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stochastic planning for low-carbon building integrated energy system considering electric-heat-V2G coupling

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年第1期151卷

作者： Xiong, Zuxun Shen, Xinwei Wu, Qiuwei Guo, Qinglai Sun, Hongbin Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Tsinghua Univ Inst Ocean Engn Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

The low-carbon building has been proposed to mitigate the climate change caused by environmental problems and realize carbon neutrality in urban areas. In addition, the integrated energy system (IES) has been developed to reduce renewable energy curtailment in the power distribution system and improve energy efficiency due to the independent operation of traditional energy systems. In this paper, we propose a stochastic planning method for low-carbon building IES, in which the Vehicle to Grid (V2G) is also considered to further increase the flexibility of low-carbon buildings. The proposed planning method optimizes the investment and operation costs, and CO2 emission of the building IES, to achieve the maximum benefit of the low-carbon building and help realize carbon neutrality. By considering the uncertainty of distributed renewable energy, multi-energy load fluctuation and the random behavior of EV users, a two-stage stochastic programming model is formulated with chance constraints, in which the heuristic moment matching scenario generation (HMMSG) and sample average approximation (SAA) methods are applied to deal with the uncertainties. In the case study, a real IES office building in Shanghai, where photovoltaic (PV), energy storage system (ESS), fuel cell (FC), EV, etc. are included as planning options, is used as the test system to verify the effectiveness of the proposed planning method, and the functions of the ESS and EV in IES are analyzed in detail in different operation scenarios. The case study results show that the proposed planning scheme can reduce the total cost and carbon emission significantly. (c) 2017 Elsevier Inc. All rights reserved.

关键词： Chance constraint Energy storage system Electric vehicle Low-carbon building integrated energy system stochastic programming

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A variant SDDP approach for periodic-review approximately optimal pricing of a slow-moving a item in a duopoly under price protection with end-of-life return and retail fixed markdown policy

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 212卷

作者： Yildiz, Baris Sutcu, Muhammed Abdullah Gul Univ Ind Engn Dept TR-38170 Kayseri Turkiye

In this paper, we examine a selling environment where a manufacturer-controlled retailer and an independent retailer sell a slow-moving A item. The manufacturer offers the independent retailer a price protection contract stipulating that the manufacturer reimburses the independent retailer in case of a reduction in the wholesale price. The price set by the independent retailer is assumed to be determined by Retail Fixed Markdown (RFM) policy. The manufacturer also offers the independent retailer a special discount rate for the replenishment orders and the retailers are assumed to follow (R, S) inventory replenishment policy. The manufacturer adopts a periodic-review pricing strategy and the mean demand observed by each retailer in a given period depends on the prices. We also take the customers choosing no-purchase option into account. We employ multinomial logit (MNL) models to forecast customers' preferences based on retail prices. The retailers' market shares are esti-mated by customized choice probability functions. We propose stochastic programming models to determine the manufacturer's pricing strategy. Then, we propose a variant stochastic Dual Dynamic programming (SDDP) algorithm to determine the manufacturer's approximately optimal pricing strategy by getting around three curses of dimensionality. Then, we move on to the observations on the impact of four critically important contractual parameters on the price, the market shares and the expected total net profits and finally discuss some possible approaches for the selection of the best compromise values of those contractual parameters.

关键词： Price protection End -of -life returns Price commitment policies Retail fixed markdown policy stochastic dual dynamic programming stochastic programming

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Planning of reliable targeted evacuation under the threat of disasters

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2023年第1期153卷

作者： Jiang, Zhoutong Shen, Shiyu Ouyang, Yanfeng Univ Illinois Urbana & Champaign Dept Civil & Environm Engn Urbana IL 61801 USA

In planning evacuation services against the threat of disasters, it is important to optimize pickup point location and rescue vehicle routing decisions while recognizing the risks of service disruptions. In this paper, a reliable location and routing model with backup service plans is proposed to plan targeted strategic evacuation, which minimizes the total expected cost for deployment of pickup stations, rescue vehicle routing, and evacuee assignments and exposure while the service is subject to probabilistic disruptions. We formulate the problem as a mixed-integer non-linear program, and develop two customized solution algorithms, one based on Lagrangian relaxation (LR) and the other based on meta-heuristic, to decompose and solve the problem. Numerical experiments with various parameters are conducted to not only demonstrate the applicability of the proposed model and effectiveness of the solution algorithms, but also to draw managerial insights. The impacts of demand distribution, facility cost, disruption probability, and evacuee exposure risk are assessed in a series of sensitivity analyses, so as to inform agencies how to carefully consider these factors while developing a reliable targeted evacuation service plan.

