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Dispatch strategies for large-scale heat pump based district heating under high renewable share and risk-aversion: A multistage stochastic optimization approach

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ENERGY ECONOMICS 2024年 136卷

作者： Siddique, Muhammad Bilal Keles, Dogan Scheller, Fabian Nielsen, Per Sieverts Tech Univ Denmark Dept Technol Management & Econ Lyngby Denmark Tech Univ Appl Sci Wurzburg Schweinfurt Inst Zero Carbon IZEC Schweinfurt Germany Ctr Appl Energy Res CAE Magdalene Schoch Str 3 D-97074 Wurzburg Germany

Heat pumps play an essential role in decarbonizing the heating sector, and their adoption is projected to rise significantly. The high share of large-scale heat pumps in district heating exposes heating utilities to uncertainty in electricity markets. This challenge is further exacerbated by 1) future high share of renewable energy resulting in increased uncertainty of electricity prices, and 2) introduction of voluntary energy bids for balancing energy markets (or regulation market). Despite the importance of this issue, little research has been conducted to address it. Therefore, this paper investigates the optimal dispatch of large-scale heat pump based district heating by adopting multi-stage stochastic optimization approach to minimize the total expected heat generation cost, taking sequential electricity markets prices as stochastic. We also evaluate the impact of high renewable energy penetration on this optimal dispatch strategy. Furthermore, we include the risk preference of district heating operator by using conditional value at risk in the objective function. We conclude that heat pump should be coupled with flexible technologies like electric boilers as more renewable energy and balancing market trading increases flexible units' dispatch. Also, most of the heat produced (50%) in our case study is used flexibly via thermal storage. We find more renewable could lead to negative expected costs and voluntary bids in balancing market further reduces the expected cost by 28-59%. Furthermore, trading risk increases significantly with renewable energy penetration, which can be mitigated to a limited extent by bidding in up and down balancing market. Risk aversion shifts trading from intraday to day-ahead market and promotes heat pump dispatch.

关键词： stochastic programming Large-scale heat pump District heating Sequential electricity markets Risk aversion

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Reliable Error Estimates for Optimal Control of Linear Elliptic PDEs with Random Inputs

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SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION 2023年第4期11卷 1139-1163页

作者： Milz, Johannes Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

We discretize a risk-neutral optimal control problem governed by a linear elliptic partial differential equation with random inputs using a Monte Carlo sample-based approximation and a finite element discretization, yielding finite dimensional control problems. We establish an exponential tail bound for the distance between the finite dimensional problems' solutions and the risk-neutral problem's solution. The tail bound implies that solutions to the risk-neutral optimal control problem can be reliably estimated with the solutions to the finite dimensional control problems. Numerical simulations illustrate our theoretical findings.

关键词： PDE-constrained optimization sample average approximation Monte Carlo sampling uncertainty quantification stochastic programming large deviations

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Risk-aware day-ahead planning of a zero energy hub integrated with green power-to-hydrogen technology using information gap decision theory and stochastic approach and considering demand side elasticity

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ENERGY REPORTS 2023年 10卷 4302-4317页

作者： Shaillan, Haider Muaelou Tohidi, Sajjad Hagh, Mehrdad Tarafdar Tabar, Vahid Sohrabi Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

Environmental issues and rapid load growth have led to increasing renewable resources infiltration toward achieving zero energy infrastructures;however, the uncertainty of such components increases the interactions between the local grid and energy markets for sustainable operation. In this study, day-ahead planning of a zero energy hub composed of wind turbine, photovoltaic, electric heat pump, boiler, chiller and storage units is optimized under the uncertainty of wind and solar units and energy market trading. In the designed structure, the required fuel for the local network is supplied through a power-to-hydrogen system to manage carbon emissions. In order to model the uncertainties and analyze the risk of decisions, a hybrid method consisting of the stochastic approach and information gap decision theory is applied. Furthermore, the impact of demand side elasticity for electrical, heating and cooling loads is evaluated. The results show that 10% of load participation significantly improves energy management actions and decreases operation costs by about 15.6%. The simulations also approve that the hybrid method handles the uncertainties under different conditions, where the risk-based cost functions change according to the operator attitude by about 522.44 $ for the ancillary parameter and scenario deviation equal to 10% and 20%, respectively.

