Optimal Configuration Plan Research Articles

Three-dimension object detection and forward-looking control strategy for non-destructive grasp of thin-skinned fruits

The dynamic non-destructive grasp of thin-skinned fruits using flexible robotic hands, which requires obtaining three-dimension(3D) spatial structure information along with adaptive planning and motion control toward the target object, is a challenging topic in agricultural intelligence. To tackle the issue of 3D detection, we utilize RGB images and LiDAR point clouds for feature extraction and construct a multi-modal depth fusion convolution neural network (MDF-CNN) to obtain the classification information and perform image segmentation. Incorporating the advantages of a variable palm structure, we establish an evaluation mechanism of the optimal grasping stability (EM-OGS) using a hybrid method of the best configuration and force closure to build a new comprehensive performance optimal configuration planning (CPO-CP) method that is based on the multiple grasping performance indexes. We also create three cross-related nonlinear prediction models P-MGF, P-ODAP, and P-OBA along with a forward-looking non-destructive grasp control algorithm (FL-NGCA) using minimum grasping force to grasp thin-skinned fruits. The control algorithm carries out online, self-directed learning in the actual grasping process of the flexible hand and constantly optimizes the accuracy of the prediction model. Experimental results show that our proposed approach greatly improves the flexible hand comprehensive performance of grasping, outperforming state-of-the-art methods for non-destructive grasping of delicate fruits in most areas.

Techno-economic-environmental analysis of seasonal thermal energy storage with solar heating for residential heating in China

Seasonal thermal energy storage (STES) of solar heat is an option of interest for clean heat transition, as residential heating is often fossil fuel-based. This study 1) proposes an integrated optimization criterion to examine how local context influences the optimal configuration planning, techno-economic-environmental performance, and feasibility of STES application; 2) identifies the position of STES in comparison to other sustainable heating options considering the local context; and 3) provides a comprehensive and transparent showcase highlighting the importance of the local context in determining the feasibility of STES in the clean heating transition. The TRNSYS modeling tool is adopted to analyze the performance, and Pareto optimization is applied to treat the multi-objective optimization. The solar fractions and storage efficiencies of the four case studies range between 58-67% and 57–69%, respectively. STES has significant potential to reduce CO2 emissions (52–72%) compared to conventional heating systems. However, the heating cost of the STES system (5.4–8.7 €-ct/kWh) is more than twice that of the conventional heating system. The CO2 avoidance cost of the four case studies ranges between 114 and 368 €/t. Properly reducing the borehole number in cold climate zones and increasing the solar collector area in warm climate zones help improve the system performance.

Optimal Configuration Planning of Multi-Energy Systems using Optimization-based Deep Learning Technique

There has been discovered that the freestanding hybrid renewable resource, which combines energy sources like solar, wind, etc., is an efficient substitute for supplying electricity to isolated places that are not connected to utility grids. The appropriate supply of electricity is subject to several restrictions, including variations in peak load, power outage supply, and other factors like swings. This study suggests an effective strategy for managing and sizing hybrid renewable energy resources to get around the drawbacks. First, the prediction of weather and loading demand is carried out using Bi-Directional Gated Recurrent Unit (Bi-GRU) network, in which past data are utilized to predict the uncertainties in load. This is done to assist the right sizing of resources for that purpose, a Long Short-Term Memory (LSTM) is used. Furthermore, the Search and Rescue Optimization Algorithm (SRO) employed for optimizing the parameter of power management. Utilizing MATLAB R2020a, the proposed model is tested by simulating it and comparing its performance to that of other models using metrics like variation rate, battery energy, Loss of Power Supply Probability (LPSP), forecasting error, net present cost, and cost of energy.

Optimal configuration planning of vehicle sharing station-based electro-hydrogen micro-energy systems for transportation decarbonization

With the popularization of electric vehicles and hydrogen fuel vehicles, establishing an eco-friendly and economic-affordable fuel supply system turns out to be critical for the decarbonization of transportation sector. To achieve this, this paper proposes a systematic methodological framework to facilitate optimal configuration planning of an electric-hydrogen micro-energy system with vehicle sharing stations (EHVS). The proposed approach is based on the notion of integrated resource planning, which combines supply side with demand side simultaneously. The problem is formulated as a two-stage hybrid stochastic-interval programming, where the first-stage is aimed at minimizing the investment cost by determining the optimal configuration planning, while the second-stage is aimed at maximizing the expected benefits through optimal operation simulation of the system. The potential flexibility of vehicular demands in response to the rental price-setting has been explicitly considered in this work and represented using an evolutionary game model. Through this, the substitutability between different vehicle demands and its impact on the long-term planning of electric-hydrogen integrated energy system can be properly captured. A hybrid interval-stochastic method is presented to handle different types of uncertainties involved in the discussed problem, wherein the scenario analysis, the interval order relation and the fuzzy preference methods are jointly utilized. The resultant problem is solved with a genetic-algorithm based framework, and implemented in the MATLAB environment. The simulation results show that, compared with conventional independent planning, there could be an improvement in the carbon emission reduction by 24.74% and an additional cost saving of 11.97% achievable using the proposed approach.

