Upper-level Planning Research Articles

An Online Data-Driven Method for Accurate Detection of Thermal Updrafts Using SINDy

Utilizing thermal updrafts shows potential for enabling long-endurance cruising of fixed-wing unmanned aerial vehicles without energy consumption. This article presents a novel online method based on sparse identification of nonlinear dynamics (SINDy) approach to achievement identification of thermal sources in the atmosphere. Initially, the algorithm is incorporated into the upper-level planning system, interacting with the lower-level controller. Then, experiments are conducted through software-in-the-loop simulations (SITL) to validate the implementation of the proposed algorithm. It is found that direct observation of thermal sources through measurements using SINDy is unfeasible during straight and circular flight modes. Nevertheless, simulation analysis of the proposed approach indicates that under unobservable conditions, a portion of the parameters can still be identified. By comparing results obtained using the particle filter algorithm, this method is shown to accurately estimate the parameters with negligible errors under observability conditions. The novelty of this approach lies in its significant improvement of the localization accuracy of the thermal source, without the need for parameter adjustments in the algorithm. Finally, the proposed methods are integrated into commonly used hardware platforms, and their online feasibility is verified through hardware-in-the-loop simulations.

Dynamic event-triggered integrated task and motion planning for process-aware source seeking

The process-aware source seeking (PASS) problem in flow fields aims to find an informative trajectory to reach an unknown source location while taking the energy consumption in the flow fields into consideration. Taking advantage of the dynamic flow field partition technique, this paper formulates this problem as a task and motion planning (TAMP) problem and proposes a bi-level hierarchical planning framework to decouple the planning of inter-region transition and inner-region trajectory by introducing inter-region junctions. An integrated strategy is developed to enable efficient upper-level planning by investigating the optimal solution of the lower-level planner. In order to leverage the information acquisition and computational burden, a dynamic event-triggered mechanism is introduced to enable asynchronized estimation, region partitioning and re-plans. The proposed algorithm provides guaranteed convergence of the trajectory, and achieves automatic trade-offs of both exploration-exploitation and accuracy-efficiency. Simulations in a highly complicated and realistic ocean surface flow field validate the merits of the proposed algorithm, which demonstrates a significant reduction in computational burden without compromising planning optimality.

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.

Research on a Distributed Photovoltaic Two-Level Planning Method Based on the SCMPSO Algorithm

In response to challenges such as voltage limit violations, excessive currents, and power imbalances caused by the integration of distributed photovoltaic (distributed PV) systems into the distribution network, this study proposes at two-level optimization configuration method. This method effectively balances the grid capacity and reduces the active power losses, thereby decreasing the operating costs. The upper-level optimization enhances the distribution network’s capacity by determining the siting and sizing of distributed PV devices. The lower-level aims to reduce the active power losses, improve the voltage stability margins, and minimize the voltage deviations. The upper-level planning results, which include the siting and sizing of the distributed PV, are used as initial conditions for the lower level. Subsequently, the lower level feeds back its optimization results to further refine the configuration. The model is solved using an improved second-order oscillating chaotic map particle swarm optimization algorithm (SCMPSO) combined with a second-order relaxation method. The simulation experiments on an improved IEEE 33-bus test system show that the SCMPSO algorithm can effectively reduce the voltage deviations, decrease the voltage fluctuations, lower the active power losses in the distribution network, and significantly enhance the power quality.

Incorporating Human–Machine Transition into CACC Platoon Guidance Strategy for Actuator Failure

This study proposes a guidance strategy based on human–machine transition (HMT) for cooperative adaptive cruise control (CACC) truck platoon actuator failures. Existing research on the CACC platoon mainly focuses on upper-level planning and rarely considers platoon planning failures caused by actuator failures. This study proposes that the truck in the platoon creates sufficient space on the target lane through HMT when the actuator fails, thereby promoting lane changes for the entire team. The effectiveness of the proposed strategy is evaluated using the Simulation of Urban Mobility (SUMO) simulation. The results demonstrate that under conditions ensuring the normal operation of traffic flow, this guidance strategy enhances the platoon’s lane-changing capability. In addition, this strategy exhibits stronger robustness and efficiency in different traffic densities. This guidance strategy provides valuable insights into improving the driving efficiency of CACC truck platoons in complex road environments.

