residential demand response operation in a microgrid

SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 1

Residential Demand Response Operation in a Microgrid

Pierluigi Siano

Professor of Electrical Energy Engineering

University of Salerno, Italy

e-mail: [email protected]

Short Course on Residential Demand Response

Operation in a Microgrid Universidad de Córdoba. Campus de Rabanales,

Bldg. Leonardo Da Vinci.


The University of Salerno

The University of Salerno, one of the

largest universities in Italy, this year

was ranked as the first university in

southern Italy.

Its structure is that of a University

Campus and its modern buildings

offer many efficient services for

teaching, research and student life in

general such as laboratories,

multimedia equipment, a language

centre, libraries, a canteen, gyms and

other sports facilities.


Outline

Demand response: motivations, capabilities and key drivers

Enabling Smart Technologies for Demand Response

Energy Management Systems

Results of a pilot Demand Response project in Italy

Developing Demand Response research activities at University of Salerno

Key challenges for Demand Response


Demand Response

The objective of Demand Response (DR) is to make the load an active participant in balancing

electricity supply and demand around the clock via side-by-side competition with supply-side

resources

DR allows loads curtailment/management in response to changes in the price of electricity over time,

or to incentive payments designed to induce lower electricity use at times of high wholesale market

prices or when system reliability is at risk1

1http://ieeechicago.org/Portals/18/IEEE%20Chicago%20April%2013%20Newsletter%20FINAL.pdf


Demand Response Activities

Strategic conservation

Load shifting Valley filling

Flexible load shape

Peak clipping

Strategic load growth


Demand Response implementation drivers

The main drivers for Demand Response implementation are:

Environmental concerns

Reliability

Smart grids technologies

Advent of energy management service provider

Policy incentives


Key Features of Smart Grids

Smart grid applications increase the opportunities for Demand Response by providing real time data to

producers and consumers

Advanced metering solutions: to replace the legacy metering infrastructure

Deployment of appropriate technologies, devices and services: to access and influence energy usage

information in smart appliances and in the integration of renewable energy

Combined digital intelligence and real-time communications: to improve the control of the transmission

and distribution grids


Smart Grids


Active Networks

Different conceptual models can be mentioned such as: Active Networks supported by ICT, Microgrids,

Virtual Power Plants and an ‘Internet’ model - all of which could find applications, depending on

geographical constraints and market evolution.

Active Distribution Networks (ADNs) represent a possible development of “Smart Grid” concepts within

distribution power systems.

The active networks have been specifically identified as facilitators to offer connectivity and interaction

capability for both DGs and customers.


Active Networks

In the initial stage, ANM will allow monitoring and remote control of the generation at the

connection point to facilitate it integration in the system.

In the intermediate stage, ANM will permit the complete control system for all the distributed

energy resources (DER) in a controlled area.


Active Networks

Source: ADINE project

The more advanced and emerging

concept of AM is based on real-time

monitoring and control of the grid.

The AM scheme allows

communication between coordinated

voltage control and generator controls,

loads and network devices, such as

reactive compensators, voltage

regulators, and on-load tap changers.


Microgrids

They are low voltage networks with DG sources, together with local storage devices and controllable

loads (e.g. water heaters and air conditioning).

They have a total installed capacity in the range of between a few hundred kilowatts and a couple of

megawatts.

EMS


Microgrids – USA concept

The Consortium for Electric

Reliability Technology Solutions

(CERTS) microgrid (CM) concept

is one of the most world famous

research project on microgrids.

Its background is into the will to use

the DERs to reduce the cost of

electrical energy and improve the

Power Quality Requirements

principally considering the needs of

industrial power plants.

DERs are supervised by a centralized

Energy Manager which maintains

economic dispatch sending active

power and voltage set-point to each

Microsource Controller.


Microgrids –European concept

The main differences with the US concept are in the attention here devoted to the market participation on

which is based the optimal operation of the microgrid. The Figure shows a possible configuration of a

microgrid and a general control scheme.

The MGCC which is always responsible for the optimization of the Microgrid operation.

Load Controllers are installed at the controllable loads to provide load control capabilities following

demands from the MGCC, under a Demand Side Management policy or for load shedding.

The hierarchical system

control architecture

comprises three critical

control levels:

• Local Micro Source

Controllers (MC) and Load

Controllers (LC)

• MicroGrid System Central

Controller (MGCC)

• Distribution Management

System (DMS).

EMS


Virtual power plant

DER units are too small and too numerous to be visible or manageable on an individual basis. Because of

their size and multitude, distributed generators and responsive loads are currently not fully integrated

into system operation and market-related activities.

The concept of Virtual power plant (VPP) counteracts this problem by aggregating DER units into a

portfolio that has similar characteristics to transmission connected generation today.

A portfolio of smaller generators and demands. The concept is closely related to DER aggregation.


The ‘Internet’ model

The vision of the internet model is:

- “Every node in the electrical network of the future will be awake, responsive, adaptive, price-smart,

eco-sensitive, real-time, flexible, humming - and interconnected with everything else”

In the Internet model:

decision-making and control are distributed across nodes spread throughout the system

flows are bi-directional

the supplier of power for a given consumer vary from one time period to the next

the network use could vary as the network self-determines its configuration.


Enabling Smart Technologies for Demand Response

Automated response technologies, enabling

both enhanced and remote control of the

energy consumption and peak load can be

divided into three general categories:

control devices,

monitoring systems,

communication systems.


Control Devices for Demand Response

Load control devices are both stand-alone and integrated into an EMS for large facilities and consist of

technologies such as:

Load control switches are used for remote control of specific end use loads such as compressors or

motors and are connected to the utility by means of communications systems.

