Planning Model Research Articles

PE059 Mixed reality in robot-assisted partial nephrectomy: Use of three-dimensional models in preoperative planning and intraoperative navigation

PE059 Mixed reality in robot-assisted partial nephrectomy: Use of three-dimensional models in preoperative planning and intraoperative navigation

Evaluation of distributed and semi-distributed hydrological models in complex River Basin system, Nepal

Hydrological information is essential for planning water resource projects and for simulating hydrological models to calibrate and generate streamflow data. This study evaluates the performance of distributed and semi-distributed hydrological models in the Tamakoshi River Basin (TRB), Nepal. The models assessed include the Spatial Process in Hydrology (SPHY), Hydrologiska Byråns Vattenbalansavdelning (HBV), and Hydrologic Engineering Centre Hydrologic Modeling System (HEC-HMS). The models were evaluated using metrics such as Nash-Sutcliffe Efficiency (NSE) ranging from 0.62 to 0.77, Coefficient of Determination (R2) from 0.76 to 0.77, and Percentage Bias (P Bias) from 26.16 % to −3.54 % during daily calibration. All three models were successfully calibrated and validated on a daily and monthly basis using observed discharge data at the Busti station in the TRB. The SPHY model performed better during the calibration period compared to the validation period, while the HBV and HEC-HMS models showed better performance during validation than calibration. Among the statistical indicators, the coefficient of determination closely matched, but NSE and P Bias showed some deviations. This comparative analysis of calibrated and validated models is valuable for filling gaps in hydrological data records and for selecting appropriate hydrological models for planning, managing, and developing water resource projects in the TRB.

Classifying 15-minute Cities: A review of worldwide practices

Amongst the most popular alternatives to the current car-dominant planning model to have emerged in recent times is the 15-minute City (15mC). The 15mC prioritises the notion of local living. It seeks to address the pressing challenges of urban life by promoting lifestyles largely independent of motorised travel modes by focusing on active mobility and proximity to essential amenities. Despite the popularity of this concept, a knowledge gap exists regarding the diverse range of international practical definitions, strategies, instruments, and implementation experiences, as well as the requirements for, and challenges involved in, operationalising the 15mC and related concepts.Consequently, this paper seeks to conduct a comprehensive analysis of the implementation of 15-minute cities and associated practices worldwide by combining an extensive literature review of both academic and grey literature sources with an expert survey. This culminates in the creation of a classification typology for the 15mC, based on the diversity of contexts and practices found, and informed by the Diffusion of Innovations Theory (DOI). Almost one hundred cities were identified as either in the process of implementing or planning to implement the 15mC concept. A diverse assortment of associated practices was identified, ranging from supporting active mobility to repurposing car space for other uses, promoting mixed land use and densification, and encouraging citizen engagement, among others. However, most cities are in the initial stages of executing their 15mC strategies, with the majority of practices still in the planning phase.Although cities worldwide have shown considerable interest in the 15mC concept, many have yet to embark on adopting comprehensive strategies or measures aimed at achieving more radical change, notably in areas such as logistics and governance.

Optimal planning of wind and solar complementary AC/DC microgrids under distributed power capacity constraints

Abstract The conventional AC/DC microgrid wind-solar complementary optimization planning method mainly uses the CvaR (conditional value at risk) risk value stochastic model to calculate the randomness of output electricity price, which is vulnerable to changes in load expectations, resulting in the per unit value of photovoltaic load output not meeting the actual demand of the microgrid. Therefore, under the constraints of distributed generation capacity, an optimal planning method of wind-solar complementation for AC/DC microgrids is designed. That is to say, considering the distributed generation capacity constraints, the optimal planning model of wind-solar complementation of AC and DC microgrids is constructed, and the bidirectional adjustment equation of the optimal planning of wind-solar complementation of AC and DC microgrids is generated, thus realizing the optimal planning of wind-solar complementation of AC and DC microgrids. The experimental results show that the PV output per unit value is higher and the load output per unit value is lower in the design of the wind-solar complementary optimization planning method of AC/DC microgrid considering distributed generation capacity constraints, which meets the requirements of efficient operation of the microgrid and has certain economic value.

