The authors focus on a model for system operators that uses centralized scheduling of multiple flexibility assets and services to minimize the cost of managing problems with grid congestion, voltages, and losses. The ...
详细信息
The authors focus on a model for system operators that uses centralized scheduling of multiple flexibility assets and services to minimize the cost of managing problems with grid congestion, voltages, and losses. The model schedules flexibility assets using stochastic optimization for AC optimal power flow in an active distribution network. The novelty of the contribution lies in its focus on how the dynamic capabilities of the flexibility resources are defined with regard to how uncertainty is resolved in the model. The impact of uncertainty is studied by using well-known quality measures from stochastic programming, such as the value of the stochastic solution. Moreover, the authors introduce a new measure related to the impact of representing uncertainty and flexibility when considering reactive power. By changing the time attributes of flexibility assets, the authors show the impact of uncertainty and time structure on a scheduling problem. The uncertainties considered are price and load levels. The findings reveal that the quality of the scheduling of each flexibility resource depends on using a stochastic model with a rigorous consideration of time and uncertainty.
This paper is devoted to the study of the expected-integral multifunctions given in the form E-Phi (x) := integral(T) Phi(t)(x)d mu, where Phi: T x R-n paired right arrows R-m is a set-valued mapping on a measure spac...
详细信息
This paper is devoted to the study of the expected-integral multifunctions given in the form E-Phi (x) := integral(T) Phi(t)(x)d mu, where Phi: T x R-n paired right arrows R-m is a set-valued mapping on a measure space (T, A, mu). Such multifunctions appear in applications to stochastic programming, which require developing efficient calculus rules of generalized differentiation. Major calculus rules are developed in this paper for coderivatives of multifunctions E-Phi and second-order subdifferentials of the corresponding expected-integral functionals with applications to constraint systems arising in stochastic programming. The paper is self-contained with presentation in the preliminaries of some needed results on sequential first-order subdifferential calculus of expected-integral functionals taken from the first paper of this series.
This article presents a framework for profit-maximizing strategic bio-energy supply chain design by taking into account variability in biomass as a response to price set as well as uncertainty in biomass yield. We pre...
详细信息
This article presents a framework for profit-maximizing strategic bio-energy supply chain design by taking into account variability in biomass as a response to price set as well as uncertainty in biomass yield. We present our model as a two-stage stochastic integer program for a multi-period integrated design of a network in which the here-and-now strategic decisions include biorefinery locations and size as well as base biomass price. To efficiently solve our model, we suggest an L-shaped-based algorithm along with a Sample Average Approximation approach. Finally, we demonstrate our results in a case study in Texas using realistic data. Within our framework, we present the relationship between biomass and biofuel price, as well as the optimal network design for the biofuel producer.
This article studies the distribution problem of brick-and-mortar retailers aiming to integrate the online channel into their operations. The article presents an integrated modeling approach addressing the online chan...
详细信息
This article studies the distribution problem of brick-and-mortar retailers aiming to integrate the online channel into their operations. The article presents an integrated modeling approach addressing the online channel-driven distribution network deployment (e-DND) problem under uncertainty. The e-DND involves decisions on operating fulfillment platforms and on assigning a fulfillment mission to those platforms, while anticipating the revenues and costs induced by order fulfillment, replenishment, delivery, and inventory holding. To model this problem while taking into account the uncertain nature of multi-item online orders, store sales, and capacities, a two-stage stochastic program with mixed-integer recourse is developed. Two alternative deployment strategies, characterized by allocation of orders, inventory positioning, delivery schema, and inbound flow pattern decisions, are investigated using this model. The first deployment strategy investigates the ship-from stores practice where the on-hand inventory is used for all sales channels. The second deployment strategy additionally considers the advanced positioning of inventory at a fulfillment center in the urban area where the online orders are requested. To solve the two-stage stochastic model with integer recourse, an exact solution approach combining scenario sampling and the integer L-shaped method is proposed. Numerical results, inspired by the case of a European retailer, are provided to evaluate the performance of the deployment strategies and the efficiency of the proposed solution approach. (c) 2020 Elsevier B.V. All rights reserved.
stochastic decomposition (SD) has been a computationally effective approach to solve large-scale stochastic programming (SP) problems arising in practical applications. By using incremental sampling, this approach is ...
