ict-enabled behavioural change in smart cities
DESCRIPTION
Siobhan Clarke (Trinity College Dublin) ICT-Enabled Behavioural Change in Smart Cities Abstract:Limited resources in urban environments, such as road networks, energy and water, are under increasing strain as a result of population growth. However, such resources could be managed in a better way through behavioural change. This presentation explores ICT-enabled autonomous behavioural change as a means to ameliorate sustainability issues in future cities. In particular, the focus is on multi-agent systems applied to energy demand-side management and to traffic congestion. Bio:Prof. Siobhán Clarke is a Professor in the School of Computer Science and Statistics at Trinity College Dublin. She joined Trinity in 2000, having previously worked for over ten years as a software engineer for IBM. Her current research focus is on software engineering models for the provision of smart and dynamic software services to urban stakeholders, addressing research challenges in the engineering of dynamic software in ad hoc, mobile environments. She is the founding Director of Future Cities, the Trinity Centre for Smart and Sustainable Cities, which includes contributors from a wide range of disciplines, including Computer Science, Statistics, Engineering, Social Science, Geography, Law, Business and the Health Sciences. She leads the School’s Distributed Systems Group, and was elected Fellow of Trinity College Dublin in 2006.TRANSCRIPT
Future Cities: Trinity Smart and Sustainable Cities Research Centre
Prof. Siobhán Clarke
Director
1
Trinity’s Future Cities Centre launched in July 2013
www.tcd.ie/futurecities/
Energy Demand-Side Management
3
Demand-Side Management
• Energy usage not distributed evenly during the day
• Morning peak, large evening peak, valley during the night
• Demand side management (DSM): modification of the consumers' electricity consumption with respect to their expected consumption
• DSM techniques – peak clipping, valley filling, load shifting …
• Based on prediction – influence consumers to defer loads that are not essential during the peaks and run them during low demand periods instead
• Can influence use of renewable too – defer loads during the periods of low availability etc
4
Demand-Side Management
5
Centralised: Evolutionary Algorithms in EV Scenario A schematic view of the problem used in this work with two main goals: • (a) that each EV is as
fully charged as possible at time of departure
• (b) reduce transformer load by avoiding charging more than one EV at a particular time, whenever possible.
6
Centralised: Evolutionary Algorithms in EV Scenario
Evolutionary Algorithms are search methods that take their inspiration from natural selection and survival of the fittest in the biological world.
1: Randomly create an initial population of individuals (a.k.a. candidate solutions). 2: repeat 3: Execute each individual and ascertain its fitness. 4: Select one or two individuals from the population with a probability based on fitness to participate in genetic operations (e.g., mutation, crossover, …). 5: Create new individuals by applying genetic operations with specified probabilities. 6: until an acceptable solution is found or some other stopping condition is met (e.g., a maximum number of generations is reached). 7: return the best-so-far individual.
7
Centralised: Evolutionary Algorithms in EV Scenario
Transformer load, averaged for 28 days, for 9 EVs with different initial state of charge (SoC).
All 9 EVs charged between 18:00 and 07:30. • Red squares shows the transformer average load for the greedy approach
(i.e., EVs start charging as soon as they reach home) • Blue circles show the average load using our proposed approach.
8
Centralised: Set Point Control Approach • Two types of demand:
base load
flexible load (e.g., electric vehicles, water heaters)
• Two set point algorithms to control
flexible load
Variable charging rate
Uses an EV charger that can vary its power (0-100%)
The transformer broadcasts the charging rate (0-100%) that each of the available EVs should charge at.
The feedback is the measured aggregate power demand at the transformer.
Variable connection rate
Uses a much simpler on-off type of charger
The transformer broadcasts the connection rate (0-100% probability) that each of the available EVs should attempt to connect at.
The feedback is the measured aggregate power demand at the transformer.