A Defense Planning Model for a Power System Against Coordinated Cyber-Physical Attack

A Defense Planning Model for a Power System Against Coordinated Cyber-Physical Attack

Lane-changing and overtaking trajectory planning for autonomous vehicles with multi-performance optimization considering static and dynamic obstacles

Affected by the complex traffic environment, lane-changing and overtaking have become daily driving operations of autonomous vehicles, and providing a drivable trajectory is one of the critical tasks of planning processes. To this end, this paper aims to propose an optimization-algorithm-based double quintic polynomial trajectory planning model considering static and dynamic obstacles for lane-changing and overtaking maneuvers of the autonomous vehicle. Firstly, an improved double quintic polynomial planning model considering different motion states and sizes of obstacles is constructed by introducing the lane change transition state to ensure the autonomous vehicle’s driving safety. Secondly, a multi-objective performance function considering various influencing factors is established to improve the driving performances of the autonomous vehicle during lane-changing and overtaking. Finally, a particle swarm optimization (PSO) algorithm is used to optimize parameters of the proposed planning model, such as the lane change time, transition speed, and longitudinal displacement, to generate a driveability trajectory that meets the driving safety, comfort, stability, and low emission requirements of the autonomous vehicle during lane-changing and overtaking. The effectiveness and advantages of the proposed planning model are verified by comparing it with several existing planning models under different driving conditions.

Optimal planning for integrated electricity and heat systems using CNN-BiLSTM-Attention network forecasts

This paper investigates optimal planning of integrated electricity and heat systems (IEHS) based on convolutional neural network-bidirectional long short term memory with attention mechanism (CNN-BiLSTM-Attention) network forecasts. Firstly, CNN and BiLSTM are employed to extract the spatio-temporal features of IEHS data, and the attention mechanism automatically assigns corresponding weights to BiLSTM to distinguish the importance of different time load sequences, then a combined CNN-BiLSTM-Attention network is constructed, which allows for a more accurate load forecasting. Furthermore, we establish optimal planning strategy to improve efficient operation and energy efficiency of the IEHS, in which the upper-level planning model with the optimization objective of minimizing comprehensive operation costs is formulated, the lower-level efficiency model aiming to balance revenue and cost is considered, then genetic algorithm and iterative solution strategy of Cplex solver are applied to optimize the bi-level objective functions. Simulation results show that the proposed CNN-BiLSTM-Attention model obtained smaller RMSE, MAE and MAPE compared to several other forecasting models. In addition, the optimization method could obtain global optimal solution of the bi-level objective functions, which improves the energy utilization efficiency of the IEHS.

Strategic planning: attracting investments in agro-industrial enterprises

Since agriculture is an important sector of the Russian economy, the problem of lack of capital in this industry is still relevant. The article shows that the effective functioning of agricultural enterprises in modern conditions of a dynamic and rapidly changing business environment in the long term depends on the development of new and more advanced approaches to investment planning. The problem of the study is related to the absence or imperfection of strategic management of investment policy at many enterprises in the agricultural sector, which leads to their difficult financial situation, slows down their development and reduces competitiveness. The main purpose of the presented research is to form proposals for improving the strategic planning model by developing and implementing investment strategies of enterprises of the agro-industrial complex (hereinafter referred to as AIC). In the course of the study, a review of domestic and foreign literature has been conducted reflecting the issues of the investment planning. Within the framework of the modern approach to the problem, the following research methods are used in the article: literature analysis, classification, comparative analysis, description, observation, and statistical method. Special attention in the methodology is paid to the digital transformation of the investment activity of the AIC in Russia and the world. The author substantiates that the modern strategy of attracting investments in the AIC enterprises should radically differ from previous approaches and be implemented with consideration to the development of the information technology market. It is necessary that this approach facilitates the transition of the Russian AIC to the formats of “Agriculture 4.0” and “Agriculture 5.0”.

