OPTIMIZATION AND DESIGN OF STAND-ALONE INTEGRATED RENEWABLE ENERGY SYSTEMS

A system utilizing more than one renewable energy resource to supply a variety of energy and other needs is called an Integrated Renewable Energy System (IRES). A typical IRES will utilize two or more renewable energy resources in order to satisfy the energy and other needs of a community. One of the fundamental technical problems facing the designer of an IRES is to match the varying energy and power requirements of the loads with the mostly stochastic characteristics of the resources in an appropriate manner. The objective of this study is to formulate a mathematical model, and use it to develop a systematic approach to the design of an IRES.;In the deterministic approach, average values of resources and load variables are used. The approach employs a linear programming technique for the design of integrated renewable energy systems. The technique is based on minimizing an objective function of total annual cost, subject to a set of energy and power constraints related to resource availabilities and load requirements.;In the probabilistic approach, a combination of deterministic and probabilistic models are used to model the energy resources and load demand. The probabilistic models are in the form of probability density and distribution functions. In this approach Loss of Power Supply Probability (LPSP) is used to summarize and quantify the overall behavior of a stand-alone system with energy storage supplying a load. The LPSP-based design method, in conjunction with a judicious ordering of the energy needs and resources, is suggested to design an IRES. The technique is based on minimizing an objective function of total capital cost while maintaining a given level of reliability.;Results of several simulations designed to study the influence of different parameters of the system on the final design values have been presented and discussed. (Abstract shortened with permission of author.).;Two design approaches--one deterministic and the other probabilistic--have been presented; they both have the following common elements: (1) Categorize the needs; (2) Catalog the resources; (3) Consider the variable nature of resources and load; (4) Find the ratings of different devices and size of energy storage that will minimize the chosen performance index.