8.2.1. Improvement of network resilience in the face of catastrophic climate events

The recent failures of electrical systems during extreme weather conditions have recently drawn the attention of the public to the critical role of network operators in modern society. Network reliability is the ability of the system to meet customers’ requirements in terms of power and energy, taking into consideration forced outages (following interruption) as well as the planned maintenance of equipment outages in the system. The North American Electric Reliability Corporation defines the reliability of an interconnected electrical network by two basic and functional aspects: “adequacy and security”.

8.2.2. Increasing electrical network efficiency

8.2.2.3. Aging infrastructure planning

The electrification of developed countries has taken place over the last hundred years; it is important to constantly invest in order to continue to guarantee the reliability and the quality of the supply. While consumption requirements do develop, in quantity and quality, as well as distributed energy production tends to spread out, the aging of energy transport and distribution infrastructures has to be dealt with.

SmartGrid technologies offer the opportunity to maximize the usage of existing infrastructures through better real-time monitoring of the operating conditions of the network equipment so as to allow for an estimation of the reserve capacities of infrastructures in real-time. They also make it possible to better define replacement strategies for electrical network equipment by anticipating the replacement of the most sought after equipment and by considering the extension of their use beyond the lifetime assigned for other equipment that would have been underutilized.

8.2.3. Integration of the variability of renewable energy sources

Efforts aiming to reduce CO₂ emissions related to electricity production have led to a significant penetration of intermittent renewable production technologies. This continuous increase is gradually extending to all regions (Figure 8.3).

Unlike traditional production methods, the production of energy sources known as “variable” is very closely correlated with weather conditions in different parts of the network. This leads to new constraints for balancing the electricity system, associated with the prediction, the monitoring and the control of the adequacy between supply and demand, and with reserve management. When the contribution of variable energy sources, with respect to the global production base, exceeds 15–20%, it becomes necessary to implement new management strategies to provide network operators with new flexibility in order to ensure system balancing (Figure 8.4).

SmartGrid technologies enable the deployment of larger amounts of intermittent resources by means of integrating new artificial intelligence and machine learning technologies in order to best predict production and demand and to improve the economic “dispatch” of the system in its entirety.

8.3.1. Global SmartGrid architecture

As has been previously developed, SmartGrid technologies have the ability to coordinate the production and consumption related to the available network capacities in line with energy market managements. They consist of an integrated architecture of the value chain from start to finish according to specific principles.

• – System design. The system can no longer be considered as a system operated by a single entity, but as a “system of systems” wich allow several subsystems to interact. These are interconnected to each other through real-time transactional control systems.
• – Deployment. In the value chain, the system must realize the interconnection of a very large number of geographically dispersed components based on different IP communication formats by integrating communication standards clearly identified at each connection node (the communication infrastructure can, in fine, be the property of network managers or be partially externalized depending on the case).
• – The synchronized processing of very large Big Data volumes of time data series. Since these technologies must provide means to understand the constraints for operating the system in real-time, the IT infrastructure must be able to synchronize and process chronological series in real time, with different sampling capabilities varying from the fraction of a millisecond (for stability problems in electrical networks) to the second (for system balancing and congestion management), or even several minutes (for optimization algorithms of energy flows and pricing on energy markets).

The new IT infrastructures of SmartGrid technologies open the way for a better optimization of the electrical system as a whole, through a better real-time estimation of network assets and of means of production, through sensors distributed at the interfaces of the system, the GridEdge, by way of automata and computers distributed in substations and control centers, up to every power feeder, or even in consumers’ homes in order to enable a flexible dynamic control of expenses.

These new architectures combine centralized computerized processing in control centers, and even the Cloud depending on the nature of the data and their criticality, with concepts of distributed intelligence deployed at different levels of the network, from control centers in thermal plants up to control systems in renewable energy parks and load management in consumers’ homes.

These systems are based on hybrid unified architectures (Figure 8.5) combining On-Premise, Cloud Substation and GridEdge multilevel automated systems, incorporating the latest communication standards developed in the industry, and especially the IEC61850 Common Information Model (unified data model) and the SmartGrid SGAM architecture model.

They comprise expert user interfaces, derived from advanced technologies for real-time decision support of the nearby environment (situational awareness), as well as middleware software integration layers to enable significant scalability in terms of application, interfaces, processing mechanisms and data storage. These tools integrate with physical network infrastructures, superposing an information technology layer.

