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A scenario-based robust distribution expansion planning under ellipsoidal uncertainty set using second-order cone programming

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期213卷

作者： Akbari, Tohid Moghaddam, Saeed Zolfaghari Pooyesh Inst Higher Educ Fac Elect Engn Qom Iran Urmia Univ Technol Fac Ind Technol Dept Elect Engn Orumiyeh Iran

This paper intends to propose a new mathematical framework for distribution expansion planning (DEP) based on which the uncertainties associated with electric demand and wind production are modeled through several plausible ellipsoidal uncertainty sets. In this regard, a hybrid model combining stochastic programming and robust optimization is constructed. Data related to the demand-wind scenarios are first divided into clusters of similar elements using an existing clustering technique. Then, an ellipsoidal uncertainty set is created corre-sponding to each of these generated clusters based on the theory of minimum volume covering ellipsoid (MVCE) using the Khachiyan algorithm (KA). The hybrid robust/stochastic scheme is formulated as a two-stage tri-level min-max-min optimization problem in which the convex conic relaxation of AC power flow is used to represent the electric distribution network. The multi-uncertainty-set-based model is then solved by employing the classic column-and-constraint-generation (C&CG) technique which guarantees the convergence to the optimal solution in a limited number of iterations. In this regard, a master problem and several subproblems related to each scenario will be solved, both of which are second-order cone programs. Numerical simulations reveal the su-periority of the hybrid model compared to the existing ones.

关键词： Distribution expansion planning Khachiyan algorithm Robust optimization Second -order cone programming stochastic programming

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Profit maximization of wind power producers with risk control considering uncertainty 10

Profit maximization of wind power producers with risk contro...

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10th IEEE Power India International Conference (PIICON)

作者： Singh, Vikram Fozdar, Manoj Malaviya Natl Inst Technol Dept Elect Engn Jaipur Rajasthan India

ISBN: (纸本)9781665459303

Wind power has emerged as one of the most prominent and rapidly expanding clean energy sources. But, with the subsidies offered and reduced incentives, wind power producers (WPP) need to formulate efficient bidding schemes to make their investments profitable. The purpose of this study is to create a stochastic decision model that will maximize the earnings of WPP participating in both day-ahead (DA) and balancing markets (BM). However, the participation of WPP in the DA market can lead to significant deviations from the initial preferred schedule due to the uncertainties associated with its offering decisions. These include uncertainties from intermittent wind power, energy prices, and imbalance penalties. Hence, a set of plausible scenarios is used to model each uncertainty. Further, a conditional value-at-risk (CVaR) metric is incorporated into the model for risk control to manage the risks related to the offering decisions. Numerical results from a case study are presented to demonstrate the efficacy of the suggested approach in increasing the WPP expected profit and lowering the associated risks.

关键词： wind power risk control electricity markets stochastic programming imbalance prices

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Scenario-based operation of an integrated rural multi-energy system considering agent-based farmer-behavior modeling

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ENERGY CONVERSION AND MANAGEMENT 2024年 304卷

作者： Liu, Yi Xu, Xiao Liu, Youbo Liu, Junyong Hu, Weihao Yang, Nan Jawad, Shafqat Wei, Zhaobin Sichuan Univ Dept Elect Engn Chengdu 610065 Peoples R China Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 610054 Peoples R China China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China

In recent years, modern agriculture has become an urgent need for various countries to handle the increasing global food demand and crisis. A renewable and sustainable rural multi-energy system is essential for modern agriculture, which meets its considerable energy consumption. However, the multi-energy consumption, assumed as given input in traditional operation problems of rural multi-energy systems, is always unavailable due to the lack of monitoring facilities and the farmer behavior uncertainty in rural areas. Therefore, this work proposes a stochastic programming method based on the energy consumption estimated by farmer household behavior for optimal operation schedules of generation devices. The method integrates the energy consumption of two forms of intensive agriculture, i.e., greenhouses and livestock houses, with a rural multi-energy system by the principle of energy balance. Further, each farmer household is established as an agent in the agent-based modeling system to analyze their behavior using household appliances and agricultural equipment. Then, overall stochastic scenarios of the stochastic programming are proposed, and the energy consumption in these scenarios is estimated in a bottom-up way based on the farmer's behavior. Based on the multi-energy consumption, the model of the proposed stochastic programming method is established to solve the optimal operation problem of the integrated rural multi-energy system with greenhouses and livestock houses. An integrated rural multi-energy system consisting of 490 farmer households, a greenhouse, and a livestock house is conducted for a case study. The results show that compared with the deterministic programming method, the optimal solutions by the proposed SP method are more accurate and it also can reduce the system cost, which can reach the highest by 50.68%.

