutions. Finally, the hypervision core is one system for all operators through which tasks can be shared, coordinated, and tracked without any loss of information.
Typical control center systems, used nowadays to operate the power system, were initially designed to meet the system operation and control requirements defined in the late 1960s. The design practices of the first system were based on the available technology of that time. Nevertheless, the legacy of typical all-encompassing and centralised software solutions is often still present today in a form of a monolithic energy management system (EMS) or data management system (DMS), provided by one vendor. As the system outdates and expires, it gets typically completely replaced by a newer version, also bringing long-enduring and costly impacts on the organisation. Such customer specific maintenance is consuming a great deal of time and resources to adapt, integrate, and interconnect the new system with the existing processes and vice versa. Yet, that still leads to limitations due to vendor lock-in, in particular, with respect to the ability to continuously and simply adjust and extend the system functionality according to user needs.
However, as elaborated in Section II, the power system operation challenges and requirements have changed significantly and are expected to change even further, mainly as a result of the ever-evolving grids and energy markets, and wide-spread digitisation among others. Additionally, the necessity for system-wide security coordination and market transparency drives the need for more and more data and information exchange between stakeholders and market participants, respectively. In order to provide reliable, safe, and economically efficient energy supply today and in the future, and comply with regulation in all times, there is a need for continuous advancement and adaptation of control center functionalities and applications. To timely meet the increasingly complex requirements, there is a pressing need for a paradigm shift in the design of control center systems from typical monolithic, all-encompassing, and closed vendor solutions towards modular, decentralized, distributed, vendor-neutral, and open systems.
As discussed in [
The main aim of the presented modular architecture is to provide high-level design directions of the digital platform with a goal to unlock: ① the potential of ever increasing operational and non-operational data; ② use of event-driven technologies for design of new applications; ③ seamless information exchange between modules via standardized interfaces; and ④ unbound flexibility with respect to maintainability of modules. Another benefit is the possibility to reuse the modules by other stakeholders, which also facilitates stakeholder collaboration and speeds up the innovation. Notably, the proposed platform can at first run in parallel to the existing legacy EMS or supervisory control and data acquisition (SCADA) system to complement the functionality, and over time in steps takes over the remaining legacy system functionality.
As visualized on
The first layer acts as a platform foundation spanning from the central location all to the edge (substations) and enabling ① data ingress and storage, ② real-time data analytic on the edge or central location to extract business value, ③ distributed applications, and ④ remote management of the platform components. One of the most promising platform implementation includes hybrid cloud, which is partly realised using on-premise and public cloud infrastructure offering additional gains in terms of resource flexibility and security, in particular, for disaster recovery. The on-premise part of the cloud infrastructure is used to host internal applications, spanning from the central location all to the edge in substations. Besides, the public part of the cloud infrastructure is used to accommodate energy market related services and data exchange gateways between stakeholders for grid security coordination and market transparency as examples. Then, a container management system can be used to ease and automate continuous integration, development, and scaling of various container-based applications anywhere in the hybrid cloud. On top of it, an event streaming platform enables high-performance data pipelining, real-time event and batch stream processing, and high availability of hosted (distributed) applications. This layer is particularly important for efficient design of data-driven online and offline event-based applications [
The second layer includes various distributed yet centrally located business functions as services that are shared and fundamental for operation of multiple end-user applications. Examples include but are not limited to (static/hybrid) state estimation, operational and non-operational data storage, alarm management, and data exchange gateways for inter-TSO/DSO security coordination, energy market and transparency purposes. Next, it also includes decentralised functions that run on the cloud edge in substations, such as asset condition monitoring, asset data acquisition, distributed (dynamic) state estimation, and vitalized protection and control schemes. The crucial parts of this layer are open software development kit (SDK) and API, which enable simple application design and seamless data exchange for application integration and visualisation purposes, respectively.
The third layer includes advanced applications for improved situational awareness and decision support, power system optimization and control, and energy market participation. Examples include but are not limited to (dynamic) security assessment and optimization, congestion management, event detection and analysis, load frequency controller, and optimal power flow. The hosted applications are residual anywhere in the cloud, typically near data sources, and make use of the various shared functions and services that are residual in the lower layer through using the shared API.
Finally, the top user engagement layer consists of a stateless intuitive front-end or cockpit, which is used to connect users and applications, allowing funneling of information and immersive performance overview of the whole power system down to the level of individual power system components, as well as effective decision-making as emphasized in Section III. The cockpit interface is stateless, meaning that it can be dynamically and automatically adapted to meet the user needs for optimal user experience and performance. A brain-computer interface or simpler bio-sensors could possibly be used to monitor workload and stress of a user and dynamically adapt the level of decision support offered by applications. Besides, the human-machine interfaces and interactions also support voice control or other advance interaction modalities for simple confirmation of actions and recording of user actions for logging purpose.
