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Unit commitment for multi-terminal VSC-connected AC systems including BESS facilities with energy time-shifting strategy

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 134卷 107367-107367页

作者： Guzman-Feria, Juan S. Castro, Luis M. Tovar-Hernandez, J. H. Gonzalez-Cabrera, N. Gutierrez-Alcaraz, G. Natl Autonomous Univ Mexico UNAM Posgrad Ingn Elect Mexico City DF Mexico Natl Autonomous Univ Mexico UNAM Dept Elect Energy Mexico City DF Mexico Inst Tecnol Morelia ITM Res & Postgrad Program Elect Engn Mexico City DF Mexico

This paper proposes a novel modelling framework for Unit Commitment (UC) studies in multi-terminal VSCconnected AC grids. Battery Energy Storage Systems (BESS) are also considered with an energy time-shifting strategy whose charge and discharge modes are defined within the 24-hour planning horizon to promote a high competition level of energy trading in the power grid. The entire transmission network is formulated by shift factors and power losses of AC/DC branches and VSC units are included by piecewise linear functions. Thus, this novel UC approach retains a mixed-integer linear programming model with a high modelling versatility for arbitrary hybrid power grids accommodating any number of VSC stations and BESS units. The method described in this paper enables the optimal generation scheduling combined with the charge and discharge BESS modes. Its applicability is confirmed using two case studies, one featuring a four-terminal VSCHVDC system with two BESS, and another characterised by seven VSC-connected AC systems with five BESS. To validate the proposed method, results are compared against those furnished by the classic UC representing the network through nodal equations. It is concluded that both methods favourably agree with each other as the errors are inferior to 2%. The usefulness of the proposed method to the real-time operation of AC/DC grids with BESS facilities is valuable.

关键词： AC/DC networks Battery energy storage systems mixed-integer linear programming Shift factors Unit commitment VSC stations

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Evaluation for Maximum Allowable Capacity of Renewable Energy Source Considering AC System Strength Measures

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2022年第2期13卷 1123-1134页

作者： Kim, Yong-Kyu Lee, Gyu-Sub Yoon, Jong-Su Moon, Seung-Il Seoul Natl Univ Sch Elect & Comp Engn Seoul 08826 South Korea Korea Inst Energy Technol KENTECH Sch Energy Engn Naju 58330 South Korea Korea Elect Power Res Inst KEPRI Daejeon 34056 South Korea

In this paper, a method to calculate the maximum allowable capacity (MAC) of renewable energy sources (RESs) in terms of AC system strength is presented;the equivalent short-circuit ratio (ESCR) and the newly proposed reactive power considered equivalent short-circuit ratio (QESCR) are considered. Unlike conventional measures, the reactive power afforded by RESs is considered when calculating the QESCR;the MAC of RESs increases because their reactive power outputs stabilize transmission networks. Based on the AC strength measures, an optimization problem to evaluate the MAC of RESs and determine their optimal locations is developed. The optimization problem is formulated in mixed-integer linear programming (MILP). To validate the effectiveness of the proposed method, it is applied to two test systems: the IEEE RTS-79 test system and a real-world Korean transmission network. The proposed method allows facile calculation of the MAC of RESs, which ensures that the transmission network remains stable.

关键词： Reactive power Power measurement Power system stability Optimization Energy measurement Stability analysis Reactive power control Maximum allowable capacity renewable energy source strength measure power system stability mixed-integer linear programming

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Optimal scheduling of a multiproduct batch chemical plant with preemptive changeover tasks

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COMPUTERS & CHEMICAL ENGINEERING 2022年 162卷

作者： Castro, Pedro M. Univ Lisbon Fac Ciencias Ctr Matemat Aplicacoes Fundamentais & Invest Oper P-1749016 Lisbon Portugal

Most short-term scheduling formulations in Process Systems Engineering assume a time horizon with-out interruptions. However, local working patterns may prevent execution of activities at nights or dur -ing weekends. One such problem is reported in Egli and Rippin (1986) and deals with a multiproduct batch chemical plant featuring recipes with stable and unstable intermediates, shared resources with limited availability of electricity and steam, non-instantaneous transfer times, and the possibility of inter-rupting changeover tasks when encountering a pre-defined break period. The objective function balances sequence-dependent changeovers with product holding costs. In this paper, we model these features with a Resource-Task Network (RTN) formulation based on a discrete-time representation. After solving the original problem to optimality for savings of 5%, we evaluate the alternative of forcing complete exe-cution of changeover tasks before or after a break, for a couple more scenarios. The results show that preemption is always beneficial, with improvements ranging from 0.57% to 36.4%.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Preemptive scheduling mixed-integer linear programming Multipurpose equipment Utility demand constraints Buffer stock

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Optimal schedule generation for single-channel crude transfer using a multi-model approach

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COMPUTERS & CHEMICAL ENGINEERING 2022年 160卷 107732-107732页

