open demand response optimization framework and tools for

Horizon 2020 – LCE-2017 - SGS

FLEXCoop

Democratizing energy markets through the introduction of innovative

flexibility-based demand response tools and novel business and market models

for energy cooperatives

WP5 – Open Demand Response Optimization Framework and

Tools for Aggregators

D5.7 – FLEXCoop Global Demand

Manager - Final Version

Due date: 31.05.2020 Delivery Date: 05.06.2020

Author(s): Germán Martínez, Rafael Peris (ETRa), Gregorio Fernández (CIRCE), Chazapi

Francesca, Petridis Kosmas (Hypertech)

Editor: Germán Martínez, Laura Morcillo (ETRa)

Lead Beneficiary of Deliverable: ETRa

Contributors: Hypertech, DTU, CIRCE, CIMNE

Dissemination level: Confidential Nature of the Deliverable: Demonstrator

Internal Reviewers: René van Vliet (ODE), Peder Bacher (DTU)

HORIZON 2020 –773909 - FLEXCoop D5.7 - FLEXCoop Global Demand


WP5 – Open DR Optimization Framework

And tools for Aggregators FLEXCoop Consortium Page 53 of 58

FLEXCoop Key Facts

Topic: LCE-01-2016-2017 - Next generation innovative technologies

enabling smart grids, storage and energy system integration with

increasing share of renewables: distribution network

Type of Action: Research and Innovation Action

Project start: 01 October 2017

Duration: 36 months from 01.10.2017 to 30.09.2020 (Article 3 GA)

Project Coordinator: Fraunhofer

Consortium: 13 organizations from nine EU member states

FLEXCOOP CONSORTIUM PARTNERS

Fraunhofer Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.

ETRa ETRA INVESTIGACION Y DESARROLLO SA

HYPERTECH HYPERTECH (CHAIPERTEK) ANONYMOS VIOMICHANIKI

DTU DANMARKS TEKNISKE UNIVERSITET

GRINDROP GRINDROP LIMITED

CIRCE FUNDACION CIRCE CENTRO DE INVESTIGACION DE RECURSOS

Y CONSUMOS ENERGETICOS

KONCAR KONCAR - INZENJERING ZA ENERGETIKUI TRANSPORT DD

SUITE5 SUITE5 DATA INTELLIGENCE SOLUTIONS Limited

S5 SUITE5 DATA INTELLIGENCE SOLUTIONS Limited

CIMNE CENTRE INTERNACIONAL DE METODES NUMERICS EN

ENGINYERIA

RESCOOP.EU RESCOOP EU ASBL

SomEnergia SOM ENERGIA SCCL

ODE ORGANISATIE VOOR HERNIEUWBARE ENERGIE DECENTRAAL

Escozon ESCOZON COOPERATIE UA - affiliated or linked to ODE

MERIT MERIT CONSULTING HOUSE SPRL

Disclaimer: FLEXCoop is a project co-funded by the European Commission under the Horizon

2020 – LCE-2017 SGS under Grant Agreement No. 773909.

The information and views set out in this publication are those of the author(s) and do not

necessarily reflect the official opinion of the European Communities. Neither the European

Union institutions and bodies nor any person acting on their behalf may be held responsible for

the use, which may be made of the information contained therein.

© Copyright in this document remains vested with the FLEXCoop Partners





EXECUTIVE SUMMARY

This deliverable is the outcome of the final version of the Task 5.3 “Dynamic demand-based

VPP module and Global Demand Manager”. This deliverable is a demonstrator, which provides

technical information about the Global Demand Manager (GDM) component.

The component is continuously analysing demand/storage flexibility, in combination with

Demand Response (DR) received signals and rapidly decide the optimal configuration of

demand-based dynamic Virtual Power Plants (VPPs) to respond in time and provide the

required flexibility to the grid.

The GDM is responsible for dispatching automated control signals to Local Demand Managers

(LDMs) with the objective of using the flexibility of the end-users. In addition to this, the GDM

will be constantly monitoring the evolution of each DR event to identify overrides (or failures)

of the deployed strategies, to respond and to revise the initially defined strategies with the aim

to achieve the expected flexibility.





Table of Contents

EXPLANATIONS FOR FRONT PAGE .................................. FEHLER! TEXTMARKE NICHT DEFINIERT.

FLEXCOOP KEY FACTS ................................................................................................................................... 2

FLEXCOOP CONSORTIUM PARTNERS ....................................................................................................... 2

EXECUTIVE SUMMARY ................................................................................................................................... 3

LIST OF FIGURES .............................................................................................................................................. 6

LIST OF TABLES ................................................................................................................................................ 6

ABBREVIATIONS ............................................................................................................................................... 7

1. INTRODUCTION ............................................................................................................................................. 8

2. THE GLOBAL DEMAND MANAGER IN THE FLEXCOOP ARCHITECTURE .................................. 8

3. VERSIONING/VERSION OF THE SOFTWARE DEMONSTRATOR ..................................................... 9

4. RELEASE DATE .............................................................................................................................................. 9

5. RELEVANT LICENCES USED IN THE DEMONSTRATOR ................................................................... 9

6. OVERVIEW OF THE GDEM ....................................................................................................................... 10

7. PROGRAMMING LANGUAGE .................................................................................................................. 11

8. CONTENTS OF THE CURRENT RELEASE ............................................................................................. 11

8.1. BS2 LOCAL OPTIMIZATION ........................................................................................................................ 12 8.1.1. Introduction ....................................................................................................................................... 12 8.1.2. Theoretical definition ........................................................................................................................ 12 8.1.3. Optimization process ......................................................................................................................... 13

8.2. BS2 GLOBAL OPTIMIZATION ...................................................................................................................... 17 8.2.1. Introduction ....................................................................................................................................... 17 8.2.2. Theoretical definition ........................................................................................................................ 17 8.2.3. Optimization process ......................................................................................................................... 18

8.3. EV FLEXIBILITY PROFILING TOOL ............................................................................................................... 20

9. SOURCE CODE OF THE RELEASE .......................................................................................................... 25

10. RELATED DOCUMENTATION ................................................................................................................ 25

11. INSTALLATION GUIDE ............................................................................................................................ 25

12. USER GUIDE ................................................................................................................................................ 25

1.1. BIDDING ..................................................................................................................................................... 25 1.2. DR CAMPAIGNS MANAGER ........................................................................................................................ 26 1.3. OPTIMIZER ................................................................................................................................................. 27 1.4. VPP MANAGER .......................................................................................................................................... 28

13. INTERFACES WITH OTHER COMPONENTS AND THEIR INTEROPERABILITY ..................... 29

14. REQUIREMENTS COVERAGE ................................................................................................................ 30

15. DEVELOPMENT AND INTEGRATION STATUS .................................................................................. 30

16. CONCLUSION.............................................................................................................................................. 31

REFERENCES .................................................................................................................................................... 32

APPENDIX A: LOCAL OPTIMIZATION EXAMPLES ............................................................................... 33

MEDIUM INSULATION ....................................................................................................................................... 33 Example 1 .................................................................................................................................................... 33 Example 2 .................................................................................................................................................... 36 Example 3 .................................................................................................................................................... 38




And tools for Aggregators FLEXCoop Consortium 3 of 58

Example 4 .................................................................................................................................................... 41 LOW INSULATION .............................................................................................................................................. 43

Example 5 .................................................................................................................................................... 43 Example 6 .................................................................................................................................................... 46 Example 7 .................................................................................................................................................... 48 Example 8 .................................................................................................................................................... 51

APPENDIX B: GLOBAL OPTIMIZATION EXAMPLES ............................................................................ 54





LIST OF FIGURES

Figure 1: FLEXCoop Conceptual Architecture Design ............................................................. 8

