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Energy-efficient dispatch of multiple-chiller systems using hybrid exchange market and genetic algorithm

ENERGY AND BUILDINGS 2022年第0期255卷 111571-111571页

作者： Akbari-Dibavar, Alireza Farahmand-Zahed, Amir Mohammadi-Ivatloo, Behnam Zare, Kazem Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Mugla Sitki Kocman Univ Elect & Elect Engn Dept Mugla Turkey

The inefficient dispatch of multiple-chiller systems results in drastic energy consumption, which should be investigated by optimal allocating of cooling loads between chillers in a building. Due to the nonlinearity of the dispatching problem, heuristic algorithms are more prevalent in this field. In this paper, a hybrid exchange market and genetic algorithm (EMGA) is established to solve the optimal chiller loading (OCL) problem. The decision variables of the problem are the partial load ratio (PLR) of chillers determining the power consumption of each chiller unit, and the objective of the OCL problem is to minimize the total power consumption of the multiple-chiller system. The effectiveness of the proposed algorithm is verified by a fair comparison of the results with similar attributes in three identical cases. It is shown that the proposed EMGA outperforms comparable algorithms in terms of convergence speed and finding optimal solutions. Moreover, the problem is modeled as mixed-integer non-linear programming (MINLP) in the general algebraic modeling system (GAMS) to solve mathematically using different solvers. It was shown that the mathematical approaches adopted by GAMS have a high probability of trapping in local optima when the decision variables are increasing, whiles the optimum solutions by the proposed EMGA are superior. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Multiple chiller dispatching Exchange market algorithm Genetic algorithm Hybrid heuristic algorithm mixed-integer non-linear programming

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Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach

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APPLIED ENERGY 2020年第0期259卷 114199-000页

作者： Shen, Feifei Zhao, Liang Du, Wenli Zhong, Weimin Qian, Feng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 200092 Peoples R China

In the large-scale industries, optimization of multi-type energy systems to minimize the total energy cost is of great importance and has received worldwide attentions. In the real industrial plants, the deterministic optimization may encounter difficulties because of various uncertainties. In this paper, the deterministic and robust optimization frameworks are proposed for energy systems optimization under uncertainty. A hybrid modeling method is applied to develop building block models based on the mechanism and process historical data. The deterministic optimization model can be further formulated as a mixed-integer non-linear programming problem. Considering enthalpy uncertainties, a generalized intersection kernel support vector clustering is employed to construct the uncertainty set. By introducing the derived uncertainty set in the deterministic optimization model, a robust optimization model is presented. A case study on the energy system of a real ethylene plant is carried out to illustrate the performance of the proposed approach and the effect of regularization parameter kappa on the optimization results is studied. The results show that the optimized energy costs are 15148.84 kg/h and 16209.81 kg/h in deterministic and robust optimization methods. Despite higher energy consumption in robust optimization, the proposed method yields a trade-off between energy cost and robustness. The conservatism of the solution can be adjusted by the regularization parameter, and in this system kappa = 0.02 is recommended.

关键词： Large-scale industrial energy systems Uncertainty Data-driven robust optimization Operational optimization mixed-integer non-linear programming

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Integrated planning for public health emergencies: A modified model for controlling H1N1 pandemic

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2020年第5期71卷 748-761页

作者： Liu, Ming Xu, Xifen Cao, Jie Zhang, Ding Nanjing Univ Sci & Technol Sch Econ & Management 200 Xiaolingwei Rd Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Reading Acad Nanjing Jiangsu Peoples R China SUNY Coll Oswego Sch Business Oswego NY 13126 USA

Infectious disease outbreaks have occurred many times in the past decades and are more likely to occur in the future. Recently, Buyuktahtakin et al. (2018) proposed a new epidemics-logistics model to control the 2014 Ebola outbreak in West Africa. Considering that different diseases have dissimilar diffusion dynamics and can cause different public health emergencies, we modify the proposed model by changing capacity constraint, and then apply it to control the 2009 H1N1 outbreak in China. We formulate the problem to be a mixed-integer non-linear programming model (MINLP) and simultaneously determine when to open the new isolated wards and when to close the unused isolated wards. The test results reveal that our model could provide effective suggestions for controlling the H1N1 outbreak, including the appropriate capacity setting and the minimum budget required with different intervention start times.

