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Flexibility and Technology Transform the Power Grid in a Resilient Digital Infrastructure

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Roberto Quadrini's picture
CEO & CSO Tecnalogic

Inventor of energy flexibility, computer science and automation engineer, modeling and optimization energy expert.Owner of international patents on methods of energy optimization using...

  • Member since 2007
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  • May 26, 2021 5:41 am GMT

This item is part of the Special Issue - 2021 - 05 - Grid Modernization, click here for more


To deal mainly with destructive events of a meteorological nature, it is necessary to evolve the current power grid into a decentralized, distributed communicating vessels model made up of virtual zones, which are made up of Consumption Unit (CU) and Production Units (PU) that can be aggregated according to affinity and homogeneity, with different degrees of flexibility priority “mapped” in their consumption / production program (baseline).

The new Adaptive Power Grid model adapts to the resources available and present within each Virtual Zone (VZ), maximizing the use of renewable sources, minimizing imbalances using the flexibility of CUs via the Virtual Node Dispatching (VND)  (IoT gateway that manages all CU/PU systems belonging to the different zones) and valoring the baseline (energy programs) of the PU and CU resources. This model extended to all Virtual Power Plant (VPP), Microgrids (MG), districts industrials, energy communities, consortiums, etc., introduces the resilience property of grid power which is able to adapt by absorbing any anomaly that jeopardizes the integrity and electrical service safety.

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Each Virtual Zone (VZ) is composed of several Virtual Nodes (VN) which group, according to their affinity and homogeneity properties, the CU/PU, selected from areas belonging to the same domain and which are directly connected to the Microgrid (MG). Each CU behaves as a node for input and/or withdrawal from the grid (Distribution Zone) in order to achieve the intrinsic property of systemic resilience and able to “absorb” any type of deviation between the consumption program (baseline) and that of production and able to resolve any “destructive anomalies” of the electrical infrastructure.

The property of Resilience manifests itself in two system components:

  1. NegaW corresponds to the ability of the network to adapt in the presence of “destructive anomalie”, expressed in available power; 1 NegaW is equal to 1 kW of feed-in/withdrawal/ mixed modulation respecting all parameters of the grid.
  2. NegaWh corresponds to the level of flexibility of the network, in the presence of “destructive anomalies”, of the grid phase with the aim of absorbing those (through the mixed modulation of CU/PU); 1 NegaWh is equal to 1 kWh of modulation (flexibility) of feed-in/withdrawal/mixed (CU/PU) respecting all system parameters.

This evolution of the Power Grid is possible using technologies that integrate the data network on the power infrastructure on the Demand Side (DS) with IoT technology. The adoption of IoT technology with open communication protocols, which enable bi-directional communication, allows to model the load, managing it in an integrated way in the customer's infrastructure and with the management of Demand-Response platforms, microgrids having the ability to absorb any external and internal anomaly of the system (electrical resilience properties of the infrastructure).

One of the most pressing problems in the energy sector is that of evolving the “monolithic electrical infrastructure”, that is resilient to “external” impulse (weather, social, etc.) into a new flexible and resilient infrastructure to changes that are due the evolution of the electricity market (primarily the introduction of mass storage systems).


Example of some transient exogenous factors that affect the integrity of the electrical network:

  • Extreme weather conditions;
  • Accidental environmental disasters (e.g. tree falls);
  • Conventional terrorism (damage to high voltage pylons) and cyber-terrorism (modification of CU set-points or of the distributed production unit program);
  • Electric peaks due to the random use of batteries for recharging mobility systems (unpredictable absorption program);
  • Energy from renewable sources not present or not available on the grid due to excessive production;
  • Concentration of localized consumption in an area or zone that cannot be foreseen as it arose from human behavior (unforeseeable unforeseen events);
  • New profiles of electricity consumption as a consequence of pandemic crises, economic crises (flattened consumption profiles, baseload growth, shift of evening and daytime load points to correct the lack of production from renewables, etc.);
  • The current model of the electricity market mainly values ​​Production, and considers Demand as a passive element in support of power plants. This GENCO-CORE (GENeration COmpany) model presents a series of problems, additional costs and infrastructure limitations, which grow exponentially with the introduction of energy from renewable sources and with the introduction of electric mobility.

The main consequence of the current model is manifested in the nature of the information flows essential for the operation of the electricity service:

  • uni-directional: the data flows are created from production (marginal price, $), are aggregate in distribution (energy load, kWh) and are presented to the end customer (in the form of an energy tariff kWh/$), who accepts them in passive mode (the data generated on the demand side are EX-POST and needed only for the billing of consumption and/or recognition of production from PV, WIND by suppliers of energy contracts). “Demand is passive because it is created by monolithic production”.
  • centralized: the production data (through time series) “build” electricity demand based on a national scale (states/regions) and not on distributed demand (industrial districts, microgrids, residential communities, consortiums, buildings, ...), introducing a level of not “knowledge” which is reflected in the failure to enhance the level of flexibility of the CU, which reduces the resilience level of the Network.
  • monolithic: the consumption data recorded on the Customer's smart meter does not present any element of flexibility since it does not arise from the programming of the demand (mapping of the consumption profile of the CUs in a granular way) but is created from the measurement that took place in an EX-POST timeline with respect to its enhancement on the EX-ANTE market. The absorption profile of each CU is not a forecast data but the result of a mapping carried out prior to its use (industrial, residential).


