多损失条件风险值的多层规划模型

对于多个损失函数,在给定的置信水平下,首先定义了不超过给定损失值的最小风险值(即Va R值)和基于权值的累积期望损失值(即CVa R损失值)概念,然后建立了一个多损失条件风险值的多层规划模型.该模型的目标是求各层多损失CVa R值达最小的最优策略,并证明了它等价于另一个较容易求解的多层规划模型.最后,给出了三级供应链中多产品的定价与订购的条件风险值模型(三层线性规划模型).     

一个经典规划问题最优解的讨论

本文就一个经典规划问题"合理利用线材"展开讨论,分析不同文献上所给出的解及它们之间的关系,创造性地给出了该问题最优解的结构.     

地市级可再生资源能源利用优化

对地市级可再生能源管理问题,建立了在确保有限的传统能源和可再生能源能满足地区各个部门在规划期内的能源总需求条件下,使整个系统的运作成本最低的可再生能源利用优化模型,并根据实际情况假设模型中除太阳能和风能的可利用量为随机变量外,其他参数均为区间数时,利用处理区间数的可能度方法和满意度方法推导该类问题的确定性等价模型．     

区间数线性规划问题的解法

基于区间数与实数之间的关系,提出了区间数线性规划的激进最优解,保守最优解的定义.利用约束集之间以及目标函数值之间的关系,在原有区间数线性规划的基础之上,给出了两个求解激进最优解、保守最优解的方法.数值例子验证了该方法的有效性和可行性.     

考虑风电与光伏机组出力下农村微网系统运行优化模型研究

我国农村电网架构薄弱,限制了光伏扶贫等政策的开展,造成了可再生能源中弃风、弃光、弃水等现象的出现,如何将农村丰富的生物质能与风电、光伏构建成具有多能互补特性微网系统是解决上述问题主要途径.因此,本文建立了包含风力机组、光伏发电、蓄能系统和生物质能的农村微网系统,考虑了风电与光伏出力的不确定性,满足机组运行、系统备用、系统负荷平衡多能约束条件,构建了基于不确定性多情景下农村微网多能调度鲁棒优化模型.针对传统启发式算法早熟、容易陷入局部最优的问题,运用GAMS求解农村多能流微能网调度模型.算例结果表明,算法有效降低系统日运行成本,证明了模型的正确性.农村多能流微网系统的建设能够有效消纳富裕的风、光可再生能源及秸秆粪便等生物质能,对改善农村环境污染问题具有重要意义.     

区间二次双层规划的最好最优解

双层规划问题是一类具有递阶结构的优化问题.在不确定的双层规划优化问题中,目标函数系数或约束条件系数为区间数的双层规划模型在实际问题中有着广泛的应用.在二次-线性双层规划模型的基础上,提出了上、下层目标函数以及约束条件系数均具有区间系数的二次-线性双层规划模型,给出了求解其最好最优解的方法.首先,通过选取约束条件中不同的基矩阵,求得区间二次-线性双层规划的可能最优解.再比较求得的全部可能最优解,便可得到区间二次-线性双层规划模型的最好最优解.最后给出数值算例验证该方法的有效性.     

模糊系数规划

给出了一模糊系统规划的定义,该定义与通常的模糊规划义有所不同,它容许规划中的目标函数系数和所有约束函数系数可以是模糊数,并且容许既有模糊系数不等式的束函数又有模糊和所有约束函数系数都可以提模糊数,并且容许既有模糊系数不等式的束函数又有模糊系数等式约束函数,本文还对满足一定条件的模糊系数规划,包括模糊系数线性规划和模糊系数二次规划,给出了切实可行的求解方法。     

基于随机规划的建筑集群冷热电联供系统优化研究

面向建筑集群的冷热电联供系统的设计和优化是实现建筑楼宇能源成本节约的重要途径。随机因素对该联供系统的优化决策,具有显著的影响。考虑建筑楼宇的能源需求为随机变量,构建随机混合整数规划模型,解决以最小化建筑楼宇总费用为目标时建筑集群冷热电联供系统的优化问题;其次,提出采用Benders多割平面方法求解多目标规划问题,从而寻找冷热电联供系统的设备配置和系统运行的Pareto最优决策;最后,通过实验验证了模型和算法的有效性。实验结果表明建筑集群在协作模式下,相比于非协作模式,具有更低的总费用。     

