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Optimal Participation in Manual Frequency Restoration Reserve-Based Demand Response Programs for Large Consumers

IEEE ACCESS 2025年 13卷 59866-59885页

作者： Arellano, Jose Garcia-Cerezo, Alvaro Carrion, Miguel Univ Castilla La Mancha Elect Engn Dept Campus Toledo Toledo 45004 Spain

The increasing penetration of intermittent, weather-dependent generation has heightened the need for flexibility in power systems. Traditionally passive, consumers are now being encouraged to actively support ancillary services to help balance generation and consumption. This work examines the participation of a large electricity consumer in manual frequency restoration reserve-based demand response programs. In particular, the Active Demand Response Service (ADRS), recently introduced in Spain, is addressed. Participation involves submitting power-price bids in an annual auction, indicating the amount of grid consumption the consumer is willing to reduce upon request by the system operator. The problem is modeled as a three-stage stochastic optimization, considering three key decisions: the power bid submitted to the auction, the operational decisions of the consumer before ADRS participation, and operational adjustments when ADRS is activated. A cement producer serves as the case study, with several scenarios comparing different electricity procurement options: grid-only supply, photovoltaic self-generation, and battery storage. The results show that consumers with alternative energy sources, such as solar or batteries, are less inclined to participate in ADRS due to the requirement to maintain a minimum level of grid consumption. Nevertheless, participation in ADRS can offer substantial economic benefits, especially at auction prices observed in previous editions. In this regard, the cement producer considered in this case study could achieve an expected cost savings of up to 30% annually by participating in this service. Moreover, the fixed remuneration from the ADRS could significantly offset the plant's operational costs, covering between 28% and 42% when participating with approximately 5 MW in ADRS.

关键词： Batteries Electricity Demand response Power system stability Renewable energy sources Production Photovoltaic systems Cement industry Remuneration Manuals Active demand response service cement plant large electricity consumer stochastic programming manual frequency restoration reserve three-stage model

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Duality in convex stochastic optimization

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MATHEMATICAL programming 2025年 1-47页

作者： Pennanen, Teemu Perkkioe, Ari-Pekka Kings Coll London Dept Math London WC2R 2LS England Ludwig Maximilian Univ Munich Math Inst Theresienstr 39 D-80333 Munich Germany

This paper studies duality and optimality conditions in general convex stochastic optimization problems introduced by Rockafellar and Wets in (Math Programm Stud 6:170-187, 1976). We derive an explicit dual problem in terms of two dual variables, one of which is the shadow price of information while the other one gives the marginal cost of a perturbation much like in classical Lagrangian duality. Existence of primal solutions and the absence of duality gap are obtained without compactness or boundedness assumptions. In the context of financial mathematics, the relaxed assumptions are satisfied under the well-known no-arbitrage condition and the reasonable asymptotic elasticity condition of the utility function. We extend classical portfolio optimization duality theory to problems of optimal semi-static hedging. Besides financial mathematics, we obtain several new results in stochastic programming and stochastic optimal control.

关键词： Convex duality stochastic programming stochastic optimal control Financial mathematics

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Data-driven models for optimizing second-hand ship trading strategies under contextual information

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NAVAL RESEARCH LOGISTICS 2025年第2期72卷 275-291页

作者： Wang, Haoqing Sun, Qinghe Wang, Shuaian Hong Kong Polytech Univ Fac Business Dept Logist & Maritime Studies Kowloon Hong Kong Peoples R China

Second-hand ship online trading platforms (SOTPs) are reshaping the traditional broker-reliant second-hand ship transactions. This study investigates the decision-making process within the context of SOTP from a shipowner's perspective. We introduce a comprehensive framework tailored to guide shipowners in strategically navigating pivotal decisions, including the adoption of SOTP and the specification of optimal minimum starting prices while leveraging the value of online transaction data. Our approach is rooted in data-driven decision-making under uncertainty, employing quantile regression forests (QRF), and weighted sample average approximation (wSAA). The latter encompasses a predictive wSAA model, a local wSAA model, and a residual-based wSAA model. Each of these models provides a unique perspective on weight determination within the wSAA paradigm. To validate our proposed approach, we draw upon extensive real-world data sourced from a Chinese SOTP between January 2017 and May 2023. Within this context, our numerical experiments pursue three primary objectives: (i) identifying performance disparities among the models, (ii) assessing the value of contextual information, and (iii) evaluating the optimal strategy for shipowners. Our findings not only underscore the efficacy of our approaches but also provide invaluable insights into the adoption of SOTPs, establishing a robust foundation for informed decision-making in the continually evolving SOTP landscape.