Demand prediction and optimization of workshop manufacturing resources allocation: A new method and a case study

At present, great changes are taken place in the internal production management and resource allocation model of manufacturers. Under the premise of rational resource allocation, the completion period of products largely depends on the timeliness of resource allocation. The related studies mostly tackle the allocation of a single type of production resources in a single workshop, without considering much about the mutual influence between workshops. Through in-depth research on workshop manufacturing practices, this paper chooses to explore the planning, allocation, and demand prediction of manufacturing resources, which has long been a difficulty in workshop production. The research has great scientific research significance and practical value. The authors designed an algorithm based on the difference of the mean stagnation time of different production processes in the execution process, and used the algorithm to predict the number of production resources required in each period, before formulating the optimal configuration plan. This method is highly reasonable and applicable. After presenting a prediction method for the allocation demand of workshop manufacturing resources, the authors discussed whether the manufacturing resource allocation between different workshops is balanced in a fixed period. Then, a new idea was proposed for collaborative production between machines of different workshops in a specific environment, and an optimization algorithm was put forward to optimize the manufacturing resource allocation to machines facing the operation execution process. Through experiments, the authors compared the utilization rate of material, technological or human production resources in each period, and thereby verified the effectiveness of the proposed algorithm.

Optimal configuration planning of rule and optimization-based driven storage coupled micro cogeneration systems by the implementation of constraint programming

In this paper, an optimization model for optimal size and dispatch planning of micro combined heat and power systems regarding distinct structures for the inclusion of the cogeneration unit, auxiliary boiler, heat storage and battery storage in the configuration of the system is introduced. The presented model is developed in a unified form by the implementation of constraint programming to consider different strategies for the system's operation. Furthermore, important details such as minimum/maximum permissible state of charge, maximum charge/discharge rate and battery inverter for modeling practical storages as technical specifications and simultaneous consideration of calendar life and life cycle as service life as well as assessment of heat dumping avoidance has been taken into account in this study. The model has been applied to three operation strategies and four kinds of buildings while considering two single-objective and one multi-objective optimization for minimization of the greenhouse gas emissions and annual costs of the system. The study has been validated by multiple study cases and the analyzes indicate that the implementation of different structures for the configuration of the micro CHP systems can impressively affect the optimum size and dispatch of these systems in each operation strategy. Comparison of the obtained results for the candidate multi-criteria solutions considering the exploitation of different storage options indicates that the inclusion of the heat storage by up to 21.5% further reduction in annual expenditures with an equal amount of emissions is a more influential energy storage option for utilization in the proposed residential cogeneration system. Regarding heat dumping assumption, dumping avoidance can be a powerful key factor in the system's management to prevent extra greenhouse generations by restricting the heedless minimization of the annual costs when it adversely affects the generation of the emissions.

Optimal clean heating mode of the integrated electricity and heat energy system considering the comprehensive energy-carbon price

Clean heating is a powerful solution for satisfying the building heat demand by synergizing energy efficiency and carbon emission. For satisfying the newly increased heat load, this paper constructs an alternative integrated electricity and heat energy system (IEHES) to consider different clean heating supply modes, namely electrical-heating mode (EH, electric boiler or electric heat pump), CHP unit with high back-pressure turbine heating mode (HBP–CHP), and auxiliary heat source heating mode (AHS, coal-fired boiler or gas-fired boiler). An optimal configuration planning and dispatch model is proposed for comparing the performance of the three heating modes. Wherein, a new comprehensive energy-carbon price is proposed as the optimization objective to minimize this system's total production costs and carbon emission costs. The results show that the HBP-CHP mode is the optimal solution when the heat load growth rate λ is lower than 14%, followed by the electric heat pump heating way under the EH mode. However, the HBP-CHP mode will limit the flexibility of the IEHES when λ is larger than 8%. Therefore, we propose a combined heating mode by introducing the electric heat pump into the HBP-CHP heating mode that facilitates the flexibility of the IEHES and brings the optimal system coal-saving effect.