Figure 2: Global Demand Manager Architecture Design ........................................................ 10

Figure 3: First optimization process ......................................................................................... 14

Figure 4: Optimization time for a 4-hour time horizon ............................................................ 16

Figure 5: Second optimization process .................................................................................... 18

Figure 6: Power flows in the model of an EV acting as an ESS. ............................................. 20

Figure 7. Simulation example 1, inputs. ................................................................................... 21

Figure 8. Simulation example 1, outputs. ................................................................................. 21

Figure 9. Simulation example 2, outputs. ................................................................................. 22

Figure 10. Simulation example 3, inputs. ................................................................................. 23

Figure 11. Simulation example 3, outputs. ............................................................................... 23

Figure 12. Simulation example 4, EV flexibility outputs. ....................................................... 24

Figure 13: Bidding workflow ................................................................................................... 26

Figure 14: DR Campaigns Manager workflow ........................................................................ 27

Figure 15: Optimization workflow ........................................................................................... 28

Figure 16: VPP generation workflow ....................................................................................... 29

LIST OF TABLES

Table 1: Requirements Coverage ............................................................................................. 30

Table 2: Development and integration status ........................................................................... 31





ABBREVIATIONS

BS Business Scenario

CIM Common Information Model

DR Demand-Response

DRS Demand-Response Settlement

DRSR DRS and Remuneration

DSO Distribution System Operator

DoW Description of Work

FFSAM Flexibility Forecasting, Segmentation and Aggregation Module

GDM Global Demand Manager for Aggregators

GDPR General Data Protection Regulation

ISP Imbalance Settlement Period

LDM Local Demand Energy Manager

MOM Message Oriented Middleware

TSO Transmission System Operator

VPP Virtual Power Plant





1. INTRODUCTION

The FLEXCoop Global Demand Manager (GDM) is one of the main components of the Open

Demand Response (DR) Optimization Framework. It is composed by the following submodules

(their details are depicted at Section 6):

VPP Manager

DR Campaigns Manager

Bidding

Optimizer

All those submodules, in direct collaboration with the DR Settlement and Remuneration

(DRSR), Flexibility Forecasting Segmentation and Aggregation (FFSAM), Local Demand

Manager (LDM) and Message Oriented Middleware (MOM) components of the FLEXCoop

solution, are combined to successfully process the DR events at global level (also known as

aggregator side level or district level).

Considering that this deliverable is a demonstrator, the main delivered product is a piece of

software. This document contains details about the functionalities of the Global Demand Manager

component developed within this task.

This is the final version of the component, so no new functionalities will be added in the future. The

only pending work to be done with this component is to correct some bugs detected during the

integration and testing phases.

2. THE GLOBAL DEMAND MANAGER IN THE FLEXCOOP ARCHITECTURE

Figure 1: FLEXCoop Conceptual Architecture Design





On the FLEXCoop Architecture, the GDM is placed at the Aggregator side. In the conceptual

architecture diagram shown in Figure 1 it appears indicated with a red frame. It is a back-end

system whose results can be visualized via the GDM View of the Visualization – Aggregator

Toolkit module, while its functionalities will be used by all the FLEXCoop components through

the Message Oriented Middleware (MOM).

3. VERSIONING/VERSION OF THE SOFTWARE DEMONSTRATOR

This is the final version of the GDM component, according with the Description of Work

(DoW).

Comparing it with its preliminary version delivered on Month 24, its main differences are the

following ones. These modifications have been applied in order to fulfil the requirements of the

2 business scenarios (BS) that are being implemented in the project (documented on D7.2

“Evaluation Framework and Respective Validation Scenarios”).

The DSO Daemon submodule previously depicted has been removed. As part of the

optimization process (BS2) that takes place on the Optimizer submodule, and the

bidding process (BS3) running on the Bidding submodule, a DR Signal is generated and

directly communicated to the DR Campaigns Manager submodule for processing it.

The Flexibility Collector submodule that was presented on the preliminary version has

been removed. The needed flexibility information is retrieved from the MOM by the

Bidding and the DR Campaigns Manager submodule, which is the one that needs this

information for doing their job.

Also, the MongoDB storage part has been removed. In order to fulfil the GDPR compliance,

this component does not store any information locally. Everything is stored on the MOM,

making accessible that information through the API it offers.

4. RELEASE DATE

The Global Demand Manager was released at 30.09.2019. Some new versions of it will be released

until the end of the integration phase, which ends at M34.

5. RELEVANT LICENCES USED IN THE DEMONSTRATOR

The Global Demand Manager has been implemented using Open Source licenses. The most relevant

software packages and licenses are listed below:

JavaScript ES6

Node.js 8.x – MIT license

Docker 17.11.0-ce – Apache 2.0 License

Python 3.8





6. OVERVIEW OF THE GDM

Figure 2: Global Demand Manager Architecture Design1

As aforementioned, the Global Demand Manager is the core component in charge of managing

the DR campaigns. For that purpose, it has been structured in several submodules:

VPP Manager: This submodule uses the results of the clustering algorithms of the FFSA

module for creating the VPPs and the flexibility that could be provided by each one of

them; this information is updated every day.

Bidding: (Only for the Dutch pilot site) This subcomponent simulates the process of

creating bids (with the format [1] and restrictions [2] depicted on TenneT’s TSO

documentation), taking into account the available flexibility, and storing those results

on the MOM.

1 The communication between the GDM and the other components (FFSA, DRSR and LDM) is done through

the MOM.





Optimizer: (Only for the Spanish pilot site) Once a day it gathers the results of all the

local optimizations and performs a global optimization for calculating the amount of

energy that has to be purchased on the market for the next day.

DR Campaigns manager: Apart from their own results, the Bidding and Optimizer sub-

components can generate new DR Campaigns:

o Bidding: If a bid activation signal is received, a DR Campaign will be generated

at the moment for fulfilling that concrete bid. Within the scope of this project,

and due to no real connection to a TSO exists, some random bid activation

signals will be generated as part of the tests performed in T7.4 “Pilot Roll Out

and Demonstration”.

o Optimizer: The DR Campaign generated for the next day is focused on maintain

the expected demand forecasted of the entire portfolio taking into account the

users with self-production.

On this sub-module it will be processed the received DR signals, along with some other

parameters, for elaborating the planning for being able to succeed on that DR Campaign.

As inputs, those modules will need the following data coming from the MOM:

Clusters: Results of the clustering algorithms of the FFSA.

Local optimization: Result of the daily optimizations executed at local level on the

LDM.

Flexibility: The flexibility information that has been previously processed on the LDM

(that flexibility takes into account the DR attributes of the devices and the information

of the published contracts).

VPP: This information, previously generated on the own GDM, is needed for the

elaboration of the DR Campaigns.

And they will generate:

VPP: To store the last version of the calculated VPPs and their flexibility.

DR signal: DR signals generated that will be retrieved by the LDM.

7. PROGRAMMING LANGUAGE

The major part of the backend module has been developed using Node.js framework, based on

JavaScript programming language.

FOR THE OPTIMIZATIONS IT HAS BEEN DEVELOPED A SOFTWARE LIBRARY WITH THE OPTIMIZATION METHODS

AND SEVERAL AUXILIARY FUNCTIONS. THIS SOFTWARE HAS BEEN DEVELOPED IN PYTHON AND SUPPORTED BY

THE NUMPY AND THE SCIPY LIBRARIES. SEVERAL PROGRAMS HAS BEEN BUILT OVER THE OPTIMIZATION

LIBRARY FOR RUNNING EXAMPLES WITH DIFFERENT DATA SETS AND CONFIGURATIONS (THEY

HAVE BEEN INCLUDED ON

REFERENCES

https://numpy.org/

https://scipy.org/scipylib/index.html





[1

]

Tennet, “Manual Bidding of Balancing- and Transport Power,” 13 01 2020. [Online].