Smart thermostats are remotely controlled by the utility and/or the customer and allow the control of

variations in temperatures’ settings with a softer control instead of using on-off switching devices.


Monitoring and Communications Systems for Demand Response

Smart meter systems measure customer consumption in a certain time-interval and transmits

measurements over a communication network to the utility or other actor responsible for metering.

This information can be shared with end-use devices informing the customers about their energy

consumption and related costs.

Smart-meter types are distinguished according to the combination of some features such as the data-storage capability of the meter,

the communication type (i.e. one-way or two-way), the connection with the energy supplier.

The accuracy requirements of static billing meters are defined in IEC 61036 standards in order to preserve the accuracy of the

measurement data.

Smart meters generally exist within a broader infrastructure which is often called Advanced Metering

Infrastructure (AMI).



Home Area Networks

Smart Meters

Neighborhood

Area Networks

Edge Routers (Collectors)

Neighborhood

Area Networks

Meter Data

Management System

Utility Wide Area Network

AMI System

AMI denotes a system that, on request or on a pre-defined

schedule, measures, saves and analyses energy usage, receiving

information from devices such as electricity meters using various

communication media.

The smart grid communication architecture consisting of two-

way communicating devices with the central SG controller, exhibits

a hierarchical structure.

An AMI network consists of a number of integrated

technologies and applications including smart meters, wide-area

networks (WANs), home area networks (HANs), meter data

management systems (MDMS), operational gateways and systems

for data integration into software application platforms,

Neighborhood Area Networks (NANs).



Home Area Networks (HANs) allow connecting smart meters to controllable electrical devices and

implement energy management functions by using devices such as programmable communicating

thermostats and other load-control devices.

Neighborhood Area Networks (NANs) are networks used for meter data collection. These data are

transferred to a central database and used for various purposes.

A Meter Data Management System (MDMS) is a database performing validation, editing and

estimation on the AMI data in order to guarantee that the data are accurate and complete. It is also

endowed with analytical tools that enable the cooperation with other information systems (operational

gateways) thanks to which AMI can also support advanced management systems.

The standard for the exchange of information of the distribution networks is based on CIM

(Common Information Model) defines a control architecture that can deal with the complexity of

smart grids and a bus of information, accessible to the different control functions, that can exchange the

information related to the state of the system on the basis of a common format.


Monitoring and Communication Systems for Demand Response

Networked Appliances

Appliances can be designed for control via a network with access ports that connect to a communications

bus sharing a common medium, as shown in Figure.

A key component in any local area network

is the network interface module (access

port for remote control) contained in

every device that uses the network.

The interface converts internal device

signals to a uniform format for the

communications medium of the HAN.

The technical elements for remote control

for an appliance with energy mode control

are:

a connection to a communication

medium,

circuits to encode and decode the

communication signals and embedded

messages, plus a link to the appliance

controller.

Smart Grid Impact on Consumer

Electronics

Consumer Electronics Association (CEA),

2013


Monitoring and Communication Systems for Demand Response

Both wireless and wired communication technology should accomplish to IEC 61850.

Wireless communication technology can be either an option for HANs, NANs and WANs, or

obligatory in case of Vehicle-to-Grid (V2G) communications, and various communication technology

and standards could coexists in different part of the smart grid.

IEEE 802.15.4 (ZigBee) and IEEE 802.11 (Wi-Fi) are appropriate technologies for smart meters in

HANs and NANs, where the coverage range varies from tens to hundreds of meters.

The coverage requirements (of tens of kilometers) for WANs impose the use of cellular wireless

networks like GPRS, UMTS, LTE, or broadband wireless access networks like IEEE 802.16m

(WiMax).

Wired communication systems: depending on the desired coverage area, various technologies can be

used for wired communication. Power Line Communications (PLCs) may be adopted for HANs and

NANs in order to cover local/micro SG portions (up to hundreds of meters).

Fiber optic communications may instead be implemented for WANs (tens of kilometers, and more).


Customer conceptual model in smart grids

The ESI provides a secure interface for Utility-to- Consumer interactions.

The ESI can act as a bridge to Building Automation System (BAS) or Energy Management System (EMS).

The ESI serves as the information management gateway through which the customer domain interacts with

energy management service providers. Basic functions of the ESI include demand response signaling (for example, communicating price information or critical peak period signals) as well as provision

of customer energy usage information to residential energy management systems or in-home displays.

The National Institute of Standards and

Technology (NIST) elaborated the

Framework and Roadmap for Smart

Grid Interoperability Standards.

It describes a high-level conceptual

reference model for the Smart Grid.

The boundaries of the Customer

domain are typically considered to be

the utility meter and the Energy

Services Interface (ESI).

NIST Framework and Roadmap for Smart Grid

Interoperability


Energy Management Service Provider - Aggregator

ESCOs offer commercial customers comprehensive energy usage analysis and recommendations for

savings. They usually propose a financial arrangement to share in the savings, rather than just being

paid for their advice.

The EMSP is authorized to act as an

intermediary between the Independent

System Operator (ISO)/Regional

Transmission Organization (RTO) and

the users to deliver DR capabilities to

meet ISO/RTO needs in its markets.

Commercial service providers are also

called Energy Service Companies

(ESCOs) or Curtailment Service

Providers (Aggregators).

NIST Framework and Roadmap for Smart Grid Interoperability



Load Aggregators are energy management companies that offer to help utilities shed load in response to

supply or distribution limitations.

A Load Aggregator acts as intermediator between electricity end-users, who provides distributed energy

resources, and those power system participants who wish to exploit these services.

The aggregator's job is to enable the demand response and bring it to the wholesale market.

