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7th IEEE International Conference on Power and Renewable Energy, ICPRE 2022

作者： Yin, Jianbing Qin, Hongpei Wang, Junhai Zhu, Chunjie Chen, Lin Xu, Hang State Grid Hangzhou Electric Power Supply Company Hangzhou China Zhejiang University College of Electrical Engineering Hangzhou China

ISBN: (数字)9781665450638

ISBN: (纸本)9781665450638

The County Energy Internet (CEI) is an emerging trend of energy utilization under energy transition, which integrates multiple energy resources and microgrids (MGs), and can promote system economy, reliability as well as efficiency. Nevertheless, the operation of the CEI faces challenges and opportunities due to the distinct operational characteristics of subsystems and the privacy-preserving issues among entities. In this paper, a decentralized stochastic operation method is presented to achieve energy and reserve co-optimization and to mitigate the impact of intermittent photovoltaic. Besides, reserve capacity of every independent energy autonomy is determined by chance-constrained programming (CCP), and these probabilistic constraints are transformed into deterministic counterpart based on probabilistic prediction results. Moreover, a decentralized coordination algorithm based on analytical target cascading (ATC) is proposed to coordinate the CEI operator and MG operators with least information sharing. Case study on a CEI with a modified 15-bus power distribution system and a 6-bus district heating system has validated the proposed method. © 2022 IEEE.

关键词： stochastic programming

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Overview of Power System Planning under Uncertainty 2

Overview of Power System Planning under Uncertainty

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2nd International Conference on Electrical Engineering and Control Science, IC2ECS 2022

作者： Zhao, Yuanshang Lin, Weifang China Electric Power Research Institute Beijing100192 China

ISBN: (纸本)9798350399165

With the gradual increase of the proportion of renewable energy generation dominated by wind power and photovoltaic power and the change of load characteristics, the source-load uncertainty of power system will be one of the new power system characteristics with high proportion of new energy. Therefore, at this stage, the uncertainty method should be mainly used for power system planning. For the planning of power system under uncertain conditions, the uncertain variables that affect the planning of power system are first introduced, including new energy output and load forecasting, new factors of power system, investment cost, policy and environment. Then, the application of stochastic programming method in power system uncertainty planning is introduced, including the general scenario model of stochastic programming and the stochastic programming model of power system with opportunity constraints, and the probability prediction of uncertain variables in stochastic programming is introduced. Secondly, the application of power system robust optimization method in power system uncertain planning is introduced, including classical robust optimization method and distributed robust optimization method, and the interval prediction of uncertain variables in classical robust optimization method is introduced. stochastic programming and classical robust optimization should study how to better describe the information of uncertain variables in the future. At present, the distributed robust optimization method should first solve the planning adaptability problem of high-proportion new energy power system. Finally, the future research on uncertain planning of power system is prospected. © 2022 IEEE.

关键词： stochastic programming

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Intraday PV-storage power reporting model considering temporal correlation of forecast error 5

Intraday PV-storage power reporting model considering tempor...

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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Liu, Shujun Wang, Bo Zhuang, Weihuan Zhao, Weiran Co. Ltd. Beijing China China Three Gorges Corporation Fujian Energy Investment Co. Ltd. Engineering Management Department Fuzhou China China Electric Power Planning & Engineering Institute Technology Innovation and Industrial Planning Center Beijing China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

This paper proposes a strategy for reporting the intraday output plan of photovoltaic (PV) power plants considering the power correction of energy storage devices. First, an intraday PV power scenario generation method which considers the temporal correlation of intraday rolling forecasts is proposed. Secondly, considering the adjustment capability of energy storage devices, an intraday joint power reporting model of PV-storage system at different time periods is established. Finally, based on the actual data of the PV-storage power station in China, the simulation is carried out to verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： stochastic programming

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A data-driven approach for a class of stochastic dynamic optimization problems

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2021年第3期80卷 687-729页

作者： Silva, Thuener Valladao, Davi Homem-de-Mello, Tito Pontifical Catholic Univ Rio de Janeiro PUC Rio Dept Ind Engn Rua Marques de Sao Vicente 225 BR-22451900 Rio De Janeiro RJ Brazil Univ Adolfo Ibanez Sch Business Diagonal Torres 2640 Santiago Chile

