Agent-Based Simulation for Interconnection-Scale Renewable Integration and Demand Response Studies

This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council(WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methods presented.

Agent-Based Simulation for Analyzing Network Formation with Learning Dynamics

In game theoretic context, it is assumed that the decision maker has the extraordinary skills of reasoning and calculation. This assumption is called ＂perfect rationality＂. A player with perfect rationality can solve complex problems without making mistakes. However, recently, many studies have restricted this rationality or the structure of game. These restrictions are called ＂bounded rationality.＂ The authors also focus on bounded rationality, but with learning dynamics and complex networks. A complex network covers a wide area. Currently, a wide range of studies have not only investigated network formation and the characteristics of a formed network, but also analyzed situations where a network is already provided. In addition, in an analysis using game theory, a prisoners＇ dilemma type game was used to investigate how a change in the network structure would affect the players＇ relationships Therefore, our model employs decision makers with learning dynamics and describes the interaction of decision makers as a network. The purpose of this study is to examine the behavior of the decision maker with learning dynamics and the formation of networks by the interaction of decision makers through an agent-based simulation.

An agent-based simulation with NetLogo platform to evaluate forward osmosis process(PRO Mode)

Forward osmosis(FO), as an emerging technology, is influenced by different factors such as operating conditions,module characteristics, and membrane properties. The general aim of this study was to develop a suitable(flexible,comprehensive, and convenient to use) computational tool which is able to simulate osmosis through an asymmetric membrane oriented in pressure retarded osmosis(PRO) mode in a wide variety of scenarios. For this purpose, an agent-based model was created in NetLogo platform, which is an easy-to-use application environment with graphical visualization abilities and well suited for modeling a complex system evolving over time. The simulation results were validated with empirical data obtained from literature and a great agreement was observed. The effect of various parameters on process performance was investigated in terms of temperature,cross-flow velocity, length of the module, pure water permeability coefficient, and structural parameter of the membrane. Results demonstrated that the increase in all parameters, except structural parameter of the membrane and the length of module led to the increase of average water flux. Moreover, nine different draw solutes were selected in order to assess the influence of net bulk osmotic pressure difference between the draw solution(DS) and feed solution(FS)(known as the driving force of FO process) on water flux. Based on the findings of this paper, the performance of FO process(PRO mode) can be efficiently evaluated using the NetL ogo platform.

Using agent-based simulation to assess diseaseprevention measures during pandemics

Despite the growing interest in macroscopic epidemiological models to deal with threats posed by pandemics such as COVID-19,little has been done regarding the assessment of disease spread in day-to-day life,especially within buildings such as supermarkets where people must obtain necessities at the risk of exposure to disease.Here,we propose an integrated customer shopping simulator including both shopper movement and choice behavior,using a force-based and discrete choice model,respectively.By a simple extension to the force-based model,we implement the following preventive measures currently taken by supermarkets;social distancing and one-way systems,and different customer habits,assessing them based on the average individual disease exposure and the time taken to complete shopping(shopping efficiency).Results show that maintaining social distance is an effective way to reduce exposure,but at the cost of shopping efficiency.We find that the one-way system is the optimal strategy for reducing exposure while minimizing the impact on shopping efficiency.Customers should also visit supermarkets less frequently,but buy more when they do,if they wish to minimize their exposure.We hope that this work demonstrates the potential of pedestrian dynamics simulations in assessing preventative measures during pandemics,particularly if it is validated using empirical data.

Assessment of Multiple Working Condition System Reliability with Agent-Based Simulation Method

The mission reliability assessment plays a great role in logistics planning and supporting resource optimization of complex system.But the current problem,which is difficult to solve,is how to model and analyze the characters of system reliability under the complex mission profile.In order to solve the problem,an agentbased simulation method was used to assess reliability for complex systems with various random working conditions.A multi-working condition simulation agent(MA)was designed and used to simulate the random transferring process of working conditions of system,and it cooperated with system simulation agents(SAs)and unit simulation agents(UAs)to realize system mission reliability(MR)simulation.Through simulation experiments,effect of multiple working conditions mission on the reliability of system was analyzed by comparing with the basic reliability condition.Feasibility and efficiency of the method were proved through simulation experiments of the case system.The research result provides a viable and useful method and a solution for MR analysis and assessment of complex systems in multi-working conditions,which can help to evaluate the reliability of operating system orienting to the practical mission and environment,and it is meaningful for the reliability analysis and the design of complex systems.

