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URL : https://hal.archives-ouvertes.fr/hal-02131022

C. , B. Suryanarayanan, S. Maciejewski, A. A. Siegel, H. J. Sharma et al., A comparison of three parallel processing methods for a resource allocation problem in the smart grid Available: https, 2017.

E. L-'electricite-en-reseau, Available: http://www.enedis.fr/ classes-temporelles, 2017.

E. Technology, . Innovation, and . Platforms, ETP smart grids: Vision and strategy Available: http://www.etip-snet, 2017.

M. Passing and . Forum, MPI: A message-passing interface standard Available: http://mpi-forum.org/docs, 2017.

O. Architecture and . Board, OpenMP application program interface Available: http://www.openmp.org/specifications, 2017.

T. Smartgrids, . Technology, and . Platform, What is a smartgrid? Available: http://ftp.smartgrids, 2017.

Z. , Y. Chen, W. And-gao, and W. , A survey on the development status and challenges of smart grids in main driver countries, Renewable and Sustainable Energy Reviews, vol.79, pp.137-147, 2017.

S. Conceptual-model-of-the,

F. Dsm, shifting, (b) peak shaving, (c) strategic conservation, (d) strategic load growth, (e) valley filling, (f) flexible load

D. Types-of and .. ,

R. ,

.. , Total energy and electric energy breakdown for each sector

A. Smart-home,

R. Peak-effect-with-uncoordinated and D. , , p.14

.. , CES: Community energy storage), System architecture with neighborhood area networks, p.23

.. , Principle of centralized coordination (DM: decision making), p.24

. .. , Fully-dependent decentralized coordination (DM: decision making), p.26

, Fully-independent decentralized coordination (DM: decision making), p.26

, Partially-independent decentralized coordination (DM: decision making), p.27

.. , Smart home model (SM: smart meter), p.42

S. Pv, , p.45

, Neighborhood agent model with power and communication structures, p.46

T. , F. , and N. , , p.47

.. , Each block represents a 60-minute time interval, p.49

, Representation of the battery control interval (15 minutes) in one hour, p.50

M. , , p.51

.. , Actual and communication data of a consumption profile, p.52

M. , , p.59

.. , Installation capacity of the resources in the neighborhood. (a) PV capacities , (b) battery capacities in smart homes, p.60

.. Rolling-horizon-approach-principle, , p.60

.. Co-simulation-platform, , p.61

.. , Smart home 01 (with PV and battery) electricity consumption profiles: (a) baseline, (b) selfish, (c) decentralized (d) centralized, p.63

, Smart home 03 (with PV and without battery) electricity consumption profiles: (a) baseline, (b) selfish, (c) decentralized (d) centralized, p.64

.. , Smart home 04 (without PV and battery) electricity consumption profiles: (a) baseline, (b) selfish, (c) decentralized (d) centralized, p.65

.. , State-of-charge of the battery system for each case, p.66

, Energy analysis of the smart homes in the decentralized coordination, p.66

, Energy analysis of the smart homes in the centralized coordination, p.67

.. , 19 Determined neighborhood profits for each control method and neighborhood size, p.68

.. , Breakdown of the probability values of the different consumption rates among smart homes, p.71

.. , Start time probability of lights, p.73

K. , , p.73

.. , Start time probability of the microwave, p.73

.. , Start time probability of the vacuum cleaner, p.73

.. , Start time probability of the television, p.74

.. , , p.74

.. , Start time probability of the iron, p.74

D. , , p.74

.. , Start time probability of the coffee maker, p.74

T. , , p.74

.. , Start time probability of the washing machine, p.74

.. , Start time probability of the clothes dryer, p.74

.. , Start time probability of the dish washer, p.75

P. Neighborhood, , p.76

, Full dynamic price in the neighborhood (condition 1: when surplus generation is increased, condition 2: when electricity consumption is increased), p.77

