electric vehicles vs. (micro) smartgrids school 2013... · mini cooper mini e (v2g) ... component...

Competence Center Agent Core Technologies

DATA SIM Summer School 2013

Electric Vehicles vs. (Micro) SmartGrids

Marco Lützenberger

16. Juli 2013

Agenda

2 16. Juli 2013

►Why are Electric Vehicles important for us (as a researcher)?

►Part One: The Driver of an Electric Vehicle

A user-centric approach

Demo 1

►Part Two: Electric Vehicle Fleets

A provider-centric approach

Demo 2

The Electric Vehicle

3 16. Juli 2013

►the (electric) vehicle

►regular vs. electric

► Interesting:

the (flaw of the) battery makes the difference

Optimise range and charging intervals

the features of the battery

Utilisation of renewable energy, grid load balancing, minimising emissions

►different stakeholders

Mini Cooper Mini E (V2G)

Power 90 kW/122HP 150 kW/204HP

Torque 160 Nm 220 Nm

Weigth 1090 kg 1.465kg

Acceleration 9.1 s 8.5 s

Maximum Speed 203 km/h 152 km/h

Range 740 km 250 km

Battery 40 l 35 kWh

Charging 1-2 min 2.4 h (230V, 50A)

3.8 h (230V, 32A)

10.1h (230V,12A) source: www.mini.de

Stakeholders and their interest in EVs

4 16. Juli 2013

►„Common“ stakeholders

The driver

The vehicle manufacturer

►„Uncommon“ stakeholders

The battery manufacturer

The charging station operator

The energy provider

The government

►Conflicting interests!

Driver Battery/Vehicle Charging station Energy provider Government

mobility lifetime money grid safety image

money service availability exploiting renewable

energy

reducing emissions

image reducing

emissions utilisation

regulatory energy

distribution

image

The researcher

5 16. Juli 2013

►The challenge: different stakeholders, different interests

►The task: bring them together

►The aim: maximise the stakeholders profit

Driver

Battery/Vehicle

Manufacturer

Charging Station

Operator

Energy Provider

Government

Developing a Solution - Constraints

6 16. Juli 2013

►Driver

scheduled and unscheduled appointments

►Vehicle/Battery manufacturer

battery characteristics: capacity, charging profile, feeding profile, CO2 fingerprint

►Charging station operator

amount, characteristics, local grid infrastructure

►Energy provider

local and global grid infrastructure, prognoses, CO2 fingerprint

►Government

amount of vehicles, CO2 emissions

The Problem... In a nutshell

7 16. Juli 2013

►Different stakeholder

driver, vehicle/battery manufacturer, charging station operator, energy provider, (government)

►Different interests

mobility, CO2 efficiency, lifetime, service, utilisation of infrastructures, utilisation of wind energy, money, image, ...

►The aim: maximise the stakeholders profit

►Developing such system is…

Modelling

Implementation

Deployment

Monitoring

► logical distribution, autonomy, reactivity, proactivity, interaction

► { The | A } solution: The agent paradigm

►Consider the stakeholders as (software-) agents

difficult

Agentoriented Software Engineering

8 16. Juli 2013

► „The Agent“ as constituting concept

► What is the definition of an Agent? There is no (common) definition!

Wooldridge and Jennings (1995): […] the term agent is used to denote a hardware or (more usually) software-based computer system that enjoys the following properties:

Autonomy: agents operate without the direct intervention of humans or others, and have some kind of control over their actions and internal state

Social ability: agents interact with other agents (and possibly humans) via some kind of agent-communication languate

Reactivity: agents perceive their environment, (which may be the physical world, a user via a graphical user interface, a collection of other agents, the INTERNET, or perhaps all of these combined), and respond in a timely fashion to changes that occur in it;

Pro-activeness: agents do not simply act in response to their environment, they are able to exhibit goal-directed behaviour by taking the initiative.

► Why agents?

AOSE Methodologies, Documentation, Development Tools, Frameworks, Monitoring Tools

JADE, JACK, Jason, JASDL, Jadex, JIAC, 3APL, Cougaar, …

► Back to the problem: autonomy, reactivity, proactivity, interaction, logical distribution

The W2V2G System I - Design

9 16. Juli 2013

► User Agent(s)

Accesses mobility patterns (derived, upcoming)

► Car Agent(s)

Accesses vehicle, generates consumption profile

► Charging Station Agent(s)

Local grid management, infrastructure information

► Energy Provider Agent

Information about grid load and available wind energy

► System Functionality

Energy Management (W2V, V2G, Controlling)

► Additional functionality:

Route Planning

The W2V Algorithm

10 16. Juli 2013

►Backend Software

►Triggered by Vehicle Agent (VA) and User Agent (UA)

Calculated consumption (VA) < Minimum SOC (UA)

The W2V Trigger I

11 16. Juli 2013

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Expected SOC

Minimum

The W2V Trigger II

12 16. Juli 2013

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Expected SOC

Minimum

The W2V Algorithm

13 16. Juli 2013

►Backend Software (BS)

►Triggered by Vehicle Agent (VA) and User Agent (UA)

Calculated consumption (VA) < Minimum SOC (UA)

►Preceeding Time intervals are examined

Effect of charging time on energy progression (BS – UA – VA)

Grid state (BS – Energy Provider Agent (EA))

W2V - Filtering

14 16. Juli 2013

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Expected SOC

Minimum

The W2V Algorithm

15 16. Juli 2013

►Backend Software (BS)

►Triggered by Vehicle Agent (VA) and User Agent (UA)

Calculated consumption (VA) < Minimum SOC (UA)

►Preceeding time intervals are filtered

Effect of charging time on energy progression (BS – UA – VA)

Grid state (BS – Energy Provider Agent (EA))

►Remaining time intervals are assessed

Wind energy (BS – EA)

Local grid state (BS – Charging Station Agent)

W2V - Filtering

16 16. Juli 2013

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Expected SOC

Minimum

0

2

1 3 5 7 9 11 13 15 17 19 21 23

W2V - Charging

17 16. Juli 2013

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Expected SOC

Minimum

The W2V Algorithm

18 16. Juli 2013

►Backend Software (BS)

►Triggered by Vehicle Agent (VA) and User Agent (UA)

Calculated consumption (VA) < Minimum SOC (UA)

►Preceding (violation) time intervals are filtered

Effect of charging time on energy progression (BS – UA – VA)

Grid state (BS – Energy Provider Agent (EA))

►Remaining time intervals are assessed

Wind energy (BS – EA)

Local grid state (BS – Charging Station Agent (CA))

►Vehicle is mainly charging renewable energy

►Additional consumption serves for load peak grading

The V2G Algorithm

19 16. Juli 2013

►Backend Software

►Triggered by User Agent or by Vehicle Agent (BS – UA – VA)

Detected change in mobility pattern

►Potential time intervals are analysed (BS – EA – UA – CA)

Grid load expected wind energy (EA)

Quotient > 0.9 discarded

Availability (CA)

►Constraint check for identified feeding intervals (UA – VA)

SOC violation?

If not possible compensation by charging (W2V)

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Expected SOC

Minimum

W2V - Charging

20 16. Juli 2013

Original W2V Strategy

V2G Feeding Intervals

V2G Compensation

Results

21 16. Juli 2013

►Field test Evaluation

►No SOC violation

►A few grid violations (our fault)

►Mini Cooper S CO2 Emissions (estimated): 18.126 gram

►Mini E (user controlled charging): 4.283,53 gram

►Mini E (W2V2G application): 2364,57 gram

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

Mini E (W2V2G) Mini E (User Controlled) Mini Cooper S

Implementation Details

22 16. Juli 2013

►Java Intelligent Agent Componentware V (JIAC V)

►Framework with a focus on industrial applications/projects

►Reliability, robustness, scalability, modularity, reusability

►Merging agents and services

►Third party API integration

Java Intelligent Agent Componentware

23 16. Juli 2013

► features

reliable communication, extensibility, reuse, performance, monitoring, maintenance, documentation, comprehensive tool support, state-of-the-art concepts/paradigms

►project requirements

robustness, scalability, support for service management, monitoring, extensibility, SOA, Cloud, webservices, OSGi Bundles, ...

►extensibility by modular assembly tailored solutions

component based architecture (agent/node beans)

►state-of-the-art libraries and languages

Java, Spring, ActiveMQ, JMX, …

The JIAC V Framework – Libraries

24 16. Juli 2013

►Agent platform agent nodes (+node beans) agents agent beans

►Runtime deployment (Spring)

►Java based implementation

►agent interaction by (ActiveMQ)

service invocation (SOA), messages, custom protocols

►Knowledge

tuple-space based memory

►runtime monitoring (JMX)

