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9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024

作者： Sharma, Swati Gautam, Anubha Sharma, Gulshan Bokoro, Pitshou N. Jamia Millia Islamia Department of Electrical Engineering New Delhi India J C Bose UST YMCA Department of Electrical Engineering Haryana Faridabad India University of Johannesburg Department of Electrical Engineering Technology Johannesburg2006 South Africa

ISBN: (纸本)9798350385403

This paper introduces a hierarchical control strategy for energy management in a hybrid AC/DC microgrid, emphasizing the influence of electric vehicle (EV) charging patterns. The power flow and standard operating conditions within the AC and DC segments of the microgrid are controlled at both primary and secondary levels. An EV aggregator model is proposed to manage the EV converter based on a planned schedule, enabling efficient power distribution and improving microgrid stability. To optimize the EV charging process and reduce costs, a non-linear stochastic programming model is employed. The performance of both the microgrid controller and EV aggregator is thoroughly evaluated in a real-time lab setting, demonstrating its effectiveness in managing EV power, enhancing voltage and frequency stability, and optimizing scheduling in both grid-connected and islanded modes. © 2024 IEEE.

关键词： stochastic programming

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Towards a sustainable power grid: stochastic hierarchical planning for high renewable integration

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第1期302卷 381-391页

作者： Atakan, Semih Gangammanavar, Harsha Sen, Suvrajeet Univ Southern Calif Daniel J Epstein Dept Ind & Syst Engn CA Los Angeles CA 90007 USA Southern Methodist Univ Dept Operat Res & Engn Management Dallas TX 75275 USA

Driven by ambitious renewable portfolio standards, large-scale inclusion of variable energy resources (such as wind and solar) are expected to introduce unprecedented levels of uncertainty into power systems operations. The current practice of operations planning with deterministic optimization models may be ill-suited for a future with abundant uncertainty. To overcome the potential reliability and economic challenges, we present a stochastic hierarchical planning (SHP) framework for power systems coordinated by a centralized planner. This framework captures operations at day-ahead, short-term and hour-ahead timescales, along with the interactions between the stochastic processes and decisions. In contrast to earlier studies where stochastic optimization of individual problems (e.g., unit commitment, economic dispatch) have been studied, this paper studies an integrated framework of planning under uncertainty , where stochastic optimization models are stitched together in a hierarchical setting, which parallels the deterministic hierarchical planning approach that is widely adopted in the power industry. Our experiments, based on the NREL-118 dataset, reveal that under high renewable integration, significant operational improvements can be expected by transitioning to the SHP paradigm. In particular, the computational results show that significant improvements can be achieved in several metrics, including system reliability, environmental sustainability, and system economics, solely by making a strategic choice to adopt the new SHP paradigm. (c) 2022 Elsevier B.V. All rights reserved.

关键词： OR in energy Power systems operations planning stochastic programming Hierarchical planning

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Generation and Transmission Expansion Planning Towards a 100% Renewable Future

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第4期37卷 3274-3285页

作者： Yin, Shengfei Wang, Jianhui Southern Methodist Univ Lyle Sch Engn Dallas TX 75275 USA

This paper proposes a novelmodeling framework and decomposition-based solution strategy combining stochastic programming (SP) and robust optimization (RO) to dealwith multiplex uncertainties in coordinated mid- and long-term power system planning. The problem is formulated as a multi-year generation and transmission planning problem from an independent system operator (ISO)'s perspective to minimize both expansion and operational costs under binary and continuous uncertainties, i.e., system component contingency and load/generation variation. N-k contingencies are captured in RO using the reformulated contingency criteria, while the load/generation uncertainty is considered in SP embedded with RO using operating scenarios generated from the historical data with spatiotemporal correlations. The original hybrid model is highly intractable, but the intractability can be relieved by the proposed decomposition strategy based on the column-and-constraint generation and L-shaped algorithms. We apply our model to perform long-term system planning under extremely high renewable penetration and investigate the case of 100% renewables in long-term planning. Numerical experiments on multi-scale test systems verify the efficacy of the proposed approach.

