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Energy supply through integrated renewable energy sources (RESs) and battery systems will be of higher importance for future residential sectors. Optimal energy management and sizing for the components of residential systems can enhance efficiency, self-suffiency, and meanwhile can be cost-effective by reducing investment as well as operating costs. Accordingly, this paper proposes an exhaustive optimization model for determining the capacity of RESs, namely: wind turbines and photovoltaic (PV) systems. In this study, batteries and electric vehicles (EVs) are utilized in line with other sources to capture fluctuations of RESs. To model the uncertainties of RESs, energy prices, and load demands a linearized stochastic programming framework is applied. The proposed framework involves long-term and efficient resource development alongside with short-term management and utilization of these resources for supplying the demand load. In our study, we utilize the roulette wheel mechanism (RWM) method as well as proper probability distribution functions (PDFs) to generate scenarios for all sources of uncertainties, including wind turbines, PV systems, demand, and electricity market price. The approach is verified in two different cases, including an individual home and a larger micro-grid (MG). The results of multiple numerical simulations demonstrate the effectiveness of the proposed stochastic model.

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Power transmission network physically connects the power generators to the electric consumers extending over hundreds of kilometers. There are many components in the transmission infrastructure that requires a proper inspection to guarantee flawless performance and reliable delivery, which, if done manually, can be very costly and time taking. One of the essential components is the insulator, where its failure could cause the interruption of the entire transmission line or widespread power failure. Automated fault detection of insulators could significantly decrease inspection time and its related cost. Recently, several works have been proposed based on convolutional neural networks to deal with the issue mentioned above. However, the existing studies in the literature focus on specific types of fault for insulators. Thus, in this study, we introduce a two-stage model in which we first segment insulators from the background images and then classify its state into four different categories, namely: healthy, broken, burned, and missing cap. The test results show that the proposed approach can realize the effective segmentation of insulators and achieve high accuracy in detecting several types of faults.

This study proposes an optimal day-ahead (DA) electricity market offering model for a virtual power plant (VPP) formed by a mix of renewable distributed energy resources along with energy storage, such as electric vehicles. Two sources of uncertainty are considered, namely, wind power generation, modelled by an uncertainty set, and DA market price, modelled by scenarios. Opposite to classical robust optimisation approaches, the authors model maps minimal (worst-case) profits to a conservativeness parameter, while the classical robust optimisation maps conservativeness parameter to worst-case profits. In this regard, by using their optimisation framework, a VPP operator only deals with setting a minimum-profit constraint, which is more sensible and easy for interpretation, while the required conservativeness is endogenously determined. The proposed mathematical model for constructing the offering curve is a hierarchical four-level robust optimisation problem. The first level represents the optimal decision on the price-quantity offer bids; the second-and third-level relate to the optimal identification of conservativeness parameter; and the fourth-level represents the optimal operation of the VPP managed assets. The four-level model is reformulated as a single-level mixed-integer linear programming problem. The proposed approach and its applicability are verified using numerical simulations.

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The fast development of technologies in the smart grids provides new opportunities such as co-optimization of multi-energy systems. One of the new concepts that can utilize multiple energy sources is a hybrid fuel station (HFS). For instance, an HFS can benefit from energy hubs, renewable energies, and natural gas sources to supply electric vehicles along with natural gas vehicles. However, the optimal operation of an HFS deals with uncertainties from different sources that do not have similar natures. Some may lack in term of historical data, and some may have very random and unpredictable behavior. In this study, we present a stochastic mathematical framework to address both types of these uncertainties according to the innate nature of each uncertain variable, namely: epistemic uncertainty variables (EUVs) and aleatory uncertainty variables (AUVs). Also, the imprecise probability approach is introduced for EUVs utilizing the copula theory in the process, and a scenario-based approach combining Monte Carlo simulation with Latin Hypercube sampling is applied for AUVs. The proposed framework is employed to address the daily operation of a novel HFS, leading to a two-stage mixed-integer linear programming problem. The proposed approach and its applicability are verified using various numerical simulations.