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

Demand Response Activities
Strategic conservation
Load shiftingValley 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
Enviromental
Parameters
UserRequirement
Tariff
Available
Components
ContractConstrain
Messagesfrom
DNO
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
INPUTDATA
OUTPUTDATA

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
.
2
Applications 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 24
15
20
25
30
35
40
Time [h]
Degrees[C°]
Tout
Troom
TSet-Point

Shiftable loads without control
0 4 8 12 16 20 24
0
0.5
1
1.5
2
Time [h]
Power[kW]
Steady Loads
Lights
Dishwasher
Washer
Dryer

Air-conditioning energy consumption without control
0 4 8 12 16 20 24
0
0.05
0.1
0.15
0.2
0.25
Energy[kWh]
Time [h]
Air-Conditioning Energy

Daily cost without control
0 4 8 12 16 20 24
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Time [h]
Cost[€]
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 24
15
20
25
30
35
40
Time [h]
Degrees[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 24
0
0.5
1
1.5
2
Time [h]
Power[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 24
0
0.05
0.1
0.15
0.2
0.25
Energy[kWh]
Time [h]
Air-Conditioning Energy

Daily cost with control
0 4 8 12 16 20 24
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Time [h]
Cost[€]
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
0 100 200 300 400 500 600
5
5.5
6
6.5
7
7.5
N simulations
Cost[€/day]
Energy Cost without control
0 100 200 300 400 500 600
2.6
2.8
3
3.2
3.4
3.6
3.8
4
4.2
N simulations
Cost[€/day]
Energy Cost with control

Daily cost without control Daily cost with control
5 5.5 6 6.5 7 7.5
0
10
20
30
40
50
60
70
80
90
Cost [€/day]
Frequency
2.5 3 3.5 4 4.5
0
20
40
60
80
100
120
140
Cost [€/day]
Frequency

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.
3
DSOs 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
3
The 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
𝑁 𝑗
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
9
A
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 (𝑇𝑒𝑥𝑡 𝑡
) Time series have been collected for winter period in the south of Italy
Thermal loads User’s air
conditioning comfort
setting
At average, 200
C with an allowed variation of ± 20
C
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 40
0
8
16
24
32
Average percentage cost savings for the buses with DR [%]
Percentagefrequency[%]

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
Costsavingspercentage[%]
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
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
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
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 24
0
100
200
D-LMP
[euro/MWh]
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 24
0
0.05
0.1
Active
Power[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 24
15
20
25
Time [h]
Temperature[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 24
0
100
200D-LMP
[euro/MWh] 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 24
0
0.05
0.1
Active
Power[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 24
15
20
25
Time [h]
Temperature[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.
4
European 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.

Residential Demand Response Operation in a Microgrid

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (20)

Similar to Residential Demand Response Operation in a Microgrid

Similar to Residential Demand Response Operation in a Microgrid (20)

More from Antonio Moreno-Munoz

More from Antonio Moreno-Munoz (19)

Recently uploaded

Recently uploaded (20)

Residential Demand Response Operation in a Microgrid