Search

This paper proposes the method pELECTRE Tri, a novel probabilistic Multi-Criteria Decision Making (MCDM) method using the traditional ELECTRE Tri, probability distribution, and Monte Carlo simulation to support informed decision-making in complex and uncertain decision environments. The proposed method is illustrated through a case study involving the renovation of three social housing buildings. The paper provides new insights into the application of probabilistic sorting MCDM in the context of energy efficiency in buildings and highlights the benefits of using probabilities rather than crisp values to categorize alternatives enabling stakeholders to make better use of available resources, especially when dealing with a large dataset of energy measures with different features. The methodology implemented in Python (DOI: 10.5281/zenodo.7967655) is available as an open source. [ABSTRACT FROM AUTHOR]

Exploring underwater acoustic sensing networks to detect and monitor ocean characteristics provides a novel approach for marine environment monitoring, disaster warning and resource exploration. However, factors such as underwater acoustic communication, ocean currents and sensor power prevent the direct adoption of terrestrial wireless sensing networks. Moreover, most previous studies have simply increased the number of nodes while ignoring the interference caused by high node density. In this paper, data collection in an offshore tidal scenario is investigated by table forwarding based election (TFBE) scheme. To balance node energy and reduce energy consumption of autonomous underwater vehicles (AUVs), the improved algorithm (TFBE-A) is proposed. Meanwhile, in this scheme, we adopt AUVs to assist in collecting and put forward a 3D precision adjustable trajectory planning (3DPATP) algorithm. To better perform 3DPATP, the maximum minimum error estimation algorithm is presented to simplify the computer operations. Additionally, we study the probability distribution function of nodes in space, providing a foundation for arranging nodes and optimizing topology. Simulation results indicate that our algorithm outperforms compared strategies, particularly in latency and energy consumption. [ABSTRACT FROM AUTHOR]

Mobile Adhoc Networks (MANETs) typically employ with the aid of new technology to increase Quality-of-Service (QoS) when forwarding multiple data rates. This kind of network causes high forwarding delays and improper data transfer rates because of the changes in the node's vicinity. Although an optimized routing technique to transfer energy has been used to lessen the delay and improve the throughput by assigning a proper data rate, it does not consider the objective of minimizing the energy use, which results in less network lifetime. The goal of the proposed work is to minimize the energy depletion in a MANET, which results in an extended Lifespan of the network. In this research paper, an Extended Life span and QSSM-ML routing algorithm is proposed, which minimizes energy use and enhances the network lifetime. First, an optimization problem is formulated with the purpose of increasing the network's lifetime while limiting the energy utilization and stability of the path along with residual. Second, an adaptive policy is applied for the asymmetric distribution of energy at both origin and intermediate nodes. In order to achieve maximum network lifespan and minimal energy depletion, the optimization problem was framed when power usage is a constraint by allowing the network to make use of the leftover power. An asymmetric energy transmission strategy was also designed for the adaptive allocation of maximum transmission energy in the origin. This made the network lifespan extended with the help of reducing the node's energy use for broadcasting the data from the origin to the target. Moreover, the node's energy use during packet forwarding is reduced to recover the network lifetime. The overall benefit of the proposed work is that it can achieve both minimal energy depletion and maximizes the lifetime of the network. Finally, the simulation findings reveal that the ELQSSM-ML algorithm accomplishes a better network performance than the classical algorithms. [ABSTRACT FROM AUTHOR]

In recent years, many methods have been presented for clustering in wireless sensor networks (WSNs). Some areas have high density, and the random distribution of the nodes reduces the clustering quality. Moreover, the number of clusters is manually determined before clustering. In this paper, a new clustering algorithm called NEMOCED is presented based on the node distribution. In the NEMOCED, the best cluster head is selected according to the node distribution. Moreover, we propose a new energy model to estimate proper clusters. One of the main features of the energy model is selecting the proper clusters. It is performed based on the number of nodes and the network size. In each round, two cluster heads are selected by using the tree structure. Finally, we introduce five criteria for assessing the quality and accuracy of the proposed algorithm. The NEMOCED can perform the clustering based on the local density of nodes and choose more proper cluster heads in high-density areas. The simulation results demonstrate that the NEMOCED can significantly improve lifetime and energy consumption. Furthermore, the simulation results show that the NEMOCED algorithm has good adaptability and works well under different network lifetime definitions. All the results prove that the NEMOCED algorithm has the advantage of being suitable and efficient for large-scale WSN applications. [ABSTRACT FROM AUTHOR]