Capacity Optimal Configuration of Wind-Hydrogen Low Carbon Energy System

Wind power is the most promising renewable energy source, and hydrogen energy is a clean energy carrier. The combination of the two will provide a feasible solution for achieving carbon neutrality. We combine natural gas and carbon capture systems with typical wind-hydrogen coupling systems to improve system economy, and hydrogen power generation is not considered. Oxygen-enriched combustion technology uses the oxygen obtained from hydrogen production to realize the coupling of natural gas and wind energy hydrogen production systems, which improves the efficiency of gas turbines and reduces Energy consumption of carbon capture. Then, with the goal of minimizing the annualized cost including the cost of investment, maintenance, operation and wind curtailment penalty, and taking wind curtailment, carbon capture, gas storage, and power balance as constraints, construct a wind-hydrogen low carbon energy system optimal configuration model. Finally, based on CPLEX to simulate the energy supply system in a certain area, the optimal configuration plan of the system’s wind power, gas storage capacity, electrolytic cell.

Configuration of product plan based on case reasoning of extenics

Through extenics-based case reasoning, this research puts forward a method to configure the modules of complex machine products. Firstly, the knowledge on the process of product design was represented, and the convergence modes of design plans were analyzed, with the aid of product semantics and the knowledge representation model of the primitives. Next, the semantic correlation function was constructed, and a priority degree evaluation method for product family was created, in order to obtain the optimal configuration plan. In this way, the excellent design genes in product plans with relatively low correlations will be preserved. Finally, the proposed method was verified on the design of computer numerically controlled (CNC) roll grinder. Specifically, the quantitative and qualitative user needs were converted into the design objectives, producing the set of primitives (design objectives). Based on sematic correlations and priority degrees, the product/product family that best match the conceptual product was searched for in the case library, and the optimal configuration plan was prepared, in the light of semantic correlation network and local correlation evaluation. The results show that the local correlations of the bed and workbench module, headstock module, grinding system, measuring system, and support of MK8420 with design objectives were 0.298, 0.450, 0.600, 0.500, and 0.298, respectively, indicating that the configuration plan is in line with user needs and suitable for case-based reasoning. The proposed method has been successfully applied in actual production, laying a good basis for the intelligent design of products.

The optimal configuration planning of energy hubs in urban integrated energy system using a two-layered optimization method

In the modern metropolitan regions, the coupling among distribution networks (DNs), gas networks (GNs), and transportation networks (TNs) is increasingly intensified, which gives rise to the urgent demand for efficient modeling and solving methods. In this context, a mathematical model for the configuration planning of integrated energy systems is proposed in this paper. With the aim of minimizing total planning costs, the model jointly considers the investment and operation strategies for distribution lines, distributed wind power generators (DWGs), combined heat and power units (CHPs), power to gas units (P2Gs), gas furnaces (GFs), fast charging facilities (FCFs) and slow charging facilities (SCFs). Since the proposed model comes down to a non-convex and nonlinear optimization problem, a two-layer algorithm is designed to solve it. In the outer layer, a genetic algorithm (GA) is performed to obtain a set of individuals representing the investment decisions on distribution lines, DWGs, and FCFs, which will be delivered to the inner layer model. With fixed decision variables, the inner model optimizes the operating costs, charging profit coefficients, and strategies for DWGs and energy converters in a coordinated manner. A real urban area in China is employed in the numerical experiment, which corroborates the effectiveness of the proposed model and solution algorithm.

Optimal configuration planning of multi-energy microgird based on deep joint generation of source-load-temperature scenarios

An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of source-load-temperature scenarios is proposed to improve the multi-energy complementation and the reliability of energy supply in extreme scenarios. Firstly, based on the historical meteorological data, the typical meteorological clusters and extreme temperature types are obtained. Then, to reflect the uncertainty of energy consumption and renewable energy output in different weather types, a deep joint generation model of radiation-electric load-temperature scenario based on denoising variational autoencoder is established for each weather module. At the same time, to cover the potential high energy consumption scenarios with extreme temperatures, the extreme scenarios with fewer data samples are expanded. After that, the scenarios are reduced by clustering analysis. The typical days of different typical scenarios and extreme temperature scenarios are determined, and the cooling and heating loads are determined by temperature. Finally, the optimal configuration of a multi-energy microgrid system is carried out. Experiments show that the optimal configuration based on the extreme scenarios and typical scenarios can improve the power supply reliability of the system. The proposed method can accurately capture the complementary potential of energy sources. And the economy of the system configuration is improved by 14.56%.

