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Showing papers on "Stochastic programming published in 2022"


Journal ArticleDOI
15 Jan 2022-Energy
TL;DR: A hybrid teaching-learning-based optimization (TLBO) and crow search algorithm (CSA) is used to obtain a reliable optimal solution with a low standard deviation for flexible EH in the presence of renewable energy sources and active loads.

54 citations


Journal ArticleDOI
TL;DR: In this paper , a multi-portfolio approach and scenario-based stochastic MIP (mixed integer programming) models for optimization of supply chain operations under ripple effect is presented.
Abstract: This paper presents a multi-portfolio approach and scenario-based stochastic MIP (mixed integer programming) models for optimization of supply chain operations under ripple effect. The ripple effect is caused by regional pandemic disruption risks propagated from a single primary source region and triggering delayed regional disruptions of different durations in other regions. The propagated regional disruption risks are assumed to impact both primary and backup suppliers of parts, OEM (Original Equipment Manufacturer) assembly plants as well as market demand. As a result, simultaneous disruptions in supply, demand and logistics across the entire supply chain is observed. The mitigation and recovery decisions made to improve the supply chain resilience include pre-positioning of RMI (Risk Mitigation Inventory) of parts at OEM plants and ordering recovery supplies from backup suppliers of parts, located outside the primary source region. The decisions are spatiotemporally integrated. The pre-positioning of RMI implemented before a disruptive event is optimized simultaneously with the RMI usage and recovery supply portfolios for the backup suppliers in the aftermath periods. The recovery supplies of parts and production at OEM plants, are coordinated under random availability of suppliers and plants and random market demand. The resilient solutions for the resilient supply portfolios are compared with the non-resilient solutions with no recovery resources available. The findings indicate that the resilient measures commonly used to mitigate the impacts of region-specific disruptions can be successfully applied for mitigation the impacts of multi-regional pandemic disruptions and the ripple effect.

47 citations


Journal ArticleDOI
TL;DR: In this paper , a stochastic optimization model based on value at best (VaB) is developed for a risk-seeking wind power producer, which is formulated as a mixed-integer linear programming problem.
Abstract: This study proposes a statistical measure and a stochastic optimization model for generating risk-seeking wind power offering strategies in electricity markets. Inspired by the value at risk (VaR) to quantify risks in the worst-case scenarios of a profit distribution, a statistical measure is proposed to quantify potential high profits in the best-case scenarios of a profit distribution, which is referred to as value at best (VaB) in the best-case scenarios. Then, a stochastic optimization model based on VaB is developed for a risk-seeking wind power producer, which is formulated as a mixed-integer linear programming problem. By adjusting the parameters in the proposed model, the wind power producer can flexibly manage the potential high profits in the best-case scenarios from the probabilistic perspective. Finally, the proposed statistical measure and riskseeking stochastic optimization model are verified through case studies.

47 citations


Journal ArticleDOI
TL;DR: In this paper , the operation of an MGs with dispatchable generators and wind turbine has been formulated as a two-stage stochastic optimisation problem, wherein DA and RT stages are seen in one shot and decision variables are determined in a way that the expected MG operation cost is minimised.

42 citations


Journal ArticleDOI
TL;DR: This paper focuses on developing a three-level defender–attacker–defender model to solve resilience-driven optimal sizing and pre-positioning problems of mobile energy storage systems in networked microgrids with decentralized control.

35 citations


Journal ArticleDOI
TL;DR: In this paper, a hybrid planning of distributed generation and distribution automation in distribution networks aiming to improve the reliability and operation indices is presented, where the objective function minimizes the sum of the expected daily investment, operation, energy loss and reliability costs.

27 citations


Journal ArticleDOI
15 Jan 2022-Energy
TL;DR: In this article, a flexible coordinated power system expansion planning (CPSEP) framework is proposed to minimize the summation of the expansion planning, operation and reliability costs while taking the network model based on AC optimal power flow constraints, and the reliability and flexibility considerations into account.

26 citations


Journal ArticleDOI
15 Jan 2022-Energy
TL;DR: The results demonstrate demand response program can significantly reduce the operation cost in worst scenarios and it is indicated that the risk-averse decisions reduce the risk of experiencing costly scenario.