Dynamic stochastic optimization models provide a powerful tool to represent sequential decision-making processes. Typically, these models use statistical predictive methods to capture the structure of the underlying stochastic process without taking into consideration estimation errors and model misspecification. In this context, we propose a data-driven prescriptive analytics framework aiming to integrate the machine learning and dynamic optimization machinery in a consistent and efficient way to build a bridge from data to decisions. The proposed framework tackles a relevant class of dynamic decision problems comprising many important practical applications. The basic building blocks of our proposed framework are: (1) a Hidden Markov Model as a predictive (machine learning) method to represent uncertainty;and (2) a distributionally robust dynamic optimization model as a prescriptive method that takes into account estimation errors associated with the predictive model and allows for control of the risk associated with decisions. Moreover, we present an evaluation framework to assess out-of-sample performance in rolling horizon schemes. A complete case study on dynamic asset allocation illustrates the proposed framework showing superior out-of-sample performance against selected benchmarks. The numerical results show the practical importance and applicability of the proposed framework since it extracts valuable information from data to obtain robustified decisions with an empirical certificate of out-of-sample performance evaluation.

关键词： stochastic programming Distributionally robust dynamic optimization Hidden Markov models Risk constraints stochastic dual dynamic programming

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Robust multi-product newsvendor model with uncertain demand and substitution

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期293卷 190-202页

作者： Zhang, Jie Xie, Weijun Sarin, Subhash C. Virginia Tech Dept Ind & Syst Engn Blacksburg VA 24061 USA

This work studies a Robust Multi-product Newsvendor Model with Substitution (R-MNMS), where the demand and the substitution rates are stochastic and are subject to cardinality-constrained uncertainty sets. The goal of this work is to determine the optimal order quantities of multiple products to maximize the worst-case total profit. To achieve this, we first show that for given order quantities, computing the worst-case total profit, in general, is NP-hard. Therefore, we derive the closed-form optimal solutions for the following three special cases: (1) if there are only two products, (2) if there is no substitution among different products, and (3) if the budget of demand uncertainty is equal to the number of products. For a general R-MNMS, we formulate it as a mixed-integer linear program with an exponential number of constraints and develop a branch and cut algorithm to solve it. For large-scale problem instances, we further propose a conservative approximation of R-MNMS and prove that under some certain conditions, this conservative approximation yields an exact optimal solution to R-MNMS. The numerical study demonstrates the effectiveness of the proposed approaches and the robustness of our model. (C) 2020 Elsevier B.V. All rights reserved.

关键词： stochastic programming Robust Cardinality-constrained uncertainty set Mixed-integer program Branch and cut algorithm

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Optimal postponement in supply chain network design under uncertainty: an application for additive manufacturing

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2021年第17期59卷 5198-5215页

作者： Ramon-Lumbierres, Daniel Heredia Cervera, F. Javier Minguella-Canela, Joaquim Muguruza-Blanco, Asier Univ Politecn Catalunya BarcelonaTech Dept Stat & Operat Res GNOM C5 BldgNorth CampusJordi Girona 1-3 Barcelona 08034 Spain Univ Politecn Catalunya BarcelonaTech Fundacio Ctr CIM Barcelona Spain

This study presents a new two-stage stochastic programming decision model for assessing how to introduce some new manufacturing technology into any generic supply and distribution chain. It additionally determines the optimal degree of postponement, as represented by the so-called customer order decoupling point (CODP), while assuming uncertainty in demand for multiple products. To this end, we propose here the formulation of a generic supply chain through an oriented graph that represents all the deployable alternative technologies, which are defined through a set of operations that are characterised by lead times and cost parameters. Based on this graph, we develop a mixed integer two-stage stochastic programme that finds the optimal manufacturing technology for meeting each market's demand, each operation's optimal production quantity, and each selected technology's optimal CODP. We also present and analyse a case study for introducing additive manufacturing technologies.

关键词： Manufacturing postponement stochastic programming supply chain strategy 3D printing additive manufacturing

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A Two-Stage stochastic Distribution Network Restoration Model with Waste Incineration Power Plants as Black-Start Power Resources 7

A Two-Stage Stochastic Distribution Network Restoration Mode...

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7th Asia Conference on Power and Electrical Engineering, ACPEE 2022

作者： Shi, Shanshan Zhou, Jian Su, Yun Wei, Xinchi Zhou, Sunyang Liu, Haoming State Grid Corporation of China Shanghai Electric Power Research Institute Shanghai China Hohai University College of Energy and Electrical Engineering Nanjing China

ISBN: (纸本)9781665418195

Waste Incineration Power Plant (WIPP) is a promising sustainable and environmentally-friendly generation technique. This paper explores the impacts of WIPPs as black-start resources on distribution network restoration. Specifically, the black-start and operation characteristics of WIPPs equipped with gas storage tanks are analyzed and modeled. Then, a two-stage stochastic distribution network restoration model is established with WIPPs as black-start resources and uncertainties from distributed renewable generations. The first stage is to solve the recovery sequence of nodes and lines, and the second stage comprises the scenario-wise operation of distributed generators and dispatchable loads. The objective is to maximize the expected restored load demand while comprehensively satisfying restoration constraints, including the black-start and operation constraints of WIPPs. The proposed restoration model is transformed into a mixed-integer linear programming (MILP) based on the linearization of AC power flow. An example analysis on an improved IEEE 13-bus distribution system demonstrated the impacts of WIPP on distribution network restoration. © 2022 IEEE.