Evaluating the flooding level impacts on urban metro networks and travel demand:behavioral analyses,agent-based simulation,and large-scale case study

With urban residents’increasing reliance on metro systems for commuting and other daily activities,extreme weather events such as heavy rainfall and flooding impacting the metro system services are becoming increasingly of concern.Plans for such emergency interruptions require a thorough understanding of the potential outcomes on both the system and individual component scales.However,due to the complex dynamics,constraints,and interactions of the elements involved(e.g.,disaster,infrastructure,service operation,and travel behavior),there is still no framework that comprehensively evaluates the system performance across different spatiotemporal scales and is flexible enough to handle increasingly detailed travel behavior,transit service,and disaster information data.Built on an agent-based model(ABM)framework,this study adopts a data-driven ABM simulation approach informed by actual metro operation and travel demand data to investigate the impact of flood-induced station closures on travelers as well as the overall system response.A before-after comparison is conducted where the traveler behaviors in disaster scenarios are obtained from a discrete choice model of alternative stations and routes.A case study of the Shanghai Metro is used to demonstrate the ability of the proposed approach in evaluating the impacts of flood-induced station closures on individual traveler behavior under normal operation and a series of water level rise scenarios of up to 5m.It was found that,when the flood-induced station closures only affect a few river-side stations in the city center,the travelers experience only minor disruptions to their trips due to the availability of unaffected stations nearby as a backup.However,as the water level increases and more stations(mainly in the suburban area)are affected,up to 25%of trips are no longer being fulfilled due to the loss of entrances,exits,or transfer links.The system experiences overall less crowdedness in terms of passenger volume and platform waiting time with a few exceptions of increased passenger load due to concentrations of passenger flows to alternative stations under flooding-induced station closures.The proposed approach can be adapted to other disaster scenarios to reveal the disaster impacts on both aggregated and disaggregated levels and guide the design of more spatio-and temporally-targeted emergency plans for metro systems.

Assessing the impacts of traffic calming at network level:A multimodal agent-based simulation

The reduction of speed limits in urban roads through traffic calming schemes intends to ensure safer traffic conditions among road users by reducing the probability related to the occurrence of severe accident.Looking it from a different perspective,traffic calming measures can potentially resolve congestion problems at the same time by lowering the overall accessibility and attractiveness of private cars in urban areas.This study proposes a new methodological approach to explore and assess the direct impacts of traffic calming in the transport system efficiency of a metropolitan area.The multi-agent transport simulation(MATSim)and Open-Berlin scenario are utilized to perform this simulation experiment.By developing a new external tool,the free flow speed and road capacity of each network link is updated based on new speed limits and different compliance rates,which are defined per road hierarchy level.The test scenarios that are formulated present radical conditions,where the speed limit in most urban roads of Berlin drops to 30 km/h or even 15 km/h.The findings of this study show a considerably high increase in trips,passenger hours,and passenger kilometers using public transport modes,when traffic calming links are introduced,the reserve change is observed in private cars trips.Although the speed limits are decreased in inner urban roads in most of the scenarios,the decrease of average travel speed of private cars is not so high as it was expected.Surprisingly,private cars are used for longer distances in all test scenarios.Car drivers seem to use already existed motorways and private road to commute.In simulations,driver compliance to the new speed limits seems to be a determinant factor that is strongly influenced by the design interventions applied in a traffic calming area.

Complex adaptive system theory,agent-based modeling,and simulation in dominant technology formation

Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to investigate the dominant technology by exploring its formation process and mechanism.Specifically,based on complex adaptive system theory and the basic stimulus-response model,we use a combination of agent-based modeling and system dynamics modeling to capture the interactions between dominant technology and the socio-technical landscape.The results indicate the following:(i)The dynamic interaction is“stimulus-reaction-selection”,which promotes the dominant technology’s formation.(ii)The dominant technology’s formation can be described as a dynamic process in which the adaptation intensity of technology standards increases continuously until it becomes the leading technology under the dual action of internal and external mechanisms.(iii)The dominant technology’s formation in the high-tech industry is influenced by learning ability,the number of adopting users and adaptability.Therein,a“critical scale”of learning ability exists to promote the formation of leading technology:a large number of adopting users can promote the dominant technology’s formation by influencing the adaptive response of technology standards to the socio-technical landscape and the choice of technology standards by the socio-technical landscape.There is a minimum threshold and a maximum threshold for the role of adaptability in the dominant technology’s formation.(iv)The socio-technical landscape can promote the leading technology’s shaping in the high-tech industry,and different elements have different effects.This study promotes research on the formation mechanism of dominant technology in the high-tech industry,presents new perspectives and methods for researchers,and provides essential enlightenment for managers to formulate technology strategies.