关键词： Location Routing Targeted evacuation stochastic programming Reliability

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Sustainable supply chain design under correlated uncertainty in energy and carbon prices

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JOURNAL OF CLEANER PRODUCTION 2023年第1期414卷

作者： Garcia-Castro, Florencia Lujan Ruiz-Femenia, Ruben Salcedo-Diaz, Raquel Caballero, Jose A. Univ Alicante Inst Chem Proc Engn Apartado Correos 99 Alicante 03080 Comunidad Valen Spain

This paper aims to provide an improvement in the modeling of supply chain designs by incorporating correlated uncertainty among multiple parameters, resulting in a more resilient design. A new methodology to generate forecasts for historically correlated time series, regardless of their underlying probability distributions, is presented and applied to generate scenarios for energy and carbon prices, which historically proved to be correlated. These scenarios are then used in a stochastic computation to obtain a three-echelon supply chain design in Europe maximizing the economic performance. The emissions were monetarized through the incorporation of the European Union cap-and-trade emissions trading system into the model. The social impact of the supply chain network is measured in terms of the direct, indirect and induced jobs it creates, which are proportional to the economic performance. By combining the developed methodology with data mining algorithms, a reduction in the number of required scenarios by more than 90% was achieved. The numerical case study moreover shows that the stochastic design ensures an average reduction of emissions by more than 3 ktons compared to the use of a deterministic approach. In comparison, the computation of a stochastic supply chain design without parameter correlation takes 5 times longer.

关键词： Correlated uncertainty Energy price Carbon price Supply chain stochastic programming

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Coordinated active and reactive power management for enhancing PV hosting capacity in distribution networks

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IET RENEWABLE POWER GENERATION 2023年第0期

作者： Golnazari, Rezvaneh Hasanzadeh, Saeed Heydarian-Forushani, Ehsan Kamwa, Innocent Qom Univ Technol Dept Elect & Comp Engn Qom Iran Laval Univ Fac Sci & Engn Dept Elect Engn & Comp Engn Quebec City PQ Canada

This paper proposes an operational planning model based on optimal active and reactive power control strategies to enhance solar photovoltaics (PV)' hosting capacity in distribution networks. Reactive power control is carried out through optimum static VAr compensators (SVCs) placement, while active power control is performed through flexible loads, particularly shiftable and interruptible loads. The first stage of the proposed two-stage stochastic model assigns decision-making regarding calculating PV hosting capacity at different nodes, in addition to the allocation and capacity of SVCs. In the second stage, the first stage decisions are assessed to ensure the power flow constraints under various uncertainties such as daily load and stochastic PV generation. The presented model is investigated through numerical analyses on modified IEEE 15-bus and IEEE 33-bus distribution systems considering different active-reactive strategy cases. While most previous works only rely on one type of active or reactive power control strategy, this study investigates the challenges of the respective application of active and reactive power control in various modes of fundamental practices. The obtained results prove the superiority of the proposed hybrid active-reactive control strategy for enhancing PV hosting capacity compared to respective active or reactive power controls.

关键词： energy management systems flexible AC transmission systems photovoltaic power systems reactive power stochastic programming

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Infectious waste management during a pandemic: A stochastic location-routing problem with chance-constrained time windows

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期177卷 109066页

作者： Hassanpour, Saeed Tasouji Ke, Ginger Y. Zhao, Jiahong Tulett, David M. Mem Univ Newfoundland Fac Business Adm St John NF A1B 3X5 Canada Guangdong Univ Technol Sch Civil & Transportat Engn Guangzhou 510006 Peoples R China

The COVID-19 pandemic has presented tremendous challenges to the world, one of which is the management of infectious waste generated by healthcare activities. Finding cost-efficient services with minimum threats to public health has become a top priority. The pandemic has induced extreme uncertainties, not only in the amount of generated waste, but also in the associated service times. With this in mind, the present study develops a mixed-integer linear programming (MILP) model for the location-routing problem with time windows (LRPTW). To handle the uncertainty in the amount of generated waste, three scenarios are defined respectively reflecting different severity levels of a pandemic. Furthermore, chance constraints are applied to deal with the variation of the service times at small generation nodes, and time windows at the transfer facilities. The complexity of the resulting mathematical model motivated the application of a branch-and-price (B&P) algorithm along with an -constraint technique. A case study of the situation of Wuhan, China, during the initial COVID-19 outbreak is employed to examine the performance and applicability of the proposed model. Our numerical tests indicate that the B&P algorithm outperforms CPLEX in the computational times by more than 83% in small-sized problem instances and reduces the gaps by at least 70% in large-scale ones. Through a comparison with the current and deterministic systems, our proposed stochastic system can timely adjust itself to fulfill nearly four times the demand of other systems in an extreme pandemic scenario, while maintaining a cost-efficient operation with no outbreak.

关键词： Infectious waste Location-routing problem Time windows stochastic programming Chance constrained programming Branch-and-price algorithm

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A mixed-integer quasi-convex optimization model for joint transmission network and wind power investment problem

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期217卷

作者： Alshamrani, Ahmad M. King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia

As one of the important renewable energy sources (RESs), the integration of wind energy into the electric grid is growing fast. This higher penetration level of wind power calls for requirements for reinforcing the existing transmission network to reduce wind power curtailment. In this context, this research focuses on developing a mathematical methodology for joint transmission network and wind power investment problem under a centralized approach. Unlike the existing models, where the objective function to be minimized is the overall cost, the objective function of this work is different. It is defined as the ratio of the total cost to the total wind power generation. The definition of this objective function allows the operator to minimize the total cost while maximizing the wind power output from wind farms. The convex AC power flow is utilized to model the power flow equations. The proposed investment model is mixed-integer quasi-convex programming (MIQCP) that is converted into a mixed-integer convex programming (MICP) problem. The numerical study indicates that the resulting MICP problem is computationally efficient, making it suitable for a realistic electric grid. In addition, it will promote the wind power output of wind farms.

