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Generation Capacity Expansion Considering Hydrogen Power Plants and Energy Storage Systems

IEEE ACCESS 2023年 11卷 15525-15539页

作者： Gomez-Villarreal, Hernan Canas-Carreton, Miguel Zarate-Minano, Rafael Carrion, Miguel Univ Castilla La Mancha Escuela Ingn Ind & Aerosp Toledo Dept Elect Engn Toledo 45071 Spain Univ Castilla La Mancha Dept Elect Elect & Control Engn Toledo 45002 Spain Univ Castilla La Mancha Escuela Ingn Min & Ind Almaden Dept Elect Engn Almaden 13400 Spain Univ Castilla La Mancha Dept Elect Engn Toledo 45002 Spain

The design of decarbonized power systems is one of the most relevant and challenging problems that power system planners are facing nowadays. In this sense, the replacement of natural gas turbines by H-2-fired gas turbines in future power systems may constitute a solution to reduce greenhouse gas emissions maintaining the dispatchability of the system. This work develops a novel generation capacity expansion formulation that considers the possibility of installing new H-2-fired gas turbines, as well as renewable generation and different storage technologies. The proposed model also determines the investment decisions related to the installation of electrolyzers to produce H-2, as well as H-2 storage facilities. The provision of reserve capacity by generating units, storage and electrolyzers is also considered to determine the optimal investment decisions. The uncertainty related to the demand growth and the capital costs of electrolyzers and H-2 storage is explicitly considered by using a stochastic programming formulation. A realistic case study based on an actual isolated power system is solved to test the proposed formulation. The obtained results indicate that H-2 power plants are installed in all cases in which this technology is available. Additionally, the capacity installed of electrolyzers is over 2.5 times higher than that of H(2 )power units.

关键词： Costs Renewable energy sources Reservoirs Power systems Batteries Production Power generation hydrogen turbines electrolyzers hydrogen tanks pumped storage hydropower renewable generation stochastic programming

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Asymptotically tight conic approximations for chance-constrained AC optimal power flow

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期305卷 738-753页

作者： Fathabad, Abolhassan Mohammadi Cheng, Jianqiang Pan, Kai Yang, Boshi Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA Hong Kong Polytech Univ Fac Business Dept Logist & Maritime Studies Kowloon Hong Kong Peoples R China Clemson Univ Sch Math & Stat Sci Clemson SC USA

The increasing penetration of renewable energy in power systems calls for secure and reliable system op-erations under significant uncertainty. To that end, the chance-constrained AC optimal power flow (CC-ACOPF) problem has been proposed. Most research in the literature of CC-ACOPF focuses on one-sided chance constraints;however, two-sided chance constraints (TCCs), albeit more complex, provide more accurate formulations as both upper and lower bounds of the chance constraints are enforced simul-taneously. In this paper, we introduce a fully two-sided CC-ACOPF problem (TCC-ACOPF), in which the active/reactive generation, voltage, and power flow all remain within their upper/lower bounds simulta-neously with a predefined probability. Instead of applying Bonferroni approximation or scenario-based approaches, we present an efficient second-order cone programming (SOCP) approximation of the TCCs under Gaussian Mixture (GM) distribution via a piecewise linear (PWL) approximation. Compared to the conventional normality assumption for forecast errors, the GM distribution adds an extra level of accu-racy representing the uncertainties. Moreover, we show that our SOCP formulation has adjustable rates of accuracy and its optimal value enjoys asymptotic convergence properties. Furthermore, an algorithm is proposed to speed up the solution procedure by optimally selecting the PWL segments. Finally, we demonstrate the effectiveness of our proposed approaches with both real historical data and synthetic data on the IEEE 30-bus and 118-bus systems. We show that our formulations provide significantly more robust solutions (about 60% reduction in constraint violation) compared to other state-of-art ACOPF for-mulations. (c) 2022 Elsevier B.V. All rights reserved.

关键词： stochastic programming Two-sided chance constraint AC optimal power flow Second-order cone programming Piecewise linear approximation

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Sample average approximations of strongly convex stochastic programs in Hilbert spaces

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OPTIMIZATION LETTERS 2023年第2期17卷 471-492页

作者： Milz, Johannes Tech Univ Munich Dept Math Boltzmannstr 3 D-85748 Garching Germany

We analyze the tail behavior of solutions to sample average approximations (SAAs) of stochastic programs posed in Hilbert spaces. We require that the integrand be strongly convex with the same convexity parameter for each realization. Combined with a standard condition from the literature on stochastic programming, we establish non-asymptotic exponential tail bounds for the distance between the SAA solutions and the stochastic program's solution, without assuming compactness of the feasible set. Our assumptions are verified on a class of infinite-dimensional optimization problems governed by affine-linear partial differential equations with random inputs. We present numerical results illustrating our theoretical findings.