关键词： stochastic programming

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Day-ahead multi-criteria energy management of a smart home under different electrical rationing scenarios

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RENEWABLE ENERGY FOCUS 2024年 51卷

作者： Issa, Haala Haj Abedini, Moein Hamzeh, Mohsen Anvari-Moghaddam, Amjad Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran Iran Aalborg Univ Dept Energy AAU Energy DK-9220 Aalborg Denmark Damascus Univ Sch Elect & Mech Engn Damascus Syria

With the recent global energy crisis, some countries have implemented electrical rationing (ER), making it necessary for smart homes to play a pivotal role in optimizing energy consumption and contributing to sustainable practices. To effectively manage smart home consumption, a stochastic programming approach for a grid-connected smart home energy management system (SHEMS) is proposed in this paper. The system includes PV, battery, diesel, and gas-based heating/cooling systems (HCS). Additionally, a demand response program (DRP) has been employed under time-of-use tariffs in the Syrian energy market. The main objective is to minimize the day-ahead expected cost and consumer discomfort by optimizing the operation of dispatchable units and loads. To manage the risks associated with the expected cost due to potential uncertainties in PV energy generation and electrical rationing programs, the conditional value-at-risk (CVaR) approach is adopted. Two methods are proposed to model the uncertainty in PV energy generation;interval bands and interval-based scenarios. The problem is modeled as a mixed-integer non-linear programming (MINLP) model, and coded in GAMS to test different cases. Based on the results obtained, substantial reductions reached 56.2% in worst-case cost scenarios when employing concurrent DRP-risk management.

关键词： Electrical rationing Smart home Energy management stochastic programming Demand response Energy market Conditional value at risk

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Optimal investment for a retirement plan with deferred annuities

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INSURANCE MATHEMATICS & ECONOMICS 2021年 98卷 51-62页

作者： Owadally, Iqbal Jang, Chul Clare, Andrew City Univ London Business Sch 106 Bunhill Row London EC1Y 8TZ England

We construct an optimal investment portfolio model with deferred annuities for an individual investor saving in a retirement plan. The objective function consists of power utility in terms of consumption of all secured retirement income from the deferred annuity purchases, as well as bequest from remaining wealth invested in equity, bond, and cash funds. The asset universe is governed by a vector autoregressive model incorporating the Nelson-Siegel term structure and equity returns. We use multi-stage stochastic programming to solve the optimization problem numerically. Deferred annuity purchases are made continuously over the working lifetime of the investor, increasing particularly in the years before retirement. The investment strategy hedges price changes in deferred annuities, and bond holding and deferred annuity purchases increase when interest rates are high. Optimal investment and deferred annuity choices depend on realized and expected values of state variables. The optimal strategy is also compared with typical retirement plan strategies such as glide paths. Our results provide support for deferred annuities as a major source of retirement income. (C) 2021 Elsevier B.V. All rights reserved.

关键词： stochastic programming Retirement planning Deferred annuities Nelson-Siegel model Vector autoregressive Glide path

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Optimization and self-dispatching of hydro-power companies in the electricity market 2

Optimization and self-dispatching of hydro-power companies i...

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2022 2nd International Conference on Power System and Energy Internet, PoSEI 2022

作者： Liang, Zhencheng Liang, Qingqing Li, Ling Luo, Cuiyun Dispatching Control Center of Guangxi Power Grid Corporation Guangxi Nanning China Xingjian College of Arts and Sciences Guangxi University Nanning China

The deepening reform of the power system has resulted in increased competition between electricity market participants. The ADP method is applied to solve the optimal self-dispatch problem of hydro-power companies under uncertain conditions. Firstly, according to the characteristics of hydropower generation technology and self-dispatching, the operation model of hydropower is given. Then, according to the organization and trading behavior of the forward contract market and day-ahead market, a medium and long-term contract quantity decision framework based on stochastic programming is given. Based on the stochastic programming decision framework, the ADP method is used to solve the generation and water decisions in each stage, and the distribution strategy of the electricity quantity of forward transactions and day-trading electricity quantity is obtained. Finally, an example is given to demonstrate the effectiveness of the proposed model and ADP method. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： stochastic programming

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