Scenario Generation Research Articles

Hydrogen leakage risk analysis of hydrogen emergency power supply vehicles using the AET-RM method

In recent years, research and application of hydrogen fuel emergency power supply vehicle technologies have been steadily advancing. However, due to the highly flammable and explosive nature of hydrogen gas, the safety risk of hydrogen leakage has been taken seriously. Addressing the current inadequacies in the understanding of hydrogen leakage risks, unclear sources of risk, incomplete coverage of risk scenarios, and insufficiencies in risk assessment methods associated with hydrogen fuel emergency power supply vehicles, this study firstly establishes a multi-scenario and multi-factor coupled hydrogen leakage risk event database and an accident risk system. This effort focuses on the diversity of application scenarios, types of hydrogen leakage hazards and complexity of interfering factors. Subsequently, by refining existing risk assessment methods, a more balanced subjective-objective evaluation and more granular risk ratings approach, termed AET-RM, is proposed, which ensures finer risk grading. Finally, through this method, the types of hydrogen leakage hazards and their harm boundaries are clarified, achieving a systematic and precise graded quantitative assessment of risk sources, probabilities, and consequences. The study results indicate that the established event database contains no risk events rated as I (acceptable) or V (unacceptable). There are 37 events classified as low risk (rating II), accounting for 17.54% of the total. Medium-risk events (rating III) comprise 142 incidents, making up 67.30%, while high-risk events (rating IV) include 32 cases, representing 15.17%. The highest risk score, determined by the AET-RM method, pertains to a risk event in the power generation scenario, Contingencies: metal corrosion within the battery.

Multi-period optimal capacity expansion planning scheme of regional integrated energy systems considering multi-time scale uncertainty and generation low-carbon retrofit

With increasingly challenging climate crisis, carbon neutrality has become a necessary path for the whole world. However, there still lacks a comprehensive investigation on the optimal capacity expansion planning for a low-carbon transformation while considering multiple uncertainties. In this paper, a multi-period optimal capacity expansion planning scheme for regional integrated energy systems considering multi-time scale uncertainties and generation low-carbon retrofit is proposed. First, to better describe the short-time scale uncertainty caused by renewable energy and load fluctuations, a multi-dimensional tempo-spatial correlated scenario generation method is proposed to more accurately capture stochastic features with the least amounts of representative scenarios. Second, a multi-period optimal planning model considering generation low-carbon retrofit and short-/long-time scale uncertainties is developed. The proposed model combines information gap decision theory and chance constraints to stress both uncertainties simultaneously, which is further linearized to facilitate the computation. Third, an improved bilinear Benders decomposition (IBBD) method is utilized to efficiently solve the proposed large-scale optimal planning problem. Finally, numerical experiments of a real world test system of WF city show: 1) the maximum errors of PDF and CDF between generated scenarios and historical data are 0.108 and 0.016, validating the effectiveness of the proposed scenario generation method; 2) the low-carbon retrofit reduces system carbon emissions by 14.12 × 107 kg, while the total system cost is $956,411,387 with additional energy coupling units and renewables installed, indicating the reliability of the multi-period planning model; 3) the IBBD algorithm demonstrates applicability and efficiency in solving the proposed multi-period optimal expansion planning problem, reducing computation time by over 80.39 % compared to commercial solvers, such as GUROBI.

Joint Optimization of Age of Information and Energy Consumption in NR-V2X System Based on Deep Reinforcement Learning.

As autonomous driving may be the most important application scenario of the next generation, the development of wireless access technologies enabling reliable and low-latency vehicle communication becomes crucial. To address this, 3GPP has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio (NR) technology, where Mode 2 Side-Link (SL) communication resembles Mode 4 in LTE-V2X, allowing direct communication between vehicles. This supplements SL communication in LTE-V2X and represents the latest advancements in cellular V2X (C-V2X) with the improved performance of NR-V2X. However, in NR-V2X Mode 2, resource collisions still occur and thus degrade the age of information (AOI). Therefore, an interference cancellation method is employed to mitigate this impact by combining NR-V2X with Non-Orthogonal multiple access (NOMA) technology. In NR-V2X, when vehicles select smaller resource reservation intervals (RRIs), higher-frequency transmissions use more energy to reduce AoI. Hence, it is important to jointly considerAoI and communication energy consumption based on NR-V2X communication. Then, we formulate such an optimization problem and employ the Deep Reinforcement Learning (DRL) algorithm to compute the optimal transmission RRI and transmission power for each transmitting vehicle to reduce the energy consumption of each transmitting vehicle and the AoI of each receiving vehicle. Extensive simulations demonstrate the performance of our proposed algorithm.