关键词： Coordinated planning discrete and continuous uncertainties stochastic programming robust optimization decomposition 100% renewable penetration

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Simultaneous schedule of trains and track maintenance according to stochastic blockage time

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INTERNATIONAL JOURNAL OF RAIL TRANSPORTATION 2022年第5期10卷 562-580页

作者： Bababeik, Mostafa Farjadamin, Mohammad Khademi, Navid Fani, Amir-Hossein Univ Tehran Coll Engn Sch Civil Engn Tehran Iran Amirkabir Univ Technol Dept Civil & Environm Engn Tehran Iran

Regarding the railway infrastructures, planning for track maintenance is a challenging task given the coordination required between train traffic and maintenance operations. In this paper, using stochastic mathematical programming, we have investigated the simultaneous scheduling of trains and operations for a single-track line to minimise both the travel time of trains and maintenance duration. The uncertainty in the blockage duration of the track is considered in the model to reduce the impact of unexpected delays in maintenance operations. The proposed model also considers the practical aspects, which have been less addressed in previous studies regarding track maintenance. We reformulated the stochastic problem as its deterministic equivalent to facilitate solutions for realistic sizes. The computational result obtained for a real-world track shows that the model can efficiently find simultaneous scheduling of trains and operations and suggest optimal blockage duration at different confidence levels. The impact of timetable comparison on the blockage duration for a heavily utilised line has also been evaluated.

关键词： Track maintenance simultaneous scheduling blockage stochastic programming

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Diversified Software Deployment for Long-Term Risk Mitigation in Cyber-Physical Power Systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第1期37卷 377-387页

作者： Zhang, Zhimei Huang, Shaowei Chen, Ying Li, Boda Mei, Shengwei Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Since modern cyber-physical power systems are vulnerable to coordinated wide-area cyber attacks, it is necessary to mitigate the potential risk as much as possible. At the planning stage, the defender can utilize software diversity, which is a common phenomenon that the cyber software of different substations comes from different competing vendors. Therefore, different kinds of software may not be exposed to the same zero-day security loophole, preventing the attacker from taking charge of multiple substations at the same time. In this paper, the optimal scheme of software deployment considering long-term risk mitigation is studied. Firstly, the framework of diversity-based cyber defense against malicious attacks is formulated. Secondly, the risk index based on representative attack patterns is constructed, which is the objective to be minimized. Thirdly, considering that the deployment scheme is long-term stable while the operating mode varies with time, we construct a multiobjective nonlinear stochastic programming to mitigate the average risk of operating modes. Then the optimization problem is solved by the multiobjective genetic algorithm. Lastly, results of the IEEE 39-node CPPS and and the Virtual European Grid demonstrate that the proposed method can considerably reduce the attack risk.

关键词： Substations Cyberattack Software Power systems Indexes Security Risk management Cyber-physical power system multiobjective genetic algorithm (MOGA) nonlinear integer programming risk mitigation stochastic programming software diversity

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AI Assisted Real Time Response Systems for National Emergency Preparedness and Disaster Management 2

AI Assisted Real Time Response Systems for National Emergenc...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Sakpal, Shreya Sudesh Golisano College of Computing and Information Sciences Rochester Institute Of Technology Department of Computer Science RochesterNY14623 United States

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388916

This paper focuses on a real-time response system supported by AI developed to improve national emergency readiness and disaster response. The proposed system includes decision support for disaster response through real-time data processing with the application of machine learning/deep learning algorithms applied to the data collected from IoT sensors, satellite images and social media commenting. Hence, the proposed system optimizes the resources, the probability of the forecasts and decisions made through the integration of linear programming, probabilistic models, and stochastic optimization. Having positively noted the effect of the system on response time of aggregated queries, the Level 2 priority simulations also revealed that the offered optimization of resource allocation is comprised at 90%, while the deep learning statistical information of disaster prediction conveys an important accuracy of 89%. The Resource management policies ensured effective manner of delivering personal and operational necessities, which flexibly changed depending on the situation. This research also shows the ability of the system to improve Disaster management by increasing efficiency in managing large-scale emergencies. © 2024 IEEE.

关键词： stochastic programming

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Benders decomposition for internal truck renewal decision in green ports

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MARITIME POLICY & MANAGEMENT 2023年第8期50卷 1057-1079页

作者： Zhen, Lu Jin, Ying Wu, Yiwei Yuan, Yingying Tan, Zheyi Shanghai Univ Sch Management Shang Da Rd 99 Shanghai 200444 Peoples R China

To effectively decrease emissions and pollution emitted by equipment in container ports, many ports began to update equipment using diesel fuels to achieve the aim of green ports. This paper studies an internal truck renewal problem in container ports and proposes a two-stage stochastic programming model to optimally determine internal truck composition optimization adjustment including three renewal modes, i.e. purchasing, retrofitting, and chartering. An exact solution procedure based on Benders decomposition accelerating by Pareto-optimal cuts is developed for the proposed model. Given the problem background of Shanghai Yangshan Deep Water Port, comprehensive computational experiments are carried out to verify the effectiveness of the proposed mathematical model and the performance of the solution approach. According to the numerical experiments, some suggestions for the management of renewing internal trucks in green ports are summarized at the end of the paper.

