Scenario Generation Research Articles

Integrated BIM + Parametric Modelling: A decision-support tool for human-centric campus simulation, automation, and optimization

Developing masterplans of large-scale areas such as campuses involves formulating multiple scenarios, considering various social, functional, environmental, and aesthetic factors, and pursuing multi-objective optimization to fulfill various conflicting quantifiable and non-quantifiable design objectives. However, current practice lacks an integrated methodology that enables automatic evaluation of those scenarios and visualization of their results to stakeholders. Using computer-performed simulations with innovative design methods to dynamically investigate and empirically assess how campus characteristics influence its inclusiveness, users’ behavior, and social interactions can help identify designs that stimulate human-centered principles. This paper introduces the integrated parametric BIM-based campus life simulation as a decision-support tool. It unifies the design and analysis cycles with scenario generation and design automation through a human-centered methodology. The supports interactive design processes that involve space exploration, analysis, and optimization, allowing the usage of multi-objective optimization methods to develop detailed design alternatives that fulfill conflicting quantifiable and non-quantifiable objectives. Thus, it functions as a descriptive and predictive tool and supports theory-testing and development.

Stochastic Scenario Generation Methods for Uncertainty in Wind and Photovoltaic Power Outputs: A Comprehensive Review

Stochastic Scenario Generation Methods for Uncertainty in Wind and Photovoltaic Power Outputs: A Comprehensive Review

Two-layer optimization model of distribution network line loss considering the uncertainty of new energy access

The integration of a distributed generator (DG) into the distribution network alters the topology structure and power flow distribution, subsequently causing changes in network loss. Moreover, existing distribution network optimization methods face high computational complexity, low efficiency, and susceptibility to local optima. This article proposes a scenario generation method using a generative adversarial network (GAN) to handle the uncertainty associated with DGs and constructs a two-layer optimization model for the distribution network. The upper layer model determines the installation location and capacity of distributed power and energy storage systems with the lowest economic cost. The lower layer model establishes an optimization model, including wind, solar, and storage, with active power network loss and voltage deviation as objective functions. Both layers are solved using the Improved Whale Optimization algorithm (IWOA). Then, the IEEE-33 node distribution system was taken as a simulation example to verify the effectiveness and superiority of the proposed model and algorithm.

Hybrid stochastic and robust optimization of a hybrid system with fuel cell for building electrification using an improved arithmetic optimization algorithm

This paper proposes a hybrid stochastic-robust optimization framework for sizing a photovoltaic/tidal/fuel cell (PV/TDL/FC) system to meet an annual educational building demand based on hydrogen storage via unscented transformation (UT), and information gap decision theory-based risk-averse strategy (IGDT-RA). The hybrid framework integrates the strengths of UT for scenario generation and IGDT-RA (hybrid UT-IGDT-RA) for optimizing the system robustness and maximum uncertainty radius (MRU) of building energy demand and renewable resource generation. The deterministic model focuses on minimizing the cost of energy production over the project’s lifespan (CEPLS) and considers a reliability constraint defined as the demand shortage probability (DSHP). The study utilizes an improved arithmetic optimization algorithm (IAOA) to optimize component sizes and MRUs, incorporating a neighborhood search operator to enhance performance and prevent premature convergence. The deterministic findings revealed that the PV/TDL/FC system configuration offers the lowest CEPLS and the highest reliability level (lowest DSHP) compared to the hybrid PV/FC and TDL/FC configurations. Additionally, these results indicated that enhancing the reliability of the energy supply for the educational building entails higher CEPLS, particularly due to increased costs associated with hydrogen storage. The robust framework findings for the PV/TDL/FC system using IGDT-RA show that for an uncertainty budget of 21%, the MRUs for educational building demand and renewable generation are obtained at 10.34% and 2.65%, respectively, which are higher compared to other configurations. This indicates that the hybrid PV/TDL/FC system is more robust in handling worst-case scenario uncertainties. Furthermore, the hybrid UT-IGDT-RA outcomes found that the stochastic scenarios incorporated to simulate a range of uncertainties beyond the conventional IGDT-RA based-nominal scenario, and it provides a broader range of robust solutions, enabling operators to align strategies with their risk tolerance and improves system flexibility, and decision-making precision in the face of uncertainties.

