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Coordinated bidding in sequential electricity markets: Effects of price-making

ENERGY ECONOMICS 2025年 144卷

作者： Miskiw, Kim K. Kraft, Emil Fleten, Stein-Erik Karlsruhe Inst Technol KIT Inst Informat Syst & Mkt IISM Karlsruhe Germany Karlsruhe Inst Technol KIT Inst Ind Prod IIP Karlsruhe Germany Norwegian Univ Sci & Technol NTNU Dept Ind Econ & Technol Management Trondheim Norway

As the uncertainty and time granularity of short-term electricity markets increase and as intraday trading gains importance, deriving good trading decisions becomes increasingly complex. This paper analyses the potential benefit of coordinating bids in three sequential electricity markets using a three-stage stochastic optimisation. The modelled markets include a typical European market setting consisting of a balancing reserve, a day- ahead, and an intraday market. Due to limited intraday market liquidity, the trading strategies also take price impacts into account. The results indicate that coordinated bidding can increase profitability, with the extent of gains depending on the price impacts. In a case study with a biomass and photovoltaic portfolio operating in Germany, we find that coordinated bidding increases the average revenue by around 18% over all analysed type days. As renewable generation continues to increase, trading strategies that coordinate bids across markets are expected to become increasingly important.

关键词： Balancing reserve market Coordinated bidding Electricity markets multi-market trading multi-stage stochastic programming Uncertainty

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multiperiod interval-based stochastic dominance with application to dynamic portfolios

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QUANTITATIVE FINANCE 2025年第4期25卷 543-575页

作者： Consigli, Giorgio Campos, Brian Vasquez Liu, Jia Khalifa Univ Sci & Technol Dept Math Abu Dhabi U Arab Emirates Xi An Jiao Tong Univ Sch Math & Stat Xian Shaanxi Peoples R China

We consider a multi-stage generalization of the interval-based stochastic dominance (ISD) principles introduced by Liu et al. [Interval-based stochastic dominance: Theoretical framework and application to portfolio choices. Ann. Oper. Res., 2021, 307, 329-361]. The ISD criterion was motivated specifically in a financial context to allow for contiguous integer SD orders on different portions of a portfolio return distribution against a benchmark distribution. A continuous spanning of SD conditions between first-, second-, and third-order stochastic dominance was introduced in that context, relying on a reference point. Here, by extending the partial order to random data processes, we apply ISD conditions to a multi-period portfolio selection problem and verify the modeling and computational implications of such an extension. Several theoretical and methodological issues arise in this case that motivate this contribution. The problem is formulated in scenario form as a multi-stage stochastic recourse program, and we study two possible generalizations of ISD principles in which we either enforce ISD constraints at each stage, independently from the scenario tree process evolution, or we do so conditionally along the scenario tree. We present a comprehensive set of computational results to show that, depending on the benchmark investment policy and the adopted ISD formulation, stochastic dominance conditions of first- or second-order can be enforced dynamically over a range of possible values of the reference point, and their solution carries a specific rationale. The computational constraints induced by the multistage ISD formulation are also emphasized and discussed in detail.

关键词： multi-stage stochastic programming Interval stochastic dominance multi-period portfolio selection Large-scale stochastic programming

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multi-stage stochastic districting: optimization models and solution algorithms

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ANNALS OF OPERATIONS RESEARCH 2025年第3期346卷 2225-2251页

作者： Pomes, Anika Diglio, Antonio Nickel, Stefan Saldanha-da-Gama, Francisco Karlsruhe Inst Technol KIT Inst Operat Res Kaiserstr12 D-76131 Karlsruhe Germany Univ Studi Napoli Federico II Dept Ind Engn Piazzale Tecchio 80 I-80125 Naples Italy Res Ctr Informat Technol FZI Haid und Neu Str 10-14 D-76131 Karlsruhe Germany Univ Sheffield Management Sch Sheffield S10 1FL England

