This paper introduces a design and optimization methodology for autonomous hybrid photovoltaic (PV)/wind/battery energy system. The main function of the proposed methodology is to determine the optimum size of each component of the energy system for the lowest cost of kWh generated and the best loss of load probability. This methodology uses the hourly wind speed, hourly radiation, and hourly load power with many different types of wind turbines and PV module types to get the optimum size of each component and the minimum cost of kWh generated at highest reliability. This methodology changes the share ratio of wind/PV with certain increments and calculates the required size of all components and the optimum battery size to get the predefined lowest acceptable probability. This methodology is implemented using a new computer program in flexible fashion which is not available in any market available software, such as homer or retscreen software. Actual data for ten Saudi sites are used with this computer program. The results obtained from the proposed program are compared with homer software. The proposed computer program performed the optimal design steps in very short time and with accurate results. Very valuable results can be extracted from the new computer program that could help researchers and decision makers. The results obtained from the proposed computer program have established the economic feasibility of installing hybrid energy systems in many sites of Saudi Arabia.
The objective of this paper is to assess the economic viability of Saudi Arabia's renewable energy resources in electricity production in the rural and remote areas as against the use of diesel generators (DG). The methodology employed is to pick an existing isolated DG electric station for a rural community and assess the levelized cost of energy (LCOE) generated for incremental generation by adding either DG, wind electric conversion system(WECS), or solar Photo Voltaic (PV) electric system. Cost figures are derived from current technology price indices and consultancies studies as contained in the National Renewable Energy Laboratory cost report of November 2012. The existing power station of Addfa in the Southern region is analyzed based on the requirement to increase its current capacity to cater for surrounding smaller communities. An annual extra energy requirement of 4 GWh is to be met by scenarios of using DG, WECS, PV or a hybrid system. Two ownership structures are considered namely a public utility that pays no tax and a private independent power producer (IPP) that pays tax. Both the constant and current LCOE generated are determined for each ownership structure. The results indicate that the WECS is the first investment choice with a constant LCOE of $0.0922/kWh and $0.1090/kWh for the public utility and the IPP, respectively. The DG is the second choice with LCOE of $0.1082/kWh and $0.1175/kWh, the third is a hybrid DG plus WECS with LCOE of $0.1102/kWh and $0.1234/kWh for each ownership structure, respectively. Finally, the PV electric system ranks fourth with LCOE of $0.2791/kWh and $0.3279/kWh, respectively. The results of sensitivity analysis show that these values of LCOE are more sensitive to the initial capital cost and less sensitive to the operation and management costs.
A generalized approach for the economic selection of wind turbine for a given wind regime is proposed in this paper. It draws from the literature and standards being used in the field to arrive at an economic site specific wind turbine based on minimizing the annual cost of energy produced (AEP) while tracking the initial capital cost (ICC) of investment required. It is meant to provide an initial study to guide decision makers who are contemplating using wind energy as a power source to generate electricity in commercial quantity for community usage. It is a general estimation approach which does not require surfing for manufacture prices and wind turbine parameters. The input data consists of site specific wind data, hub height, rotor diameter and turbine power rating. The output gives a range of plots of feasible wind turbine ratings, rotor diameters, rated speed against initial capital cost (ICC) and also cost of energy produced (COE).
Purpose – The paper aims to report on an integrated techno-economic framework for the performance analysis of energy production based on the renewable energy resources (RERs). Whilst the majority of existing studies have focussed on technical aspects of RER modelling, the proposed framework incorporates financial assessment into the process of appraising the alternatives of hydropower, wind energy and solar energy infrastructures. An approach to the optimal choice of RER deployment for a specific developing region is formulated and applied to Ghana. Design/methodology/approach – A model comprising technical and economic parameters was developed for analysing the investment rankings of different RERs and comparing them to that of conventional energy sources such as the natural gas combined cycle (NGCC) electric plant. The analysis also included the carbon cost and power generation capacity. The total life cycle costing and levelised cost of energy generated from each resource were modelled for three corporate ownership structures: a public utility that is not tax-liable (no-tax case); organisational power generation for internal use, ultimately concerned with its after-tax costs (after-tax case); and an independent power producer for the market, with before-tax revenues covering all costs (before-tax case). Findings – Using the empirical data from Ghana together with the proposed framework, it is shown that when carbon incentives are provided, the hydroelectric and wind conversion infrastructures can effectively compete with the conventional NGCC in this country, whilst with no carbon credit, NGCC still appears to be the most viable option. Practical implications – Policy-related recommendations on carbon incentives and preferential power purchase prices, which are critical for widespread RER deployment, can be directly derived from this research. Originality/value – The study represents a comprehensive decision-making tool that can be used in regulatory and investment analysis on the expansion of RER systems in the developing countries.
This research paper presents a novel droop control strategy for sharing the load among three independent converter power systems in a microgrid. The proposed method employs a machine learning algorithm based on regression trees to regulate both the system frequency and terminal voltage at the point of common coupling (PCC). The aim is to ensure seamless transitions between different modes of operation and maintain the load demand while distributing it among the available sources. To validate the performance of the proposed approach, the paper compares it to a traditional proportional integral (PI) controller for controlling the dynamic response of the frequency and voltage at the PCC. The simulation experiments conducted in MATLAB/Simulink show the effectiveness of the regression tree machine learning algorithm over the PI controller, in terms of the step response and harmonic distortion of the system. The results of the study demonstrate that the proposed approach offers an improved stability and efficiency for the system, making it a promising solution for microgrid operations.
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