Flexibility management model of home appliances to support DSO requests in smart grids

Abstract Several initiates have been taken promoting clean energy and the use of local flexibility towards a more sustainable and green economy. From a residential point of view, flexibility can be provided to operators using home-appliances with the ability to modify their consumption profiles. These actions are part of demand response programs and can be utilized to avoid problems, such as balancing/congestion, in distribution networks. In this paper, we propose a model for aggregators flexibility provision in distribution networks. The model takes advantage of load flexibility resources allowing the re-schedule of shifting/real-time home-appliances to provision a request from a distribution system operator (DSO) or a balance responsible party (BRP). Due to the complex nature of the problem, evolutionary computation is evoked and different algorithms are implemented for solving the formulation efficiently. A case study considering 20 residential houses equipped each with seven types of home-appliances is used to test and compare the performance of evolutionary algorithms solving the proposed model. Results show that the aggregator can fulfill a flexibility request from the DSO/BRP by re-scheduling the home-appliances loads for the next 24-h horizon while minimizing the costs associated with the remuneration given to end-users.

[1]  Ioannis Lampropoulos,et al.  A system perspective to the deployment of flexibility through aggregator companies in the Netherlands , 2016, Energy Policy.

[2]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[3]  Antonello Monti,et al.  A real-time commercial aggregator for distributed energy resources flexibility management , 2017, Sustainable Energy, Grids and Networks.

[4]  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.

[5]  Amit Konar,et al.  Differential Evolution Using a Neighborhood-Based Mutation Operator , 2009, IEEE Transactions on Evolutionary Computation.

[6]  Sergio Ramos,et al.  A Flexibility Home Energy Management System to Support Agreggator Requests in Smart Grids , 2018, 2018 IEEE Symposium Series on Computational Intelligence (SSCI).

[7]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[8]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[9]  Abdullah Abusorrah,et al.  A Game Theoretic Approach to Risk-Based Optimal Bidding Strategies for Electric Vehicle Aggregators in Electricity Markets With Variable Wind Energy Resources , 2016, IEEE Transactions on Sustainable Energy.

[10]  Miika Rämä,et al.  Increasing flexibility of Finnish energy systems—A review of potential technologies and means , 2018, Sustainable Cities and Society.

[11]  Tamer Ölmez,et al.  A new metaheuristic for numerical function optimization: Vortex Search algorithm , 2015, Inf. Sci..

[12]  Emad Samuel Malki Ebeid,et al.  SEMIAH: An Aggregator Framework for European Demand Response Programs , 2015, 2015 Euromicro Conference on Digital System Design.

[13]  Mitchell Curtis,et al.  Demand side response aggregators: How they decide customer suitability , 2017, 2017 14th International Conference on the European Energy Market (EEM).

[14]  Juan M. Corchado,et al.  An Ising Spin-Based Model to Explore Efficient Flexibility in Distributed Power Systems , 2018, Complex..

[15]  Vladimiro Miranda,et al.  EPSO-evolutionary particle swarm optimization, a new algorithm with applications in power systems , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[16]  Ronnie Belmans,et al.  Demand flexibility versus physical network expansions in distribution grids , 2016 .

[17]  Aleksandra Roos,et al.  Modeling Consumer Flexibility of an Aggregator Participating in the Wholesale Power Market and the Regulation Capacity Market , 2014 .

[18]  Weihua Zhuang,et al.  Aggregating a Large Number of Residential Appliances for Demand Response Applications , 2018, IEEE Transactions on Smart Grid.

[19]  Ricardo Faia,et al.  A New Hybrid-Adaptive Differential Evolution for a Smart Grid Application Under Uncertainty , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[20]  Pablo Hernandez-Leal,et al.  Local Energy Markets: Paving the Path Toward Fully Transactive Energy Systems , 2019, IEEE Transactions on Power Systems.

[21]  Behnam Mohammadi-Ivatloo,et al.  A Bayesian game theoretic based bidding strategy for demand response aggregators in electricity markets , 2020 .

[22]  Fernando Lezama,et al.  Survey on Complex Optimization and Simulation for the New Power Systems Paradigm , 2018, Complex..

[23]  Pierluigi Siano,et al.  Demand response and smart grids—A survey , 2014 .

[24]  Feifei Gao,et al.  Efficient and Autonomous Energy Management Techniques for the Future Smart Homes , 2017, IEEE Transactions on Smart Grid.

[25]  Carlos Henggeler Antunes,et al.  An Energy Management System Aggregator Based on an Integrated Evolutionary and Differential Evolution Approach , 2015, EvoApplications.

[26]  Pedro Faria,et al.  Clustering optimization of distributed energy resources in support of an aggregator , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[27]  Ricardo Faia,et al.  Hybrid-adaptive differential evolution with decay function (HyDE-DF) applied to the 100-digit challenge competition on single objective numerical optimization , 2019, GECCO.

[28]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[29]  Alessandro Farinelli,et al.  Agent-Based Microgrid Scheduling: An ICT Perspective , 2019, Mob. Networks Appl..

[30]  Daniel E. Olivares,et al.  Participation of Demand Response Aggregators in Electricity Markets: Optimal Portfolio Management , 2018, IEEE Transactions on Smart Grid.

[31]  Zita A. Vale,et al.  Multi-dimensional signaling method for population-based metaheuristics: Solving the large-scale scheduling problem in smart grids , 2016, Swarm Evol. Comput..

[32]  Andreas Sumper,et al.  Local Flexibility Market Design for Aggregators Providing Multiple Flexibility Services at Distribution Network Level , 2018 .

[33]  Pierluigi Siano,et al.  Optimal Bidding Strategy for a DER Aggregator in the Day-Ahead Market in the Presence of Demand Flexibility , 2019, IEEE Transactions on Industrial Electronics.