In this paper, a distributed convex optimization framework is developed for energy trading between islanded microgrids. More specifically, the problem consists of several islanded microgrids that exchange energy flows by means of an arbitrary topology. Due to scalability issues and in order to safeguard local information on cost functions, a subgradient-based cost minimization algorithm is proposed that converges to the optimal solution in a practical number of iterations and with a limited communication overhead. Furthermore, this approach allows for a very intuitive economics interpretation that explains the algorithm iterations in terms of "supply-demand model" and "market clearing." Numerical results are given in terms of convergence rate of the algorithm and attained costs for different network topologies.

The fifth generation wireless networks must provide fast and reliable connectivity while coping with the ongoing traffic growth. It is of paramount importance that the required resources, such as energy and bandwidth, do not scale with traffic. While the aggregate network traffic is growing at an unprecedented rate, users tend to request the same popular contents at different time instants. Therefore, caching the most popular contents at the network edge is a promising solution to reduce the traffic and the energy consumption over the backhaul links. In this paper, two scenarios are considered, where caching is performed either at a small base station, or directly at the user terminals, which communicate using Device-to-Device (D2D) communications. In both scenarios, joint design of the transmission and caching policies is studied when the user demands are known in advance. This joint design offers two different caching gains, namely, the pre-downloading and local caching gains. It is shown that the finite cache capacity limits the attainable gains, and creates an inherent tradeoff between the two types of gains. In this context, a continuous time optimization problem is formulated to determine the optimal transmission and caching policies that minimize a generic cost function, such as energy, bandwidth, or throughput. The jointly optimal solution is obtained by demonstrating that caching files at a constant rate is optimal, which allows reformulation of the problem as a finite-dimensional convex program. The numerical results show that the proposed joint transmission and caching policy dramatically reduces the total cost, which is particularised to the total energy consumption at the Macro Base Station (MBS), as well as to the total economical cost for the service provider, when users demand economical incentives for delivering content to other users over the D2D links

With transition towards 5G, mobile cellular networks are evolving into a powerful platform for ubiquitous large-scale information acquisition, communication, storage and processing. 5G will provide suitable services for mission-critical and real-time applications such as the ones envisioned in future Smart Grids. In this work, we show how emerging 5G mobile cellular network, with its evolution of Machine-Type Communications and the concept of Mobile Edge Computing, provides an adequate environment for distributed monitoring and control tasks in Smart Grids. In particular, we present in detail how Smart Grids could benefit from advanced distributed State Estimation methods placed within 5G environment. We present an overview of emerging distributed State Estimation solutions, focusing on those based on distributed optimization and probabilistic graphical models, and investigate their integration as part of the future 5G Smart Grid services.

In this paper, we address the case where two microgrids are isolated from the main power grid but can exchange energy with each other in a peer-to-peer (P2P) manner. The goal is to minimize the total cost resulting from energy generation and transportation, while each microgrid satisfies its local power demand. We first propose a centralized solution. In this approach, a central controller must have access to all the information. For those situations where privacy must be protected, we propose a distributed approach, in which each microgrid solves a local optimization problem in an iterative fashion by exchanging some information with the other one. I. INTRODUCTIONWorldwide energy demand is expected to increase steadily over the incoming years, driven by energy demands from humans, industries and electrical vehicles: more precisely, it is expected that the growth will be in the order of 40% by year 2030. This demand is fueled by an increasingly energydependent lifestyle of humans, the emergence of electrical vehicles as the major source of transportation, and further automation of processes that will be facilitated by machines.In today's power grid, energy is produced in centralized and large energy plants (macrogrid energy generation); then, the energy is transported to the end client, often over very large distances and through complex energy transportation meshes. Such a complex structure has a reduced flexibility and will hardly adapt to the demand growth, thus increasing the probability of grid instabilities and outages. The implications are enormous as demonstrated by recent outages in Europe that have caused losses of millions of Euros.Given these problems at macro generation, it is of no surprise that a lot of efforts have been put into replacing or at least complementing macrogrid energy by means of local renewable energy sources. In this context, microgrids are emerging as a promising energy solution in which distributed (renewable) sources are serving local demand that does not surpass the secondary substation. When local production cannot satisfy microgrid requests, energy is bought from the main utility distribution grid. Microgrids are envisaged to provide a number of benefits: reliability in power delivery (e.g., by islanding), efficiency and sustainability by increasing the penetration of renewable sources, scalability and investment deferral, and the provision of ancillary services. From this list, it is necessary to stress the islanding capability [1]- [3]. Islanding is one of the highlighted features of microgrids, which refers to the ability

This paper provides a view to Peer-to-Peer (P2P) approach for smart grid operation adopted in P2P-SmarTest project. It provides an overview on solutions proposed for distributed P2P energy trading, P2P grid control and wireless communication enabling the proposed P2P operation. The paper proposes some business models that can be adopted in a P2P setting. We also outline the barriers and enablers against and for adopting local or regional P2P based electricity operations.