9
Centralised: Set Point Control Approach
0
100
200
300
400
500
600
700
800
0
20000
40000
60000
80000
100000
120000
140000
0:0
0
6:0
0
12
:00
1
8:0
0
0:0
0
6:0
0
12
:00
1
8:0
0
Vo
lt A
mp
s
Variable connection rate - set point 100 kVA
Total Load
Base Load
Connection Rate
0 100 200 300 400 500 600 700 800
0
20000
40000
60000
80000
100000
120000
140000
0:0
0
6:5
2
13
:44
20
:36
3:2
8
10
:20
17
:12
Vo
lt A
mp
s
Variable connection rate - set point 75 kVA
Total Load
Base Load
Connection Rate
0
200
400
600
800
0
20000
40000
60000
80000
100000
120000
0:0
0
9:3
6
19
:12
4:4
8
14
:24
Vo
lt A
mp
s
Variable charging rate - set point 100kVA
Total Load
Base Load
Charge Rate
0
100
200
300
400
500
600
700
800
0
20000
40000
60000
80000
100000
120000
0:0
0
4:4
8
9:3
6
14
:24
19
:12
0:0
0
4:4
8
9:3
6
14
:24
19
:12
Vo
lt A
mp
s
Variable charging rate - set point 75 kVA
Total Load
Base Load
Charge Rate
10
Centralised: Set Point Control Approach
• Shaping demand
• Enriched with urgency protocol with inherent backoff to ensure user utility
When the time to charge gets close to the time left (< 10 minutes difference), the device agent changes to the urgent state and starts to charge fully at each time step
• Set point control can be used to determine the bounds on transformer – if they are set too low utility might not be met and spikes occur
0
200
400
600
800
0
20000
40000
60000
80000
100000
120000
140000
0:0
0
8:0
0
16
:00
0:0
0
8:0
0
16
:00
0:0
0
8:0
0
16
:00
Vo
lt A
mp
s
100-80-60-80-100kVA control schedule
Total Load
Base Load
Control
Connection Rate 0
200
400
600
800
0
20000
40000
60000
80000
100000
120000
140000
0:0
0
8:0
0
16
:00
0:0
0
8:0
0
16
:00
0:0
0
8:0
0
16
:00
Vo
lt A
mp
s
Constant 75kVA
Total Load
Base Load
Control
Connection Rate
11
Decentralised approaches
12
Decentralised: Enhancements to demand side management in residential communities
• Target: Avoid high peak usage of electric appliances
• Short term load forecasting:
Estimate a residential community’s power demand ahead of time (day ahead, week ahead) based on historical load and weather information
Tackle issues of small scale (i.e. unpredictable human factor)
Detect anomalous changes from expected demand; match anomalies with previously encountered information and attempt re-prediction
13
Decentralised: Enhancements to demand side management in residential communities
• Normal days prediction:
Combines several techniques with various advantages (ANN, WNN, ARIMA, NF)
Focus on a community of 230 houses (half-hourly recorded demand from the CER smart-meter trial)
Uses historical weather information from Dublin airport station
Achieves 2.39% NRMSE (evaluation over 20 consec. days)
Monday Tuesday Wednesday Thursday Friday
14
Decentralised: Enhancements to demand side management in residential communities
• Anomalous days prediction: Detects anomalous
pattern changes in day demand as it progresses
Matches anomaly type from previously classified days and triggers re-prediction based on them
3.63% prediction error, 65% detection rate at 2:30pm
15
Decentralised: Multi-agent residential demand side management based on load forecasting
• Implement the grid as a multi-agent system - each EV is controlled by an RL-agent which implements 3 policies:
Policy 1: achieve at least the minimum required battery charge
Policy 2: charge at the minimum possible price/during the lowest load
Policy 3: keep under set transformer limits
• Agents given
Current load/ current price only
Predicted load/ predicted price too
16
Decentralised: Multi-agent residential demand side management based on load forecasting
• Uses reinforcement learning
• Distributed W-Learning (DWL) Multiple policies on each agents
Multiple agents collaborating
Learn dependencies between neighbouring agents
• Each agent learns how its actions affect its neighbours
17
Decentralised: Multi-agent residential demand side management based on load forecasting
• Simulations performed with 9 households (with EV agent + base load each)
• The required daily mileage differs ranges from 50 miles (requiring about 35% of full battery charge) to 80 miles (requiring about 50% of full battery charge). Charging decisions made every 15 minutes
• Base load taken from the data recorded in smart meter trial performed in Ireland in 2009-2010.
