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Feasibility of Behind-the-Meter Battery Storage in Wind Farms Operating on Small Islands

BATTERIES-BASEL 2022年第12期8卷

作者： Dratsas, Pantelis A. Psarros, Georgios N. Papathanassiou, Stavros A. NTUA Sch Elect & Comp Engn Zografou Campus 9Iroon Polytech Str Athens 15780 Greece

This paper investigates the anticipated benefits from the introduction of a battery energy storage system (BESS) behind-the-meter (BtM) of a wind farm (WF) located in a small non-interconnected island (NII) system. Contrary to the standard storage deployment applications for NII, where storage is either installed in front of the meter as a system asset or integrated into a virtual power plant with renewable energy sources, the BESS of this paper is utilized to manage the power injection constraints imposed on the WF, aiming to minimize wind energy curtailments and improve WF's yield. A mixed integer linear programming generation scheduling model is used to simulate the operation of the system and determine the permissible wind energy absorption margin. Then, a self-dispatch algorithm is employed for the operation of the WF-BESS facility, using the BESS to manage excess wind generation that cannot be directly delivered to the grid. Additionally, the contribution of BESS to the capacity adequacy of the NII system is investigated using a Monte Carlo-based probabilistic model, amended appropriately to incorporate storage. Finally, an economic feasibility analysis is carried out, considering the possible revenue streams. By examining several BESS configurations, it has been shown that BtM BESS reduces energy curtailments and contributes substantially to resource adequacy as its energy capacity increases. However, the investment feasibility is only ensured if the capacity value of the BtM storage is properly monetized or additional dependability of wind production is claimed on the ground that the inherent intermittency of the wind production is mitigated owing to storage.

关键词： battery energy storage stations wind energy management principle autonomous power systems non-interconnected island renewable penetration unit commitment capacity adequacy economic assessment mixed integer linear programming

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On a Pickup to Delivery Drone Routing Problem: Models and algorithms

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 172卷

作者： Gomez-Lagos, Javier Rojas-Espinoza, Benjamin Candia-Vejar, Alfredo Univ Talca Fac Engn Doctoral Program Engn Syst Curico 3340000 Chile Univ Talca Fac Engn Sch Civil Ind Engn Curico 3340000 Chile Univ Talca Fac Engn Dept Ind Engn Curico 3340000 Chile Univ Finis Terrae Fac Engn Providencia 7500000 Chile

A new variant of the Pickup and Delivery Routing problem is presented. Given a set of customers, facilities, a depot, and a homogeneous fleet of drones, the Pickup to Delivery Drone Routing Problem (PDDRP) aims to find a drone scheduling such that a drone serves the customer's order from a set of available facilities. Each drone starts in the depot, flies to pickup the customer's order in a facility, and continues its flight to deliver the parcel to a customer. Then, the drone begins another service, and once its last service is completed, it returns to the depot. The objective is to minimize the makespan associated with the drone fleet. The layer of facilities forcing drones to visit one of them to pickup the parcel makes the problem different from traditional pickup and delivery routing problems. Three mixed-linear programming models are presented to obtain optimal solutions for the problem. The first model is related to the multiple Traveling Salesman Problem (m-TSP), the second is associated with the Parallel Machine Scheduling Problem (PMS), and the third was developed specifically for the new *** the high computational complexity of the PDDRP, a Greedy Randomized Adaptive Search Procedure (GRASP) was designed to find near-optimal solutions when exact approaches cannot achieve (near) optimal solutions. Computational experiments show that a commercial solver could solve only small problem instances. GRASP can find reasonable solutions in a short time when medium and large instance sizes need to be solved. Finally, is shown that some routing problems for delivery, allowing truck-drone collaboration, could be formulated as an extension of PMS.

