The ability to accurately forecast power generation from renewable sources is nowadays recognised as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the impact of single components in providing accurate predictions. In this paper we extensively compare simple forecasting methodologies with more sophisticated ones over 32 photovoltaic plants of different size and technology over a whole year. Also, we try to evaluate the impact of weather forecasts on the prediction of PV power generation.

Motivated by the problems of charging a number of electric vehicles via limited capacity infrastructure, this article considers the problem of individual load adjustment under a total capacity constraint. For reasons of scalability and simplified communications, distributed solutions to this problem are sought. Borrowing from communication networks (AIMD algorithms) and distributed convex optimisation, we describe a number of distributed algorithms for achieving relative average fairness whilst maximising utilisation. We present analysis and simulation results to show the performance of these algorithms. In the scenarios examined, the algorithm's performance is typically within 5% of that achievable in the ideal centralised case, but with greatly enhanced scalability and reduced communication requirements.

This paper introduces distributed algorithms that share the power generation task in an optimized fashion among the several Distributed Energy Resources (DERs) within a microgrid. We borrow certain concepts from communication network theory, namely Additive-Increase-Multiplicative-Decrease (AIMD) algorithms, which are known to be convenient in terms of communication requirements and network efficiency. We adapt the synchronized version of AIMD to minimize a cost utility function of interest in the framework of smart grids. We then implement the AIMD utility optimisation strategies in a realistic power network simulation in Matlab-OpenDSS environment, and we show that the performance is very close to the full-communication centralized case.

Inspired by the ability of Markov chains to model complex dynamics and handle large volumes of data in Google's PageRank algorithm, a similar approach is proposed here to model road network dynamics. The central component of the Markov chain is the transition matrix which can be completely constructed by easily collecting traffic data. The proposed model is validated using the popular mobility simulator SUMO. Markov chain theory and spectral analysis of the transition matrix are then shown to reveal non-evident properties of the underlying road network and to correctly predict consequences of road network modifications. Preliminary results from possible applications are shown and simple practical examples are provided throughout this article to clarify and support the theoretical expectations.

Motivated by the need to provide services to alleviate range anxiety of electric vehicles, we consider the problem of balancing charging demand across a network of charging stations. Our objective is to reduce the potential for excessively long queues to build up at some charging stations, although other charging stations are underutilized. A stochastic balancing algorithm is presented to achieve these goals. A further feature of this algorithm is that it is fully decentralized and facilitates a plug-and-play type of behavior. Using our system, the charging stations can join and leave the network without any changes to, or communication with, a centralized infrastructure. Analysis and simulations are presented to illustrate the efficacy of our algorithm.

This paper introduces and illustrates some novel stochastic policies that assign parking spaces to cars looking for an available parking space. We analyze in detail both the main features of a single park, i.e., how a car could conveniently decide whether to try its luck at that parking lot or try elsewhere, and the case when more parking lots are available, and how to choose the best one. We discuss the practical requirements of the proposed strategies in terms of infrastructure technology and vehicles' equipment and the mathematical properties of the proposed algorithms in terms of robustness against delays, stability, and reliability. Preliminary results obtained from simulations are also provided to illustrate the feasibility and the potential of our stochastic assignment policies.