This paper investigates a multi-stage stochastic Districting Problem (MSSDP). The goal is to devise a districting plan (i.e., clusters of Territorial Units-TUs) accounting for uncertain parameters changing over a discrete multi-period planning horizon. The problem is cast as a multi-stage stochastic programming problem. It is assumed that uncertainty can be captured by a finite set of scenarios, which induces a scenario tree. Each node in the tree corresponds to the realization of all the stochastic parameters from the root node-the state of nature-up to that node. A mathematical programming model is proposed that embeds redistricting recourse decisions and other recourse actions to ensure that the districts are balanced regarding their activity. The model is tested on instances generated using literature data containing real geographical data. The results demonstrate the relevance of hedging against uncertainty in multi-period districting. Since the model is challenging to tackle using a general-purpose solver, a heuristic algorithm is proposed based on a restricted model. The computational results obtained give evidence that the approximate algorithm can produce high-quality feasible solutions within acceptable computation times.

关键词： Districting multi-stage stochastic programming Heuristics

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Should the Occupational Pension Plans' Investment be Long-Term or Short-Term? Evidence from China

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COMPUTATIONAL ECONOMICS 2025年第6期65卷 3391-3418页

作者： Liu, Wenling Xu, Fengmin Jing, Kui Hua, Ziyue Xi An Jiao Tong Univ Sch Econ & Finance Yanta West Rd Xian 710049 Shannxi Peoples R China Xi An Jiao Tong Univ Sch Marxism Xianning West Rd Xian 710061 Shannxi Peoples R China

Contrary to the long-term nature of pension funds, occupational pension plans' investments are often short-sighted under the constraints of appraisal mechanisms. This paper investigates whether the investment horizon of occupational pension plans should be long-term or short-term with a multi-stage portfolio selection model. The model takes investments' accumulated wealth and CVaR as the objective function and constrains the investment proportion on equity assets. We solve the model with a scenario tree constructed by the copula-based method. The numerical experiments compare the performance of occupational pension plans under various investment horizons within the Chinese fund market. The results illustrate the advantages of long-term investment while also revealing the existence of an optimal investment horizon, beyond which there is no additional benefit. For long-term investment, we show that a preference for risk and relaxed constraints on equity assets can lead to better performance. The robustness tests concerning parameter sensitivity and time-varying risk attitudes further confirm the main conclusions.

关键词： Occupational pension plan Long-term portfolio multi-stage stochastic programming C51 G11 G23 J23

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Production planning for semiconductor manufacturing under demand and yield uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 196卷

作者： Rashidi, Eghbal Bhuiyan, Tanveer Hossain Mason, Scott J. San Francisco State Univ Dept Decis Sci San Francisco CA 94132 USA Univ Texas San Antonio Dept Mech Engn San Antonio TX USA Amazon Pendleton SC USA

In this paper, we study a complex semiconductor production planning problem under uncertainties due to product demand, and production and process yields. The studied semiconductor manufacturing processes consists of facilities for wafer fabrication, sort, assembly, test, and demand centers. We consider the fabrication and sort facilities as a single unit called the Front-End and the assembly and test facilities as another single unit called the Back-End. The production processes are considered to follow a push policy in the Front-End and a pull policy in the Back-End. We propose a multi-stage stochastic programming model for this problem and apply the model to a large data set. We perform various analyses to understand the effect of these uncertainties on the semiconductor manufacturing decisions and its profit and provide interesting managerial insights. Our experiments verify the usefulness of our proposed stochastic programming model.

关键词： Supply chain management under uncertainty multi-stage stochastic programming Semiconductor supply chain

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A stabilised Benders decomposition with adaptive oracles for large-scale stochastic programming with short-term and long-term uncertainty

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COMPUTERS & OPERATIONS RESEARCH 2024年 167卷

作者： Zhang, Hongyu Mazzi, Nicolo Mckinnon, Ken Nava, Rodrigo Garcia Tomasgard, Asgeir Norwegian Univ Sci & Technol Dept Ind Econ & Technol Management Hogskoleringen 1 N-7491 Trondheim Norway Univ Edinburgh Sch Math James Clerk Maxwell Bldg Peter Guthrie Tait Rd Edinburgh EH9 3FD Scotland aHead Res Corso Svizzera 185 I-10149 Turin Italy