Security-constrained stochastic optimal power flow analysis using optimally reduced scenarios for wind generation

Security-constrained stochastic optimal power flow analysis using optimally reduced scenarios for wind generation

Optimal allocation of energy storage coordinated with thermal power units for ramp events considering the correlation among offshore wind farms

AbstractDuring extreme meteorological events, the output of wind farms may undergo noticeable fluctuations within short timeframes, leading to wind power ramp events (WPREs), which can result in severe disruptions to the power system, potentially causing widespread outages. However, limited studies have dedicated to addressing the adverse impacts of WPREs on the power system. Moreover, few articles consider the influence of power correlations among multiple wind farms on WPREs. This paper proposes an improved multi‐parameter segmentation algorithm for detecting WPREs based on the generation of wind farm output scenarios with different correlation coefficients. And a bi‐level optimization model is developed for the allocation of electrochemical energy storage and thermal power units to address offshore WPREs, considering the correlations among wind farms. The effectiveness of the proposed approach is validated through case study based on the modified IEEE RTS‐24 node system. Results demonstrate that stronger correlation among wind farms lead to fewer WPREs and lower requirements for thermal power units and electrochemical energy storage capacity, thereby reducing both investment and operational costs.

An autonomous vehicles' test case extraction method: Example of vehicle-to-pedestrian scenarios.

Testing autonomous vehicles (AVs) in hazardous scenarios is a crucial technical approach to ensure their safety. A key aspect of this process is the generation of hazard scenarios. In general, such scenarios are generated through cluster analysis of traffic accident data. However, this approach may not fully capture the criticality of the generated scenarios, as it tends to emphasize the statistical characteristics of the data rather than its real-world applicability. This paper proposes a novel method to enhance scenario adaptation by integrating quantization weights with a new clustering algorithm. These weights, representing the correlation between scenario elements and the AV system, are calculated using fuzzy comprehensive evaluation (FCE). The proposed method is applied to 1044 pedestrian accident cases in China, resulting in the identification of nine categories of typical scenarios and corresponding test schemes for both the perception and decision-making systems of AVs. The results show that the new method increases the proportion of critical scenarios by 17.4% and 13.6%, respectively, compared to traditional methods. Overall, the critical scenarios generated in this paper can significantly improve the testing efficiency and safety of AVs.

Novel demand response-based stochastic optimisation in a grid-connected microgrid considering outlier detection and imputation

To address the challenges in renewable generation, the proposed work presents a risk-based stochastic optimisation approach. This method considers uncertainties by employing Monte Carlo simulations for scenario generation and scenario reduction employing K-means clustering. The unsupervised K-means clustering technique is particularly effective for outlier detection and imputation, ensuring more reliable data for decision-making. Additionally, the authors proposed a novel demand response programme called Dynamic Sustainability Pricing (DSP), designed to reduce both the environmental impact and the total cost of operating a microgrid. To evaluate the performance of the DSP, several indices such as the personalised sustainability index, the carbon intensity index, the renewable energy participation index, and the environmental impact index have been proposed. It is evident that the DSP has caused the PAR to decrease from 5.73 to 4.98 and that the load factor's percentage increase from before to after the DSP application has improved to 3.603%. The improvement in LF from 0.48 to 0.51 is the result of DSP employment. In addition, the TCPI was raised from 1 to 1.148, which helps consumers by lowering the overall consumer tariff and raising the profit index. The PSI increased from 3.3 to 3.069, indicating that RES's participation was maximized.