26 citations


Journal ArticleDOI
TL;DR: In this article , a multi-stage distributionally robust optimization (MSDRO) model is set up to address the temporal uncertainties in the day-ahead economic dispatch model, which provides more flexibility so that the decision variables can be adjusted at each time period, leading to a complex nested formulation.
Abstract: A virtual energy storage (VES) model is proposed in this paper to accommodate renewable energy under a special market regulation. Such VESs can provide or consume electricity to the main power grid under the premise that the daily net electricity energy is balanced. Furthermore, a multi-stage distributionally robust optimization (MSDRO) model is set up in this paper to address the temporal uncertainties in the day-ahead economic dispatch model. Compared with the traditional two-stage distributionally robust optimization, the proposed multi-stage approach provides more flexibilities so that the decision variables can be adjusted at each time period, leading to a complex nested formulation. To efficiently solve the MSDRO model, a stochastic dual dynamic programming method is employed to decompose the original large-scale optimization model into several sub-problems in the stages, as two steps: forward pass and backward pass. In the forward pass, the expected cost-to-go function is approximated by piecewise-linear functions and then several samples are used to generate a lower bound; the backward pass will generate Benders’ cuts at each stage from the solution of the forward pass. The forward and backward passes are performed iteratively until the convergence is reached. Numerical results on an IEEE 118-bus system and a practical power system in China verify the proposed method.

24 citations


Journal ArticleDOI
01 Feb 2022-Energy
TL;DR: In this article , a two-stage multi-objective stochastic-robust hybrid optimization model for the optimal device capacity of a hybrid combined cooling, heating and power (CCHP) system integrated with renewable energy is proposed.

24 citations


Journal ArticleDOI
Guanguan Li1, Qiqiang Li1, Yi Liu1, Huimin Liu1, Wen Song1, Ran Ding1 
TL;DR: A bi-level energy management framework that can help the retail market to coordinate peer-to-peer energy trading among multiple prosumers is presented, where a retailer acts as the leader that determines price discrimination for various prosumers with the goal of maximizing the social welfare.

Journal ArticleDOI
TL;DR: Two-stage risk-averse and risk-neutral stochastic optimization models are proposed to schedule repair activities for a disrupted CI network with the objective of maximizing system resilience and assessing the risks associated with post-disruption scheduling plans.

Journal ArticleDOI
TL;DR: In this paper , a coordinated charging scheduling approach for battery electric buses (BEBs) in a hybrid charging scheme is proposed, where both plug-in fast charging and battery swapping charging modes are incorporated in a single charging station.
Abstract: This paper proposes a coordinated charging scheduling approach for battery electric buses (BEBs) in a hybrid charging scheme, i.e., both plug-in fast charging and battery-swapping charging modes are incorporated in a single charging station. To accommodate the uncertain battery energy consumption during bus operation, a two-stage stochastic program is formulated, where the first stage decision determines the battery inventory level of each station and the second stage determines the charging mode and designs when, where, and how long each bus should be charged. Future uncertainties associated with energy consumption are captured by a set of possible discrete scenarios from historical data. A progressive hedging algorithm is developed to decompose the two-stage stochastic program into sub-problems. A case study is conducted to verify the proposed models and solution algorithms.

Journal ArticleDOI
TL;DR: In this article , a stochastic management algorithm is proposed to find a day-ahead optimal operation of the renewable energy resources, including wind turbine, photovoltaic (PV) unit, Fuel Cell (FC), electrolyzer, microturbine, and energy storage that simultaneously considers the participation of the smart homes in demand response programs.

Journal ArticleDOI
TL;DR: In this paper , a two-stage hybrid stochastic programming/robust optimization (SP/RO) day-ahead scheduling of interconnected transactive MGs (ITMGs) is proposed to reduce the operation cost of the whole ITMG.

Journal ArticleDOI
TL;DR: In this paper, a two-stage hybrid stochastic programming/robust optimization (SP/RO) day-ahead scheduling of interconnected transactive MGs (ITMGs) is proposed.

Journal ArticleDOI
TL;DR: In this article , two-stage risk-averse and risk-neutral stochastic optimization models are proposed to schedule repair activities for a disrupted critical infrastructures (CIs) network with the objective of maximizing system resilience.

Journal ArticleDOI
01 Jan 2022-Energy
TL;DR: In this article , a risk-based stochastic model is used to model the prevailing uncertainties such as loads, wind generation, and main-grid availability in a market-based operation framework.

Journal ArticleDOI
TL;DR: In this article , an integrated stochastic programming-information gap decision theory (IGDT) approach is proposed to handle multiple uncertainties in the optimization of IES operation and planning.

