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Scheduling of Energy Hub Resources Using Robust chance-constrained Optimization

IEEE ACCESS 2022年 10卷 129738-129753页

作者： Esmaeel Nezhad, Ali Nardelli, Pedro H. J. Sahoo, Subham Ghanavati, Farideh LUT Univ Sch Energy Syst Dept Elect Engn Lappeenranta 53850 Finland Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark Univ Aveiro Dept Ind Engn & Management P-3810193 Aveiro Portugal

This paper develops a robust chance-constrained model for handling the uncertainties of generation and consumption in multi-carrier energy hubs. The proposed model incorporates corresponding loading factors for each type of electrical, heating, and cooling loads. This is done to assess the maximum loadability of the whole system. In this respect, the chance-constrained approach is implemented for the feasibility assessment of the operation problem with uncertainties. The uncertainties which are assumed here include the forecast errors of electrical, heating, and cooling load demands, and the volatile solar power generation. The overall problem formulation is developed in the mixed-integer linear programming (MILP) framework. The standard chance-constrained approach is converted to a deterministic optimization model by utilizing the Big M method. The main objective of the proposed model is to maximize the loadability index with uncertainties while addressing the permissible risk index of the decision-maker. The studied energy hub comprises electrical, heating, and cooling loads, and the energy flow technique is adopted in this paper to model the load balance equations. The simulation results are presented for different scenarios while addressing features of the proposed model for the summer and winter seasons. Furthermore, the developed model is evaluated for different scenarios and a comparison is made with the information-gap decision theory (IGDT) method.

关键词： chance-constrained programming energy hub robust optimization loadability index mixed-integer linear programming

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Uncertainties in the resource conservation problems: a review

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CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2022年第9期24卷 2681-2699页

作者： Arya, Deepika Bandyopadhyay, Santanu Indian Inst Technol Dept Energy Sci & Engn Mumbai 400076 Maharashtra India

Process integration, which started its development in the early 1970s, is an emerging branch of study for conserving various resources. Process integration studies the interdependencies among various process units at the system level and the development and use of tools for holistically designing process networks with generic optimization of resources and sustainable development. The problems addressed in process integration are often referred to as resource conservation or source-sink allocation problems. Most of these problems are solved with precise input parameters. However, due to a wide range of known and unknown factors, these input parameters are uncertain in practical applications. To make the designed network more reliable, these uncertainties should be incorporated at the targeting stage of the problem. Over the years, researchers have used various approaches for managing resource conservation networks under uncertainty. This review examines the different mathematical optimization approaches adopted for handling uncertainties associated with the resource conservation networks along with their practical applications in recent years. The paper primarily examines the four most common approaches used to address uncertainties in process integration: sensitivity analysis, chance-constrained programming, fuzzy optimization, and interval programming. Recent advances in handling uncertainties within the framework of process integration, covering both mathematical programming and Pinch analysis, are also discussed. The review ends with a discussion on the significance and contributions of recent approaches. Some of the important future research directions are also identified to be addressed using process integration and Pinch analysis. [GRAPHICS] .

关键词： Process integration Sensitivity analysis chance-constrained programming Fuzzy optimization Interval programming Pinch analysis

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Dynamic Operation Strategy of Battery Energy Storage System Considering Energy Arbitrage and Frequency regulation 5

Dynamic Operation Strategy of Battery Energy Storage System ...

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5th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Zhang, Shengqi Wang, Yilin Shanghai Univ Dept Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781665490542

With the rapid development of battery energy storage systems (BESS) in power system, the multifunctional application of BESS in the energy arbitrage and frequency regulation is regarded as an effective way in improving the economic benefits. However, current operation strategy for BESS cannot fully considered the time-varying demand of energy arbitrage and the randomness of frequency regulation. Thus, this paper proposes a dynamic operation strategy (DOS) by considering the randomness of frequency regulation, and the results of the DOS consist of two core parameters as arbitrage power and droop coefficient. At last, the promising results shown that the BESS DOS can adjust the dispatch priority of BESS resource according to demand of the energy arbitrage, and enable BESS continuously track the frequency deviation signals.

关键词： battery energy storage system energy arbitrage frequency regulation dynamic programming chance-constrained programming

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Stochastic day-ahead optimal scheduling of multimicrogrids: an alternatingdirection method of multipliers (ADMM) approach

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2022年第4期30卷 1354-1369页

作者： Safari, Amin Nasiraghdam, Hossein Azarbaijan Shahid Madani Univ Dept Elect Engn Tabriz Iran Islamic Azad Univ Ahar Branch Dept Elect Engn Ahar Iran

Multimicrogrid system is a novel notion in modern power systems as a result of developing renewable-based generation units and accordingly microgrids in distribution networks. Their energy management might be challenging due to presence of independent units. Thus, in this paper, a decentralized method for energy management of multimicrogrid systems has been proposed. Decentralized methods can enhance the privacy of users and reduce the burden of calculations. Alternating direction method of multipliers (ADMM) is selected as a decentralized approach which has the capability of breaking problems with complicating constraints in order to facilitate the solving process. Using decentralized approach not only reduces the burden of calculations, but also increases the privacy of entities. Wind turbines as renewable based generators are assumed to participate in this system. To model the uncertainties of these units, chance-constrained programming is employed. Also, due to clean output of hydrogen storage systems and fuel cells, the inclination for using these systems has expanded. Simulations on the test case study demonstrate the applicability and performance of the proposed methodology. Considering the reliability level as 0.9 results in 12, 989$ in the test case. By considering the reliability level as 0.8, the operational cost becomes 11, 712$ which shows a reduction of 1277$ which is achieved by jeopardizing the system reliability by 10%.

