The penalty function method is one basic method for solving constrained nonlinear programming, in which simple smooth exact penalty functions draw much attention for their simpleness and smoothness. This article offer...The penalty function method is one basic method for solving constrained nonlinear programming, in which simple smooth exact penalty functions draw much attention for their simpleness and smoothness. This article offers a new kind of simple smooth approximative exact penalty function of general constrained nonlinear programmings and analyzes its properties.展开更多
为实现集成智能楼宇(intelligent building,IBs)的主动配电网(active distribution network,ADN)灵活运行,该文提出一种基于机会约束规划的含IBs的ADN分布式能量管理策略。首先,基于建筑物的热惯性,构建含空调柔性负荷的IBs数学模型;其...为实现集成智能楼宇(intelligent building,IBs)的主动配电网(active distribution network,ADN)灵活运行,该文提出一种基于机会约束规划的含IBs的ADN分布式能量管理策略。首先,基于建筑物的热惯性,构建含空调柔性负荷的IBs数学模型;其次,综合考虑楼宇侧与网络侧的运行约束,建立基于Dist Flow的集成IBs的ADN数学模型;然后,考虑到光伏(photovoltaic,PV)出力与外界温度的不确定性,利用机会约束规划将集成IBs的ADN优化问题转化为混合整数二阶锥规划(mixed integer second-order cone programming,MISOCP)问题;最后,为了保护配电网运营商与用户的隐私性,利用交替方向乘子法(alternating direction method of multipliers,ADMM)实现了集成IBs的ADN的分布式能量管理。基于ADMM的解耦机制,原MISOCP问题可以被分解为楼宇侧的混合整数线性规划(mixed-integer linear programming,MILP)子问题以及网络侧的二阶锥规划(second-order cone programming,SOCP)子问题进行求解。结果表明,在保障各主体信息隐私性的前提下,所提策略利用IBs灵活性实现了集成IBs的ADN全局最优能量管理。展开更多
Engineering optimization problems can be always classified into two main categories including the linear programming(LP)and nonlinear programming(NLP)problems.Each programming problem further involves the unconstraine...Engineering optimization problems can be always classified into two main categories including the linear programming(LP)and nonlinear programming(NLP)problems.Each programming problem further involves the unconstrained conditions and constrained conditions for design variables of the optimized system.This paper will focus on the issue about the design problem of NLP with the constrained conditions.The employed method for such NLP problems is a variant of particle swarm optimization(PSO),named improved particle swarm optimization(IPSO).The developed IPSO is to modify the velocity updating formula of the algorithm to enhance the search ability for given optimization problems.In this work,many different kinds of physical engineering optimization problems are examined and solved via the proposed IPSO algorithm.Simulation results compared with various optimization methods reported in the literature will show the effectiveness and feasibility for solving NLP problems with the constrained conditions.展开更多
文摘The penalty function method is one basic method for solving constrained nonlinear programming, in which simple smooth exact penalty functions draw much attention for their simpleness and smoothness. This article offers a new kind of simple smooth approximative exact penalty function of general constrained nonlinear programmings and analyzes its properties.
文摘为实现集成智能楼宇(intelligent building,IBs)的主动配电网(active distribution network,ADN)灵活运行,该文提出一种基于机会约束规划的含IBs的ADN分布式能量管理策略。首先,基于建筑物的热惯性,构建含空调柔性负荷的IBs数学模型;其次,综合考虑楼宇侧与网络侧的运行约束,建立基于Dist Flow的集成IBs的ADN数学模型;然后,考虑到光伏(photovoltaic,PV)出力与外界温度的不确定性,利用机会约束规划将集成IBs的ADN优化问题转化为混合整数二阶锥规划(mixed integer second-order cone programming,MISOCP)问题;最后,为了保护配电网运营商与用户的隐私性,利用交替方向乘子法(alternating direction method of multipliers,ADMM)实现了集成IBs的ADN的分布式能量管理。基于ADMM的解耦机制,原MISOCP问题可以被分解为楼宇侧的混合整数线性规划(mixed-integer linear programming,MILP)子问题以及网络侧的二阶锥规划(second-order cone programming,SOCP)子问题进行求解。结果表明,在保障各主体信息隐私性的前提下,所提策略利用IBs灵活性实现了集成IBs的ADN全局最优能量管理。
基金This work was partially supported by the Ministry of Science and Technology of Taiwan Under Grant No.MOST 108-2221-E-366-003.
文摘Engineering optimization problems can be always classified into two main categories including the linear programming(LP)and nonlinear programming(NLP)problems.Each programming problem further involves the unconstrained conditions and constrained conditions for design variables of the optimized system.This paper will focus on the issue about the design problem of NLP with the constrained conditions.The employed method for such NLP problems is a variant of particle swarm optimization(PSO),named improved particle swarm optimization(IPSO).The developed IPSO is to modify the velocity updating formula of the algorithm to enhance the search ability for given optimization problems.In this work,many different kinds of physical engineering optimization problems are examined and solved via the proposed IPSO algorithm.Simulation results compared with various optimization methods reported in the literature will show the effectiveness and feasibility for solving NLP problems with the constrained conditions.