作者： Paranjape, Aditya A. Baranwal, Mayank Wagle, Satyavrat Lotti, Rushi Majumder, Sushanta Bulliere, Anne-Laure Imperial Coll London Dept Aeronaut London England Tata Consultancy Serv Ltd Tata Consultancy Serv Res Mumbai Maharashtra India Tata Consultancy Serv Ltd Engn & Ind Serv Mumbai Maharashtra India TotalEnergies Courbevoie France

This paper presents three techniques for scheduling for crude transfer between a port and a refinery on a single pipeline in the presence of stringent flow constraints. The three techniques are based on metaheuristics (business rules), mixed integer linear programming and reinforcement learning. In addition to comparing the three techniques, we also demonstrate how knowledge gleaned from one technique (in our case, the metaheuristics) can be used to design an algorithm based on another technique (in our case, reinforcement learning). A novel feature of our approach to reinforcement learning, in particular, is the use of low-fidelity, reduced-order simulators for training the scheduler and supporting it with a post-processor based either on business rules or on integer programming for ensuring compatibility with the constraints. The set of constraints considered here includes temporal restrictions on the use of the pipeline, flow constraints in the tanks that feed the column distillation units in the refinery, and the need to ensure a certain minimum residence time for crude in a given tank for dewatering. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Crude transfer scheduling Reinforcement learning Optimal control mixed-integer linear programming

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Charging station optimization for balanced electric car sharing

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DISCRETE APPLIED MATHEMATICS 2022年 308卷 187-197页

作者： Deza, Antoine Huang, Kai Metel, Michael R. McMaster Univ Dept Comp & Software Adv Optimizat Lab Hamilton ON Canada McMaster Univ DeGroote Sch Business Hamilton ON Canada RIKEN AIP Continuous Optimizat Team Tokyo Japan

This work focuses on finding optimal locations for charging stations for one-way electric car sharing programs. The relocation of vehicles by a service staff is generally required in vehicle sharing programs in order to correct imbalances in the network. We seek to limit the need for vehicle relocation by strategically locating charging stations given estimates of traffic flow. A mixed-integer linear programming formulation is presented with a large number of potential charging station locations. A column generation approach is used which finds an optimal set of locations for the continuous relaxation of our problem. Results of a numerical experiment using real traffic and geographic information system location data show that our formulation significantly increases the balanced flow across the network, while our column generation technique was found to produce a superior solution in much shorter computation time compared to solving the original formulation with all possible station locations. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Electric vehicle One-way car sharing Column generation mixed-integer linear programming

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Real-time order picking planning framework for warehouses and distribution centres

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第18期60卷 5468-5487页

作者： Dauod, Husam Won, Daehan SUNY Binghamton Dept Syst Sci & Ind Engn Binghamton NY 13902 USA

This paper proposes a dynamic-order picking (DOP) framework where batches and picker routes are continuously modified in response to urgent orders arrival and disruption events. In the proposed framework, we consider conflicts between pickers in narrow aisles to ensure a smooth replanning function. For this purpose, we first formulate a static joint-order batching and sequencing model (J-OBS) that takes into account congestion time. Then, we propose a replanning model (D-J-OBS) that utilises real-time input from multiple information systems. As a solution approach and due to the NP-hardness of the problem, we use a tabu search algorithm to solve practical sizes of the static and the dynamic problems. The results indicate that the DOP framework generates significant tardiness savings compared to online-order picking, especially for systems with high degree of dynamism.

关键词： Logistics dynamic-order picking joint-order batching and sequencing mixed-integer linear programming tabu search

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Continuous Piecewise linear Approximation of Plant-Based Hydro Production Function for Generation Scheduling Problems

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ENERGIES 2022年第5期15卷 1699页

作者： Abreu, David Lucas dos Santos Finardi, Erlon Cristian Univ Fed Santa Catarina Elect Energy Syst Planning Lab LabPlan BR-88040900 Florianopolis SC Brazil Inst Syst & Comp Engn Res & Dev Brazil INESC P&D BR-11055300 Santos SP Brazil

An essential challenge in generation scheduling (GS) problems of hydrothermal power systems is the inclusion of adequate modeling of the hydroelectric production function (HPF). The HPF is a nonlinear and nonconvex function that depends on the head and turbined outflow. Although the hydropower plants have multiple generating units (GUs), due to a series of complexities, the most attractive modeling practice is to represent one HPF per plant, i.e., a single function is built for representing the plant generation instead of the generation of each GU. Furthermore, due to the computation time constraints and representation of nonlinearities, the HPF must be given by a piecewise linear (PWL) model. This paper presented some continuous PWL models to include the HPF per plant in GS problems of hydrothermal systems. Depending on the type of application, the framework allows a choice between the concave PWL for HPF modeled with one or two variables and the nonconvex (more accurate) PWL for HPF dependent only on the turbined outflow. Basically, in both PWL models, offline, mixed-integer linear (or quadratic) programming techniques are used with an optimized pre-selection of the original HPF dataset obtained through the Ramer-Douglas-Peucker algorithm. As a highlight, the framework allows the control of the number of hyperplanes and, consequently, the number of variables and constraints of the PWL model. To this end, we offer two possibilities: (i) minimizing the error for a fixed number of hyperplanes, or (ii) minimizing the number of hyperplanes for a given error. We assessed the performance of the proposed framework using data from two large hydropower plants of the Brazilian system. The first has 3568 MW distributed in 50 Bulb-type GUs and operates as a run-of-river hydro plant. In turn, the second, which can vary the reservoir volume by up to 1000 hm(3), possesses 1140 MW distributed in three Francis-type units. The results showed a variation from 0.040% to 1.583% in term

