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OPTIMIZATION AND SENSITIVITY ANALYSES OF A COMBINED COOLING, HEAT AND POWER SYSTEM FOR A RESIDENTIAL BUILDING

THERMAL SCIENCE 2021年第5期25卷 3969-3986页

作者： Melo, Filipi Maciel Antonio Pina, Eduardo Carvalho, Monica Univ Fed Paraiba Joao Pessoa Paraiba Brazil Univ Zaragoza Zaragoza Spain

In the quest for a better use of energy resources, energy integration, and cogeneration strategies have been employed in the industrial and commercial sectors with considerable benefits realized. However, the residential sector remains underexplored. An optimization procedure should be carried out whenever there is a need to ensure or verify the economic viability of an energy system. This study uses mixed integer linear programming to optimize the energy supply to a residential building, with 20 floors and 40 apartments, located in the city of Joao Pessoa (northeast Brazil). The equipment available includes gas engines, electric and natural gas boilers, heat exchangers, cooling towers, and absorption and mechanical chillers. The optimization establishes the optimal system configuration and operational strategy (operation throughout the year). Economic, technical, and legal aspects were considered in the minimization of the total annual costs associated with the building's energy supply. The energy demands were calculated on an hourly basis, throughout one year, by the EnergyPlus software and corresponded to hot water (83 MWh per year), electricity (171 MWh per year), and cooling (242 MWh per year) demands. The optimal system was entirely reliant on the electric grid to meet the electricity demand directly and to satisfy heating and cooling demands by means of an electric hot water boiler and a mechanical chiller. The optimal solution is tested by varying, within reasonable limits, selected parameters: natural gas and electricity tariffs, the behavior of residents, amortization factor, and relationship between the tariffs of electricity and natural gas.

关键词： optimization mixed integer linear programming energy building residential sensitivity analysis northeast Brazil

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Interpreting Deep-Learned Error-Correcting Codes

Interpreting Deep-Learned Error-Correcting Codes

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IEEE International Symposium on Information Theory (ISIT)

作者： Devroye, N. Mohammadi, N. Mulgund, A. Naik, H. Shekhar, R. Turan, G. Y. Wei, Y. Zefran, M. Univ Illinois Chicago IL 60680 USA ELRN MTA SZTE Res Grp Artificial Intelligence Szeged Hungary

ISBN: (纸本)9781665421607;9781665421591

Deep learning has been used recently to learn error-correcting encoders and decoders which may improve upon previously known codes in certain regimes. The encoders and decoders are learned "black-boxes", and interpreting their behavior is of interest both for further applications and for incorporating this work into coding theory. Understanding these codes provides a compelling case study for Explainable Artificial Intelligence (XAI): since coding theory is a well-developed and quantitative field, the interpretability problems that arise differ from those traditionally considered. We develop post-hoc interpretability techniques to analyze the deep-learned, autoencoder-based encoders of TurboAE-binary codes, using influence heatmaps, mixed integer linear programming (MILP), Fourier analysis, and property testing. We compare the learned, interpretable encoders combined with BCJR decoders to the original black-box code.

关键词： Training Deep learning Neural networks Turbo codes Robustness Decoding mixed integer linear programming

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Scheduling Optimization of Household Equipment using a Wireless Home Automation System 5

Scheduling Optimization of Household Equipment using a Wirel...

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5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Alshehhi, Aaesha Alawadhi, Fatma Baqer, Meera Dhaouadi, Rached Amer Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates

ISBN: (纸本)9781665482370

In this paper, we present a low-cost home automation system that can generate an energy-efficient and cost-reducing electricity consumption schedule. The system is comprised of an energy management system running on a raspberry pi 4 called Home Assistant with wireless smart switches connected to the desired home appliances. The communication between all components in the system is configured to be wireless, which means that the control interface for this system is flexible. The proposed optimization method for generating the schedule is based on mixed integer linear programming. The home energy management system is realized on a raspberry pi. The optimization algorithm is implemented using Python and the Gurobi solver package. The optimized scheduling for the home appliances was obtained for different cases of user time preferences and results prove that the proposed method efficiently reduced the total cost of electricity of a typical household.

关键词： home automation system schedule optimization mixed integer linear programming genetic algorithm nested loops

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Brayton pumped thermal energy storage: Optimal dispatchment in multi-energy districts

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ENERGY CONVERSION AND MANAGEMENT 2024年 314卷

作者： Ghilardi, Alessandra Frate, Guido Francesco Kyprianidis, Konstantinos Tucci, Mauro Ferrari, Lorenzo Univ Pisa Dept Energy Syst Terr & Construct Engn Largo Lucio Lazzarino I-56122 Pisa Italy Malardalen Univ Sch Business Soc & Engn Dept Sustainable Energy Syst Vasteras 72123 Sweden