Capacity Planning Method for Wind–Solar–Thermal-Storage Bundled HVDC Sending System Considering Transient Overvoltage Constraints

High-voltage direct current (HVDC) sending systems have been the main means of renewable power cross-regional sharing and consumption. However, the transient overvoltage problems restrict the transmission capacity and renewable energy accommodation. The allocation of wind–solar–thermal storage capacity has become an important factor affecting the safety and stability of renewable energy sending. A capacity planning method is proposed for a wind–solar–thermal-storage bundled HVDC sending system considering transient overvoltage constraints. Firstly, based on quantile regression analysis and Gaussian mixture modeling, the typical scenario generation method is proposed to depict the uncertainty of renewable energy. Then, the transient overvoltage characteristics of the integrated HVDC transmission system are analyzed. The relationship between the power output of power sources and the system short-circuit capacity is derived. Meanwhile, the calculation method of the minimum short-circuit capacity of the HVDC system is proposed. Based on the calculation method, the transient overvoltage constraint corresponding to the voltage support strength is constructed. Finally, considering the transient overvoltage constraints, the capacity planning model of the wind–solar–thermal storage is established. The upper-layer model optimizes the configuration scheme of the wind–solar–thermal storage to minimize the total system cost. The lower-layer model optimizes the operation scheduling under the typical operation scenarios of renewable energy and delivery load. The optimal capacity planning scheme for the wind–solar–thermal storage is determined through the coordinated optimization of the two-layer model. The feasibility and effectiveness of the proposed method are verified through a case analysis. The results show that the proposed planning method can effectively maintain a higher short-circuit ratio and improve the voltage support strength under the premise of completing the sending plan.

A Survey of Scenario Generation for Automated Vehicle Testing and Validation

This survey explores the evolution of test scenario generation for autonomous vehicles (AVs), distinguishing between non-adaptive and adaptive scenario approaches. Non-adaptive scenarios, where dynamic objects follow predetermined scripts, provide repeatable and reliable tests but fail to capture the complexity and unpredictability of real-world traffic interactions. In contrast, adaptive scenarios, which adapt in real time to environmental changes, offer a more realistic simulation of traffic conditions, enabling the assessment of an AV system’s adaptability, safety, and robustness. The shift from non-adaptive to adaptive scenarios is increasingly emphasized in AV research, to better evaluate system performance in complex environments. However, generating adaptive scenario is more complex and faces challenges. These include the limited diversity in behaviors, low model interpretability, and high resource requirements. Future research should focus on enhancing the efficiency of adaptive scenario generation and developing comprehensive evaluation metrics to improve the realism and effectiveness of AV testing.

Systematic Analysis Based on the Optimal Design of Photovoltaic Inverter Circuit

As a clean and renewable energy source, solar energy is of great significance to reduce greenhouse gas and harmful gas emissions, stabilize energy supply, promote industrial innovation, and help achieve sustainable development and carbon neutrality goals. In this paper, the structure and working principle of photovoltaic cells are introduced, and the characteristics of solar photovoltaic cells and thin film photovoltaic cells are compared and analyzed. Then, the application scenarios of solar photovoltaic power generation and thin film photovoltaic power generation are compared, and the future development direction of photovoltaic cells is prospected. Finally, the structure and working mechanism of the inverter circuit are introduced, some problems in the current photovoltaic inverter circuit are summarized, and then some optimization design of photovoltaic inverter circuit based on the maximum power point tracking technology and leakage current related problems are analyzed, and suggestions on how to optimize the design of photovoltaic inverter circuit are given.

Hybrid Proximal Policy Optimization—Wasserstein Generative Adversarial Network Framework for Hosting Capacity Optimization in Renewable-Integrated Power Systems

The rapid integration of distributed energy resources (DERs) such as photovoltaics (PV), wind turbines, and energy storage systems has transformed modern power systems, with hosting capacity optimization emerging as a critical challenge. This paper presents a novel Hybrid Proximal Policy Optimization-Wasserstein Generative Adversarial Network (PPO-WGAN) framework designed to address the temporal-spatial complexities and uncertainties inherent in renewable-integrated distribution networks. The proposed method combines Proximal Policy Optimization (PPO) for sequential decision-making with Wasserstein Generative Adversarial Networks (WGAN) for high-quality scenario generation, enabling robust hosting capacity enhancement and operational efficiency. Simulation results demonstrate a hosting capacity improvement of up to 128.6% in high-penetration scenarios (90% renewable), with average operational cost reductions of 22%. Voltage deviations are minimized to within ±5% of nominal levels, while energy losses are reduced by 18%. Scenario quality, evaluated using the Wasserstein metric, achieved convergence with an average score of 0.95 after 80 iterations, highlighting the WGAN’s ability to generate realistic and diverse scenarios. This study advances the state of the art in distribution network optimization by integrating machine learning techniques with robust mathematical modeling. The PPO-WGAN framework enhances scalability, ensures grid stability, and promotes efficient renewable integration, providing a robust foundation for future applications in modern power systems.

