optimizing economic operations for residential …...to appear at ieee smartgridcomm 2016 in nov....

Wei-Peng Chen and Daisuke Mashima

Fujitsu Laboratories of America, Inc.October 11th, 2016

Optimizing Economic Operations for Residential Demand Response Programs

Copyright 2016 Fujitsu Laboratories of America, Inc.

Residential DR has great potential but not yet popular

Objective 1: to expand residential DR adoption

Copyright 2016 Fujitsu Laboratories of America, Inc.1

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2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

C&I

Residential

Unit($ Million)

Market forecast for Demand Response (DR) services in C&I (Commercial & Industrial) and Residential sectors

Source: Navigant Research

Complexity of managing multiple DR programs

Multiple types of DR programs exist e.g. Critical Peak Pricing (CPP), Peak Time Rebate (PTR), Direct Load

Control (DLC) program

Utilities would like to employ different DR programs to attract residential customers with different preferences

BUT, the complexity of handling multiple programs is non-trivial


CPP PTR DLC

Category Time-based Time-based Incentive-basedRewards Lower off-peak rates Performance-basis Compliance-basis

Electricity rate Dynamic Flat Flat

Limited calls Yes No Yes

Direct control No No Yes

Objective 2: jointly managing multiple DR programs and optimally controlling DR resources in a large scale deployment

Solution: Comprehensive residential DR framework

Mobile app serves as an end-customer facing DR client Support multiple DR programs, and enables fine-granularity

control.


VTN: Virtual Top NodeVEN: Virtual End Node

•"Residential Demand Response System Framework Leveraging IoT Devices." To appear at IEEE SmartGridComm 2016 in Nov. 2016. •"SPOT: A Smartphone-Based Platform to Tackle Heterogeneity in Smart-Home IoT Systems." To appear at the IEEE 3rd World Forum on Internet of Things in Dec. 2016.

http://sgc2016.ieee-smartgridcomm.org/

http://wfiot2016.ieee-wf-iot.org/

Advantages of mobile app DR solution

A bi-directional, real-time channel between utilities and consumers Better reliability

Cost-effective platformNo hardware hub/gateway neededLow cost, easy installationSupport multiple DR programsLower customer acquisition cost

Automated, informative features to enhance customer engagement Automated control of multiple IoT devices Informative energy data analytics

Copyright 2015 FUJITSU LABORATORIES OF AMERICA4

Summary of contributions Present mathematical modeling of optimal DR resource control

for three representative DR programs, (CPP, PTR, and DLC programs), with simple solutions that minimize utilities’ operation cost for DR. The model is based on the two-settlement electricity market adopted by

most of the wholesale electricity markets in the U.S. such as ERCOT, PJM, MISO, and CAISO.

That is, we consider both day-ahead and real-time market.

Compare performances of multiple DR programs through simulations based on real-world data.Real statistical data from PJMReal DR program settings from utilities


Basics of two-settlement electricity market

𝑪𝑪𝑬𝑬𝒅𝒅𝒅𝒅 = cost of energy procurement at the hour ℎ of the day 𝑑𝑑from both day-ahead (DA) and real-time (RT) markets


Real-Time Market

Day-Ahead Market

DemandSupply

𝒑𝒑𝑫𝑫𝑫𝑫𝒅𝒅𝒅𝒅𝑫𝑫𝑫𝑫𝑫𝑫𝒅𝒅𝒅𝒅

𝒑𝒑𝑹𝑹𝑹𝑹𝒅𝒅𝒅𝒅𝑫𝑫𝑹𝑹𝑹𝑹𝒅𝒅𝒅𝒅

𝑪𝑪𝑬𝑬𝒅𝒅𝒅𝒅 = 𝒑𝒑𝑫𝑫𝑫𝑫𝒅𝒅𝒅𝒅 � 𝑫𝑫𝑫𝑫𝑫𝑫𝒅𝒅𝒅𝒅 + 𝒑𝒑𝑹𝑹𝑹𝑹𝒅𝒅𝒅𝒅 � 𝑫𝑫𝑹𝑹𝑹𝑹𝒅𝒅𝒅𝒅 − 𝑫𝑫𝑫𝑫𝑫𝑫𝒅𝒅𝒅𝒅

DA price RT energy demandRT priceDA energy

procurement

Optimized DR operations Objective function = to maximize the utility’s profit of DR operations Control variables: utilities decide when and which participants to