Wasserstein generative adversarial networks-based photovoltaic uncertainty in a smart home energy management system including battery storage devices

Rooftop photovoltaic (PV) power generation uncertainty is one of the prominent challenges in smart homes. Home Energy Management (HEM) systems are essential for appliance and Energy Storage System (ESS) scheduling in these homes, enabling efficient usage of the installed PV panels' power. In this context, effective solar power scenario generation is crucial for HEM load and ESS scheduling with the objective of electricity bill cost reduction. This paper proposes a two-step approach, where a machine learning technique, Wasserstein Generative Adversarial Networks (WGANs), is used for PV scenario generation. Then, the generated scenarios are used as input for the HEM system scheduler to achieve the goal of cost minimization. The generated solar energy scenarios are considered in a single household case study to test the presented method's effectiveness. The WGAN scenarios are evaluated using different metrics and are compared with the scenarios generated by Monte Carlo simulation. The results prove that WGANs generate realistic solar scenarios, which are then used as input to a Mixed Integer Linear Programming (MILP) problem aiming for electricity bill minimization. A 41.5% bill reduction is achieved in the presented case study after scheduling both the load and ESS, with PV fluctuations taken into account, compared to the case where no scheduling, PV, or ESS are considered.

Intelligent system for monitoring the operational properties of ship power equipment

The peculiarity of monitoring the technical condition of ship vehicles is that during the service life the main energetic installation is not changed, but its continuous maintenance and periodic repairs are carried out. In the organization of such activities, the leading role belongs to technical diagnostics, which allows you to determine the technical condition of the material, as well as predict possible changes for a certain period. Intelligent system of monitoring of operational properties of ship power equipment with Markov circuits, probabilistic dynamics elements, calculations on probabilistic models, multi-criteria optimization of diagnostic parameters, simulation and scenario generation is proposed. In detail, the content of the main structural units of the system for monitoring the operational properties of ship power equipment is considered. The input information was a generalization of experience in the operation of vehicles. The stages of construction of Markov circuits are described in relation to diagnostics of turbochargers, the feature of which is a replacement of discrete time by a continuous sequence of states. Considered by a separate unit of calculation on probabilistic models on the basis of spectra of vibration signals by combining their main discrete features and establishing new diagnostic parameters. The visualization of relationships consists in the construction of digraphs of interactions of the main structural elements of the SES taking into account probabilistic models. Multi-criteria optimization in the presented system is considered from the standpoint of statistical criteria and their convolution. Simulation and generation of basic scenarios is described, from the point of view, the conversion of analog data about workflows to digital form. Provided information support and tools of the system for monitoring the operational properties of ship power equipment

Short-term stochastic multi-objective optimization scheduling of wind-solar-hydro hybrid system considering source-load uncertainties

Uncertainties in energy outputs (source side) and load side are simultaneously present and cannot be ignored in the actual operation of multi-energy systems. Adopting a reasonable uncertainty modeling method and understanding the impact of source-load uncertainties on optimization scheduling are key to formulating accurate and effective multi-energy scheduling plans. To address the uncertainties on both the source side and the load side in wind-solar-hydro hybrid systems, this paper proposes a multi-objective optimization scheduling model based on stochastic programming theory. The model aims to maximize the net profit of the system's power generation and minimize the fluctuation of the remaining load. It employs Vine-Copula coupled with Monte Carlo simulation and the deep learning method TimeGAN to generate joint wind and solar power output and load scenario sets. The generated source-load uncertainty scenarios are then reduced to representative scenarios using the K-Means clustering method, which are used as inputs for the scheduling model. The proposed model is applied to a wind-solar-hydro energy base in China, and the results show that: 1) The Vine-Copula-based source-side scenario generation method can quantitatively consider the correlations among meteorological factors. The relative errors of the generated scenarios' statistics compared to the original data are all less than 5%, and the relative errors of the correlation coefficients are less than 10%. 2) The TimeGAN-based load-side scenario generation method avoids the presupposition of the load probability distribution. Compared to the original data, the generated scenarios have R2 and Pearson correlation coefficients of 0.77 and 0.87, respectively. Additionally, TimeGAN shows significant advantages over traditional random sampling methods in simulating extreme scenarios. 3) Both source-side and load-side uncertainties significantly impact the optimization scheduling results of multi-energy systems, leading to increased fluctuation of the remaining load and decreased net profit. 4) The combined source-load uncertainties have a synergistic negative impact on the multi-objective optimization scheduling results. 5) The Pareto front of the optimization results is a concave function with low marginal benefits. Decision-makers should adopt a compromise solution as a guide for the operation of multi-energy systems.