Agent-based simulation for symmetric electricity market considering price-based demand response

With the development of electricity market mechanism and advanced metering infrastructure(AMI),demand response has become an important alternative solution to improving power system reliability and effi-ciency. In this paper, the agent-based modelling and simulation method is applied to explore the impact of symmetric market mechanism and demand response on electricity market. The models of market participants are established according to their behaviors. Consumers’ response characteristics under time-of-use(TOU) mechanism are also taken into account. The level of clearing price and market power are analyzed and compared under symmetric and asymmetric market mechanisms. The results indicate that the symmetric mechanism could effectively lower market prices and avoid monopoly.Besides, TOU could apparently flatten the overall demand curve by enabling customers to adjust their load profiles,which also helps to reduce the price.

STUDIES ON HORIZONTAL COMPETITION AMONG HOMOGENOUS RETAILERS THROUGH AGENT-BASED SIMULATION

This paper adopts agent-based simulation to study the horizontal competition among homogenous price-setting retailers in a one-to-many supply chain (a supply chain consists of one supplier and multiple retailers). We model the supplier and retailers as agents, and design their behavioral rules respectively. The results show that although the agents learn individually based on their own experiences, the system converges asymptotically to near Nash equilibrium steady states. When analyzing the results, we first discuss the properties of these steady states. Then based on these properties, we analyze the effects of the retailers’ horizontal competition on the retail prices, retailers’ profits and supplier’s revenue.

Agent-Based Simulation of Rumor Propagation on Social Network Based on Active Immune Mechanism

To simulate the rumor propagation process on online social network during emergency, a new rumor propagation model was built based on active immune mechanism. The rumor propagation mechanisms were analyzed and corresponding parameters were defined. BA scale free network and NW small world network that can be used for representing the online social network structure were constructed and their characteristics were compared. Agent-based simulations were conducted on both networks and results show that BA scale free network is more conductive to spreading rumors and it can facilitate the rumor refutation process at the same time. Rumors paid attention to by more people is likely to spread quicker and broader but for which the rumor refutation process will be more effective. The model provides a useful tool for understanding and predicting the rumor propagation process on online social network during emergency, providing useful instructions for rumor propagation intervention.

COVID-19 transmission in U.S.transit buses:A scenario-based approach with agent-based simulation modeling(ABSM)

The transit bus environment is considered one of the primary sources of transmission of the COVID-19(SARSCoV-2)virus.Modeling disease transmission in public buses remains a challenge,especially with uncertainties in passenger boarding,alighting,and onboard movements.Although there are initial findings on the effectiveness of some of the mitigation policies(such as face-covering and ventilation),evidence is scarce on how these policies could affect the onboard transmission risk under a realistic bus setting considering different headways,boarding and alighting patterns,and seating capacity control.This study examines the specific policy regimes that transit agencies implemented during early phases of the COVID-19 pandemic in USA,in which it brings crucial insights on combating current and future epidemics.We use an agent-based simulation model(ABSM)based on standard design characteristics for urban buses in USA and two different service frequency settings(10-min and 20-min headways).We find that wearing face-coverings(surgical masks)significantly reduces onboard transmission rates,from no mitigation rates of 85%in higher-frequency buses and 75%in lower-frequency buses to 12.5%.The most effective prevention outcome is the combination of KN-95 masks,open window policies,and half-capacity seating control during higher-frequency bus services,with an outcome of nearly 0%onboard infection rate.Our results advance understanding of COVID-19 risks in the urban bus environment and contribute to effective mitigation policy design,which is crucial to ensuring passenger safety.The findings of this study provide important policy implications for operational adjustment and safety protocols as transit agencies seek to plan for future emergencies.

Agent-based Simulation Systems for Emergency Management

Emergencies, which are very difficult to be forecasted, would always bring about huge harm to people. Therefore, to find ways to reduce such devastating effects, researches on emergency management have turned to be paramount. Nowadays, the rapid development of computer technology has supplied a new and effective idea for the researches of emergency management, namely that the researches can be done in computers by performing simulation experiments according to the artificial societies, computational experiments, parallel execution （ACP） approach. Guided by this approach, this paper has proposed one agent-based prototype simulation system to research emergency management. Firstly, structure of the simulation system oriented to emergency management was analyzed and designed. Then a simulation system oriented to public health emergency management was constructed to study the transmission of infectious diseases. Finally, several experiments were carried out based on the system, with several significant conclusions having also been obtained.

An Agent-based Simulation Model of Wheat Market Operation:The Benefit of Support Price

Grain security is one of the most important issues worldwide.Many developing countries,including China,have adopted the Agriculture Support Price(ASP)program to stimulate farmers’enthusiasm for growing grain,to ensure self-sufficiency in grain and the stable development of the grain market.To propose decision support for the government in designing a more reasonable support price in the ASP program,we formulate an agent-based model to simulate the operation of the wheat market in the harvest period.To formulate the formation process of the market price influenced by farmers’expected sale price,processors’expected purchase price,and the ASP,the time series and regression methods are adopted.Based on the proposed market price model,to quantitatively analyze the grain transaction process and the ASP program’s impacts on market agents,we develop an agent-based simulation model to describe the adaptive evolution and interaction among market agents.Furthermore,we validate and implement the simulation model with public wheat market data.Finally,insights and suggestions about the decision of the ASP program are provided.