关键词： Rural multi-energy system Greenhouse Livestock house Farmer household behavior stochastic programming

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Fair trading strategy in multi-energy systems considering design optimization and demand response based on consumer psychology

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ENERGY 2024年 306卷

作者： Li, Li Fan, Shuai Xiao, Jucheng Zhou, Huan Shen, Yu He, Guangyu Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Minist Educ Shanghai 200240 Peoples R China Shanghai Noncarbon Energy Convers & Utilizat Inst Shanghai 200240 Peoples R China Tsinghua Univ Zhejiang Energy Internet Res Ctr Yangtze Delta Reg Inst Jiaxing 314006 Peoples R China

Multi-energy systems can complement and convert various energy sources, bring opportunities and challenges to community energy markets. However, energy system configuration and trading prices change as a result of demand response behavior interventions and have a reverse effect on them, which is generally ignored. Therefore, it is necessary to provide an energy trading strategy that considers interactions between the supply and demand sides. To this end, this study proposes a holistic approach for electricity, cooling and heating trading, which is based on the Nash-type game to guarantee energy trading fairness under such interactions. The integrated demand response model is established considering uncertainty based on consumer psychology, where the capacity of flexible energy demand is a critical decision variable influenced by the supply side. Based on this, the design optimization model of multi-energy systems is developed, where commonly used energy supply technologies are considered. The impact of interaction with the demand side on the design optimization of multi- energy systems is highlighted. The case study shows that the energy retailer can avoid sacrificing 21 % of its profits to achieve 9 % energy cost savings for the demand response aggregator considering trading fairness;both players have better economic performance when demand-side interactions are considered.

关键词： Multi-energy system stochastic programming Integrated demand response Consumer psychology Game theory

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Traffic emergency vehicle deployment and dispatch under uncertainty

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2024年 183卷

作者： Zhen, Lu Wu, Jingwen Chen, Fengli Wang, Shuaian Shanghai Univ Sch Management Shanghai Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hong Kong Peoples R China

To manage traffic emergencies, cities require multiple types of traffic rescue vehicles, which need to be dispatched from various rescue stations dispersed throughout the city. A reasonable way of deploying the rescue vehicles must be determined given that the times and locations at which the traffic emergencies occur are uncertain. In this paper, we propose a two-stage stochastic programming approach to deploying multiple types of emergency vehicles in response to traffic accidents in the context of uncertainty. The first stage of the proposed model concerns decisions on the quantities of the different types of vehicles to be stocked at each rescue station. In the second stage, when the locations and accident rescue demands are realized in each scenario, the decision-making involves dispatch of the emergency vehicles to the traffic accidents. To solve the proposed model, we suggest a variable neighborhood search method. Using the road network of Jiading District, Shanghai, as an example, we perform numerical experiments to investigate the efficiency of the proposed method and the model validity. Some managerial implications are also outlined in the sensitivity analysis.

关键词： Traffic accident rescue Emergency vehicle dispatching stochastic programming Variable neighborhood search

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A modelling and solution approach for wind-affected drone-truck routing problem under uncertainty

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 257卷

作者： Dodge, Mercedeh MirHassani, Seyed Ali Hooshmand, Farnaz Amirkabir Univ Technol Tehran Polytech Dept Math & Comp Sci Tehran Iran

In the field of logistics and transportation, drones and trucks effectively enhance each other's capabilities by offering complementary benefits in terms of speed, cargo capacity, and charging frequency. Thus, efficient management of their collaboration is an important task. Although there is a vast literature addressing different aspects of drone-truck combined operations (DTCO), only a few studies incorporate the energy consumption of drones into the optimization model, and the existing ones have made simplified assumptions. This paper proposes an optimization model for DTCO by incorporating a comprehensive energy function affected by the drone speed, cargo weight, wind speed, and wind direction, paying attention to environmental viewpoints. Due to this energy function, the problem is formulated as a mixed-integer nonlinear programming (MINLP) model. To enhance tractability and efficiency, we provide a linear approximation for the MINLP model. Given the stochastic nature of wind conditions throughout the day, we extend the deterministic model as a scenario-based stochastic one. Incorporating uncertainty makes the model more complex and hence, we adopt a modified progressive hedging algorithm (PHA) to efficiently solve the model. Computational results over a variety of instances confirm the effectiveness of the proposed approach.

关键词： Drone-truck routing Energy consumption function Wind-affected routing stochastic programming Progressive hedging algorithm

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Linking Predictive and Prescriptive Analytics of Elderly and Frail Patient Hospital Services 10

Linking Predictive and Prescriptive Analytics of Elderly and...

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10th IEEE International Conference on Healthcare Informatics (IEEE ICHI)

作者： Williams, Elizabeth Gartner, Daniel Harper, Paul Cardiff Univ Sch Math Cardiff Wales Cardiff Univ Math Modelling Unit Cardiff Wales Natl Hlth Serv Cardiff Wales

ISBN: (数字)9781665468459

ISBN: (纸本)9781665468459

Predictive and prescriptive techniques are being evaluated to predict demand for inpatient services within South East Wales. This work is specifically focusing on multi-site hospital services for elderly and frail patients, using classification and regression trees to determine patient clusters with similar attributes, yielding results of up to 89.62% accuracy. By incorporating these results into mathematical models we aim to quantify the value of incorporating the clustering results in a deterministic and stochastic mathematical programme.

