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Simultaneous disaggregation of multiple appliances based on non-intrusive load monitoring

ELECTRIC POWER SYSTEMS RESEARCH 2021年 193卷 106887-106887页

作者： Hua, Dong Huang, Fanqi Wang, Longjun Chen, Wutao South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China

Non-intrusive load monitoring (NILM) can achieve the disaggregation of power consumption through electricity data at the power entrance without changing the existing circuit structure, which is helpful for saving energy, reducing emission, and improving the utilization of electric energy. This paper studies event-based NILM, which can disaggregate multiple appliances that are switched simultaneously. That is a mixed linear integer programming (MILP) model, where the 0-1 indicates whether the appliances were switched in the event process. And the 0-1 variable constraints of the power feature vectors during the event are constructed. The proposed MILP runs only when events occur, which can reduce the computational complexity. In event detection, non-dominated sorting genetic algorithm II is used to select the parameters of cumulative sum (CUSUM), such as threshold and the length of sliding windows, which realizes the tradeoff between precision and recall. The case studies demonstrate that the improved CUSUM has a precision of 92% in event detection;the proposed event-based NILM approach on the REDD database and laboratory data achieves an accuracy of over 90% on power draws;the appliance with long switching process, such as the inverter air conditioner, can also be monitored by the sequential combination.

关键词： Non-intrusive load monitoring Simultaneous disaggregation mixed-integer linear programming Event detection Cumulative sum Smart metering

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MANGO: A novel optimization model for the long-term, multi-stage planning of decentralized multi-energy systems

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APPLIED ENERGY 2021年 288卷 116585-116585页

作者： Mavromatidis, Georgios Petkov, Ivalin Swiss Fed Inst Technol Grp Sustainabil & Technol CH-8092 Zurich Switzerland

This study presents MANGO (Multi-stAge eNerGy Optimization), a novel optimization model that incorporates a multi-year planning horizon, along with flexible, multi-stage investment strategies for the effective, longterm design of decentralized multi-energy systems (D-MES). By considering the dynamic surrounding energy and techno-economic landscape that evolves over time, MANGO harnesses the strategic value of investment flexibility and can optimally phase D-MES investments in order to benefit, for instance, from projected future reduced technology costs and technical improvements. To achieve this, the model considers the most relevant dynamic aspects, such as year-to-year variations in energy demands, changing energy carrier and technology prices, technical improvements and equipment degradation. MANGO is also capable of optimizing the design of complex configurations composed of multiple, interconnected D-MES installed at different locations. Finally, the model?s formulation also addresses end-of-horizon effects that can distort solutions in multi-stage energy system models. Besides presenting the key aspects and the mathematical formulation of MANGO, this study also uses the model to develop a six-stage energy design plan, along a 30-year project horizon, for an urban district composed of 3 sites in Zurich, Switzerland. One candidate D-MES is considered per site and different scenarios are examined regarding building retrofitting and D-MES interconnections. Results overall show that retrofitting leads to lower emission levels, but significantly higher costs. On the other hand, D-MES interconnections improve both the economic and the environmental system performance. Finally, regarding optimal D-MES configurations, a variety of technologies is used, with combinations of air-source heat pumps and natural gas boilers leading to better economic performance and combinations of ground-source heat pumps and biomass boilers to more environmentally-friendly designs. Overal

关键词： Decentralized multi-energy systems Multi-stage energy planning Energy system design Renewable energy Techno-economic optimization mixed-integer linear programming

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Optimal Energy Management Framework for Truck-Mounted Mobile Charging Stations Considering Power Distribution System Operating Conditions

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SENSORS 2021年第8期21卷 2798-2798页

作者： Jeon, Soi Choi, Dae-Hyun Chung Ang Univ Sch Elect & Elect Engn 84 Heukseok Ro Seoul 156756 South Korea

A high charging demand from many electric vehicles (EVs) at a fixed charging station (FCS) with a limited number of charging poles can increase the waiting time of EVs and yield an abnormal power grid condition. To resolve these challenges, this paper presents an optimization framework in which a mobile charging station (MCS) is dispatched to the overloaded FCS to reduce the number of waiting EVs while maintaining normal power grid operation. Compared to existing MCS scheduling methods that do not consider actual power distribution system operations, the proposed framework takes into account the (i) active/reactive power flow and consumption of EVs, (ii) reactive power capability of MCS, and (iii) voltage quality in power distribution systems. In coupled transportation and power distribution systems, the proposed algorithm conducts optimal operation scheduling of MCS for both road routing and charging and discharging, thereby leading to the reduction of waiting EVs within the allowable voltage range. The proposed MCS optimization algorithm was tested in IEEE 13-bus and 33-bus distribution systems coupled with 9-node and 15-node transportation systems, respectively. The test results demonstrate the effectiveness of the proposed algorithm in terms of number of waiting EVs, voltage magnitude deviation, and reactive power of the MCS.

