R. Chedid

Unit sizing and control of hybrid wind-solar power systems

The aim of this paper is to provide the core of a CAD/CAA tool that can help designers determine the optimal design of a hybrid wind-solar power system for either autonomous or grid-linked applications. The proposed analysis employs linear programming techniques to minimize the average production cost of electricity while meeting the load requirements in a reliable manner, and takes environmental factors into consideration both in the design and operation phases. While in autonomous systems, the environmental credit gained as compared to diesel alternatives can be obtained through direct optimization, in grid-linked systems emission is another variable to be minimized such that the use of renewable energy can be justified. A controller that monitors the operation of the autonomous/grid-linked systems is designed. Such a controller determines the energy available from each of the system components and the environmental credit of the system. It then gives details related to cost, unmet and spilled energies, and battery charge and discharge losses.

A decision support technique for the design of hybrid solar-wind power systems

This paper presents a decision support technique to help decision makers study the influencing factors in the design of a hybrid solar-wind power system (HSWPS) for grid-linked applications. These factors relate mainly to political and social conditions, and to technical advances and economics. The analytic hierarchy process (AHP) is used to quantify the various divergencies of opinions, practices and events that lead to confusion and uncertainties in planning HSWPS. The trade-off/risk method is used to generate multiple plans under 16 different futures and obtain the corresponding trade-off curves. Unlike the traditional 2-D simulation, a novel modeling of a trade-off surface in 3-D space is presented where the knee set is determined using the minimum distance approach. Robust and inferior plans are segregated based on their frequent occurrence in the conditional decision set of each future and hedging analysis to reduce risk is performed in order to assign alternative options in case risky futures occur.

Probabilistic performance assessment of autonomous solar-wind energy conversion systems

This paper describes the development of a general probabilistic model of an autonomous solar-wind energy conversion system (SWECS) composed of several wind turbines (wind farm), several photovoltaic (PV) modules (solar park), and a battery storage feeding a load. The model takes into consideration outages due to the primary energy fluctuations and hardware failure. It allows the simulation of wind farms and solar parks containing either identical or different types of wind turbines and PV modules with the load being fed from either the renewable sources, or the battery storage, or both. A methodology is also presented to determine an upper limit on the size of the battery storage required to satisfy a given load profile taking into consideration the charging/discharging of the batteries.

Intelligent control of a class of wind energy conversion systems

This paper discusses the control problem for a class of wind energy conversion systems (WECS). It first develops a detailed model and then compares four control algorithms based on conventional and intelligent control theories. A simple PI conventional controller for the exciter loop is carried out by using a first order model. When the system operating points change, the PI controller fails to provide sufficient damping or acceptable performance. Therefore both fuzzy voltage and fuzzy power regulators are introduced. Also a conventional adaptive pitch controller is proposed to adjust the pitch angle of the rotor blades in order to maximize the energy capture and reduce the mechanical loads. As an alternative to this controller, a neural network controller is also designed. Using the existing nonlinear wind model and the different control algorithms, the dynamic behavior of the controlled (WECS) is simulated. By selecting convenient wind data, the system characteristics such as its tracking performance, its robustness and its ability to recover from large disturbances are studied and discussed.

Adaptive fuzzy control for wind-diesel weak power systems

This work is concerned with the development of an adaptive fuzzy logic controller for a wind-diesel system composed of a stall regulated wind turbine with an induction generator connected to an AC busbar in parallel with a diesel generator set having a synchronous generator. In this work we propose to use an adaptive network based inference system (ANFIS) in order to generate fuzzy membership functions and control rules for the controller. A feedback linearized proportional integral controller is used to provide the required expert knowledge. A controller design process is identified; it consists of generating input-output data pairs to identify the control variables range and initial fuzzy memberships, and then to tune or adapt them using an ANFIS network structure. The controller inputs are the frequency error and its integral for the governor part of the controller, and the voltage and frequency errors for the automatic voltage regulator. These are readily measurable quantities leading to a simple controller which can be easily implemented.

Probabilistic performance assessment of wind energy conversion systems

This paper describes the development of a general probabilistic model of an autonomous wind energy conversion system (WECS) composed of several wind turbines (wind farm) connected to a load and a battery storage. The proposed technique allows the simulation of wind farms containing identical or different wind turbines types and considers a bidirectional flow of power in and out of the battery. The model is based upon a simple procedure to estimate the joint probability distribution function of the total available wind power and that of the turbines operating modes due to hardware failure. A methodology is also developed to use the proposed model to determine an upper limit on the size of the battery storage required for a given number of turbines to satisfy the load with a certain expected energy not supplied (EENS). The model can also be used to evaluate the energy purchased from or injected to the grid in the case of grid-connected systems.

Probabilistic production costing of diesel-wind energy conversion systems

This paper describes the development of a general probabilistic model of a diesel-wind energy conversion system (DWECS) composed of several diesel units, several wind turbines (wind farm), and battery storage feeding a load. The model allows the simulation of a diesel system with a wind farm of different wind turbine types considering system stability, and outages due to hardware failure and primary energy fluctuations. It is based on a modification of the convolution method, which considers a given penetration level selected by the utility for stability consideration. The production costs of the diesel units are then deduced from the expected energy not supplied (EENS) using a unit de-convolution in reverse economic order. A methodology is also presented to determine the size of the battery storage based on the excess wind energy available during operation, or that disconnected for stability consideration, while accounting for the charging/discharging cycles.

Probabilistic model of a two-site wind energy conversion system

A general probabilistic model of a two-site wind-energy conversion system (WECS) is described. The wind speeds at the two sites are not assumed independent, thus preventing the convolution theorem from being directly applicable. Instead, a model based on the conditional probability theorem and mutually exclusive events is presented. The model allows the assessment of the energy resource available to supply a given load represented by its load duration curve, taking into consideration wind-turbine failure modes and the intermittent nature of the wind resource. The model is based on a capacity-probability table constructed using fundamental probability theorems on conditional and mutually exclusive events. The table is then combined with a load model in order to assess its performance and determine the expected energy not supplied (EENS) of the system. A planning study is then reported on a realistic system to illustrate how the most efficient wind turbines can be selected to support an existing thermal system using a procedure based on a minimization of the incremental marginal cost of operation.

Energy resource allocation using multi-objective goal programming: the case of Lebanon

The traditional energy-resources allocation problem is concerned with the allocation of limited resources among the end-uses such that the overall return is maximized. In the past, several techniques have been used to deal with such a problem. In this paper, the energy allocation process is looked at from two points of view: economy and environment. The economic objectives include costs, efficiency, energy conservation, and employment generation. The environmental objectives consider environmental friendliness factors. The objective functions are first quantified and then transformed into mathematical language to obtain a multi-objective allocation model based upon pre-emptive goal programming techniques. The proposed method allows decision-makers to encourage or discourage specific energy resources for the various household end-uses. The case of Lebanon is examined to illustrate the usefulness of the proposed technique.

A fuzzy programming approach to energy resource allocation

This paper presents a fuzzy multi-objective linear programming approach to solve the energy allocation problem. For this, nine energy resources and six household end uses are considered. An optimal solution will be extracted, and an explicit interactive sensitivity analysis will be dealt with. As the results obtained depend on the fuzzy nature of the objective functions and on the conflicting nature of some of the objectives among themselves, the proposed method can be regarded as a decision support tool for the decision makers who can be guided by its results to arrive at an appropriate solution interactively. It will be shown that optimization using fuzzy logic can provide the decision-makers with more flexibility that would assist them in the allocation of various resources to meet the various end-uses by studying the effects of several factors such as price variations, membership function shapes and membership function reference values. Copyright