关键词： data-driven optimization second-hand ship stochastic programming weighted sample average approximation

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An integrated blood supply chain network design during a pandemic

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年第9期63卷 3384-3409页

作者： Yang, Hengfei Yin, Yunqiang Wang, Dujuan Cheng, T. C. E. Zhang, Ruisan Hu, Hai Sichuan Univ Business Sch Chengdu 610064 Peoples R China Univ Elect Sci & Technol China Sch Management & Econ Chengdu Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hung Hom Kowloon Hong Kong Peoples R China Sichuan Univ West China Hosp Emergency Management Off Chengdu Peoples R China

The emergency supply of blood in the wake of pandemic has proved challenging. We develop in this paper a bi-objective blood supply chain (BSC) model that uses two-stage stochastic optimisation to optimise the expected cost of the supply chain and the average blood delivery time when a pandemic occurs. The model considers not only vertically and horizontally integrated mechanisms of the BSC network, but also several specific assumptions about the pandemic, such as the risk of disruption in donor groups, and the restriction of 'no cross blood donation'. To alleviate blood shortages in the context of a pandemic, we further extended our model, which allows for blood type substitution according to ABO/Rh(d)-compatibility. To efficiently solve the model and generate the Pareto front, we develop a hybrid solution algorithm to solve the proposed model, including the epsilon-constraint method to transform the bi-objective model into a single-objective model, the subgradient method for finding a lower bound, and two heuristic methods for finding an upper bound. Moreover, we perform extensive numerical studies to assess the performance of the proposed approach. We also discuss the impacts of the key parameters, integration mechanisms, and planning blood type substitution on the BSC. Finally, we also apply the proposed model to a real case study of the BSC in Chongqing, China.

关键词： Blood supply chain integrated mechanism pandemic stochastic programming Lagrangian relaxation

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Dynamic and stochastic optimization of algae cultivation process

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COMPUTERS & CHEMICAL ENGINEERING 2025年 198卷

作者： Kivanc, Sercan Beykal, Burcu Deliismail, Ozgun Sildir, Hasan Gebze Tech Univ Dept Chem Engn TR-41400 Kocaeli Turkiye Univ Connecticut Dept Chem & Biomol Engn Storrs CT 06269 USA Univ Connecticut Ctr Clean Energy Engn Storrs CT 06269 USA SOCAR Turkey R&D & Innovat Co TR-35800 Izmir Turkiye Izmir Inst Technol Dept Chem Engn Turkiye TR-35430 Izmir Turkiye

This study offers a realistic representation of system dynamics which accounts for light intensity, biomass, substrate, and nitrogen concentration, by employing stochastic programming techniques to account for spatial and temporal variations for algae growth. The optimization task focuses on lipid productivity and selectivity, which are crucial factors in the context of algal biofuel production. Different scenarios from likely and unlikely cases of model parameters were evaluated. Optimal initial conditions for key variables such as nitrogen, substrate, light, biomass, lipid, and surface light intensity are calculated, considering the uncertainty of the parameters as well as other governing equations. The results show that a remarkable 11.18% increase in lipid productivity compared to a reference scenario. Furthermore, in the stochastic case, our results highlight that uncertainty has a disproportionately large effect on biomass in comparison to lipid concentration, providing valuable insights into the behavior of the system under varying conditions. This provides a comprehensive exploration of the parameter uncertainty on lipid productivity and algal growth.

