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A sampling-based solution approach for electricity capacity expansion planning with chance constraint

COMPUTERS & INDUSTRIAL ENGINEERING 2021年 162卷

作者： Kim, Hansung Min, Daiki Choi, Dong Gu Pohang Univ Sci & Technol Dept Ind & Management Engn 77 Cheongam Ro Pohang 37673 Gyeongbuk South Korea Ewha Womans Univ Sch Business 52 Ewhayeodae Gil Seoul 03760 South Korea Pohang Univ Sci & Technol Open Innovat Big Data Ctr 77 Cheongam Ro Pohang 37673 Gyeongbuk South Korea

In order to achieve carbon neutrality, several countries and regions have set specific targets for the reduction of carbon emissions. Although many policy makers agree on the uncertainty of the several factors which are involved in the planning of future energy systems, they have yet to consider the risks that their policies will incur, should they fail to meet current carbon emission targets. In this study, we propose a stochastic programmingbased energy system model which takes into account various economic and technical uncertainty factors in addition to a chance-constraint, in order to assess the feasibility of meeting carbon emission targets. To determine the chance-constrained programming model efficiently, we propose a solution-based approach which adopts a sample average approximation approach. In addition, we combine the sampling-and-discarding approach with a newly devised heuristic algorithm that is suitable for our proposed model. Furthermore, as it is a numerical study, we apply our model and solution approach to evaluate the long-term capacity expansion plan under the carbon emission target within the Korean electricity sector. Through this numerical study, we verify both the solution quality and computational time of our solution approach, as well as the usability of our model for the trade-off analysis between costs and risks.

关键词： Carbon emission target Energy system model chance-constrained programming Sample Average Approximation Sampling-and-discarding Heuristic removal algorithm

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Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications

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ENERGIES 2021年第20期14卷 6658页

作者： Hong, Ying-Yi Apolinario, Gerard Francesco DG. Chung Yuan Christian Univ Dept Elect Engn Taoyuan 32023 Taiwan Technol Inst Philippines Elect Engn Dept Manila 1001 Philippines

The unit commitment problem (UCP) is one of the key and fundamental concerns in the operation, monitoring, and control of power systems. Uncertainty management in a UCP has been of great interest to both operators and researchers. The uncertainties that are considered in a UCP can be classified as technical (outages, forecast errors, and plugin electric vehicle (PEV) penetration), economic (electricity prices), and "epidemics, pandemics, and disasters " (techno-socio-economic). Various methods have been developed to model the uncertainties of these parameters, such as stochastic programming, probabilistic methods, chance-constrained programming (CCP), robust optimization, risk-based optimization, the hierarchical scheduling strategy, and information gap decision theory. This paper reviews methods of uncertainty management, parameter modeling, simulation tools, and test systems.

关键词： chance-constrained programming hierarchical scheduling strategy information gap decision theory probabilistic methods risk-based optimization robust optimization stochastic programming unit commitment problem uncertainty

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A chance-constrained small modular reactor siting model -- a case study for the Province of Saskatchewan, Canada

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2021年 148卷 111320-111320页

作者： Luo, Bin Huang, Guohe Chen, Jiapei Zhang, Xiaoyue Zhao, Kai Univ Regina Fac Engn & Appl Sci Regina SK S4S 0A2 Canada Beijing Normal Univ China Canada Ctr Energy Environm & Ecol Res State Key Joint Lab Environm Simulat & Pollut Con URB NUSch Environm Beijing 100875 Peoples R China

In this study, a chance-constrained small modular reactor siting (CCSS) model has been first proposed for planning regional electric power systems in the Province of Saskatchewan under the pressure of greenhouse gas emission mitigation. Through incorporating three programming methods (interval linear programming, chanceconstrained programming, and mixed-integer programming) in an optimization framework, the CCSS model could effectively deal with multiple uncertainties expressed as probability distribution and intervals in constraints and objectives. Since locations of retired coal-fired power stations have been proposed as new sites for small modular reactor (SMR), this model could provide the construction planning for SMR in terms of its sizes and sites. Various solutions to power generation and capacity expansion with different risk levels were obtained under the objective of minimal system costs. Results are helpful for identifying optimized strategies to increase the proportion of renewable energy under emission constraints. In addition, model without introducing the technology of SMR has been comprehensively compared with the CCSS model to recognise the effects of SMR on system costs and greenhouse gas emissions. Results indicate that the CCSS model is effective for supporting longterm clean and renewable energy utilization as well as emission reduction policy formulation in Saskatchewan.