Bi-level programming optimization method of rural integrated energy system based on coupling coordination degree of energy equipment

Promoting energy transformation in rural areas, building a Rural Integrated Energy System, and developing multi-energy complementary and comprehensive utilization of rural energy in accordance with local conditions are important paths to support global low-carbon transformation and rural revitalization. However, how to consider the coupling synergy of multiple devices and plan the capacity of multiple devices in rural energy systems in an economical, low-carbon, and reliable manner is an urgent issue facing the development of Rural Integrated Energy System. This paper proposes a bi-level programming optimization method of Rural Integrated Energy System based on coupling coordination degree of energy equipment. Firstly, a multi-dimensional rural user energy utilization framework is designed that comprehensively considers agriculture, animal husbandry, industry, and lifestyle. Secondly, the bi-level optimization model of the Rural Integrated Energy System based on the coupling coordination degree of energy equipment is constructed. The upper-level planning model puts forward the equipment coupling coordination degree, and determines the equipment selection scheme for different types of Rural Integrated Energy System planning. The lower-level planning model considers economy, low carbon, and reliability, and uses the improved multi-objective Harris Hawk optimization algorithm to solve the capacity of different energy equipment in the Rural Integrated Energy System. Finally, the effectiveness and scientific nature of the model and method proposed in this paper are verified by the Integrated Energy Systems of a rural area in the north of China. In addition, by comparing the equipment selection scheme with the traditional method, it can be found that the optimization scheme in this paper that considers the coupling coordination of energy equipment reduces the annualized total cost by 29.34 %, reduces the annual carbon emissions by 60.43 %, and increases the reliability by 15.85 %. The collaborative planning of energy economy, low carbon energy and reliable energy has been realized.

Bi-Level Optimization for De-Icing Position Allocation and Unmanned De-Icing Vehicle Fleet Routing Problem.

Aircraft icing due to severe cold and local factors increases the risk of flight delays and safety issues. Therefore, this study focuses on optimizing de-icing allocation and adapting to dynamic flight schedules at medium to large airports. Moreover, it aims to establish a centralized de-icing methodology employing unmanned de-icing vehicles to achieve the dual objectives of minimizing flight delay times and enhancing airport de-icing efficiency. To achieve these goals, a mixed-integer bi-level programming model is formulated, where the upper-level planning guides the allocation of de-icing positions and the lower-level planning addresses the collaborative scheduling of the multiple unmanned de-icing vehicles. In addition, a two-stage algorithm is introduced, encompassing a Mixed Variable Neighborhood Search Genetic Algorithm (MVNS-GA) as well as a Multi-Strategy Enhanced Heuristic Greedy Algorithm (MSEH-GA). Both algorithms are rigorously assessed through horizontal comparisons. This demonstrates the effectiveness and competitiveness of these algorithms. Finally, a model simulation is conducted at a major northwestern hub airport in China, providing empirical evidence of the proposed approach's efficiency. The results show that research offers a practical solution for optimizing the use of multiple unmanned de-icing vehicles in aircraft de-icing tasks at medium to large airports. Therefore, delays are mitigated, and de-icing operations are improved.

Research on the Cooperative Development Mode of Rural Tourism Resorts — Taking Huangpu River Source Tourism Resort as an Example

The Huangpu River Source Tourism Resort, situated within Anji County, Zhejiang Province, encompasses the administrative boundaries of Baofu Town, Zhangcun Town, and Hanggai Town. With the establishment of the resort's management committee, upper-level planning and coordination have gradually fallen into place, facilitating the full-scale construction of the resort. As a rural-oriented tourism destination aimed at fostering comprehensive development across these three townships, disparities are anticipated in terms of development foundations, tourism competitiveness, and development approaches. The purpose of this paper is to establish an evaluation index system for rural tourism competitiveness within the region. Utilizing factor analysis via the SPSS tool, it seeks to assess the strengths and weaknesses of each town's development factors, thereby evaluating the tourism development foundation and potential across the three towns. This endeavor aims to provide more effective guidance for the scientific construction and management of the Huangpu River Source Tourism Resort in Anji County and to explore a linked development model for the resort.

A System Optimization Approach for Trains’ Operation Plan with a Time Flexible Pricing Strategy for High-Speed Rail Corridors

Optimizing the train plan for high-speed rail systems should consider both the passengers’ demands and enterprise’s benefits. The choice of the departure time period is the most important factor affecting the passenger demand distribution. In this paper, the optimization problem of a train operation plan based on time period preference is studied for a high-speed rail corridor. First, according to the travel process of the passengers, the extended service network for a high-speed rail system is established. The main factors that influence the passengers’ travel choices are analyzed, and the departure time period preference, stop time and flexible pricing strategy based on the time period preference are put forward. The generalized travel cost function, including the convenience, ticket fare and stop time costs, is constructed, and a two-level programming model is established based on the function. The upper-level planning model is formulated as a mixed 0–1 programming problem that aims at maximizing the revenue of the railway enterprise. It is mainly constrained by passenger travel demand and solved by improved genetic algorithms. The lower-level model is a user equilibrium (UE) model. The Frank–Wolfe algorithm is used to allocate multiple groups of OD (origin and destination) passenger flows to each train so that the generalized travel expenses of all the passengers with the same OD are minimized and equal. Finally, the train operation plan is solved based on the Lan-xi (Lanzhou–Xi’an) high-speed rail data, and the validity of both the model and algorithm is verified.

