Data-driven Distributionally Adjustable Robust Chance-constrained DG Capacity Assessment

Moving away from fossil fuels towards renewable sources requires system operators to determine the capacity of distribution systems to safely accommodate green and distributed generation(DG).However,the DG capacity of a distribution system is often underestimated due to either overly conservative electrical demand and DG output uncertainty modelling or neglecting the recourse capability of the available components.To improve the accuracy of DG capacity assessment,this paper proposes a distributionally adjustable robust chance-constrained approach that utilises uncertainty information to reduce the conservativeness of conventional robust approaches.The proposed approach also enables fast-acting devices such as inverters to adjust to the real-time realisation of uncertainty using the adjustable robust counterpart methodology.To achieve a tractable formulation,we first define uncertain chance constraints through distributionally robust conditional value-at-risk(CVaR),which is then reformulated into convex quadratic constraints.We subsequently solve the resulting large-scale,yet convex,model in a distributed fashion using the alternating direction method of multipliers(ADMM).Through numerical simulations,we demonstrate that the proposed approach outperforms the adjustable robust and conventional distributionally robust approaches by up to 15%and 40%,respectively,in terms of total installed DG capacity.

A Systems Approach to Assessing Sustainability Capacity in Kalobeyei Refugee Settlement in Turkana County, Kenya

Refugee settlements face several challenges in transitioning from a temporary planning approach to more sustainable settlements. This is mainly due to an increase in the number of forcibly displaced people over the last few decades, and the difficulties of sustainably providing social services that meet the required standards. The development of refugee settlements assumed that forcibly displaced people would return to their places or countries of origin. Unfortunately, displacement situations are prolonged indefinitely, forcing these people to spend most of their lives in conditions that are often deplorable and substandard, and therefore unsustainable. In most cases, the establishment of refugee settlements is triggered by an emergency caused by an influx of forcibly displaced people, who need to be accommodated urgently and provided with some form of international assistance and protection. This leaves little or no time for proper planning for long-term development as required. In addition, the current approach to temporary settlement harms the environment and can strain limited resources with ad hoc development models that have exacerbated difficulties. As a result, living conditions in refugee settlements have deteriorated over the last few decades and continue to pose challenges as to how best to design, plan, and sustain settlements over time. To contribute to addressing these challenges, this study proposes a new methodology supported by Model-Based Systems Engineering (MBSE) and a Systems Modeling Language (SysML) to develop a typical sustainable human settlement system model, which has functionally and operationally executed using a Systems Engineering (SE) approach. To assess the sustainability capacity of the proposed system, this work applies a matrix of crossed impact multiplication through a case study by conducting a system capacity interdependence analysis (SCIA) using the MICMAC methodology (Cross-impact matrix multiplication applied to classification) to assess the interdependency that exist between the sub-systems categories to deliver services at the system level. The sustainability analysis results based on capacity variables influence and dependency models shows that development activities in the settlement are unstable and, therefore, unsustainable since there is no apparent difference between the influential and dependent data used for the assessment. These results illustrate that an integrated system could improve human settlements’ sustainability and that capacity building in service delivery is beneficial and necessary.

State-of-the-art of port simulation models for risk and capacity assessment based on the vessel navigational behaviour through the nautical infrastructure

Ports play an increasingly important role in the freight transportation chain due to containerization. High vessel flows and higher densities increase the relevance of the non- terminal related processes. Several simulation models have been developed in the recent decades with different goals, but their abilities to represent realistic vessel traffic in ports differ. In this paper, we identify the main navigational processes and operations related to the port nautical infrastructure, and review and assess the current port simulation models. This survey represents an exhaustive review of the state-of-the-art of simulation models for port assessment purposes focussing on safety and capacity. The model assessment focuses on the identification of the relevant criteria to represent vessel navigation, based on which processes are covered by each model and how they have been considered in each model. The assessment covers the nautical infrastructure representation and the naviga- tional behaviour. The outcome of this review will be used for the development of a simulation based port assessment methodology. Future port simulation models should include the suitable criteria for a more realistic traffic representation that allows a proper safety and capacity port analysis and assessment.