关键词： Demand side elasticity Renewable resources Robust optimization stochastic programming Zero energy hub

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Integrated planning of emergency supply pre-positioning and victim evacuation

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SOCIO-ECONOMIC PLANNING SCIENCES 2024年 95卷

作者： Yang, Wenjie Caunhye, Aakil M. Zhuo, Maolin Wang, Qingyi Sichuan Univ Business Sch Chengdu 610064 Peoples R China Natl Univ Singapore Inst Operat Res & Analyt Singapore 117602 Singapore Univ Edinburgh Business Sch Edinburgh EH8 9JU Scotland Chengdu Ind & Trade Coll Sch Finance & Trade Management Chengdu 611731 Peoples R China

Natural disasters such as earthquakes, floods, and storms cause numerous human casualties and economic losses every year. Emergency supply pre-positioning and victim evacuation, as two important emergency relief operations, tend to be separately considered in the literature. However, there are intrinsic correlations between the two operations in reality, which calls for an integrated planning of emergency supply pre-positioning and victim evacuation. In this study, we propose a stochastic programming model to optimize, in an integrated way, the deployment of emergency facilities, pre-stocking and distribution of emergency supplies and management of self-evacuated victims under two planning goals, namely cost effectiveness and evacuation fairness. We also formulate four comparison models to highlight the benefits of our integrated planning model. With a case study on the development of an emergency preparedness and response plan in Sichuan Province, China, we not only demonstrate the effectiveness and benefits of our approach but also obtain key managerial insights and policy suggestions for better emergency management practice.

关键词： Emergency supply Victim evacuation Integrated planning Cost effectiveness and evacuation fairness stochastic programming

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An Improved Integer L-Shaped Method for the Vehicle Routing Problem with stochastic Demands

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INFORMS JOURNAL ON COMPUTING 2023年第2期35卷 423-439页

作者： Hoogendoorn, Y. N. Spliet, R. Erasmus Univ Erasmus Sch Econ NL-3062 Rotterdam Netherlands

We present an improved integer L-shaped method for the vehicle routing problem with stochastic demands. It exhibits speedups up to a factor of 325 compared with the current state-of-the-art, which allows us to solve 153 previously unsolved benchmark instances to optimality. The algorithm builds on the state-of-the-art in a few ways. First, we rectify a few technical issues found in the current literature. Second, we improve valid inequalities known as partial route inequalities. Finally, we introduce three new types of valid inequalities.

关键词： stochastic programming integer L-shaped method vehicle routing problem stochastic demands

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Blood inventory management: Ordering policies for hospital blood banks under uncertainty

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2023年第1期30卷 273-301页

作者： Meneses, Maria Marques, Ines Barbosa-Povoa, Ana Univ Lisbon Ctr Management Studies Inst Super Tecn Ave Rovisco Pais 1 P-1049001 Lisbon Portugal

The blood supply chain faces several challenges, such as stochastic demand and supply, the relation between the various stages of the chain, and the intrinsic nature of the product. Blood is a perishable, scarce, and (in most cases) voluntarily supplied product used to perform vital transfusions in patients which increases the pressure of managing its supply chain as efficiently and effectively as possible. For these reasons, it is crucial to have optimized inventory management that allows the availability of the right type of blood product, in the right place, at the right time, and in the right amount while avoiding wastage, especially in hospital blood banks that are the direct link to patients. This work aims to address these challenges with a new two-stage stochastic programming model for defining optimal ordering policies for blood products, considering demand uncertainty. This model minimizes wastage, shortages, and total costs related to blood inventory management, including ABO-substitutions. The model supports hospitals' tactical-operational decisions of when and how much blood products to order. A case study of a Portuguese hospital is used to validate and show the applicability of the model. By comparing several ordering policies, we show that it is possible to contemplate the decision maker's goals whilst obtaining substantial reductions in terms of wastage and costs. These results allow the definition of an important set of managerial insights.

关键词： blood supply chain inventory management perishable products stochastic programming ABO‐ substitution

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Supporting platelet inventory management decisions: What is the effect of extending platelets' shelf life?

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期310卷 640-654页

作者： Dillon, Mary Vauhkonen, Ilmari Arvas, Mikko Ihalainen, Jarkko Vilkkumaa, Eeva Oliveira, Fabricio Aalto Univ Sch Business Dept Informat & Serv Management Espoo Finland Aalto Univ Sch Sci Dept Math & Syst Anal Espoo Finland Finnish Red Cross Blood Serv Helsinki Finland

Managing blood product inventories can be challenging due to stochastic supply and demand, varying shelf lives of the products, and the need to account for multiple objectives such as the minimisation of costs, product shortage and expiry. This complex setting makes it difficult to include all relevant aspects, while ensuring that the computation time required to optimise the blood product supply chain remains reasonable. Consequently, existing models typically fail to solve realistic-sized problems and thus have not found much use in supporting decisions faced by blood service practitioners. This research develops a methodological framework for modelling platelet inventories, resulting in robust managerial recommen-dations. Specifically, we propose a two-stage stochastic programming model to define optimal order-up -to levels that minimise costs, shortage and expiry in a decentralised decision-making setting. We exploit the problem structure to decompose it and make the model computationally tractable. To ensure that the model is practically relevant, we develop it with practitioners from the Finnish Red Cross Blood Service. We use the model to estimate the costs of extending the shelf life of platelets from five to seven days through two methods and assess the impacts of this extension on optimal inventory decisions. These results can be used to optimise the Finnish platelet supply chain and inform future cost-effectiveness analyses regarding shelf life extension.& COPY;2023 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： Decision support systems Platelet inventory management stochastic programming Progressive Hedging