关键词： Sample average approximation PDE-constrained optimization under uncertainty Linear-quadratic optimal control under uncertainty Exponential tail bounds stochastic programming

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Risk-averse stochastic optimal control: An efficiently computable statistical upper bound

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OPERATIONS RESEARCH LETTERS 2023年第4期51卷 393-400页

作者： Guigues, Vincent Shapiro, Alexander Cheng, Yi FGV Praia Botafogo Sch Appl Math Rio De Janeiro Brazil Georgia Inst Technol Atlanta GA 30332 USA

In this paper, we discuss an application of the stochastic Dual Dynamic programming (SDDP) type algorithm to nested risk-averse formulations of stochastic Optimal Control (SOC) problems. We propose a construction of a statistical upper bound for the optimal value of risk-averse SOC problems. This outlines an approach to a solution of a long standing problem in that area of research. The bound holds for a large class of convex and monotone conditional risk mappings. Finally, we show the validity of the statistical upper bound to solve a real-life stochastic hydro-thermal planning problem. (c) 2023 Elsevier B.V. All rights reserved.

关键词： stochastic programming stochastic optimal control SDDP Dynamic programming Risk measures Statistical upper bounds

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A mixed-integer quasi-convex optimization model for joint transmission network and wind power investment problem

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期217卷

作者： Alshamrani, Ahmad M. King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia

As one of the important renewable energy sources (RESs), the integration of wind energy into the electric grid is growing fast. This higher penetration level of wind power calls for requirements for reinforcing the existing transmission network to reduce wind power curtailment. In this context, this research focuses on developing a mathematical methodology for joint transmission network and wind power investment problem under a centralized approach. Unlike the existing models, where the objective function to be minimized is the overall cost, the objective function of this work is different. It is defined as the ratio of the total cost to the total wind power generation. The definition of this objective function allows the operator to minimize the total cost while maximizing the wind power output from wind farms. The convex AC power flow is utilized to model the power flow equations. The proposed investment model is mixed-integer quasi-convex programming (MIQCP) that is converted into a mixed-integer convex programming (MICP) problem. The numerical study indicates that the resulting MICP problem is computationally efficient, making it suitable for a realistic electric grid. In addition, it will promote the wind power output of wind farms.

关键词： Mixed -integer quasi -convex optimization Mixed -integer convex programming stochastic programming Transmission and wind investment

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Uncertainty in maritime ship routing and scheduling: A Literature review

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期308卷 499-524页

作者： Ksciuk, Jana Kuhlemann, Stefan Tierney, Kevin Koberstein, Achim Europa Univ Viadna Grosse Scharrnstr 59 D-15230 Frankfurt Germany Univ Bielefeld Univ Str 25 D-33615 Bielefeld Germany

The area of maritime transportation optimization has recently begun to achieve increasing success at solving large scale models, and industry is steadily adopting operations research-based models and algorithms. However, the parameters of models in the maritime domain, like many others, are beset with uncertainty. The travel times of ships, the handling times at port, the amounts of demand available at ports, fuel prices and more are all unknown and highly variable inputs to optimization methods. Recently, the maritime literature has started to address sources of uncertainty to provide higher quality decision making. We review this nascent area of the literature and provide a unifying view of different types of uncertainty across the main areas of maritime transport and varying problem types.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： stochastic programming Literature review Maritime optimization Optimization under uncertainty

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A decentralized stochastic bidding strategy for aggregators of prosumers in electricity reserve markets

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JOURNAL OF CLEANER PRODUCTION 2023年第1期389卷

作者： Manna, Carlo Sanjab, Anibal Flemish Inst Technol Res VITO Boeretang 200 B-2400 Mol Belgium EnergyVille Thor Pk 8310 B-3600 Genk Belgium

Recent years have seen an increasing interest in demand response as a means to provide flexibility and support the penetration of renewable resources in the power system. Aggregators play a key role, in this regard, by enabling the participation of small-scale distributed energy resources and loads connected at the distribution network into local and central *** paper proposes an optimal strategy to support aggregators of a large number of small prosumers to participate in the tertiary reserve procurement market. In particular, a two-stage decentralized approach is proposed in which, first, prosumers individually optimize their flexible appliances schedule according to their own requirements and preferences, without sharing any information with the aggregator. At the aggregator level, a trading strategy is proposed for participation in the reserve market aiming at maximizing the aggregator's profit under the pay-as-bid pricing scheme, while only requiring a linear programming formulation ensuring the efficiency of the trading problem. In the developed approach, a scenario-based stochastic programming method is introduced to capture the uncertainties of market prices, weather conditions, loads, and user *** demonstrate the applicability of the proposed method, using real data, simulations on 2500 small prosumers in a realistic setting are carried out. The proposed linear and decentralized method can be executed within a few minutes, enabling its applicability for aggregators with a large number of customers.