Assessment and Analysis of Carrying Capacity of 350 km/h EMUs Operating on Beijing-Shanghai Highspeed Railway

This paper makes an analysis of the impact on the structure of the corridortype train working diagram by increasing the 350 km/h train pairs of Beijing-Shanghai High-speed Railway(HSR).According to the requirements of operation speed diversity,the multi-station receiving characteristics of cross-line trains and different train stopping schemes,this paper proposes a scheme of adding 350 km/h train pairs via computer analysis of carrying capability mode.The scheme is divided into three stages:(1)Initial stage:It is planned to increase trains running at 350 km/h,with 15~26 pairs of trains put into operation;(2)Rapid increase stage:27~142 pairs of 350 km/h trains are put into operation;(3)Full replacement stage:143~195 pairs of 350 km/h trains are put into operation,during which the number of cross-line trains under operation is controlled,the number of cross-line trains in each section is determined,the operation and connection scheme of cross-line trains is adjusted and the train stopping scheme is optimized.The results of this study were used for the adjustment of the train working diagram in the third quarter of June 25,2021 to increase the number of 350 km/h train pairs from 19 to 30 on Beijing-Shanghai HSR.350 km/h trains are evenly arranged during 7:00—19:00 on the train working diagram and 300 km/h trains are arranged by making full use of every time and space,to improve the travelling speed on Beijing-Shanghai HSR as a whole.

Potential Assessment of Spatial Correlation to Improve Maximum Distributed PV Hosting Capacity of Distribution Networks

Successful distributed photovoltaic (PV) planning now requires a hosting capacity assessment process that accounts for an appropriate model of PV output and its uncertainty. This paper explores how the PV hosting capacity of distribution networks can be increased by means of spatial correlation among distributed PV outputs. To achieve this, a novel PV hosting capacity assessment method is proposed to account for arbitrary geographically dispersed distributed PVs. In this method, the empirical relation between the spatial correlation coefficient and distance is fitted by historical data in one place and then applied to model the joint probability distribution of PV outputs at a neighboring location. To derive the PV hosting capacity at candidate locations, a stochastic PV hosting capacity assessment model that aims to maximize the PV hosting capacity under thermal and voltage constraints is proposed. Benders decomposition algorithm is also employed to reduce the computational cost associated with the numerous sampling scenarios. Finally, a rural 59-bus distribution network in Suzhou, China, is used to demonstrate the effectiveness of the proposed PV hosting capacity assessment methodology and the significant benefits obtained by increasing geographical distance.

3D Tungsten Trioxide Nanosheets as Optoelectronic Materials for On-chip Quantification of Global Antioxidant Capacity

The photoelectrochemical properties of semiconductors mainly depend on the size and shape of the corresponding nanoparticles.Herein,3D WO3 nanosheets were controllably synthesized with the aid of polyethyleneimine,which presents enhanced photocurrent responses.Based on this excellent photoelectrochemical property,a photoelectrochemical chip was prepared by lithography technology for the smart monitoring of the antioxidant capacity(AC)in red wine and exhibits a series of advantages including rapid response time,high sensitivity,and long-lasting stability.The mechanism of the present photoelectrochemical sensing was explored and shows a single electron transfer reaction.Furthermore,only 200µL of samples are required for one testing,which demonstrates that the present photoelectrochemical chip can be potentially integrated with a portable commercial device(such as a mobile phone)for further research and development of food and drug supervision.

Situation reasoning for an adjustable autonomy system

Purpose–The purpose of this paper is to provide a more capable and holistic adjustable autonomy system,involving situation reasoning among all involved information sources,to make an adjustable autonomy system which knows what the situation is currently,what needs to be done in the present situation,and how risky the task is in the present situation.This will enhance efficiency for calculating the level of autonomy.Design/methodology/approach–Situation reasoning methodologies are present in many autonomous systems which are called situation awareness.Situation awareness in autonomous systems is divided into three levels,situation perception,situation comprehension and situation projection.Situation awareness in these systems aims to make the tactical plans cognitive,but situation reasoning in adjustable autonomous systems aim to communicate mission assessments to unmanned vehicle or humans.Thus,in solving this problem,it is important to design a new situation reasoning module for the adjustable autonomous system.Findings–The contribution of this paper is presenting the Situation Reasoning Module(SRM)for an adjustable autonomous system,which encapsulates event detection,cognitive situations,cognitive tasks,performance capacity assessment and integrated situation reason.The paper concludes by demonstrating the benefits of the SRM in a real-world scenario,a situation reasoning simulation in unmanned surface vehicles(USV)while performing a navigation mission.Originality/value–The method presented in this paper represents a new SRM to reason the situation for adjustable autonomous system.While the results presented in the paper are based on fuzzy logic and Bayesian network methodology.The results of this paper can be applicable to land,sea and air robotics in an adjustable autonomous system.