关键词： Mixed -integer quasi -convex optimization Mixed -integer convex programming stochastic programming Transmission and wind investment

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Limited multi-stage stochastic programming for managing water supply systems

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ENVIRONMENTAL MODELLING & SOFTWARE 2013年 41卷 53-64页

作者： Housh, Mashor Ostfeld, Avi Shamir, Uri Technion Israel Inst Technol Fac Civil & Environm Engn IL-32000 Haifa Israel

Decision-making processes often involve uncertainty. A common approach for modeling uncertain scenario-based decision-making progressions is through multi-stage stochastic programming. The size of optimization problems derived from multi-stage stochastic programs is frequently too large to be addressed by a direct solution technique. This is due to the size of the optimization problems, which grows exponentially as the number of scenarios and stages increases. To cope up with this computational difficulty, solution schemes turn to decomposition methods for defining smaller and easier to solve equivalent sub-problems, or through using scenario-reduction techniques. In our study a new methodology is proposed, titled Limited Multi-stage stochastic programming (LMSP), in which the number of decision variables at each stage remains constant and thus the total number of decision variables increases only linearly as the number of scenarios and stages grows. The LMSP employs a decision-clustering framework, which utilizes the optimal decisions obtained by solving a set of deterministic optimization problems to identify decision nodes, which have similar decisions. These nodes are clustered into a preselected number of clusters, where decisions are made for each cluster instead of for each individual decision node. The methodology is demonstrated on a multi-stage water supply system operation problem, which is optimized for flow and salinity decisions. LMSP performance is compared to that of classical multi-stage stochastic programming (MSP) method. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Water supply systems Management Optimization stochastic programming Uncertainty

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A Fast Penalty-Based Gauss-Seidel Method for stochastic Unit Commitment with Uncertain Load and Wind Generation

IEEE Open Access Journal of Power and Energy

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IEEE Open Access Journal of Power and Energy 2021年 8卷 211-222页

作者： Palani, Ananth M. Wu, Hongyu Morcos, Medhat M. Department of Electrical and Computer Engineering Kansas State University ManhattanKS United States

stochastic network-constrained unit commitment (S-NCUC) can be used to manage the uncertainty of an increasing penetration level of renewable energy effectively. However, the drawbacks of the progressive hedging algorithm (PHA) based solutions are that they are not provably convergent due to the non-convexity of S-NCUC. Additionally, the solution obtained is usually fractional-valued (non-binary) and therefore not readily implementable. In this paper, we apply a novel Penalty-Based Gauss-Seidel (PBGS) algorithm in solving S-NCUC using an exact augmented Lagrangian representation with proven convergence. To improve the computational efficiency of the PBGS, we further propose an accelerating technique with rigorous proof to skip solving scenarios when certain conditions are met during iterations. We numerically validate the proposed algorithms on the IEEE 118-bus system and a practically-sized ERCOT-like system, both with variable wind generation. Numerical results demonstrate the efficacy of the proposed algorithms in yielding high-quality S-NCUC solutions. The merits of the proposed algorithms are also revealed in comparison with PHA and extensive-form (EF) based mixed-integer programming solutions. © 2020 IEEE.

关键词： stochastic programming

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Coordinated preparation and recovery of a post-disaster Multi-energy distribution system considering thermal inertia and diverse uncertainties

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APPLIED ENERGY 2023年第1期336卷

作者： Li, Zhengmao Xu, Yan Wang, Peng Xiao, Gaoxi Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

Low-probability but high-impact extreme events, such as floods, earthquakes, hurricanes, etc., could threaten the security of a multi-energy system, especially on the distribution level, and cause severe energy supply outages. In this paper, a coordinated restoration method is presented for the renewable energy-integrated multi-energy distribution system (MDS) with several coupling points to coordinate the preparation and load recovery stages after the extreme event. First, the MDS restoration is comprehensively modeled with coupled power and thermal network constraints. Especially, the thermal inertia, which is in the form of pipe storage and thermal demand response of smart buildings to serve as a buffer when the source fails, is fully utilized to reduce the energy supply cost after disasters. Secondly, both preparation and load recovery stage measures are employed to facilitate efficient and reliable system restoration. Furthermore, multiple uncertainties from the renewable generation and power demands in the MDS restoration are dealt with via a risk-averse two-stage stochastic programming approach. Finally, simulation results validate the effectiveness of our method and its superiority over the traditional restoration methods.

关键词： Multi -energy distribution systems (MDSs) Thermal inertia stochastic programming Coordinated restoration

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