A systematic decision-making approach for planning and assessment of hybrid renewable energy-based microgrid with techno-economic optimization: A case study on an urban community in Egypt

This paper proposes a systematic and integrative decision-making approach for efficient planning and assessment of hybrid renewable energy-based microgrid (HRE-MG) systems. The approach is combined with an extensive techno-economic optimization analysis. The proposed methodology is validated with an actual case study on an urban city in Egypt, with the developed HRE-MG comprising solar, wind, diesel, and fuel cell energies. Different hybridization scenarios are modeled, evaluated and compared to obtain the most practicable solution with minimum cost and emission impact. The obtained results reveal that solar, wind, fuel cell show optimal configuration plan that yields a minimum net present and energy costs. Also, it achieves a significant reduction in carbon gases and increases system renewable fraction compared to other scenarios. Further, the reliability of the proposed hybrid energy system is examined under loss of power supply probability concept. A sensitivity analysis is then conducted to verify the model accuracy and examine the cost performance of the designed system concerning uncertain parameters.

Beijing subsidiary administrative center multi-energy systems: An optimal configuration planning

Multi-energy systems (MES) contribute to increasing energy utilization efficiency and renewable energy accommodation by coupling multiple energy sectors. Beijing is planning to build a subsidiary administrative center in Tongzhou District. A new MES will be built in this center from scratch to jointly meet electricity, heat and cooling demands. This raises the need for optimizing the configuration of MES from scratch at the planning stage. In this paper, the configuration planning of the MES in Beijing’s new subsidiary administrative center is conducted using a two-stage mixed-integer linear programming (MILP) approach based on the energy hub (EH) model. Given the load demand, distributed renewable energy sources, energy prices and candidate system component parameters, the MES configuration planning, equipment selection and capacity planning are jointly managed without limiting the possibility of MES configuration. A sensitivity analysis is performed to show the impacts of load profiles and energy prices on the optimal MES configuration. Operation simulations are carried out based on the obtained optimal planning scheme and another four alternative planning schemes presented by Beijing subsidiary administrative center. The operating cost, CO2 emission and overall system efficiency of each planning scheme are calculated and compared with each other.

Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning

The need for innovative pathways for future zero-emission and sustainable power development has recently accelerated the uptake of variable renewable energy resources (VREs). However, integration of VREs such as photovoltaic and wind generators requires the right approaches to design and operational planning towards coping with the fluctuating outputs. This paper investigates the technical and economic prospects of scheduling flexible demand resources (FDRs) in optimal configuration planning of VRE-based microgrids. The proposed demand-side management (DSM) strategy considers short-term power generation forecast to efficiently schedule the FDRs ahead of time in order to minimize the gap between generation and load demand. The objective is to determine the optimal size of the battery energy storage, photovoltaic and wind systems at minimum total investment costs. Two simulation scenarios, without and with the consideration of DSM, were investigated. The random forest algorithm implemented on scikit-learn python environment is utilized for short-term power prediction, and mixed integer linear programming (MILP) on MATLAB® is used for optimum configuration optimization. From the simulation results obtained here, the application of FDR scheduling resulted in a significant cost saving of investment costs. Moreover, the proposed approach demonstrated the effectiveness of the FDR in minimizing the mismatch between the generation and load demand.

Optimal Configuration Planning of Multi-Energy Systems Considering Distributed Renewable Energy

Multi-energy systems (MESs) contribute to increasing energy utilization efficiency and renewable energy accommodation by coupling multiple energy sectors. The preferable characteristic of MESs raises the need for optimizing the configuration of MESs across multiple energy sectors at the planning stage. Based on the energy hub (EH) model, this research presents a two-stage mixed-integer linear programming approach for district level MES planning considering distributed renewable energy integration. The approach models an MES as a directed acyclic graph with multiple layers. The proposed EH configuration planning procedure includes two stages: 1) optimizing what equipment (e.g., energy converters, distributed renewable energy sources and storages) should be invested in for each layer and 2) optimizing the connection relationships between the invested equipment in each two adjacent layers. The proposed approach is able to optimize both the equipment selection and the MES configuration. It can be applied to both expansion planning and initial planning of MESs from scratch. An illustrative example of planning a typical MES is given. A sensitivity analysis is performed to show the impacts of load profiles, energy prices and equipment parameters on the optimal MES configuration. A case study based on the MES in Beijing’s new subsidiary administrative center is conducted using the proposed approach.