关键词： SMR siting Electric power systems Optimization GHG emission mitigation Multiple uncertainties chance-constrained programming

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A chance-constrained urban agglomeration energy model for cooperative carbon emission management

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ENERGY 2021年 223卷 119885-119885页

作者： Cao, R. Huang, G. H. Chen, J. P. Li, Y. P. He, C. Y. Beijing Normal Univ Sch Environm Beijing 100875 Peoples R China Beijing Normal Univ UR BNU Ctr Energy Environm & Ecol Res Beijing 100875 Peoples R China Univ Regina Inst Energy Environm & Sustainable Communities Regina SK S4S 0A2 Canada

The impacts of climate change need to be mitigated by controlling the emissions of carbon dioxide. The restriction of carbon dioxide emissions will limit fossil-fuel -fired electricity and affect water consumption in energy-related industries. In this study, a chance-constrained urban agglomeration energy model is developed to plan a cooperative energy-water nexus system with carbon dioxide emission constraints at the regional scale. The model can not only reflect uncertainties expressed as random variables and interval values but also manage energy-water nexus systems under cooperation mechanism effectively. The model is applied to the Guangzhou-Shenzhen-Dongguan region, wherein two scenarios with/ without cooperative carbon dioxide emission strategies under different water resource risks are analyzed. Results reveal that the system cost of the study region would decrease under the cooperative carbon dioxide emission strategy. Compared with the case without mitigation cooperation, the cost of those with cooperation would reduce by [4.3, 4.8] % (with 6.2% more carbon dioxide emission) in Shenzhen. The results also demonstrate that water consumption would increase as constraint-violation level decreases from 0.2 to 0. The developed model is efficient for facilitating regional dynamic analyses of carbon dioxide emission mitigation and the associated economic cost under different policy scenarios and system risks. ? 2021 Elsevier Ltd. All rights reserved.

关键词： chance-constrained programming Cooperative management Emission mitigation Energy-water nexus Regional energy system Uncertainty

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On the sustainable perishable food supply chain network design: A dairy products case to achieve sustainable development goals

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JOURNAL OF CLEANER PRODUCTION 2021年 278卷 123060-123060页

作者： Jouzdani, Javid Govindan, Kannan Golpayegan Univ Technol Dept Ind Engn Golpayegan Iran Shanghai Maritime Univ China Inst FTZ Supply Chain Shanghai 201306 Peoples R China Univ Southern Denmark Danish Inst Adv Study Dept Technol & Innovat Ctr Sustainable Supply Chain Engn DK-5230 Odense M Denmark

Perishable products require special handling measures that may have social and environmental impacts along with their well-known economic aspects. Therefore, the sustainability of food supply chains has gained ground;however, the sustainability of perishable food supply chains, is still not a fully explored field of study. Therefore, in this research, a multi-objective mathematical programming model is developed to optimize the cost, energy consumption, and the traffic congestion associated with such supply chain operations. In this study, product lifetime uncertainty is explicitly modeled as a Weibull random variable, and food perishability is assumed to be affected by vehicle refrigerator utilization, which is considered as a decision variable. In addition, multiple vehicle types and multiple product types are considered. A dairy supply chain case is investigated, and the interrelations and interactions of all three aspects of sustainability, also known as the three triple bottom lines (TBL) of sustainability, are studied. The results indicate that emphasizing the economic aspect, for highly perishable products, the environmental impact of the chain may increase by 120%, and for the highly congested road networks, the social impact may rise by 51%. However, a 15% economic compromise can improve the sustainability of the supply chain network design by 150%. It is also shown that road congestion and the uncertain perishability of the products are critical factors that can, although differently, affect the operation and the design of the supply chain. This study contributes to the sustainable development goals (SDG's) such as Zero Hunger (SDG 2);Affordable and Clean Energy (SDG 7);Decent Work and Economic Growth (SDG 8);Industry, Innovation, and Infrastructure (SDG 9);Responsible Consumption and Production (SDG 12);Climate Action (SDG 13) and Peace, Justice, and Strong Institutions (SDG 16). The results suggest that decision-makers can significantly reduce the enviro

