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The potential availability of renewable energy sources is unquestionable and the government is setting steep targets for renewable energy usage. Renewable‐based DGs, reduce dependence on fossil fuels, mitigate global climate change, ensure energy security, and reduce emissions of CO2 and other greenhouse gases. This study addresses microgrid system analysis with hybrid energy sources and reconfiguration simultaneously for efficient operation of the system. Microgrid zones are formulated categorically with the existing distribution system. In this study, wind, solar and small hydro‐based DGs are considered. Uncertainties of renewable power generation and load are also taken care in the optimization problem. A multi‐objective optimisation method proposed in this paper for optimal integration of renewable‐based DGs and reconfiguration of the network to minimise power loss and maximise annual cost savings. Optimal location and sizes of DG units are determined using gravitational search algorithm and general algebraic modelling system respectively. Optimal reconfiguration of the microgrid system is obtained using genetic algorithm. Simulation results are obtained for the IEEE 33‐bus system and compared with existing methods as available in the literature. Furthermore, this study has been carried out with a 24‐hr time‐varying distribution system. The simulation results show the efficiency and accuracy of the proposed technique.

Insect navigation arises from the coordinated action of concurrent guidance systems but the neural mechanisms through which each functions, and are then coordinated, remains unknown. We propose that insects require distinct strategies to retrace familiar routes (route-following) and directly return from novel to familiar terrain (homing) using different aspects of frequency encoded views that are processed in different neural pathways. We also demonstrate how the Central Complex and Mushroom Bodies regions of the insect brain may work in tandem to coordinate the directional output of different guidance cues through a contextually switched ring-attractor inspired by neural recordings. The resultant unified model of insect navigation reproduces behavioural data from a series of cue conflict experiments in realistic animal environments and offers testable hypotheses of where and how insects process visual cues, utilise the different information that they provide and coordinate their outputs to achieve the adaptive behaviours observed in the wild.

Photovoltaic (PV) systems are a clean energy source that allows for power generation integration into electrical networks without destructive environmental effects. PV systems are usually integrated into electrical networks only to provide active power during the day, without taking full advantage of power electronics devices, which can compensate for the reactive power at any moment during their operation. These systems can also generate dynamic reactive power by means of voltage source converters, which are called PV-STATCOM devices. This paper presents a convex formulation for the optimal integration (placement and sizing) of PV-STATCOM devices in electrical distribution systems. The proposed model considers reducing the costs of the annual energy losses and installing PV-STATCOM devices. A convex formulation was obtained to transform the hyperbolic relation between the products of the voltage into a second-order constraint via relaxation. Two simulation cases in the two IEEE test systems (33- and 69-node) with radial and meshed topologies were implemented to demonstrate the effectiveness of the proposed mixed-integer convex model. The results show that PV-STATCOM devices reduce the annual cost of energy losses of electrical networks in a more significant proportion than PV systems alone.

With the rising interest in sustainable transportation efforts, the optimal integration of Electric Vehicles (EVs) in the transportation business has come into existence very drastically. However, because of the increased power consumption, their impact on the electric power market might result in more enormous losses in the active power, a reduced voltage profile, as well as reduced voltage stability limits. In Radial Distribution System (RDS), getting the reduced effect of EVs load is crucial. It is significantly important to build charging stations for EVs and Distributed Generators (DGs) at the best locations. This article presents a combined approach to solve the optimal integration of DGs and EVs problem. First, the desired locations of DGs and EVs are found using the Voltage Stability Index (VSI) method. Further, the resource ratings are obtained by the Firefly algorithm (FA) method. EVs charging dump and smart charging strategies are used, and comparison is done with both systems. Furthermore, the main objective of this work is to reduce the power loss and maintain a good voltage profile at each bus in the RDS. Finally, the developed approach is tested on IEEE 69 bus system.

