ENGN8831
Smart Grids 2

Paul Scott

Outline

Smart Grids 1

  • Future Challenges
  • Smart Grids
  • Technologies
  • Applications

Smart Grids 2

  • Prosumers
  • Incentives
  • Residential EMSs
  • Coordination

Prosumer

A flexible customer who has can both produce and consume power

Flexibility through Distributed Energy Resources (DERs):

  • Distributed Generation
  • Battery Storage
  • Electric Vehicles
  • Smart Appliances
  • Controllable Loads

Can trade power and provide network support services

Prosumer Types

  • Homes
  • Industry
  • Office buildings
  • Electric vehicles

We focus on residential prosumers:

  • Most numerous type of consumer
  • Around 25% of total network load
  • Have most interesting behaviour

Prosumer Activities

  • Communicate with network
  • Buy and sell power
  • Balance supply and demand
  • Regulate voltage
  • Regulate frequency
  • Avoid overloading network

Prosumer Coordination

Coordinating the actions of many prosumers

  • Reduce network peaks
  • Balance renewable supply
  • Provide network support

Historic Incentives

  • Fixed tariffs don't accurately reflect the costs associated with network and generation
  • 5kW Air Con at peak times can cause $1000 a year in network costs
  • User might only pays for $300 of this
  • Similarly for PV, often get more benefit from self consumption than they offset in costs
  • Comes down to no incentive to reduce consumption from peak periods

AER. State of the energy market 2014. Technical report, Australian Energy Regulator, 2014.

New Incentives

  • Network utility
  • Retailer
  • Aggregator

may offer:

  • Support contracts
  • Time-varying pricing
  • A market

Support Contracts

Participants are offered contracts to provide services, either as requested or continuously

Utility requests 1000 houses to drop consumption by 500W each for 30mins, in order to prevent upstream line rating violation

Utility enters into an agreement with owners to control the power factor of 15 inverters, for voltage regulation purposes at the end of a feeder

Time-Varying Pricing

Time-varying prices for the consumption of real/reactive power

  • Time-of-use pricing
  • Critical peak pricing
  • Real-time pricing

Might represent marginal prices of an underlying market

Retail Prices

Trading in a Market

Energy traded in a pool or directly with other agents

A trading agent offers Margaret's excess 2kWh late afternoon solar generation on the market for 40c. Sarah's agent accepts the offer as it is expecting the family EV to be home and in need of charging by this time.

Aggregators

Intermediate between wholesale market and its customers (could be retailer) who takes advantage of the purchasing strength and greater certainty of a collective

Bids directly into the wholesale market on behalf of customers. Rewards are shared between customers. The aggregator still needs a method of coordinating its customers to get the desired effect.

Flexible Prosumers

Smart Appliances

Existing smart appliances offer:

  • Wi-Fi connectivity
  • Smart phone apps
  • Notifications

Further offerings will develop along with the IoT

Energy Management System

Device Scheduling

For the price incentives, the EMS decides on behalf of occupants how to schedule devices to minimise costs.

Device Scheduling

Occupant Constraints

Occupants have hard constraints about the operation of devices. They can also have softer preferences, which are modelled as "comfort" costs.

Specified explicitly, or learnt by the EMS (e.g., Nest).

For example, an occupant is happy with an ambient temperature between 20-22°C from 5pm-11pm. Outside this temperature range they experience a "comfort" cost of 40c/°C/hr. They have a hard constraint that the temperature must not stray outside the 18-26°C range.

Considerations

  • Continuous or discrete time
  • Reactive or proactive control
  • Policy based or online

Receding Horizon Control

Receding Horizon Control

Receding Horizon Control

Optimisation Problem

For one horizon, assuming we have constant power devices, and ignoring the reactive component


  • \(i:\) device index
  • \(t:\) time index
  • \(p_{i,t}:\) powers
  • \(x_{i}:\) other variables
  • \(\psi_t:\) prices
  • \(f_i:\) comfort cost
  • \(g_i:\) constraint funcs

  • \(\min_{p_{i,t},x_i} \sum_t \psi_t \sum_i p_{i,t}\)
    \(+ \sum_i f_i(p_i,x_i)\)
  • \(g_i(p_i,x_i) \leq 0\)
  • \(\underline{p} \leq \sum_i p_{i,t} \leq \bar{p}\)