关键词： Pickup and Delivery Problem Network optimization mixed integer linear programming Drone Routing Problem GRASP

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Energy-carbon neutrality optimization in production scheduling via solar net metering

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JOURNAL OF CLEANER PRODUCTION 2022年 380卷

作者： Trevino-Martinez, Samuel Sawhney, Rapinder Sims, Charles Univ Tennessee Dept Ind & Syst Engn Knoxville TN 37996 USA Univ Tennessee Howard H Baker Jr Ctr Publ Policy Knoxville TN 37996 USA Univ Tennessee Dept Econ Knoxville TN 37996 USA

Energy is an indispensable resource in manufacturing, and it carries both financial and environmental pressures for enterprises. Considering this importance, this research presents an optimization framework to allow energy -intensive industries to move towards energy-carbon neutrality via production scheduling and renewable energy. This involves integrating renewable energy system decisions into production scheduling, incorporating solar photovoltaic modules and energy storage systems, and leveraging energy policies, such as net metering programs, that provide credits for net excess generation. The proposed optimization framework is built under a multi-objective mixed integer linear programming formulation, using time-indexed variables. The model is showcased within the context of a case study involving an energy-intensive plastic injection molding process. Furthermore, a robust optimization approach using Monte Carlo simulations is presented for the construction of confidence intervals used in estimating the lifespan of solar installations. Results demonstrate that manufacturing systems that enable their solar photovoltaic system with an energy storage dispatching capability are more cost competitive to lower net metering compensation rates than those that do not.

关键词： Production scheduling mixed integer linear programming Solar energy Energy storage Net metering

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Risk Management of Stochastic Power Generation Outages by Load Curtailment Program

Risk Management of Stochastic Power Generation Outages by Lo...

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Power Engineering Application (ICPEA), IEEE International Conference in

作者： Khalid Alqunun Electrical Engineering Department College of Engineering University of Hail Hail Saudi Arabia

The challenges of random generation outages are one of the major concerns in modern power systems. The unexpected power generation outages lead to a severe damage in one or more of the power components. This damage requires immediate scheduling of the available generation units to minimize the risks and increase the system security. This paper presents an optimization technique of load curtailment to be immediately applied in case of power generation outages. The optimization method involves a prompt response of the generation outages through predefined variables and constraints. The objective function of the proposed technique takes into account the power dispatch cost and the compensation cost, which will be paid directly to the end-user customers as a reward for their contribution of curtailing some of their loads. The amount of the power dispatch and the load curtailment are precisely evaluated using mixed-integer linear programming, where all the possible variables are tested before reaching a feasible solution. Example studies are presented to explore the performance of the load curtailment and to schedule all the energy sources during stochastic scenarios of the generation outages.

关键词： Schedules Costs Energy resources Stochastic processes Software Power systems mixed integer linear programming

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SALEM: Service Fairness in Wireless Mesh Environments

SALEM: Service Fairness in Wireless Mesh Environments

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Henrique Silva Noé Godinho Bruno Sousa DEI CISUC University of Coimbra Coimbra Portugal

The utilisation of resources from a fairness perspective has been an extensively researched topic, either to enhance the quality of services or to allow spectrum sharing, among others. One method to define forwarding routes is through flow algorithms, which aim to find optimal paths between nodes where capacity constraints are of interest. SALEM is an approach to enable fairness in services competing for resources in wireless mesh environments. SALEM implements a multicommodity flow heuristic, based on a mixed integer linear programming (MILP) formulation, to consider fairness in reliability, delay and energy objectives and is constrained by bandwidth and link usage criteria. SALEM is implemented in the ONOS controller to enable a fair utilisation of links in a topology replicating a real-world scenario. Within a time complexity that is directly related to the topology, the number of users and available services, the results of SALEM demonstrate its feasibility to run in production-ready networks with minimal overhead.

关键词： Wireless communication Network topology Quality of service Bandwidth Topology Delays mixed integer linear programming

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Optimal Operation of Energy Hubs in an Auction-Based Peer-To-Peer Energy Trading Framework

Optimal Operation of Energy Hubs in an Auction-Based Peer-To...

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IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East)

作者： Kirkland Rowe Geev Mokryani Kavian Cooke F. Campean Therese Chambers Faculty of Engineering and Informatics University of Bradford UK Faculty of Engineering and Computing University of Technology Jamaica

This paper presents a bi-level stochastic mixed-integer linear programming (MILP) model for the optimal operation of the energy hubs participating in the peer-to-peer (P2P) energy market in an integrated electricity-gas energy system. The upper-level problem represents the strategic behaviour of the Distribution Network Operator (DNO) to maximize their profit. The lower level corresponds to the P2P market clearing problem, where energy hubs operate to minimize cost and strategically exchange energy with each other and the distribution network. The model is transformed into a single-level model using the Karush-Kuhn-Tucker (KKT) optimality conditions. The proposed model is validated on an integrated 16-bus 33 kV UK generic distribution system (UKGDS) and a 6-node natural gas system, which is used to demonstrate the model’s effectiveness.