A planning model for coal power exit scales based on minimizing idle and shortage losses: A case study of China

Owing to the rising energy demand and coal dependency in developing countries, the losses incurred from coal power exit are substantial, leading to a low willingness to exit and a high risk of tragedy of the commons. Concurrently, the normalization of peak loads and increasing penetration of renewable energy have exacerbated the contradiction between the overall electricity surplus and peak power shortages in these countries. In response to these challenges, we propose an optimal planning model for a coal power exit scale based on the minimization of losses from idle coal power and industrial power shortages. The main findings are presented as follows: First, compared with the coal power exit models guided by the energy trilemma, our model can reduce losses (the primary demand in developing countries) by at least 22 %, and, in some scenarios, it can increase the exit scale by up to 147 GW. Second, unlike existing methods that measure the overall impact of power shortages, our approach is better at capturing industries' production loss characteristics, which is important for developing countries (positioned in the production segment of the industrial chain), including the evolution patterns of resource and time losses, recovery trajectories of power interruptions, and main influencing factors. Third, China's optimal coal power exit scale for 2030 fluctuates between −7 and 282 GW, with total losses between 48.34 and 188.86 billion yuan. This study provides quantitative tools and decision-making references for optimizing coal power exit and power use.

A Study of the Improved A* Algorithm Incorporating Road Factors for Path Planning in Off-Road Emergency Rescue Scenarios.

To address the problem of ignoring unpaved roads when planning off-road emergency rescue paths, an improved A* algorithm that incorporates road factors is developed to create an off-road emergency rescue path planning model in this study. To reduce the number of search nodes and improve the efficiency of path searches, the current node is classified according to the angle between the line connecting the node and the target point and the due east direction. Additionally, the search direction is determined in real time through an optimization method to improve the path search efficiency. To identify the path with the shortest travel time suitable for emergency rescue in wilderness scenarios, a heuristic function based on the fusion of road factors and a path planning model for off-road emergency rescue is developed, and the characteristics of existing roads are weighted in the process of path searching to bias the selection process toward unpaved roads with high accessibility. The experiments show that the improved A* algorithm significantly reduces the travel time of off-road vehicles and that path selection is enhanced compared to that with the traditional A* algorithm; moreover, the improved A* algorithm reduces the number of nodes by 16.784% and improves the search efficiency by 27.18% compared with the traditional 16-direction search method. The simulation results indicate that the improved algorithm reduces the travel time of off-road vehicles by 21.298% and improves the search efficiency by 93.901% compared to the traditional A* algorithm, thus greatly enhancing off-road path planning.

A double-layer optimization strategy for distribution networks considering 5G base station clusters

The reliability of the power supply for 5G base stations (BSs) is increasing. A large amount of BS backup energy storage (BES) remains underutilized. This study establishes a double-layer optimization distribution network (DN) considering BS clusters. An energy consumption characteristics and scheduling ability model of the BSs was established to address the differences in the characteristics of different traffic flows. A double-tier planning model for BS-joining grid market ancillary services is proposed. The upper-layer model addresses optimal tidal flow problems in DNs to minimize integrated operating costs, while the lower-layer model focuses on BES economic optimization. The double-layer model changes into a single-layer linear model using the Karush–Kuhn–Tucker (KKT) condition and the Big M method. Simulation validation using the IEEE-33 node DN proves that this approach can reduce DN operating costs, regulate voltage fluctuations, and guarantee economical and safe DN operation.