Available:

https://www.tennet.eu/fileadmin/user_upload/SO_NL/Manual_Bidding_BTP.pdf.

[Accessed 21 05 2020].

[2

]

Tennet, “Product information automatic Frequency Restoration Reserve,” 18 12 2018.

[Online]. Available:

https://www.tennet.eu/fileadmin/user_upload/SO_NL/Product_information_aFRR_20

18-12-18.pdf. [Accessed 21 05 2020].





APPENDIX A: LOCAL OPTIMIZATION EXAMPLES AND APPENDIX B: GLOBAL

OPTIMIZATION EXAMPLES

Below are some examples of optimization of energy management for the areas described in

8.1.3 Optimization process. The optimization has been carried out for 8 hours with a time step

of 15 minutes. Nominal maximum electric power of the HP 4000.0 Watts. The internal

minimum and maximum temperature set points are 21 and 24 respectively. The internal and

walls/air initial temperature is 22ºC. Energy results are in Watts-hour.

Medium Insulation

Example 1

Prediction Horizon 8

Data Time Step (seconds) 3600.0

Optimization Time Step (seconds) 900

Conduction, convection and infiltration resistances of the zone 1.0 0.0037 0.0206

Walls and air thermal capacitances of the zone 2274900.55 810000.0

Solar gains distribution to air and wall nodes coefficients 1.3137 0.2706

HVAC power distribution to air and wall nodes coefficients 1.0 0.01

Nominal maximum electric power of the HP 4000.0

Internal Minimum Temperature Set points





[21. 21. 21. 21. 21. 21. 21. 21.]

Internal Maximum Temperature Set points

[25. 25. 25. 25. 25. 25. 25. 25.]

Initial Internal Temperature 22.0

Expected Ambient Temperature

[12. 13. 14. 15. 16. 17. 18. 18.]

Expected Global Horizontal Irradiance

[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]

Expected Produced PV power

[0. 0. 0. 0. 0. 0. 0. 0.]

Grid price

[1. 1. 1. 1. 1. 1. 1. 1.]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]

--------------------------------------------------------------------------------

Total time: 8

Internal Temperatures

21.5 21.3 21.6 21.9 22.2 22.8 22.7 22.6





Wall Temperatures

21.8 21.6 21.5 21.8 22.0 22.0 22.0 22.0

Consumed electric power bought from the grid

351.35 417.42 319.32 193.19 97.10 82.08 36.04 11.01

Consumed electric power generated by the PV

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Summary

Min & Max Temp Int: 21.28 22.77

Min & Max Temp Walls: 21.51 22.0

Total Consumed electric power bought from the grid: 1507.5

Total Consumed electric power generated by the PV: 0.0

Total Consumed electric power bought from the grid Value: 1507.5

Total Consumed electric power generated by the PV Value: 0.0

Total Value: 1507.5





Example 2










[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]







[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[0. 0. 0. 0. 0. 0. 0. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]

Total time: 8


21.5 21.3 21.6 21.9 22.2 22.8 22.7 22.6

Wall Temperatures

21.8 21.6 21.5 21.8 22.0 22.0 22.0 22.0


351.35 417.42 319.32 193.19 97.10 82.08 36.04 11.01






0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Summary







Total Value: 1568.46

Example 3














[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]



[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[ 0. 100. 200. 150. 100. 100. 100. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]

Total time: 8






21.5 21.3 21.5 21.8 22.2 22.8 22.7 22.6

Wall Temperatures

21.9 21.9 22.2 22.3 22.0 22.0 22.0 22.0


351.35 317.42 119.32 43.19 15.04 0.00 0.00 11.01


0.00 100.00 200.00 150.00 100.00 100.00 100.00 0.00

Summary







Total Value: 875.09





Example 4










[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]







[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[ 0. 100. 200. 150. 100. 100. 100. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[-0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9]

Total time: 8


21.5 21.3 21.5 21.8 22.2 22.8 22.7 22.6

Wall Temperatures

21.9 21.9 22.2 22.3 22.0 22.0 22.0 22.0


351.35 317.42 119.32 43.19 15.04 0.00 0.00 11.01






0.00 100.00 200.00 150.00 100.00 100.00 100.00 0.00

Summary






Total Consumed electric power generated by the PV Value: -675.0

Total Value: 200.09

Low Insulation

Example 5














[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]



[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[0. 0. 0. 0. 0. 0. 0. 0.]

Grid price

[1. 1. 1. 1. 1. 1. 1. 1.]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]





Total time: 8


21.3 21.2 21.4 21.8 22.5 22.9 22.8 23.0

Wall Temperatures

22.3 21.8 21.9 22.3 22.0 22.0 22.0 22.0


675.68 816.82 677.68 505.51 367.37 309.31 219.22 183.18


0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Summary












Example 6










[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]







[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[0. 0. 0. 0. 0. 0. 0. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]

Total time: 8


21.3 21.2 21.4 21.8 22.5 22.9 22.8 23.0

Wall Temperatures

22.3 21.8 21.9 22.3 22.0 22.0 22.0 22.0


675.68 816.82 677.68 505.51 367.37 309.31 219.22 183.18


0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00





Summary








Example 7














[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]



[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[ 0. 100. 200. 150. 100. 100. 100. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]

Total time: 8


21.3 21.2 21.4 21.7 22.5 22.8 22.7 22.9





Wall Temperatures

22.3 21.9 22.2 22.7 22.0 22.0 22.0 22.0


675.68 716.82 477.68 355.51 267.37 209.31 119.22 183.18


0.00 100.00 200.00 150.00 100.00 100.00 100.00 0.00

Summary












Example 8










[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]







[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[ 0. 100. 200. 150. 100. 100. 100. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[-0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9]

--------------------------------------------------------------------------------

Total time: 8


21.3 21.2 21.4 21.7 22.5 22.8 22.7 22.9

Wall Temperatures

22.3 21.9 22.2 22.7 22.0 22.0 22.0 22.0


675.68 716.82 477.68 355.51 267.37 209.31 119.22 183.18






0.00 100.00 200.00 150.00 100.00 100.00 100.00 0.00

Summary








APPENDIX B: Global optimization examples).

8. CONTENTS OF THE CURRENT RELEASE

This release contains all the functionalities that should be covered by this component. The list

of those functionalities can be seen in the Requirements Coverage section.

As part of the details of those functionalities, a couple of optimization processes are executed

for covering the needs of the BS2. These optimizations are split in two steps:

1. The first is executed one on the LDM at local level for every user,

2. and the second one at global level on the GDM.

8.1. BS2 Local Optimization

8.1.1. Introduction

This first optimization is initially defined in the “Chapter 4 - USE CASE 2 (SPAIN’S CASE;

PILOT 2)” of the deliverable “D7.2 – FLEXCoop Evaluation Framework and Respective

Validation Scenarios”. After a meticulous study of this optimization, in which different

alternatives have been analysed, especially for the differential equations that define the thermal

behaviour of the asset, an optimization process has been defined, which is detailed below.

Its output is the amount of energy that needs to be imported from the grid at each step of time

(Pgrid(tk)) and the total amount of self-produced energy to be used also at each step of time

(Ppv(tk)). In addition to this, also the needed set points of the heat pumps for fulfilling those

results are provided, which already take into account the building parameters and user settings.