This requires that the aggregator:

1) studies which customers can provide profitable demand response,

2) actively promotes the demand response service to customers,

3) installs control and communication devices at customer's premises and

4) provides financial incentives to the customers to provide demand response.



Who can be an aggregator?

In the current liberalized regime, DSO’s cannot perform demand response aggregation because they cannot

participate in electricity markets.

Currently retailers are in the best position to become aggregators because they have connections to the

electricity market and an existing relationship with the customers.

The aggregator could also be a third party, a company who does not have any existing relationship with the

customers as far as electricity business is considered. However, it could have a relationship in another field,

such as facility management.



Customer’s remuneration

Aggregators write contracts with commercial customers who are offered lower energy costs in exchange for

occasional load shedding. They can arrange better energy prices for their customers by pooling loads.

This offloads the marketing and management of load control from the utility.

An availability fee is given for customers who make a contract with the aggregator and enable load

control or control of other types of DER. The availability fee may be reduced by penalty payments if

the customer does not follow the aggregator's control signals.

An opposite to the availability fee is a rental payment for the control and communication equipment

which the aggregator has installed. Payment can also be based explicitly on following the control calls

(yes/no) or the power reduced due to control call in a demand response event.

The customer's benefit can be based on dynamic tariffs provided by an aggregator retailer.

The customer can be given a certain percentage of the aggregator's gross profit from selling DER to the

market.

A combination of the different payment components can be used to achieve a suitable risk and

incentive level.



The remuneration, especially for the call payment, is closely connected to the way the customer’s resources

are controlled. There are several ways to affect the customer behaviour to obtain DR.

In case of small and medium-sized customers these can be divided into price-based options and direct load

control.

Price-based control refers to changes in electricity use by customers in response to changes in the prices

they pay (electricity tariff). In other words, the customer receives price information from his aggregator

at specified intervals. The time resolution of the prices can be from several hours to less than one hour.

Volume-based control where the aggregator controls the total power drawn by a consumer, without

regard to individual appliances.

In direct load control the aggregator can directly control the power drawn by one or more appliances at

customer’s premises. This can take place automatically so that the aggregator can remote-control the

appliances or so that he first notifies the customer who performs the actual control.


Energy Management Systems for Demand Response

In DR applications, Indirect Load Control is replacing Direct Load Control (where utility suppliers do

operate customer appliances or devices remotely).

Customers’ choices about using appliances are influenced by price or event messages and carried out

with:

manual action by consumers

automatic actions by smart appliances

decisions by an Energy Management Agent (EMA) that manages appliance operation.

A system with EMA is called Distributed Load Control exploiting microprocessor based and

combining Local and Direct Load Control with much increased flexibility and customer control.

It is also possible to implement Distributed Load Control by sending utility prices and event

notifications directly to smart appliances (Prices-to-Devices).


Energy Management Systems for Demand Response

The EMA may switch energy sources from the public grid to local generators or a battery.

The utility or service provider send price or event messages to all houses in real-time over a HAN such

as the Internet. These signals enter the house through a residential gateway (Energy Service Interface) that

also serves as a line of demarcation between utility and home owner equipment.

Distributed Load Control with an Energy Management Agent (via Utility or Indipendent Service Providers)


Results of a pilot DR project in Italy



Decision Support and Energy

Management System (DSEMS)Messages for

Customers

Loads

Configuration

Storage System

Supply from the grid

Supply local sources

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Energy Management System

An Energy Management System has been designed and implemented that receives price and system

signals and provides energy management of loads, air conditioning units, storages, local generation units

according to user preferences. Outputs are the command signals used for the control of thermal and electrical

loads and the messages for the end user.

Pilot industrial research project

“System for Energy Savings with

Integrating of Air Conditioning”

funded by the Italian Ministry of

Economic Development and carried

out with BTicino and other Italian

Universities.

Coordinator and Principal

Investigator for the University of

Salerno (2010-2014).



The EMS ensures the power supply and performs detachment or control of the electrical loads based

on given priorities and according to different control functions that may be selected by the user.

The Economy function determines during each period the best electrical load configuration to reduce

the energy cost also considering user requirements and constraints and assuming a TOU tariff with

different costs for “peak” and for “off-peak” hours.

Shiftable loads (dishwasher, washing machine) are moved to the off-peak tariff period.

The temperature set point of the air-conditioning units is controlled to reduce energy consumption:

during the peak tariff period

during the off-peak tariff period when the power consumption exceeds the available power

(including local resources).



The Emergency function is automatically selected by the EMS after a failure in the distribution grid

supply. In this case, the electrical supply is provided by a local generation (photovoltaic, micro-wind,

micro-turbine, etc.) and by an electric energy storage system.

The Energy function aims at assuring a given electrical energy consumption or economic expense in a

prefixed period of time (according to the contract agreed with the supplier). The EMS sends messages

to the user informing it about:

the daily-average consumption;

the allowed consumption to achieve the prefixed target.

The Thermal Storage function changes the temperature set point of the air-conditioning in order to

allow an anticipated cooling/heating in each controlled zone also on the basis of the local generation.



The Power function is selected so that the absorbed active power is limited by a prefixed threshold

value and may allow user receiving an economic benefit from the DSO.

The NET-Service function allows DSO controlling some selected electric loads in order to achieve

benefits for the grid, while the end-user will receive a premium for the service it offers to the DSO.

The Comfort function is selected when the user is willing to assure the maximum comfort in the house

in terms both of indoor temperature and of electrical load usage.

Controllable and shiftable loads are managed only to avoid that the maximum available active power is

exceeded, thus improving the continuity of supply for the end-user.



TiDomus: mask for the selection of the electric loads.