关键词： Supply chain network design Sustainability Perishable food Goal programming chance-constrained programming Dairy product

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Identifying optimal land-use patterns using a copula-based interval stochastic programming model for urban agglomeration under uncertainty

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ECOLOGICAL ENGINEERING 2020年 142卷 105616-000页

作者： Li, H. W. Li, Y. P. Huang, G. H. Gao, P. P. North China Elect Power Univ Sino Canada Energy & Environm Res Ctr Beijing 102206 Peoples R China Beijing Normal Univ Sch Environm Beijing 100875 Peoples R China Univ Regina Inst Energy Environm & Sustainable Communities Regina SK S4S 7H9 Canada

In this study, a copula-based interval stochastic programming (CISP) model is developed for land-use planning management (LUPM), where uncertainties presented as interval values and random variables can be tackled. CISP can also reflect the interactions among random variables with different probability distributions. A CISP-LUPM model is formulated for identifying desired land-use patterns for the Guangzhou-Zhongshan urban agglomeration. Solutions related to land-use pattern, ecosystem services value (ESV), and system benefit have been generated under multiple scenarios with different constraint-violation levels. Results reveal that the forest and grass cover rate for the urban agglomeration would be [42.0, 44.4] % under synergistic scheme at the end of planning period. Results also show that the ESV supported by water area accounts for [44.6, 46.3] % the total ESVs, and waterbody plays a major role in maintaining the balance of the urban agglomeration ecosystem. Results also disclose that, under the influence of interactive ecological factors, the change in land-use patterns for Guangzhou is more sensitive to system violation-risk level than that for Zhongshan. The findings are helpful for reflecting interaction among ecological factors and disclosing their joint effects on land-use patterns, thus supporting an in-depth analysis of tradeoff between socio-economic development and ecological sustainability.

关键词： chance-constrained programming Copula Ecosystem services Joint probability distribution Land-use planning management

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Stochastic optimal planning scheme of a zero-carbon multi-energy system (ZC-MES) considering the uncertainties of individual energy demand and renewable resources: An integrated chance-constrained and decomposition algorithm (CC-DA) approach

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ENERGY 2021年 232卷 121000-121000页

作者： Alabi, Tobi Michael Lu, Lin Yang, Zaiyue Southern Univ Sci & Technol Dept Mech & Energy Engn Shenzhen Peoples R China Hong Kong Polytech Univ Dept Bldg Serv Engn Renewable Energy Res Grp RERG Hong Kong Peoples R China

Renewable resources stochasticity and energy demand variation are inevitable during the operation of energy systems. Thus, this paper presents novel stochastic planning and operation of a zero-carbon multi-energy system (ZC-MES) taking the uncertainties of individual energy demand and environmental conditions into consideration. The comprehensive mathematical model is developed as an optimization problem using Monte Carlo scenario generation, fast forward scenario reduction approach, chance-constrained programming, duality theory, and big-M linearization approach. Furthermore, benders decomposition is applied to split the large-scale optimization problem into an investment master problem (MP) and operation subproblem (SP), which are then solved iteratively. The obtained results indicate that by considering all the energy demand uncertainties as an individual entity in the model, the selected capacities of PV, AC, and HWS increase by 60%, 21%, and 10.6%, respectively, while, WT, WSHP, BES, and EC reduce by 14%, 15%, 11%, and 1.09%, respectively. Also, the optimal operation cost for the proposed model reduced by 5-10% compared to other scenarios, however, its annualized investment cost is 3-12% higher but the overall economic implication is optimal. In summary, the underlying reason for this optimal result is the combination of energy storage temporal arbitrage and energy output shifting technique that was implemented by the algorithm to maintain an optimal interrelationship balance and to favour the optimal sizing of the chosen technologies. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Stochastic optimization Zero-carbon Multi-energy system Renewable energy chance-constrained programming Benders decomposition

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chance-constrained energy and multi-type reserves scheduling exploiting flexibility from combined power and heat units and heat pumps

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ENERGY 2021年 233卷 121176-121176页