Adaptive robust steering strategy for electro-hydraulic hybrid steering system based on backstepping method

Aiming at the angle tracking problem of electro-hydraulic hybrid steering (EHHS) system of heavy commercial vehicles in intelligent driving mode, the dynamic model of the system is established. In order to weaken the influence of nonlinear disturbance and parameters, an adaptive robust controller based on backstepping method is designed, a linear extended state observer is established to predict the disturbance. Triangular programming and trapezoidal programming are introduced into the upper level planning of steering angle position to improve the performance, so as to realize the tracking control of steering angle. In order to verify the effectiveness of the control algorithm, the designed controller is compared with DRC control. The results show that the controller used in this paper has higher servo accuracy, smaller error, and better robustness to interference. And to verify the improvement of the speed planning algorithm, the simulation is compared with the non-planning algorithm. The results show that adding the planning algorithm can reduce the control effect error and avoid the response oscillation to a certain extent. Finally, HIL bench test shows that the control strategies under various working conditions have good effect, and meet the steering angle tracking requirements of commercial vehicles in intelligent driving mode.

Optimal dispatch of a novel integrated energy system combined with multi-output organic Rankine cycle and hybrid energy storage

Integrated energy system (IES) is characterized as a heterogeneous system, providing energetic, economic and environmental benefits by cooperating with multiple energy carriers. However, current studies rarely focus on utilizing hybrid electric/thermal storage in coordination with harvesting heat flexibly in IES, which should be further considered in the planning period. To this end, a novel IES combined with hybrid electric/thermal storage and multi-output organic Rankine cycle (ORC) is proposed. Considering operating modes of gas turbine, ORC’s working manners, and converting patterns of harvesting heat in IES, comparisons of case study and operating characteristics on typical season days are analyzed. A bi-level optimal planning model of hybrid electric/thermal storage with ORC is proposed, consisting of a lower-level operating model and an upper-level planning model. Results show that there is the least operating cost of IES on typical season days under the off-design mode of gas turbine, ORC’s combined heat and power (CHP) mode and harvesting heat flexibly, when enabling multi-flows to adjust thermoelectric ratio effectively. ORC’s pure power generating mode in IES with low electrical load is not economically attractive, while ORC’s CHP mode is beneficial to enhance ORC’s consistent dispatching time and electricity producing amount. State of charge of battery energy storage system has similar varying patterns, while heat storage degree and cold storage degree of thermal energy storage system change differently owing to varying thermal loads. The IES is thermodynamically feasible and economically attractive thanks to the bi-level optimization, achieving optimal battery energy storage system capacity of 1773 kWh, thermal energy storage system capacity of 4823 kWh and ORC capacity of 91.25 kW.

Research on market trading strategy of multi-microgrid intelligent power distribution system based on Bi-level optimization

With the continuous promotion of the “dual carbon” idea, future power generation will rely heavily on renewable energy sources. As an effective utilization form of clean power sources, it is of positive significance to study the trading strategy of microgrids in the intelligent power distribution system under the influence of carbon quota. In this research, a bi-level optimization method is used to build a trading model of the distribution-side power market, with the upper-level planning aiming to reduce the cost of distribution system operators and the lower-level planning aiming to increase the revenue of microgrids. Secondly, the genetic algorithm and sequential quadratic planning algorithm are applied to the upper and bottom models to determine the optimal clearing strategy for the microgrid and the optimal scheduling scheme for the distribution system operator, respectively. Finally, a typical day is used as an example to analyze in detail the market trading strategy of a multi-microgrid intelligent distribution system under the influence of carbon quota, and the effectiveness of the method described in this paper is verified.