These control functions have been implemented in a simulation tool, named TiDomus that is able to

reproduce different house environments by varying:

the type and the nominal power of the electric loads;

the thermal characteristics of the building;

the type and the technical characteristics of the air conditioning system;

the presence/absence of inhabitants in the house.



ESS

Electric Source

Simulator

TBS

Thermal Behaviour

Simulator

ELS

Electric Load

Simulator

CLS

Control Logic

Simulator

MAIN

INP

UT

DA

TA

OU

TP

UT

DA

TA



Mask for the calculation of the primary energy requirements. Mask for the evaluation of the economic saving.

TiDomus uses Monte Carlo Simulation for the extraction process of the daily power profile of the house

starting from the knowledge of some social and economic factors.



The control functions have been coded

with Stateflow of Matlab and then

implemented on an ARM9 processor.



A new device (an IR interface) has been produced that is able to control the temperature set point of the air

conditioning system by sending infrared commands.

The interface is connected to the fieldbus and is therefore directly manageable by the EMS2.

2Applications made with SW OpenWebNetProtocol operating in various operating systems through appropriate gateway (SCS/SCS or

USB/IP). SCS is an acronym for “Simplified Wiring System”. It uses a fieldbus network protocol and has applications in the field of home

automation and building automation. It is used mainly in BTicino and Legrand installations.



Simulation tests have been performed.

The system considers the following electrical loads:

• Fixed loads;

• Lights;

• Dishwasher;

• Washing Machine;

• Dryer.

The active absorbed power of dishwasher, washing machine and dryer (shiftable loads) is assumed to be

constant in a cycle of work, while steady loads and lights have a fixed power consumption.

The air-conditioning system is used both for summer cooling and for the winter heating. Its consumptions

depends on the outdoor temperature and on the temperature set-point defined by the user.



The following inputs/outputs to the EMS have been considered:

Inputs.

• contractual power;

• absorbed active power;

• load on signal;

• net availability;

• tariff profile;

• load priority list;

• temperature set-point;

• outdoor temperature.

Outputs.

• load control signals;

• supply energy;

• energy cost.



- Normal scenario

In Fig. 3 is shown the outdoor temperature, the constant set-point temperature, TSet-Point (of 20 °C) and the

indoor temperature, following TSet-Point. The power absorbed by the shiftable loads without considering the EMS are shown in in Fig..

Indoor, outdoor and Set-Point temperatures

0 4 8 12 16 20 2415

20

25

30

35

40

Time [h]

Deg

rees

[C°]

Tout

Troom

TSet-Point



Shiftable loads without control

0 4 8 12 16 20 240

0.5

1

1.5

2

Time [h]

Po

wer

[kW

]

Steady Loads

Lights

Dishwasher

Washer

Dryer



Air-conditioning energy consumption without control

0 4 8 12 16 20 240

0.05

0.1

0.15

0.2

0.25

En

erg

y [

kW

h]

Time [h]

Air-Conditioning Energy



Daily cost without control

0 4 8 12 16 20 240.02

0.04

0.06

0.08

0.1

0.12

0.14

Time [h]

Co

st [

€]

Energy Cost without control



Economy scenario

In this scenario the EMS modifies the temperature set-point of the air-conditioning system in order to reduce the cost. As shown in Fig. , in the period from 8.00 to 19.00 (high tariff) the TSet-Point is increased up to 24 °C, while a constant temperature set-point of 22 °C has been set by the user during the day. As during the time period from 0.00 to 8.00 the windows are closed and there are some people inside the house, the air-conditioner works normally and the indoor temperature reaches the user set-point. On the other side, when there are no people inside the house and/or one window is opened, the air-conditioner switches off (see Fig.).

Indoor and Set-Point temperatures

0 4 8 12 16 20 2415

20

25

30

35

40

Time [h]

Deg

rees

[C

°]

Troom

TSet Point



The way the EMS translates shiftable loads and turn on these loads in correspondence of a low price tariff period is shown in Fig. . In this case, the dishwasher and washing machine are shifted. Moreover, the system turn off the lights in absence of people in the home.

In details, the system reduces the absorbed power by the lights of 20% after 15 minutes and turn off the lights after 30 minutes.

Shiftable loads with control

0 4 8 12 16 20 240

0.5

1

1.5

2

Time [h]

Po

wer

[kW

]

Lights

Dishwasher

Washer

Dryer



The Figures show the energy consumption of the air-conditioning and the daily cost, respectively. It’s worth noting that the amount of energy consumption and the daily cost are lower with respect to the previous case without EMS.

Air-conditioning energy consumption with control

0 4 8 12 16 20 240

0.05

0.1

0.15

0.2

0.25

En

erg

y [

kW

h]

Time [h]

Air-Conditioning Energy



Daily cost with control

0 4 8 12 16 20 240

0.02

0.04

0.06

0.08

0.1

0.12

0.14

Time [h]

Co

st [

€]

Energy Cost with control



SUMMER DAY RESULTS

Weekday

Energy

Consumption

[kWh/day]

Holiday

Energy

Consumption

[kWh/day]

Weekday

Cost

[€/day]

Holiday

Cost

[€/day]

With

EMS 4.1 6.3 3.0 4.5

Without

EMS 8.2 8.4 5.7 4.8



A comparison between the normal and economy scenario is obtained by using the MCS.

Some results are shown in next figures in order to evidence the difference in terms of daily cost, considering

a summer weekday without control and with control.