SDO: A novel sled dog-inspired optimizer for solving engineering problems

A new bio-inspired meta-heuristic algorithm, named Sled Dog Optimizer (SDO), is proposed in this paper. The inspiration for SDO is primarily drawn from the various behavioral patterns of sled dogs. It focuses on constructing mathematical models by simulating the process of dog sledding, training and retiring behaviors. The mutual constraints of multiple steps and the variation of several parameters make the exploitation and exploration capabilities of SDO well balanced. To test the ability of SDO, this paper sets up several different sets of experiments to be analyzed from a number of different aspects. First, SDO is qualitatively analyzed through several performance metrics, which include search history, search trajectory and population diversity. Second, SDO is compared with several novel and excellent metaheuristics at CEC 2017 as well as CEC 2020. The results show that SDO has extremely good performance in solving unconstrained optimization problems. SDO is then further applied to the CEC 2020 real-world problem test set to test its ability to solve real-world optimization problems with constraints. Finally, this paper extends and proposes a new path planning model to which SDO is applied. In these problems with constraints, SDO performs well and has a promising application. SDO demonstrates outstanding control of exploration and exploitation capabilities in all experiments. It can be said that SDO promotes the progress of meta-heuristic algorithms and provides another new powerful technology for complex optimization problems in the real world.

How to choose mobile energy storage or fixed energy storage in high proportion renewable energy scenarios: Evidence in China

With the large-scale integration of renewable energy and changes in load characteristics, the power system is facing challenges of volatility and instability. Therefore, enhancing the safe and stable operation capability of the power system is an urgent problem that needs to be solved. Mobile energy storage can improve system flexibility, stability, and regional connectivity, and has the potential to serve as a supplement or even substitute for fixed energy storage in the future. However, there are few studies that comprehensively evaluate the operational performance and economy of fixed and mobile energy storage systems. To comprehensively evaluate the economic benefits of large-scale mobile energy storage systems, this paper constructs an overall horizontal cost model for energy storage systems that considers the power system and train transportation system. This model not only introduces the traditional concept of lifecycle cost of energy storage systems, but also considers the costs of traditional transmission lines and battery transportation. Secondly, to achieve simulation of large-scale mobile energy storage system planning and operation, this paper establishes a multi-region power planning and operation simulation (MPO) model and a battery transportation and logistics (BTL) model to accurately reflect the operation mode of fixed energy storage and mobile energy storage in the power system. Finally, taking the actual power grids and railway networks in Northeast and North China as case studies, this article provides an in-depth analysis of the technical, economic, and environmental performance of large fixed and mobile energy storage systems, and conducts a detailed comparison. The research results indicate that under high grid connection ratios (using 75% and 66% as examples), the overall cost of mobile energy storage systems continues to decrease to 1.42 CNY/kWh and 0.98 CNY/kWh. Compared to fixed energy storage at 5.45 CNY/kWh and 4.79 CNY/kWh, it has an absolute economic advantage and shows significant carbon reduction capabilities, reaching 241,800 to 1,394,600 tons. This discovery fully confirms the enormous potential and application value of mobile energy storage in high proportion renewable energy scenarios, providing strong technical support and economic analysis basis for the sustainable development of the power system.

Multi-Objective Real-Time Planning of Evacuation Routes for Underground Mine Fires

When a fire occurs underground, pre-existing emergency escape routes may become ineffective. Such scenarios necessitate the real-time planning of escape routes that consider evolving conditions and prioritize safety. This paper proposes a multi-objective real-time search method for emergency escape routes using dynamic programming to address these challenges. Firstly, this paper discusses how the evacuation of an underground mine fire caused by external factors depends on the fire monitoring system, personnel positioning system, and emergency evacuation system, as well as the modeling and solution methods of evacuation route planning. Then, based on fire smoke, CO, and temperature sensor data from the mine ventilation network structure and monitoring system, the possible smoke spread in the event of an underground fire was calculated. The objectives are to optimize both the shortest equivalent length of the path and the shortest time to walk through the smoke flow while ensuring that temporary escape time does not exceed the rated protection time of the self-rescuer. A mathematical model for emergency escape route planning is established under these conditions. A labeling algorithm based on dynamic programming is employed to find the Pareto optimal solution set of emergency evacuation routes that meet emergency requirements. Finally, two path evaluation indicators, namely “escape target priority” and “personnel temporary escape time”, are introduced to re-rank the solutions in the Pareto optimal set, thereby obtaining disaster evacuation routes with different priorities. Example verification shows that the algorithm can quickly solve the disaster evacuation routes that meet the actual disaster evacuation needs in complex networks. Example verification shows that the algorithm can quickly solve the emergency escape routes in complex networks that meet actual emergency escape needs.