In this article, a novel voltage stability index is proposed for the optimal placement and an analytical as well as particle swarm optimization method is implemented for optimal sizing of different types of distributed generation (DG) in a radial distribution system. The three types of DG considered are Type I, Type II and Type IV model. The methods account for changes in long-term system load profile in the planning phase to enhance power system performance. The load model chosen is composite. The optimal power factor and the cost of the DG are also considered. The efficiency of the proposed technique is tested and validated on IEEE 33-bus and 69-bus systems. The significant savings in annual energy loss, reduction in system power losses with upgraded voltage profile is observed for Type IV DG at optimal PF over other types. The results attained are further compared to other analytical and nature-inspired methods to exemplify the dominance of the proposed methodology. The statistical analysis has also been carried out for both bus systems without and with considering all types of DG.

Wedge prisms shifting the visual field laterally create a mismatch between the straight ahead position signalled by vision and that encoded by extraretinal and head-on-trunk proprioceptive information. Short-term adaptation to left-deviating prisms in normal subjects results in a visuomotor attentional bias towards the right-hand side (aftereffect). Prismatic adaptation (PA) is usually induced through a training consisting in repeated ballistic movements of the dominant arm towards visual targets, while participants are wearing prismatic goggles. The present study demonstrates that an original oculomotor PA procedure with leftward deviating prisms—without pointing movements and only consisting in repeated gaze shifts towards visual targets—can induce a rightward bias in normal subjects as assessed by visual straight ahead and line bisection tasks (Experiments 1 and 2). We show that oculomotor PA induces a bias in line bisection similar to that reported after visuomotor PA (Experiment 2). We suggest that a conflict between retinal, extraretinal and proprioceptive information about the straight ahead location causes the observed effects. In follow-up experiments 3, 4, and 5, we demonstrate that neither eye deviation without prisms nor shift of the visual field without eye deviation induces PA biases. We propose that an optimal integration model of visual and proprioceptive inputs can best account for the observed results.

Passive movement through an environment is typically perceived by integrating information from different sensory signals, including visual and vestibular information. A wealth of previous research in the field of multisensory integration has shown that if different sensory signals are spatially or temporally discrepant, they may not combine in a statistically optimal fashion; however, this has not been well explored for visual–vestibular integration. Self-motion perception involves the integration of various movement parameters including displacement, velocity, acceleration and higher derivatives such as jerk. It is often assumed that the vestibular system is optimized for the processing of acceleration and higher derivatives, while the visual system is specialized to process position and velocity. In order to determine the interactions between different spatiotemporal properties for self-motion perception, in Experiment 1, we first asked whether the velocity profile of a visual trajectory affects discrimination performance in a heading task. Participants performed a two-interval forced choice heading task while stationary. They were asked to make heading discriminations while the visual stimulus moved at a constant velocity (C-Vis) or with a raised cosine velocity (R-Vis) motion profile. Experiment 2 was designed to assess how the visual and vestibular velocity profiles combined during the same heading task. In this case, participants were seated on a Stewart motion platform and motion information was presented via visual information alone, vestibular information alone or both cues combined. The combined condition consisted of congruent blocks (R-Vis/R-Vest) in which both visual and vestibular cues consisted of a raised cosine velocity profile and incongruent blocks (C-Vis/R-Vest) in which the visual motion profile consisted of a constant velocity motion, while the vestibular motion consisted of a raised cosine velocity profile. Results from both Experiments 1 and 2 demonstrated that visual heading estimates are indeed affected by the velocity profile of the movement trajectory, with lower thresholds observed for the R-Vis compared to the C-Vis. In Exp. 2 when visual–vestibular inputs were both present, they were combined in a statistically optimal fashion irrespective of the type of visual velocity profile, thus demonstrating robust integration of visual and vestibular cues. The study suggests that while the time course of the velocity did affect visual heading judgments, a moderate conflict between visual and vestibular motion profiles does not cause a breakdown in optimal integration for heading.