关键词： Stochastic programming multimicrogrid systems decentralized optimization energy management chance-constrained programming hydrogen storage system

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A chance-constrained programming Model for the Optimal Stopping Decision of Power R&D Project

A Chance-constrained Programming Model for the Optimal Stopp...

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International Conference on Advances in Energy and Environmental Science (ICAEES)

作者： Yi, Changsheng Guan, Xuebin Gao, Jianbing Anhui Univ Technol Sch Management Sci & Engn Maanshan 243032 Anhui Peoples R China

ISBN: (纸本)9783037858615

A power R&D process can be regarded as a jump process of scientific knowledge full of exploration and complexity. Every jump represents a scientific breakthrough or a new discovery in this setting. In this paper, the inter-arrival times are treated as random variables observe arbitrary distributions. The a-optimistic net return of project performance is proposed and a chance-constrained programming model is established to model the power R&D optimal stopping decision problem. Considering the complexity of the model, the stochastic simulation is designed to estimate the values of project return performance and the simultaneous perturbation stochastic approximation (SPSA) algorithm is employed to solve the proposed model. Finally, the effectiveness of the hybrid algorithm and the applicability of the model are illustrated by numerical examples.

关键词： Power R&D project Optimal stopping chance-constrained programming SPSA

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A mathematical model for managing the multi-dimensional impacts of the COVID-19 pandemic in supply chain of a high-demand item

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ANNALS OF OPERATIONS RESEARCH 2022年 1-46页

作者： Paul, Sanjoy Kumar Chowdhury, Priyabrata Chakrabortty, Ripon Kumar Ivanov, Dmitry Sallam, Karam Univ Technol Sydney UTS Business Sch Sydney NSW Australia RMIT Univ Sch Accounting Informat Syst & Supply Chain Melbourne Vic Australia Univ New South Wales Sch Engn & Informat Technol Canberra ACT Australia Berlin Sch Econ & Law Dept Business & Econ Supply Chain & Operat Management Block BB 3-49Badensche Str 50 D-10825 Berlin Germany Univ Canberra Sch IT & Syst Canberra ACT Australia Zagazig Univ Fac Comp & Informat Zagazig Egypt

The COVID-19 pandemic has wreaked havoc across supply chain (SC) operations worldwide. Specifically, decisions on the recovery planning are subject to multi-dimensional uncertainty stemming from singular and correlated disruptions in demand, supply, and production capacities. This is a new and understudied research area. In this study, we examine, SC recovery for high-demand items (e.g., hand sanitizer and face masks). We first developed a stochastic mathematical model to optimise recovery for a three-stage SC exposed to the multi-dimensional impacts of COVID-19 pandemic. This allows to generalize a novel problem setting with simultaneous demand, supply, and capacity uncertainty in a multi-stage SC recovery context. We then developed a chance-constrained programming approach and present in this article a new and enhanced multi-operator differential evolution variant-based solution approach to solve our model. With the optimisation, we sought to understand the impact of different recovery strategies on SC profitability as well as identify optimal recovery plans. Through extensive numerical experiments, we demonstrated capability towards efficiently solving both small- and large-scale SC recovery problems. We tested, evaluated, and analyzed different recovery strategies, scenarios, and problem scales to validate our approach. Ultimately, the study provides a useful tool to optimise reactive adaptation strategies related to how and when SC recovery operations should be deployed during a pandemic. This study contributes to literature through development of a unique problem setting with multi-dimensional uncertainty impacts for SC recovery, as well as an efficient solution approach for solution of both small- and large-scale SC recovery problems. Relevant decision-makers can use the findings of this research to select the most efficient SC recovery plan under pandemic conditions and to determine the timing of its deployment.

关键词： Supply chain resilience COVID-19 pandemic Recovery planning Stochastic modelling chance-constrained programming

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chance-constrained programming Method by Constraints Aggregation for Social Consensus Making on IT Risk Countermeasure

Chance-constrained Programming Method by Constraints Aggrega...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Katsunori Sato Masaki Samejima Ryoichi Sasaki Graduate School of Information Science and Technology Osaka University 2-1 Yamadaoka Suita Osaka Japan School of Science & Technology for Future Life Tokyo Denki University 5 Senju Asahi-cho Adachi-ku Tokyo Japan

ISBN: (纸本)9781479906505

This paper addresses the chance-constrained programming for the social consensus making on IT risk counter-measures. The sample average approximation method is a well-known method for solving the chance-constrained programming. However, the computational time is still so long that the opinion leaders can not use the solution of the chance-constrained programming in their discussion. So, we proposed the interactive chance-constrained programming method to show the solutions that are being searched to the opinion leaders. Through the preliminary experiment, we clarified the issues of sampling for the approximation and controlling the search of the solution.