关键词： electric vehicle mobile charging station transportation network power distribution network mixed-integer linear programming

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Optimal sizing and economic analysis of Photovoltaic distributed generation with Battery Energy Storage System considering peer-to-peer energy trading

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2021年 28卷

作者： Yaldiz, Abdurahman Gokcek, Tayfur Sengor, Ibrahim Erdinc, Ozan Yildiz Tech Univ Dept Elect Engn Istanbul Turkey Univ Coll Cork Environm Res Inst MaREI Cork Ireland Izmir Katip Celebi Univ Dept Elect & Elect Engn Izmir Turkey

Demand for distributed generation (DG) systems has increased in recent years as costs have decreased, policies pursuing zero carbon emission objectives have been implemented, and energy demand has increased, in addition to technological advancements in renewable energy systems. With this increase in the number of DGs, a concept known as Peer-to-Peer (P2P) energy trading has been emerging, which offers innovative solutions in which new generation users take an active role in the market. This concept enables further efficient and optimal resource utilization by providing buying and selling via the P2P market together with the grid. With optimal resource sizing in the proposed structure, maximum self-sufficiency, shorter payback periods, and economical use of energy resources are supplied. This study maximizes the net profit by deducting the gain to customers from the use of Photovoltaic (PV) and Battery Energy Storage Systems (BESS) from their costs. Moreover, an optimal PV/BESS sizing for prosumers is attained through the use of a mixed-integer linear programming (MILP) based algorithm structure. Consumers offer energy with the most economical price in this proposed system through the P2P energy market. On the other hand, capital, replacement, and maintenance costs are all taken into account for a further realistic approach. The case studies of prosumers and consumers trading energy in Peer-to-Grid (P2G) and P2P are contrasted, as is the return on investment and the economic benefit of PV/BESS sizing during the operational time. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Optimal sizing Peer-to-peer energy trading mixed-integer linear programming Battery energy storage system Optimization

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A MILP Model for Revenue Optimization of a Compressed Air Energy Storage Plant with Electrolysis

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ENERGIES 2021年第20期14卷 6803-6803页

作者： Klaas, Ann-Kathrin Beck, Hans-Peter Tech Univ Clausthal Inst Elect Power Engn & Energy Syst Leibnizstr 28 D-38678 Clausthal Zellerfeld Germany

Energy storage, both short- and long-term, will play a vital role in the energy system of the future. One storage technology that provides high power and capacity and that can be operated without carbon emissions is compressed air energy storage (CAES). However, it is widely assumed that CAES plants are not economically feasible. In this context, a mixed-integer linear programming (MILP) model of the Huntorf CAES plant was developed for revenue maximization when participating in the day-ahead market and the minute-reserve market in Germany. The plant model included various plant variations (increased power and storage capacity, recuperation) and a water electrolyzer to produce hydrogen to be used in the combustion chamber of the CAES plant. The MILP model was applied to four use cases that represent a market-orientated operation of the plant. The objective was the maximization of revenue with regard to price spreads and operating costs. To simulate forecast uncertainties of the market prices, a rolling horizon approach was implemented. The resulting revenues ranged between EUR 0.5 Mio and EUR 7 Mio per year and suggested that an economically sound operation of the storage plant is possible.