关键词： Epidemic outbreak resource allocation mixed-integer non-linear programming public health emergencies optimisation

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A strategy for advanced biofuel production and emission utilization from macroalgal biorefinery using superstructure optimization

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ENERGY 2021年 221卷 119883-119883页

作者： Dickson, Rofice Liu, J. Jay Lahore Univ Management Sci LUMS SBA Sch Sci & Engn Dept Chem & Chem Engn Lahore 54792 Pakistan Pukyong Natl Univ Dept Chem Engn Busan 48513 South Korea

The rapid increase in the emission of greenhouse gases over the years signifies the urgent need to explore fuels that emit less CO2, such as biofuels. CO2 emission can be reduced further by productively utilizing the CO2 generated during industrial processes. In this study, we have proposed a strategy to produce advanced biofuel from macroalgal biorefinery and to utilize all waste streams from processing into value-added products. To achieve this aim, we developed a novel superstructure for biorefinery process synthesis based on Saccharina japonica (macroalgae) to determine an optimal design of the biorefinery. Process integration was performed to utilize direct greenhouse gas emissions from the biorefinery and to reduce pollutant emissions and freshwater consumption. A techno-economic and environmental mixed-integer non-linear model was formulated based on this superstructure. To achieve the economic and environmental goals, two objective functions were studied: maximization of the net present value and minimization of CO2 emissions. A comprehensive sensitivity and Monte Carlo simulation model was formulated to evaluate the effects of variation in key model parameters on the overall economics and to perform the risk assessment, respectively. The minimum ethanol selling price range for the integrated design was found to be USD 0.36-0.56/L. The optimal design achieved a 90% reduction in CO2 emissions, from 4.86 kg/s to 0.42 kg/s, as well as a 38.6% reduction in freshwater consumption. The risk of the optimal design was found to be 20-44% on the basis of the minimum selling price of ethanol. Therefore, this design can be implemented as an economically and environmentally feasible approach to biofuel production. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Superstructure optimization Process synthesis Process integration Macroalgae mixed-integer non-linear programming Biofuel

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Sustainable retrofit of petrochemical energy systems under multiple uncertainties using the stochastic optimization method

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COMPUTERS & CHEMICAL ENGINEERING 2021年 151卷 107374-107374页

作者： Qian, Qiming Liu, Hua He, Chang Shu, Yidan Chen, Qing L. Zhang, Bing J. Sun Yat Sen Univ Guangdong Engn Ctr Petrochem Energy Conservat Sch Chem Engn & Technol Key Lab Low Carbon Chem & Energy Conservat Guangd Zhuhai 519082 Peoples R China

We report a multi-objective stochastic mixed-integer non-linear programming (MINLP) framework for sustainable retrofit and capability expansion of traditional energy systems in petrochemical complexes. Multiple uncertainties including energy demands, solar radiation and wind speeds are considered in the optimization framework, these are characterized by historical data or normal distributions which are predefined with assumed mean values and standard variations. A stochastic reduced order model approach is introduced to describe the uncertainties by a small number of scenarios and their individual probabilities. The optimization framework further accounts for system configuration selection and sizing of the candidate energy conversion equipment, such as thermal storage units, gas turbines, boilers, steam turbines, as well as their operating capacities in each time period. A case study is investigated to demonstrate the performance of the proposed strategy and then the optimization results under three modes (deterministic, stochastic and semi-stochastic programs) are compared. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Sustainable energy system Retrofit mixed-integer non-linear programming Stochastic reduced order model Multiple uncertainties

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The effect of investment in green technology in a two echelon supply chain under strict carbon-cap policy

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BENCHMARKING-AN INTERNATIONAL JOURNAL 2020年第6期27卷 1875-1891页

作者： Ghosh, Arindam Sarmah, S. P. Kanauzia, Radhey Krishna ICFAI Business Sch IBS Dept Operat & IT Hyderabad Telangana India IIT Kharagpur Dept Ind & Syst Engn Kharagpur W Bengal India