This paper evaluates the feasibility of using wind power for power supply to coastal communities isolated from the main supply grid. The case study is Stewart Island, where the cost of electricity provided by a central diesel power station is higher than the grid network in New Zealand. The Princeton Ocean Model (POM) conducted by MetOcean Solutions Limited (MSL) is used to find Foveaux as an optimized site for generating wind power. Global Wind Atlas is used to plot the wind rose of current wind patterns in New Zealand. In the next step, wind speed data from each site are imported from the NASA database to WRPLOT view software and Homer Pro to find wind frequency distribution and output power in the area. The maximum annual power can be seen in WSW (32,299 kW hours/year), SW (20,111 kW hours/year) and W (15,622 kW hour/year) directions, respectively. [ABSTRACT FROM AUTHOR]

This paper addresses the design of a torque vectoring architecture to control the four electrical machines in a four-wheel-drive (4WD) formula-type competition vehicle. The scheme includes a new yaw-rate controller and a novel optimal torque distribution algorithm. Two yaw-rate controllers are proposed: one based on $\mathcal {H}_\infty$ optimal control and another based on linear parameter varying (LPV) system concepts. Both controllers are designed using an extended bicycle model validated with experimental data. Simulation results shown the effectiveness of the proposed overall control scheme in terms of energy efficiency, cornering speed and stability no matter the high-demanding working conditions. Such an effectiveness is quantitatively demonstrated by means of several key performance indicators chosen to ease the comparison of the proposed approach with respect to other reported works. [ABSTRACT FROM AUTHOR]

Wind energy (WE), which is one of the renewable energy (RE) sources for generating electricity, has been making a significant contribution to obtaining clean and green energy in recent years. Fitting an appropriate statistical distribution to the wind speed (WS) data is crucial in analyzing and estimating WE potential. Once the best suitable statistical distribution for WS data is determined, WE potential and potential yield could be estimated with high accuracy. The main objective of this paper is to propose a novel approach for calculating wind energy potential. For this purpose, the Efficient Global Optimization (EGO) technique was proposed for fitting a statistical distribution to WS data and the performance of the technique was compared with genetic algorithm (GA), simulated annealing (SA), and differential evolution (DE). Performance metrics showed that EGO is providing better estimations compared with GA, SA, and DE. Based on Weibull parameters obtained by using EGO, potential WE and potential annual revenue were estimated for Gdańsk, which is the capital of Pomerania Voivodeship in Poland, in the case of having city-type wind turbines in the city center. Estimations for Gdańsk showed that city-type wind turbines might be helpful for producing electricity from WE in the city without being limited by constraints such as having a long distance between wind turbines and buildings. If such wind turbines were erected on the roofs of residential buildings, malls, or office buildings, there is a possibility that part of the electric energy needed for such buildings could be generated using WE. However, this topic should be further investigated from technical and financial perspectives. [ABSTRACT FROM AUTHOR]

This paper considers the power allocation problem in device-to-device (D2D) communication underlaying a cellular network and investigates the impact of different transmitting and interference power constraints on the energy efficiency and spectral efficiency of the network. We formulate the power allocation problem in D2D communication as a nonlinear fractional programming problem with an objective to maximize the energy efficiency of a D2D communication link subject to four different combinations of transmitting and interference power constraints. To solve the original formulated nonlinear fractional programming problem, we first convert it into a dual nonlinear parametric programming problem, and then decouple the dual problem into several solvable concave problems. Further, a closed-form solution is derived to each dual problem and an efficient power allocation algorithm is proposed to find a numerical solution to the formulated problem. Simulation results show that the proposed power allocation algorithm outperforms an ergodic capacity maximization algorithm and a uniform power distribution algorithm in terms of the energy efficiency of a D2D link, and can efficiently improve the overall ergodic capacity of a cellular network. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the enhancement of RF–RF energy harvesting efficiency (erf–rf) in multipath environments in the context of wireless power transfer (WPT). For this, we used a retrodirective transmitting (Tx) antenna array resonating at 2.4 GHz and a receiving (Rx) antenna surrounded by scatterers placed in the Rx near field. The Rx resides in the Tx far field. We showed that in a medium made of a random distribution of scatterers, a time-reversed wave field interacts with the random medium to regenerate not only the propagating waves but also the evanescent waves required to refocus the energy at the receiver location. The system was enclosed inside a 3 m3 cubical reverberating room to create a strong multipath environment. The study was done for homogeneous (free space) and heterogeneous (multipath environment) media. Different WPT scenarios were considered for different applications: 4 × 1, 4 × 2 and 4 × 4 multi input-multi output (MIMO) systems. The simulation results show that using near-field scattering generates signal focusing at the source location, which increases the RF–RF energy harvesting efficiency, especially in a multipath environment. The average received power in the frequency band 2.4–2.5 GHz was greatly enhanced in the presence of the scatterers. The investigated WPT approach showed encouraging results for charging/powering-up sensors, IoT and smart devices in indoor environments. [ABSTRACT FROM AUTHOR]