AGENT-BASED SIMULATION FOR KANSEI ENGINEERING： TESTING A FUZZY LINEAR QUANTIFICATION METHOD IN AN ARTIFICIAL WORLD

This paper argues that agent-based simulation can be used as a way for testing Kansei Engineering methods which deal with the human reaction from sensory to mental state, that is, sensitivity, sense, sensibility, feeling, esthetics, emotion affection and intuition. A new fuzzy linear quantification method is tested in an artificial world by agent-based modeling and simulations, and the performance of the fuzzy linear method is compared with that of a genetic algorithm. The simulations can expand people＇s imagination and enhance people＇s intuition that the new fuzzy linear quantification method is effective.

Molecular simulation study of the microstructures and properties of pyridinium ionic liquid[HPy][BF_(4)]mixed with acetonitrile

The microstructures and thermodynamic properties of mixed systems comprising pyridinium ionic liquid[HPy][BF_(4)]and acetonitrile at different mole fractions were studied using molecular dynamics simulation in this work.The following properties were determined:density,self-diffusion coefficient,excess molar volume,and radial distribution function.The results show that with an increase in the mole fraction of[HPy][BF_(4)],the self-diffusion coefficient decreases.Additionally,the excess molar volume initially decreases,reaches a minimum,and then increases.The rules of radial distribution functions(RDFs)of characteristic atoms are different.With increasing the mole fraction of[HPy][BF_(4)],the first peak of the RDFs of HA1-F decreases,while that of CT6-CT6 rises at first and then decreases.This indicates that the solvent molecules affect the polar and non-polar regions of[HPy][BF_(4)]differently.

Simulation of game analysis based on an agent-based artificial stock market re-examined

This work re-examined the simulation result of game analysis (Joshi et al., 2000) based on an agent-based model, Santa Fe Institute Artificial Stock Market. Allowing for recent research work on this artificial model, this paper’s modified game simulations found that the dividend amplitude parameter is a crucial factor and that the original conclusion still holds in a not long period, but only when the dividend amplitude is large enough. Our explanation of this result is that the dividend amplitude pa- rameter is a measurement of market uncertainty. The greater the uncertainty, the greater the price volatility, and so is the risk of investing in the stock market. The greater the risk, the greater the advantage of including technical rules.

Agent-based simulation of alternative classroom evacuation scenarios

Due to the growing number of emergency accidents occurring around students, evacuation issues have become significantly important for both school officials and architects. Simply following construction codes cannot ensure that a building＇s layout is suitable for evacuation behaviors; therefore, to discover the suitable planning schemes, we have introduced an agentbased simulation model via Netlogo to investigate the interrelationships between evacuation efficiency and classroom layouts. Before conducting modeling experiments, both the simulation structure and the sensitivity to its parameter settings are examined by validation research and sensitivity analysis. Furthermore, to demonstrate the importance of conducting fire drills with students, two different types of behavior rules are designed to reflect the distinctive characteristics of students evacuating without instructions and students evacuating in good order. The general comparison results show us that the classroom layout with two exits shortens students＇ evacuation time, and the premeditated behavior rules, meaning that students who follow preset instructions to arrange their activities, not only escape faster but also have some advantages in ensuring their safety during the evacuation process. Moreover, at the end of this paper, several methods of improving this simulation model are proposed for more complex research in the future.

Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression

An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. This model was built to approximately reproduce some essential findings that were previously reported for a rather complex model of diabetes progression. Our models are translations of basicelements of this previously reported system dynamics model of diabetes. The system dynamics model, which mimics diabetes progression over an aggregated US population, was disaggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. The four estimated models attempted to replicate stock counts representing disease states in the system dynamics model while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent’s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. All three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model, although behavioral factors appeared to contribute more than the elderliness factor. The results illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.

Industry effect of job hopping: an agent-based simulation of Chinese construction workers

Job hopping affects the development of industries in terms of efficiency and quality of work. It is a problem for the Chinese construction industry, where excessive job hopping is detrimental to meeting the current daunting challenges involved in the industry's transformation and efficiency improvement. To provide an exhaustive analysis of this effect, game theory is combined with social relationship networks to create an agent-based simulation model. Simulation results indicate that the frequent job moves of Chinese construction workers have a negative effect on their skill development, employment, and worker relationships, as well as results in sharp increase in employer labor costs. The findings point to the need to act for the benefit of workers and employers and maintain the development of the industry.