8.1.2. Theoretical definition





Where:

N: Number of discrete timesteps

tk: Discrete time variable

Pgrid(tk): Consumed electric power bought from the grid at time tk

Ppv(tk): Consumed electric power generated by the PV at time tk

kgrid(tk), kpv (tk): Parameters to define inclusion/exclusion of the respective term in the

objective function

Tin(tk): Zone air temperature at time tk

Tw(tk ): Averaged surface/walls temperature at time tk

Tin,1: Value of Tin(tk) at time t1

𝑇𝑚𝑖𝑛(𝑡𝑘): Minimum accepted indoor temperature at time tk

𝑇𝑚𝑎𝑥(𝑡𝑘): Maximum accepted indoor temperature at time tk

Tamb(tk): Ambient temperature at time tk

𝑃𝑝𝑣𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑(𝑡𝑘): Produced electric power by the PV at time tk

𝑃𝐻𝑃𝑚𝑎𝑥: Nominal maximum electric power of the HP

C1, C2: Walls and air thermal capacitances of the zone

R1, R2, R3: Conduction, convection and infiltration resistances of the zone

p1, p2: Solar gains distribution to air and wall nodes coefficients

p3, p4: HVAC power distribution to air and wall nodes coefficients

Notes:

A, B. Matrices of the state-space 3R2C grey box model.

Tw(t1) = Tin,1. The averaged surface/walls temperature is not measured; the assumption

that its initial value equals the zone air temperature at t1 is acceptable and does not affect

the zone air temperature prediction's accuracy.

The objective function can be written as follows. It is now easier to see that the first

term is the electricity taken from the grid, while the second term is the PV electricity

sent to the grid:

kgrid(tk) and kpv(tk) could also play the role of prices if needed.

Possible combination of parameters values for the objective function:

kgrid(tk) = 1, kpv(tk) = 0, ∀𝑘. This leads to the explicit minimization of electric power

consumed by the grid for the optimization period. Indirectly it also finds a good PV

consumption curve.

kgrid(tk) = 1, kpv(tk) = -1, ∀𝑘. This combines the minimization of electric power consumed

by the grid and the maximization of PV-generated electric consumption for the

optimization period.

kgrid(tk) = 0, kpv(tk) = -1, ∀𝑘. Leads to the explicit maximization of PV-generated electric

consumption for the optimization period. We are against this option, it can result in

increased grid consumption.





SOME EXAMPLES OF RUNNING THIS OPTIMIZATION HAVE BEEN INCLUDED ON

REFERENCES

[1

]


Available:


[Accessed 21 05 2020].

[2

]




18-12-18.pdf. [Accessed 21 05 2020].





APPENDIX A: Local optimization examples.

8.1.3. Optimization process

The following figure shows the optimization process, the required data sets and the results

obtained.

Figure 3: First optimization process

The optimization data defines the optimization process and includes the time horizon for

optimization and the duration of the time interval of the input data.

The asset data includes the maximum available power and the parameters that characterize the

thermal behaviour of the asset.

The user settings data includes user preferences with the minimum and maximum temperatures

that the user is willing to support, without compromising the personal comfort.

The Predicted Data includes predicted data of environmental type (ambient temperature and

radiation) and market prices (purchase price of grid energy and sale of PV energy).

As a result of the optimization process, a vector is generated with the internal temperatures that

must be selected by the user. Complementing this information is an estimate of the energy

required to be consumed by the user from the grid and of the PV production energy to be sold.

8.1.3.1 Comments about the result

The correct energy management of the aggregator requires that it has an estimation of the

energy that its users will consume over a period of time and mechanisms so that the actual use

of that energy is similar to the estimated one.

The use of energy made by each user depends on the actions carried out by the user on the

equipment that consumes energy. One of the equipment that consumes the most energy is

HVAC. In this case, to determine the behaviour of the HVAC, the temperature at which it must





operate at a given moment must be established. To cover a time interval [1..N] it is required to

have a temperature for each instant of that interval:

𝑇𝑠𝑒𝑡 = [𝑇𝑠𝑒𝑡1 ⋯ 𝑇𝑠𝑒𝑡𝑖 … 𝑇𝑠𝑒𝑡𝑁]

Each user, to meet the demands of the aggregator must be able to establish the vector of

temperatures for that time interval. This can be done automatically by acting directly on the

HVAC equipment or manually by the user.

The optimization of a user's energy consumption results in a vector that defines the time

evolution of internal energy and two other vectors with the energy consumption associated with

that evolution (Grid and PV consumption).

𝑇𝑖𝑛 = [𝑇𝑖𝑛1 ⋯ 𝑇𝑖𝑛𝑖 … 𝑇𝑖𝑛𝑁]

𝐸𝑔𝑟𝑖𝑑 = [𝐸𝑔𝑟𝑖𝑑1 ⋯ 𝐸𝑔𝑟𝑖𝑑𝑖 … 𝐸𝑔𝑟𝑖𝑑𝑁]

𝐸𝑝𝑣 = [𝐸𝑝𝑣1 ⋯ 𝐸𝑝𝑣𝑖 … 𝐸𝑝𝑣𝑁]

From the data of the temporal evolution of the internal temperature Tin, the set points Tset at

which the HVAC must operate can be derived.

The elementary solution to optimization is to set the Tset to the minimum possible temperature

Tmin in a cold environment and to the maximum possible Tmax in a warm environment.

However, it is possible to improve this optimization by storing thermal energy in the building

itself without using specific devices.

The optimization that is carried out makes use of the characteristics of each zone, the

environmental information (solar radiation, ambient temperature) and the minimum and

maximum temperature values that define the user's comfort zone. This optimization does not

make use of energy storage systems and only considers the acceptable temperature ranges to

manage user’s comfort.

If a user does not have their own energy production, the only possible optimization is to use the

price differences in the energy to accumulate thermal energy in the building to take advantage

of it when the energy price is higher. This only results in slight differences in the final cost. If

the user has also his/her own energy production, he/she can take advantage of this cheap energy

to accumulate the thermal energy in the building and use it when he/she does not have his/her

own energy.

The variable to be optimized X is formed by the vector of internal temperatures Tin, the vector

of temperatures in the walls Tw, the vector of the power needed from the grid Pgrid and the

vector of the power from own production Ppv. Each vector has N components where N is the

number of time steps used in the optimization. For example, for 24 hours with a time step of 15





minutes, a vector of N = 24 * 4 = 96 data is required, so the variable X has 4 * 96 = 384

components.

X =

[

𝑇𝑖𝑛1

⋮𝑇𝑖𝑛𝑁

𝑇𝑤1

⋮𝑇𝑤𝑁

𝑃𝑔𝑟𝑖𝑑1

⋮𝑃𝑔𝑟𝑖𝑑

𝑁

𝑃𝑝𝑣1

⋮𝑃𝑝𝑣

𝑁 ]

Optimization requires an initial variable that must meet all constraints. To determine it, it is

necessary to define feasible Tin and Tw temperatures and then calculate the energy required by

simulation.

The time required for the simulation depends on the size of the variable to be optimized (4N).

This size depends on the time horizon to be optimized and the time step. The following figure

shows the time required for optimization with a time horizon of 4 hours and different time steps.

Figure 4: Optimization time for a 4-hour time horizon

The user comfort temperature range is usually very small so there is very little temperature

range in the optimization. This implies that after optimization, the results hardly differ from the

elemental solution. In general, the best strategy is to consume as little energy as possible and,

if you have your own production, sell the surplus.





8.2. BS2 Global Optimization

8.2.1. Introduction

The second optimization is defined in the section “4.5.4 Second optimization: 24 h electricity

price and imbalance deviations” of the deliverable “D7.2 – FLEXCoop Evaluation Framework

and Respective Validation Scenarios”.

This optimization is executed considering the behaviour of all users. In this optimization, in

addition to the cost for the purchase or sale of energy by the aggregator, deviations from the

assumptions made during the first optimization are also considered.