This paper proposes a holistic framework of data-driven distributionally robust joint chance constrained economic dispatch (ED) optimization, which seamlessly incorporates deep learning-based optimization for effective utilization of renewable energy in power systems. By leveraging a deep generative adversarial network (GAN), an f-divergence-based ambiguity set of wind power distributions is constructed as a ball centered around the probability distribution induced by a generator neural network. In particular, the GAN is well suited for capturing complicated spatial and temporal correlations of wind power. Based upon this ambiguity set, a distributionally robust joint chance constrained ED model is developed to hedge against distributional uncertainty present in multiple constraints, without assuming a perfectly known probability distribution. The proposed deep learning based ED optimization framework greatly mitigates the conservatism inflicting on distributionally robust individual chance constrained optimization. Theoretical a priori bound on the required number of synthetic wind power data generated by GAN is explicitly derived for the multi-period ED problem to guarantee a predefined risk level. The effectiveness and scalability of the proposed approach are demonstrated in the six-bus and IEEE 118-bus systems by comparing with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper develops a vehicle scheduling method for the electric bus (EB) route considering stochastic volatilities in trip travel time and energy consumption. First, a model for estimating the trip energy consumption is proposed based on field‐collected data, and the probability distribution function of trip energy consumption considering the stochastic volatility is determined. Second, we propose the charging strategy to recharge buses during their idle times. The impacts of stochastic volatilities on the departure time, the idle time, the battery state of charge, and the energy consumption of each trip are analyzed. Third, an optimization model is built with the objectives of minimizing the expectation of delays in trip departure times, the summation of energy consumption expectations, and bus procurement costs. Finally, a real bus route is taken as an example to validate the proposed method. Results show that reasonable idle times can be generated by optimizing the scheduling plan, and it is helpful to stop the accumulation of stochastic volatilities. Collaboratively optimizing vehicle scheduling and charging plans can reduce the EB fleet and delay times while meeting the route operation needs. [ABSTRACT FROM AUTHOR]

Summary This paper proposes a new management algorithm and operation of a hybrid renewable energy system (HRES) connected to the power system. The whole hybrid system is managed in such a manner that it can produce as much needed by the grid system. The proposed algorithm is used to distribute proportionally the active and reactive power references to the PV source and wind generators according to their ability contribution. Based on the available active powers and the ability on reactive power of each sub‐system, the references are calculated using a proportional distribution algorithm and sent individually by the principal controller to each auxiliary controller. The analysis of the simulation results obtained under Matlab/Simulink shows the effectiveness of the proposed management algorithm and the flexibility of the hybrid renewable energy system studied. [ABSTRACT FROM AUTHOR]

The growing awareness of climate change is constantly moving the attention of designers and policymakers from typical and current scenarios to future layouts. This new approach introduces a degree of uncertainty that should be accounted in the building design process. The multitude of possible scenarios suggests considering dynamic system that can adapt themselves to unpredicted operating conditions. The aim of this study is to test a new approach and a new flexibility metric analysing the behaviour of a high-performance dynamic internal curtain in current and future scenarios. The first part of the paper focuses on the optimization of the dynamic system in the current scenario; this preliminary analysis represents the traditional design approach and is considered the baseline for all the comparisons. Hence, defining a matrix of likely scenarios, this paper explores the behaviour of three different selected control strategies – always off (i.e., no curtain), fixed control, optimized control – in the different scenarios considering possible variations of i) climate, ii) urban context, iii) internal loads, and iv) building use. The main outcomes of the research are, on the one hand, the comparisons of the control strategies and the benefits of the dynamic system and, on the other hand, the definition of a new metric that can properly describe the flexibility of the building systems with reference to the future scenarios analysed. This metric is based on six classes – which describe the statistical distribution of the energy consumptions of all the scenarios simulated in the matrix – and an index called Energy Flexibility Index (EFI) that quantifies the flexibility of the technology considered. The case study analysed highlights how implementing a dynamic shading system, considering the same building use, can increase the EFI of nearly 21% for non-optimized control strategy and up to 23% when optimized. While changing the building use in residential reduces these values respectively, to 6% and 7%. [ABSTRACT FROM AUTHOR]