Appliance Modelling

  • Battery
  • Washing machine

Battery

Linear approximation of battery

Parameters

  • \(\eta:\) efficiency
  • \(\bar{p}:\) max power
  • \(\bar{E}:\) capacity

Variables

  • \(p_t^c:\) charge power
  • \(p_t^d:\) discharge power
  • \(E_t:\) stored energy

Constraints

  • \(p_t^c \in [0,\bar{p}]\)
  • \(p_t^d \in [0,\bar{p}]\)
  • \(E_t \in [0,\bar{E}]\)
  • \(E_t = E_{t-1} + t^{stp}(\eta p_t^c - p_t^d)\)

Washing Machine

Constant power shiftable load

Parameters

  • \(t^e:\) earliest time
  • \(t^l:\) latest time
  • \(d:\) duration
  • \(p^{nom}:\) nom power

Variables

  • \(u_t:\) run
  • \(p_t:\) power

Constraints

  • \(u_t = \{0,1\}\)
  • \(p_t = p^{nom} \sum_{t'=t-d+1}^t u_{t'}\)
  • \(\sum_{t=t^e}^{t^l} u_t = 1\)

EMS Example

  • Simple implementation of an EMS that controls a battery
  • Optimise battery operation over a week (no uncertainty)
  • Costs account for approximate purchase + installation

EMS Example

Applications

  • Optimal solar and battery sizing
  • Adoption incentives for different DERs
  • Develop/compare control strategies
  • Develop/compare pricing and tariffs

The Battery Proposition

House+ PV+ Bat
ACT$18.8$7.7$12.3
ACT -- TOU$18.9$7.2$11.9
NSW$29.5$17.4$19.9
NSW -- TOU$31.3$17.1$19.3
  • 3 kW PV and LG Chem RESU 3.3 LV Battery
  • Prices are for one week, not representative of year

Prosumer Coordination

Demand Response

  • Special contracts with large loads
  • Network operator sends request to defer load
  • We want to extend this to small loads/prosumers

Traditional Grid

Relatively few remote generators, inflexible loads and centralised control

Smart Grid

Many distributed generators, flexible prosumers and decentralised control

Comparison

Comparison

Comparison

Comparison

Reduce Wholesale Prices

Herding

Considerations

  • Quality
  • Scalability
  • Reliability
  • Communications
  • Equipment costs
  • Ease of use
  • Privacy
  • Greed

Global Objective

Before designing an approach to coordinating prosumers we need to agree what our goal should be as a benevolent network operator

  • Maximise generator profits?
  • Minimise consumer costs?
  • Equal outcome for all (egalitarian)?
  • Best combined utility (utilitarian)?

Network-Aware Coordination

A distributed iterative marginal-pricing market approach

  • Near optimal (utilitarian)
  • Preserves privacy
  • Models network constraints
  • Prices at each bus
  • Fast enough for RHC

Model

  • \(C:\) set of components (houses, gens, lines, buses)
  • \(T:\) set of terminals
  • \(L:\) set of connections between terminals

Model

  • \(y_i = [p,q,v,\theta]^\mathsf{T}:\) variables for terminal \(i\)
  • \(x_c:\) variables for component \(c\) (inc. terminal vars)

Optimisation Problem

\begin{align} &\min_x\sum_{c \in C} f_c(x_c)\quad\color{red}{\text{costs}}\\ &\text{ s.t. } \forall c \in C: g_c(x_c) \leq 0\quad\color{red}{\text{constraints}}\\ &\phantom{\text{ s.t. }} \forall (i, j) \in L: h(y_i, y_j) = 0\quad\color{red}{\text{connections}} \end{align}


where \(h(y, y') := y + Ay'\)

ADMM

Iterative algorithm that allows the problem to be decomposed across linear constraints. Proven to converge when the problem is convex, and linear constraints take on particular form.

Split Into Two

House/Generator Payments

\(\lambda_pp + \lambda_qq\)

Visualisation of Algorithm

Visualisation of Algorithm

Visualisation of Algorithm

Visualisation of Algorithm

Visualisation of Algorithm

Visualisation of Algorithm

Visualisation of Algorithm

CONSORT: Bruny Island

ARENA funding to deploy up to 40 batteries in homes on Bruny Island and reward owners for supporting the network

  • ANU
  • Reposit Power
  • TasNetworks
  • USyd
  • UTAS

CONSORT: Bruny Island

Demo

Convergence