The additional cost corresponds to deviations due to excess or default consumption, although

in general they will be due to excess. There is a maximum consumption that must not be

exceeded or alternatively a minimum consumption that must not be lowered.

As a result of this optimization, some commands are extracted to the user (a_down and a_up)

that indicate to the user that he/she should reduce consumption or consume more. These values

are in the range [0, 1] and if one of the values is greater than zero the other must be zero. In

other words, if one action must be taken in one period, the other cannot be done.

The cost for non-compliance is made by comparing with two reference powers (Pmin, Pmax).

8.2.2. Theoretical definition

𝑐𝑎𝑔𝑔 = 𝑚𝑖𝑛(𝑢𝑖 , 𝑖 ∈ {1,2, . . . , 𝑛}) ∑(∑𝑐𝑏𝑢𝑦(�̂�𝑖,𝑡∗ , 𝑡)

𝑛

𝑖=1

+ ∑𝑐𝑠𝑒𝑙𝑙(�̂�𝑖,𝑡∗ , 𝑡) +

𝑛

𝑖=1

𝑡𝑒𝑛𝑑

𝑡=𝑡𝑗

+𝑐𝑑,𝑢𝑝(∑(

𝑛

𝑖=1

�̂�𝑖,𝑡∗ , 𝑡) + 𝑐𝑑,𝑑𝑜𝑤𝑛(∑(

𝑛

𝑖=1

�̂�𝑖,𝑡∗ , 𝑡))

w.r.t.

∀𝑡 ∈ {𝑡𝑗, 𝑡𝑗+1, . . . , 𝑡𝑒𝑛𝑑}

�̂�𝑖,𝑡∗ = (�̂�𝑖,𝑡 + 𝑎𝑖,𝑡

𝑑𝑜𝑤𝑛(�̂�𝑖,𝑡𝑚𝑎𝑥 − �̂�𝑖,𝑡) − 𝑎𝑖,𝑡

𝑢𝑝(�̂�𝑖,𝑡 − �̂�𝑖,𝑡𝑚𝑖𝑛) − �̂�𝑖,𝑡

𝑃𝑉)

𝑐𝑏𝑢𝑦(�̂�𝑖,𝑡∗ , 𝑡) = 𝑐𝑡

𝑒�̂�𝑖,𝑡∗ for �̂�𝑖,𝑡

∗ > 0

𝑐𝑠𝑒𝑙𝑙(�̂�𝑖,𝑡∗ , 𝑡) = 𝑐𝑡

𝑟�̂�𝑖,𝑡∗ for �̂�𝑖,𝑡

∗ ≤ 0

𝑐𝑑,𝑢𝑝(∑ �̂�𝑖,𝑡∗ , 𝑡𝑛

𝑖=1 ) = 𝑐𝑡𝑑,𝑢𝑝(𝑃𝑡

𝑏𝑖𝑑 − ∑ �̂�𝑖,𝑡∗𝑛

𝑖=1 ) for ∑ �̂�𝑖,𝑡∗ < 𝑃𝑡

𝑏𝑖𝑑𝑛𝑖=1

𝑐𝑑,𝑑𝑜𝑤𝑛(∑ �̂�𝑖,𝑡∗ , 𝑡𝑛

𝑖=1 ) = 𝑐𝑡𝑑,𝑑𝑜𝑤𝑛(∑ �̂�𝑖,𝑡

∗ − 𝑃𝑡𝑏𝑖𝑑𝑛

𝑖=1 ) for 𝑃𝑡𝑏𝑖𝑑 ≤ ∑ �̂�𝑖,𝑡

∗𝑛𝑖=1

where





● 𝑢𝑖 = {𝑎𝑖,𝑡𝑗

𝑢𝑝 , 𝑎𝑖,𝑡𝑗+1𝑢𝑝 , . . . , 𝑎𝑖,𝑡𝑒𝑛𝑑

𝑢𝑝 , 𝑎𝑖,𝑡𝑗𝑑𝑜𝑤𝑛, 𝑎𝑖,𝑡𝑗+1

𝑑𝑜𝑤𝑛 , . . . , 𝑎𝑖,𝑡𝑒𝑛𝑑

𝑑𝑜𝑤𝑛}

● 𝑎𝑖,𝑡𝑢𝑝 = [0,1]: The signal which represents the requested up-regulation activation, i.e.

load reduction. From 0 indicating no activation to 1 indicating full up-regulation

activation.

● 𝑎𝑡𝑑𝑜𝑤𝑛 = [0,1] : The signal which represents the requested down-regulation activation,

i.e. load increase. From 0 indicating no activation to 1 indicating full down-regulation

activation.

● At most one activation to one side can be carried out at any time t, thus 𝑎𝑡𝑑𝑜𝑤𝑛 > 0 ⇒

𝑎𝑡𝑢𝑝 = 0 ∧ 𝑎𝑡

𝑢𝑝 > 0 ⇒ 𝑎𝑡𝑑𝑜𝑤𝑛 = 0always hold.

● �̂�𝑖,𝑡 is the forecasted baseline power level.

● �̂�𝑖,𝑡𝑚𝑖𝑛 is the forecasted minimum power level, which is the reference load of prosumer

𝑖 if up-regulation activation is activated.

● �̂�𝑖,𝑡𝑚𝑎𝑥 is the forecasted maximum power level, which is the reference load of prosumer

𝑖 if down-regulation activation is activated.

SOME EXAMPLES OF RUNNING THIS OPTIMIZATION HAVE BEEN INCLUDED ON APPENDIX B:

GLOBAL OPTIMIZATION EXAMPLES










Medium Insulation

Example 1










[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]







[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[0. 0. 0. 0. 0. 0. 0. 0.]

Grid price

[1. 1. 1. 1. 1. 1. 1. 1.]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]

--------------------------------------------------------------------------------

Total time: 8


21.5 21.3 21.6 21.9 22.2 22.8 22.7 22.6

Wall Temperatures

21.8 21.6 21.5 21.8 22.0 22.0 22.0 22.0






351.35 417.42 319.32 193.19 97.10 82.08 36.04 11.01


0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Summary







Total Value: 1507.5

Example 2














[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]



[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[0. 0. 0. 0. 0. 0. 0. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price





[0. 0. 0. 0. 0. 0. 0. 0.]

Total time: 8


21.5 21.3 21.6 21.9 22.2 22.8 22.7 22.6

Wall Temperatures

21.8 21.6 21.5 21.8 22.0 22.0 22.0 22.0


351.35 417.42 319.32 193.19 97.10 82.08 36.04 11.01


0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Summary












Example 3










[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]







[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[ 0. 100. 200. 150. 100. 100. 100. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]

Total time: 8


21.5 21.3 21.5 21.8 22.2 22.8 22.7 22.6

Wall Temperatures

21.9 21.9 22.2 22.3 22.0 22.0 22.0 22.0


351.35 317.42 119.32 43.19 15.04 0.00 0.00 11.01






0.00 100.00 200.00 150.00 100.00 100.00 100.00 0.00

Summary







Total Value: 875.09

Example 4














[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]



[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[ 0. 100. 200. 150. 100. 100. 100. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[-0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9]





Total time: 8


21.5 21.3 21.5 21.8 22.2 22.8 22.7 22.6

Wall Temperatures

21.9 21.9 22.2 22.3 22.0 22.0 22.0 22.0


351.35 317.42 119.32 43.19 15.04 0.00 0.00 11.01


0.00 100.00 200.00 150.00 100.00 100.00 100.00 0.00

Summary







Total Value: 200.09





Low Insulation

Example 5










[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]




And tools for Aggregators FLEXCoop Consortium of 58



[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[0. 0. 0. 0. 0. 0. 0. 0.]

