The ubiquitous expansion and transformation of the energy supply system involves large-scale power infrastructure construction projects. In the view of investments of more than a million dollars per kilometre, planning authorities aim to minimise the resistances posed by multiple stakeholders. Mathematical optimisation research offers efficient algorithms to compute globally optimal routes based on geographic input data. We propose a framework that utilizes a graph model where vertices represent possible locations of transmission towers, and edges are placed according to the feasible distance between neighbouring towers. In order to cope with the specific challenges arising in linear infrastructure layout, we first introduce a variant of the Bellman-Ford algorithm that efficiently computes the minimal-angle shortest path. Secondly, an iterative procedure is proposed that yields a locally optimal path at considerably lower memory requirements and runtime. Third, we discuss and analyse methods to output k diverse path alternatives. Experiments on real data show that compared to previous work, our approach reduces the resistances by more than 10% in feasible time, while at the same time offering much more flexibility and functionality. Our methods are demonstrated in a simple and intuitive graphical user interface, and an open-source package (LION) is available at https://pypi.org/project/lion-sp.
This paper investigates inland port infrastructure investment planning under uncertain commodity demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The two-stage stochastic model minimizes the total expected costs, including the capacity expansion investment costs associated with handling equipment and storage, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The Arkansas section of the McCllean-Kerr Arkan…
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
The optimal charging infrastructure planning problem over a large geospatial area is challenging due to the increasing network sizes of the transportation system and the electric grid. The coupling between the electric vehicle travel behaviors and charging events is therefore complex. This paper focuses on the demonstration of a scalable computational framework for the electric vehicle charging infrastructure planning over the tightly integrated transportation and electric grid networks. On the transportation side, a charging profile generation strategy is proposed leveraging the EV energy con…
Planning resilient hydrogen supply chains under disruption risk
Brandenburg University of Technology Cottbus–Senftenberg（勃兰登堡工业大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Interdisciplinary Transformation University Austria（奥地利跨学科转型大学）
About 26% of total U.S. energy consumption is used in the transportation sector. Conventional vehicles use fuels such as gasoline, emit harmful gases, and have adverse effects on the environment. Electric vehicles (EVs) provide an alternative solution that decreases the dependency on traditional fuels such as gasoline and reduces hazardous gas emissions. EVs can drive longer distances by employing dynamic wireless power transfer systems (DWPT) without increasing their battery size or having stopovers. Additionally, developing a decision system that avoids an excessive load on the power grid is…
Investigating State-of-the-Art Planning Strategies for Electric Vehicle Charging Infrastructures in Coupled Transport and Power Networks: A Comprehensive Review
Large Language Model-Assisted Planning of Electric Vehicle Charging Infrastructure with Real-World Case Study
Department of Data Science and AI, Faculty of Information Technology, Monash University（数据科学与人工智能系，信息科技学院，莫纳什大学）；Monash Energy Institute, Monash University（莫纳什能源研究所，莫纳什大学）
Integrated investment, retrofit and abandonment energy system planning with multi-timescale uncertainty using stabilised adaptive Benders decomposition
This paper describes a generation and transmission (G&T) expansion planning tool based on Benders decomposition and multistage stochastic optimization: (i) A MIP-based "investment module" determines a trial expansion plan; (ii) an SDDP-based "operation module" calculates the expected operation costs for the trial plan and Benders cuts to the investment module based on the expected marginal costs of the capacity constraints at the optimal solution. The integrated G&T planning approach is illustrated with a realistic planning study for the Bolivian power system.