dispatch DR events in each DR programWhen 𝑠𝑠𝑖𝑖𝑖𝑖 = 1, the utility send a DR event to a DR participant 𝑖𝑖 at the day 𝑑𝑑 Fine granularity of DR resource selection and control is key to efficiency

Model of DR participants’ behaviors Regular load of a DR participant 𝑖𝑖 when 𝑠𝑠𝑖𝑖𝑖𝑖 = 0 ⇒ 𝐷𝐷𝑖𝑖𝑖𝑖𝑖 Curtailment at hour ℎ of day 𝑑𝑑 of participant 𝑖𝑖 when 𝑠𝑠𝑖𝑖𝑖𝑖 = 1 ⇒ ∆𝐷𝐷𝑖𝑖𝑖𝑖𝑖

Optimization problem:

𝐸𝐸𝑖𝑖𝑖 = profit of a utility at hour h of day 𝑑𝑑 = 𝑅𝑅𝑖𝑖𝑖 − 𝐶𝐶𝐷𝐷𝐷𝐷𝑑𝑑𝑑 − 𝐶𝐶𝐸𝐸𝑑𝑑𝑑= Revenue – DR cost – Energy procurement cost

𝐶𝐶𝐸𝐸𝑑𝑑𝑑 = 𝑝𝑝𝐷𝐷𝐷𝐷𝑑𝑑𝑑𝐷𝐷𝐷𝐷𝐷𝐷𝑑𝑑𝑑 + 𝑝𝑝𝐷𝐷𝑅𝑅𝑑𝑑𝑑 ∑𝑖𝑖 𝐷𝐷𝑖𝑖𝑖𝑖𝑖 − 𝑠𝑠𝑖𝑖𝑖𝑖∆𝐷𝐷𝑖𝑖𝑖𝑖𝑖 + 𝐷𝐷𝑂𝑂𝑑𝑑𝑑 − 𝐷𝐷𝐷𝐷𝐷𝐷𝑑𝑑𝑑Copyright 2016 Fujitsu Laboratories of America, Inc.7

Constraints depend on the contract of each program

max[𝑠𝑠𝑖𝑖𝑑𝑑]

∑𝑖𝑖=1𝑖𝑖=365∑𝑖=𝑖𝑠𝑠𝑖=𝑖𝑒𝑒 𝐸𝐸𝑖𝑖𝑖

s.t. the constraints on 𝑠𝑠𝑖𝑖𝑖𝑖 ∀𝑖𝑖 ∈ 𝑁𝑁

Comparison of formulations of 3 DR programs

DR program Critical Peak Pricing (CPP)

Peak-Time Rebate (PTR)

Direct Load Control (DLC)

Characteristic During critical peak day, the high rate 𝑝𝑝𝑖 is applied compared to the low rate 𝑝𝑝𝑙𝑙is used in normal days

The pre-determined rate of the PTR credit is 𝑝𝑝𝐷𝐷𝐷𝐷

A lump sum DLC credit is paid in a year, so no need to consider the cost of individual DR event

revenue and DR cost

𝑅𝑅𝑖𝑖𝑖 = 𝑝𝑝𝑓𝑓𝐷𝐷𝑂𝑂𝑑𝑑𝑑 + 𝑝𝑝𝑙𝑙 ∑𝑖𝑖 1 − 𝑠𝑠𝑖𝑖𝑖𝑖 � 𝐷𝐷𝑖𝑖𝑖𝑖𝑖 +𝑝𝑝𝑖 ∑𝑖𝑖 𝑠𝑠𝑖𝑖𝑖𝑖 𝐷𝐷𝑖𝑖𝑖𝑖𝑖 − ∆𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝐶𝐶𝐷𝐷𝐷𝐷𝑑𝑑𝑑 = 0