An efficient tool for real-time global carbon neutrality with credibility of delicacy management: A Modelx + MRV + O system

Current carbon neutralization systems are time-consuming, which generally require at least one to two months. We propose a highly efficient real-time carbon neutralization mechanism, a Modelx+MRV + O system based on the Internet of Things and blockchain technology for all the carbon reduction scenarios. This mechanism includes an accounting model for a certain distributed carbon reduction scenario, and real-time M, RV, and O systems, enabling enterprises, products, or individuals to reliably reach carbon neutrality in real time. We demonstrated how to build a real-time model (Modelx) by proposing a carbon emission reduction methodology for the returnable packaging scenario and a photovoltaic power generation scenario combining IoT technology for a traditional methodology for polar electricity. We found that the proposed system can achieve real-time analysis based on the monitored turnover number and electricity generated and avoid falsified values. Because carbon neutrality is essential to reduce carbon emissions and combat climate change globally, this system can accelerate the sustainable transformation by managing carbon neutrality globally.

The value of real-time adjustable remedial action schemes

Remedial action schemes (RAS) are often seen as an inexpensive way to relieve contingency-related grid congestion without building new transmission infrastructure. However, RAS settings often remain fixed during real-time operation and do not adapt to variation in operating conditions due to renewable and distributed generation. This lack of adaptability may cause suboptimal settings and possibly insecure operations. To assess the value of allowing RAS settings to vary real time and the benefit of considering multiple load and generation scenarios, we propose a mixed integer optimization framework which identifies optimal RAS actions while incorporating multiple load and renewable energy scenarios. We also propose an iterative algorithm that efficiently solves the optimization problem, leveraging the fact that only a few scenarios and contingencies are binding at optimality. We demonstrate the benefits of (i) updating RAS more frequently and (ii) considering multiple load scenarios by performing case studies on the RTS-GMLC system.

Bovine Alphaherpesvirus 1, Bovine Alphaherpesvirus 5 and Bubaline Alphaherpesvirus 1 in Palatine Tonsils from Water Buffaloes in Northern Brazil and Possible Links with the Origin of Bovine Alphaherpesvirus Type 5.

Herpesviruses are significant pathogens of ruminants. In water buffaloes (Bubalus bubalis), however, herpesviruses have not been thoroughly studied. Although bubaline alphaherpesvirus 1 (BuAHV1) and bovine alphaherpesvirus 1 (BoAHV1) have already been recovered from water buffaloes, to date, no reports on the occurrence of bovine alphaherpesvirus 5 (BoAHV5) in these animals have been published. Therefore, the aim of this study was to search for BuAHV1, BoAHV1, and BoAHV5 in palatine tonsils of apparently healthy water buffaloes from the Pará state, Northern Brazil. Tissue samples of tonsils (n = 293) were screened by a nested PCR (nPCR) targeting a region of UL44 (gC coding gene), followed by sequencing, to detect and differentiate between the viral types. Viral genome segments were detected in 18 out of 293 (6.1%) of the palatine tonsil samples. Two animals carried genomes of BoAHV1 only, eleven animals carried BoAHV5 genomes only, and four animals carried BuAHV1 only. Another animal had both BoAHV1 and BoAHV5 genomes in its tonsils. No infectious virus could be recovered from any of the samples. The BuAHV1 sequences identified here were more closely related to BuAHV1 genomes identified in India. Phylogenetic analyses suggested a closer relationship between the recovered BoAHV5 and BuAHV1 genomes. Therefore, evidence is provided here to confirm that not only BoAHV1 and BuAHV1, but also BoAHV5, can infect water buffaloes. This report highlights (i) the first detection of BoAHV5 in water buffaloes and (ii) the occurrence of coinfections with BoAHV1 and BoAHV5 in that species. Such findings and the similarity of BoAHV5 to Indian herpesvirus genomes suggest that the origin of type 5 may be linked to recombinations between bovine and bubaline herpesviruses within bubalines, since the scenario for generation of recombinants in buffaloes is potentially present.