Purpose Strict carbon-cap policy is one of the basic policies proposed by the regulatory bodies to reduce the anthropogenic greenhouse gas emission. The purpose of this paper is to examine whether it is beneficial for a company to invest in green technology or not under the strict carbon-cap policy and for that a two echelon supply chain model is developed. This paper gives insight about judicious decision about investment on green technology. Design/methodology/approach Mathematical modeling approach has been adopted to understand the effect of investment on green technology. All the cost and emissions parameters have been derived and the total cost (TC) and total emission equations have been formulated mathematically. Two constrained mixed-integer nonlinear programming (MINLP) problems have been formulated and solved considering with or without green investment. Further, supply chain cost is optimized without carbon constraint to understand the effect of carbon constraint. Findings The investment in green technology can reduce the total supply chain cost. The study reveals that handling different parameters optimally can reduce both cost and emissions. Originality/value This paper tries to assess the effectiveness of green investment on technology under strict carbon-cap policy on a supply chain and, thereby, added value to the existing work. It examines the role played by various parameters under strict carbon-cap policy to draw insights, which will be beneficial for the academic community and managers.

关键词： Supply chain Strict carbon cap Investment in green technology mixed-integer non-linear programming

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Exploring DC microgrid: Advanced applications and their control strategies

Energy 360

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Energy 360 2025年 3卷

作者： C. Elwely Salem A. Et-taleby Y. Chaibi N. Ayadi B. Elkari M. Benslimane Z. Chalh Sidi Mohamed Ben Abdellah University Fez Morocco ESME Research Lab Paris France IDEA School Euromed University Fez Morocco

The increasing reliance on microgrids (MG) as a power delivery system underscores the critical importance of advanced control strategies and application-specific solutions. With a focus on their technological advantages, possible uses and control mechanisms, this review evaluates the emerging role of DC microgrids as a viable substitute for conventional AC systems. Examining recent advancements, the paper explores key applications, including electric vehicle (EV) charging, smart buildings, hydrogen production, data centers and residential energy systems, highlighting the versatility of DC microgrids in optimizing renewable energy integration and enhancing energy efficiency. The review further categorizes and analyzes different control methods, including centralized, decentralized and distributed control, detailing their key features, limitations and mathematical representations. The findings emphasize that DC microgrids offer improved energy efficiency, reduced conversion losses and enhanced power reliability. Additionally, advanced control strategies play a crucial role in ensuring optimal energy management, system stability and operational resilience. By providing a critical analysis of these aspects, this review serves as a guide for future research and innovation in DC microgrid control and application optimization, contributing to the development of more sustainable and efficient energy systems.

关键词： AC, Alternative current AEMS, Automated energy management system BEMSs, Building energy management systems BESS, Battery energy storage system CAC, Centralized automated control CEMPC, Centralized economic model predictive control COS, Cooperative Online Scheduling CRAC, Computer room air conditioning CSWG, Climate Sustainability Work Group CVC, Clusters of vehicle control DC, Direct current DEMPC, Distributed economic model predictive control DERs, Distributed energy resources DGs, Distributed generators DMM, Direction method of multipliers DMS, Distributed management system DRC, Demand response control DTLR, Dynamic thermal line rating EMS, Energy management system ESS, Energy storage system EVCSs, Electric Vehicle Charging Stations EVs, Electric Vehicles FLC, Fuzzy logic control GA, Genetic algorithm GTBA, Game-theory based approach HDR, Holistic Demand Response HEMS, Hybrid energy management system HPWOA, Hybrid particle whale optimization algorithm HRSO, Hybrid Robust-Stochastic Optimization HV, High voltage HVAC, Heat, ventilation and air conditioning IRENA, International Renewable Energy Agency IT, Information technology LC, Local control LCOE, Levelized Cost of Energy LV, Low voltage LVAC, Low voltage alternative current LVDC, Low voltage direct current MCO, Multiple criteria optimization MG, Microgrid MINLP, mixed-integer non-linear programming MPPT, Maximum power point tracking MREAI, Microgrid renewable energy availability index MREEI, Microgrid renewable energy energy index MRI, Microgrid resiliency index MV, Medium voltage NPC, Net present cost NZB, Net zero building NZEB, Net-zero energy buildings OCO, Online Convex Optimization P2P, Peer to peer PED, Peak energy demands PEVs, Plug-in electric vehicles PFF, Power fluctuation factor PHEVs, Plug-in hybrid electric vehicles PMS, Power management system PSO, Particle Swarm Optimization PV, Photovoltaic RESs, Renewable energy sources RMOO, Robust Multi-Objective Optimization SIU, Smart interlinking unit SLA, Service legal agreement SMC, Sliding mode cont