关键词： Costs Stochastic processes Distribution networks Peer-to-peer computing Smart grids mixed integer linear programming Natural gas

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Energy storage integration with run of river power plants to mitigate operational environmental constraints: Case study of Sweden

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JOURNAL OF ENERGY STORAGE 2022年 56卷

作者： Sridhar, Araavind Baskar, Ashish Guhan Thakur, Jagruti KTH Royal Inst Technol Dept Energy Technol Div Energy Syst Stockholm Sweden LUT Univ Sch Energy Syst Lappeenranta Finland Polytech Univ Milan Dept Elect Informat & Bioengn Milan Italy

Increasing environmental restrictions, create stringent regulations for the use and management of rivers and water bodies, thereby jeopardizing the operational flexibility of hydropower plants. The national plan for new environmental regulations in Sweden is expected to be implemented in 2025. Hence, it is imperative for the hydropower producers to consider alternatives which can overcome the environmental restrictions for providing flexibility. In this paper, a mixed integer linear programming (MILP) based algorithm for the short-term regulation of hydropower plants with energy storage is proposed. There are two stages of the algorithm: Future electricity market prices model using machine learning techniques, and the combined hydropower plant and battery system (CHBS)'s operation model using MILP. The designed algorithm is used to analyze the techno-economic feasibility of the operation of three hydropower plants Skattungbyn, Unnan and Hansjo, located in lower Orealven river in Sweden. Three different electricity market scenarios were developed based on Swedish nuclear energy targets for 2040. The forecasted market prices act as inputs for the designed optimization algorithm. The algorithm optimizes the operation of hydropower plants with an objective to maximize revenue through harnessing flexibility enabled by storage. It is observed from the results that with the current battery costs (approximate to 0.36 million euro/MWh), the CHBS for short-term regulation is not profitable with NPV ranging from -1 to -4 million euro and the cost of battery needs to be less than 50 000 euro/MWh to make it economically feasible.

关键词： Short term regulation Hydropower plant Battery energy storage system mixed integer linear programming Machine learning

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Robust liner ship routing and scheduling schemes under uncertain weather and ocean conditions

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2022年 137卷

作者： Li, Mingyu Xie, Chi Li, Xinghua Karoonsoontawong, Ampol Ge, Ying-En Tongji Univ Key Lab Rd & Traff Engn Minist Educ 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn 1239 Siping Rd Shanghai 200092 Peoples R China Tongji Univ Sch Transportat Engn Shanghai Peoples R China Tongji Univ Natl Inst Innovat & Dev Shanghai Peoples R China King Mongkuts Univ Technol Thonburi Dept Civil Engn 126 Pracha Utid Rd Bangkok 10140 Thailand Changan Univ Coll Transportat Engn Middle Sect Naner Huan Rd Xian 710064 Shaanxi Peoples R China

Since sailing along the Northern Sea Route (NSR) through the Arctic Ocean comes into reality, commercial shipping developments including opening new liner services along this route have been put on the agenda. However, uncertainties within weather and ocean conditions in this region, especially during temperature-varying seasons, may cause failure to on-time arrivals, resulting unignorable or even unaffordable monetary losses. As a result, these uncertainties will inevitably affect cargo shippers' willingness of choosing this being-explored route, even if on average its shipping time is much shorter than that of the traditional Asia-Europe shipping lines. In this context, the present paper describes a liner ship routing and scheduling problem considering schedule-sensitive demand and late-arrival penalty for operating incoming NSR shipping lines in the future. By using two types of uncertainty sets, bounded and budget-bounded uncertainty sets, we construct and solve two robust counterparts of this problem. The deterministic model and bounded robust model can be seen as special cases of the budget-bounded robust model when the uncertainty budget is set to null and full, respectively. A multifaceted case study of planning liner shipping routes and schedules along the NSR is conducted to validate the efficacy and efficiency of the proposed models and identify the effects of uncertainty bounds and budgets on the solution performance. The following facts and conclusions are revealed from the optimization results of the case study: The budget-bounded robust model functions the best in the worst condition;low revenue rate and high demand sensitivity are the major factors for excluding a candidate port from the route;the order of a visited port in the route decides the weight of its uncertainty budget on the entire shipping line;setting the uncertainty budget in a decelerative way will enhance the scheduling robustness of solutions.