关键词： hydro production function piecewise linear model mixed-integer linear programming

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The Two Echelon Open Vehicle Routing Problem: Optimization of Crowdshipping Based Parcel Delivery

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KSCE JOURNAL OF CIVIL ENGINEERING 2022年第9期26卷 4073-4085页

作者： Wu, Xue Hu, Dawei Ma, Bingshan Jiang, Ruisen Changan Univ Sch Transportat Engn Xian 710064 Peoples R China Changan Univ Sch Automobile Xian 710064 Peoples R China

Crowdshipping is a revolutionary concept of the sharing economy. In this study, two carriers are used to perform the following expedition: the truck starts from the depot to complete part of the deliveries, shares part of the load with the crowdshipper at the relay point, and the private driver selected as the crowdshipper continues from that point onward. This study proposes the two-echelon open vehicle routing problem with crowdshipping (2EOVRP-CS) and formulates a mathematical model to determine the crowdshipper, parcel relay location, truck route, and crowdsource route. A tangible nested genetic algorithm (NGA) is proposed, and its efficiency is demonstrated by comparison with CPLEX and genetic algorithm (GA). A real case study is investigated in Xi'an city to test the applicability of the proposed model. The results show that using crowdshipping instead of truck delivery alone can save approximately 14% of the total cost and 26% of truck vehicle miles traveled (VMT). Moreover, several sensitivity analyses are performed. The results show that crowdshipping is sensitive to the detour limit and the time value of carriers. For the detour limit, after the acceptable detour distance increases by 8%, the total cost can be reduced by up to 5.94%.

关键词： Sharing economy Crowdshipping Two-echelon open vehicle routing problem mixed-integer linear programming Nested genetic algorithm

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Extensions for Benders cuts and new valid inequalities for solving the European day-ahead electricity market clearing problem efficiently

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第2期300卷 713-726页

作者： Ceyhan, Gokhan Koksalan, Murat Lokman, Banu Middle East Tech Univ Dept Ind Engn TR-06800 Ankara Turkey Univ Michigan Ross Sch Business 701 Tappan Ave Ann Arbor MI 48109 USA Univ Portsmouth Ctr Operat Res & Logist Dept Operat & Syst Management Portsmouth PO1 3DE Hants England

We study the day-ahead electricity market clearing problem under the prevailing market design in the European electricity markets. We revisit the Benders decomposition algorithm that has been used to solve this problem. We develop new valid inequalities that substantially improve the performance of the algorithm. We generate instances that mimic the characteristics of past bids of the Turkish day-ahead electricity market and conduct experiments. We use two leading mixed-integer programming solvers, IBM ILOG Cplex and Gurobi, in order to assess the impact of employed solver on the algorithm performance. We compare the performances of our algorithm, the primal-dual algorithm, and the Benders decomposition algorithm using the existing cuts from the literature. The extensive experiments we conduct demonstrate that the price-based cuts we develop improve the performance of the Benders decomposition algorithm and outperform the primal-dual algorithm. (C) 2021 Elsevier B.V. All rights reserved.

关键词： OR in energy Day-ahead electricity market clearing problem mixed-integer linear programming Benders decomposition

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The multi-stage framework for optimal sizing and operation of hybrid electrical-thermal energy storage system

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ENERGY 2022年第0期245卷 123248-123248页

作者： He, Yi Guo, Su Zhou, Jianxu Song, Guotao Kurban, Aynur Wang, Haowei Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210024 Peoples R China Hohai Univ Coll Energy & Elect Engn Nanjing 211000 Peoples R China

In order to mitigate the intermittency of renewable energy, the paper proposes a hybrid electrical thermal energy storage system, which complementarily utilizes the cost-effective two-tank direct molten salt thermal energy storage system and the flexible lead-acid battery. A multi-stage framework is further proposed for optimal sizing and operation of the hybrid energy storage system. First, the renewable energy capacity is optimized considering the minimization of total net load. Then, the typical net load profiles are selected by K-means clustering algorithm. Finally, a bi-level optimization model considers the minimization of levelized cost of storage (LCOS) and power deviation to optimize the sizing and operation strategy of the hybrid energy storage system, and the optimization problem is solved by meta-heuristic algorithm and mixed-integer linear programming. The case studies show that: (1) the hybrid energy storage system is more reliable than single thermal energy storage and more cost-effective than single battery;(2) the multi-stage framework outperforms the commonly-used rule-based operation strategy;(3) demand response strategy can effectively reduce the investment cost of the proposed system. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Hybrid energy storage system Sizing and operation co-optimization Levelized cost of storage Particle swarm optimization mixed-integer linear programming Demand response

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