Aerodynamic characteristic analysis and layout optimization design for compound UAVs by using hybrid Fuzzy–PSO algorithm

In order to further improve the performance of compound UAVs at high and low speed with heavy loading, a type of compound coaxial UAV with fixed wings and tail ducted-rotor thrusters was proposed to meet technology requirement. The lift and thrust aerodynamic characteristics were analyzed to get some relative characteristic and the aerodynamic coupling relationships between the fixed wings and tail propulsion system. And an optimization method based on hybrid Fuzzy–PSO (HFPSO) algorithm was proposed to obtain the optimal configuration plan that can reach the best aerodynamic performance without considering the coaxial architecture. The optimal configuration plan was finally obtained by applying the HFPSO method which also proves the method is effective and reasonable through comparison.

Design Optimization and Model Predictive Control of a Standalone Hybrid Renewable Energy System: A Case Study on a Small Residential Load in Pakistan

Renewable energy sources (RESs) offer a promising prospect for covering the fundamental needs of electricity for remote and isolated regions. To serve the customers with high power quality and reliability, design optimization methodology and a possible power management strategy (PMS) for wind-diesel-battery-converter hybrid renewable energy system (HRES) is proposed in this paper. The analysis is applied to a real case study of a standalone residential load located in a remote rural area in Pakistan. Firstly, optimal component sizing is investigated according to actual meteorological and load profile data. Different hybrid configurations are modeled, analyzed, and compared in terms of their technical, economic and environmental metrics with the aid of HOMER® software. The main objective is to determine the most feasible and cost-effective solution with least life-cycle cost, keeping in view the impact of carbon emissions. Secondly, a suitable PMS based on the state of charge (SOC) of the battery is proposed and implemented in MATLAB/Simulink® software for the designed HRES. The PMS is targeted to maintain load balance and extract maximum wind power while keeping the battery SOC within the safe range. Model predictive control (MPC) approach is applied to improve the output voltage profile and reduce the total harmonic distortion (THD). The boost converter is used for maximum power extraction from the wind. The DC-DC buck-boost battery controller is utilized to stabilize the DC bus voltage. The design optimization results show that the hybridization of wind, battery, and converter presents optimal configuration plan with minimum values of total net present cost (14,846 $) and cost of energy (0.309 $/kWh), which means 76.7% reduction in both total system cost and energy cost and 100% saving in harmful emissions when compared to the base case using diesel generator. The proposed system is able to support hundred percent of the load demand with excess energy of 30.1%. Performance analysis of PMS under variable load and fluctuating wind power generation is tested, and promising results with efficient load voltage profile is observed. Further, THD is reduced significantly to 0.26% as compared to 2.62% when the conventional PI controller is used. The output of this work is expected to open a new horizon for researchers, system planners for efficient design and utilization of HRES to curb drastic increase in load demand for urban as well as rural areas.

An integration model for generating and selecting product configuration plans

In the developed market, time-to-market and market shares require companies to provide products that satisfy customer requirements in a timely manner, and the variety in product configurations has been analyzed thoroughly. Against this background, this study addresses an integration model for generating feasible configuration plans based on market transaction data and for selecting the optimal configuration plan(s) based on customer requirements. Transaction data can be used for clustering products to analyze the characteristics of segmented markets and yield the probabilities of configuration plans; along with the constraint conditions, feasible configuration plans can be generated, as well as market strategies for different segmented markets. In addition, a probabilistic classifier, the Naive Bayes Classifier, is applied to map the customer requirements to the configuration plan with the highest probability. The classifier is suitable for handling imprecise and uncertain information, such as product requirements expressed by customers. A case study of a mouse device is illustrated, and the results indicate the integration model can achieve a good performance in terms of time advantages in project design.

Optimal distributed generation planning at a local level – A review of Serbian renewable energy development

Abstract With the Law on Energy and its acceptance by the Covenant of Mayors, local authorities in Serbia are obliged to prepare action plans for the development of distributed generation and energy efficiency measures aiming at reducing CO 2 emissions. The development of distributed generation (wind, photovoltaic, hydro and combined heat and power plant) is an asset to the local authorities wishing to become energy independent microgrids that could relieve the national transmission grid in the way of reducing amount of electricity to be transported from centralized generation to the end-user. Using the simulation tool HOMER, an optimal configuration plan of the municipal microgrids for Serbia has been drawn, in order to obtain the lowest total net present costs during the planning period under various levels of CO 2 reduction constraint. The increase of the end-user specific costs for energy has been quantified in a sensitivity analysis based on this constraint in the local authority microgrid.