Hardware and Software Implementation of Decentralized Active Demand Response (DADR) System Supporting Primary Regulation

The concept of demand response (DR) is relatively well-known. There is an extensive literature on possible DR system services and evaluation of regulative potential, based on statistical analyses of data concerning loads suitable for provision of the service. Some of the presented concepts have been implemented in real applications. However, as far as the authors know, this paper presents the first attempt at an implementation of a decentralized active DR system (DADR), without communication between devices, in the form of a laboratory setup consisting 1000 devices enabling primary regulation support. The proposed implementation covers an original hardware design, a software platform for flexible system development, a laboratory arrangement enabling an objective assessment of the quality of service provided by the DADR system, as well as results of a pilot test in real power system. The practical realization of the DADR system is based on an earlier elaborated and theoretically investigated control algorithm, presented in previous papers.

[1]  N. Kunwar,et al.  Area-Load Based Pricing in DSM Through ANN and Heuristic Scheduling , 2013, IEEE Transactions on Smart Grid.

[2]  Xin Jin,et al.  Frequency Regulation Services from Connected Residential Devices: Short Paper , 2016, BuildSys@SenSys.

[3]  Mehdi Rahmani,et al.  LMI-Based Robust Predictive Load Frequency Control for Power Systems With Communication Delays , 2017, IEEE Transactions on Power Systems.

[4]  P. Mancarella,et al.  Decentralized Participation of Flexible Demand in Electricity Markets—Part II: Application With Electric Vehicles and Heat Pump Systems , 2013, IEEE Transactions on Power Systems.

[5]  Grzegorz Benysek,et al.  Decentralized Active Demand Response (DADR) system for improvement of frequency stability in distribution network , 2016 .

[6]  Kazunori Sakurama,et al.  Communication-Based Decentralized Demand Response for Smart Microgrids , 2017, IEEE Transactions on Industrial Electronics.

[7]  Jianzhong Wu,et al.  Primary Frequency Response From Electric Vehicles in the Great Britain Power System , 2013, IEEE Transactions on Smart Grid.

[8]  Pedro Faria,et al.  Aggregation and Remuneration of Electricity Consumers and Producers for the Definition of Demand-Response Programs , 2016, IEEE Transactions on Industrial Informatics.

[9]  Mario Paolone,et al.  GECN: Primary Voltage Control for Active Distribution Networks via Real-Time Demand-Response , 2014, IEEE Transactions on Smart Grid.

[10]  Grzegorz Benysek,et al.  Modified stochastic algorithm for decentralized active demand response (DADR) system supporting primary load frequency control , 2017, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society.

[11]  Jianhui Wang,et al.  A Distributed Direct Load Control Approach for Large-Scale Residential Demand Response , 2014, IEEE Transactions on Power Systems.

[12]  Jaeseok Choi,et al.  DSM Considered Probabilistic Reliability Evaluation and an Information System for Power Systems Including Wind Turbine Generators , 2013, IEEE Transactions on Smart Grid.

[13]  Grzegorz Benysek,et al.  Electric vehicle charging infrastructure in Poland , 2012 .

[14]  C. Y. Chung,et al.  Well-Being Analysis of Generating Systems Considering Electric Vehicle Charging , 2014, IEEE Transactions on Power Systems.

[15]  Tingwen Huang,et al.  Second-Order Continuous-Time Algorithms for Economic Power Dispatch in Smart Grids , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[16]  Jiming Chen,et al.  A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches , 2015, IEEE Transactions on Industrial Informatics.

[17]  K. Kalsi,et al.  Loads as a Resource Frequency Responsive Demand November 2015 , 2015 .

[18]  Grzegorz Benysek,et al.  AC/DC/DC Interfaces for V2G Applications—EMC Issues , 2013, IEEE Transactions on Industrial Electronics.

[19]  Azam Khalili,et al.  Fully Distributed Demand Response Using the Adaptive Diffusion–Stackelberg Algorithm , 2017, IEEE Transactions on Industrial Informatics.

[20]  Alessandro Astolfi,et al.  A stochastic approach to distributed power frequency control by means of smart appliances , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[21]  Kazunori Sakurama,et al.  Privacy Masking for Distributed Optimization and Its Application to Demand Response in Power Grids , 2017, IEEE Transactions on Industrial Electronics.

[22]  Taisuke Masuta,et al.  Supplementary Load Frequency Control by Use of a Number of Both Electric Vehicles and Heat Pump Water Heaters , 2012, IEEE Transactions on Smart Grid.

[23]  João P. S. Catalão,et al.  Optimal Household Appliances Scheduling Under Day-Ahead Pricing and Load-Shaping Demand Response Strategies , 2015, IEEE Transactions on Industrial Informatics.

[24]  Marko Aunedi,et al.  Economic and Environmental Benefits of Dynamic Demand in Providing Frequency Regulation , 2013, IEEE Transactions on Smart Grid.

[25]  Grzegorz Benysek,et al.  Application of Stochastic Decentralized Active Demand Response (DADR) System for Load Frequency Control , 2018, IEEE Transactions on Smart Grid.

[26]  G. Strbac,et al.  Decentralized Participation of Flexible Demand in Electricity Markets—Part I: Market Mechanism , 2013, IEEE Transactions on Power Systems.

[27]  Dane Christensen,et al.  Foresee: A user-centric home energy management system for energy efficiency and demand response , 2017 .