Energy storage systems play a crucial role in supporting the integration of renewable energy sources. In this framework, Brayton Pumped Thermal Energy Storage is an emerging technology thanks to many positive features, including geographical and raw materials independence, long lifetime, and peculiar sector-coupling capabilities. By storing electric energy as thermal exergy, this technology offers the flexibility to discharge energy directly for heating or cooling applications or convert it back into electricity as needed by the grid. This dual functionality fits well with the multi-energy intrinsic nature of urban districts in which electrical and thermal energy carriers are involved. This paper aims then to evaluate the potential economic benefit due to the usage of a Brayton based Pumped Thermal Energy Storage as multi-energy device instead of a solely electric-to-electric. An urban district with thermal and electric requirements is used as a case study to investigate the technoeconomic performance of the mentioned storage capacity when coupled to photo-voltaic plants to simulate deep-decarbonization scenarios. The system day-ahead optimization, performed through a mixed integer linear programming approach, aims to minimize the operational cost computed over a 24-h horizon. The results highlight that operational yearly cost savings are 5-10 % when using the multi-energy storage functionalities compared to the standard electric-to-electric operation. Despite the cost reduction, allowing only direct heating causes unavoidable thermal curtailment losses in the 6-10 % range. However, these losses can be reduced to 3 % by introducing the additional direct cooling functionality, bringing the best performances from the economic and thermodynamic standpoints.

关键词： Pumped thermal energy storage Sector-coupling Multi -energy storage mixed integer linear programming

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A coordinated generation and voyage planning optimization scheme for all-electric ships under emission policy

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 156卷

作者： Gao, Jingjie Lan, Hai Zhang, Xinan Iu, H. H. C. Hong, Ying-Yi Yin, He Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Univ Western Australia Sch Elect Elect & Comp Engn Perth WA 6009 Australia Chung Yuan Christian Univ Dept Elect Engn Chung Li Dist 320 Taoyuan Taiwan

The International Maritime Organization has introduced the concept of Emission Control Area to reduce sulfur emissions from coastal areas. The expensive low-sulfur fuels are required to be used in Emission Control Area, leading to increased sulfide emissions due to bypassing by ship operators. It creates new challenges for navigation and power generation scheduling of all-electric ships. This article presents a new bi-objective joint optimization scheme for ship sailing routes and energy management. It considers emission control strategies to balance the total cost of operation and sulfur dioxide emissions throughout the ship's voyage. Unlike previous optimization problems with fixed routes and sailing time, the influence of emission policies on the operator's navigation decisions is considered. The route options and sailing time are treated as variables embedded in the model. In addition, the optimization accuracy is improved by integrating the model into mixed integer linear programming through a modified piecewise linearization model. The results show that the total operating cost for the same route is reduced by 6.05%, and the SO2 emission is 8.36% after integrating the emission policy influencing factor in the model. The effectiveness of the proposed scheme is validated, and the model's necessity for optimizing ship power generation and voyage within Emission Control Area is also illustrated.

关键词： Emission control area All-electric ships Bi-objective joint optimization Joint voyage planning and energy management mixed integer linear programming

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Adaptive robust electric vehicle routing under energy consumption uncertainty

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 160卷

作者： Jeong, Jaehee Ghaddar, Bissan Zufferey, Nicolas Nathwani, Jatin Univ Waterloo Dept Management Sci Waterloo ON Canada Ivey Business Sch London London ON Canada Tech Univ Denmark Dept Technol Management & Econ Lyngby Denmark Univ Geneva Geneva Sch Econ & Management Geneva Switzerland

Electric vehicles (EVs) have been highly favoured as a mode of transportation in recent years. EVs offer numerous benefits over traditional fuel -based vehicles, particularly in terms of the environmental impact. Although electric vehicles offer several advantages, there are certain restrictions that limit their usage. One of the significant issues is the uncertainty in their driving range. The driving range of EVs is closely related to their energy consumption, which is highly affected by exogenous and endogenous factors. Since those factors are unpredictable, uncertainty in EVs' energy consumption should be considered for efficient operation. This paper proposes a two -stage adaptive robust optimization framework for the electric vehicle routing problem. The objective is to minimize the worst -case energy consumption while guaranteeing that services are delivered at the appointed time windows without battery level deficiency. We postulate that EVs can be recharged on route, and the charging amount can be adjusted depending on the circumstances. A column -and -constraint generation based heuristic algorithm, which is coupled with variable neighbourhood search and alternating direction algorithm, is proposed to solve the resulting model. The computational results show the economic efficiency and robustness of the proposed model, and that there is a tradeoff between the total required energy and the risk of failing to satisfy all customers' demand.