Grid price

[1. 1. 1. 1. 1. 1. 1. 1.]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]

Total time: 8


21.3 21.2 21.4 21.8 22.5 22.9 22.8 23.0

Wall Temperatures

22.3 21.8 21.9 22.3 22.0 22.0 22.0 22.0






675.68 816.82 677.68 505.51 367.37 309.31 219.22 183.18


0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Summary








Example 6














[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]



[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[0. 0. 0. 0. 0. 0. 0. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]





Total time: 8


21.3 21.2 21.4 21.8 22.5 22.9 22.8 23.0

Wall Temperatures

22.3 21.8 21.9 22.3 22.0 22.0 22.0 22.0


675.68 816.82 677.68 505.51 367.37 309.31 219.22 183.18


0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Summary












Example 7










[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]







[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[ 0. 100. 200. 150. 100. 100. 100. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]

Total time: 8


21.3 21.2 21.4 21.7 22.5 22.8 22.7 22.9

Wall Temperatures

22.3 21.9 22.2 22.7 22.0 22.0 22.0 22.0


675.68 716.82 477.68 355.51 267.37 209.31 119.22 183.18


0.00 100.00 200.00 150.00 100.00 100.00 100.00 0.00





Summary








Example 8














[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]



[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[ 0. 100. 200. 150. 100. 100. 100. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[-0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9]

--------------------------------------------------------------------------------

Total time: 8






21.3 21.2 21.4 21.7 22.5 22.8 22.7 22.9

Wall Temperatures

22.3 21.9 22.2 22.7 22.0 22.0 22.0 22.0


675.68 716.82 477.68 355.51 267.37 209.31 119.22 183.18


0.00 100.00 200.00 150.00 100.00 100.00 100.00 0.00

Summary








APPENDIX B: Global optimization examples.

8.2.3. Optimization process

The following figure shows the optimization process, the required data sets and the results

obtained.

Figure 5: Second optimization process

The general data includes:

Optimization Time Horizon

Price of buying from the grid

Rewards of selling to the grid

Day ahead imbalances deviation costs for less energy consumption

Day ahead imbalances deviation costs for more energy consumption

Power bid

The user data includes for each user:

Forecasted baseline power level

Forecasted minimum power level, which is the reference load of prosumer - if up-

regulation activation is activated

Forecasted maximum power level, which is the reference load of prosumer - if down-

regulation activation is activated

Forecasted of the PV generation

The optimization results include:

For each time step:

o For each user: The down and up settings

o The total energy consumed

o The cost of the consumed energy

The cost of the total energy consumed





8.3. EV flexibility profiling tool

This subsection shows some simulations results carried out to evaluate the performance of the

developed EV flexibility profiling tool: economically optimal charging profile and flexibility

possibilities. Only simulations and no real field test have been made because no real

manageable EV charging points were available in friendly users’ houses.

This text is the continuation of previous works shown in other documents as D3.1 - DER

Modelling and Forecasting Algorithms, D3.4 – FLEXCoop EVs flexibility profiling models or

D5.1 – Demand Flexibility Profiling Mechanism Configuration. These documents were focused

on storage and EV storage modelling (see

Figure 6), flexibility calculation model and first simulations (review those documents for a

theoretical review of EV flexibility profiling tool). Current results will include the calculation

of optimal charging processes and flexibility possibilities in residential framework, were EV

charging point is near other electric demands and results can be influenced by those ones.

Figure 6: Power flows in the model of an EV acting as an ESS.

Next simulations results are made using input data similar to the historical (energy demand, for

example) and characterization (energy prices, contracted power, etc…) data provided by

friendly users of FLEXCoop Project.

EXAMPLE 1

V2G charging point in an average house.

o Other consumptions included but no storage or renewables systems.

General consumer data:

o Contracted power: 5.75kW (25 Amps at 230V and no power penalization in the

optimization)

o Electric tariff: 2.0DHA (with discrimination tariff, more info in

https://www.somenergia.coop/es/tarifas-de-electricidad/#tarifa2.0)

DERs:

o Local generation systems: none.

o Batteries: none.

EV and charging point:

o Single-phase charging point, 3,7 kW maximum charging power.

https://www.somenergia.coop/es/tarifas-de-electricidad/#tarifa2.0





o Batteries capacity: 24 kWh.

o Batteries minimum absolute SoC:5 kWh

o Batteries minimum SoC when charging: 10kWh

o Batteries minimum SoC at departure time: 15kWh

o Batteries arrival SoC: 8kWh

Figure 7. Simulation example 1, inputs.

In Figure 7 it can be seen the inputs of the first simulation example. In blue line (P_dem) is the

normal demands of a house along three days, in orange (Energy_price) the energy prices (two

periods), in grey (VE_at_home) the availability of the EV in the house (1 when it is available

and 0 when it is out of the house) and in yellow (min_SoC) the minimum SoC of the EV (5kWh

in general, 10 kWh when connected to the charging point and 15 just when de departure is

forecasted) .

Figure 8. Simulation example 1, outputs.

Figure 8 shows part of the results of the simulation. The blue line (P_dem(t)) shows the normal

house demand, the orange line (P_grid(t)) the demand in the grid connection point including

the EV charging demand, the grey (P_VE(t)) the charging demand of the EV, the yellow one

(SoC_EV(t)) the electric vehicle state of charge and the blue line (Energy_price) the energy

prices. As it can be seen, when the EV is connected the optimization engine starts to charge it,





even at high prices, until it reached the minim SoC when connected and continues until the

departure SoC only in low prices period. The EV charging power is limited by the whole house

contracted power. “P_grid(t)” shows the optimal consumption curve, including home demands

and EV charge, minimizing energy costs managing vehicle charge.

EXAMPLE 2

Same conditions as example 1 but with longer EV availability.


As it can be seen in Figure 9 the EV continues the charge at low power and in low prices period

until all the maximum SoC is reached, In high prices period discharges de EV batteries until

the minimum SoC when connected and starts re-charging in low price periods until the

departure SoC. These operation set points reduces energy bill by buying energy in low price

periods and using it when prices are higher. The minimum SoC when connected is maintained

if any non-forecasted departure is needed and them minimum departure SoC is ensured.

EXAMPLE 3

Same conditions as example 2 but with Solar PV generation.

DERs:

o Local generation systems: 3.975 kWp and 4kW inverter (similar to Spanish

friendly user, see Figure 10 “perf_gen1” line).

o Batteries: none.





Figure 10. Simulation example 3, inputs.


With local solar PV generation (Figure 11), the EV charging profile changes ensuring minimum

SoCs and reducing global energy costs. The curve “P_grid(t)” shows the optimal consumption

curve minimizing energy costs managing EV charge.

EXAMPLE 4

Same conditions as example 1.

Flexibility possibilities calculation

Figure 12 shows the flexibility that could be provided by an EV in the basic situation. The blue

line (P_EV_opt(t)) shows the charging set-points that minimise energy cost, the orange line

(P_up(t)) shows the maximum power that could follow the charging point following charger

limits and maximum contracted power (3.7kW and 5.75kW) and the grey line

(P_down(t))shows the minimum power that could follow the charging point following charger

limits, maximum contracted power and V2G capabilities. As it can be seen even calculation

flexibility capabilities minimum SoCs must be respected (state of charge cannot be under that

values).





Figure 12. Simulation example 4, EV flexibility outputs.





9. SOURCE CODE OF THE RELEASE

Git has been used for versioning the source code, which is hosted at ETRA’s git repositories.

The developed software is not an open source software, and thus its source code is not public.

If requested by a partner, the access to the source code could be discussed under an exploitation

agreement of the software.

