Demand Response Implementation in an Intelligent Energy Management - - PowerPoint PPT Presentation

Demand Response Implementation in an Intelligent Energy Management System

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Community Grid Model

Community

Community Manager

ISO

Electricity Market

• Community Manager (CM)
• manages community members
• has direct connection with

electricity market

• Community is a subset of the main grid
• Community Members
• Producers, Consumers, Prosumers
• Some members have contracts

with CM, some others do not have any contract.

• Prosumers
• Offer surplus of generation
• Execute demand response programs
• Receive fixed payment for generation
• Receive incentives for demand response

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Members Contracts with Community Manager

• Producers
• Offer the max generation capacity to

the CM

• Receive fixed payment mentioned in

contract

• Consumers
• Execute demand response programs

provided by CM

• Receive incentives based on

consumption curtailment/reduction

DR Type M / V** Remuneration Activation/ Signal Measure/ Contract

DLC* M Power tariff discount DLC per equipment X events per month Red. T1 V Cost/kWh reduced Reduction notification Actual kWh reduction Red. T2 M Cost/kWh reduced Actual consumption level notification Actual kWh reduction Pricing V N/A (Reduced/Increased energy costs) Electricity price notification N/A

* Direct Load Control ** Mandatory – Voluntary

Members can have more than one demand response contracts Demand Response Contracts

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Real distribution network for Community model

Location:

• ISEP/IPP – Porto, Portugal

Network features:

• Internal low voltage network
• f university campus
• Underground electrical lines

(3.350 km)

• 15 kV / 400V-230V, 2050 kVA
• 21 Buses
• MV/LV transformer

in bus #21

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iEMS (intelligent Energy Management System) Description

• 4 Producers
• Wind Turbines
• PV
• 3 Consumers
• Houses
• Factories
• Commercial

Buildings

• 13 Prosumers
• Houses
• Factories
• Commercial

Buildings

Proposed Community members BUS #15: GECAD Research Center GECAD Building:

• 11 Offices
• 2 Laboratories
• 1 Server room
• 1 Meeting room
• 1 Kitchen
• 2 Bathroom
• 10 kW PV Generation Capacity

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iEMS Web Panel

4 Energy meters Sensors

• CO2
• VOC
• Temperature
• Humidity
• Light
• Temperature Outside
• Light Outside

19 DALI Ballasts for lighting control

• Presence

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Optimization Model for Lighting System

Inputs

Total consumption PV generation Number of offices Number of lights Consumption of each light Max consumption of each light Max reduction of each light Required Reduction Max reduction Priority of each light

Lights Scheduling Results Algorithm for Minimizing Consumption

• Lower priorities first
• Do not turn all off
• Keep a minimum

illumination level for each room

Optimization

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Real-Time Demonstration

Optimization

Scheduling

iEMS

Real-Time data Input Output

iEMS

Real-Time Actuation MODBUS TCP/IP MODBUS TCP/IP

• Min. Lights Consumption = lp (“min”, priorities, coefficientsMat, directions, rhs)

Direction of Optimization Constraints of Optimization

9 The present work has been developed under the EUREKA - ITEA2 Project FUSE-IT (ITEA-13023), Project GREEDI (ANI|P2020 17822), and has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013.

Thank you for your attention!

Questions