18
Decentralised: Parallel Transfer Learning • There are repeated patterns in smart grid
• Both over time and across the grid
• Dynamism in the grid can require relearning
• While learning performance tends to be bad
• Transfer Learning accelerates learning by providing initial information.
• Requires converged information prior to execution
• Information must be relevant to target task to accelerate learning
• Cannot capture the dynamism of policy relationships
19
Decentralised: Parallel Transfer Learning • Parallel Transfer Learning is an on-line version of Transfer
Learning Source and target tasks learn simultaneously, sharing information
whenever they deem it necessary
• This allows the relatedness of tasks to be exploited
• Multiple transfers allow dynamicity of inter-policy relationships to be shared
Transfer Learning Parallel Transfer Learning
20
Decentralised: Parallel Transfer Learning
• Using PTL reduces learning time to 1/3 of previous
21
Energy DSM Papers to date Conferences • E. Galvan-Lopez, A. Taylor, S. Clarke, and V. Cahill. Design of an Automatic Demand-Side Management System
Based on Evolutionary Algorithms . Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC '14, Gyeongju, Korea, March 24 - 28, 2014. ACM
• C. Harris, I. Dusparic, E. Galvan-Lopez, A. Marinescu, V. Cahill, and S. Clarke. Set Point Control for Charging of Electric Vehicles on the Distribution Network. . In Innovative Smart Grid Technologies (ISGT). IEEE PES, 2014.
• A. Marinescu, I. Dusparic, C. Harris, S. Clarke, and V. Cahill. A hybrid approach to very small scale electrical demand forecasting. In Innovative Smart Grid Technologies (ISGT). IEEE PES, 2014.
• Ivana Dusparic, Colin Harris, Andrei Marinescu, Vinny Cahill, and Siobhan Clarke. Multi-agent residential demand response based on load forecasting. In Technologies for Sustainability (SusTech), 1st IEEE Conference on, pages 90-96. IEEE, 2013
• E. Galvan-Lopez, C. Harris, I. Dusparic, S. Clarke, and V. Cahill. Reducing Electricity Costs in a Dynamic Pricing Environment. IEEE SmartGridComm, pages 169 - 174, Tainan City, Taiwan, 5 - 8 November, 2012, IEEE Press.
• C. Harris, R. Doolan, I. Dusparic, A. Marinescu, V. Cahill, and S. Clarke. A Distributed Agent Based Mechanism for Shaping of Aggregate, ENERGYCON 2014
• E. Galvan-Lopez, C. Harris, L. Trujillo, K. Rodriguez, S. Clarke and V. Cahill. Autonomous Demand-Side Management System Based on Monte Carlo Tree Search, ENERCYCON 2014
• A. Marinescu, I. Dusparic, C. Harris, S. Clarke, and V. Cahill. A Dynamic Forecasting Method for Small Scale Residential Electrical Demand, International Joint Conference on Neural Networks (IJCNN), IEEE, 2014
• A. Taylor, I. Dusparic, E. Galvan-Lopez, S. Clarke, and V. Cahill. Accelerating Learning in Multi-Objective Systems through Transfer Learning, World Congress on Computational Intelligence (WCCI), IEEE, 2014
Workshops • A. Taylor, I. Dusparic, E. Galvan-Lopez, S. Clarke, and V. Cahill. Transfer Learning in Multi-Agent Systems Through
Parallel Transfer. The 30th International Conference on Machine Learning, Atlanta, USA, 16 - 21, 2013. • A. Marinescu, C. Harris, I. Dusparic, S. Clarke, and V. Cahill. Residential electrical demand forecasting in very small
scale: An evaluation of forecasting methods. In Software Engineering Challenges for the Smart Grid, 2nd International Workshop on (SE4SG). IEEE, 2013
• A. Taylor, E. Galvan-Lopez, S. Clarke, and V. Cahill. Management and Control of Energy Usage and Price using Participatory Sensing Data. Eleventh International Conference on Autonomous Agents and Multiagent Systems, pages 111-119, Valencia, Spain, 2012.
Thank you.
http://www.tcd.ie/futurecities/
Prof. Mélanie Bouroche
Trinity College Dublin