10. RELATED DOCUMENTATION

This demonstrator implements the functionalities specified on the DoW in addition with the

results of the Task 3.5 “Prosumer-centric local optimization strategies definition” and Task 7.1

“Detailed Pilot Evaluation, Impact Assessment and Cost-Benefit Analysis Framework”; it is

also an evolved version of what was presented on the preliminary version of this T5.3 “Dynamic

demand-based VPP module and Global Demand Manager” task. Their details can be read on

the proper deliverables:

D3.5 “Local Demand Manager Specifications and Intra-building Optimization

Algorithms”

D7.2 “FLEXCoop Evaluation Framework and Respective Validation Scenarios”

D5.3 “FLEXCoop Global Demand Manager – Preliminary Version”

The integration with the other FLEXCoop components and testing of this module is taking place

in T6.4 “Integration of FLEXCoop Components, Preliminary Testing, Parametrization and Pre-

Pilot Validation” and described on D6.4 “FLEXCoop Integrated DR Optimization Framework

and Pre-validation results – Preliminary Version” and D6.8 “FLEXCoop Integrated DR

Optimization Framework and Pre-validation results – Final Version”. The final integration of

components will be available at the end of June 2020.

11. INSTALLATION GUIDE

No installation guide is needed. The service is already installed at ETRA’s servers and its

services can be used through the API that is being configured on the MOM component.

12. USER GUIDE

Considering it is a backend component, no user guide depicting how this should be used by

him/her is needed. Instead of that it will explain the workflow of the automatic processes

running on it, so the user can have a better understanding of how it works.

1.1. Bidding

It is executed every day at 14:30 hr. (the bids for the next day have to be placed before 14:45

hr.).

As depicted on Figure 13: Bidding workflow, this process starts on the LDEM getting all the

available flexibility from all the devices of the portfolio and crosschecking that data with the

contracts between users and the aggregators and the DR Attributes of each devices. The output

of this is the real available flexibility that could be used for creating the bids for the next day.





With all the updated available flexibility, the GDM retrieves all of it and aggregates it. With

that information, the GDM can create the 96 bids for the next day (1 bid for each ISP2) and

store them on the MOM.

More details about this process can be read at D7.2 “Evaluation Framework and Respective

Validation Scenarios”.

Figure 13: Bidding workflow

1.2. DR Campaigns Manager

It can be executed for two reasons:

When a DR signal is sent from the Optimizer

When a “bid activation” signal is received on the Bidding modules and it forwards it

here as a DR signal.

On this case the workflow is as shown in Figure 14. When this module receives as an input a

DR signal, it stores that information on the Middleware. If that signal is for a campaign starting

within the next 15’, then it is also triggered. On parallel with this, a process checking if a DR

Campaign has to be triggered within the next 15’ is constantly running.

When a DR Campaign must be triggered, individual signals are sent to the LDMs. The rest of

the process occurs on that component, triggering the needed actions at each step of time for

getting the requested flexibility.

2 Imbalance Setttlement Period. Each ISP is calculated quaterhourly, that means it contains information for the

following 15 minutes





Figure 14: DR Campaigns Manager workflow

More details about how the DR Campaigns are handled at local level is documented on D3.5

“Local Demand Manager Specifications and Intrabuilding Optimization Algorithms”.

1.3. Optimizer

This module is executed every day at 23:50 hr., being executed in two steps, detailed both of

them in the BS2 Local and BS2 Global sections of this document.

As it can be seen on it workflow at Figure 15, firstly the LDM optimizes the consumption of

each user for taking into their own self-production. With that information, combined with the

price of buying energy from the grid, rewards of selling it to the grid, day ahead imbalances

deviation costs for less energy consumption and day ahead imbalances deviation costs for more

energy consumption, performs a second optimization in order to optimize the general

consumption of its entire portfolio. As a result of this, a new DR Campaign is generated.





Figure 15: Optimization workflow

1.4. VPP Manager

This module is executed every day at 23:50 hr..

As depicted in Figure 16, the GDM gathers the following information from the FFSA:

Cluster results: The clusterization of the entire portfolio taking into account the

specified criteria.

Cluster forecasts: The forecasts for all the devices included on the requested cluster

Reliability: From the list of devices provided, it returns the ones with at least the

indicated reliability.

Combining this information, the GDM calculates the VPPs, where all the portfolio is clustered

and it is know their forecast for the next 24 hours, but considering only the devices with a

minimum level of reliability.





Figure 16: VPP generation workflow

13. INTERFACES WITH OTHER COMPONENTS AND THEIR INTEROPERABILITY

The component has been deployed in a Docker container, communicating with the REST API

of the MOM component for making them available to all the other modules of the FLEXCoop

architecture. Through this API, the GDM will interact with:

With the Flexibility Forecasting Segmentation and Aggregation module for getting the

clusters of devices, their reliability delivering requested flexibility, and the total amount

of it that could be provided by each cluster.

With the Demand Response Settlement and Remuneration module for creating the

baseline of each affected user at the beginning of each DR campaign, and for

remunerating them during the settlement phase.

With the Local Demand Managers module for sending the proper signal to the affected

users for triggering and monitoring the DR campaigns. Also through this module, the

aggregated flexibility per user will be obtained.

With the Visualization – Aggregator Toolkit module for the visualization of all the

information related to the DR campaigns that have been executed or are planned to be

executed.

The details about the interoperability interfaces with all the other components of the FLEXCoop

solution is being provided in D4.7 “FLEXCoop Common Information Model – Final Version”.

The implementation of the respective interfaces is being covered on Task 6.4 “Integration of

FLEXCoop Components, Preliminary Testing, Parameterization and Pre-Pilot Validation”, and

it is being documented on its proper deliverables D6.4 ”FLEXCoop Integrated DR Optimization

Framework and Pre-validation results – Preliminary Version” and D6.8 ”FLEXCoop Integrated

DR Optimization Framework and Pre-validation results – Final Version”.





Additional to this, the following endpoints have been provided for being able to see the results

generated by the Global Demand Manager. It has to be taken into account that due to the

integration process is still in progress, most of the data used for feeding the inputs of this module

is dummy data. Also, the format how this responses are provided through these endpoints

shouldn’t be considered the final format for presenting this information; the results of this

module will be visualized in both GUI applications (T5.5 “FLEXCoop Real-time Monitoring

and Control Platform/User Interfaces for Aggregators” and T6.3 “Prosumer Portal and User

Interfaces for Prosumers”) developed within this project:

Visualization of the created VPPS

Visualization of the last created bid

Visualization of the last optimization results

Visualization of a new DR Campaign requested

14. REQUIREMENTS COVERAGE

The following table summarizes the functionalities that have been covered within this

demonstrator:

Calculation of the VPPs

Simulation of the participation in the aFRR market (in the Netherlands)

Promotion of self-consumption concept (in the Spanish pilot site)

Management of DR campaigns

Table 1: Requirements Coverage

15. DEVELOPMENT AND INTEGRATION STATUS

The following table summarizes the actual status of the Global Demand Manager component

and the actions needed for completing it.

Current Status Final demonstrator

Development status Finished

Pending development

actions

Bug corrections if detected

Integration status To be debugged

Pending integration

actions

Communication with the Middleware has been successfully

done. The remaining work to be done is to correct the bugs that

http://cloudtec.etra-id.com:6100/api/vpp/get

http://cloudtec.etra-id.com:6100/api/bid/get

http://cloudtec.etra-id.com:6100/api/optimization/get

http://cloudtec.etra-id.com:6100/api/drcampaign/get





may arise when the rest of components are also communicating

with the Middleware and reporting data to it

Table 2: Development and integration status

16. CONCLUSION

This demonstrator provides a view of the Global Demand. The main objectives of this

component are available and its final version has been implemented.

