Towards a Distributed,
Service-Oriented Control
Infrastructure for Smart Grid
Muhammad Umer Tariq , Santiago Grijalva, Marilyn Wolf
1
Department of Electrical and Computer Engineering,
Georgia Institute of Technology, Atlanta, USA.
ASU - Cyber Physical Systems Lab
Professor G. Fainekos
Presenter: Ramtin Raji Kermani
Some Background …

What is Smart Grid?
◦ A smart grid is a digitally enabled electrical
grid that gathers, distributes, and acts on
information about the behavior of all
participants (suppliers and consumers) in
order to improve the efficiency, importance,
reliability, economics, and sustainability of
electricity services. (Wikipedia)
Abstract
What is missing?
 Smart Grid initiatives aim to overlay the
existing power grid infrastructure with a
communication and computation
infrastructure to enable integration of
renewable resources and increased
efficiency and reliability of the electric
power grid.

Abstract
Smart Grid
Initiative
Overlay



Electric
Power Grid
+
Communication
Infrastructure
Computation
Infrastructure
Enable integration of renewable energy
Increased Efficiency
Reliability
How?
- Substantial changes to Centralized Control Infrastructure
- More Powerful Comm. and Comp. Infrastructure
In This Paper …

We describe:
The elements required for implementation of a “Prosumer”
based distributed control architecture for smart grid.

We propose:

A Hard Real Time enabled, Web Services based
1- Computing infrastructure
2- Distributed Control infrastructure
For Development
For Operation
In This Paper …

Main components of this computing infrastructure:
◦
◦
◦
◦

Generic Real Time API (Generic RT-API)
A Rule based Configurable API Translator
A Hard Real Time Web Services (HRT-WS) Engine
The ability to receive XML based configuration/control instructions
remotely.
We have used LXRT/RTAI based Hard Real Time Linux
environment.
In This Paper …
Keywords:
 Smart Grid,
 Cyber Physical Systems,
 Power System Informatics
 Hard Real-Time Systems
 Web Services
 Model Transformation

Introduction

Energy Management Systems (EMS)
Traditionally, the control of the bulk electric power grid has been
realized by Energy Management Systems (EMS) that allow
control devices located at substations, generation units and loads
to achieve safe and economical operation of the grid.

Main components of these EMS systems:
◦
◦
◦
◦
◦

Supervisory Control and Data Acquisition (SCADA)
Computing Resources,
User Interface
Network Economic
Security Application Software
Distributed management System (DMS)
◦ Increased automation level compared to EMS
Introduction

EMS and DMS  Based on Centralized Control

Limitations of Centralized Control:
◦
◦
◦
◦
◦

Too much data is needed for operation
Communication bottlenecks
Intractable control & optimization problems
Exponential growth of EMS and DMS complexity
Vulnerability of energy control centers to security attacks
Also EMS/DMS:
◦ Bad software stack
◦ Introduction of More software for power applications 
◦ Suitable software engineering techniques.
◦  Ensure
the fulfillment of mission critical requirements
Introduction

Transition towards Distributed Control Architecture Needs:
Appropriate Computing and Control infrastructure

Using Web Services and Service Oriented Architecture?
smart grid applications would require enhancements to the
current Web Services infrastructure, such as support for
Timeliness
Proposed Computing Structure in this paper:
- Development Support
- Run-time Support
Introduction

Contributions of this paper:
◦ A Futuristic distributed control architecture for
Smart Grid built on the concept of “Prosumer”.
◦ A Hard Real Time-enabled, Web Services based
Computing Infrastructure required to implement
our proposed distributed control architecture
◦ Implementation steps towards a lab prototype of
the proposed computing infrastructure
Related Work

Smart grid Architecture
◦ Some works support just one type of apps
◦ Some don’t support a generic infrastructure
◦ The Smart Grid initiative of the National Institute
of Standards and Technology (NIST) is developing
standards to ensure interoperability of the smart
grid components
Related Work

Our Architecture represents an abstraction
layer which hides the NIST Conceptual
Reference Model and GWAC interoperability
stack from smart grid applications and
presents them with an interface which is
more suitable to realizing flexible and reliable
smart grid applications
Related Work
NIST Conceptual Reference Model
Related Work

Service Oriented Architecture
◦ Web Services has emerged as the most popular technology
for developing SOA based solutions
◦ SOA is not just a set of standards. It is a design philosophy
which aims at developing systems that are loosely coupled,
flexible, reusable and adaptable. Our proposed infrastructure
moves the concepts of Web Services and SOA beyond the
enterprise domain and applies them to the development of a
smart grid control infrastructure.
Concept of “Prosumer”

Prosumer: Producer + Consumer
◦ Prosumer may consist of a combination of: a)
components: energy sources, loads, and storage, b)
an electric grid, c) a grid control system, and d) a
market
◦ Any electric system can be represented as a prosumer, and all
the power system control interactions can be modelled as
interactions between prosumers
Proposed Control Architecture

Architecture level
◦ 4 (hierarchical) Control Layers
◦ Interactions are defined based on interfaces
Proposed Control Architecture

Prosumer Services and Interfaces
◦ We use the concept of Prosumer and its associated control
layers to propose a Web Services-based SOA control
infrastructure for smart grid by standardizing a set of entitycentric web services.
Proposed Control Architecture
Proposed Control Architecture
The details of power system quantities like
voltage, active power and reactive power are
contained inside the messages used in the
service definitions above.
 For example, giveRealTimeStatusMsg of LCTRL
service would include all the real time status
values that are of interest to a service trying
to control that particular device. Moreover,
we propose to modify the traditional WSDL
definition to associate a timing constraint
with each of the operation

Proposed Control Architecture
 Case
Study:
Residential Demand Response
◦ Utility  U_PRSMR
◦ Neighbourhood  N_PRSMR
◦ Home  H_PRSMR
How it works?
Proposed Control Architecture
-
Advantages:
-
Autonomous Local Control
Smaller Optimization Problems
Automated Configuration and Operation
Reliability and Certification
Flexibility for Market Innovations
IMPLEMENTATION REQUIREMENTS OF PROPOSED
DISTRIBUTED CONTROL ARCHITECTURE
◦ Location of Computing resources
 LCRTL  Devices
◦
◦
◦
◦
Prioritizing Applications
Supporting different data rates
Timelines
Model driven Development and operation
Proposed computing infrastructure
◦ Hardware
 Heterogeneous hardware (Servers to embedded)
◦ Operating System
 RTAI (Real-time Application Interface) (Kernel)
 LXRT (Linux Real-time) (User Space)
◦ Generic RT API and API translator
 Generic Real-time API
 Rule-based Configurable API translator
◦ Middleware
 Hard Real-Time Web Services (HRT-WS) Engine
Proposed computing infrastructure
Program Structure using the Generic RT-API

Once we have a program in terms of our Generic RT-API, we can
translate it to a particular RTOS platform using an API Translator
Proposed computing infrastructure
Proposed computing infrastructure
Proposed computing infrastructure
Steps towards Implementation

We propose to use model driven tools to transform model
based simulations into actual machine readable configurations for
the individual elements of computing platform proposed here, in
an automated and reliable manner.

Analogy:
Simulink model for Automotive industry  Executable Code generation
Steps towards Implementation
Conclusion and Future Work

The proposed hard real time enabled, Web Services
based automatically configurable computing
infrastructure can support and accelerate the
application development for the Prosumer- driven,
distributed smart grid control architecture.

The scope of the paper is at the architecture level
which will open multiple opportunities for research in
future.
How this paper could be useful in our Lab?
More Questions?