关键词： Liner ship routing and scheduling Uncertain weather and ocean conditions Uncertainty bound Uncertainty budget Robust optimization mixed integer linear programming

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An internet of things-enabled decision support system for freight transportation: A case study of Indian special freight transport operator

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 172卷

作者： Kumar, Gaurav Kumar, Akhilesh Indian Inst Technol Dept Ind & Syst Engn Kharagpur 721302 W Bengal India Indian Inst Management Raipur Operat & Quantitat Methods Abhanpur 493661 Chhattisgarh India

Railway organizations are spending on digitizing rail freight transportation for better resource utilization and revenue generation. In 2010, the Indian Railway (IR) proposed a "Special Freight Train Operator" scheme to build an IR stake in freight transportation in high-capacity rakes. IR allowed freight train operators (FTOs) and manufacturers to invest in wagons and take benefit of the largest rail network to move selected goods to their end customers. In the absence of an optimized support system, FTOs are often confronted with the decision of rolling stock (rake) scheduling, rake assignment, and rescheduling on a real-time basis. Today, most of the rakes have GPS devices. Thus, railway train management systems readily provide data to create a dynamic optimization model for FTOs using IoT-based real-time information. We first formulate a mixed-integer linear programming (MILP) model that aims at revenue maximization incorporating optimal rake assignment and optimal rake scheduling. Furthermore, to incorporate real-time GPS data, we propose an "IoT enabled real-time rake schedular-reschedular heuristic" for rescheduling. The computational investigations exhibit that the proposed heuristic performs effectively both in terms of run time and the quality of the solution. These models will help FTOs smoothly run day-to-day businesses and lead to better revenue realization in the calendar month by increasing the number of trips.

关键词： Internet of things (IoT) Mathematical optimization mixed integer linear programming Heuristics Real-time Rake rescheduling Special Freight Train Operator Indian Railways

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Warm-starting constraint generation for mixed-integer optimization: A Machine Learning approach

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KNOWLEDGE-BASED SYSTEMS 2022年 253卷

作者： Jimenez-Cordero, Asuncion Miguel Morales, Juan Pineda, Salvador Univ Malaga OASYS Grp Ada Byron Res Bldg Malaga 29010 Spain Univ Malaga Dept Math Anal Stat & Operat Res & Appl Math Area Ada Byron Res Bldg Malaga 29010 Spain

mixed integer linear Programs (MILP) are well known to be NP-hard (Non-deterministic Polynomial -time hard) problems in general. Even though pure optimization-based methods, such as constraint generation, are guaranteed to provide an optimal solution if enough time is given, their use in online applications remains a great challenge due to their usual excessive time requirements. To alleviate their computational burden, some machine learning techniques (ML) have been proposed in the literature, using the information provided by previously solved MILP instances. Unfortunately, these techniques report a non-negligible percentage of infeasible or suboptimal instances. By linking mathematical optimization and machine learning, this paper proposes a novel approach that speeds up the traditional constraint generation method, preserving feasibility and optimality guarantees. In particular, we first identify offline the so-called invariant constraint set of past MILP instances. We then train (also offline) a machine learning method to learn an invariant constraint set as a function of the problem parameters of each instance. Next, we predict online an invariant constraint set of the new unseen MILP application and use it to initialize the constraint generation method. This warm-started strategy significantly reduces the number of iterations to reach optimality, and therefore, the computational burden to solve online each MILP problem is significantly reduced. Very importantly, all the feasibility and optimality theoretical guarantees of the traditional constraint generation method are inherited by our proposed methodology. The computational performance of the proposed approach is quantified through synthetic and real-life MILP applications. (C) 2022 The Author(s). Published by Elsevier B.V.

关键词： mixed integer linear programming Machine learning Constraint generation Warm-start Feasibility and optimality guarantees

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