Hypervision interface, as a part of one cockpit module in

Fig. 4 Proposed enabling digital platform architecture featuring modular design and standardised API.
A card can first be automatically created and notified to the operator ahead of time based on forecasted alerts and contextual information, with a preliminary diagnosis. This can be refined and refreshed as refreshed forecasts or new information that comes in. Then recommendations for actions can be made available within the card or the operator can propose another one. The operator can further tag the card as representing a certain problem and objective. The operator can preferably select one option that will be considered as active. The card can eventually come with a procedure and configuration choices for execution. The card can then be send for automatic action execution on the grid once needed. A card can also be manually edited from scratch by the operator for more unusual situations. The card finally is shared across operators allowing for effective coordination.
As structured decision-making is applied to any field, hypervision interface frameworks in the end are applied to any industrial domain, only the underlying information management remains domain-specific. OperatorFabric [
This solution facilitates the interactions between operation control centers, who can share information in real time, as pre-formatted cards that can be sent either manually by operators or automatically by external solutions.
The goal of AI is to turn machines into intelligent agents that are able to learn from experience in order to optimally perform complex tasks [
The most promising applications for using AI outside of the power system are within the domains of autonomous driving systems [
In the domain of power systems, the emerging AI methods are promising for future software tools to make stability analysis and control in smart grids tractable [
AI applications deliver initial but promising results that include online security assessment in multiple renewable energy scenarios, fault location identification under different operating conditions, stability control, or others [
However, AI applications still face several challenges in practice that are currently actively studied and developed, which relate to the methods or applications. The methodological challenges are, for instance, related to learning from imbalanced data, difficulties in transfer learning, or robustness against attack or adversarial examples. The application-specific challenges include high requirements on data (both quantity and quality), platform design for efficient and effective development and deployment in production, as shown in Section IV, a collaboration between the power systems and AI communities, and generally accepted and shared benchmarks. One specific challenge to use AI for critical tasks such as operation or planning of power systems relates to the trust required by operators before using tools with AI. Making AI trustworthy in a systematic way is highly important in critical infrastructure workflows. This means that on top of satisfying basic performance measures, AI needs to satisfy requirements related to reliability, human interaction, interpretability, and bias, and eventually offer explanations. Implementing a common language between human experts and machines such as ontologies [
Digitalization paves the way for energy transition towards carbon neutrality and energy system integration. The physical energy infrastructure, e.g., power plants, substations, and power lines, is increasingly dependent on operation technology (OT) systems and industrial IoT for real-time monitoring and control of the physical facilities. Utilities, aggregators, and service providers use high-speed information technology (IT) networks for business operations. It can be imagined that on top of the power infrastructure reside IT-OT network layers. Together they form a complex and interdependent CPS for the power system. CPS combines the cyber system comprising of communication, control, and computation functionalities with the physical world, which typically consists of a natural and/or man-made system governed by the laws of physics. Modern CPS involves multiple and complex physical subsystems with varying degrees of interactions via communication networks. Hence, their holistic analysis is a challenge that needs to be addressed, as comprehensively posited in [
It is well recognized that information and communication technologies are vulnerable to cyber attacks. Examples of cyber security incidents related to power systems already exist around the world. On December 23, 2015, cyber attacks were conducted on the power grid in Ukraine. Hackers intruded into IT-OT systems of the control center of three DSOs. Attackers took control of the SCADA systems and disconnected seven 110 kV and twenty-three 35 kV substations from the grid for hours. The cyber attacks in Ukraine resulted in power outages, which affected 225000 customers [
Utilities play a central role in grid digitalization, spearheading the energy transition and energy system integration. They invest in cyber security solutions to secure the control centers from cybercrime and hacktivism. However, they are also the main targets of state-sponsored cyber attacks. Video evidence of the 2015 cyber attack in Ukraine shows an engineering workforce not adequately responding to attackers taking remote control of the power grid OT system and not knowing if it is a cyber attack or their own IT department is controlling the SCADA system. The kill chain of cyber attacks on power system operators typically starts by exploiting vulnerabilities in the utility IT system through phishing emails and similar methods. Malware is installed to open gateways and facilitate remote access for system reconnaissance, weaponization, and OT targeting. Attackers can intrude from the IT system into the OT system by stealing login credentials, escalating access privileges, and discovering networked OT systems and hosts. In the OT system, they can take control and tamper with the SCADA system, disconnect power plants and entire substations, and cause physical damage to power equipment by interfering with their control systems.
