面向辅助服务的新能源场站共享储能容量优化配置

随着新能源快速发展,储能的作用日益凸显。针对新能源场站共享储能提供一次调频的定容问题,提出以共享储能投资商总成本最小为目标的储能优化配置方法。首先对历史频率数据的经验分布进行拟合,利用拟合结果生成配置储能所需的频率数据。然后基于所生成的频率数据,综合考虑一次调频约束、储能的倍率特性约束和一次调频参与率约束等,建立了满足一次调频要求的储能优化配置模型。所建模型为混合整数线性规划模型,可采用成熟的求解器进行求解。最后,基于实际频率数据对所提方法进行仿真分析,并对锂电池和飞轮两种储能类型的配置结果进行对比。结果表明,相比各自独立加装储能,多个新能源场站加装共享储能可以降低成本;配置锂电池储能系统的能量容量虽然显著大于飞轮储能,但其综合成本更低。     

解决混合整数非线性规划问题的混沌局部搜索差分进化算法

为提高差分进化算法的局部搜索能力和避开罚函数方法中罚参数选择问题,提出一种混沌局部搜索策略的差分进化算法(CLSDE)用于解决非线性混合整数规划问题.CLSDE中,只对目标函数中的变量进行编码,约束条件函数中的变量随机产生,每代进化完毕后,对最优个体进行混沌局部搜索.6个基本的测试函数实验结果证明CLSDE比MIHDE具有较好的寻优能力.     

A dynamic convexized method for nonconvex mixed integer nonlinear programming

We consider in this paper the nonconvex mixed-integer nonlinear programming problem. We present a mixed local search method to find a local minimizer of an unconstrained nonconvex mixed-integer nonlinear programming problem. Then an auxiliary function which has the same global minimizers and the same global minimal value as the original problem is constructed. Minimization of the auxiliary function using our local search method can escape successfully from previously converged local minimizers by taking increasing values of parameters. For the constrained nonconvex mixed-integer nonlinear programming problem, we develop a penalty based method to convert the problem into an unconstrained one, and then use the above method to solve the later problem. Numerical experiments and comparisons on a set of MINLP benchmark problems show the effectiveness of the proposed algorithm.     

加速分支定界算法在化工过程合成中的应用

在化工过程合成中,人们在确定研究系统的最大超结构后,通常采用混合整数非线性规划模型将其表达,而后通过计算机对该模型求解,从而找到最佳的流程结构.然而,近年来出现了1种新的求解过程,称为加速分支定界法(ABB),是在最大结构已知的基础上,采用分支定界法进行求解的思路.该算法克服了传统方法在处理整型变量时出现的麻烦,不需要建立复杂的混合整数非线性规划模型,就可以实现计算机自动寻找最优的过程流程,为快速有效地求解化工过程综合优化问题提供了1种新的途径.本文对分支定界法与加速分支定界法进行了详细比较,证实了ABB算法在实现自动寻找最优流程结构的合理性与可靠性.最后,以生化法制备丁醇、乙醇和丙酮的下游分离提纯为实例,研究了ABB算法在过程优化中的应用.结果表明,该算法克服了传统方法在处理整型变量时出现的麻烦,是1种快速有效地求解化工过程综合优化问题的新途径.     

WSN中层次型拓扑控制与网络资源配置联合设计方法

综合考虑异构无线传感器网络中节点速率分配、簇的划分规则和链路层网络频带资源占用情况, 提出一种基于拓扑控制与资源优化分配的层次型路由算法. 在网络层, 该算法根据成员节点和簇首节点的速率分配机制建立节点流量平衡模型. 在链路层, 分析无线传感器网络频谱共享行为, 研究邻近用户间访问冲突的规避抑制模型, 重构网络频带资源. 通过引入带宽比例因子将可用频带划分成若干子带, 提高网络频带资源的利用效率. 本文基于跨层联合设计思路, 建立一个混合整数非线性规划问题,对异构无线传感器网络中拓扑控制和网络资源分配问题联合设计, 得到最优的分簇结果和资源分配方案. 最后, 在设定网络拓扑中评估性能, 仿真结果证实该算法在网络频带资源充分利用的同时, 可实现最优的簇首匹配和路由建立结果.     

