Good Morning. I am MB responsible of the Operations team in Digital Grid Software Solutions Italy. In my team we develop different kind of software solutions starting from SCADA systems up to energy management applications.
Today I will talk about Digitalization and how it may enable our customers to convert threats into opportunities.
Challenges like grid balancing, peak avoidance, grid resilience can be addressed by digitally enhanced electrification and automation systems and devices.
I will focus on real-time data acquisition and data analysis for energy management – more precisely on how digitalization is an enabler for creating innovative energy services toward efficiency and demand-response.
Let’s now talk about how to design an IT system that can provide such innovative services. In my opinion there are three key pillars:
Adopt an IOT paradigm, that means standard protocols and general-purpose data acquisition layer allowing integration of a wide range of systems and devices. Among the different raising standards I would like to highlight two of them: MQTT and AMQP. Just to make some concrete examples – the two main cloud PaaS providers Amazon and Microsoft they both have IOT layers based respectively on MQTT and AMQP.
Go on Cloud. I believe that going in the Cloud for such applications is a must. As consequence Software as a Service business model is most likely to be the natural choice. Here a micro-services architecture with independent small services guarantees the modularity, reliability, and scalability of the system.
Real Time Analytics. Data needs to be analyzed if we want value them. What we really need is real-time aggregation and data analysis if we need to manage energy flows. We are not talking about monitoring and reporting only but we are talking about services that requires real-time decisions for implementing efficiency or demand-response functionality.
Real-time data acquisition and data aggregation are enabler for energy efficiency advanced services; having:
Real-time measures
Load & Generation Profile/ Forecast
User Preferences / Environmental data
The EMS system can define rules to optimize energy consumption (minimize KWh) or to minimize energy costs (minimize €). Rule actions can be:
Load Control/Shifting
Storage Control
Generation dispatching
Comfort variables set points integrating Smart Buildings
While optimization logics in terms of consumption or cost are the objective of a single stakeholder the system can also put together the needs of different stakeholders by supporting demand-profile workflows. This will allow the DSO or the Energy Vendor to agree a specific demand profile with the user (or an aggregation of users) implementing active demand scenarios. Scenarios covering different objectives, for instance:
Grid emergency
Peak shaving
Electric mobility grid integration
Most of the concepts I just introduced have been tested on a real scenario such as Milano Expo 2015.
That was an unique opportunity to build a Smart City from green field:
1 million square meters area
75MW of planned power
145 countries present and 53 self-built pavilions
In addition there is full fiber optics backbone connecting everything and a wifi for the public but also for the services.
In this project Siemens is a strategic partner of Enel for the Smart Grid technology at EXPO Milano 2015.
In details @ Expo we had:
-100 Smart distribution substations
-200 Smart meters in the delivery substations and in the pavilions LV lines
-photovoltaic plants and energy storage
-30 Smart Buildings with more than 300 room automation devices for climate and lighting management and load control
-A public lighting system with more than 8 thousands lamps
-50 electric vehicles charging units
This slide represents the data flows implemented in the Expo project.
First of all we have data coming from the network SCADA. Power measures from each substation every 5 minutes.
Then we get data from each delivery – we have a GME meter on each MV/LV transformer – 15’ data via GSM.
Then we get data from the main LV lines after the main power panel with the Smart Info devices – 5’ data via WiFi.
Finally on specific loads we have multi-meter.
We integrate also temperature & lighting sensors for which we get data every 5’.
Here it is a reference architecture for an IOT based Energy Management System.
From the bottom we have the so –called technical systems such as:
Distribution network SCADA
Smart Meters or Meter Data Management Systems
Other systems that are typically in the Grid: public lighting, building management, electric mobility
All these systems still require a Local Control that means that they have automation functionalities that have to be executed locally. But all these systems will send real-time data to the EMS system – data that are currently available by these kind of systems but that are still kept in the control rooms.
Additionally we have devices, typically on the end-user side that can be also integrated in the EMS system.
On the cloud dedicated services take care of the data acquisition and data analysis exposing their functionality via a standard REST API interface.
The API can then be accessed by specific EMS web applications or by any other enterprise application.
Here the conclusion. With Smart Grid we added an IT/communication network to the Electric Grid, now that we are moving in the IOT era we are going to integrate connected Things to the Smart Grid systems.
In other words we are moving from Smart Grid to Digital Grid where innovative services will be reality soon.