Benders decomposition with adaptive oracles was proposed to solve large-scale optimisation problems with a column-bounded block -diagonal structure, where subproblems differ only in the right -hand side and cost coefficients. Adaptive Benders reduces computational effort significantly by iteratively building inexact cutting planes and valid upper and lower bounds. However, Adaptive Benders and standard Benders may suffer severe oscillation when solving degenerate models. Therefore, we propose stabilising Adaptive Benders with the level method and adaptively selecting which subproblems to solve each iteration for more accurate information. In addition, we propose a dynamic level method to improve the robustness of stabilised Adaptive Benders by adjusting the level set each iteration. We compare stabilised Adaptive Benders with the unstabilised versions of Adaptive Benders with one subproblem solved per iteration and standard Benders on a multi -region longterm power system investment planning problem with short-term and long-term uncertainty. The problem is formulated as multi-horizon stochastic programming. Four algorithms were implemented to solve linear programming with up to 1 billion variables and 4.5 billion constraints. The computational results show that: (a) for a 1.00% convergence tolerance, the proposed stabilised method is up to 113.7 times faster than standard Benders and 2.1 times faster than unstabilised Adaptive Benders;(b) for a 0.10% convergence tolerance, the proposed stabilised method is up to 45.5 times faster than standard Benders and unstabilised Adaptive Benders cannot solve the largest instance to convergence tolerance due to severe oscillation and (c) dynamic level method makes stabilisation more robust.

关键词： Large scale optimisation multi-stage stochastic programming multi-horizon stochastic programming Stabilised Benders decomposition with adaptive oracles Level method

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multi-stage Co-planning Model for Power Distribution System and Hydrogen Energy System Under Uncertainties

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Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 80-93页

作者： Qirun Sun Zhi Wu Wei Gu Pengxiang Liu Jingxuan Wang Yuping Lu Shu Zheng Jingtao Zhao School of Electrical Engineering Southeast UniversityNanjing 210096China NARI Technology Development Co.Ltd NanjingChina

The increased deployment of electricity-based hydrogen production strengthens the coupling of power distribution system(PDS)and hydrogen energy system(HES).Considering that power to hydrogen(PtH)has great potential to facilitate the usage of renewable energy sources(RESs),the coordination of PDS and HES is important for planning purposes under high RES *** this end,this paper proposes a multi-stage co-planning model for the PDS and *** the PDS,investment decisions on network assets and RES are optimized to supply the growing electric load and *** the HES,capacities of PtHs and hydrogen storages(HSs)are optimally determined to satisfy hydrogen load considering the hydrogen production,tube trailer transportation,and storage *** overall planning problem is formulated as a multistage stochastic optimization model,in which the investment decisions are sequentially made as the uncertainties of electric and hydrogen load growth states are revealed gradually over *** studies validate that the proposed co-planning model can reduce the total planning cost,promote RES consumption,and obtain more flexible decisions under long-term load growth uncertainties.

关键词： Power distribution system vehicle-based hydrogen energy system co-planning model multi-stage stochastic programming uncertainty

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A Bayesian approach to data-driven multi-stage stochastic optimization

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JOURNAL OF GLOBAL OPTIMIZATION 2024年第2期90卷 401-428页

作者： Chen, Zhiping Ma, Wentao Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Xian Int Acad Math & Math Technol Ctr Optimizat Tech & Quantitat Finance Xian 710049 Peoples R China

Aimed at sufficiently utilizing available data and prior distribution information, we introduce a data-driven Bayesian-type approach to solve multi-stage convex stochastic optimization, which can easily cope with the uncertainty about data process's distributions and their inter-stage dependence. To unravel the properties of the proposed multi-stage Bayesian expectation optimization (BEO) problem, we establish the consistency of optimal value functions and solutions. Two kinds of algorithms are designed for the numerical solution of single-stage and multi-stage BEO problems, respectively. A queuing system and a multi-stage inventory problem are adopted to numerically demonstrate the advantages and practicality of the new framework and corresponding solution methods, compared with the usual formulations and solution methods for stochastic optimization problems.