Daily cost without control Daily cost with control

0 100 200 300 400 500 6005

5.5

6

6.5

7

7.5

N simulations

Co

st [

€/d

ay

]

Energy Cost without control

0 100 200 300 400 500 6002.6

2.8

3

3.2

3.4

3.6

3.8

4

4.2

N simulationsC

ost

[€

/day

]

Energy Cost with control



Daily cost without control Daily cost with control

5 5.5 6 6.5 7 7.50

10

20

30

40

50

60

70

80

90

Cost [€/day]

Fre

qu

en

cy

2.5 3 3.5 4 4.50

20

40

60

80

100

120

140

Cost [€/day]

Fre

qu

en

cy



SUMMER WEEKDAY RESULTS OBTAINED WITH MCS

Energy

Consumption

[kWh/day]

Cost

[€/day]

With

EMS 4.0 3.2

Without

EMS 10.4 6.1



Experimental tests have been performed on two real apartments and compared with simulation results

in order to validate the models and the implemented functions.

With the economy function, a mean percentage annual costs reduction in the range 5% - 10%

depending on the efficiency class (from A to G) of the house can be evidenced.

The first site, located in Cantù, near Como’s lake, is a cottage of 160 m2 (with energy performance class

B). The installed electric power capacity is 6 kW with a 6 kW rated power PV system and a controlled

air conditioning system.

The Thermal Storage function exploits the excess electrical energy generated by the PV system for

thermal storage by increasing the power consumption of the air conditioning systems for an anticipated

cooling of the involved zones.

Scenario Simulation Results

Consumption

[kWh/day]

Experimental Results

Consumption

[kWh/day]

Deviation [%]

COMFORT 12.93 13.30 2.78%

ECONOMY 9.58 9.23 3.79%



Cantù site with PV production. August 7, 2013.

The programmed temperature profile is set every day at 26 °C, from 24.00 p.m. to 06.00 a.m., at 30 °C, from 06.00 a.m. to 18.00 p.m., and 26°C, from 18.00 p.m.

to 24.00 a.m. The Thermal Storage is enabled during the whole day. With a change of 6 °C, the control system modifies the preset temperatures for each time slots.

The activation of the “Thermal Storage” function

occurs in the time period from 10 a.m. to 12 a.m.,

when the energy not consumed exceeds the set

threshold. In the "zone 4 living" there is a change in

the set-point (from 30 °C to 24 °C) and an increase in

the energy consumption for the air conditioning.



References

Int. Journals

P. Siano, “Demand response and smart grids - A survey.” Renewable & Sustainable Energy Reviews,

vol. 30, p. 461-478, 2014.

P. Siano, G. Graditi, M. Atrigna, A. Piccolo, “Designing and testing decision support and energy

management systems for smart homes”. Journal of Ambient Intelligence and Humanized Computing,

Vol. 4, pp. 651- 661, 2013.

P. Siano, G. Graditi, M.G. Ippolito, R. Lamedica, A. Piccolo, A. Ruvio, E. Santini, G. Zizzo,

Innovative Control Logics for a Rational Utilization of Electric Loads and Air-Conditioning Systems in

a Residential Building, Energy and Buildings, 102 (2015) 1–17

Int. Conferences

P. Siano, G. Graditi M. Atrigna, A. Piccolo,“Energy management system for smart homes: Testing

methodology and test-case generation”, 2013 International Conference on Clean Electrical Power

(ICCEP 2013), pp. 766-771, 2013.

P. Siano, M.G. Ippolito, G. Zizzo, A. Piccolo, “Definition and application of innovative control logics

for residential energy optimization”, SPEEDAM 2014, Ischia, Italy, 18-20 June 2014.

P. Siano, et alii, “Designing an Energy Management System for Smart Houses”, IEEE International

Conference on Enviroment and Electrical Engineering, EEEIC 2015, Rome, 2015.

Smart Grid Impact on Consumer Electronics, Consumer Electronics Association (CEA), 2013.


Developing DR research activities:

a probabilistic methodology for evaluating the

benefits of residential DR in a real time

distribution energy market


Developing DR research activities: a probabilistic methodology for evaluating the

benefits of residential DR in a real time distribution energy market

The idea is that of considering real time nodal prices (D-LMPs) at the distribution level instead of a TOU

tariff with different costs for “peak” and for “off-peak” hours that is, instead, based on transmission prices

(without considering the distribution system constraints and power losses).

The proposed approach introduces nodal prices3 at the distribution level in a distribution energy market

(as in a microgrid). D-LMPs are based on three cost components (energy costs, congestions and power

losses).

In the developed probabilistic methodology the uncertainties related to the stochastic variations of the

involved variables (load demand, user preferences, environmental conditions, house thermal behavior and

wholesale market trends) are modeled by using Monte Carlo Simulation.

3DSOs are in charge of purchasing high voltage energy from the wholesale market and transferring it to clients of distribution networks

at a flat energy price generally calculated on the basis of the transmission nodal price, which can cause market inefficiencies because

of the lack of consideration of the distribution system constraints.


A probabilistic methodology for evaluating the benefits of residential DR

Transactive controllers are designed to control air conditioning units and some shiftable loads and to

make bids on the distribution electricity market in response to D-LMPs and according end-user

requirements.

Temperature set-point and its maximum allowable variations are considered for the air conditioning.

The desired operating period is taken into account for shiftable loads.



System architecture

market

DR aggregator

gateway controller house

appliances

DSO

DGs

offers

bids

Distribution network

A DR aggregator,

according to the signals

received by the transactive

controllers, makes the bids

and gives feedback signals

(bid acceptance or

rejection).


Demand Response Economics

Under inelastic demand (D1) extremely high price (P1) may result on a strained electricity market.

If DR measures are employed the demand becomes more elastic (D2) and a much lower price will result in the market (P2).

