A Planning Method for Operational Test of UAV Swarm Based on Mission Reliability

The unmanned aerial vehicle(UAV)swarm plays an increasingly important role in the modern battlefield,and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm.Due to the high cost and long duration of operational tests,it is essential to plan the test in advance.To solve the problem of planning UAV swarm operational test,this study considers the multi-stage feature of a UAV swarm mission,composed of launch,flight and combat stages,and proposes a method to find test plans that can maximize mission reliability.Therefore,a multi-stage mission reliability model for a UAV swarm is proposed to ensure successful implementation of the mission.A multi-objective integer optimization method that considers both mission reliability and cost is then formulated to obtain the optimal test plans.This study first constructs a mission reliability model for the UAV swarm in the combat stage.Then,the launch stage and flight stage are integrated to develop a complete PMS(Phased Mission Systems)reliability model.Finally,the Binary Decision Diagrams(BDD)and Multi Objective Quantum Particle Swarm Optimization(MOQPSO)methods are proposed to solve the model.The optimal plans considering both reliability and cost are obtained.The proposed model supports the planning of UAV swarm operational tests and represents a meaningful exploration of UAV swarm test planning.

Integrated operational planning of hydrothermal power and natural gas systems with large scale storages

The growing installation of natural gas fired power plants has increased the integration of natural gas and electricity sectors. This has driven the need investigate the interactions among them and to optimize energy resources management from a centralized planning perspective. Thus, a combined modeling of the reservoirs involved in electric power and gas systems and their locations on both networks are essential features to be considered in the operational planning of energy resources.This paper presents a modeling and optimization approach to the operational planning of electric power and natural gas systems, taking into account different energy storage facilities, such as water reservoirs, natural gas storages and line packs of pipelines. The proposed model takes advantage of captures both energy systems synergy and their associated networks. This approach identifies the interactions between the energy storage facilities and their economic impact over their optimal scheduling. The results show the benefits of an integrated operational planning of electric power and natural gas systems, the close interdependency between the energy resources stored in both systems, and the effects of a combined scheduling.

Mathematical modeling of electrochemical storage for incorporation in methods to optimize the operational planning of an interconnected micro grid

We extract a mathematical model to simulate the steady-state charging and discharging behaviors of an electrochemical storage over a 24-hour time interval.Moreover,we develop a model for optimizing the daily operational planning of an interconnected micro grid considering electrochemical storage.The optimization model is formulated to maximize the total benefit of the micro grid via selling power to its end consumers and also exchanging power with the wholesale energy market so that the constraints of distributed energy resources(DERs) and low-voltage grid are met.The optimization problem is solved by a genetic algorithm,and applied on two micro grids operating under different scenarios containing the absence or presence of electrochemical storages.Comparison of the results of the optimization model for this micro grid,with and without electrochemical storage,shows that the electrochemical storage can improve the economical efficiency of the interconnected micro grids by up to 10.16%.

Levy-flight moth-flame optimisation algorithm-based micro-grid equipment sizing:An integrated investment and operational planning approach

Bridging the gap between simulation and reality for successful micro-grid(MG)implementation requires accu-rate mathematical modelling of the underlying energy infrastructure and extensive optimisation of the design space defined by all possible combinations of the size of the equipment.While exact mathematical optimisa-tion approaches to the MG capacity planning are highly computationally efficient,they often fail to preserve the associated problem nonlinearities and non-convexities.This translates into the fact that the available MG sizing tools potentially return a sub-optimal(inferior)MG design.This brings to light the importance of nature-inspired,swarm-based meta-heuristic optimisation algorithms that are able to effectively handle the nonlinear and non-convex nature of the MG design optimisation problem–and better approximate the globally optimum solution–though at the expense of increased computational complexity.Accordingly,this paper introduces a robust MG capacity planning optimisation framework based on a state-of-the-art meta-heuristic,namely the Lévy-flight moth-flame optimisation algorithm(MFOA).An intelligent linear programming-based day-ahead en-ergy scheduling design is,additionally,integrated into the proposed model.A case study is presented for a real grid-tied community MG in rural New Zealand.A comparison of the modelling results with those of the most popular tool in the literature and industry,HOMER Pro,verifies the superiority of the proposed meta-heuristic-based MG sizing model.Additionally,the efficiency of the Lévy-flight MFOA is compared to nine well-established meta-heuristics in the MG capacity planning literature.The comparative analyses have revealed the statistically significant outperformance of the Lévy-flight MFOA to the examined meta-heuristics.Notably,its superiority to the original MFOA,the hybrid genetic algorithm-particle swarm optimisation,and the ant colony optimiser,by at least~6.5%,~8.4%,and~12.8%,is demonstrated.Moreover,comprehensive capital budgeting analyses have confirmed the financial viability of the test-case system optimised by the proposed model.

