Carleton University IoT presentation

Smart Homes in the Smart Grid

Thomas Kunz

Professor, Systems and Computer Engineering

The Many Aspects of “Smart Grid”

Energy Management in Smart Homes

Whole system involves three networks1. Utilities communicate ToD pricing info: here RBDS (one-way broadcast network)2. Appliances and Smart Controller communicate over in-home networks:

• HomePlug C&C: powerline in-home network• ZigBee: wireless in-home network

Research Challenges/Issues

, How to optimize energy consumption: home controller (centralized) or smart appliances (distributed) need to perform optimizations– Having residential users operate appliances manually will be less promising– Complexity of optimization problem?

, Appliances and home controller need to communicate– Network alternatives– Improving existing network protocols

, Utilities broadcast ToD price info– Subject to impersonation attacks– Can we authenticate messages in a one-way broadcast network?

, Explore impact of design alternatives– For example: what impact will EVs have on residential energy consumption– Exploring new coordination mechanisms

Optimizing Energy Consumption

Optimizing Residential Energy Usage

• Goal: wide-spread participation of users to reduce peak power consumptions and balance load

• The potential for profit and the cost saving features of smart grids are excellent motivating factors• Needs automation to be really convenient

• In smart grid – the user is considered as a ‘Prosumer’ because• The user produces energy (renewables, selling via microgrid, etc.)• The user consumes energy (appliances, buying from microgrid, etc.)

Optimization Inputs: Energy Consumption

, Energy consuming components:

– Inelastic load cannot be delayed.

– Elastic load can be delayed and its quantity depends on price of electricity.

– The storage can be considered as an elastic load.

– Selling energy to microgrid can be considered as load.

Demand

Microgrid

<[e1,e2,...,et]>

Storage

<[e1,e2,...,et]>

Elastic Load

<[e1,e2,...,et]>

Inelastic Load

<[e1,e2,...,et]>

Optimization Inputs: Energy Sources

, Energy sources:

– Utility is considered to have infinite supply and dynamic price.

– Storage provides time varying supply.

– Microgrid has different energy quantity with different price.

– Renewables have different generation profile, price is considered as 0.

Supply

Utility

<[∞, ∞,... ∞],[p1,p2,....,pt]>

Storage

<[e1,e2,...,et]>

Microgrid

<[e1,e2,...,et],[p1,p2,...,pt]>

Renewables

<[e1,e2,...,et]>

et =Energypt=Price

Unified Optimization Model Problems

, Both storage and microgrid can act as both load and energy source.

, This reciprocal relationship makes it more complex to formulate an optimization problem

, Unified Optimization: solve many issues at the same time: load scheduling, trading in the microgrid (both amount and price), storage charging, ….

– Optimization problem not linear

– Multiple households: multiple objective functions, pareto-optimal solutions

– Solution time grows rapidly with number of households, planning horizon, number of appliances, etc.

Proposed Optimization Model

, Determine the user’s energy consumption and generation characteristics – Module 1: considers renewables and

storage.

, Buying components – Module 2: Considers utility, microgrid

(buyer) and storage.

, Selling Components– Module 3: Considers microgrid

(seller) and storage.

, Solve iteratively

The Modular Optimization Model

Home Networking

Home Networking

, Many choices: Wireless, Powerline, Wired….

, If mixed networking, which network protocols

, Developed/modified existing network simulator (NS2) to support multiple interfaces/networking technologies, explored alternative routing protocols:– Flooding– AODV/ZigBee routing

, Joint-path strategy, Backbone-based path strategy (packet forwarded firstly through the

backbone), Dual-path strategy (wireless path strategy plus backbone-based path

strategy)

Summary of Routing Insights

, Combined network performance better than using a single network (powerline or wireless)

, Flooding best network layer strategy when communicating information to ALL devices in the home

, To communicate with a specific device, dual-path and backbone-based routing superior to joint-path routing in terms of PDR– Dual-path: lowest latency– Backbone-based routing: lowest energy costs

Security

Security Challenges

, ToD messages broadcast in one-way RDBS network – what happens if intruder broadcasts fake messages– For example: broadcast low price during heat wave => AC units will kick in => grid

load rises, potentially leading to overload

, Network Security:– Confidentiality not important– Source authentication crucial

, Common solutions not applicable in the absence of two-way communication– Certificates: complex verification algorithm, need occasional access to

certificate authorities– Challenge-Response: less computationally complex, based on shared key,

requires bi-directional communication

One-way Authentication Protocol Evaluations

Security NOT only a protocol issue: on-air monitors to monitorfor bogus messages, outlier detection to detect obviously faultyinformation

Simulation Framework

Evaluating Policy Alternatives: A User-Centered Simulation Framework

Simulator Validation

Sample Study: Charging EVs over Night based on Threshold Price

Conclusion

, Lots of specific challenges, some solutions, typically we need to write papers to talk about them

, Hard to do a complete “system” when in university– Real smart home data– Actual smart devices/appliances

, Was offered an electric hot water tank once, not sure where to put it….

, Sometimes problems are those that we think are important, but may not be the most pressing issues in the real world

, Collaborations with industry helpful, various ways to do this and get funding for it– MITACS, NSERC Engage, ……