关键词： compressed air energy storage CAES Huntorf hydrogen electrolysis day-ahead-market mixed-integer linear programming MILP rolling horizon optimization

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Open-shop production scheduling with reverse flows

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 153卷 107077-107077页

作者： Aghighi, Saba Niaki, Seyed Taghi Akhavan Mehdizadeh, Esmaeil Najafi, Amir Abbas Islamic Azad Univ Fac Ind & Mech Engn Dept Ind Engn Qazvin Branch Qazvin Iran Sharif Univ Technol Dept Ind Engn Azadi Ave Tehran Iran KN Toosi Univ Technol Fac Ind Engn Tehran Iran

The purpose of this paper is to present a mixed-integer linear programming model for the scheduling problem in an open-shop manufacturing system involving reverse flows, where two job flows (direct and reverse) in two opposite directions are processed on the same machines. The aim is to minimize the maximum completion time of all jobs on all machines (i.e., the makespan). A numerical example is presented and solved using the GAMS software (version 25.1.2) to validate the proposed mathematical model. As this problem is NP-hard, a vibration damping-based optimization (VDO) algorithm is proposed to solve large-scale problems in reasonable execution times. Besides, the Taguchi experimental design approach is employed to adjust and estimate the appropriate values of the VDO's parameters. Finally, the computational results of this algorithm are compared to the results obtained by a Simulated Annealing (SA), Cuckoo Search (CS), Ant Colony Optimization (ACO), Harmony Search (HAS), Imperialist Competitive (ICA), and Bat algorithm (BA). The statistical comparison results on several randomly generated problems of different sizes using the Kruskal-Wallis test reveals the better performance of the VDO algorithm.

关键词： Scheduling Open shop Reverse flows mixed-integer linear programming Vibration damping optimization algorithm

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Investigating Hydrogen-Based Non-Conventional Storage for PV Power in Eco-Energetic Optimization of a Multi-Energy System

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ENERGIES 2021年第23期14卷 8096页

作者： Di Somma, Marialaura Caliano, Martina Cigolotti, Viviana Graditi, Giorgio Italian Natl Agcy New Technol Energy & Sustainabl Dept Energy Technol & Renewable Sources I-00123 Rome Italy

Through the integration of multiple energy carriers with related technologies, multi-energy systems (MES) can exploit the synergies coming from their interplay for several benefits towards decarbonization. In such a context, inclusion of Power-to-X technologies in periods of excess renewable electricity supply, removes the need for curtailment of renewable electricity generation. In order to achieve the environmental benefits of MES without neglecting their economic feasibility, the optimal design problem is as crucial as challenging and requires the adoption of a multi-objective approach. This paper extends the results of a previous work, by investigating hydrogen-based non-conventional storage for PV power in the eco-energetic optimization of an MES. The system under study consists of a reversible fuel cell (r-SOC), photovoltaic (PV), electric heat pump, absorption chiller and thermal storage, and allows satisfying the multi-energy needs of a residential end-user. A multi-objective linear problem is established to find the optimal MES configuration including the sizes of the involved technologies with the goal of reducing the total annual cost and the fossil primary energy input. Simulation results are compared with those obtained in previous work with a conventional nanogrid where a combined heat and power (CHP) system with gas-fired internal combustion engine and a battery were present instead of an r-SOC. The optimized configuration of the non-conventional nanogrid allows achieving a maximum primary energy reduction amounting to 66.3%, compared to the conventional nanogrid. In the face of the environmental benefits, the non-conventional nanogrid leads to an increase in total annual costs, which, compared to the conventional nanogrid, is in the range of 41-65%.

关键词： multi-energy system multi-objective optimal design hydrogen carrier Power-to-X primary energy saving annual cost mixed-integer linear programming

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A multi-objective model to optimize country-scale municipal solid waste management with economic and environmental objectives: A case study in Malaysia

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JOURNAL OF CLEANER PRODUCTION 2021年 316卷 128366-128366页

作者： Ooi, Jun Keat Woon, Kok Sin Hashim, Haslenda Xiamen Univ Malaysia Sch Energy & Chem Engn Jalan SunsuriaBandar Sunsuria Sepang 43900 Selangor Malaysia Univ Teknol Malaysia Fac Chem & Energy Engn Sch Chem & Energy Engn Proc Syst Engn Ctr PROSPECT UTM Johor Bahru Skudai 81310 Johor Malaysia