详细信息
stochastic decomposition (SD) has been a computationally effective approach to solve large-scale stochastic programming (SP) problems arising in practical applications. By using incremental sampling, this approach is designed to discover an appropriate sample size for a given SP instance, thus precluding the need for either scenario reduction or arbitrary sample sizes to create sample average approximations (SAA). When compared with the solutions obtained using the SAA procedure, SD provides solutions of similar quality in far less computational time using ordinarily available computational resources. However, previous versions of SD were not applicable to problems with randomness in second-stage cost coefficients. In this paper, we extend its capabilities by relaxing this assumption on cost coefficients in the second stage. In addition to the algorithmic enhancements necessary to achieve this, we also present the details of implementing these extensions, which preserve the computational edge of SD. Finally, we illustrate the computational results obtained from the latest implementation of SD on a variety of test instances generated for problems from the literature. We compare these results with those obtained from the regularized L-shaped method applied to the SAA function of these problems with different sample sizes.
Coordinated operation of various energy sources has drawn the attention of many power producers worldwide. In this paper, a Concentrating Solar Power Plant (CSPP) along with a wind power station, a Compressed Air Ener...
详细信息
Coordinated operation of various energy sources has drawn the attention of many power producers worldwide. In this paper, a Concentrating Solar Power Plant (CSPP) along with a wind power station, a Compressed Air Energy Storage (CAES) unit, and a Demand Response Provider (DRP) constitute the considered Hybrid Power Producer (HPP). In this regard, this paper deals with the optimal participation of the mentioned HPP in the Day-Ahead (DA), and intraday electricity markets by benefiting from the joint configuration of all accessible resources. To attain risk-averse strategies in the suggested model, Conditional Value-at-Risk (CVaR) based on the epsilon-constraint technique is employed, while its efficiency is validated compared to the previously applied method to such problems. On the whole, the main contributions of this work lie in: 1) proposing a novel model for optimal behavior of a CSPP-based HPP in DA, and intraday markets using a three-stage decision-making architecture, and 2) developing a bi-objective optimization framework to improve the functioning of the risk-constrained algorithm. Simulation results reveal that taking advantage of the CSPP in the intraday market, and coordinated operation of all resources not only enhance the profitability of the system but also lessen the associated risk compared to the previous models.
In this paper, a class of stochastic mathematical programs with probabilistic complementarity constraints is considered. We first investigate convergence properties of sample average approximation (SAA) approach to th...
详细信息
In this paper, a class of stochastic mathematical programs with probabilistic complementarity constraints is considered. We first investigate convergence properties of sample average approximation (SAA) approach to the corresponding chance constrained relaxed complementarity problem. Our discussion can be not only applied to the specific model in this paper, but also viewed as a supplementary for the SAA approach to general joint chance constrained problems. Furthermore, considering the uncertainty of the underlying probability distribution, a distributionally robust counterpart with a moment ambiguity set is proposed. The numerically tractable reformulation is derived. Finally, we use a production planing model to report some preliminary numerical results.
Influence maximization aims at identifying a limited set of key individuals in a (social) network which spreads information based on some propagation model and maximizes the number of individuals reached. We show that...
详细信息
Influence maximization aims at identifying a limited set of key individuals in a (social) network which spreads information based on some propagation model and maximizes the number of individuals reached. We show that influence maximization based on the probabilistic independent cascade model can be modeled as a stochastic maximal covering location problem. A reformulation based on Benders decomposition is proposed and a relation between obtained Benders optimality cuts and submodular cuts for correspondingly defined subsets is established. We introduce preprocessing tests, which allow us to remove variables from the model and develop efficient algorithms for the separation of Benders cuts. Both aspects are shown to be crucial ingredients of the developed branch-and-cut algorithm since reallife social network instances may be very large. In a computational study, the considered variants of this branch-and-cut algorithm outperform the state-of-the-art approach for influence maximization by orders of magnitude. (C) 2020 Elsevier B.V. All rights reserved.
Under the dual-carbon target, distributed energy sources often cause power mismatches between supply and load, challenging the stability and safety of distribution networks. This paper proposes a comprehensive evaluat...
详细信息
Capacities of lines and generators in a power grid are regularly increased to satisfy the increasing demand due to electrification or population growth. Since some lines and generators go out of service in extreme wea...
详细信息
暂无评论