Segregating the IT-OT systems of control centers by using firewalls is not enough for the cyber security of power systems. Advanced mathematical and computational foundations, methods, and technologies are needed for incident response to protect utilities from state-sponsored cyber attacks to ensure cyber security of the future control room. Operation resilience of power systems to such cyber threats is achieved by combining innovative technologies, incident response strategies, and human factors. Furthermore, it is imperative to build trained human capital for grid operators to deal with the ever-growing cyber threats. Threat intelligence plays an important role in preventive and reactive cyber security. Utilities share knowledge among a network of trust via information sharing and analysis centers (ISACs). Cyber threat management is emerging as the best practice for managing threats beyond the basic risk assessment found in security information and event management systems.
Dynamic analysis of a very large, fast, interconnected, and complex power system is currently only possible with numerical models. Despite calibration efforts, widely used physical-based models fail to be general and accurate enough for describing the system in any state of functioning, not capturing, for instance, system uncertainties, asset health status and life-cycle effects, cyber-interactions, or usual operation schemes. As defined in [
DTs hence offer the possibility to connect and tune the digital models with measurements of the real assets to mimic the reality. A detailed and virtual replica of the power grid provides system operators with the enhanced capabilities for real-time prediction and fast and reliable decision support. It is foreseen in the next decade that DTs will be widely deployed for various industrial applications due to recent advances in parallel computing, solvers, data processing and management tools, big data, and AI [
The wide-area monitoring system (WAMS), in addition to the conventional SCADA systems, greatly enhances the situational awareness since it provides information on the essential variables for system operation with a high resolution. This enables the near real-time monitoring of the dynamic power system phenomena and facilitates a dynamic security assessment (DSA). A DT mirrors the system state in real time and consolidates the control center system architecture. It improves the model accuracy by combining WAMS and DSA. The DT facilitates an operator assistant system for fast and reliable decision support [
DT may be part of the enabling digital platform given in Section IV as the modules used for cyber resilience analysis, planning, and operation of integrated CPS. They comprise of detailed discrete-time models of OT networks for substation automation and continuous-time models of the power system. The discrete-time systems are used to model and simulate the substation communication networks and processes. Therefore, DTs extend the current modeling, simulation, and analysis capabilities of power system planners from only a physical domain to the integrated cyber-physical domains. DTs allow the real-time simulation of cyber attacks at the cyber system layer and the impact analysis at the physical layer in an integrated co-simulation environment. Power system operators can assess and improve the grid operation resilience to cyber attacks and plan the cyber security operation of the integrated CPS. Cyber resilience should be an integral part of control center systems and should be taken into account when designing new EMS applications.
New operating tools for advanced monitoring in support of managing the operational reliability are needed in order to improve the situational awareness in a system growing in complexity, decentralization, and uncertainty [
When assessing the dynamic security, a model that considers equipment failures such as the failure of a generator or a transmission line is simulated by current tools. While DTs consider rather active type of simulations and have a broad capability supporting active decision-making including modeling the entire intelligence of system operations, the simulations for security assessments are rather passive. An analysis of the post-fault simulation results provides the security information. Unfortunately, with current tools, the computational time is too long to analyze many faults (combinations) for possible operating conditions in real time. Hence, the tools limit the DSA to offline studies, which makes the simulation results inaccurate and unsuitable for real-time DSA. The reason for such long computational time is that the methods that underlie the current tools rely on numerical integration that solves the dynamical model described by ordinary differential equations (ODEs) in the time domain [
New promising AI and ML methods for real-time assessment of security and control (preventive and corrective) of reliability are emerging [
Operators will get assisted through an hypervision interface with smart recommendations, continuous situational awareness of projected operational trajectories augmented by AI, as shown in Section V-B, and in the end more automatic execution functions when actions get implemented. Operators can choose when to delegate further a task to the hypervision assistant at any step of the decision ladder if appropriate. This goes in the direction of semi-automation as described by [
A usual trend when going through more automation while not considering the demands for human decision-making is to see operators slowly going towards on-the-loop mere verification of recommendation (much like security scanner airport operators), hence moving from strong human operation intelligence to strong machine intelligence. Not to mention an unrealistic target of eventually getting them out of the loop of a fully autonomous grid. While on the loop, an operator will be prone to anchoring bias (i.e., over-relying on the first piece of given information) and automation bias (i.e., accepting without thinking the single recommendation displayed) [
An assistant [
Resulting hybrid intelligence [
More specifically,

Fig. 5 Desired functionalities for an operator when designing proactive decision support through hypervision.
In summary, hybrid intelligence essentially represents a form of human-in-the-loop decision-making in which assessing the operator’s situation and vivid human-machine interactions are key. For instance, different underlying strategies for human-machine interface design, annotation, and data sampling continuously need to be aligned [
The current operating paradigm is to assess the steady-state security of power system operation with security, meaning to study when single fault occurs at maximal at the same time. The assumptions of this paradigm are no longer suitable [
The new probabilistic paradigm for security assessment can be used to quantify risks [
As the system becomes more complex, especially considering the current numerous cyber and multi-agent interactions, as well as rapidly evolving technologies and applications, comprehensive and continuous testing and training become of utmost importance. Continuous testing of new functionalities also helps speed up acceptance and improve the users’ satisfaction. As a healthy check, a process which we cannot be tested on demand is probably too complex to manage and should probably not be eligible for deployment. Testing should support proper design of application ecosystem development and ensure that this still make the system predictable and controllable enough to be run under various operating conditions (normal, emergency, cyber attack) or in degraded modes. Continuous training should be offered to operators to learn how to best use evolving applications, revise their intuitions and understanding of changing system behavior, and best coordinate with various operators under different configurations.