JIAC Applications - Nodes

25 16. Juli 2013

►Default Nodes

JMX, Secured JMX, Service Directory, Registry

►Component Specification in Spring parent description

JMX capable parent node

Agent references

Agent description

JIAC Applications - Agents

26 16. Juli 2013

►Default Agents

Simple Agent, non-blocking agent, custom

►Component specification in Spring parent description

non-blocking parent agent

bean reference

JIAC Applications – (Agent) Beans

27 16. Juli 2013

►Specification in Spring

► Implementation in Java (extends AbstractMethodExposingBean)

fully qualified java name

Bean attribute

JIAC V – Agents

28 16. Juli 2013

►agent standard components

execution cycle, local memory, communication adaptors

►component based architecture

agent behaviours and capabilities in AgentBeans

► flexible activation schemes

regular, life cycle, observers, action methods

►AgentBeans and NodeBeans

►available AgentBeans (and NodeBeans)

communication, JADL++ interpreter, Drools rule engine, migration, persistence, load measurement and –balancing, user management, human agent interface, webserver, webservice gateway, OSGi gateway

Demo 1

29 16. Juli 2013

Lessons Learned

30 16. Juli 2013

► It worked!

►CO2 was decreased

►Agent technology supported:

Transparent distribution

Distributed development

Programming behaviour

►Not everything was good!

Communication

Planning performance

Lessons Learned

31 16. Juli 2013

►Stakeholder

Driver Battery/Vehicle Charging station Energy provider Government

mobility lifetime money grid safety image

money service availability exploiting renewable

energy

reducing emissions

image reducing

emissions utilisation

regulatory energy

distribution

image

Electric Vehicles and Micro SmartGrids

32 16. Juli 2013

►Wishlist:

Not one but many (electric) vehicles

Valid information on vehicle utilisation

Consuming AND producing infrastructure

Ability to (temporarily) store electric energy

► (Electric) car sharing + the Micro SmartGrid

►The vision

Use EVs and local storage to ‘buffer’ surpluses of energy

Make the grid autarkic

Area of application: Companies, car sharing enterprises

Test Site Setup

16. Juli 2013 ISGT 2012 33

▶ Real-life test system of ‚Micro Smart Grid‘

Photovoltaic 50 kWp

Wind Turbines 5 kWp

Hydrogen Fuel Cell 1 kWel, 1 kWth

Stirling Engine 1 kWel, 16 kWth

Grid Buffer Battery 140 - 160 kWh; 18 kW

13 Electric Vehicle charging stations with distinct specifications, mostly 16 A, 400 V

Single Point of Common Coupling at 630 kVA transformer

The (second) Problem

34 16. Juli 2013

►Factors:

Consumption

Production (wind and solar energy)

Vehicle utilisation

►The challenge:

Not one but many vehicles

Vehicles are REALLY required (time critical)

Minimise grid procurement

Maximise utilisation of renewable energy minimise C02 emissions

The Second Problem

35 16. Juli 2013

►W2V2G Approach

Inapplicable time critical environment

►Deterministic optimisation

Brute force?

Complex (NP-hard problem) time critical environment

►Stochastic optimisation

Possible! …but is it good?

Evolution strategy

Modelling – Formulate the problem

36 16. Juli 2013

►Consider charging schedules (the arrangement of charging and feeding processes) as ‘population’

►Populations are moduled as follows:

►A ‘process graph’ contains all energy consumption and generation processes

►These are modelled as activities (duration+energy demand)

►Activities are linked to ‘inventory resources’ (EVs or charging stations)

Constraints: maximum load, minimum capacity, ...

►Minimize the externally procured energy

Depending on a (dynamic) tariff

Optimsation Algoritm (Evolution Strategy)

37 16. Juli 2013

► (μ/ρ + λ) strategy

►Generate initial population of μ individuals

►Based on these μ ‘parents’, λ ‘offsprings’ are generated...

by recombining a random selection of ρ parents

and slightly altering ‘mutating’ the result

► Initial population is created by very simple scheduler (charge when vehicles return)

►Mutate by shifting individual or groups of activities to another place in the process plan

►Recombination difficult due to many dependencies

►Measure the quality and mutate, again

►Terminate when there is no increase in quality

Problems

38 16. Juli 2013

►Results are ok!

►Well know problem of stochastic approaches

Local optima

Algorithm gets ‚stuck ‘

►Solution: Not one but many problem solvers (agents)

Develop interaction protocol

Distribute agents on local (multi-core) machine

Optimisation protocol

39 16. Juli 2013

►Optimisation client

Proposes optimisation job

►Optimisation server

Accepts (or rejects) optimisation job

Performance

40 16. Juli 2013

►Quality versus populations

►Quality versus time

Conclusion

41 16. Juli 2013

►Agents can not only be used for physical distribution, but also for logical distribution

►Avoided well known problem of stochastic optimisation

►Exploited multi-core architecture

►No ‘real’ agency

autonomy? social ability? reactiveness? pro-activeness?

►recombination

Demonstration 2

42 16. Juli 2013