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Data-driven stochastic optimization for power grids scheduling under high wind penetration

ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2023年第1期14卷 41-65页

作者： Xie, Wei Yi, Yuan Zhou, Zhi Wang, Keqi Northeastern Univ Boston MA 02115 USA Rensselaer Polytech Inst Troy NY 12180 USA Argonne Natl Lab 9700 S Cass Ave Argonne IL 60439 USA

To address the environmental concern and improve the economic efficiency, the wind power is rapidly integrated into smart grids. However, the inherent uncertainty of wind energy raises operational challenges. To ensure the cost-efficient, reliable and robust operation, it is critically important to find the optimal decision that can correctly and rigorously hedge against all sources of uncertainty. In this paper, we propose data-driven stochastic unit commitment (SUC) to guide the power grids scheduling. Specifically, given the finite historical data, the posterior predictive distribution is developed to quantify the wind power prediction uncertainty accounting for both inherent stochastic uncertainty of wind power generation and input model estimation error. For complex power grid systems, a finite number of scenarios is used to estimate the expected cost in the planning horizon. To further control the impact of finite sampling error induced by using the sample average approximation (SAA), we propose a parallel computing based optimization solution methodology, which can quickly find the reliable optimal unit commitment decision hedging against various sources of uncertainty. The empirical study over six-bus and 118-bus systems demonstrates that our approach can provide more cost-efficient and robust performance than the existing deterministic and stochastic unit commitment approaches.

关键词： stochastic programming Unit commitment Parallel computing Wind power Power grids scheduling Renewable energy

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Multistage stochastic dominance: an application to pension fund management

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ANNALS OF OPERATIONS RESEARCH 2023年 1-21页

作者： Kopa, Milos Moriggia, Vittorio Vitali, Sebastiano Charles Univ Prague Fac Math & Phys Dept Probabil & Math Stat Sokolovska 83 Prague 18675 Czech Republic Univ Bergamo Dept Econ Via Caniana 2 I-24127 Bergamo BG Italy

A pension fund manager typically decides the allocation of the pension fund assets taking into account a long-term sustainability goal. Many asset and liability management models, in the form of multistage stochastic programming problem, have been proposed to help the pension fund manager to define the optimal allocation given a multi-objective function. The recent literature proposes univariate stochastic dominance constraints to guarantee that the optimal strategy is able to stochastically dominate a benchmark portfolio. In this work we extend previous results (i) considering alternative types of multivariate stochastic dominance that appear more suitable in a multistage framework, (ii) proposing a way to measure the economic cost of introducing stochastic dominance constraints, (iii) proposing a sort of augmented stochastic dominance through a safety margin. Numerical results show the difference between the alternative ways to interpret and apply the multivariate stochastic dominance. These results are evaluated thanks to the proposed economic cost of the stochastic dominance constraints and either in presence or not of a safety margin.

关键词： stochastic programming Portfolio selection Sensitivity analysis Asset and liability management Pension fund stochastic dominance

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Coordinated Generation Expansion Planning for Transmission and Distribution Systems via ADMM

Coordinated Generation Expansion Planning for Transmission a...

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International Conference on Smart Energy Systems and Technologies (SEST) - Driving the Advances for Future Electrification

作者： Chowdhury, Nahid-Ur-Rahman Munoz-Delgado, Gregorio Alguacil, Natalia Contreras, Javier Univ Castilla La Mancha ETS Ingn Ind Ciudad Real Spain

ISBN: (纸本)9798350386509;9798350386493

This paper presents a practical algorithm designed to address the generation expansion planning problem across both transmission and distribution systems. Conventional and wind-based generators are eligible to be installed under a set of scenarios that captures the uncertainty arising from different operating conditions, demand fluctuations, and wind dynamics. Assuming there is one planner at the transmission level and distinct planners for each of the analyzed distribution systems, an optimal expansion plan for the whole power system is obtained. The problem presented, framed as a scenario-based linear programming instance, is addressed here using a coordinated but distributed approach through the Alternating Direction Method of Multipliers (ADMM). Unlike other works that also facilitate transmission and distribution planners' coordination, the proposed solution methodology not only guarantees the finite convergence to optimality but also the privacy of information related to each planner's assets. Privacy is achieved by solely exchanging information on the injections at the interface nodes among neighboring transmission and distribution systems. The effectiveness of the ADMM algorithm is assessed using a 36-node test system, revealing a reasonable tradeoff between solution quality and computation times for a practical setting.

关键词： Coordinated transmission and distribution planning Distributed optimization Generation expansion planning stochastic programming Uncertainty

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Energy Storage Cluster Planning Method Accelerated by Progressive Hedging Algorithm Considering Uncertainty in Renewable Energy and Load 2

Energy Storage Cluster Planning Method Accelerated by Progre...