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Dealing with the stochastic prosumager problem with controllable loads

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SOFT COMPUTING 2023年第18期27卷 12913-12924页

作者： Violi, Antonio Beraldi, Patrizia Carrozzino, Gianluca Univ Sannio Dept Law Econ Management & Quantitat Methods Benevento BN Italy Univ Calabria Dept Mech Energy & Management Engn Arcavacata Di Rende CS Italy

This paper focuses on the home energy management for a residential prosumager with flexible loads. In particular, three different types of controllable appliances (shiftable, interruptible, thermostatically controllable) have been considered, each one with a specific representation of energy consumption profile and a potential discomfort rate for the user. The inherent uncertainty affecting the main model parameters (i.e., non- controllable loads, solar production, external temperature) is explicitly accounted for by adopting the two-stage stochastic programming modeling paradigm. The model solution provides the prosumager with the optimal scheduling of the controllable loads and the operation of the storage system that guarantee the minimum expected energy procurement cost, taking into account the overall discomfort. A preliminary computational experience has shown the effectiveness of the proposed approach in terms of cost savings and the advantage related to the use of a stochastic programming approach over a deterministic formulation.

关键词： Home energy management stochastic programming Controllable appliances User discomfort modeling

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Optimization of Green Energy Portfolio in Retail Market Using stochastic programming

Optimization of Green Energy Portfolio in Retail Market Usin...

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North American Power Symposium

作者： Hessam Golmohamadi Reza Keypour A. Hassanpour Masoud Davoudi Department of Power Electrical Engineering ECE School Semnan University Electrical and Computer Engineering Department University of North Carolina at Charlotte

ISBN: (纸本)9781467373906

In this paper, a mathematical optimization approach for green energy portfolio is presented to strike a right balance between risk and profit associated with retailing in power market. This approach emphasizes on the increasing use of renewable resources to overcome conservation concerns to some degrees. Three different uncertainties are considered for electricity price and energy output of wind and solar distributed generation units. In order to model the uncertainties properly, scenario construction schemes, namely Monte Carlo and time series with ARIMA (Auto regressive integrated moving average) are implemented in this paper. Moreover, risk and elasticity analysis are considered simultaneously to enable consumers and retailers to manage their risk and incomes. Two-stage stochastic programming with fixed recourse is used to model the probabilistic space of decision making process in this paper. At the end, numerical results and simulations are presented which demonstrate the applicability of the proposed approach in a retail electricity market.

关键词： Electricity price Retailer Risk management stochastic programming Uncertainty

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Coordinated Planning of Wind Power Generation and Energy Storage With Decision-Dependent Uncertainty Induced by Spatial Correlation

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IEEE SYSTEMS JOURNAL 2023年第2期17卷 2247-2258页

作者： Yin, Wenqian Li, Yujia Hou, Jiazuo Miao, Miao Hou, Yunhe Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China HKU Shenzhen Inst Res & Innovat Shenzhen 518053 Peoples R China

The global experience on wind farm development reveals that due to the spatial correlation, the prediction error of wind power is related to the scale of wind farms. This evidence indicates that the uncertainty features of wind power output from large-scale wind farms are not fixed but dependent on expansion decisions. The decision-dependent uncertainty (DDU) will alter the traditional optimization process and pose solution challenges. This article proposes a coordinated planning model for large-scale wind farms and energy storage considering DDU. First, a DDU model, which quantifies the relationship between wind power prediction errors and the wind farm size, is established based on historical data. The proposed DDU model for a single wind farm is extended to multiple wind farms with their spatial correlation captured by a Gaussian Mixture Model. Then, tackling the coupling relationship between decisions and uncertainty, an affine function-based solution method for the stochastic model with decision-dependent probability distributions is proposed. The constructed affine function maps planning decisions to decision-dependent wind power scenario sets via linear transformation. The difference between the planning model with and without the DDU in wind power is compared and discussed. Case studies verify the proposed model and solution method.

关键词： Wind power generation Uncertainty Wind farms Correlation Planning Renewable energy sources Generators Decision-dependent uncertainty (DDU) energy storage Gaussian mixture model (GMM) spatial correlation stochastic programming wind power generation planning

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