These systems provide operators with real-time information about every asset in the network (quality, measures, oscillations, counters, etc.) to enable the real-time management of transactions between means of production, storage and flexible demand while ensuring an optimal balancing of the system as a whole.

8.3.2. Basic technological elements for SmartGrids

The previous “systems of systems” SmartGrid architecture is composed of several key technological building blocks interacting with one another at different levels of the electric network. Some of these components already exist in the network, others considered as mature are being deployed while some still require development and demonstration stages.

For this purpose, close collaborations between electric operators and market participants are essential to update associated technologies and deployment strategies.

When examining the various technology layers of systems, multiple blocks can be distinguished:

• – at the lowest levels, new Smart Inverter technologies which integrate power electronics technology associated with high-speed controllers deployed for the management of power conversion and of the quality of AC-DC power. These include in particular:
  • - AC/DC conversion substations for interconnections between networks or renewable farms’ remote connections. Originally intended for connection purposes, their use is evolving towards a more complex function for network support,
  • - FACTS and “smart” STATCOM to provide compensation for reactive power to the system. Initially deployed in very high voltage, the technology is developing for low voltage (at the GridEdge’s level) where the massive integration of photovoltaic resources generates new voltage constraints,
  • - inverters associated with photovoltaics and storage incorporating DC/AC conversion functions and providing auxiliary services – frequency and voltage control – to manage network stability and its inertial reserve,
  • - fast electric vehicle chargers;
• – at a higher level, computerized substation technologies, for MicroGrid control and operating electric feeders. They include all fast protection and control equipment to protect assets or to self-heal the system during emergency situations. These solutions are usually composed of the following technology components:
  • - digital and analog current and voltage sensors for detecting electrical waveforms,
  • - devices for the surveillance of the good health of substations and primary equipment,
  • - controllers associated with substations and electric feeders,
  • - controllers associated with MicroGrids and regional defense plans (Wide Area Measurement System);
• – at a third level, control technologies of distributed energy resources. These technologies are usually deployed on the consumer side, that is, close to flexible distributed energy resources (located downstream of GridEdge interfaces). They usually include:
  • - smart meter management systems (AMI, MDM and others),
  • - decentralized production controllers,
  • - energy management and control systems in buildings,
  • - energy management and control systems in residences, in particular the recharge infrastructure for electric vehicles;
• – at the last level, the management tools of the energy system as a whole. Although historically they were very centralized in a few control centers, associated architectures tend to evolve into new hybrid distributed architectures. They thus allow for a distribution of application catalogues at different levels of the subsystems located within the IT infrastructures of Utility Premise network operators, in the Cloud or even at the GridEdge interfaces level depending on the criticality of associated data and computational constraints. These tools comprise all ICTs required for monitoring, controlling and the optimization of the energy system, in particular, tools for:
  • - congestion management and contingency analysis,
  • - stability management,
  • - dynamic management of asset conditions,
  • - renewable energy forecasts and real-time estimation of the system state,
  • - dynamic management of incidents and reconfiguration-reconnection strategies,
  • - management of distributed energy resources (integrating decentralized production, storage and demand management),
  • - energy market management,
  • - geographical data management.

8.3.3. Integration of new MicroGrid layers: definition

A MicroGrid is a section contiguous to the electrical network. The commercial incorporation of its distributed energy resources (namely, its means of production, expenses, storage means and the electric vehicles allocated thereto) all contribute to its operation in a coordinated manner in response to its consumption requirements and to the specific constraints of the network, generally related to its congestion and its quality problems.

MicroGrids can operate in a completely autonomous fashion (small disconnected electrical networks) in scenarios where the main network requires it. They are generally attached to an “aggregator” unit operating a virtual power plant (VPP) composed of the aggregation of distributed energy resources of MicroGrids.

The growing demand in distributed renewable energy, the development of information system architectures towards distributed architectures and the active participation of customers in the market through services such as demand-side management are points of convergence towards decentralized MicroGrid architectures, thus offering new business models to network operators. The main advantages of MicroGrids can be outlined as follows:

• – they naturally encourage the self-consumption of private renewable energy by end-users produced within the boundaries of MicroGrids, thus minimizing operating losses and the impact on congestions in the main network;
• – it offers new opportunities of service continuity on the critical loads of MicroGrids during a network incident at “GridEdge” interfaces;
• – it allows the optimization of distributed energy production with regard to the requirements of the energy market, even the balancing and reserve requirements of the network;
• – it offers new options for reprofiling capital investments necessary to the consolidation of the network;
• – it allows for the emergence of new business models among MicroGrid users.