It is estimated that a 5% lowering of demand would result in a 50% price reduction during the peak hours of the California

electricity crisis in 2000/2001.3

3The Power to Choose - Enhancing Demand Response in Liberalised Electricity Markets Findings of IEA Demand Response Project, Presentation 2003

MCP

MCQ



Distribution acquisition market

DR aggregators and DGs owners submit active power bids and offers to the DSO acquisition market in form

of blocks for each time slot.

The DSO carries out a RT intraday optimization every time slot (15 minutes).

The market clearing quantity and prices (D-LMPs) at each bus are determined by maximizing the social

welfare considering inter-temporal constraints as follows:

𝑀𝑎𝑥𝑖𝑚𝑖𝑧𝑒 𝑆𝑊(𝐱, 𝐮) = ∑ 𝐵𝑗(𝑑𝑗

𝑁𝑗

𝑗=1

) − ∑ 𝐶ℎ(𝑔ℎ

𝑁ℎ

ℎ=1

)

0)(

0)(

osubject t

gd,u, x,g

gd,u, x,h



For air conditioning units, the bid quantity is computed based on the required energy to achieve

the desired indoor temperature.

The bid price is computed on the basis of the mean of D-LMP at the bus in the previous 24 hours

and on the indoor temperature distance from the set point.

The air conditioning unit is switched on during the subsequent time slot only if the bid is

accepted.

A similar approach is adopted for shiftable loads whose bid prices are determined on the basis of

a price forecast and of a prediction error on it. The bid price increases with time, also according

to the appliance working time interval allowed by the user.



Thermal loads management: HVAC algorithm



Bidding curve (t)



Shiftable loads algorithm



Optimal interval in the float



Blocks of the simulation model



S/S - 2S/S - 1

49 4748

53

5051

52

5455

58 565762 596061

63

64

67 656671 68

69

70

72

75 737476

79 7778

83 80

8182

43

46

4544

30 353433

32

31 36 37 38

39

41

40

42

25 29282726

15

22

20

19181716 21 23 24

12

1311

14

2 3 4 5 6 7

8

1

10

9A

B

C

D

E

F

G

H

I

M

DG

DG

DG

L

n

n

Legend

Bus with dispatchable

loads with DR

Bus with fixed loads

Diesel GeneratorDG

Distribution Network used to test the model - 84-bus network 11.4-kV radial distribution system



Simulation data

Class of input Type of Input Data

Network Feeders supply two 20 MVA, 33/11.4 kV transformers

Feeder thermal limits between 150 A and 60 A

Voltage limits ±10% of nominal value

Buses’ loads 83 buses with fixed and dispatchable loads (each one with 120 residential loads) 29 buses with

dispatchable loads and the remaining buses with non dispatchable loads

Diesel generators

(DGs)

each of 660 kW, located at groups of 4 at buses 53, 69, and 83, and characterized by constant

offer quantity equal to the size of the generators and a constant offer price equal to 160

euro/MWh (with takes into account both start-up, shutdown costs and operation costs)

Houses A house has about 150 m2 useful floor area. Transmittances and thickness of walls, roof, floor,

windows and doors make the energy efficiency class of the house being G as defined by EN

15217.

In accordance to a usual practice for residential loads, power factors equal to 0.9 and constant

in time have been assumed.

External temperature (𝑇𝑒𝑥𝑡𝑡) Time series have been collected for winter period in the south of Italy

Thermal loads

User’s air

conditioning comfort

setting

At average, 200C with an allowed variation of ± 2

0C

Shiftable

loads

Washing

machines (𝑚 = 1)

Rated power (𝑃𝑊𝑠ℎ𝑖𝑓𝑡1) of 2 kW and Operations Time (𝑂𝑇1) of 2 h.

Dishwashers (𝑚 = 2) Rated power (𝑃𝑊𝑠ℎ𝑖𝑓𝑡2) of 2 kW and Operations Time (𝑂𝑇2) of 1.5 h.



Average percentage of cost savings for a house considering all 29 dispatchable loads involved in the DR

program (100% DR involvement)

0 5 10 15 20 25 30 35 400

8

16

24

32

Average percentage cost savings for the buses with DR [%]

Per

cen

tag

e fr

equ

ency

[%

]



Average percentage of cost savings for a house considering all dispatchable loads involved in the DR program

5%

15%

25%

35%

45%

55%

65%

75%

85%

5 8 13 27 29 37 39 43 44 45 46 50 52 53 54 55 58 61 62 63 66 68 72 74 76 78 80 81 83

Cost

sav

ings

per

centa

ge

[%]

Bus [identification number]

100% DR involvement

50% DR involvement

25% DR involvement

feeder G

feeder M

feeder I

Higher cost savings since without

DR there are congestions on the

lines 47-48 and M-77

Cost savings always

higher than 10%




5%

15%

25%

35%

45%

55%

65%

75%

85%

5 8 13 27 29 37 39 43 44 45 46 50 52 53 54 55 58 61 62 63 66 68 72 74 76 78 80 81 83

Cost

sav

ings

per

centa

ge

[%]


100% DR involvement

50% DR involvement

25% DR involvement

feeder G

feeder M

feeder I

The transactive controller of

the air conditioning unit

operates in such a way to

decrease the temperature set

point to its lower bound when

the D-LMPs are high due to

congestions and expensive

electrical power from the DG.

This is more frequent when the

customers’ involvement is

equal to 25% and causes

higher average daily energy

savings.

Cost savings tends largely to increase with the

reduction of costumers’ involvement in DR.