Research on the Spatial Perception of Stakeholders in Brownfield Redevelopment Based on Value Compatibility Analysis

Under the current urban renewal background, the subjective attitude of stakeholders directly affects the feasibility of planning projects in the development or protection activities related to brownfield redevelopment. It is key that the public effectively participates in planning and decision-making to explore the suitable expression method of public attitude. In this paper, Jigang, Jinan, Shandong Province is taken as an example. By using participatory mapping and semi-structural interviews, the landscape perceived value of 365 (342 valid) stakeholders in the original site of Jigang is investigated. By using hot spot analysis, correspondence analysis and compatibility index analysis, the spatial composition of public perceived landscape value, the correspondence between landscape value and land use and its compatibility with existing conservation and renewal schemes are revealed. On this basis, three types of brownfield land redevelopment attitude areas are identified. The results show that: 1. The attitude of Jinan Iron and Steel Group’s renewal based on the degree of compatibility is location-dependent, and the spatial difference analysis of this attitude provides more detailed data support for the protection and renewal design, planning management and conflict control; 2. The landscape perceived value of case stakeholders has regularity in spatial distribution and is related to a certain material landscape foundation (land use), which is beneficial in explaining the possible social phenomena caused by landscape change; 3. Participatory cartography combined with landscape value investigation provides an effective method for the study of perceived landscape. Through cartographic visualization, statistical analysis and index model construction, the spatial structure characteristics of perceived landscape value can be revealed. It can provide effective decision support for brownfield urban renewal projects, solve the problem that the current upper-level planning of such renewal is not matched with the actual demand, and improve the vitality of brownfield sites in the development area.

A Project Scheduling Game Equilibrium Problem Based on Dynamic Resource Supply

In a resource-constrained project scheduling problem, most studies ignore that resource supply is a separate optimization problem, which is not in line with the actual situation. In this study, the project scheduling problem and the resource supply problem are regarded as a dynamic game system, with interactive influences and constraints. This study proposes a Stackelberg dynamic game model based on the engineering supply chain perspective. In this model, the inherent conflicts and complex interactions between the Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP) and the Multi-Period Supply Chain Problem (MPSCP) are studied to determine the optimal equilibrium strategy. A two-level multi-objective programming method is used to solve the problem. The MRCPSP is the upper-level planning used to optimize project scheduling and activity mode selection to minimize project cost and duration; MPSCP is a lower-level planning method that seeks to make resource transportation decisions at a lower cost. A two-layer hybrid algorithm, consisting of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), is proposed to determine the optimal equilibrium strategy. Finally, the applicability and effectiveness of the proposed optimization method are evaluated through a case study of a large hydropower construction project, and management suggestions for related departments are provided.

Research on the optimal allocation method of source and storage capacity of integrated energy system considering integrated demand response

The optimal allocation of energy storage capacity is an important issue for integrated energy systems (IES). To reduce the impact of volatility and intermittency of renewable energy sources, the impact of volatility needs to be smoothed out by rational allocation of energy storage. To address the above issues, this paper proposes a method for the optimal allocation of source storage capacity considering integrated demand response(IDR). Firstly, the basic mechanism of IES based on energy hub(EH) is constructed, and the model and coupling relationship of equipment components in the system are introduced. The IDR model is built to optimize the load curve according to the real-time market tariff scheme, and then the confidence interval method is used to analyze the scenery uncertainty in IES and determine the scenery output curve under different confidence levels. On this basis, a bi-level optimization model is built that takes into account both source and storage capacity allocation and operational optimization. The upper-level planning model takes into account the uncertainty of wind power and photovoltaic output, and solves the allocation scheme of energy storage intending to minimize the total planning cost; the lower-level operation optimization model takes into account the output constraints of each unit and optimizes the output of the equipment to minimize the operation cost. Finally, it is verified that considering IDR can reasonably optimize the allocation of the system’s source storage capacity and reduce the system’s investment and operating costs.

Optimal Control of Connected and Automated Vehicles at Multiple Adjacent Intersections

In this paper, we establish a decentralized optimal control framework for connected and automated vehicles (CAVs) crossing multiple adjacent, multi-lane signal-free intersections to minimize energy consumption and improve traffic throughput. Our framework consists of two layers of planning. In the upper-level planning, each CAV computes its optimal arrival time at each intersection recursively along with the optimal lane to improve the traffic throughput. In the low-level planning, we formulate an energy-optimal control problem with interior-point constraints, the solution of which yields the optimal control input (acceleration/deceleration) of each CAV to cross the intersections at the time specified by the upper-level planning. Moreover, we extend the results of the proposed bi-level framework to include a bounded steady-state error in tracking the optimal position of the CAVs. Finally, we demonstrate the effectiveness of the proposed framework through simulation for symmetric and asymmetric intersections and comparison with traditional signalized intersections.