Municipal solid waste (MSW) is a universal issue affecting all countries. In Malaysia, almost 90% of the MSW is disposed of in open landfills with no energy recovery system. Due to the heterogeneous characteristics of MSW, it is essential to develop a combination of different waste processing facilities with simultaneous cost and greenhouse gas (GHG) minimization. A novel multi-objective mixed-integer linear programming with the augmented e-constraint method is developed to assess the optimum MSW allocation on seven conventional waste processing facilities, including open landfills, material recycling facility, sanitary landfills, anaerobic digestion, composting, incineration, and plasma gasification. The avoided emissions of the valuable products valorized from MSW, which have been often neglected in the optimization model, are included in the study to investigate the extent of emissions reduction in the MSW management system. Compared to the current scenario in Malaysia (total cost: 66.66 MYR/tonne MSW, net GHG emissions: 0.42 tonne CO2-eq/tonne MSW), the least-cost solution (i.e., 74% open landfill, 10% sanitary landfill, and 15% material recycling facility) shows 26% reduction for both cost and GHG emissions, while the least GHG emissions solution (i.e., 20% sanitary landfill, 21% anaerobic digestion, 21% composting, 21% plasma gasification, and 17% material recycling facility) indicates 159% reduction of GHG emissions with 15% increase in cost. Among the 11 Pareto-optimal solutions, the 10th solution (34% sanitary landfill, 21% anaerobic digestion, 21% composting, 17% material recycling facilities, and 8% plasma gasification) reduces 3% of national greenhouse gas emissions and is recommended for future MSW management in Malaysia. The sensitivity analysis indicates that plasma gasification is more favorable when the electricity price is higher. A change of waste separation rate from 30% to 90% reduces total MSW management cost and the net GHG emissions by 18.24

关键词： Augmented epsilon-constraint Cost minimization Greenhouse gas mixed-integer linear programming Municipal solid waste Pareto-optimal solutions

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A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions

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ENERGIES 2021年第21期14卷 6963页

作者： Rippel, Daniel Foroushani, Fatemeh Abasian Luetjen, Michael Freitag, Michael Univ Bremen BIBA Bremer Inst Prod & Logist GmbH Hsch Ring 20 D-28359 Bremen Germany Univ Bremen Fac Prod Engn Badgasteiner Str 1 D-28359 Bremen Germany

In the literature, different authors attribute between 15% to 30% of a wind farm's costs to logistics during the installation, e.g., for vessels or personnel. Currently, there exist only a few approaches for crew scheduling in the offshore area. However, current approaches only satisfy subsets of the offshore construction area's specific terms and conditions. This article first presents a literature review to identify different constraints imposed on crew scheduling for offshore installations. Afterward, it presents a new mixed-integer linear Model that satisfies these crew scheduling constraints and couples it with a scheduling approach using a Model Predictive Control scheme to include weather dynamics. The evaluation of this model shows reliable scheduling of persons/teams given weather-dependent operations. Compared to a conventionally assumed full staffing of vessels and the port, the model decreases the required crews by approximately 50%. Moreover, the proposed model shows good runtime behavior, obtaining optimal solutions for realistic scenarios in under an hour.

关键词： offshore installations crew scheduling mixed-integer linear programming model predictive control

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Optimal Charging Schedule Planning for Electric Buses Using Aggregated Day-Ahead Auction Bids

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ENERGIES 2021年第16期14卷 4727页

作者： Zoltowska, Izabela Lin, Jeremy Warsaw Univ Technol Inst Control & Computat Engn PL-00665 Warsaw Poland New Grid Solut Austin TX 78731 USA

This study aims to plan a cost-minimizing charging schedule for electric buses with fast charging stations. The paper conceptualizes the problem as a three-stage procedure, which is oriented around the participation of an electric bus aggregator in a day-ahead energy auction. First, the aggregation stage determines the bid parameters of buses. With bid parameters, aggregated cost-minimizing charging plans are obtained in the second stage conceived as the hourly day-ahead auction. The disaggregation of hourly plans into feasible minutely charging schedules is the third stage. The main contribution is the formulation of mixed-integer linear programming aggregation models to determine charging availability expressed as minimum and maximum hourly energy requirements taking into account detailed, minutely characteristics and constraints of the charging equipment and the buses. No price forecasts are required, and the plans adjust to the wholesale prices of energy. Defining only a few aggregated bids parameters used in linear programming constraints and incorporating them into the auction model is another contributing factor of this paper, allowing the scheduling of storage-based participants economically. The proposed methodology has been verified on a recently published case study of a real-world bus service operated on the Ohio State University campus. We show that the auction-based charging of all 22 buses outperforms as-soon-as-possible schedules by 7% to even 28% of daily cost savings. Thanks to the aggregated bids, buses can flexibly shift charges between high- and low-price periods while preserving constraints of the charging equipment and timetables.

关键词： aggregator coordinated charging double auction mixed-integer linear programming

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