Electricity Demand must be the core of the new adaptive grid model, made up of “flexible” resources to be used as “virtual batteries” to build the property of the resilience. Distributed generation (programmable or not), microgrids, integrated with “flexible” resources (building, HVAC, industrial production systems, pumps, lighting systems, ...), define the basic layer of the grid's ability to absorb abnormal and make the electrical system adaptive. To transform demand into a resilient absorption model, the following methodology: “Profiling, Scheduling and Balancing of energy program (baseline)



  • Real-time acquisition of the consumption data of each load;
  • Construction of the Characteristic Energy Profile;
  • Definition of the consumption program associated with the operational activity, in compliance with the predetermined performance indexes.


  • Dynamic implementation of consumption programs through ordinary modulation of setpoints;
  • Periodic verification of actual compliance with the predetermined performance indexes.


  • Dynamic correction of the consumption program through an extraordinary modulation of the set-points, drawing as needed from a predetermined list of possible interventions;
  • Activation in the event of operational criticalities or remuneration opportunities.


  • Identification of the actual availability of energy that can be modulated in compliance with the predetermined performance indexes;
  • Periodic communication of the consumption program and availability to modulation towards the aggregator;
  • Implementation of the modulation requested by the aggregator in case of actual grid need

The flexibility of the Virtual Zones (VZ) can be quantified in NegaWh, with the creation of zones with different absorption priorities (NegaW), ie the ability to absorb the potential network anomaly.

The evolution of the current network into an adaptive model, through the adoption of IoT technology in Micro Grids and more generally in the Demand Side, enables utilities and various operators to propose their portfolio in Energy-as-a-Service mode.

On the data network infrastructure level, machine learning, deep learning agents (to resolve all math linear e not linear programming) can be developed that make the entire grip infrastructure adapt automatically up to the point of tokenizing energy. When a service for dispatching is requested by power grid operator to the Distributions/VPP,Microgrid, the Virtual Zones (VZ) can create smart contract in the provision of balancing services, modulation up to creation of the services in Resilicence-as-a-Service (RaaS) mode for the power grid maintenance.

The adaptive power grid was born thanks to the creation of electrical flexibility, which is the result of the balancing of the program of each CU belonging to the Demand Side, and solves the criticalities and problems that have impacts on the management of the power grid that can undermine the principles on which the electricity network is based:


  • Adequacy - “System equipped with sufficient production, storage, demand control and transport capacity resources to meet the expected demand, with a margin of adequacy in any given period”;
  • SafetyAbility of the electrical system to withstand changes in the operating status as a result of sudden disturbances, without violating the operating limits of the system itself”;
  • QualityAbility to ensure the continuity of the service (lack of interruptions in the supply of electricity, frequency and voltage within the allowable ranges) and the quality of the same (voltage level, waveform, …)”;
  • EfficiencyAbility to manage the electricity system respecting the safety, adequacy and quality requirements, at the minimum overall cost for the citizen/user".
Roberto Quadrini's picture
Thank Roberto for the Post!
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Julian Jackson's picture
Julian Jackson on Jun 3, 2021

Thanks for your interesting post. I have some reservations, due to the complexity of what is being proposed. I am also wondering about the connectivity issues, both physical and digital. I know many US states have all sorts of different systems, and here in Europe each national grid grew up independently and are now required by both legislation and circumstances to be linked up. Will these emergent systems be prone to new types of unforeseen issues and failures in the worst case scenarios?

Roberto Quadrini's picture
Roberto Quadrini on Jun 4, 2021

Thank you so much for the comment.
The proposed methodology is based on a mathematical engine equipped with a series of linear and non-linear optimization algorithms, machine learning, which transforms the EX-ante and EX-post data of the various operators (TSO, DSO, BRP, ESCO, Consortium, energy communities , Clients, etc.) in information flows that realize flexibility on the DEMAND SIDE and make it available to the power grid. To realize the adaptive and resilient network model, the physical network must be virtualized, using all the systems already present in the electrical infrastructure.

To use existing systems:
- smart meter
- scada system
- OPC server
- Gateway
- etc
The architecture is logical and is a virtualization of the electricity networks that are identical from the point of view of the infrastructure, only the usage model and the management times between the various operators in the different countries change.

The virtualization of electrical zones, areas and nodes is possible thanks to the level of electrical flexibility that is "extracted" in the micro-grid and distributed generation, and which is made available to the TSO. To communicate the level of flexibility to the various operators, the protocols defined by the local authorities are used. In Italy, for example, IEC 60870-5-104 is used to communicate with the TSO (Terna).

Communication with the consumption units (Demand Side) is achieved by interfacing the SCADA systems already present and using their protocols (MOBbus, OPC, etc). So the connectivity used is the one already present at the customer (industry, residential). The novelty is in the nature of the data that create maps of virtual zones aggregated by areas and that are made available with different levels of flexibility.

The aggregate of these profiles towards the TSO generates a map that is able to deal with anomalies in the electricity grid by modulating consumption, dispatching renewables, using the communication systems that are already present. The physical and communication criticality is the same, the resilience capacity is instead very broad, as the flexibility model towards the TSO changes.

With regard to the new emerging communication systems, it becomes strategic to adopt new innovative business models such as SaaS or HaaS (Hardware-as-a-Service) which increase the scalability and security of the systems in the field, increasing the security of the data network.

The real challenge is to virtualize the physical network, to make it resilient and adapted, optimizing the demand that becomes a system that absorbs both renewables and all anomalies, avoiding always using production plants that have a significant impact on energu transitions using the same physical sistems. I'm avaible for any information or comment. Thanks. 

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