Modeling and optimization of a combined cooling, heating and power plant system

In this paper, we develop a modeling and optimization procedure for minimizing the operating costs of a combined cooling, heating, and power (CCHP) plant at the University of California, Irvine, which uses co-generation and Thermal Energy Storage (TES) capabilities. Co-generation allows the production of thermal energy along with electricity, by recovering heat from the generators in a power plant. TES provides the ability to reshape the cooling demands during the course of a day, in refrigeration and air-conditioning plants. Therefore, both cogeneration and TES provide a potential to improve the efficiency and economy of energy conversion. The proposed modeling and optimization approach aims to design a supervisory control strategy to effectively utilize this potential, and involves analysis over multiple physical domains which the CCHP system spans, such as thermal, mechanical, chemical and electrical. Advantages of the proposed methodology are demonstrated using simulation case studies. © 2012 AACC American Automatic Control Council)

A Multi-Level Optimization Algorithm and a Ship Design Application

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97130/1/AIAA2012-5555.pd

A globally convergent primal-dual interior point algorithm for general non-linear programming

This paper presents a primal-dual interior point algorithm for solving general constrained non-linear programming problems. The initial problem is transformed to an equivalent equality constrained problem, with inequality constraints incorporated into the objective function by means of a logarithmic barrier function. Satisfaction of the equality constraints is enforced through the incorporation of an adaptive quadratic penalty function into the objective. The penalty parameter is determined using a strategy that ensures a descent property for a merit function. It is shown that the adaptive penalty does not grow indefinitely. The algorithm applies Newton's method to solve the first order optimality conditions of the equivalent equality problem. Global convergence of the algorithm is achieved through the monotonic decrease of a merit function. Locally the algorithm is shown to be quadratically convergent

A primal-dual interior point algorithm that uses an exact and differentiable merit function to solve general nonlinear problems

A primal-dual interior point algorithm for solving general nonlinear programming problems is presented. The algorithm solves the perturbed optimality conditions by applying a quasi-Newton method, where the Hessian of the Lagrangian is replaced by a positive definite approximation. An approximation of Fletcher's exact and differentiable merit function together with line-search procedures are incorporated into the algorithm. The line-search procedures are used to modify the length of the step so that the value of the merit function is always reduced. Different step-sizes are used for the primal and dual variables. The search directions are ensured to be descent for the merit function, which is thus used to guide the algorithm to an optimum solution of the constrained optimisation problem. The monotonic decrease of the merit function at each iteration ensures the global convergence of the algorithm. Finally, preliminary numerical results demonstrate the efficient performance of the algorithm for a variety of problems

Global optimization in the 21st century: Advances and challenges

This paper presents an overview of the research progress in global optimization during the last 5 years (1998–2003), and a brief account of our recent research contributions. The review part covers the areas of (a) twice continuously differentiable nonlinear optimization, (b) mixedinteger nonlinear optimization, (c) optimization with differential-algebraic models, (d) optimization with grey-box/black-box/nonfactorable models, and (e) bilevel nonlinear optimization. Our research contributions part focuses on (i) improved convex underestimation approaches that include convex envelope results for multilinear functions, convex relaxation results for trigonometric functions, and a piecewise quadratic convex underestimator for twice continuously differentiable functions, and (ii) the recently proposed novel generalized �BB framework. Computational studies will illustrate the potential of these advances