A multi-user decision support system for online city bus tour planning

Tourism is rapidly becoming a sustainable pathway toward economic prosperity for host countries and communities. Recent advances in information and communications technology, the smartphone, the Internet and Wi-Fi have given a boost to the tourism industry. The city bus tour （CBT） service is one of the most successful businesses in the tourism industry. However, there exists no smart decision support system determining the most efficient way to plan the itinerary of a CBT. In this research, we report on the ongoing development of a mobile application （app） and a website for tourists, hoteliers and travel agents to connect with city bus operators and book/purchase the best CBT both in terms of cost and time. Firstly, the CBT problem is formulated as an asymmetric sequential three-stage arc routing problem. All places of interest （PoI） and pickup/dropout points are identified with arcs of the network （instead of nodes）, each of which can be visited at least once （instead of exactly once）. Secondly, the resulting pure integer programming （IP） problem is solved using a leading optimization soft- ware known as General Algebraic Modeling System （GAMS）. The GAMS code developed for this project returns： （1） the exact optimal solution identifying the footprints of the city bus relative to all the arcs forming the minimal cost network; （2） the augmenting paths corre- sponding to the pickup stage, the PoI visiting stage and the drop-off stage. Finally, we demonstrate the applicability of the mobile app/website via a pilot study in the city of Melbourne （Australia）. All the computations relative to the initial tests show that the ability of the app to answer users＇ inquiries in a fraction of a minute.

Optimization method of urban rail train operational plan based on O-D time-varying demand

Purpose–Under the constraints of given passenger service level and coupling travel demand with train departure time,this study optimizes the train operational plan in an urban rail corridor to minimize the numbers of train trips and rolling stocks considering the time-varying demand of urban rail passenger flow.Design/methodology/approach–The authors optimize the train operational plan in a special network layout,i.e.an urban rail corridor with dead-end terminal yard,by decomposing it into two sub-problems:train timetable optimization and rolling stock circulation optimization.As for train timetable optimization,the authors propose a schedule-based passenger flow assignment method,construct the corresponding timetabling optimization model and design the bi-directional coordinated sequential optimization algorithm.For the optimization of rolling stock circulation,the authors construct the corresponding optimization assignment model and adopt the Hungary algorithm for solving the model.Findings–The case study shows that the train operational plan developed by the study’s approach meets requirements on the passenger service quality and reduces the operational cost to the maximum by minimizing the numbers of train trips and rolling stocks.Originality/value–The example verifies the efficiency of the model and algorithm.

COMPARISON OF TWO METHODS OF OPERATING THEATRE PLANNING:APPLICATION IN BELGIAN HOSPITAL

Operating Theatre is the centre of the hospital management＇s efforts. It constitutes the most expensive sector with more than 10% of the intended operating budget of the hospital. To reduce the costs while maintaining a good quality of care, one of the solutions is to improve the existent planning and scheduling methods by improving the services and surgical specialty coordination or finding the best estimation of surgical case durations. The other solution is to construct an effective surgical case plan and schedule. The operating theatre planning and scheduling is the two important steps, which aim to make a surgical case programming with an objective of obtaining a realizable and efficient surgical case schedule. This paper focuses on the first step, the operating theatre planning problem. Two planning methods are introduced and compared. Real data of a Belgian university hospital ＂Tivoli＂ are used for the experiments.