In 2007, Cheng and colleagues published their influential review wherein they analyzed the literature on spatial cue interaction during navigation through a Bayesian lens, and concluded that models of optimal cue integration often applied in psychophysical studies could explain cue interaction during navigation. Since then, numerous empirical investigations have been conducted to assess the degree to which human navigators are optimal when integrating multiple spatial cues during a variety of navigation-related tasks. In the current review, we discuss the literature on human cue integration during navigation that has been published since Cheng et al.’s original review. Evidence from most studies demonstrate optimal navigation behavior when humans are presented with multiple spatial cues. However, applications of optimal cue integration models vary in their underlying assumptions (e.g., uninformative priors and decision rules). Furthermore, cue integration behavior depends in part on the nature of the cues being integrated and the navigational task (e.g., homing versus non-home goal localization). We discuss the implications of these models and suggest directions for future research.

This research addresses the efficient integration and sizing of flexible alternating current transmission systems (FACTS) in electrical distribution networks via a convex optimization approach. The exact mixed-integer nonlinear programming (MINLP) model associated with FACTS siting and sizing aims for the minimization of the expected annual operating costs of the network (i.e., energy losses and FACTS purchasing costs). The constraints of this problem include power equilibrium equalities, voltage regulation bounds, and device capacities, among others. Due to the power equilibrium constraints per node and period, the MINLP model is a non-convex optimization problem. To transform the exact MINLP model into a mixed-integer convex one, the approximation of the product between two variables in the complex domain is relaxed through its hyperbolic equivalent, which generates a set of convex cones. The main advantage of the proposed mixed-integer convex model is that it ensures the global optimum of the problem, even when considering objective multiplexes. Numerical simulations in the IEEE 33-, 69-, and 85-bus grids demonstrate the effectiveness and robustness of FACTS integration via the proposed convex approach in comparison with the exact solution of the MINLP model in the GAMS software as well as with combinatorial optimization algorithms (i.e., the black widow optimizer and the vortex search algorithm). All simulations were carried out in MATLAB with Yalmip optimization and the Gurobi and Mosek solvers. The simulation results show that, for a fixed operation of the FACTS devices (i.e., a VAR compensator) during the day, the annual operating costs are reduced by 12.63%, 13.97%, and 26.53% for the IEEE 33-, 69-, and 85-bus test systems, respectively, while for the operation variable, the reductions are by 14.24%, 15.79%, and 30.31%, respectively.

In recent years, considerable growth was about the integration of renewable energy sources in the Radial Distribution Systems (RDS), as Photovoltaic Distributed Generators (PVDG) due to their importance in achieving plenty desired technical and economic benefits. Implementation of the Distribution Static Var Compensator (DSVC) in addition to the PVDG would be one of the best choices that may provide the maximum of those benefits. Hence, it is crucial to determine the optimal allocation of the devices (PVDG and DSVC) into RDS to get satisfactory results and solutions. This paper is devoted to solve the allocation problem (locate and size) of hybrid PVDG and DSVC units into the standards test systems IEEE 33-bus and 69-bus RDSs. Solving the formulated problem of the optimal integration of hybrid PVDG and DSVC units is based on minimizing the proposed Multi-Objective Function (MOF) which is represented as the sum of the technical-economic parameters of Total Active Power Loss (TAPL), Total Reactive Power Loss (TRPL), Total Voltage Deviation (TVD), Total Operation Time (TOT) of the overcurrent relays (OCRs) installed in the RDS, the Investment Cost of PVDGs (ICPVDG) and the Investment Cost of DSVCs (ICDSVC), by applying various recent metaheuristic optimization algorithms. The simulation results reveal the superiority and the effectiveness of the Slime Mould Algorithm (SMA) in providing the minimum of MOF, including minimization of the power losses until 16.209 kW, and 12.11 kVar for the first RDS, 4.756 kW and 7.003 kVar for the second RDS, enhancing the voltage profiles and the overcurrent protection system. Moreover, the ability to reach the optimal allocation of PVDG and DSVC and maintain the voltage profiles in the allowable limit, whatever the load demand variation.