𝑅𝑅𝑖𝑖𝑖 = 𝑝𝑝𝑓𝑓�𝐷𝐷𝑂𝑂𝑑𝑑𝑑 + ∑𝑖𝑖 𝐷𝐷𝑖𝑖𝑖𝑖𝑖 − 𝑅𝑅𝑖𝑖𝑖 = 𝑝𝑝𝑓𝑓�𝐷𝐷𝑂𝑂𝑑𝑑𝑑 + ∑𝑖𝑖 𝐷𝐷𝑖𝑖𝑖𝑖𝑖 −

Constraint Maximal CPP events in a year = 𝑀𝑀

No limitation on the number of PTR events

Maximal DLC events in a year = 𝑀𝑀

Optimization problem

𝑚𝑚𝑚𝑚𝑚𝑚[𝑠𝑠𝑖𝑖𝑑𝑑]

∑𝑖𝑖=1𝑖𝑖=365∑𝑖𝑖 𝑠𝑠𝑖𝑖𝑖𝑖 ∑𝑖=𝑖𝑠𝑠𝑖=𝑖𝑒𝑒� 𝑝𝑝𝑖 − 𝑝𝑝𝑙𝑙 𝐷𝐷𝑖𝑖𝑖𝑖𝑖 − �𝑝𝑝𝑖 −

𝑚𝑚𝑚𝑚𝑚𝑚[𝑠𝑠𝑖𝑖𝑑𝑑]

�𝑖𝑖

𝑠𝑠𝑖𝑖𝑖𝑖 � �𝑖=𝑖𝑠𝑠

𝑖=𝑖𝑒𝑒

𝑝𝑝𝐷𝐷𝑅𝑅𝑑𝑑𝑑 − 𝑝𝑝𝐷𝐷𝐷𝐷 − 𝑝𝑝𝑓𝑓 � ∆𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑚𝑚𝑚𝑚𝑚𝑚[𝑠𝑠𝑖𝑖𝑑𝑑]

∑𝑖𝑖=1𝑖𝑖=365∑𝑖𝑖 𝑠𝑠𝑖𝑖𝑖𝑖 � ∑𝑖=𝑖𝑠𝑠𝑖=𝑖𝑒𝑒 𝑝𝑝𝐷𝐷𝑅𝑅𝑑𝑑𝑑 − 𝑝𝑝𝑓𝑓 � ∆𝐷𝐷𝑖𝑖𝑖𝑖𝑖

s.t. ∑𝑖𝑖=1𝑖𝑖=365 𝑠𝑠𝑖𝑖𝑖𝑖 ≤ 𝑀𝑀 ∀𝑖𝑖 ∈ 𝑁𝑁

Solution Greedy algorithm: compare today’s potential profit of each CPP participant against the highest X values of the rest of the year (X is the remaining quota)

Simple comparison with a threshold (a function of real-time price)

𝑠𝑠𝑖𝑖𝑖𝑖 = �1, 𝑖𝑖𝑖𝑖 𝑝𝑝𝐷𝐷𝑅𝑅𝑑𝑑𝑑 − 𝑝𝑝𝐷𝐷𝐷𝐷 − 𝑝𝑝𝑓𝑓 > 00, 𝑖𝑖𝑖𝑖 𝑝𝑝𝐷𝐷𝑅𝑅𝑑𝑑𝑑 − 𝑝𝑝𝐷𝐷𝐷𝐷 − 𝑝𝑝𝑓𝑓 ≤ 0