Personalized Shared Control for Automated Vehicles Considering Driving Capability and Styles.

The shared control system has been a key technology framework and trend, with its advantages in overcoming the performance shortage of safety and comfort in automated vehicles. Understanding human drivers' driving capabilities and styles is the key to improving system performance, in particular, the acceptance by and adaption of shared control vehicles to human drivers. In this research, personalized shared control considering drivers' main human factors is proposed. A simulated scenario generation method for human factors was established. Drivers' driving capabilities were defined and evaluated to improve the rationality of the driving authority allocation. Drivers' driving styles were analyzed, characterized, and evaluated in a field test for the intention-aware personalized automated subsystem. A personalized shared control framework is proposed based on the driving capabilities and styles, and its evaluation criteria were established, including driving safety, comfort, and workload. The personalized shared control system was evaluated in a human-in-the-loop simulation platform and a field test based on an automated vehicle. The results show that the proposed system could achieve better performances in terms of different driving capabilities, styles, and complex scenarios than those only driven by human drivers or automated systems.

Robust planning of production networks at an automotive supplier

As global trends like individualisation continue to drive significant changes in industrial production within international networks, it has become increasingly crucial for companies to maintain competitiveness through the efficient utilisation of resources. The rising complexity in production networks originates from an increasing number of constraints due to company-specific requirements, coupled with expanding networks that broaden the solution space, ultimately leading to prolonged planning processes. Furthermore, current planning tasks are predominantly performed manually, as the extensive efforts required for data acquisition often render the use of solution algorithms infeasible due to incomplete or inaccurate data. Therefore, this study explores robust planning of production networks, employing Monte–Carlo simulation and clustering for scenario generation, stochastic modelling concepts to tackle the mathematical problem, and utilising DT concepts for data integration at the network level.

Secondary Reconnection Between Interlinked Flux Tubes Driven by Magnetic Reconnection With a Short X‐Line

AbstractA three‐dimensional particle‐in‐cell simulation is performed to study secondary reconnection between two interlinked flux tubes produced by neighboring guide field reconnection x‐lines. The reconnecting magnetic fields of this secondary reconnection is enhanced toward the diffusion region, agree well with that in observations. The magnetic field pileup is attributed to the upstream magnetic tension force, that smashes the flux tubes into each other. We propose that the primary reconnection x‐line length is a key parameter to determine the formation of interlinked flux tubes and secondary reconnection therein. Interlinked flux tubes will form only if the x‐line is short; when the x‐line is long enough, the regular flux ropes are formed instead. The critical x‐line length to form interlinked flux tubes is determined by the distance between two neighbor x‐lines and the magnetic shear angle of the primary reconnection. The results provide a novel scenario of secondary reconnection generation during three‐dimensional reconnection.

Sensitivity analysis of thermal phenomena in APB (Annular Pressure Buildup) in oil wells

This paper presents a sensitivity analysis aimed at understanding how certain parameters of the thermal problem affect the increase in Annular Pressure Buildup (APB) in oil wells. The structure of an oil well experiences high-temperature gradients throughout its lifespan, directly affecting its components such as casings, cement, and rock formation. Among the undesirable effects caused by temperature variation, APB stands out, related to the expansion of fluids confined between casings, being a severe phenomenon that can lead to well collapse. This justifies studies aimed at better understanding temperature distributions and, consequently, the increase in pressure in the annular spaces of oil wells. On the other hand, changes in fluid and component temperatures during well operation depend on many factors, such as operating flow rate, inlet pressure and temperature, operating time, formation type, and properties of the produced/injected fluid. Some of these parameters are chosen for inclusion in the parametric analysis proposed in this work. To achieve the proposed objective, the methodology adopted in this work is based on four steps: a) selection of a reference well used for the analyses; b) definition of the variables to be studied and their assumed values; c) generation and simulation of scenarios formed by the combination of chosen variables; and d) selection and description of the sensitivity analysis method to be employed. The main contribution of this work is to present a sensitivity analysis of certain well parameters in annular pressure buildup, allowing inference about the importance of some isolated properties in the thermomechanical response. Additionally, the methodology presented can be applied to other parameters and thus improve understanding of how each property influences well APB.