作者： Tan, Jin Wu, Qiuwei Zhang, Menglin Wei, Wei Liu, Feng Pan, Bo Tech Univ Denmark Ctr Elect Power & Energy Dept Elect Engn DK-2800 Lyngby Denmark Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Jiaxing Guodiantong New Energy Technol Co Ltd Zhifu Ctr Bldg 29966 Xiuyuan Rd Jiaxing 314000 Zhejiang Peoples R China

District heating systems can provide considerable flexibility for electric power systems through combined heat and power units and heat pumps. This paper proposes a chance-constrained two-stage energy and multi-type reserves scheduling scheme for integrated electricity and heating systems to handle both wind power forecast errors and the outage of the largest generator. The combined heat and power units and heat pumps not only provide following reserves for offsetting wind power forecast errors under normal conditions, but also provide primary frequency response reserves for arresting system frequency decline after the outage of the largest generator. The primary frequency response reserves from combined heat and power units and heat pumps are optimized satisfying system steady-state frequency requirement while considering the reserve costs. To manage the risk level of load shedding caused by wind power forecast errors, chance constraints are adopted to achieve the trade-off between the sufficiency of following reserves and system economic efficiency. The nonlinear and nonconvex scheduling model is reformulated as a mixed-integer linear program via linearization and convex approximation based on conditional value-at-risk. The effectiveness of the proposed scheduling scheme in improving the system frequency regulation, system economic efficiency, and wind power integration is verified through the case studies on a 6-bus and 6-node integrated electricity and heating system and a regional large-scale test system. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： chance-constrained programming CHPs and HPs Conditional value-at-risk Following reserves Primary frequency response reserve

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Subband Scheduling for NOMA with Probabilistic QoS and Aligned Transmission Constraints

Subband Scheduling for NOMA with Probabilistic QoS and Align...

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IEEE International Conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond

作者： Thieu, Quang-Tuan Hsieh, Hung-Yun Natl Taiwan Univ Grad Inst Commun Engn Taipei 106 Taiwan Natl Taiwan Univ Dept Elect Engn Taipei 106 Taiwan

ISBN: (纸本)9781728174402

Non-Orthogonal Multiple Access (NOMA) is a promising direction to meet the demand for high spectral efficiency in the SG network. To fully utilize its advantage, an efficient algorithm for scheduling multiple subbands while taking into account practical requirements such as probabilistic QoS and aligned transmission constraints is needed. In this paper, we apply a probabilistic method to relax the constraints and decouple the optimal scheduling problem into two sub-problems, namely user assignment and power allocation. We solve each sub-problem by using parametric and iterative algorithms. Simulation results show that the proposed method outperforms the baseline.

关键词： chance-constrained programming probabilistic clustering user assignment power allocation

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chance-constrained optimization for integrated local energy systems operation considering correlated wind generation

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 132卷 107153-107153页

作者： Huo, Da Gu, Chenghong Greenwood, David Wang, Zhaoyu Zhao, Pengfei Li, Jianwei Newcastle Univ Sch Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England Univ Bath Dept Elect & Elect Engn Bath BA2 7AY Avon England Iowa State Univ Ames IA 50011 USA Coventry Univ Inst Future Transport & Cities Ctr Adv Low Carbon Prop Syst Coventry W Midlands England

Energy hubs, which integrate multiple energy vectors through converters, can enhance the value of Integrated Local Energy Systems (ILES) via increased flexibility and reduced costs. However, uncertain renewable energy and the non-convex, non-linear properties of energy flows complicate the modelling and operation of energy hub systems. This paper develops chance-constrained optimization methods for planning and operation of energy hub systems under uncertainty. The non-linear formulations of power and gas flows are relaxed by convexification methods, leading to a formulation of Second Order Cone Problem (SOCP), which can be efficiently solved to global optimality. The correlation between geographically close wind generators connected to the hub systems is modelled by establishing their relation using Gaussian copula. The proposed chance-constrained optimization is demonstrated on a six-hub system within a multi-vector energy distribution network with 7 electrical buses and 7 gas nodes. The value of different levels of system integration through the installation of energy hubs is investigated. The results show that by combining system integration via energy hubs with chance constrained operation, the proposed method can reduce operating costs and increase renewable energy yields, thereby benefitting hub system operators and customers with reduced energy infrastructure investment and energy costs.

关键词： chance-constrained programming Copula Correlation Distribution network Energy hub Integrated Local Energy Systems

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