5%

15%

25%

35%

45%

55%

65%

75%

85%

5 8 13 27 29 37 39 43 44 45 46 50 52 53 54 55 58 61 62 63 66 68 72 74 76 78 80 81 83

Cost

sav

ings

per

centa

ge

[%]


100% DR involvement

50% DR involvement

25% DR involvement

feeder G

feeder M

feeder I

Cost savings for a 100% of

customers’ involvement are

lower than those related to a

50% of customers’

involvement.

Simultaneously displacement

of many shiftable loads to

hours characterized by a lower

price forecast determines the

peak rebound effect (due to

the generation of electrical

power from expensive DG

during some few hours and

consequent higher D-LMPs).




5%

15%

25%

35%

45%

55%

65%

75%

85%

5 8 13 27 29 37 39 43 44 45 46 50 52 53 54 55 58 61 62 63 66 68 72 74 76 78 80 81 83

Cost

sav

ings

per

centa

ge

[%]


100% DR involvement

50% DR involvement

25% DR involvement

feeder G

feeder M

feeder I

Differently from what happens

on other feeders, a percentage

of 25% of customers’

involvement cannot generally

alleviate congestions on

feeder G.

This implies lower cost savings

for a 25% of customers’

involvement if compared to a

50% of customers’

involvement allowing

alleviating congestions in most

cases.



Bus 52. D-LMP, active power and indoor temperature 50%DR

(some relevant variables during a winter day considered in the MCS)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200D

-LM

P

[eu

ro/M

Wh]

DR

WODR

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

0.05

0.1

Act

ive

Pow

er [

MW

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 2415

20

25

Time [h]

Tem

per

ature

[C

]



Bus 52. D-LMP, active power and indoor temperature 25%DR

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

100

200D

-LM

P

[eu

ro/M

Wh]

DR

WODR

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

0.05

0.1

Act

ive

Pow

er [

MW

]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 2415

20

25

Time [h]

Tem

per

ature

[C

]



Bidding curve (t)


Discussion

The method is able to guarantee a percentage of cost savings always higher than 10% both in the case

without and with congestions on the distribution network.

The transactive controllers generally allow reducing the peak of daily load curve.

The adoption of D-LMPs in a RT electrical energy market can in most cases prevent congestions.

This method enables a case by case detailed analysis that, as evidenced by the previous analysis, is in

general required to evaluate cost savings (due to the complexity of interactions among transactive

controllers, distribution network topology and technical constraints).

References

P. Siano, D. Sarno “Assessing the Benefits of Residential Demand Response in a Real Time Distribution

Energy Market”, Applied Energy 161 (2016) 533–551.

P. Siano et alii “A Novel Method for Evaluating the Impact of Residential Demand Response in a Real

Time Distribution Energy Market” Journal of Ambient Intelligence and Humanized Computing, in press.


Demand Response regulatory

and policy frameworks

Potential benefits of Demand Response


Demand Response regulatory and policy frameworks - US

Regulatory and policy frameworks, such as the Energy Policy Act of 2005, have been recently

enacted that promote DR and allow customers and load aggregators taking part by means of DR

resources in energy, capacity, and ancillary services markets.

Also, the FERC (Federal Energy Regulatory Commission) Order 719 contributed to remove

obstacles to the participation of DR in wholesale markets by allowing load aggregators bidding DR on

behalf of retail customers into markets.

In 2011, FERC Order 745, determined that DR resources should be compensated at the Locational

Marginal Price (LMP) for their participation in wholesale markets, thus establishing an equal treatment

between demand-side resources and generation.

The order is highly controversial and has been opposed by a number of energy economists.


Demand Response regulatory and policy frameworks - Europe

The existing EU regulatory framework makes DR possible, but its full potential will not be realized

without further action from national policy-makers, regulators and energy companies, additional

efforts should aim at:4

(i) Creating market-based and transparent incentives for DR that reward participation through dynamic

prices without unnecessary constraints whilst respecting legal considerations on data security and

protection, privacy, intrusion.

(ii) Opening up the market to exploit the potential of DR, treating demand side resources fairly in

relation to supply and elaborating clear and transparent market rules and technical requirements.

(iii) Bringing the technology into the market through the roll-out of smart metering with the

appropriate functionalities, creating the necessary framework for smart appliances and energy

management systems.

4European Commission Staff Working Document, Incorporing demand side flexibility, in particular demand response, in electricity markets, 2013.


Demand Response regulatory and policy frameworks - Europe

Jessica Stromback, Demand Response: the value of non-use Smart Energy Demand Coalition,

Smart Energy Demand Coalition, Berlin Energy Forum, Berlin, 10-11 February 2014

Demand side products and

programmes are being created

within the wholesale electricity

market, with an increasing

number of aggregators active in

the markets (e.g. UK).

Entry barriers to balancing and

reserve markets are gradually

being removed and time of use

tariffs are available in several

Member States for residential

consumers (e.g. UK, FR, IT,

ES).

More comprehensive residential

pricing and industrial load

balancing programmes are

being developed (e.g. FR).


Demand Response regulatory and policy frameworks in Italy

In Italy, policy has focused mostly on efficiency, supported by tariffs related to peak loads (i.e.,

capacity) for many residential customers.

Actually, demand side resources can participate in the day ahead market, but the interest has been

low. Wholesale market operators can act as demand aggregators (dispatching user). However, there are

no independent DR aggregators in Italy. (The bidder can decide to make a bid with an indication of

price or to bid at 0 price). The interest in making bids with an indication of price in the day ahead

market is currently low. 5

5 However, in 2012 there was an increase of 23% in the bids with indication of price, showing that consumers are willing to use more

suitable pricing strategies, probably due to the effect that the economic crisis is having on the wholesale electricity market.