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IEEE 2nd International Conference on Power Science and Technology (ICPST)

作者： Wang, Weihua Zhao, Shuai Lin, Yingming Liu, Yang Xie, Pingping Li, Yiran Li, Gengfeng Guangdong Power Grid Co Ltd Elect Power Dispatching & Control Ctr Guangzhou Peoples R China Xi An Jiao Tong Univ Smart Grid Key Lab Shaanxi Prov Xian 710049 Peoples R China

ISBN: (纸本)9798350349047;9798350349030

The high proportion of renewable energy presents numerous new features in the power system, which poses new challenges for the planning and operation of the power system. The energy storage system is an important technology and basic equipment to support the construction of the new power system under the dual carbon goals. Rational configuration of energy storage cluster can effectively improve the operation of the power system. To solve the issue that the current requirements on the energy storage cluster scale of power systems with substantial renewable energy output are too general to provide a suitable energy storage cluster configuration scheme, this paper proposes an energy storage cluster planning method to describe the uncertainty of renewable energy output and load. Considering the characteristics of the system, the proposed method is formulated as a stochastic programming model to plan the energy storage cluster. The progressive hedging algorithm is employed to solve the proposed model. Finally, the modified IEEE RTS79 test system is used to verify the method, and the best RER range is obtained.

关键词： uncertainty stochastic programming progressive hedging algorithm energy storage cluster

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Electric Vehicle-Based Virtual Batteries in Power Distribution System Operation

Electric Vehicle-Based Virtual Batteries in Power Distributi...

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International Conference on Smart Energy Systems and Technologies (SEST) - Driving the Advances for Future Electrification

作者： Diaz-Cachinero, Pablo Guldorum, Hilmi Cihan Cuadra-Garcia, Alejandro Munoz-Delgado, Gregorio Contreras, Javier Univ Carlos III Madrid Leganes Spain Yildiz Tech Univ Istanbul Turkiye Univ Castilla La Mancha ETS Ingn Ind Ciudad Real Spain

ISBN: (纸本)9798350386509;9798350386493

Environmental concerns and the need for sustainable energy are transforming energy systems, highlighting the role of electric vehicles (EVs) and renewable energy sources in reducing the emissions in transportation and power distribution systems, respectively. In this paper, a scenario-based stochastic energy schedulingmodel for the power distribution system operation is proposed, considering the presence of EVs in the form of virtual batteries. This model also takes into account conventional generation, wind and solar generation, battery banks (BBs), and ac power flow. Furthermore, since an EV-based virtual battery is a system that combines the stored energy of some EVs to act as a single large battery, it is necessary to characterize its operation in terms of EV behavior. To that end, a probability-based methodology is developed to characterize the uncertain parameters of EV-based virtual batteries relying on historical data. Such parameters include initial energy, minimum and maximum energy limits, and net balance of energy entering and leaving the virtual battery, as well as the charging and discharging power limits. In addition, the uncertainty associated with renewable generation, demand, and prices is also considered. Finally, the proposed model is tested in the IEEE-37 bus system. Numerical results demonstrate the relevance of EVs for the operation of power distribution systems.

关键词： Conic Power Flow Distribution Systems Electric Vehicle stochastic programming Virtual Battery

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stochastic Bike-Sharing Transport Network Design 2nd

Stochastic Bike-Sharing Transport Network Design

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2nd Southwest Data Science Conference (SDSC)

作者： Wang, Gang Fan, Yiwei Lu, Xiaoling Univ Massachusetts Dartmouth N Dartmouth MA 02747 USA Renmin Univ China Beijing 100872 Peoples R China

ISBN: (纸本)9783031618154;9783031618161

As a transport mode, bike-sharing has gained popularity worldwide because it is environmentally friendly and cost-efficient. However, as a bike-sharing network grows, operating costs at rental centers increase. The problem is determining the locations of rental centers to open and the number of bicycles that will be transferred daily between rental centers while minimizing the total operating costs. We present a stochastic programming model and a Benders decomposition-based hybrid algorithm. We consider two scenarios for demand-return machine learning models - time series-based prediction and weather-based forecasting. Finally, we provide a case study of developing a bike-sharing network in New York City to verify the significance of the proposed models. We also evaluate the performances of demand-return prediction models and the impact of the relative ratio between demand and return on bike-sharing network design. We find no bicycle transfer if the penalty cost for a rental station has an inverse linear relationship with the ratio of returns to rentals. Nevertheless, when the penalty cost is exponentially dependent on the negative ratio of returns to rentals, bicycle transfer occurs between rental stations with large ratios.