关键词： Adaptive robust optimization Electric vehicle routing Uncertainty Decomposition mixed integer linear programming

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A novel dynamic batching energy management strategy for multi-microgrid networks with enhanced privacy

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ENERGY AND BUILDINGS 2024年 318卷

作者： Arunkumar, C. P. Albert Paul Selvakumar, K. SRM Inst Sci & Technol Coll Engn & Technol Dept Elect & Elect Engn Chennai Tamil Nadu India

This article presents a novel energy management approach termed Dynamic Batching EMS (DBEMS) designed specifically for day -ahead scheduling within multi-microgrid (MMG) networks. The prime aim of this strategy is to curtail operational costs within the MMG network, fortify the privacy of individual microgrids, and optimize the utilization of available resources, surpassing existing methodologies like centralized, decentralized, and nested management strategies. The emphasis is chiefly on orchestrating cost-effective operations while bolstering data privacy for each microgrid within the complex MMG framework. The efficacy of the proposed strategy is evaluated across both grid -connected and islanded modes, focusing on a network comprising four microgrids. The model formulation is executed utilizing the mixed integer linear programming method. In the islanded mode, optimization is conducted for scenarios both inclusive and exclusive of Battery Energy Storage Systems (BESS). Comparative assessments are made against centralized and nested strategies, considering parameters including operational costs, privacy enhancements, load -shedding, computation time, and computational burden.

关键词： Multi-microgrids Energy management Optimisation Cost-optimization mixed integer linear programming Privacy enhancement Distributed generation

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Virtual Platoon based CAVs Cooperative Driving at Unsignalized Intersection 25

Virtual Platoon based CAVs Cooperative Driving at Unsignaliz...

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IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

作者： Cong, Xiangyue Yang, Bo Gao, Fengkun Chen, Cailian Tang, Yuliang Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Shanghai Engn Res Ctr Ind Intelligent Control & M Shanghai 200240 Peoples R China Xiamen Univ Dept Informat & Commun Engn Xiamen 361005 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

The emergence of connected and automated vehicles (CAVs) poses a promising future of transportation systems. Complicated traffic conditions at intersections lead to challenges for cooperative driving of CAVs while ensuring safety and efficiency. This paper proposes a conflict-free cooperative control method for CAVs at unsignalized intersections. Based on the conflict points inside the intersection, we project each conflict vehicles cluster onto one virtual lane and construct multiple virtual platoons for ensuring safety. To maximize the intersection utilization, a packing problem is proposed for developing optimal car-following order, which is formulated as a mixed-integer linear programming (MILP). Considering the practical conditions at the intersection, we remove the redundant constraints to obtain nearly global-optimal solutions in real time. Numerical simulations validate that the proposed method saves 5.5%-9.3% total evacuation time under normal traffic flow.

关键词： Transportation Numerical simulation Real-time systems Safety mixed integer linear programming

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Simultaneous Charger Placement and Power Scheduling for On-Demand Provisioning of RF Wireless Charging Service 21st

Simultaneous Charger Placement and Power Scheduling for On-D...

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21st International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Jiang, Huatong Li, Yanjun Gao, Meihui Zhejiang Univ Technol Sch Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9783030953881;9783030953874

To provision on-demand radio frequency (RF) charging service in a designated place, the problem of simultaneous charger placement and power scheduling is studied. Based on users' historic spatial and temporal distribution information, we formulate the problem as how to place a given number of chargers and how to adjust the chargers' power levels in each time interval so that the revenue of the charging service can be maximized, given the total power limitation constraint. The formulated problem is a mixed integer linear programming (MILP) problem and we propose a branch and bound (B&B) algorithm to solve it. Extensive simulations in both small-scale and large-scale networks, as well as simulations based on the real data set are conducted to validate the effectiveness of our proposed algorithm. The results show that, our proposed algorithm outperforms greedy algorithm, an algorithm that deals charger placement and power scheduling separately, in most of the simulation scenarios, and reaches the optimum in small-scale instances.

关键词： RF charging Charger placement Power scheduling mixed integer linear programming

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Potential of Formation Flight for Commercial Aviation: Three Case Studies

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JOURNAL OF AIRCRAFT 2021年第2期58卷 320-333页

作者： Kent, Thomas E. Richards, Arthur G. Univ Bristol Dept Comp Sci Queens Bldg Bristol BS8 1TR Avon England Univ Bristol Dept Engn Dynam & Control Queens Bldg Bristol BS8 1TR Avon England

Formation flight has the potential to significantly reduce aircraft fuel consumption by allowing "follower" aircraft to fly in the aerodynamic wake of "leader" aircraft. However, this requirement for aircraft to be in close proximity for large parts of their journey raises questions about the suitability of flying in formation given the diverse range of existing flights and geographical regions. This paper demonstrates the potential for two-aircraft formation flight for three distinct case studies: long-haul airline, transatlantic airline, and low-cost airline, encompassing a range of typical airline regions and characteristics. The results indicate, even with only minor scheduling alterations, the potential fuel savings could result in saving hundreds of millions of dollars in fuel costs and reducing millions of tonnes of carbon dioxide emissions. An analytical geometric method for calculating all possible combinations of optimal routes is presented. This is coupled with a mixed integer linear program for providing an assignment of aircraft into formation pairs. A number of different key metrics, correlations, and predictive indicators help to determine which flights, airlines, and regions show "good" formation potential. Importantly, this paper also demonstrates these results for a wide range of drag-reduction possibilities and the impact this has on achievable fuel saving.

关键词： Commercial Aviation Aerodynamic Simulation Direct Operating Cost mixed integer linear programming Fuel Consumption Transatlantic Flight Flight Testing Wake Vortices Base of Aircraft Data Burning Rate

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