Journal ArticleDOI
24 Jan 2022-Energies
TL;DR: This review paper discusses microgrid and home energy management, demand-side management, unit commitment, microgrid integration, and economic dispatch as examples of applications of probabilistic optimization techniques in smart power systems.
Abstract: Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy. It is further divided into stochastic, robust, distributionally robust, and chance-constrained optimizations. The topics of probabilistic optimization in smart power systems are covered in this review paper. In order to account for uncertainty in optimization processes, stochastic optimization is essential. Robust optimization is the most advanced approach to optimize a system under uncertainty, in which a deterministic, set-based uncertainty model is used instead of a stochastic one. The computational complexity of stochastic programming and the conservativeness of robust optimization are both reduced by distributionally robust optimization.Chance constrained algorithms help in solving the constraints optimization problems, where finite probability get violated. This review paper discusses microgrid and home energy management, demand-side management, unit commitment, microgrid integration, and economic dispatch as examples of applications of these techniques in smart power systems. Probabilistic mathematical models of different scenarios, for which deterministic approaches have been used in the literature, are also presented. Future research directions in a variety of smart power system domains are also presented.

Journal ArticleDOI
TL;DR: In this article , a stochastic programming approach is proposed for determining an optimum maintenance plan to minimize maintenance costs and expected failure costs, while maximizing the probability of successful accomplishment of the next mission under uncertainties in future operating conditions.

Journal ArticleDOI
TL;DR: In this article , a two-stage stochastic model is put forward for electricity procurement in large consumers (LCs) with storage system, photovoltaic, wind and geothermal units, slow demand response (SDR) and fast demand response(FDR), bilateral contracts and pool market.
Abstract: In this paper, a two-stage stochastic model is put forward for electricity procurement in large consumers (LCs) with storage system, photovoltaic, wind and geothermal units, slow demand response (SDR) and fast demand response (FDR), bilateral contracts and pool market. The model considers the uncertainties of pool market prices, demands, wind and photovoltaic power. Conditional-value-at-risk (CVaR) as a risk metric is used to take the variability of procurement cost into account. Major findings indicate that SDR and FDR collectively decrease the expected procurement cost by 9.4% and CVaR by 10.5% and also show that FDR is more efficient than SDR in decreasing the electricity procurement cost and its associated risk. The superiority of FDR over SDR is attributed to its flexibility which enables it to adjust its shift-ups and shift-downs according to the occurred scenario. The results imply that demand response programs and in particular, FDR decrease the purchased electricity from pool market. According to the results, the increase in confidence level increases CVaR, but does not significantly change the expected procurement cost. The results also show that at low weight factors, the increase in weight factor increases expected procurement cost and decreases CVaR, but at weight factors beyond 8, the increase in weight factor changes neither expected procurement cost nor CVaR.

Journal ArticleDOI
TL;DR: In this paper , a risk-averse two-stage stochastic programming model is developed to incorporate the uncertainties associated with the demand, initial inventory and level of reusability of the reusable packaging material.

Journal ArticleDOI
TL;DR: In this paper , Bertsimas, Shtern, and Sturt investigated a simple approximation scheme, based on overlapping linear decision rules, for solving data-driven two-stage distributionally robust optimization problems with the type-infinity Wasserstein ambiguity set.
Abstract: In “Two-Stage Sample Robust Optimization,” Bertsimas, Shtern, and Sturt investigate a simple approximation scheme, based on overlapping linear decision rules, for solving data-driven two-stage distributionally robust optimization problems with the type-infinity Wasserstein ambiguity set. Their main result establishes that this approximation scheme is asymptotically optimal for two-stage stochastic linear optimization problems; that is, under mild assumptions, the optimal cost and optimal first-stage decisions obtained by approximating the robust optimization problem converge to those of the underlying stochastic problem as the number of data points grows to infinity. These guarantees notably apply to two-stage stochastic problems that do not have relatively complete recourse, which arise frequently in applications. In this context, the authors show through numerical experiments that the approximation scheme is practically tractable and produces decisions that significantly outperform those obtained from state-of-the-art data-driven alternatives.

Journal ArticleDOI
TL;DR: In this article, a hybrid approach to optimize the operation and handle the uncertainties of a residential energy system composed of photovoltaic, fuel cell, boiler and storage units is presented.

Journal ArticleDOI
TL;DR: In this article , a multi-stage stochastic program is developed to optimize various strategies for planning limited and necessary healthcare resources to improve patient access to care during epidemics and pandemics.

