Sami H. Karaki

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.

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.

Robust Multi-Period OPF With Storage and Renewables

Renewable energy sources and energy storage systems present specific challenges to the traditional optimal power flow (OPF) paradigm. First, storage devices require the OPF to model charge/discharge dynamics and the supply of generated power at a later time. Second, renewable energy sources necessitate that the OPF solution accounts for the control of conventional power generators in response to errors of renewable power forecast, which are significantly larger than the traditional load forecast errors. This paper presents a sparse formulation and solution for the affinely adjustable robust counterpart (AARC) of the multi-period OPF problem. The AARC aims at operating a storage portfolio via receding horizon control; it computes the optimal base-point conventional generation and storage schedule for the forecasted load and renewable generation, together with the constrained participation factors that dictate how conventional generation and storage will adjust to maintain feasible operation whenever the renewables deviate from their forecast. The approach is demonstrated on standard IEEE networks dispatched over a 24-h horizon with interval forecasted wind power, and the feasibility of operation under interval uncertainty is validated via Monte Carlo analysis. The computational performance of the proposed approach is compared with a conventional implementation of the AARC that employs successive constraint enforcement.

Power generation expansion planning with environmental consideration for Lebanon

This work describes the development and usage of a generation expansion planning (GEP) tool based on dynamic programming, probabilistic production simulation, and environmental assessment. The problem of GEP is solved in stages using tunnel dynamic programming to determine the optimal investment plan of unit additions. The objective function of the planning exercise is to minimize either the cost or the environmental impact or some weighed function of the two. The production costing methodology is based on combining a probabilistic generation model with the load duration curve of the system to deduce a risk model from which the expected energy not supplied and the expected energy produced by each unit are estimated. Estimation of environmental emissions is conducted based on fuel type, heat rate, and energy produced by each unit. The program can model hydroelectric units as well as energy limited units, under economical and environmental load dispatches. The model is illustrated by a planning case study of the Lebanese electric power system to examine the impact of various technical, economic and environmental parameters on the proposed plans.

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.

An adaptive fuzzy-synchronous machine stabilizer

Describes an adaptive fuzzy-synchronous machine power system stabilizer (PSS) that behaves like a proportional integral derivative (PID) controller. The implemented adaptive technique predicts tracking-error divergence and makes online adjustments to the controller gain parameters in order to obtain a faster regulation of the error signal. The proposed PSS is less sensitive to the quality of expert knowledge, and thus is more robust than a non-adaptive fuzzy PSS when subjected to large disturbances. Hence, adequate controls are derived for the various error cases, and smooth control surfaces are achieved. Simulations are carried out using the proposed PSS to show that it consistently gives a stable response with acceptable overshoots and settling times on the frequency, angle and voltage errors.

A multi-objective design methodology for hybrid renewable energy systems

This paper describes a methodology to design a hybrid renewable energy system over a certain planning horizon. Traditionally a system plan was developed to achieve a minimum cost objective (MCO) while satisfying the energy demand, reliability, stability and battery constraints. The minimum emissions objective (MEO) is now an important target to achieve subject to the above mentioned constraints. Each of the above problems may be solved using linear programming, but minimizing the two preceding objectives at the same time forms a multi-objective problem which is solved by the epsiv-constraint and the goal attainment methods. The epsiv-constraint method minimizes the total cost while the emissions are less than a certain value epsiv determined by the linear programming when minimizing emissions only or by the designer. The goal attainment method tries to balance all the objectives and make them as close as possible to the initial goals determined by MCO and MEO. A case study is presented to illustrate the applicability and the usefulness of the proposed method.

Contingency Constrained VAr Planning Using Penalty Successive Conic Programming

Summary form only given. This paper presents a new method for VAr planning under multiple operating scenarios, optimizing sizes and locations of new reactive compensation equipment to ensure that both the system voltage profile and voltage stability requirements are met. The approach is based on L1-norm regularization for finding a solution with minimum VAr installation sites and on the L2-norm penalty function for satisfying the multiple state constraints. The L2-norm penalty function is exact in the sense that a finite penalty parameter is required to establish equivalence with the VAr planning problem, thus avoiding numerical ill-conditioning. The solution is obtained from a successive conic programming algorithm which makes use of adaptive trust-region control. The results of the new method are compared with those of an optimal power flow based program for VAr planning.