关键词： Bike sharing transport network Uncertain demand and return stochastic programming Machine learning Benders decomposition

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Maritime fleet composition under future greenhouse gas emission restrictions and uncertain fuel prices

MARITIME TRANSPORT RESEARCH

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MARITIME TRANSPORT RESEARCH 2024年 6卷

作者： Loennechen, Olav Fagerholt, Kjetil Lagemann, Benjamin Stalhane, Magnus Norwegian Univ Sci & Technol Dept Ind Econ & Technol Management Trondheim Norway Norwegian Univ Sci & Technol Dept Marine Technol Trondheim Norway SINTEF Ocean Trondheim Norway

This paper studies the maritime fleet composition problem with uncertain future fuel and carbon prices under the restriction of complying with future greenhouse gas (GHG) emission restrictions. We propose a two -stage stochastic programming model that can be adapted to two different variants of this problem. The first variant considers the Maritime Fleet Renewal Problem where there is an existing initial fleet to be renewed through scrapping and acquisitions, as well as retrofitting of ships in the current fleet. The second variant considers the Maritime Fleet Size and Mix Problem, where also the initial fleet must be determined. When applying the model to a fleet of Supramax bulk carriers as a case study, we find that LNG- and methanol -based power systems are favorable initial choices. Two different scenario sets, with 50% and 90% reduction restrictions by 2045, are investigated. Depending on the ambition level, retrofits towards ammonia can be cost-effective.

关键词： Maritime transportation Greenhouse gas emissions Alternative fuels Retrofit stochastic programming

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Resilient Post-Disaster Rescue Framework Using Mobile and Connected Electric Vehicles 99

Resilient Post-Disaster Rescue Framework Using Mobile and Co...

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IEEE 99th Vehicular Technology Conference (VTC-Spring)

作者： Zheng, Yang Chen, Nan Wang, Miao Tennessee Technol Univ Dept Elect & Comp Engn Cookeville TN 38505 USA Univ North Carolina Charlotte Energy Prod & Infrastruct Ctr Charlotte NC USA

ISBN: (纸本)9798350387414

The increasingly frequent occurrence of natural disasters has severely interfered with the operation of fundamental infrastructures such as power, transportation, and communication systems. For these decades-old infrastructures, enhancing the system resilience requires extremely high upgrade expenditure. Therefore, more flexible and cost-efficient solutions are in urgent demand. Equipped with on-broad large-capacity batteries, electric vehicles (EVs) could serve as mobile post-disaster rescue devices, namely mobile energy storage (MES). This paper proposes a flexible post-disaster rescue scheme using mobile and connected EVs as MESs to supply emergency resources before the fundamental infrastructures fully recover. Different from existing literature, this paper uncovers the potential energy supply and communication capabilities of MESs to provide damaged areas with on-demand energy and communication resources. Specifically, the uncertainty of natural disasters of tornadoes and flooding is modelled during different scenario generations. Then, a two-stage stochastic programming problem is formulated to determine the MES deployment location in the pre-disaster stage and the MES service operation in the post-disaster stage. The generated disaster scenarios are integrated into the formulated problem to ensure a statistically optimal result. Simulation results validate the optimality of the proposed scheme compared to benchmark schemes.

关键词： stochastic programming

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Matching Optimization of Wind-solar Complementary Power Generation System Based on Joint Probability Distribution 9

Matching Optimization of Wind-solar Complementary Power Gene...

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9th International Conference on Power and Renewable Energy

作者： Bao, Daorina Liu, Lihui Gao, Fan He, Jiawei Huang, Qianqian Inner Mongolia Univ Technol Sch Energy & Power Engn Hohhot Peoples R China

ISBN: (纸本)9798350377477;9798350377460

The intermittency, randomness and volatility of wind power and photovoltaic power generation bring trouble to power system planning. The capacity configuration of integrated energy system including wind power photovoltaic usually does not consider the uncertainty of wind power output, resulting in too idealized system capacity configuration and low equipment utilization. In this paper, Copula theory is applied to establish the joint probability distribution model of wind farm and photovoltaic power plant output, and the typical daily output sequence is obtained. A capacity matching method of wind-wind complementary system based on stochastic programming is proposed to effectively suppress the output fluctuation of new energy generation. Finally, a typical daily wind-solar output scenario is generated according to the measured data, and the capacity ratio of the wind-solar complementary system is optimized by analyzing the example.

关键词： Wind power generation Photovoltaic power generation Joint probability distribution stochastic programming Ratio optimization

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A Large Neighborhood Search Metaheuristic for the stochastic Mixed Model Assembly Line Balancing Problem with Walking Workers 15th

A Large Neighborhood Search Metaheuristic for the Stochastic...

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15th International Conference of the Metaheuristics International Conference (MIC)

作者： Thompson, Joseph Orion Lahrichi, Nadia Meyer, Patrick Mohammadi, Mehrdad Thevenin, Simon Polytech Montreal Montreal PQ Canada IMT Atlantique Nantes France Eindhoven Univ Technol Eindhoven Netherlands IMT Atlantique Brest France

ISBN: (纸本)9783031629211;9783031629228

This work proposes a Large Neighborhood Search Metaheuristic for solving a mixed-model assembly line balancing problem with walking workers and dynamic task assignment. The considered problem is a multi-stage stochastic program with integer recourse. These problems are very hard to solve because the number of binary variables increases exponentially with the number of production cycles. We study different decomposition approaches, and our results suggest that re-optimizing for a sub-tree outperforms other decompositions, such as model-based or station decomposition.

关键词： Assembly Line Balancing Large neighborhood search stochastic programming

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