正态随机规划的确定性规划及其灵敏度分析

在大量的管理决策问题中,经常会遇到目标函数的系数和右端常数为相互独立的正态随机变量的随机线性规划模型.利用对偶规划将正态随机规划化为具有α可靠度的线性规划,给出了解决该正态随机规划的一个有效方法,并对正态随机变量的参数进行了灵敏度分析,避免了由于参数估计偏差给决策带来的风险,保证了最优方案的α可靠度.     

多目标模糊系数规划

在单目标模糊系数规划的理论基础上，对多目标模糊系数规划进行讨论，在以目标间的协调程度尽可能大为最优性条件的要求下提出多目标模糊系数规划最优解的定义，并给出一种可行的求解方法。     

Renewable energy investments under different support schemes: A real options approach

This paper adopts a real options approach to analyze investment timing and capacity choice for renewable energy projects under different support schemes. The main purpose is to examine investment behavior under the most extensively employed support schemes, namely, feed-in tariffs and renewable energy certificate trading. We consider both multiple sources of uncertainty under each support scheme and uncertainty with respect to any change of support scheme, and we obtain both analytical (when possible) and numerical solutions. In a Nordic case study based on wind power, we find that the feed-in tariff encourages earlier investment. Nevertheless, as investment has been undertaken, renewable energy certificate trading creates incentives for larger projects. In our baseline scenario and taking the fixed feed-in tariff as a base, the revenue required to trigger investments is 61% higher with renewable certificates. At the same time, investment capacity is 61% higher.     

VIABILITY MODEL OF TROPHIC INTERACTIONS IN MARINE ECOSYSTEMS

ABSTRACT. An attempt to use viability models for studying marine ecosystems is proposed as a possible alternative to classical ecosystem modeling. Viability models do not consider optimal solutions but instead define all possible evolutions of a dynamical system under given constraints. Applied to marine ecosystems, a viability model is formulated based on the trophic coefficients of a mass‐balanced model. This requires relatively few assumptions about the processes involved and can integrate uncertainty associated with the required estimates of input parameters. An iterative algorithm is proposed to calculate the viability kernel, i.e., the envelope of all viable trajectories of the ecosystem. An application to the Benguela ecosystem is presented, considering interactions between detritus, phytoplankton, zooplankton, pelagic fish, demersal fish and fisheries. Results show how a viability kernel could be used to better define the healthy states of a marine ecosystem, by defining what states should be avoided. The paper discusses how viability models of trophic interactions could help to define a new ecosystem‐based indicator for fisheries management. It then discusses how this approach can potentially contribute to a paradigm shift that is emerging in the management of renewable resources.     

电力市场环境下计及V2G和多类型需求响应的综合能源系统多目标优化模型

园区综合能源系统(Park integrated energy system,PIES)是满足用户多种用能需求的一种重要途径,但由于内部分布式能源机组出力具有不确定性,且与负荷在时间维度上存在一定的不匹配性,限制了能源利用效率和经济效益的提升.因此,本文首先在构建耦合热电的PIES的基础上,构建了多类型需求响应模型;其次,构建了考虑风光机组出力不确定性的以系统净收益、清洁能源弃能率为多目标函数的优化模型;然后,构建了"不确定性处理-多目标处理-单目标处理"的三阶段求解模型,最后,以V2G和需求响应为考虑条件,设计多情景.算例结果表明:V2G可以为清洁能源不稳定发电提供有效备用,在一定程度上提供清洁能源利用效率;引入电热需求响应,能够改变用户负荷,增加经济效益.     