Journal ArticleDOI
01 Jan 2022
TL;DR: In this article , a hybrid approach to optimize the operation and handle the uncertainties of a residential energy system composed of photovoltaic, fuel cell, boiler and storage units is presented.
Abstract: Hybrid energy systems are developed recently due to their applicability in small scales. This paper presents a hybrid approach to optimize the operation and handle the uncertainties of a residential energy system composed of photovoltaic, fuel cell, boiler and storage units. In this regard, the uncertain parameters are divided into two categories including badly-behaved and well-behaved parameters and then, robust optimization and stochastic programming are utilized for modeling, respectively. As well, conditional value-at-risk is implemented to evaluate the risk of well-behaved parameters. According to the simulations, it is proved that badly-behaved uncertainties have higher impacts on system operation. Moreover, changing the control parameters of robust optimization and conditional value-at-risk from 0 to 24 and 0 to 1 increase the total cost by 5.2% and 0.47%, respectively. The comparative results also show that the proposed hybrid method takes less conservative decisions to optimize cost.

Journal ArticleDOI
TL;DR: Li et al. as discussed by the authors proposed a global optimization framework of information layer-physical layer-energy layer-dynamic programming (IPE-DP), which can realize the unity of different information scenarios, different vehicle configurations and energy conversions.

Journal ArticleDOI
TL;DR: In this paper , a multistage stochastic mixed-integer program (MS-MIP) formulation is proposed to keep track with the fast development of hydrogen industry, and a nested decomposition algorithm based on Stochastic Dual Dynamic Integer Programming (SDDiP) is developed.
Abstract: Integrated Hydrogen-Electrical (IHE) microgrids are desirable testbeds for the practice of carbon-neutral energy supply. This paper studies the IHE microgrids planning (IHEMP) under a dynamic perspective. To keep track with the fast development of hydrogen industry, we propose a multistage stochastic mixed-integer program (MS-MIP) formulation. It comprehensively considers the siting and sizing decisions of IHE microgrids, the dynamic expansion of distributed energy facilities, and the detailed operational model to derive a robust, flexible and profitable investment policy. Moreover, a scenario-tree based sampling strategy is leveraged to capture both the large-scale strategic uncertainties (e.g., the long-term growth of electric loads and hydrogen refueling demands, as well as the cost changes of system components) and fine-scale operating uncertainties (e.g., random variation of renewable energy outputs and loads) under different time scales. As the resulting formulation could be computationally very challenging, we develop a nested decomposition algorithm based on Stochastic Dual Dynamic Integer Programming (SDDiP). Case studies on exemplary IHE microgrids validate the effectiveness of our dynamic planning approach. Also, the customized SDDiP algorithm shows a superior solution capacity to handle large-scale MS-MIPs than the state-of-the-art solver (i.e., Gurobi) and a popular scenario-oriented decomposition method (i.e., progressive hedging algorithm).

Journal ArticleDOI
TL;DR: In this article , the authors investigate a crowd-sourced last-mile parcel delivery system supported by a network of strategically located mini-depots and present a two-stage stochastic network design problem with a time-dependent arc capacity.
Abstract: Crowd-shipping is an emergent solution to avoid the negative effects caused by the growing demand for last-mile delivery services. Previous research has studied crowd-shipping typically at an operational planning level. However, the study of support infrastructure within a city logistics framework has been neglected, especially from a strategic perspective. We investigate a crowd-sourced last-mile parcel delivery system supported by a network of strategically located mini-depots and present a two-stage stochastic network design problem with stochastic time-dependent arc capacity to fulfill stochastic express deliveries. The first-stage decision is the location of mini-depots used for decoupling flows allowing more flexibility for crowd–demand matching. The second stage of the problem is the demand allocation of crowd carriers or professional couriers for a finite set of scenarios. We propose an exact Benders decomposition algorithm embedded in a branch-and-cut framework. To enhance the algorithm, we use partial Benders decomposition, warm-start, and non-dominated cuts. We perform computational experiments on networks inspired by the public transportation network of Munich. The proposed solution method outperforms an off-the-shelf solver by solving instances 3.6 to 19 times faster. The results show the potential to exploit the stochastic crowd flows to deliver packages with deadlines of 3 or 8 h. The crowd can transport 8.3% to 32.5% of the total demand by using between 4% to 24% of the crowd capacity, and we observe average daily savings of 2.1% to 7.6% of the total expected operational cost. The results show values of the stochastic solution of at least 1% and up to 10%.