Coordinated optimization for controlling short circuit current and multi-infeed DC interaction

Due to increased penetration of renewable energies,DC links and other emerging technologies,power system operation and planning have to cope with various uncertainties and risks.In order to solve the problems of exceeding short circuit current and multi-infeed DC interaction,a coordinated optimization method is presented in this paper.Firstly,a branch selection strategy is proposed by analyzing the sensitivity relationship between current limiting measures and the impedance matrix.Secondly,the impact of network structure changes on the multi-infeed DC system is derived.Then the coordinated optimization model is established,which considers the cost and effect of current limiting measures,the tightness of network structure and the voltage support capability of AC system to multiple DCs.Finally,the non-dominated sorting genetic algorithm II combining with the branch selection strategy,is used to find the Pareto optimal schemes.Case studies on a planning power system demonstrated the feasibility and speediness of this method.

Two-stage Correction Strategy-based Real-time Dispatch for Economic Operation of Microgrids

Given the different energy rates of multiple types of power generation units,different operation plans affect the economy of microgrids.Limited by load and power generation forecasting technologies,the economic superiority of day-ahead plans is unable to be fully utilized because of the fluctuation of loads and power sources.In this regard,a two-stage correction strategy-based real-time dispatch method for the economic operation of microgrids is proposed.Based on the optimal day-ahead economic operation plan,unbalanced power is validly allocated in two stages in terms of power increment and current power,which maintains the economy of the day-ahead plan.Further,for operating point offset during real-time correction,a rolling dispatch method is introduced to dynamically update the system operation plan.Finally,the results verify the effectiveness of the proposed method.

Voice-driven fleet management system for agricultural operations

Food consumption is constantly increasing at global scale.In this light,agricultural production also needs to increase in order to satisfy the relevant demand for agricultural products.However,due to by environmental and biological factors(e.g.soil compaction)the weight and size of the machinery cannot be further physically optimized.Thus,only marginal improvements are possible to increase equipment effectiveness.On the contrary,late technological advances in ICT provide the ground for significant improvements in agriproduction efficiency.In this work,the V-Agrifleet tool is presented and demonstrated.VAgrifleet is developed to provide a “hands-free”interface for information exchange and an “Olympic view”to all coordinated users,giving them the ability for decentralized decision-making.The proposed tool can be used by the end-users(e.g.farmers,contractors,farm associations,agri-products storage and processing facilities,etc.)order to optimize task and time management.The visualized documentation of the fleet performance provides valuable information for the evaluation management level giving the opportunity for improvements in the planning of next operations.Its vendorindependent architecture,voice-driven interaction,context awareness functionalities and operation planning support constitute V-Agrifleet application a highly innovative agricultural machinery operational aiding system.

Approximating multi-purpose AC Optimal Power Flow with reinforcement trained Artificial Neural Network

Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment from renewable DERs(Distributed Energy Resource).Mathematical solvers are often able to solve the AC-OPF problem but need significant computation time.Artificial neural networks(ANN)have a good application in function approximation with outstanding computational performance.In this paper,we employ ANN to approximate the solution of AC-OPF for multiple purposes.The novelty of our work is a new training method based on the reinforcement learning concept.A high-performance batched power flow solver is used as the physical environment for training,which evaluates an augmented loss function and the numerical action gradient.The augmented loss function consists of the objective term for each use case and the penalty term for constraints violation.This training method enables training without a reference OPF and the integration of discrete decision variable such as discrete transformer tap changer position in the constrained optimization.To improve the optimality of the approximation,we further combine the reinforcement training approach with supervised training labeled by reference OPF.Various benchmark results show the high approximation quality of our proposed approach while achieving high computational efficiency on multiple use cases.