关键词： Sustainability Microalgal process Dynamic optimization stochastic programming Spatiotemporal modeling

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stochastic economic model predictive control for renewable energy and ancillary services trading with storage

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2024年 38卷

作者： Santosuosso, Luca Camal, Simon Liberati, Francesco Di Giorgio, Alessandro Michiorri, Andrea Kariniotakis, Georges PSL Univ Ctr Proc Renewable Energies & Energy Syst PERSEE MINES Paris 1 Rue Claude DaunesseCS 10207 F-06904 Sophia Antipolis France Sapienza Univ Rome DIAG Dept Via Ariosto 25 I-00185 Rome Italy

The provision of renewable -based ancillary services (AS) is paramount for the stable operation of power systems featuring high renewable penetration. The combined operation of storage with renewables enables aggregators to increase the reliability of their energy and frequency control AS offers. Existing dispatch strategies for the supply of both energy and AS are usually rule -based or involve tracking a technical reference signal, hence economically suboptimal for aggregators. This study proposes a comprehensive decision framework in which first a stochastic optimization derives bids on energy and AS markets, then stochastic economic model predictive control (SEMPC) optimizes the storage dispatch in order to maximize the profit and minimize the storage degradation, as a function of the predicted renewable production and the expected AS activation. The framework is applied to a real -world case study where storage combined with wind power participates in the energy market, the frequency containment market and the frequency restoration reserve market. The SEMPC-based approach increases market revenue by 15% compared to a standard reference -tracking MPC, and reduces storage degradation by 23%. The stochastic formulation lowers the sensitivity of the economic objectives to renewable energy forecast errors, compared to deterministic approaches.

关键词： stochastic economic model predictive control Ancillary services Frequency control stochastic programming Renewable energy Storage degradation

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Managing appointment-based services with electronic visits

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期315卷 863-878页

作者： Cai, Yun Song, Haiqing Wang, Shan Sun Yat Sen Univ Lingnan Coll Guangzhou 510275 Peoples R China Sun Yat Sen Univ Sch Business Guangzhou 510275 Peoples R China

Electronic visits, or "E-visits"for short, have emerged as a promising channel for accessing healthcare and can significantly impact daily operations in healthcare facilities. However, there is a lack of research on how to efficiently manage appointments for outpatient care providers when faced with E-visits that exhibit different waiting cost patterns. Our study investigates how providers can use appointment scheduling as a "passive"control when patients have full access to the E-visit channel, to better utilise resources and reduce patient waiting. Specifically, we demonstrate that multimodularity still applies to the model with E-visits, despite their waiting costs being typically nonlinear. Furthermore, we analyse how providers can "actively"control the arrival of E-visits by scheduling their time windows. By examining the structures of the optimal joint schedule of appointments and E-visit time windows, and reformulating the problem into a two-stage program, we have designed an Accelerated Cut Generation Algorithm, which is shown to be efficient in our numerical study. To the best of our knowledge, this is the first study to explore the optimal scheduling of both appointments and E-visit time windows. By implementing proper scheduling, the negative impact of E-visits can be mitigated, their benefits to the provider can be enhanced, and overall operational efficiency can be improved.

关键词： OR in health services E-visits Appointment scheduling stochastic programming

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Convex approximations of two-stage risk-averse mixed-integer recourse models

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2024年第1期88卷 313-347页

作者： van Beesten, E. Ruben Romeijnders, Ward Roodbergen, Kees Jan Univ Groningen Fac Econ & Business Nettelbosje 2 NL-9747 AE Groningen Netherlands

We consider two-stage risk-averse mixed-integer recourse models with law invariant coherent risk measures. As in the risk-neutral case, these models are generally non-convex as a result of the integer restrictions on the second-stage decision variables and hence, hard to solve. To overcome this issue, we propose a convex approximation approach. We derive a performance guarantee for this approximation in the form of an asymptotic error bound, which depends on the choice of risk measure. This error bound, which extends an existing error bound for the conditional value at risk, shows that our approximation method works particularly well if the distribution of the random parameters in the model is highly dispersed. For special cases we derive tighter, non-asymptotic error bounds. Whereas our error bounds are valid only for a continuously distributed second-stage right-hand side vector, practical optimization methods often require discrete distributions. In this context, we show that our error bounds provide statistical error bounds for the corresponding (discretized) sample average approximation (SAA) model. In addition, we construct a Benders' decomposition algorithm that uses our convex approximations in an SAA-framework and we provide a performance guarantee for the resulting algorithm solution. Finally, we perform numerical experiments which show that for certain risk measures our approach works even better than our theoretical performance guarantees suggest.