Optimizing cropping patterns and resource allocation for sustainable agricultural development: A case study of Ilam province, Iran

Establishing connections between ecosystems and agricultural mechanization can help address erroneous decision-making in agricultural management and prevent further harm to ecosystems. It is essential for authorities and decision-makers in the agricultural sector to understand ecosystem dynamics and influencing factors to develop appropriate policies for sustainable development. This study focuses on identifying optimal conditions for determining crop patterns and input allocation in Ilam province's agricultural practices, considering technical and energy parameters. Cultivating one hectare of wheat and barley in the region requires an average energy consumption of 1.79 GJ from various sources, with an estimated energy output of 38.04 GJ ha−1 for these crops. The research involved farmers cultivating wheat, barley, fodder maize, and canola in the southern region of Ilam province, covering cities like Abdanan, Dareshahr, and Dehloran. The study suggests adjustments to cropping patterns, including reducing wheat cultivation and increasing irrigated fodder maize cultivation under optimal conditions. It also recommends exploring rainfed cultivation or enhancing the yield of rainfed wheat to offset the reduced wheat cultivation. The goal planning model indicates a shift towards sustainability, with changes in cropping patterns focusing on wheat, barley, fodder maize, and canola crops, including expanding the cultivation areas of irrigated wheat, barley, and fodder maize. Efforts aim to enhance the productivity of irrigated crops to promote sustainability and improve agricultural practices in the province for long-term sustainability goals.

Machine Learning-Enhanced Benders Decomposition Approach for the Multi-Stage Stochastic Transmission Expansion Planning Problem

The necessary decarbonization efforts in energy sectors entail integrating flexible assets and increased levels of uncertainty for the planning and operation of power systems. To cope with this in a cost-effective manner, transmission expansion planning (TEP) models need to incorporate progressively more details to represent potential long-term system developments and the operation of power grids with intermittent renewable generation. However, the increased modeling complexities of TEP exercises can easily lead to computationally intractable optimization problems. Currently, most techniques that address computational intractability alter the original problem, thus neglecting critical modeling aspects or affecting the structure of the optimal solution. In this paper, we propose an alternative approach to significantly alleviate the computational burden of large-scale TEP problems. Our approach integrates machine learning (ML) with the well-established Benders decomposition to manage the problem size while preserving solution quality. The proposed ML-enhanced Multicut Benders Decomposition algorithm improves computational efficiency by identifying effective and ineffective optimality cuts via supervised learning techniques. We illustrate the benefits of the proposed methodology by solving multi-stage TEP problems of different sizes based on the IEEE24 and IEEE118 test systems, while also considering energy storage investment options..

Optimal allocation of wind-based distributed generators and STATCOMs using a hierarchical stochastic programming approach

Allocating static synchronous compensators (STATCOMs) to regulate given allotted wind-based distributed generators (W-DGs) during planning stages can provide high investment returns by maximizing the installed W-DGs. Amidst rising global warming concerns, commitments to adopt renewable energy gain international momentum to curb greenhouse emissions and meet environmental targets, reducing reliance on fossil-based resources. Renewable energy's intermittency complicates distribution planning, stressing voltage devices and increasing network losses. This paper presents a new hierarchical stochastic planning model that addresses uncertainties related to W-DGs and generic loads. The model optimizes allocation with voltage constraints and reactive power while incorporating a two-stage mixed-integer nonlinear program (MINLP), maximizing net profit, and adding an economic dimension to promote renewable energy investments. Improved wind power modeling with historical data and collective evaluation metrics for selecting the best-fitted probability distribution function (PDF) enhances the accuracy of wind power integration assessment. The hierarchical approach considers relaxed voltage constraints in Stage I to allow maximum allotment of W-DGs while introducing STATCOMs and DGs' reactive power in Stage II to address voltage violations. Verification on the Canadian 41-bus network demonstrates the advantage of the hierarchical approach in allocating more W-DGs and achieving higher profits than the simultaneous planning approach. These advances significantly enhance renewable energy integration in power systems.

