Consensus + Innovations Approach for Distributed Multiagent Coordination in a Microgrid

By Gabriela Hug, Soummya Kar, Chenye Wu. IEEE Transactions on Smart Grid, 2015

328 citations recorded by the source corpus.

Distributed energy resources and demand-side management are expected to become more prevalent in the future electric power system. Coordinating the increased number of grid participants in an efficient and reliable way is going to be a major challenge. A potential solution is the employment of a distributed energy management approach, which uses intelligence distributed over the grid to balance supply and demand. In this paper, we specifically consider the situation in which distributed resources and loads form microgrids within the bulk power system in which the load is supplied by local generation. A distributed energy management approach based on the consensus + innovations method is presented and used to coordinate local generation, flexible load, and storage devices within the microgrid. The approach takes advantage of the fact that, at the optimal allocation settings, the marginal costs given as a function of the power output/consumption need to be equal for all nonbinding network resources. Solutions for single time step, as well as multitime step optimization including intertemporal constraints, are presented.

Subfields: Computer Networks and Communications, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f567248ad2e5ec25726101ff306ba41fd9ff4e50

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Citations in the corpus

Peer-to-Peer Trading in Electricity Networks: An Overview (2020, background): “In [29], on the other hand, the authors propose a distributed approach based on the consensus and innovations method to coordinate local generation, flexible load, and storage devices within the microgrid to derive a distributed economic dispatch algorithm.”
Multiclass Energy Management for Peer-to-Peer Energy Trading Driven by Prosumer Preferences (2019, methodology): “To address this, distributed energy management strategies have been proposed, including dual decomposition [13], [14], alternating direction method of multipliers (ADMM) [15], [16] and distributed consensus based algorithms [17], [18].”
Consensus-Based Approach to Peer-to-Peer Electricity Markets With Product Differentiation (2018, methodology): “The Relaxed Consensus + Innovation (RCI) is a decentralized optimization method inspired by the Consensus + Innovation method presented for instance in [5] in the case of a pool-based market.”
A survey of distributed optimization (2019, background): “For example, the authors of Cherukuri and Cortés (2018) ; Hug et al. (2015) ; Wu, Yang, Stoor-vogel, and Stoustrup (2017) ; Yang, Wu, Stoorvogel, and Stous-trup (2016) and He et al. (2019) studied the coordination be-tween distributed generators and energy storages, while the authors of Li, Chen,…”
A Decentralized Bilateral Energy Trading System for Peer-to-Peer Electricity Markets (2020, methodology): “ing direction method of multipliers (ADMM) [10], primal-dual gradient methods [11], [12], and consensus-based methods [13],”
Control Strategies for Microgrids With Distributed Energy Storage Systems: An Overview (2018, background): “(c) The DOPF strategies presented in [126] and [127] are”
Peer-to-peer and community-based markets: A comprehensive review (2018, methodology): “A quadratic function [52] is commonly used to represent production/consumption costs, using three positive parameters an, bn and cn.”
Distributed Nash Equilibrium Seeking by a Consensus Based Approach (2016, background): “For instance, a class of consensus controllers were proposed for networked dynamical systems in [42] and a consensus based approach was studied in [43] for distributed coordination of the generation, load and storage devices in a microgrid.”
Exogenous Cost Allocation in Peer-to-Peer Electricity Markets (2019, methodology): “Some test cases exist for P2P markets, such as in [9], but they do not provide power system’s characteristics.”