. Neighborhood and .. Load-profile, , p.77

. Base and .. Dynamic-electricity-pricing, , p.78

.. , Communication diagram of the neighborhood agents, p.80

.. , Coordination diagram of the group-based mechanism, p.81

.. , Coordination diagram of the turn-based mechanism, p.82

, Assets and non-controllable appliance numbers in smart homes, p.84

.. , 23 PV installation capacities in smart homes, p.84

.. , 85 4.26 Comparison of the energy consumption breakdown for the different strategies, p.86

, Smart home electricity profile example, using the different strategies

C. Annual-neighborhood,

.. , Annual neighborhood peak consumption profiles, p.88

C. Electric and .. Systems-architecture, , p.93

, Forecast spot-market (PJM) and utility prices (ComEd) for, p.94, 2017.

, Actual spot market (PJM) and utility prices (ComEd) for, p.94, 2017.

.. Modeling-for-appliance-scheduling, , p.95

.. , Aggregated electricity profile of the neighborhood without considering residential PV generation, p.97

.. , Aggregated electricity profile of the neighborhood considering residential PV generation, p.97

.. , Aggregated electricity profile of the neighborhood with base loads and PV generation (PV generation is a negative load), p.98

.. , Aggregated electricity profile of the neighborhood with base loads, PV generation and non-scheduled assets, p.98

.. , Aggregated electricity profile of the neighborhood with base loads, PV generation , non-scheduled assets and a scheduled asset, p.99

Y. Calculation-of-parameter-n-u, , p.99

Y. Calculation-of-parameter-b-u, , p.100

M. Openmp-flow, , p.101

S. Openmp-implementation-of, , p.102

.. , 14 MPI application utilization model

.. , 15 MPI implementation of SGRA (D cores)

.. , OpenMP plus MPI implementation of SGRA (T × D cores), p.104

.. , Number of hybrid threads and tasks versus (a) total simulation time and (b) computation time of per-GA-iteration, p.105

.. , Aggregator profits on July 1 st for each PV penetration level, p.107

, Total profits of customers on July 1 st for each PV penetration level, p.108

, Total controlled assets number on July 1 st for each PV penetration level, p.108

.. , Aggregated electricity profiles of the neighborhood before and after scheduling: (a) 0% (b) 25% (c) 50%, (d) 75% and (e) 100% PV penetration levels, p.109

, Total asset electricity profiles of the neighborhood before and after scheduling: (a) 0% (b) 25% (c) 50%, (d) 75% and (e) 100% PV penetration levels, p.110

.. , PJM and CIP): (a) 0% (b) 25% (c) 50%, (d) 75% and (e) 100% PV penetration levels, Forecast price profiles (ComEd, p.111

.. , PJM and CIP): (a) 0% (b) 25% (c) 50%, (d) 75% and (e) 100% PV penetration levels, Actual price profiles (ComEd, p.112

.. , Total simulation time for each PV penetration level, p.114

, Weekly forecast aggregator profits for each PV penetration level, p.114

, Weekly actual aggregator profits for each PV penetration level, p.114

, Weekly total profits of customers for each PV penetration level, p.115

, Weekly total controlled assets numbers for each PV penetration level, p.115

.. Weekly-total-forecast-aggregator-profits, , p.116

.. Weekly, , p.142

.. , Residential energy consumption modeling approaches [23]

.. Comparison-of-coordination-structures, , p.36

.. , , p.37

.. , Amount, power rating and controllability of appliances (: controllable, ?: non-controllable), p.43

.. , Start time and operation duration parameters of appliances, p.44

E. Grid and .. Neighborhood-trading-tariffs, , p.47

.. , Comparison of the algorithms ( : used, -: not used), p.48

.. , Smart homes and total neighborhood electricity costs ( * * : smart home with PV and battery, * smart home with PV), p.61