With the progress of decarbonization, renewable-powered microgrids are attracting wide attention. To cope with the fluctuation of renewable power at different timescales, both long-term and short-term energy storage devices are required. This paper studies the operation of renewable-dominated isolated microgrids integrated with hybrid seasonal-battery storage. A data-driven scheduling-correction framework is proposed. By leveraging the historical data of renewable power and load, the scheduling module generates ex-post optimal state-of-charge (SoC) sequences of the seasonal energy storage ahead of the operating year. In each period of real-time operation, the correction module performs two steps: the first step is to update the reference SoC for the seasonal storage based on the ex-post optimal SoC sequences and the newly observed data; the second step is to solve a bi-objective rolling-horizon optimization problem which minimizes the instant operating cost while steering the SoC of seasonal storage to its reference value. An appealing feature of the proposed method is that the long-term renewable power forecasts are not required. A microgrid system is devised to verify the proposed framework. Numerical tests show that the scheduling-correction framework outperforms existing rolling horizon approaches in compromising economy, power supply reliability, and renewable energy utilization. • A data-driven scheduling-correction framework is proposed to provide real-time actions with a global vision, making better use of the seasonal hydrogen storage. In the scheduling module, ex-post optimal SoC sequences of the seasonal storage are generated using historical data. In the correction module, the real-time operation relies on a bi-objective rolling-horizon optimization problem which minimizes the operating cost while following the SoC reference of the seasonal storage, which is sequentially updated along with time. • The kernel regression technique is established to sequentially update the SoC reference of the seasonal hydrogen storage. This technique calibrates a conditional probability distribution based on the newly observed data; the resulting distribution (weight coefficients) interprets how likely the current year is similar to each of the past years. The reference is updated in each period according to the weighted sum of the ex-post optimal SoC sequences. • Systematic tests are conducted to compare the cost, load shedding, and renewable energy curtailment achieved by different methods. Numerical results show that the scheduling-correction framework can fully exploit the seasonal storage. The proposed method outperforms existing rolling horizon approaches in compromising economy, power supply reliability, and renewable energy utilization. [ABSTRACT FROM AUTHOR]

• Improve the performance of micro-grid based on demand response programs. • Reduce operating costs of micro-grid based on probability distribution function models. • Improving the speed and accuracy of dynamic response system based on the Modified Shuffled Frog Leaping Algorithm. • Improving micro-grid performance in the presence of stochastic and non-stochastic power generation sources based on probability models. In this paper, an energy consumption optimization problem is investigated for a representative Micro-Grid (MG) system including Distributed Energy Resources (DERs) such as Wind Turbines (WTs), Photo-Voltaic (PV) arrays, Combined Heat and Power (CHP), Fuel Cell (FC), and boiler to supply the electricity and thermal load demand instantaneously. This problem is mathematically formulated considering resources and load uncertainties using Beta, Normal, and Weibull distribution function with suitable coefficients. In this regard, a model based on the minimization of objective function consisting of operating costs, pollution, and distribution lines electrical losses is presented and solved using the Modified Shuffled Frog Leaping Algorithm (MSFLA). The main objective of this paper is to choose the best collection of DERs and other resources in MG to study which of them is optimum to achieve the objective function. Demand response program (DR) based on the electricity price is considered as a Time of Use (TOU) tariff in which the simultaneous simulations will be figured out. The comparison of the proposed method with the existing method has verified the superiority of MSFLA approach to solve this optimization problem. [ABSTRACT FROM AUTHOR]

