www.london.edu

Community Aggregation - The value of Local Flexibility

Dr. Jesus Nieto MartinSenior Research Fellow

Lisbon, 28th June 2018

How electricity was delivered?

Smart Grid

Smart Grid Architectural Model

LOW CARBON

TECHNOLOGIES

LOW CARBON GENERATION

• Limited capacity

• Passive design / operation

• Centralised Generation

• Limited Visibility

• One-way power flow

• Load centric design

• Reduced headroom

• Increased Intelligence / Active Management

• Distributed Generation

• Need for increased visibility

• Two-way power flows

• Utilisation centric design

N E T W O R K V I S I B I L I T YN E T W O R K V I S I B I L I T Y

Why is it an evolving sector?

DISTRIBUTION SYSTEM OPERATOR

• Transition to a sustainable energy system

- Increase in intermittent generation

• Electrification of everything

- Growing electricity usage

- Electrification of transport and heat

• Distributed generation

- Small-scale generation in the distribution grids

Three Trends

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• Flexibility of demand should be exploited

- Demand and supply should both be used to balance

energy systems

• Aging networks operated to their limits

- Active distribution network management needed

• Control of large numbers of small units

- Too complex to use centralised top-down control

- Control paradigm shift needed

Three Challenges

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Our world is more complex and growing faster

than our control methods can handle

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Complex systems

• Highly interconnected

• Heterogeneous device-

human participation

• Extreme data

• Pervasive intelligence

• Increasing autonomy

The move from Big Data to Distributed Control involves addressing:

• Large numbers of sensing and/or control end points

• High complexity

• Node heterogeneity

• Multiple scales of operation

• Pervasive computing /

autonomous nodes

• Wide geographical scope

The solutions must be:

Deployable, scalable, robust, resilient, and adaptable

From Big Data to Distributed Control

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• Some grid objectives:

- Reduce peak loads (lowers new capacity investments, enhances asset utilization)

- Enhance efficiency of wholesale markets and production

- Reduce impacts of transmission congestion

- Provide ancillary services, ramping, & balancing (especially in light of renewables)

• Some end-user objectives:

- Reduce energy bills

- Maintain requirements for comfort and business

- Increase net benefits of distributed generation and storage investments

• Some societal objectives

- Mitigate impacts from disasters

- Reduce environmental impact

Negotiate Multiple Objectives

with Distributed Control

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Planning vs Operations – Data driven models

Planning and Network

Development

Power System

Optimisation

Operational

Planning

Real-Time Operation (Including

Emergency)

Data Analysis

Ex-Post

Time

Scales

Long Term (1-5 years)

Midterm (1 month - 1 year)

Short Term (Intraday - 1 week)

Real Time (After Market

Closure, Including Emergency)

Post

Actions

Viewpoints Market Player Grid Optimiser

The problem:

• Power production is shifting from centralize to more dispersed and distributed

deployments, and from entirely dispatchable forms to significantly intermittent

stochastic forms.

• Operating such a grid that powers economies with reliable and affordable electric

rates will require large amounts and new form of operational flexibility.

The opportunity:

• Provide this flexibility at reasonable cost whit distributed assets: continually

responsive loads, electrical &thermal storage. smart inverters, electric vehicle

chargers, etc.

• Transactive energy systems provide the control and coordination required to

actively engage customer-owned and third-party assets to provide this flexibility

through transparent, competitive means.

The problem and the opportunity

Transactive energy systems

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• Use market mechanisms to perform distributed optimization

- Reflect value in exchangeable terms (price)

- Effectively allocate available resources and services in real-time

- Provide incentive for investment on longer time horizon

• Use communications and automation of devices and systems as real-time

agents for market interaction

- Agents convey preferences and perform local control actions

- Engage in one or more markets to trade for services, e.g.,

Real-time energy, peak-shaving

System reserves

Transactive Energy

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• Engenders the voluntary collaboration of end-user assets through incentives

• Incentives must reflect actual grid values and constraints, to offer the end

user an equitable deal

• Decision-making to respond is kept at the end-user, participant level

• Automation conveniently takes care of the details

• Uses decentralized decision-making - scalable and sensitive to privacy

• "Virtual control" - negotiation feedback loop provides smooth, stable,

predictable response required by grid operators

• Allows end-user assets to compete on a level playing field, with each other

and traditional grid assets

Characteristics of Transactive Energy Systems

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Global energy goals cannot be met without changes in how we control complex systems

- Potential for substantial efficiencies in end-use systems with new controls

- More data and devices available

- New assets difficult to coordinate

- Existing controls antiquated

- Cyber-physical systems

- Growing "edge" computing resources

- Cloud computing becoming paradigm

- Existing security models challenged

Traditional centralized control approaches are a common weakness

Transactive Energy Conclusions

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www.london.edu

CEDISON: Community Energy Dynamic Solution with

Blockchain

A project funded by:

Primary hypothesis:

A distributed control approach is the most

efficient way to advance control theory to

address the challenges posed by large-scale

digitized infrastructure systems

www.london.edu

PenileeMilton

Craigend

Impact on Tariff design

n

o Rapidly increasing number of new controllable devices within the archipelago with new

characteristics and impact on distribution networks as well as third party resources: ferry,

hydrogen plant...

o We propose a decomposition of distributed autonomous multi-level architecture

organised along: (1) substation/plant level; (2) local area/district level; (3) individual level.

o A multi-market phased approach:

Phased Stochastic multi-market clearings

Preliminary conclusions

Aggregated trading strategy at community level would decrease

wind curtailment in the Orkney’s down to 30%

(vs 60-70% provided by the ANM)

Balancing at community level

decreases DUoS and Triads activation

Compelling measurable advantages of a

local balancing area:

• Congestion Management

• Coordination of heating strategies

• Smart charging of EVs

"A set of economic and control mechanisms that allows the

dynamic balance of supply and demand across the entire

electrical infrastructure using value as a key operational

parameter.“

GridWise Architecture Council

An approach to responding to the change…

Transactive Energy

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