.. , Neighborhood area number of resource owners, p.67

.. , Comparison of proposed methods in Chapters 3 and 4, p.69

, Number of smart home appliances and corresponding probabilities, p.72

M. Appliance-operation-duration, , p.73

. Battery and R. Ownership, , p.75

.. , Daily electric energy management results with (WE) and without (WoE) forecasting errors, p.85

.. , Annual electric energy management with (WE) and without (WoE) forecasting errors, p.88

.. , Neighborhood area number of resource owners, p.89

.. , Determined neighborhood profits and peak reductions for each control method and neighborhood size, p.90

]. , Modeling parameters of assets

). B. Accepted, R. Celik, D. Roche, A. Bouquain, and . Miraoui, Decentralized neighborhood energy management with coordinated smart home energy sharing, IEEE Transactions on Smart Grid, issue.1

J. 2. Celik, R. Roche, S. Suryanarayanan, D. Bouquain, and A. Miraoui, Electric energy management in residential areas through coordination of multiple smart homes, Renewable and Sustainable Energy Reviews, vol.80, pp.260-275, 2017.
DOI : 10.1016/j.rser.2017.05.118
URL : https://hal.archives-ouvertes.fr/hal-02131022

B. 1. Celik, R. Roche, D. Bouquain, and A. Miraoui, Increasing renewable local energy use in smart neighborhoods through coordinated trading, Cyber-Physical-Social Systems and Constructs in Electric Power Engineering, chapter 9, pp.217-252, 2016.

, Conference Papers

). S. Accepted, V. Sharma, B. Durvasulu, S. Celik, T. M. Suryanarayanan et al., Metrics-based assessment of sustainability in demand response, The 15 th IEEE International Conference in Smart City, p.2017, 2017.

B. Celik, S. Suryanarayanan, A. A. Maciejewski, H. J. Siegel, S. Sharma et al., A comparison of three parallel processing methods for a resource allocation problem in the smart grid, 2017 North American Power Symposium (NAPS), 2017.
DOI : 10.1109/NAPS.2017.8107204
URL : https://hal.archives-ouvertes.fr/hal-02131020

C. 3. Celik, R. Roche, D. Bouquain, and A. Miraoui, Coordinated home energy management in community microgrids with energy sharing among smart homes, In: ELECTRIMACS, p.6, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01624464

C. 4. Celik, R. Roche, D. Bouquain, and A. Miraoui, Coordinated neighborhood energy sharing using game theory and multi-agent systems, 2017 IEEE Manchester PowerTech, pp.1-6, 2017.
DOI : 10.1109/PTC.2017.7980820
URL : https://hal.archives-ouvertes.fr/hal-02131024

C. 5. Celik, R. Roche, D. Bouquain, and A. Miraoui, Coordinated energy management using agents in neighborhood areas with RES and storage, 2016 IEEE International Energy Conference (ENERGYCON), pp.1-6, 2016.
DOI : 10.1109/ENERGYCON.2016.7514081

C. 6. Roche, B. Celik, D. Bouquain, and A. Miraoui, A framework for grid-edge resilience improvement using homes and microgrids coordination, 2015 IEEE Eindhoven PowerTech, pp.1-6, 2015.
DOI : 10.1109/PTC.2015.7232417

S. R. Workshops, D. Roche, A. Bouquain, and . Miraoui, Coordinated neighborhood energy sharing using game theory and multi-agent systems, 2017.

P. 2. Celik, S. Sharma, V. Durvasulu, S. Suryanarayanan, T. M. Hansen et al., Metric-based assessment of sustainability in demand response, International Conference and Workshop REMOO -Presentation, 2017.

P. 3. Celik, R. Roche, S. Surayanarayanan, D. Bouquain, and A. Miraoui, Cyber physical systems in the smart grid: electric energy management through smart homes coordination, Grenoble, 2016.

P. 4. Celik, R. Roche, S. Surayanarayanan, D. Bouquain, and A. Miraoui, CPS for energy management in smart homes through coordination, Presentation, 2016.