Insights into Building VR Solutions in the Police Domain

Advanced technologies such as Virtual Reality (VR) hold immense significance for society, particularly in the law enforcement domain. VR offers a unique platform for immersive, experiential learning, allowing Police Officers (POs) to engage with dynamic scenarios that mirror real-world situations, incidents, and challenges. In the Police domain, the integration of VR solutions provides valuable insights into enhancing POs’ training, situational awareness, and community relations. Learning objectives in VR-based Police training encompass a spectrum of skills, including decision-making under pressure, effective communication and collaboration, and de-escalation techniques. Furthermore, evaluation perspectives span both quantitative and qualitative measures, assessing training efficacy, officer performance, and societal impact. This research adopts a systematic literature review approach where it becomes evident that the future of VR-enabled policing lies in interdisciplinary collaboration, technological innovation, and ethical considerations. Future directions entail refining VR solutions to reflect evolving societal dynamics, integrating Artificial Intelligence (AI) for adaptive scenario generation and analytics, and addressing concerns related to algorithmic transparency, fairness, security, and privacy. By leveraging VR technologies, law enforcement agencies can enhance operational effectiveness and foster trust, accountability, and responsibility within the communities they serve.

Generative Artificial Intelligence in Pathology and Medicine: A Deeper Dive.

This review article builds upon the introductory piece in our 7-part series, delving deeper into the transformative potential of generative artificial intelligence (Gen AI) in pathology and medicine. The article explores the applications of Gen AI models in pathology and medicine, including the use of custom chatbots for diagnostic report generation, synthetic image synthesis for training new models, data set augmentation, hypothetical scenario generation for educational purposes, and the use of multimodal along with multiagent models. This article also provides an overview of the common categories within Gen AI models, discussing open-source and closed-source models, as well as specific examples of popular models such as GPT-4, Llama, Mistral, DALL-E, Stable Diffusion, and their associated frameworks (eg, transformers, generative adversarial networks, diffusion-based neural networks), along with their limitations and challenges, especially within the medical domain. We also review common libraries and tools that are currently deemed necessary to build and integrate such models. Finally, we look to the future, discussing the potential impact of Gen AI on health care, including benefits, challenges, and concerns related to privacy, bias, ethics, application programming interface costs, and security measures.

On the dynamics of treasury bond yields: From term structure modelling to economic scenario generation

On the dynamics of treasury bond yields: From term structure modelling to economic scenario generation

Quantity bidding strategy of inter-provincial and intra-provincial two-level peak regulation market of coal-fired units considering prediction error

Abstract Under the background of the ‘carbon peaking and carbon neutrality’ target, the proportion of new energy installed capacity has been greatly increased. The problem of wind and photovoltaic power abandonment has become more and more serious. In the construction of inter-provincial and intra-provincial multi-level electricity markets, it is urgent to establish a source-network coordination mechanism and tap new consumption capacity to adapt to the rapid development of new energy. This paper first sorts out the joint operation process of the inter-provincial and intra-provincial two-level electric energy market and the peak regulation market. Then, with the goal of minimizing the total cost of the inter-provincial peak regulation market and provincial joint market, a collaborative optimization model of inter-provincial and intra-provincial peak regulation quantity bidding strategy is established. The expected scenario generation method considering prediction error is proposed. The collaborative optimization solution process is explained. Finally, the proposed model is simulated to verify the effectiveness of the collaborative optimization of inter-provincial and intra-provincial peak load regulation.