Multi-objective planning of regional integrated energy system aiming at exergy efficiency and economy

This paper introduces the exergy efficiency that takes into account both the quantity and quality of energy, and constructs a bi-level planning optimization model of the regional integrated energy system. Factors such as equipment exergy efficiency are considered by the upper-level planning model. Through matter-element information theory and Frequent Patterm-growth algorithm, the energy structure of the regional integrated energy system planning is determined by quantitative means. The lower-level planning model is intended for economy and exergy efficiency, and the tabu search algorithm is embedded in the solving algorithm of the multi-objective genetic algorithm for solving, determining the capacity of each equipment in the energy structure. In the case study, three comparison schemes are used to verify the proposed model and method through the regional integrated energy system in a resort town in northern China. Among them, Scheme 1 is devised for economy, Scheme 2 is proposed for economy and energy efficiency, and Scheme 3 is intended for economy and exergy efficiency. The results show that compared with the Scheme 1 and Scheme 2, the system exergy efficiency of Scheme 3 corresponding to this model has increased by 17.34% and 11.17%, respectively. Despite an increase of 10.61% in total annualized cost in Scheme 3 compared to Scheme 1, there is a decrease of 6.37% compared to Scheme 2. Despite the conflict in improving exergy efficiency and economy, there is still a balance to draw in the proposed model in this paper.

Multi-objective optimal planning of urban virtual power plant based on electrical distance

Currently, virtual power plants (VPP) in urban areas face problems such as over-dispersion of distributed power sources, insufficient source-load coordination. Traditional VPP planning method increases the difficulty in the operation and the dispatch of VPP. Therefore, this paper proposes a planning method for urban VPP that fully considers the coupling characteristics between the buses. From the distribution network to district to bus, a multi-objective bilevel optimization planning model for urban VPP is established. The upper-level planning model aims at minimizing the annual comprehensive cost, and derive the overall capacity of wind power turbine (WT), photovoltaic(PV), and energy storage system (ESS) of VPP in each district. The lower-level planning model aims at minimizing the network loss, and realizes the site selection and capacity allocation in each selected site. The case study based on the IEEE-33 standard system was carried out to verify the advancement and effectiveness of the proposed method.

Electric/thermal hybrid energy storage planning for park-level integrated energy systems with second-life battery utilization

The use of retired batteries from electric vehicles as a second-life battery energy storage system has been recognized as a way to break the high investment cost limitation of battery energy storage systems with the associated cost reduction of a park-level integrated energy system. The battery degradation and replacements should be further considered in the planning period, especially compared with new battery energy storage systems. Additionally, there is a lack of discussion on utilizing thermal energy storage systems in coordination with second-life battery to reduce degradation. For this reason, an electric/thermal hybrid energy storage system planning method for park-level integrated energy systems with second-life battery utilization is proposed. A cumulative battery life loss calculation model is developed. A bi-level optimal planning model of electric/thermal hybrid energy storage system using second-life batteries, including an upper-level planning model and a lower-level operating model, is proposed. At the upper level, to maximize the net present value during the planning stage, the capacity of the hybrid energy storage system as well as when the second-life battery energy storage system needs to be replaced are optimized. At the lower level, the operation schemes are optimized to obtain the minimum annual operating cost, which are fed back to the upper level. The proposed method considers continuous capacity degradation of second-life batteries and mutually beneficial relationships between thermal energy storage and second-life batteries. Finally, a case study demonstrates the economic effectiveness and battery service life improvement of the proposed planning method.

A Bi-Level Model for Green Freight Transportation Pricing Strategy Considering Enterprise Profit and Carbon Emissions

Due to the impact of COVID-19, enterprises need effective pricing strategies to improve profits and viability. In order to fill research gaps in the literature relating to market competition among different freight modes and to adjust the freight transportation structure by optimizing transportation prices, we propose a multi-objective bi-level programming pricing model that considers market competition and the carbon emissions of the freight system in China. First, an objective function in upper-level planning was used to improve logistics enterprise profits and reduce the total carbon emissions of the freight system. Then, a generalized cost function for the freight transportation mode was designed to quantify the market competition among different transportation modes, and a user equilibrium assignment model was established to obtain the results of cargo flow assignment in lower-level planning. To solve the model, a sensitivity analysis algorithm was designed, and a logistics network example was used to verify the effectiveness of the model. The experimental results show that reasonable freight price adjustment can effectively increase enterprise profits and reduce the total carbon emissions of the freight system. In this paper, we provide a new method for freight pricing research, considering the market competition of multiple transportation modes, and provide a new idea for adjusting the transportation structure through freight price optimization, which will play a positive role in promoting the development of green freight.