University of Rijeka（里耶卡大学）；University of Southern Denmark（南丹麦大学）
The application of autonomous UAVs to infrastructure inspection tasks provides benefits in terms of operation time reduction, safety, and cost-effectiveness. This paper presents trajectory planning for three-dimensional autonomous multi-UAV volume coverage and visual inspection of infrastructure based on the Heat Equation Driven Area Coverage (HEDAC) algorithm. The method generates trajectories using a potential field and implements distance fields to prevent collisions and to determine UAVs' camera orientation. It successfully achieves coverage during the visual inspection of complex structur…
This study proposes a bi-level optimization program to represent the electric vehicle (EV) charging infrastructure design and utilization management problem with user-equilibrium (UE) decisions. The upper level aims to minimize total facility deployment costs and maximize the revenue generated from EV charging collections, while the lower level aims to minimize the EV users' travel times and charging expenses. An iterative technique is implemented to solve the bi-level mixed-integer non-linear program that generates theoretical lower and upper bounds to the bi-level model and solves it to glob…
This paper presents an optimization-based receding horizon trajectory planning algorithm for dynamical systems operating in unstructured and cluttered environments. The proposed approach is a two-step procedure that uses a motion planning algorithm in a first step to efficiently find a feasible, but possibly suboptimal, nominal solution to the trajectory planning problem where in particular the combinatorial aspects of the problem are solved. The resulting nominal trajectory is then improved in a second optimization-based receding horizon planning step which performs local trajectory refinemen…
An algorithm is developed to optimize vehicle speed trajectory over multiple signalized intersections with known traffic signal information to minimize fuel consumption and travel time, and to meet ride comfort requirements using sequential convex optimization method. A comparison between the proposed method and dynamic programming is carried out to verify its optimality. In addition, vehicle motion during turning is studied because of its significant effect on fuel consumption and travel time.
To realize autonomous shipping, autonomous berthing and unberthing are some of the technical challenges. In the past, numerous research have been done on the optimization of trajectory planning of berthing problems. However, these studies assumed only a simple berth and did not consider obstacles. Optimization of trajectory planning on berthing and unberthing in actual ports must consider the spatial constraints and maintain sufficient distance to obstacles. The main contributions of this study are as follows: (i) a collision avoidance algorithm based on the ship domain which has variable size…
This paper describes an area decomposition and a robust optimization strategy for the planning of large-scale transmission networks considering uncertainty in variable renewable energy (VRE) and hydropower generation. The strategy is illustrated with a study of Brazil's power grid expansion from 2027 to 2036. The country's network has over 10 thousand buses and 14 thousand circuits and covers an area equivalent to the USA. A clustering scheme based on bus marginal costs is used to decompose the grid into seven regions. Each regional planning is then solved by a heuristic and Benders decomposit…
Reshetnev Siberian State University of Science and Technology（西伯利亚国立科技大学（列舍特涅夫院士命名））
The application of combinatorial optimization problems to solving the problems of planning processes for industries based on a fund of reconfigurable production resources is considered. The results of their solution by mixed integer programming methods are presented.
University of California at Berkeley（加州大学伯克利分校）；FANUC Corporation（发那科公司）
With the development of robotics, there are growing needs for real time motion planning. However, due to obstacles in the environment, the planning problem is highly non-convex, which makes it difficult to achieve real time computation using existing non-convex optimization algorithms. This paper introduces the convex feasible set algorithm (CFS) which is a fast algorithm for non-convex optimization problems that have convex costs and non-convex constraints. The idea is to find a convex feasible set for the original problem and iteratively solve a sequence of subproblems using the convex const…
Transmission expansion planning (TEP) is critical for the power grid to meet fast growing demand in the future. Traditional TEP model does not utilize the flexibility in the transmission network that is considered as static assets. However, as the load profile may have different seasonal patterns, the optimal network configuration could be very different for different seasons in the planning horizon. Therefore, this paper proposes to incorporate seasonal network optimization (SNO) into the traditional TEP model. SNO dynamically optimizes the network for each season of each planning epoch. Two …
We develop an optimization-based framework for joint real-time trajectory planning and feedback control of feedback-linearizable systems. To achieve this goal, we define a target trajectory as the optimal solution of a time-varying optimization problem. In general, however, such trajectory may not be feasible due to , e.g., nonholonomic constraints. To solve this problem, we design a control law that generates feasible trajectories that asymptotically converge to the target trajectory. More precisely, for systems that are (dynamic) full-state linearizable, the proposed control law implicitly t…
Fluence map optimization for intensity-modulated radiation therapy planning can be formulated as a large-scale inverse problem with competing objectives and constraints associated with the tumors and organs-at-risk. Unfortunately, clinically relevant dose-volume constraints are nonconvex, so standard algorithms for convex problems cannot be directly applied. While prior work focused on convex approximations for these constraints, we propose a novel relaxation approach to handle nonconvex dose-volume constraints. We develop efficient, provably convergent algorithms based on partial minimization…