Several testing layers discussed are needed: in-silico, in-vitro, and in-vivo. Lots of physical testing processes and hardware have already existed for years for in-vitro lab testing over small-scale systems or replica as in RTDS or OPAL-RT. Parallel runs have also been done punctually in control rooms when bringing in some new applications to be validated by operators. This often requires lots of preparation, and yet only covers a reduced set of system conditions: the ones encountered during this operation testing period. More systematic and continuous in-vitro lab testing is therefore needed, especially through comprehensive “shadow control room”, as a replica of real control room (EMS/IT, audio/video/human ergonomics), with added grid simulation capabilities that can be reconfigured very quickly for tests and experiments only. Evaluations of ergonomics and decision support tools and processes could be more rigorously tested in regard to their impact on operator’s decision-making.
In-vivo lab testing also becomes more necessary to regularly assess the proper configuration of different control layers, as well as underlying asset reliability and health. As the power system needs to run continuously without interruption, invasive in-vivo lab testing have been regarded as risky and not considered extensively. Nonetheless, the open-and-close reliability testing of breakers through periodic maneuvers, power system stabilizer (PSS) power plant controller testing, as well as primary reserve frequency control verification are examples of existing in-vivo lab testing. New and more numerous in-vivo testing could be implemented to test cyber system behavior over different scales possibly in the form of frequently planned on-off asset maneuvers under different but secure system conditions. These controlled interventions should also help test the accuracy of predictive models and grid models at the core of decision support tools.
Finally, in-silico testing also comes as a new opportunity thanks to the developments of virtualisation and DTs shown in Section V-D to replay real environments, much like flight or car simulators. In particular, collections of real edge cases when captured can be simulated to be systematically tested over and over as done for autonomous vehicle development. Combined with comprehensive knowledge bases and artificial agent allowing one to virtually run operation scenarios realistically and automatically, it can test the potential of new designs or functionalities for cheap. This also limits more demanding in-vitro or in-vivo testing.
Such in-silico environments can also form the basis of advance operator’s training simulator (OTS). Instead of artificial agents virtually running power system operation scenarios, human operator could just simply run these on their own with the available decision support tool to test their decisions and learn about the behavior of contextual systems. This goes beyond today’s existing OTS limited to single canonical snapshots and low-level actions (without use of support decision tool) instead of full contextual scenarios over time with possible high-level strategies. Human operators would also learn and train themselves by watching “games” from others, either human or artificial agents. Depending on their level of expertise, specific play or games could be recommended to them. OTS should quantify and compare the operators’ performance, assessing their strength and making recommendations for improvements. Finally, it should have future operators trained in multi-domain and in coordination with other agents, either other human operators or artificial assistants.
In this paper, we present the transformative perspectives of future control centers to handle the operation consequences of ongoing and upcoming changes in the power system through the energy transition. Control centers will have to evolve continuously to adapt. Consequently, we propose an enabling digital platform architecture to unlock the potential of data, the integration of emerging technologies, and the design of new applications and their flexible integration in an always extending ecosystem. We also highlight the evolving roles of the operators as planners and coordinators. It is supported by additional automation, but also importantly by a new approach for decision-making. We indeed propose the integration of hypervision, a simplified and unified interface for all operators that instantiates structured decision-making framework based on the Rasmussen’s decision ladder. Some hypervision frameworks already exist and could be deployed in a very near future. Complemented with upcoming technologies such as AI and DTs, one perspective is to develop a comprehensive and collaborative artificial assistant for the operators within the next decade while relying on advance probabilistic security assessment. This security paradigm shift may require a profound cultural change for operators and within the company at the same time, which should be conducted as early as possible. Retraining the operators will be needed. More generally, continuous training and learning should become necessary to keep operating an always evolving system. New advance training simulator integrating all discussed dimensions should be developed. It could be further used as a testbed for experimenting the effectiveness of the new design, and hence be a fruitful intermediate milestone. In parallel, extending the testing capabilities of the system, applications, and processes before integrating this new level of complexity is as usual mandatory. The culture of testing should be enlarged and reinforced within the companies. Testing should eventually be run continuously. In the end, succeeding at the proposed control center transformation will depend on the close collaboration between stakeholders, research institutions, vendors, and possibly open communities.
Appendix
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