Distributed Optimal Dispatch of Distributed Energy Resources Over Lossy Communication Networks

Distributed Adaptive Primal Algorithm for P2P-ETS over Unreliable Communication Links

“…Distributed energy coordination and sharing algorithms aim to minimize the energy cost of each prosumer whilst meeting the aggregate energy demand and satisfying their individual generator output capacity. These algorithms have been widely investigated for use in energy networks in applications ranging from economic dispatch problems [8,15], decentralized energy management [11,21], scheduling algorithms for smart grids [26], distributed energy trading in microgrids [12,13,16,27], distributed optimal power flow [28] and distributed voltage control [29] etc. without examining the underlying communication network.…”

“…without examining the underlying communication network. However, to assess the effect of the communication infrastructure on these applications, recent works have considered network delay [15], packet loss [8] and time-varying network topologies of the communication networks. The authors in [30] applied a gradient method to handle cases of time-varying directed networks and [15] uses gradient push-sum method for cases including communication delays.…”

“…In this section, a case of an unreliable communication network with a probability of signal loss on the communication links is considered, as well as varying the step sizes. Motivated by study [8], the probability of loss was set to 0.1 for the full mesh topology. The results are as shown in Figure 5.…”

“…This can be observed from the result which shows a 20% reduction in delay compared to hop-by-hop adaptive link state routing algorithm [25]. Further, in contrast to [8,15] that examined delay and packet loss, we tested the robustness of the proposed DAP routing algorithm to more stringent communication network constraints including delay, packet loss, congestion and different network topologies. The result shows that these constraints if not checked, would impact negatively the communication and transactions of the energy trading entities.…”

Hybrid Communication Architectures for Distributed Smart Grid Applications

“…Following this concept, the emerging distributed applications for the Distribution System Operator (DSO) within an NAN can be categorized into the following three groups: (1) distributed distribution system state estimation and control strategies only at the NAN level, such as coordinated voltage control [20], distributed optimal dispatch of distributed RESs [21]; (2) distributed monitoring and control of customer-owned RESs and ESSs through both a NAN and multiple HANs; (3) while within a HAN, instead of the traditional demand-driven-supply approach, the supply-driven-demand mechanism must be implemented in a distributed way to allow an interactive matching of flexible load and available generation at a "correct" price by the ways such as the local power sharing, priority-based load curtailment and demand response [19]. Considering the totally different data rates (1-100 Kbps for HAN, 100 Kbps-10 Mbps for NAN) and coverage range requirements (1-100 m for HAN, 100 m-10 km for NAN) at a HAN and an NAN [6], it indicates that studying hybrid communications architectures is so important to accommodate the above distrinct distributed applications at different area networks.…”