关键词： Data-driven Bayesian technique multi-stage stochastic programming Consistency Scenario tree

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The assignment of project managers to projects in an uncertain dynamic environment

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ANNALS OF OPERATIONS RESEARCH 2024年第2-3期341卷 1107-1134页

作者： Rezaeian, Aidin Koosha, Hamidreza Ranjbar, Mohammad Poormoaied, Saeed Ferdowsi Univ Mashhad Dept Ind Engn Mashhad Iran Eindhoven Univ Technol Dept Ind Engn & Innovat Sci Eindhoven Netherlands

In this paper, we consider a project-based organization that deals with an assignment problem in which a set of projects must be assigned to a group of project managers. This assignment is done based on the relative contributions of projects to the organizational mission and a matching score between each pair of a project and a project manager. We assume that some projects are deterministic, and the organization has signed their corresponding contracts while others are stochastic, i.e. the organization has submitted bids for these projects and may or may not win them in the future. Furthermore, we consider a finite planning horizon and presume a predetermined start time for each deterministic and stochastic project. We develop two models including a multi-stage stochastic integer programming model and a stochastic dynamic programming model to solve the problem. The latter shows better performance for small-size and less complex instances whereas the former gives better performance for more complex instances. We also developed a heuristic algorithm to solve large-size and more complex instances. Computational results indicate that the developed heuristic algorithm can reach near-optimal solutions in reasonable CPU run times and dominates the two other solution approaches particularly for large-size instances.

关键词： Project management stochastic generalized assignment problem multi-stage stochastic programming stochastic dynamic programming Heuristic algorithm

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A non-anticipative learning-optimization framework for solving multi-stage stochastic programs

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ANNALS OF OPERATIONS RESEARCH 2024年 1-41页

作者： Yilmaz, Dogacan Buyuktahtakin, I. Esra New Jersey Inst Technol Dept Mech & Ind Engn 200 Cent Ave Newark NJ 07114 USA Virginia Tech Grad Dept Ind & Syst Engn 250 Perry St Blacksburg VA 24061 USA

We present a non-anticipative learning- and scenario-based prediction-optimization (ScenPredOpt) framework that combines deep learning, heuristics, and mathematical solvers for solving combinatorial problems under uncertainty. Specifically, we transform neural machine translation frameworks to predict the optimal solutions of scenario-based multi-stage stochastic programs. The learning models are trained efficiently using the input and solution data of the multi-stage single-scenario deterministic problems. Then our ScenPredOpt framework creates a mapping from the inputs used in training into an output of predictions that are close to optimal solutions. We present a Non-anticipative Encoder-Decoder with Attention (NEDA) approach, which ensures the non-anticipativity property of multi-stage stochastic programs and, thus, time consistency by calibrating the learned information based on the problem's scenario tree and adjusting the hidden states of the neural network. In our ScenPredOpt framework, the percent predicted variables used for the solution are iteratively reduced through a relaxation of the problem to eliminate infeasibility. Then, a linear relaxation-based heuristic is performed to further reduce the solution time. Finally, a mathematical solver is used to generate the complete solution. We present the results on two NP-Hard sequential optimization problems under uncertainty: stochastic multi-item capacitated lot-sizing and stochastic multistage multidimensional knapsack. The results show that the solution time can be reduced by a factor of 599 with an optimality gap of only 0.08%. We compare the results of the ScenPredOpt framework with cutting-edge exact and heuristic solution algorithms for the problems studied and find that our framework is more effective. Additionally, the computational results demonstrate that ScenPredOpt can solve instances with a larger number of items and scenarios than the trained ones. Our non-anticipative learning-optimization a

关键词： multi-stage stochastic programming Machine learning Lot-sizing Knapsack Sequential decision-making Combinatorial optimization Relax-and-fix heuristics

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