In regard to the balancing market, the current requirements give access only to generation units and the regulatory framework for

aggregated DR participation is under development. A capacity market administered by TERNA should be launched in 2017, but it

will only be accessible by generation side resources. Participation in the balancing market would require an always operating control

centre, which is a cost barrier for a new aggregator. The rules regarding verification and definition of baseline for demand side

resources are not explicit yet.


Demand Response regulatory and policy frameworks in Italy

Industrial loads participate via two “interruptible contracts” programs managed by the Italian TSO

(TERNA): one for the mainland and one for Sicily and Sardinia. This program foresees a payment

subject to a mechanism based on the number of interruptions called in the year.6

Italy has developed a wide interval smart meter (without an in-home display) rollout and has a long

tradition in Time of Use programs for high & medium voltage consumers.

Mandatory TOU tariffs have been introduced since July 2010 for the majority of customers who buy

from the main supplier, ENEL (two time bands: one for “peak” hours and the other for “off-peak”

hours). The regulator is running a major study in order to understand what impact this TOUP has on

household consumption7. (It is not necessarily popular: ENEL’s competitors advertise flat rates as an

inducement to switch supplier.)

6 i.e. extra €/MW for each additional interruption if the number of interruptions is >10 or paid back if the number of interruptions is

<10. In the case of Sardinia and Sicily extra €/MW for each additional interruption is paid if the number of interruptions is > 20. The

total interruptible power contracted under the two mechanisms reached 4.318 MW in June 2012. The minimum bid limit is 1 MW. 7 RES caused an increase of pries during the previously considered “off-peak” hours thus making the TOUP not useful.


Potential benefits of Demand Response

Operation Expansion Market Transmission

and

Distribution

Relieve congestion

Manage contingencies, avoiding outages

Reduce overall losses

Facilitate technical operation

Defer investment in network

reinforcement or increase long-

term network reliability

Generation Reduce energy generation in peak times:

reduce cost of energy and possibly

emissions

Facilitate balance of supply and demand

(especially important with intermittent

generation) Reduce operating reserves

requirements or increase short-term

reliability of supply

Avoid investment in peaking

units

Reduce capacity reserves

requirements or increase long-

term reliability of supply

Allow more penetration of

intermittent renewable sources

Retailing Reduce risk of

imbalances Reduce price

volatility. New products,

more consumer choice

Demand Consumers more aware of cost and

consumption, and even environmental

impacts. Give consumers options to

maximize their utility: opportunity to

reduce electricity bills or receive

payments

Take investment decisions with

greater awareness of

consumption and cost

Increase demand

elasticity



Need to establish reliable control strategies and market frameworks so that the DR resource can be

optimized.

Due to the lack of experience it is still needed to employ extensive assumptions when modelling and

evaluating this resource.

Reacting to high-prices, DR loads could all switch to the same low price-period, causing a peak

rebound (which can be, in most cases, coped with RT transactions between customers and suppliers).

If the DR is limited the system benefits may not be sufficient to cover the cost of the control and

communications infrastructure for DSO.

If differentials in real time prices vary over only a small range, the savings for consumers may not be

sufficient to induce investments in DR programs (as consumers may not be able to recoup their costs of

installation or justify the burden of responding to prices).



Electric utilities need ruling to allow consumers and consumer electronics companies using any means

and devices to manage energy, as long as they do not harm the grid.

Consumers should not need the approval of the public utility before buying an energy management

product from a consumer electronics company.

Likewise, consumers should be free to contract with third-party energy management service providers

without approval of the utility.


Additional References on Demand Response

P. Siano, “Demand response and smart grids - A survey.” Renewable & Sustainable Energy Reviews, vol. 30, p.

461-478, 2014.

Zakariazadeh A, Homaee O, Jadid S, P. Siano. A new approach for real time voltage control using demand response

in an automated distribution system. Appl Energy Elsevier2014;114:157–66.

Zakariazadeh A, Jadid S, P. Siano. Stochastic operational scheduling of smart distribution system considering wind

generation and demand response programs. Int J Electr Power Energy SystElsevier2014;63:218–25.

Zakariazadeh A, Jadid S, P. Siano. Multi-objective scheduling of electric vehicles in smart distribution system.

Energy Convers Manage Elsevier 2014;79:43–53.

Zakariazadeh A, Jadid S, P. Siano. Economic-environmental energy and reserve scheduling of smart distribution

system: A multiobjective mathematical programming approach. Energy Convers Manage Elsevier 2014;78:151–64.

Zakariazadeh A, Jadid S, P. Siano. Stochastic multi-objective operational planning of smart distribution systems

considering demand response programs. Electr Power Syst Res Elsevier 2014;111:156–68.

Mazidi M, Zakariazadeh A, Jadid S, P. Siano. Integrated scheduling of renewable generation and demand

responseprograms in a microgrid Energy Convers Manage Elsevier 2014;86:1118–26.

Zakariazadeh A, Jadid S, P. Siano. Smart microgrid energy and reserve scheduling with demand response using

stochastic optimization. Int J Electr Power Energy Syst Elsevier 2014;63:523–33.

C. Cecati, C. Citro, P. Siano, (2011). “Combined Operations of Renewable Energy Systems and Responsive

Demand in a Smart Grid”, IEEE Transactions on Sustainable Energy. Vol. 2 (4). pp. 468-476.

Shafie-khah, M., Heydarian-Forushani E., Golshan M.E.H., P. Siano., Moghaddam, M.P., Sheikh-El-Eslami,

M.K., Catalão, J.P.S., Optimal trading of plug-in electric vehicle aggregation agents in a market environment for

sustainability, Applied Energy, Vol. 162, 2016, pp. 601-612.