A novel surrogate polytope method for day-ahead virtual power plant scheduling with joint probabilistic constraints

Virtual Power Plant (VPP) is an emerging concept that can effectively manage a large number of distributed energy resources (DERs). However, the inherent uncertainty of load and renewable generation poses a challenge to reliable VPP power scheduling. This manuscript proposes a novel method for day-ahead VPP scheduling with a joint probabilistic guarantee on its power availability without violating the DER constraints and network constraints. A surrogate polytope is first used to find the inner approximation of the VPP power, implicitly including the low-level DER power, DER constraints, and network constraints. Then, a multivariate Gaussian distribution is used to fit the random parameters of the surrogate polytope, after which the iterative supporting hyperplane algorithm is used to solve the VPP scheduling problem. Extensive case studies based on real-world renewable generation scenarios demonstrate the superior performance of the proposed method in out-of-sample cost and reliability, with a manageable computing complexity.

Risk based design optimization via scenario generation and genetic programming under hybrid uncertainties

Abstract The design of complex systems often requires the incorporation of uncertainty optimization strategies to mitigate system failures resulting from multiple uncertainties during actual operation. Risk-based design optimization, as an alternative methodology that accounts for the balance between design cost and performance, has garnered significant attention and recognition. This paper proposes a risk design optimization method for tackling hybrid uncertainties via scenario generation and genetic programming. The hybrid uncertainties are quantified through scenario generation method to obtain risk assessment indicators. The genetic programming method is used to simulate the real output of the objective or constraints. To drive the optimization process, the sample-based discrete gradient expression is constructed, and the optimal scheme aligning the risk requirements is obtained. Three calculation examples of varying computing complexity are presented to verify the efficacy and usability of the suggested approach.

Exploring master scenarios for autonomous driving tests from police-reported historical crash data using an adaptive search sampling framework

Crash scenario-based testing is crucial for assessing autonomous driving safety. However, existing studies on scenario generation tend to prioritize concrete scenarios for direct testing, neglecting the construction of fundamentally functional scenarios with a broader range. Police-reported historical crash data is a valuable supplement, yet detecting all potential crash scenarios is laborious. In order to address this issue, this study proposes an adaptive search sampling framework based on deep generative model and surrogate model (SM) to extract master scenario samples from police-reported historical crash data. The framework starts with selecting representative samples from the full crash dataset as initial master scenario samples using various sampling techniques. Evaluation indexes are then constructed, and derived scenario samples are synthesized using the deep generative model. To enhance efficiency, an SM is established to replace the generative model’s training and data generation process. Based on the SM, an adaptive search sampling method is developed, which iteratively adjusts the sampling strategy using the Similarity Score to achieve comprehensive sampling. Experimental results demonstrate the notable advantage of the adaptive search sampling method over other sampling methods. Furthermore, statistical analysis and visualization assessments confirm the effectiveness and accuracy of the proposed method.

Energy supply structure optimization of integrated energy system considering load uncertainty at the planning stage

Under the trend of global carbon neutrality, the integrated energy system with the characteristics of multi-energy scheduling and gradient utilizing will be widely constructed and applied in the future energy market. For the construction of energy systems in emerging building complex, this paper analyzes the load probability characteristics of regional building complex at the planning stage, and conducts an aggregation analysis of its multiple uncertainties, and obtains the conclusion that the load factor at time t obeys the normal distribution. In order to formulate this uncertainty, this paper combines the case study with the scenario analysis method containing scenario generation and reduction to transform the stochastic programming model into several deterministic models and analyses the discrepancies of the optimization results under different objectives and different load variances. The results show that after considering load-side uncertainty, the total system capacity increases by 7 %, the total investment under the minimum investment objective by 4 %, and the carbon emission under the minimum carbon emission objective by 3 %. In addition, the system needs to pay 32.8 % of the total investment increment to obtain 80 % carbon reduction when adopting carbon emissions as the objective.