∀𝑖𝑖 ∈ 𝑁𝑁, 𝑑𝑑 = 1, . . , 365

Similar to the solution of CPP problem


Simulation settings

Simulation parameters are based on real-world data. Real statistical data from PJM: DA and RT price and energy procurement

data from Dominion Virginia Power (corresponding to DOM zone in PJM) Real DR program settings from utilities

• CPP: Dominion Virginia Power Smart Pricing [28], • PTR: Duke Energy Peak Time Rebate Pilot [29], and • DLC: Dominion Virginia Smart Cooling program

Model participant’s load, 𝐷𝐷𝑖𝑖𝑖𝑖𝑖, and curtailment behavior, ∆𝐷𝐷𝑖𝑖𝑖𝑖𝑖, based on simple mathematical models.


𝐷𝐷𝑖𝑖𝑖𝑖𝑖 = �𝑚𝑚 � 𝒩𝒩 𝐿𝐿𝑖𝑖𝑖, 𝑏𝑏 � 𝐿𝐿𝑖𝑖𝑖 , if 𝑖𝑖 is not on vacation~ 𝑈𝑈 0.8 � 𝐷𝐷𝑣𝑣𝑣𝑣𝑣𝑣 ,𝐷𝐷𝑣𝑣𝑣𝑣𝑣𝑣 , if 𝑖𝑖 is on vacation

Program CPP PTR DLC∆𝐷𝐷𝑖𝑖𝑖𝑖𝑖

(𝑛𝑛𝑛𝑛−𝑣𝑣𝑣𝑣𝑣𝑣) 𝑈𝑈(𝑘𝑘ℓ , 𝑘𝑘𝓊𝓊 ) � 𝐷𝐷𝑖𝑖𝑖𝑖𝑖 𝑈𝑈(𝑘𝑘ℓ , 𝑘𝑘𝓊𝓊 ) � 𝐷𝐷𝑖𝑖𝑖𝑖𝑖 𝑈𝑈(∆𝐷𝐷ℓ ,∆𝐷𝐷𝓊𝓊 )

Parameters 𝑘𝑘ℓ(𝐶𝐶𝐶𝐶𝐶𝐶) 𝑘𝑘𝓊𝓊

(𝐶𝐶𝐶𝐶𝐶𝐶) 𝑘𝑘ℓ(𝐶𝐶𝑅𝑅𝐷𝐷) 𝑘𝑘𝓊𝓊

(𝐶𝐶𝑅𝑅𝐷𝐷) ∆𝐷𝐷ℓ ∆𝐷𝐷𝓊𝓊Value 0.3 0.8 0.1 0.4 2 2.5

Performance comparison of 3 programs PTR

Compare with baseline (no PTR)

Show increased profit


CPP

Compare with: DOM: utility’s real

events Price: broadcast basis

Proposal reduces the loss significantly

DLC Compare with: DOM: utility’s real

events Price: broadcast basis

Proposal reduces the loss

DLC program is usually applied for ancillary service

Video: Residential Demand Response


https://drive.google.com/file/d/0B3okJ_ljkYZUU1E4ai1VaGhCWG8/view?usp=sharing

https://drive.google.com/file/d/0B3okJ_ljkYZUU1E4ai1VaGhCWG8/view?usp=sharing

Conclusions and future works Propose optimal DR resource control for utilities to deal with

multiple, heterogeneous DR programs Presented mathematical models for popular DR programs and the

computationally-lightweight solutions for optimization problems

Simulation results show the proposed solution either increase the profit or reduce the loss significantly

As the next step, we plan to find an opportunity to evaluate the effectiveness in a real-world setting.

Future works: Explore the bidding strategies when considering DR operationsCombine coupons with DR incentive: partner with merchants to jointly

offer coupons


Support Multiple DR ProgramsDirect Load Control (Smart Thermostat) Peak Time RebateDynamic Pricing

/ Critical Peak Pricing

• Initialization of a DR event can be constraint driven (e.g. emergency) or price driven (e.g. high wholesale electricity price)


optimizing economic operations for residential …...to appear at ieee smartgridcomm 2016 in nov....

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