关键词： Machine Learning Mathematical programming stochastic programming

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Designing Zonal-Based Flexible Bus Services Under stochastic Demand

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TRANSPORTATION SCIENCE 2021年第6期55卷 1280-1299页

作者： Lee, Enoch Cen, Xuekai Lo, Hong K. Ng, Ka Fai Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China Cent South Univ Sch Traff & Transportat Engn Changsha 410000 Peoples R China

In this paper, we develop a zonal-based flexible bus services (ZBFBS) by considering both passenger demands' spatial (origin-destination or OD) and volume stochastic variations. Service requests are grouped by zonal OD pairs and number of passengers per request, and aggregated into demand categories which follow certain probability distributions. A two-stage stochastic program is formulated to minimize the expected operating cost of ZBFBS, in which the zonal visit sequences of vehicles are determined in stage 1, whereas in stage 2, service requests are assigned to either regular routes determined in stage 1 or ad hoc services that incur additional costs. Demand volume reliability and detour time reliability are introduced to ensure quality of the services and separate the problem into two phases for efficient solutions. In phase 1, given the reliability requirements, we minimize the cost of operating the regular services. In phase 2, we optimize the passenger assignment to vehicles to minimize the expected ad hoc service cost. The reliabilities are then optimized by a gradient-based approach to minimize the sum of the regular service operating cost and expected ad hoc service cost. We conduct numerical studies on vehicle capacity, detour time limit and demand volume to demonstrate the potential of ZBFBS, and apply the model to Chengdu, China, based on real data to illustrate its applicability.

关键词： flexible bus demand-responsive transit stochastic programming reliability

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Maximizing the expected net present value in a project with uncertain cash flows

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第2期294卷 442-452页

作者： Peymankar, Mahboobeh Davari, Morteza Ranjbar, Mohammad Ferdowsi Univ Mashhad Fac Engn Ind Engn Dept Mashhad Razavi Khorasan Iran Univ Cote dAzur SKEMA Business Sch Lille France

This paper considers the problem of maximizing the expected net present value of a project under uncertain cash flows, which are described by a supporting set of discrete scenarios. While cash flows are often considered more or less stable in countries with stable economy, they can be quite uncertain in countries with financial and/or political crisis. In these countries, cash flows may drastically rise after a certain political event or a financial crisis. Therefore, we assume that the price changes may well occur after a predictable time (e.g., an election or the date on which a deal is agreed or broken). Such an assumption has never been made in project scheduling under uncertainty. We propose two integer linear programming (ILP) formulations and develop two-stage stochastic programming approaches that use Benders decomposition to solve the problem efficiently. Since the number of generated scenarios may be large and thus intractable, we also employ a forward scenario reduction technique to construct a rather tractable set of scenarios. Computational results indicate that the developed Benders-based methods outperform the ILP formulations. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Project scheduling Net present value stochastic programming Benders decomposition

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Optimal coordinated operation of integrated energy hubs, considering regenerative braking utilization

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IET ELECTRICAL SYSTEMS IN TRANSPORTATION 2021年第4期11卷 362-376页

作者： Akbari, Saeed Fazel, Seyed Saeed Jadid, Shahram Iran Univ Sci & Technol Sch Railway Engn Tehran 16864 Iran Iran Univ Sci & Technol Sch Elect Engn Tehran Iran

This article presents a coordinated operation model for energy management of a multi-integrated energy system based on Mixed-Integer Linear Programing (MILP). The power derived by trains from regenerative braking energy (RBE), during deceleration, is utilised to meet the interconnected energy hubs' (IEHs) demand. The recovered energy is calculated by simulating the motion of the trains in MATLAB software. The electricity and heat demand response (DR) programs are integrated into the proposed model to study their impacts on the operating cost and the carbon emission of the IEH, considering several case studies. Furthermore, the uncertainties of the RBE, photovoltaic power generation, and loads of the IEH are considered by formulating the optimisation problem stochastically through a scenario-based approach. Therefore, a scenario generation and reduction decision-making technique is employed. Finally, the GAMS optimisation software is used to assess the efficiency of the presented MILP model. The simulation results indicate that the total operating cost of the IEH reduced 2.0% and 1.4% in the case studies. Also, the CO2 emission is decreased by about 0.3% by applying the coordination scheme besides the DR programs.

关键词： carbon compounds total operating cost Matlab photovoltaic power generation reduction decision-making technique GAMS optimisation software integrated interconnected energy hubs regenerative braking utilization photovoltaic power systems DR programs carbon emission power engineering computing regenerative braking energy stochastic programming MATLAB software MILP model linear programming multiintegrated energy system RBE IEH coordination scheme integer programming energy management CO mixed-integer linear programming model energy management systems regenerative braking coordinated operation model demand side management decision making

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