Techno-economic analysis of mixed battery and fuel cell electric bus fleets: A case study

Electric city buses are promising to reduce traffic-related emissions in city centres as their drive train emits zero local emissions in an urban context. However, battery electric buses (BEBs) have limited driving range and long charging times. Although this can be managed, as city buses operate on predetermined routes, fuel cell electric buses (FCEBs) can meet more operational requirements in terms of range and charging. Thus, many bus network operators want to design a mixed full electric bus network with BEBs and FCEBs, which involves several complex and interdependent planning tasks. We extend an existing planning model to accommodate the planning of BEBs and FCEBs with their respective charging and fuelling infrastructure, and apply it to a real-world case study. Our results indicate that a heterogeneous BEB fleet with a mix of depot and opportunity charging results in lower total costs than a depot charging concept or a homogeneous FCEB fleet. We additionally find that a heterogeneous fleet including FCEBs and BEBs with a mix of charging concepts can be beneficial under specific circumstances. More specifically, the results indicate, that FCEB are a viable technology to supplement full electric bus fleets as soon as the costs for FCEB fall below 625,000€or the costs for hydrogen fall below 4.33 €/kg H2. Interestingly, rising energy prices would not increase the utilization of FCEBs, but rather influence the choice of BEB-types.

The Use of 3D Printing in Fetal Surgery for Surgical Planning: A Scoping Review.

Objectives: We sought to identify in which clinical scenarios 3D printed models are used to plan for fetal surgeries as well as the main purpose and the imaging method utilized for the models. In addition, we describe benefits and shortcomings of the models, as well as potential future improvements. Methods: In this scoping review, data were collected retrospectively from scientific databases (PubMed, Embase, Cochrane CENTRAL, CINAHL, Scopus, and the Web of Science platform) and screened by title, abstract, and full text against strict criteria. The inclusion criteria required the study be performed on a live fetus and involve 3D models used for fetal surgery. The models must have been designed from diagnostic imaging modalities such as CT, MRI, or ultrasound. The articles considered include clinical trials, review articles, cohort studies, case series, case reports, and conference abstracts. Results: Of the initial 742 articles collected, six met the inclusion criteria. Spina bifida and EXIT procedures were the most frequent use cases that inspired surgeons to print models for surgical planning. The ability to view patient-specific anatomy in a 3D handheld model was often touted as providing a great benefit to the surgical team's ability to anticipate intraoperative challenges. Conclusions: Three-dimensional printing models have been applied to plan for fetal surgeries, more specifically, for EXIT procedures and fetoscopic surgical repair of spina bifida. The potential benefits of 3D printing in fetal surgery are enormous.

Generating high-resolution land use and land cover maps for the greater Mariño watershed in 2019 with machine learning

Land Use and Land Cover (LULC) maps are important tools for environmental planning and social-ecological modeling, as they provide critical information for evaluating risks, managing natural resources, and facilitating effective decision-making. This study aimed to generate a very high spatial resolution (0.5 m) and detailed (21 classes) LULC map for the greater Mariño watershed (Peru) in 2019, using the MORINGA processing chain. This new method for LULC mapping consisted in a supervised object-based LULC classification, using the random forest algorithm along with multi-sensor satellite imagery from which spectral and textural predictors were derived (a very high spatial resolution Pléiades image and a time serie of high spatial resolution Sentinel-2 images). The random forest classifier showed a very good performance and the LULC map was further improved through additional post-treatment steps that included cross-checking with external GIS data sources and manual correction using photointerpretation, resulting in a more accurate and reliable map. The final LULC provides new information for environmental management and monitoring in the greater Mariño watershed. With this study we contribute to the efforts to develop standardized and replicable methodologies for high-resolution and high-accuracy LULC mapping, which is crucial for informed decision-making and conservation strategies.