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Locating exchangers in an EIP-wide heat integration network

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COMPUTERS & CHEMICAL ENGINEERING 2018年 108卷 57-73页

作者： Nair, Sajitha K. Soon, Melvin Karimi, I. A. Natl Univ Singapore Dept Chem & Biomol Engn 4 Engn Dr 4 Singapore 117585 Singapore

Inter-plant heat integration offers an energy-saving opportunity in eco-industrial parks (EIPs) beyond the traditional intra-plant integration. The economic feasibility of this integration depends critically on the locations of heat exchangers. In this work, we generalize our previous study on centralized HENS (Heat Exchanger Network Synthesis) for EIPs to allow exchangers to be located at either plant or central sites facilitating both intra-plant and inter-plant heat integration in a seamless manner. We propose a mixed integer non-linear programming (MINLP) model that synthesizes a maximum-NPV (Net Present Value) EIP-wide HEN, while accounting for all the capital (e.g. heat exchangers, pumps, and pipelines) and operating (pumping costs, utility savings) cash flows along with the ambient heat gains/losses during transports. The model is tested on five examples from the literature and gives better HENs compared to the previous results. This work highlights and quantifies the impact of heat exchanger locations in an EIP-wide HEN. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Eco-industrial park Heat integration mixed-integer non-linear programming Heat exchanger network Heat exchanger

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Data-driven Security-Constrained AC-OPF for Operations and Markets 20

Data-driven Security-Constrained AC-OPF for Operations and M...

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Power Systems Computation Conference (PSCC)

作者： Halilbasic, Lejla Thams, Florian Venzke, Andreas Chatzivasileiadis, Spyros Pinson, Pierre Tech Univ Denmark Ctr Elect Power & Energy Lyngby Denmark

ISBN: (纸本)9781910963104

In this paper, we propose a data-driven preventive security-constrained AC optimal power flow (SC-OPF), which ensures small-signal stability and N-1 security. Our approach can be used by both system and market operators for optimizing redispatch or AC based market-clearing auctions. We derive decision trees from large datasets of operating points, which capture all security requirements and allow to define tractable decision rules that are implemented in the SC-OPF using mixed integer nonlinear programming (MINLP). We propose a second order cone relaxation for the non-convex MINLP, which allows us to translate the non-convex and possibly disjoint feasible space of secure system operation to a convex mixed-integer OPF formulation. Our case study shows that the proposed approach increases the feasible space represented in the SC-OPF compared to conventional methods, can identify the global optimum as opposed to tested MINLP solvers and significantly reduces computation time due to a decreased problem size.

关键词： Security-constrained OPF small-signal stability convex relaxation mixed-integer non-linear programming

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An integrated analytic network process with mixed-integer non-linear programming to supplier selection and order allocation

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2010年第9-12期49卷 1195-1208页

作者： Razmi, Jafar Rafiei, Hamed Univ Tehran Univ Coll Engn Dept Ind Engn Tehran Iran

As the global business environment changed, much more commercial and technological competitions evolved in this environment. Consequently, more attention is devoted to the business partners including suppliers. In this paper, supplier selection with order allocation problem is addressed for strategic items with respect to the materials' characteristics as well as what has been mostly considered in the literature, suppliers' characteristics. The proposed model comprises an analytic network process (ANP) sub-model to qualify suppliers and filter-suitable candidates among the available ones. Additionally, the ANP sub-model is applied to evaluate suppliers regarding their qualitative attributes which cannot be modeled with mathematical tools properly. Moreover, a mixed-integer non-linear sub-model is presented to simultaneously allocate order quantities to the chosen suppliers. The model is customized to the strategic items by introducing supplier switching cost which is a notable cost component for products. Finally, experimental results and sensitivity analyses are presented to validate the proposed model.

关键词： Supplier selection Order allocation Strategic sourcing Analytic network process mixed-integer non-linear programming

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