关键词： Aggregators Electricity markets Flexibility Reserve procurement stochastic programming

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Capacity planning for effective cohorting of hemodialysis patients during the coronavirus pandemic: A case study

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期304卷 276-291页

作者： Bozkir, Cem D. C. Ozmemis, Cagri Kurbanzade, Ali Kaan Balcik, Burcu Gunes, Evrim D. Tuglular, Serhan Ozyegin Univ Ind Engn Dept Istanbul Turkiye Koc Univ Coll Adm Sci & Econ Business Adm Istanbul Turkiye Marmara Univ Med Fac Dept Internal Med Istanbul Turkiye

Planning treatments of different types of patients have become challenging in hemodialysis clinics during the COVID-19 pandemic due to increased demands and uncertainties. In this study, we address capacity planning decisions of a hemodialysis clinic, located within a major public hospital in Istanbul, which serves both infected and uninfected patients during the COVID-19 pandemic with limited resources (i.e., dialysis machines). The clinic currently applies a 3-unit cohorting strategy to treat different types of pa-tients (i.e., uninfected, infected, suspected) in separate units and at different times to mitigate the risk of infection spread risk. Accordingly, at the beginning of each week, the clinic needs to allocate the available dialysis machines to each unit that serves different patient cohorts. However, given the uncertainties in the number of different types of patients that will need dialysis each day, it is a challenge to determine which capacity configuration would minimize the overlapping treatment sessions of different cohorts over a week. We represent the uncertainties in the number of patients by a set of scenarios and present a stochastic programming approach to support capacity allocation decisions of the clinic. We present a case study based on the real-world patient data obtained from the hemodialysis clinic to illustrate the effectiveness of the proposed model. We also compare the performance of different cohorting strategies with three and two patient cohorts.(c) 2021 Elsevier B.V. All rights reserved.

关键词： OR in health services COVID-19 pandemic Hemodialysis Patient cohorting stochastic programming

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Consistency of Monte Carlo Estimators for Risk-Neutral PDE-Constrained Optimization

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APPLIED MATHEMATICS AND OPTIMIZATION 2023年第3期87卷 57-57页

作者： Milz, Johannes Tech Univ Munich Dept Math Boltzmannstr 3 D-85748 Garching Germany

We apply the sample average approximation (SAA) method to risk-neutral optimization problems governed by nonlinear partial differential equations (PDEs) with random inputs. We analyze the consistency of the SAA optimal values and SAA solutions. Our analysis exploits problem structure in PDE-constrained optimization problems, allowing us to construct deterministic, compact subsets of the feasible set that contain the solutions to the risk-neutral problem and eventually those to the SAA problems. The construction is used to study the consistency using results established in the literature on stochastic programming. The assumptions of our framework are verified on three nonlinear optimization problems under uncertainty.

关键词： stochastic programming Monte Carlo sampling Sample average approximation Optimization under uncertainty PDE-constrained optimization

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Optimal stochastic self-scheduling of a water-energy virtual power plant considering data clustering and multiple storage systems

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JOURNAL OF ENERGY STORAGE 2023年 65卷

作者： Rezaei, Navid Pezhmani, Yasin Mohammadiani, Rojiar Pir Univ Kurdistan Dept Elect Engn Sanandaj Iran Univ Kurdistan Dept Comp Engn Sanandaj Iran

As the world moves towards the integration of different water and energy resources, as well as storage systems, it is necessary to develop the conventional structure of virtual power plants (VPPs). In this work, a mixed-integer nonlinear programming (MINLP) model is established for stochastic self-scheduling problem of a new VPP structure, namely water-energy VPP. The proposed VPP, which participates in the electricity market to maximize its daily profit, aggregates different energy and water resources including wind turbines, compressed air energy storage (CAES), gas boiler, electrical boiler, absorption chiller, ice storage, water storage, and water well to supply water demand, as well as different types of energy demands such as cooling, power, and heat demands. To cope with the uncertain behavior of wind power, a scenario-based approach is employed. First, with the use of Monte Carlo simulation in MATLAB, a number of scenarios are obtained, and afterwards, K-means technique, which is a fast and efficient data clustering method, is implemented for the scenario reduction. Additionally, the impact of the consideration of CAES system on the performance of the proposed water-energy is discussed. The numerical results indicate that with the deployment of CAES system into the structure of the water-energy VPP, the expected daily profit is increased up to 4.26 %.

关键词： Virtual power plant Water -energy nexus Data clustering Renewable energy stochastic programming

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