Chance-constrained economic dispatch with renewable energy and storage

Increasing penetration levels of renewables have transformed how power systems are operated. High levels of uncertainty in production make it increasingly difficulty to guarantee operational feasibility; instead, constraints may only be satisfied with high probability. We present a chance-constrained economic dispatch model that efficiently integrates energy storage and high renewable penetration to satisfy renewable portfolio requirements. Specifically, we require that wind energy contribute at least a prespecified proportion of the total demand and that the scheduled wind energy is deliverable with high probability. We develop an approximate partial sample average approximation (PSAA) framework to enable efficient solution of large-scale chance-constrained economic dispatch problems. Computational experiments on the IEEE-24 bus system show that the proposed PSAA approach is more accurate, closer to the prescribed satisfaction tolerance, and approximately 100 times faster than standard sample average approximation. Finally, the improved efficiency of our PSAA approach enables solution of a larger WECC-240 test system in minutes.     

不确定条件下多属性决策的比较因素法(英文)

本文针对不确定条件下多属性决策问题,扩展了原有多属性决策的概念,引入了比较因素与次比较因素,获得了其一些代数性质以及相应的定理。在此基础上,给出了不确定条件下多属性决策的比较因素法。     

Electricity market equilibrium under information asymmetry

We study a competitive electricity market equilibrium with two trading stages, day-ahead and real-time. The welfare of each market agent is exposed to uncertainty (here from renewable energy production), while agent information on the probability distribution of this uncertainty is not identical at the day-ahead stage. We show a high sensitivity of the equilibrium solution to the level of information asymmetry and demonstrate economic, operational, and computational value for the system stemming from potential information sharing.     

Toward net-zero carbon manufacturing operations: an onsite renewables solution

A growing number of manufacturing firms are striving to achieve eco-friendly operations through onsite wind or solar generation. This paper proposes a zero-carbon power supply model to guide the integration of onsite renewable energy into manufacturing facilities. We intend to address two fundamental questions: (1) Is it cost-effective to deploy onsite wind turbines and solar photovoltaics (PVs) systems to achieve net-zero carbon environmental performance? (2) Is the renewable generation system able to meet the electricity demand despite the power intermittency? To answer these questions, we formulate a stochastic optimization model to minimize the levelized cost of onsite renewable energy. The goal is achieved by optimizing the sizing of wind and solar generating units. The proposed energy solution is tested in ten cities around the world under diverse climatic conditions. While PV is still expensive, we conclude that manufacturers could realize zero-carbon emissions at affordable cost provided the local wind speed is above 5 m/s.     

ENDANGERED SPECIES AND NATURAL RESOURCE EXPLOITATION: EXTINCTION VS. COEXISTENCE

A model of renewable resource exploitation under event uncertainty is formulated. The model is applied to analyze the situation in which excessive water diversion for human needs can lead to the extinction of an animal population. Special attention is given to uncertainty regarding the conditions that lead to extinction. The manner in which the potential benefit foregone due to the species' extinction (the “extinction penalty”) induces more conservative exploitation policies is studied in detail. When the extinction penalty is ignored, the optimal policy is to drive the resource stock to a particular equilibrium level from any initial state. When the extinction penalty is accounted for and the conditions that lead to extinction are not fully understood (i.e., involve uncertainty), an interval of equilibrium states is identified, which depends on the penalty and on the immediate extinction risk.     

A Crop Planning Problem with Fuzzy Random Profit Coefficients

The main purpose of this paper is to present a crop planning problem for agricultural management under uncertainty. It is significant that agricultural managers assign their limited farmlands to cultivation of which crops in a season. This planning is called the crop planning problem and influences their incomes for the season. Usually, the crop planning problem is formulated as a linear programming problem. But there are many uncertain factors in agricultural problems, so future profits for crops are not certain values. A linear programming model with constant profit coefficients may not reflect the environment of decision making properly. Therefore, we propose a model of crop planning with fuzzy profit coefficients, and an effective solution procedure for the model. Furthermore, we extend this fuzzy model, setting the profit coefficients as discrete randomized fuzzy numbers. We show concrete optimal solutions for each models.