Hierarchical robust Day-Ahead VPP and DSO coordination based on local market to enhance distribution network voltage stability

As distributed energy resources (DER) are increasing, a variety of business models and markets are appearing. Business models such as virtual power plants (VPPs) contribute to enhanced stability of the power system while improving the profits of participating resources. However, the uncertainty of the behavior of DERs and the resulting stability issues are a major barrier to DER deployment and require a coordination framework between grid operators and business models to overcome. Therefore, in this paper, a hierarchical robust day-ahead coordination of a VPP and distribution system operator (DSO) based on the local market is proposed to improve the voltage stability of the distribution network by considering the uncertainty of DERs. For fairness reasons, the contribution of DERs to the distribution system can be addressed by a market-based approach. Local markets are divided into local energy markets (LEM) and local flexibility markets (LFM), which can consider the uncertainty and profitability of DERs. First, the DER determines the seller and buyer status of the LEM based on its self- scheduling with expected prices and is matched based on a double-auction mechanism. At this time, the worst-case scenario of load and renewable energy generation predicted by the Gaussian process (GP) is considered. The mismatched amounts in the LEM are aggregated by the VPP and bid into the wholesale energy market (WEM) operated by the TSO (Transmission system operator). Subsequently, DSO operates a LFM that coordinates microgrid (MG) and VPP on the distribution grid to increase voltage stability. The LFM and WEM problems are considered as interval optimization in the form of mixed integer linear programming and are applied together. The flexibility supply resulting from the adjustment of VPP is compensated by a loss-sensitivity-based flexibility price. Since voltage stability exists in upper and lower bounds, the upper and lower bound of net load through GP-based forecasting is considered along with the linearized network constraints. The results are validated through Monte-Carlo simulation-based scenarios on IEEE benchmark system in MATLAB environment. As a result, it is proved that the proposed model improves the distribution network voltage stability and the profit of the participants.

Residential PV-battery scheduling with stochastic optimization and neural network-driven scenario generation

Residential end-users have increasingly been attracted to installing behind-the-meter (BTM) photovoltaic (PV) and battery energy storage systems (BESS). The inherent stochastic nature of PV introduces challenges to energy management for PV-BESS owners. This work presents a two-stage stochastic optimization (SO) framework for battery operation that takes into account uncertainties in PV outputs to minimize daily grid consumption and battery degradation. A combination of a feed-forward neural network (FFNN) and an error analysis statistical technique is proposed to generate a highly accurate scenario set for estimating PV behavior. Then, the scenario set is reduced using a backward scenario reduction technique to address the issue of dimensionality that SO suffers from. The reduced scenario set is then fed into the two-stage SO model to calculate expected electricity costs and battery degradation for the day-ahead PV-BESS problem. The suggested model's performance in controlling BTM batteries is evaluated using real-world data collected from smart meters installed in a house in Aran Islands, Ireland. The results show that the model can effectively withstand sudden changes in the PV output and generate results that are comparable to an ideal case with accurate PV data available.

Optimal dispatch strategy of battery energy storage system in utility-scale photovoltaic integrated grid under variability

The frequency response of a photovoltaic (PV) system integrated power grid is severely hampered due to inadequate inertial support. Integrating a battery energy storage system (BESS) can assist in maintaining frequency response by providing a rapid injection of active power into the grid. Nevertheless, instead of typical constant droop settings enabled in BESS, adopting variable PV power dependent operation of BESS can ensure frequency stability with reduced injection, especially in PV-integrated grids. From this viewpoint, this paper proposes a novel frequency control approach of BESS depending on the available PV power in the grid. A gradient descent-based optimization is utilized to evaluate the required maximum injection from the installed BESS to maintain frequency response under different PV generation values. This approach is then tested by modifying the BESS frequency controller to keep the active power dispatch from BESS within the calculated value under a given outage. The whole methodology is analyzed in a customized IEEE 39 bus New England System under different PV penetration and generation scenarios. The results indicate successful convergence to optimal dispatch levels for the BESS across all evaluated scenarios. As PV generation increases, the optimal BESS dispatch as a percentage of rated PV output declines gradually. However, at lower PV generation, this value rises rapidly and reaches the installed BESS capacity. Later, the applicability of the methodology is validated by comparing the simulated outputs with different test grid network and another support technique- Synchronous Condenser (SC). From the standpoint of frequency stability, the proposed technique can assist the grid planners in regulating BESS operations in a renewable integrated grid more effectively.