• The probability distribution of platoon size in mixed traffic flow is proposed based on the Markov chain. • The capacity of mixed traffic flow considering the maximum platoon size of CAVs is derived. • The stability of mixed traffic flow considering the maximum platoon size of CAVs is derived. • Two safety evaluation metrics are adopted to analyze the impact of the maximum platoon size of CAVs on the traffic safety of mixed traffic flow. • The fuel consumption and emissions are adopted to analyze the impact of the maximum platoon size of CAVs onmixed traffic flow. This paper analyzes the characteristics of mixed traffic flow with maximum platoon size of connected automated vehicles (CAVs) based on analytical and simulation methods. First, we discuss the car-following and headway types of mixed traffic flows with human-driven vehicles (HDVs), connected human-driven vehicles (CHVs), automated vehicles (AVs), and CAVs. Second, the probability distribution of the CAVs platoon size is derived based on the Markov chain model. Subsequently, the capacity model and stability condition of mixed traffic flow are proposed based on the probability distribution of the CAVs platoon size, and some characteristics of the influence of the maximum platoon size on traffic capacity and stability are analyzed. A numerical experiment based on car-following models was conducted to investigate the effect of the maximum platoon size on capacity, stability, safety, fuel consumption, and emission of mixed traffic flow. The results show that (1) the capacity of mixed traffic flow increases with the maximum platoon size of CAVs; (2) the stability of mixed traffic flow deteriorates with the increase in the maximum platoon size of CAVs; (3) the safety risk of vehicles in mixed traffic flow increases with the maximum platoon size of CAVs; (4) the fuel consumption and emissions (e.g., CO, HC, and NOx) increase with the maximum platoon size of CAVs. This indicates that the larger maximum platoon size can only improve traffic capacity, which is not conducive to the stability, safety, fuel consumption, and emissions of mixed traffic flow. [ABSTRACT FROM AUTHOR]

The integrated light guide plate (ILGP) is expected to integrate the functions of multiple optical films in the backlight module (BLM), so that the BLM has excellent properties such as thinness and high light energy utilization efficiency. Aiming at the difficulties in fabrication of the ILGP sample with high uniformity, such as small size and micro-nano structure with limited surface type, in this paper, a grayscale direct-write lithography system for fabricating two-dimensional distributed microprism arrays on the bottom surface of a ILGP is proposed, and a 5.0-inch ILGP with thinness and high uniformity is designed and fabricated. According to the restriction of lithography process technology, a 5.0-inch integrated BLM with two-dimensional distributed microprism arrays on the bottom surface is designed and optimized, of which the illuminance uniformity and luminance uniformity are equivalent to those in typical one. The sample of the optimized 5.0-inch ILGP is fabricated by using the constructed grayscale direct-write lithography system, which successfully solves the difficulties in fabrication of microprism arrays with two-dimensional distribution and asymmetric special surface type. The comparison test shows that the illluminance uniformity and luminance uniformity of the sample are equivalent to that of the typical structure, and a 5.0-inch ILGP sample with thin thickness and high uniformity is successfully produced. At the same time, the grayscale direct-write lithography system created in this paper has a strong ability to process three-dimensional micro-nano structure, which can be further extended to be applied to process other micro-nano components with two-dimensional random distribution and three-dimensional contour. [ABSTRACT FROM AUTHOR]

Environmental regulations, such as the IMO's GHG reduction strategy and the launch of the DCS, have led to a trend towards assessing the performance of ships in service, which has increased the focus on the actual performance of ships at sea. However, unlike sea trials, there is no established method to enable objective and transparent evaluation of a ship's performance during actual operations. For the establishment of the objective evaluation method, it can be assumed to set the single evaluation condition such. As for weather condition, which varies due to a shipping route, and a shipping route is different for each individual ship. Then, in this paper, several representative shipping routes have been considered and the relation between operating area and weather condition is investigated with the long-term statistical data. The validity of this approach is examined through a comparison with encountered weather data. Based on the survey results, the weather condition is expressed with categorized basic weather conditions and their probabilities. Through case studies, the appropriateness of the weather conditions given to each route is examined quantitatively. The results show that the weather conditions established from the long-term statistical data are reasonable, and that they are appropriate for expressing each representative route in the light of the evaluation of the actual sea performance. • Frequency distributions for weather conditions are obtained from statistical data. • Statistical data have similar tendency with data of encountered weather. • Categorized basic weather conditions are proposed for the evaluation. • The effect by assumed routes on the fuel consumption is quantitatively evaluated. [ABSTRACT FROM AUTHOR]