Storage devices play an essential role in increasing the flexibility of microgrids. In addition to improving the security and the resiliency of the microgrids, and in turn facilitating the handling of energy generated from intermittent sources, storage devices also allow microgrids to take tactical decisions to improve their efficiency or to increase their revenues. Demand side management, load following, and switching between an island mode and a grid-connected mode are typical examples of applications that are better enabled by the presence of storage devices. In this context, the objective of this paper is to explore how the size of storage devices affect the efficiency of microgrids, and their ability to gain revenues in a competing market. Results obtained in realistic Monte Carlo simulations on the IEEE 39-bus system are provided and discussed for this purpose.
The development of distributed energy generation, often aggregated to form micro- or nano- grids based on power generation from renewable sources, or small-size generators make it easier to electrify remote and isolated areas. In addition to privately owned generation capacities, it could be feasible and profitable to sell/buy the excess energy to/from the closest neighbors, giving rise to "peer-to-peer" forms of energy trades. Despite that peer-to-peer trading has been widely discussed in the literature, the organization of isolated markets with limited small capacities is still a subject of study and discussion in the power community. In this paper, we evaluate the functioning of an alternative shorter market scheme characterized by only one bidding period, closer to the delivery time, which is convenient as it profits from more accurate forecasts. Then, the final balancing is performed internally within the same microgrids. Feasibility and cost-effectiveness of the proposed market is assessed for an isolated cluster of microgrids and compared with the currently operating markets, modelled as a day-ahead and real-time balancing markets. Two-stages stochastic optimization is implemented to compare the two market options. For this purpose, a grid of five villages is considered as a case-study and simulations are performed under two possible sets of installed capacities, including both diesel generators and renewable energy sources, to operate in either a dependent or a self-sufficient regime. Simulation results show that both markets succeed in supplying the required power demand, but the proposed simpler type of market may also be cost effective with a cost reduction up to 20%.
The recent COVID-19 outbreak has motivated an extensive development of non-pharmaceutical intervention policies for epidemics containment. While a total lockdown is a viable solution, interesting policies are those allowing some degree of normal functioning of the society, as this allows a continued, albeit reduced, economic activity and lessens the many societal problems associated with a prolonged lockdown. Recent studies have provided evidence that fast periodic alternation of lockdown and normal-functioning days may effectively lead to a good trade-off between outbreak abatement and economic activity. Nevertheless, the correct number of normal days to allocate within each period in such a way to guarantee the desired trade-off is a highly uncertain quantity that cannot be fixed a priori and that must rather be adapted online from measured data. This adaptation task, in turn, is still a largely open problem, and it is the subject of this work. In particular, we study a class of solutions based on hysteresis logic. First, in a rather general setting, we provide general convergence and performance guarantees on the evolution of the decision variable. Then, in a more specific context relevant for epidemic control, we derive a set of results characterizing robustness with respect to uncertainty and giving insight about how a priori knowledge about the controlled process may be used for fine-tuning the control parameters. Finally, we validate the results through numerical simulations tailored on the COVID-19 outbreak.
A methodology to predict underwater acoustic channel communication properties (capacity, bandwidth, range) from the environmental conditions in the ocean is proposed. The methodology is based on the use of acoustic propagation models coupled to a set of equations proposed firstly by Stojanovic [1]. A parametric study of channel characteristics as a function of changing environmental conditions is presented, showing in particular how channel range and/or source transmission power are influenced by the relative position of source and receiver with respect to the ocean temperature thermocline. This kind of results is crucial to adaptively configure the relative position of mobile nodes (typically AUVs – Autonomous Underwater Vehicles) in underwater sensor networks, with the final goal of mitigating the effects of environmental changes on the network communication capabilities.
A data‐based estimation of the wind–power curve in wind turbines may be a challenging task due to the presence of anomalous data, possibly due to wrong sensor reads, operation halts, malfunctions or other. In this study, the authors describe a data‐based procedure to build a robust and accurate estimate of the wind–power curve. In particular, they combine a joint clustering procedure, where both the wind speeds and the power data are clustered, with an Earth Mover Distance‐based Extreme Learning Machine algorithm to filter out data that poorly contribute to explain the unknown curve. After estimating the cut‐in and the rated speed, they use a radial basis function neural network to fit the filtered data and obtain the curve estimate. They extensively compared the proposed procedure against other conventional methodologies over measured data of nine turbines, to assess and discuss its performance.
In this paper we use clustering algorithms to compute the typical Italian load profile in different days of the week in different seasons. This result can be exploited by energy providers to tailor more attractive time-varying tariffs for their customers. We find out that better results are obtained if the clustering is not performed directly on the data, but on some features extracted from the data. Thus, we compare some conventional features to identify the most informative ones in the Italian case.
Many centralized and distributed power sharing algorithms have been proposed in the literature for de-loading operations in wind farms with variable speed wind turbines. Typically, in these strategies, two-way communications are required between the control center and the single turbines, or among the turbines. This paper solves the same problem in a truly decentralized fashion, which only requires a greatly reduced amount of one-way communications, without exchanging information among the turbines, and shows that an optimal solution can be obtained to minimize utility functions of interest of single wind turbines. In particular, we consider utility functions that take into account mechanical fluctuations and rotor over-speeds during transient, while balancing the utilization of wind turbines at steady-state operations. This is achieved by adopting the so-called Additive Increase Multiplicative Decrease (AIMD) algorithms, which are frequently used in communication applications, for solving the power sharing problem in a decentralized fashion. Extensive simulations under different working conditions, on wind farms consisting of wind turbines of different mechanical characteristics, are provided to illustrate the potential and the efficiency of the proposed methodology.
In this work we show that simple classic models of power grids, albeit frequently utilized in many applications, may not be reliable for investigating cascading failures problems. For this purpose, we develop a novel model, based on a structure-preserving approach, to obtain a network-based description of a power grid, where nodes correspond to generators and buses, while the links correspond to the physical lines connecting them. In addition, we also consider classic voltage and frequency protection mechanisms for lines and buses. Considering the Italian power grid as a case study of interest, we then investigate the propagation of an initial failure of any line of the power system, and compare the predicted impact of the failure according to different assumptions in the model such as the presence or absence of protection mechanisms and a simplified description of the system dynamics. In particular, it can be observed that more realistic models are crucial to determine the size of the cascading failure, as well as the sequence of links that may be involved in the cascade.
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