关键词： Internal truck renewal green ports stochastic programming strategic planning Benders decomposition

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Sequential Resource Planning Decisions in an Epidemic Based on an Innovative Spread Model

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2022年第2期19卷 677-691页

作者： Li, Na Wang, Ziyan Pei, Zhi Shanghai Jiao Tong Univ Sch Mech Engn Dept Ind Engn & Management Shanghai 200240 Peoples R China Zhejiang Univ Technol Coll Mech Engn Hangzhou 310023 Peoples R China

Making rapid decisions in intervention resource planning is crucial for mitigating morbidity, mortality, and costs to the societies during epidemic outbreaks. This study presents a data-driven optimization approach for multiperiod resources planning, based on a sequential decision framework, considering up-to-date information and uncertainty in the spread of epidemics. In this method, a new (SEIHRD)-H-3-R-2 spread model is constructed to generate the most potential scenarios of an epidemic, based on all the historical information, and risk-averse stochastic programming was proposed to arrive at an optimal resource planning solution. The data-based numerical experiments demonstrate that our approach could control the epidemic by reducing the infected cases and deaths with similar or fewer resources than in the reality. In addition, we also find that the risk-averse design of the objective was able to take a steadier approach to resource planning helped avoid large fluctuations in resource allocations compared to a risk-neutral design. The other insight obtained from these experiments was that a moderate decision interval along with a planning horizon, which is slightly larger than the decision interval, would be a good choice for the sequential planning problem.

关键词： Epidemics Planning Hospitals Optimization stochastic processes COVID-19 Uncertainty Conditional value at risk (CVaR) innovative spread model sequential decision stochastic programming

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A Probabilistic Model for Driving-Style-Recognition-Enabled Driver Steering Behaviors

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第3期52卷 1838-1851页

作者： Deng, Zejian Chu, Duanfeng Wu, Chaozhong Liu, Shidong Sun, Chen Liu, Teng Cao, Dongpu Wuhan Univ Technol Intelligent Transportat Syst Res Ctr Wuhan 430063 Peoples R China Univ Waterloo Dept Mech & Mechatron Engn Waterloo ON N2L 3G1 Canada China Automot Technol & Res Ctr Co Ltd Dev Dept 1 Tianjin 300300 Peoples R China

This article presents a framework to determine driving style and design a driver steering model considering driver characteristics. First, principal component analysis (PCA) and K-means clustering are utilized to classify 30 participants into cautious, moderate, and aggressive drivers. Subsequently, a generic steering model is established based on the model predictive control method. Thereafter, the maximum lateral acceleration is extracted as a crucial indicator to represent driver characteristics, and it is calibrated through probabilistic models using the dataset, which consists of the classified drivers. Besides, point estimation model and interval estimation model are leveraged to determine driving style and adjust constraints in the stochastic programming-based steering model. Finally, simulation experiments present the variations of actual output trajectories between the aggressive drivers and the cautious drivers.

关键词： Vehicles Acceleration stochastic processes Support vector machines Predictive models Probabilistic logic Principal component analysis Driver steering model driving style model predictive control probabilistic modeling stochastic programming

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Optimal Model for Electricity Retailer Considering Demand Response and Risk Management through stochastic Formulation

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2022年第11-12期49卷 1007-1020页

作者： Apornak, Kourosh Soleymani, Soodabeh Faghihi, Faramarz Mozafari, Babak Islamic Azad Univ Sci & Res Branch Dept Elect Engn Tehran Iran

The electricity market is an economic environment of the power system in order to buy and sell energy in the presence of different players. One of the most effective part of electricity market is retail electricity market. Electricity price in the retail electricity market has a different rate for various types of customers. So, various retail prices are generally settled for different customers in the retail market. In power market, retailers purchase large volume of energy from power producers and sell it to customers. This paper from the retailer prospective while to encourage the customer to participate in priced-based demand response (DR) programs, proposes the nonlinear model by considering uncertainties in pool market and demand elasticity to decrease the energy procurement in hours with high pool *** the formulation of suggested stochastic model. Risk measurement of the proposed model is obtained by Value at Risk and Conditional Value at Risk. By applying the proposed model, electricity retailer will be capable to choose different risk-based strategies. The proposed model will lead to optimal management and decision-making of the retailer in purchasing from bilateral contracts and the pool market. Therefore, based on the suggested economic approach, the proposed model by considering the DR program reduces energy consumption during peak hours, which facilitates the supply of electricity to customers during these hours. Finally the case study is implemented in GAMS software and solved Via MINOS solver to observe the effectiveness and validation of the suggested model.

关键词： risk management power market electricity retailer priced-based demand response programs pool market bilateral contract retailer profit stochastic programming

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