Guide to developing case-based attack scenarios and establishing defense strategies for cybersecurity exercise in ICS environment

Critical infrastructure mainly performs its role through an industrial control system (ICS). Organizations conduct cyber exercises between red and blue teams, focusing on offense and defense. Practical exercises require explicit attack scenarios and corresponding defense strategies. However, systematic guides for deriving cyberattack scenarios or defense strategies still need to be improved. This paper proposes a guide for establishing realistic attack scenarios and defense strategies for cybersecurity exercises in ICS environments. Attack scenario generation is divided into four steps: generating attack references, deriving attack sequences, mapping threat information, and mapping vulnerable implementation patterns. Deriving a defensive strategy consists of two steps parallel to developing an attack scenario: deriving containment and eradication. The methodology we propose guides exercise planning based on a knowledge base, thereby assisting exercise planners in generating various scenarios and deriving clear defense strategies. We showed that a clear exercise plan could be established through a case study.

Hydrogen-fueled microgrid energy management: Novel EMS approach for efficiency and reliability

Effective energy management within microgrids is crucial, especially given system uncertainties. This study presents a novel Energy Management System (EMS) designed for microgrids with diverse energy sources, notably hydrogen and fuel cells. The EMS integrates artificial intelligence algorithms to predict and adapt to rapid changes, enhancing energy resource efficiency and minimizing wastage. Responsive in nature, the EMS enhances efficiency, reliability, and cost reduction in microgrid energy, dynamically responding to varying conditions for improved stability and environmental protection. Employing a random pattern approach, the EMS focuses on operational efficiency and reliability in supplying electrical and thermal loads. Addressing uncertainties in the electricity market and renewable production, a stochastic framework with scenario generation enables operation in grid-connected and island modes. Enhanced optimization through an improved particle swarm algorithm ensures cost-effectiveness and system reliability in dynamic environmental conditions. This research pioneers innovative microgrid energy management, emphasizing hydrogen and fuel cell technologies to elevate efficiency and performance reliability.

Climate change scenario analysis in Spree catchment, Germany using statistically downscaled ERA5-Land climate reanalysis data

This study focuses on the statistical downscaling of ERA5-Land reanalysis data using the Statistical DownScaling Model (SDSM) to generate climate change scenarios for the Spree catchment. Linear scaling was used to reduce the biases of the Global Climate Model for precipitation and temperature. The statistical analyses demonstrated that this method is a promising and straightforward way of correcting biases in climate data. SDSM was used to generate climate change scenarios, which considered three emission scenarios: RCP 2.6, RCP 4.5, and RCP 8.5. The results indicated that higher precipitation is expected under higher emission scenarios. Specifically, the summer and autumn seasons were projected to experience up to 50 mm more rainfall in the next 80 years, and the temperature was projected to increase by up to 1∘C by 2100. These projections of climate data for different scenarios are useful for assessing water management studies for agricultural and hydrologic applications considering changing climate conditions. This study highlights the importance of statistical downscaling and scenario generation in understanding the potential impacts of climate change on water resources. The results of this study can provide valuable insights into water resource management, especially on adapting to changing climate conditions.

Scenario-based anticipatory failure determination and patent technology inspiration for product innovation design

Exploring opportunities of technologies for product is critical in the highly competitive market. Anticipatory failure determination (AFD) identifies likely failure modes of a product, which provides a tool to explore the technology opportunities for product improvement. The existing methods of AFD are limited in the failure analysis of system components and the acquisition of technical solutions for the failure cause is depending on designer experience. This research proposes an approach to product innovation design based on the scenario-based evolution of AFD and patent technology inspiration. Scenarios obtained based on the proposed scenario generation rules are used to form failure events from the product function level. The failure events are then used to explore technical opportunities based on the anticipatory failure determination. A word vector model is trained with the patent data to extract patent technology terms in combination with the semantic similarity of the functional basis. The Latent Dirichlet Allocation (LDA) is used to cluster patent technologies. The best technical solution is finally selected based on the relative technology maturity and technological distance. The TRIZ tool is used to resolve possible conflicting problems. A complete product innovation design process is proposed from the technology opportunity exploration to concept design formation.