关键词： stochastic programming Mixed-integer recourse Risk aversion Convex approximations

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A cooperative transactive multi-carrier energy control mechanism with P2P energy plus reserve trading using Nash bargaining game theory under renewables uncertainty

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APPLIED ENERGY 2024年 353卷

作者： Alizadeh, Ali Esfahani, Moein Dinar, Farid Kamwa, Innocent Moeini, Ali Mohseni-Bonab, Seyed Masoud Busvelle, Eric Univ Laval Coll Engn Elect Engn Dept Quebec City PQ Canada Toulon Univ Lab Informat & Syst Toulon France Hydroquebec IREQ Power Syst Simulat & Evolut Varennes PQ Canada Hydro Quebec IREQ Digital Syst Varennes PQ Canada

Transactive Energy Control (TEC) as a market-based control is a critical notion for scheduling Multi-Carrier Energy Systems (MCESs) in local networks and forming an Energy Hub (EH). Nevertheless, implementing TEC for scheduling and controlling MCESs is extremely difficult due to the lack of a cooperative TEC model that accounts for network constraints and the uncertainty of Renewable Energy Sources (RESs). This paper defines and formulates Prosumer-Based Multi-Carrier Energy Systems (PB-MCESs), which include electricity, heat, cooling, and gas hubs to enable internal coordination of resources and flexibility extraction for PB-MCESs. Subsequently, Nash Bargaining Game Theory is employed to construct a cooperative TEC that prioritizes P2P energy trade. In addition to P2P energy trading, PB-MCESs can trade their reserve in a P2P fashion to mitigate their uncertainty. PB-MCESs estimate the level of uncertainty using stochastic programming and allot a reserve capacity based on this estimation in order to manage their uncertainty via P2P reserve trading and internal reserves. PB-MCES can also control their risk by altering their risk-taking factor in accordance with the Conditional Value-at-Risk (CVAR) index. Implementations have demonstrated that the proposed cooperative TEC decreases total costs by 17.14% and that the proposed P2P reserve trading reduces total costs by 16.32%.

关键词： Transactive energy control Prosumer-based multi -carrier energy systems Energy hub Nash bargaining game theory stochastic programming Risk management P2P trading Conditional value-at-risk

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Leveraging decision diagrams to solve two-stage stochastic programs with binary recourse and logical linking constraints

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第1期315卷 228-241页

作者： MacNeil, Moira Bodur, Merve Univ Toronto Dept Mech & Ind Engn Toronto ON Canada Univ Edinburgh Sch Math Edinburgh Scotland

We generalize an existing binary decision diagram -based (BDD-based) approach of Lozano and Smith (MP, 2022) to solve a special class of two -stage stochastic programs (2SPs) with binary recourse, where the first -stage decisions impact the second -stage constraints. First, we extend the second -stage problem to a more general setting where logical expressions of the first -stage solutions enforce constraints in the second stage. Then, as our primary contribution, we introduce a complementary problem, that appears more naturally for many of the same applications of the former approach, and a distinct BDD-based solution method, that is more efficient than the existing BDD-based approach on commonly applicable problem classes. In the novel problem, secondstage costs, rather than constraints, are impacted by expressions of the first -stage decisions. In both settings, we convexify the second -stage problems using BDDs and parameterize either the BDD arc costs or capacities with first -stage solutions. We extend this work by incorporating conditional value -at -risk and propose the first decomposition method for 2SP with binary recourse and a risk measure. We apply these methods to a novel stochastic problem, namely stochastic minimum dominating set problem, and present numerical results to support their effectiveness.

关键词： stochastic programming Binary decision diagrams Benders decomposition

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