RuleML2015: Rule-based data transformations in electricity smart grids

Rule-based data transformations in Electricity Smart Grids

The 9th International Web Rule Symposium (RuleML) August 2-5, 2015

Freie Universität Berlin, Berlin, Germany

Rafael Santodomingo, Mathias Uslar – OFFIS Institute of Oldenburg, Germany J.A. Rodríguez-Mondéjar, M.A. Sanz-Bobi – Comillas Pontifical University of Madrid, Spain

[email protected]

• Business Case

• Technological Challenges

• Rule-based Solution

• Results

• Importance and Impact

Page 2

Contents

5 August 2015 The 9th International Web Rule Symposium (RuleML)

• Smart Grids

Page 3

Business Case


V

Energy Management System

Substation Automation System

Meter Data Management System

Increasing amount of information exchanges in the electricity sector

V

Bay Controller

Transformer Controller Bay

Controller Smart Meter Energy

Box

Aggregator Meter Concentrator

A

• Standards to the rescue…

Page 4

Business Case


V



Meter Data Management System

We still need cost-effective solutions performing data transformations

V

Bay Controller

Transformer Controller Bay

Controller Smart Meter Energy

Box

Aggregator Meter Concentrator

A

CIM

SCL

DLMS/ COSEM

Open ADR

• Focusing on CIM and SCL

Page 5

Technological Challenges


scl:tConnectivityNode

scl:tTerminal

scl:tConductingEquipment scl:type = “DIS”

scl:tConductingEquipment scl:type = “CBR”

scl:tBay

cim:Terminal

cim:ConnectivityNode

cim:Disconnector

cim:Breaker

cim:Bay

cim:BusbarSection

Mismatches hinder the transformations between the data models

• Solving mismatches with SWRL & JRL rules (I)

Page 6

Rule-based Solution


scl:tBay cim:Bay

scl:tBay(?x) → cim:Bay(?x)

cim:Bay(?x) → scl:tBay(?x)

scl:tConductingEquipment scl:type = “CBR”

cim:Breaker

scl:tConductingEquipment(?x) ∧ scl:type(?x, “CBR”) → cim:Breaker(?x)

cim:Breaker(?x) → scl:tConductingEquipment(?x) ∧ scl:type(?x, “CBR”)

Naming mismatches

Multilateral correspondences

• Solving mismatches with SWRL & JRL rules (II)

Page 7

Rule-based Solution


Covering mismatches

[(?x rdf:type scl:tBay)(?x scl:ConnectivityNode ?z) noValue(?x scl:ConductingEquipment)-> [(?y cim:Equipment.EquipmentContainer ?x) <- makeInstance(?x p cim:BusbarSection ?y) (?x rdf:type scl:tBay)]]

scl:ConnectivityNode

cim:Equipment. EquipmentContainer

scl:tConnectivityNode

scl:tBay

cim:Bay

cim:BusbarSection

cim:ConnectivityNode

• SWRL & JRL can be processed by freely available reasoners

Page 8

Rule-based Solution


Pellet

Jena Generic Rule Reasoner

A rdf:type scl:tBay

scl:tBay(?x) → cim:Bay(?x)

A rdf:type cim:Bay C rdf:type cim:BusbarSection C cim:Equipment.EquipmentContainer A

[(?x rdf:type scl:tBay)(?x scl:ConnectivityNode ?z) noValue(?x scl:ConductingEquipment)-> [(?y cim:Equipment.EquipmentContainer ?x) <- makeInstance(?x p cim:BusbarSection?y) (?x rdf:type scl:tBay)]]

SWRL rules JRL rules

SCL instances

CIM instances

• Case studies (I)

Page 9

Results




Rule-based converter

SWRL & JRL rules CIM

SCL

power system model

power system model

Power system model - 1

• Case studies (II)

Page 10

Results






SCL

power system model

power system model


• Case studies (III)

5 August 2015 The 9th International Web Rule Symposium (RuleML) Page 11

Results





SCL

power system model

power system model


• Key Performance Indicators (KPIs)

5 August 2015 The 9th International Web Rule Symposium (RuleML) Page 12

Results





SCL

power system model

power system model

Accuracy = 1 Runtime = 1.472s Cost = freely available reasoners

• Interoperability is a key enabler of future electricity Smart Grids – Need for solutions that facilitate interactions among systems based on heterogeneous

data models

• Traditional integration technologies in the electricity sector are costly and inefficient

– Manual processes and ad-hoc converters

• Leveraging rule languages (SWRL & JRL) to express correspondences between two data models –> benefits for electric companies:

– Bi-directional translations between smart grid systems performed by freely available reasoners

– Improves accuracy, performance, and cost indicators compared with traditional technologies

– Transformation rules and converters can be reused in different applications

Page 13

Importance and Impact


• Future challenges – Case studies with other standard data models – Adopting rule-based data transformations for run-time communications based on OPC

UA & Semantic Web Services – Enhancing ontology matching techniques to find transformation rules automatically

Page 14

Importance and Impact


Technology

RuleML2015: Rule-based data transformations in electricity smart grids