The qualitative and quantitative evaluations of the advantages largely depend on the MicroGrid business model and on the services offered to the main network. MicroGrids can be defined according to the following usage contexts:

• – MicroGrids attached to private industrial and commercial infrastructures:
  • - they are private property, operated by private infrastructure managers and integrate limited interaction with networks,
  • - the main objective is to offer a reliable and economic supply to infrastructure owners,
  • - more recently, new developments have been observed around university campuses focusing on innovation that can be deployed in connection with these campuses;
• – MicroGrids attached to government agencies:
  • - military MicroGrid particularly focusing on the reliability of energy and infrastructure resilience,
  • - these organisms take interest in the economic impact of the approach by utilizing their MicroGrid in addition to the energy supplied through the main network,
  • - some cities have begun to consider MicroGrids as a key factor for the development of their SmartCity vision of energy;
• – MicroGrids attached to electrical network operators:
  • - electric network operators are considering the deployment of MicroGrids to supply customers with specific and localized requirements, in areas exposed to strong network constraints (Figure 8.7, example of Enedis in France as part of the NiceGrid project, around Carros),
  • - deregulated public services work together with the aggregators of energy resources in order to improve service quality through distribution networks and MicroGrid operators,
  • - the deployment of these solutions requires coordination with the aggregators of production and consumption means in order to stay in line with market rules.

MicroGrid management functionalities are mainly derived from management and portfolio optimization applications of distributed energy resources (DER). These applications derive largely from applications initially deployed in control rooms of public distribution operators which, in the case of private MicroGrids, are operated by other commercial entities.

These systems generally cover the following technological building blocks:

• – supervision, control and communication data acquisition:
  • - industrial communications equipment employing wireless technology or power-line communication (PLC).
  • - automation equipment for substations and electrical feeders,
  • - control algorithms for the dispatch of distributed energy resources;
• – MicroGrids real-time management applications:
  • - islanding detection, resynchronization and economic dispatch in interconnected mode,
  • - power balancing and frequency control,
  • - voltage control,
  • - MicroGrid topology evaluation,
  • - alarm management and event logging;
• – forecasting and planning:
  • - renewable resources forecasting, namely including photovoltaics, wind energy and electricity-steam combined cycles.
  • - load, load management and storage availability forecasting,
  • - energy price forecasting,
  • - distributed energy dispatching optimization;
• – transactional control with network operators and markets:
  • - retail aggregators with contracts, compliance and performance,
  • - real-time trading of flexibility and available reserves,
  • - billing and regulations follow-up.

In the future, the operation of electric networks will evolve into a multi-level coordinated network control interfacing with MicroGrid constellations operated through flexibility aggregators (Virtual Power Plant) which interface with network and market operators. Even if the real-time control of each of these MicroGrids will still be managed by local control facilities, a new aggregation layer is emerging in order to ensure the coordination of these MicroGrids regarding the whole of the energy system.

Figure 8.8 illustrates these new integration approaches in which the electrical network plays a new unifying optimization role between different MicroGrid infrastructures deployed at the GridEdge interfaces of the electrical network.

MicroGrids appear as a new cornerstone of interconnections between consumers/users – transformed into “prosumers” likely to offer a new level of flexibility to the system – and the infrastructures of the electric network whose control layers are evolving. This leads to the implementation of new information and communication infrastructures facilitating a global integration of these systems between them, while allowing for distributed control and optimization strategies at all levels of the electricity system, depending on the technical and business model constraints under consideration.

In cities in particular, where energy systems are particularly constrained, these architectures should also enable an extension beyond the limit of the power system, by means of interconnecting with other gas, heat or even electric vehicle recharging networks through open interfaces allowing the exchange of flexibility between these systems.

A major challenge for the deployment of these new architectures is their potential to open up sufficient new avenues to be able to interrelate historically isolated information while responding to new rules of confidentiality and minimizing risks in terms of cyber-security. These new systems will aim to establish optimal control strategies between energy infrastructures.

Over the past years, architecture specifications and the standardization of interfaces have remained confined to “single infrastructure silos” relying on highly personalized platforms and specific to each field. Recent developments in ICTs nevertheless enable the consideration of new “Industrial Internet” platforms, focusing on re-using Big Data and Machine Learning applications capable of considering new avenues for “System of systems” deployment, thus making a decompartmentalization of the energy system by data possible.