关键词： chance-constrained programming Opinion leaders programming information technology Solutions

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Distributed optimal operation of PV-storage-load micro-grid considering renewable and load uncertainties

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JOURNAL OF ENERGY STORAGE 2024年 86卷

作者： Yin, Hang Wang, Yang Wu, Gang Liu, Youbo Chen, Yongdong Liu, Junyong Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China Power China Sichuan Elect Power Engn Co Ltd Chengdu 610041 Peoples R China State Grid Sichuan Econ Res Inst Chengdu 610041 Peoples R China

Micro-grid is one of the important carriers for renewable energy integration. Optimizing the operation mode of micro-grid can improve economic benefits and reduce risks brought by uncertainties. To maximize the economic benefits of photovoltaic-storage-load micro-grid, a chance-constrained optimal operation model considering renewable and load uncertainties is proposed in this paper. Firstly, the photovoltaic (PV) power and load forecast error distribution function based on fitting method is obtained. Then, a probabilistic power balance constraint based on chance constrained theory is established to reflect risks brought by uncertainties. To protect the information privacy of different entities, the alternating direction multiplier method (ADMM) with self-adaptive parameters is used to the proposed optimal model in a distributed way. Case study demonstrates that the proposed model can achieve a profit and risk balance of micro-grid scheduling by setting different confidence levels, and the information privacy of micro-grid operator and users is well protected.

关键词： Photovoltaic power stations energy storage Uncertainties ADMM chance-constrained programming

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Stochastic modeling for the aggregated flexibility of distributed energy resources ☆

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 234卷

作者： Wen, Yilin Guo, Yi Hu, Zechun Hug, Gabriela Tsinghua Univ Dept Elect Engn Beijing Peoples R China ETH Power Syst Lab Zurich Switzerland Empa Urban Energy Syst Lab Dubendorf Switzerland

This paper proposes an uncertainty modeling method for the aggregated power flexibility of DERs. Basically, both outer and inner approximated power-energy boundary models are utilized to describe the aggregated flexibility of controllable DERs. These power and energy boundary parameters are uncertain because the availability of controllable devices, such as electric vehicles and thermostatically controlled loads, cannot be precisely predicted. The optimal operation problem of the aggregator is thus formulated as chance-constrained programming (CCP). Then, a flexibility envelope searching algorithm based on the ALSO-X+ method is proposed to solve the CCP, the result of which is a conservative approximation of the original CCP but not as conservative as the Conditional Value-at-Risk approximation. After optimizing the aggregated power of the group of DERs, the decision at the aggregator level is disaggregated into the flexibility regions of individual DERs. Finally, the numerical test demonstrates the effectiveness and robustness of the proposed method.

关键词： Aggregated flexibility chance-constrained programming Distributed energy resources Flexibility modeling and identification Uncertainty modeling

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Integrated remanufacturing scheduling of disassembly, reprocessing and reassembly considering energy efficiency and stochasticity through group teaching optimization and simulation approaches

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ENGINEERING OPTIMIZATION 2024年第12期56卷 2018-2039页

作者： Fu, Yaping Zhang, Zhengpei Liang, Pei Tian, Guangdong Zhang, Chaoyong Qingdao Univ Sch Business Qingdao Peoples R China Beijing Univ Civil Engn & Architecture Sch Mech Elect & Vehicle Engn Beijing Peoples R China Shandong Univ Key Lab High Efficiency & Clean Mech Manufacture Minist Educ Jinan Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

The energy crisis and environmental pollution are receiving increasing attention from governments and communities. This study researches energy-aware remanufacturing systems. Remanufacturing aims to reuse valuable resources from end-of-life products and produce as-new products. Since remanufacturing systems involve a series of disassembly, processing and assembly operations, remanufacturing schedule integrates disassembly, processing and assembly shops. A multi-objective scheduling of remanufacturing systems is proposed, considering workstation use, energy consumption and customer satisfaction simultaneously. A chance-constrained programming model is established to minimize makespan and energy consumption while satisfying total tardiness requirements. A hybrid method is developed, using group teaching optimization and a discrete event simulation system, which can seek and evaluate potentially favourable solutions. The approach is validated on a group of test instances using well-known methods. The results reveal that this method can find non-dominated solutions with well-converged and well-diversified performance, verifying its advantages in providing informed decisions for managers and engineers.

关键词： Remanufacturing scheduling multi-objective optimization group teaching optimization chance-constrained programming discrete event simulation

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