This component has been installed on ETRA’s server acting as a background component

automatically interacting with the Middleware, so no API has been created for manually using

it. Although tts functionalities are not human available, it is possible to visualize the data it

generates on the Visualization – Aggregator Toolkit.

It has some different functionalities depending on the Business Scenarios:

For the Spanish case, two optimizations are executed once a day for improving the usage

of self-consumption.

For the Dutch one, according with TenneT’s TSO, daily bids are generated, and some

of them can be activated by triggering specific signals for that (but Tennet won’t be

aware of this, this activation only triggers the process of getting the flexibility depicted

on the activated bid, no reports will be sent to them).

On the other hand, the rest of functionalities as the management of DR Campaigns and the

creation of VPPs is something common to both BS.

As the project progresses, for the duration of the integration phase and during the months that

the entire system is being used at the pilot sites , the detected bugs on this component will be

corrected.





REFERENCES

[1

]


Available:


[Accessed 21 05 2020].

[2

]




18-12-18.pdf. [Accessed 21 05 2020].





APPENDIX A: LOCAL OPTIMIZATION EXAMPLES






Medium Insulation

Example 1










[21. 21. 21. 21. 21. 21. 21. 21.]






[25. 25. 25. 25. 25. 25. 25. 25.]



[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[0. 0. 0. 0. 0. 0. 0. 0.]

Grid price

[1. 1. 1. 1. 1. 1. 1. 1.]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]

--------------------------------------------------------------------------------

Total time: 8


21.5 21.3 21.6 21.9 22.2 22.8 22.7 22.6





Wall Temperatures

21.8 21.6 21.5 21.8 22.0 22.0 22.0 22.0


351.35 417.42 319.32 193.19 97.10 82.08 36.04 11.01


0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Summary







Total Value: 1507.5





Example 2










[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]







[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[0. 0. 0. 0. 0. 0. 0. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]

Total time: 8


21.5 21.3 21.6 21.9 22.2 22.8 22.7 22.6

Wall Temperatures

21.8 21.6 21.5 21.8 22.0 22.0 22.0 22.0


351.35 417.42 319.32 193.19 97.10 82.08 36.04 11.01






0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Summary








Example 3














[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]



[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[ 0. 100. 200. 150. 100. 100. 100. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]

Total time: 8






21.5 21.3 21.5 21.8 22.2 22.8 22.7 22.6

Wall Temperatures

21.9 21.9 22.2 22.3 22.0 22.0 22.0 22.0


351.35 317.42 119.32 43.19 15.04 0.00 0.00 11.01


0.00 100.00 200.00 150.00 100.00 100.00 100.00 0.00

Summary







Total Value: 875.09





Example 4










[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]







[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[ 0. 100. 200. 150. 100. 100. 100. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[-0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9]

Total time: 8


21.5 21.3 21.5 21.8 22.2 22.8 22.7 22.6

Wall Temperatures

21.9 21.9 22.2 22.3 22.0 22.0 22.0 22.0


351.35 317.42 119.32 43.19 15.04 0.00 0.00 11.01






0.00 100.00 200.00 150.00 100.00 100.00 100.00 0.00

Summary







Total Value: 200.09

Low Insulation

Example 5














[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]



[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[0. 0. 0. 0. 0. 0. 0. 0.]

Grid price

[1. 1. 1. 1. 1. 1. 1. 1.]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]





Total time: 8


21.3 21.2 21.4 21.8 22.5 22.9 22.8 23.0

Wall Temperatures

22.3 21.8 21.9 22.3 22.0 22.0 22.0 22.0


675.68 816.82 677.68 505.51 367.37 309.31 219.22 183.18


0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Summary












Example 6










[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]







[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[0. 0. 0. 0. 0. 0. 0. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]

Total time: 8


21.3 21.2 21.4 21.8 22.5 22.9 22.8 23.0

Wall Temperatures

22.3 21.8 21.9 22.3 22.0 22.0 22.0 22.0


675.68 816.82 677.68 505.51 367.37 309.31 219.22 183.18


0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00





Summary








Example 7














[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]



[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[ 0. 100. 200. 150. 100. 100. 100. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[0. 0. 0. 0. 0. 0. 0. 0.]

Total time: 8


21.3 21.2 21.4 21.7 22.5 22.8 22.7 22.9





Wall Temperatures

22.3 21.9 22.2 22.7 22.0 22.0 22.0 22.0


675.68 716.82 477.68 355.51 267.37 209.31 119.22 183.18


0.00 100.00 200.00 150.00 100.00 100.00 100.00 0.00

Summary












Example 8










[21. 21. 21. 21. 21. 21. 21. 21.]


[25. 25. 25. 25. 25. 25. 25. 25.]







[12. 13. 14. 15. 16. 17. 18. 18.]


[ 16. 30.4 73.1 138.2 162.8 120.5 133.5 140.5]


[ 0. 100. 200. 150. 100. 100. 100. 0.]

Grid price

[1. 1. 1.1 1.1 1.1 1. 1. 1. ]

PV price

[-0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9]

--------------------------------------------------------------------------------

Total time: 8


21.3 21.2 21.4 21.7 22.5 22.8 22.7 22.9

Wall Temperatures

22.3 21.9 22.2 22.7 22.0 22.0 22.0 22.0


675.68 716.82 477.68 355.51 267.37 209.31 119.22 183.18






0.00 100.00 200.00 150.00 100.00 100.00 100.00 0.00

Summary








APPENDIX B: GLOBAL OPTIMIZATION EXAMPLES

This section shows an example of optimization for an aggregator with 5 users. The input data is as follows:

Price of buying from the grid

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Price of selling to the grid

0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9

Day ahead imbalances deviation costs for more energy

0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

Day ahead imbalances deviation costs for less energy

0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

Power bis

HORIZON 2020 –773909 - FLEXCoop D5.3 - FLEXCoop Global Demand Manager - Preliminary Version



0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Number of users: 5

Forecasted baseline power level (1 row for each user)

1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000

1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000

1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000

1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000

1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000

Forecasted minimum power level, which is the reference load of prosumer - if up-regulation activation is activated (1 row for each user)

500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500

500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500

500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500

500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500

500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500

Forecasted maximum power level, which is the reference load of prosumer - if down-regulation activation is activated (1 row for each user)




3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500

3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500

3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500

3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500

3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500 3500

Forecasted of the PV generation

0 0 0 0 0 0 0 0 100 200 300 400 500 500 500 500 400 200 200 100 0 0 0 0

0 0 0 0 0 0 0 0 100 200 300 400 500 500 500 500 400 200 200 100 0 0 0 0

0 0 0 0 0 0 0 0 100 200 300 400 500 500 500 500 400 200 200 100 0 0 0 0

0 0 0 0 0 0 0 0 100 200 300 400 500 500 500 500 400 200 200 100 0 0 0 0

0 0 0 0 0 0 0 0 100 200 300 400 500 500 500 500 400 200 200 100 0 0 0 0

The optimization results are as follows

Time Cost Consumption a_down & a_up for users 1 to 5

0 2750 2500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

1 2750 2500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

2 2750 2500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

3 2750 2500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0




4 2750 2500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

5 2750 2500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

6 2750 2500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

7 2750 2500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

8 2200 2000 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

9 1650 1500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

10 1100 1000 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

11 550 500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

12 0 0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

13 0 0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

14 0 0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

15 0 0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

16 550 500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

17 1650 1500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

18 1650 1500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

19 2200 2000 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

20 2750 2500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0




21 2750 2500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

22 2750 2500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

23 2750 2500 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0

Total: 44550.00

open demand response optimization framework and tools for

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