Robust formulations for production optimization of satellite oil wells

In the daily operation of oilfields operators are always seeking for the best operating conditions. In various situations, a drop in processing capacity is incurred by equipment failure, and prediction models are subject to uncertainty, making optimal decisions challenging. Therefore technical works have used mathematical optimization to find the optimal operation in those situations. However, uncertainty has been mostly overlooked and solutions often rely on nominal or average behaviour models. Neglecting the inherent uncertainty can impact the operating practices significantly. To this end, this work reformulates the uncertain production optimization problem as a robust optimization problem following the column-wise and row-wise frameworks, with cardinality-constrained sets. The latter allows the operator to regulate the level of protection of a solution against the uncertain model. The focus is on oilfields producing from satellite wells and with gas-lift systems. The formulations are analysed in a representative oilfield available in simulation software.     

Deterministic symbolic regression with derivative information: General methodology and application to equations of state

Symbolic regression methods simultaneously determine the model functional form and the regression parameter values by generating expression trees. Symbolic regression can capture the complexity of real-world phenomena but the use of deterministic optimization for symbolic regression has been limited due to the complexity of the search space of existing formulations. We present a novel deterministic mixed-integer nonlinear programming formulation for symbolic regression that incorporates derivative constraints through auxiliary expression trees. By applying the chain rule to mathematical operations, binary expression trees are capable of representing the calculation of first and second derivatives. We apply this formulation to illustrative examples using derivative information to show increased model discrimination capability. In addition, we perform a case study of a thermodynamic equation of state to gain insight on valid functional forms with thermodynamics-based constraints on the first and second derivatives.     

Solving power-constrained gas transportation problems using an MIP-based alternating direction method

We present a solution algorithm for problems from steady-state gas transport optimization. Due to nonlinear and nonconvex physics and engineering models as well as discrete controllability of active network devices, these problems lead to difficult nonconvex mixed-integer nonlinear optimization models. The proposed method is based on mixed-integer linear techniques using piecewise linear relaxations of the nonlinearities and a tailored alternating direction method. Most other publications in the field of gas transport optimization only consider pressure and flow as main physical quantities. In this work, we additionally incorporate heat power supplies and demands as well as a mixing model for different gas qualities. We demonstrate the capabilities of our method on Germany's largest transport networks and hereby present numerical results on the largest instances that were ever reported in the literature for this problem class.     

Short-term scheduling of an open-pit mine with multiple objectives

This article presents a novel algorithm for the generation of multiple short-term production schedules for an open-pit mine, in which several objectives, of varying priority, characterize the quality of each solution. A short-term schedule selects regions of a mine site, known as ‘blocks’, to be extracted in each week of a planning horizon (typically spanning 13 weeks). Existing tools for constructing these schedules use greedy heuristics, with little optimization. To construct a single schedule in which infrastructure is sufficiently utilized, with production grades consistently close to a desired target, a planner must often run these heuristics many times, adjusting parameters after each iteration. A planner's intuition and experience can evaluate the relative quality and mineability of different schedules in a way that is difficult to automate. Of interest to a short-term planner is the generation of multiple schedules, extracting available ore and waste in varying sequences, which can then be manually compared. This article presents a tool in which multiple, diverse, short-term schedules are constructed, meeting a range of common objectives without the need for iterative parameter adjustment.     

Transformer Fleet Optimal Maintenance with Risk Considerations

Abstract

This paper aims at proposing an optimization model for high voltage transformers’ maintenance scheduling to minimize operational cost and risk. The focus of the paper is on the transformer fleet management, and not on a single transformer maintenance procedure or the evaluation of a single transformer failure risk. The proposed methodology takes as inputs the importance (e.g., based on the expected energy not supplied) and failure risk of each transformer (e.g., based on on-line Dissolved Gas Analysis), and the maintenance cost for each transformer. It also considers the main practical constraints in field interventions. As output, the proposed methodology provides the best timing for the maintenance of each transformer from the fleet of interest. The problem has been modeled as mixed-integer linear programming. Eleven months of real data from the Brazilian transmission system are used for setting up the recent history of outages. The following year data are employed to test the effectiveness of the optimization model. The output of the proposed fleet maintenance scheduling tool is the optimum viable intervention calendar for a power transformer fleet within a 52-week horizon. This proposal represents an innovative and robust solution, which can support the operational planning experts’ decision-making process.