Geothermal Power Generation

Bulk power system based on fossil fuels becomes less reliable and stable in economic terms, technically more labor-consuming and harmful environmental impact. These problems have led many countries to find ways to supply the electricity from a green and sustainable energy source. The electricity derived from renewable energy sources such as hydro, solar, wind, biomass and geothermal refers to as green and sustainable energy. Geothermal energy is not only utilized for electric power generation, but it is also exploited to generate environmentally friendly heat energy. As of the end of 2018, geothermal global cumulative installed capacity exceeded 13 GW, generated an energy of about 630 peta joule (PJ). This chapter presents the geothermal energy resource in terms of the types of power plants, principle of the electricity generation and current world status of geothermal resource utilization. The issues such as advantages and disadvantages of geothermal energy economically and environmentally and means to overcome shortcomings are also considered. The main barriers for the development of geothermal industry include high resource and exploration risk, overall high development cost particularly drilling, and inadequate financing and grant support. The global averaged cost of electricity for the geothermal facility is nearly 0.072 USD/kWh as compared to 0.056 for onshore wind and 0.047 USD/kWh for hydropower. However, the technology is rather competitive to other renewables such as concentrating solar power (0.185 USD/kWh) and offshore wind (0.127 USD/kWh). Meanwhile, further research and development is critically needed to eliminate the non-condensable gases (NCGs) associated with the geothermal power generation

An improved algorithm for photovoltaic module temperature prediction and its techno- economic impact on energy yield

Photovoltaic (PV) system comprising PV modules and related control system is the sole means through which the solar energy is converted directly into electricity. The PV module is generally rated according to its maximum DC power output (Wp) which is obtained under Standard Test Condition. However, this condition is seldom encountered, especially in the high temperature and variable irradiance climate like Malaysia. On the other hand, in the actual operating conditions, the energy generated from PV module is sturdily influenced by surrounding climate; hence, a performance evaluation model for PV system is necessary. This research proposes a mathematical algorithm to calculate the hourly, monthly and annually expected PV system energy output, considering the actual PV module temperature (Tm) increase effect. The new algorithm was developed due to the limitation in the existing methodologies particularly the one used in Malaysia by Malaysian Green Technology Corporation (MGTC). The developed Tm prediction model is based on the pre-processed hourly data measured for 9 months at the 92 kWp Building Integrated Photovoltaic (BIPV) GreenTech Malaysia, Bangi, Selangor which includes Tm, ambient temperature (Ta), solar irradiance (G), wind speed (Ws) and Relative Humidity (RH). The developed algorithm was compared to the model used by MGTC and validated with actual measurements. In addition, 5 years of hourly data for Ta, G, and Ws measured at 6 different locations in Malaysia obtained from Malaysia Meteorological Department were used for development of a solar radiation and energy output estimation models. The proposed energy model gives good result since it is closer to measured data compared to the PVWatts simulation tool. Results on the techno-economic analysis are also presented. The proposed energy output estimation model is expected to be useful for the PV system installer in the pre-installation phase in terms of feasibility and performance analysis of the PV system

Agricultural waste in Libya as a resource for biochar and methane production: An analytical study

This study aims to analyse the possibility of exploiting agricultural waste in Libya to produce biochar and methane gas, and to evaluate the technical, economic and environmental aspects associated with this technology. In this study, the focus was on seven agricultural projects located in the Libyan desert, where these projects contain many varieties of Crops. A region with a total area of 5.36×106 ha was explored from Benghazi to Dernah eastward including the Green Mountain (Libya). For literary analysis, peer-reviewed scientific publications for 2018-2023 were selected from reliable bibliometric databases Scopus, Google Scholar, ScienceDirect, PubMed, since these databases have the greatest coverage of peer-reviewed publications. To study the biomass potential of the region, the Bioenergy Tool developed by IRENA was used. The study showed that agricultural residues available in Libya can be used Libya, such as grain straw, palm trees, and others, in the production of biochar and methane gas, using pyrolysis techniques to convert agricultural waste into biochar and methane gas. The study indicates that this technology can be cost-effective and environmentally effective, and that many environmental and economic benefits can be achieved, such as improving air quality, increasing agricultural land productivity, and providing new job opportunities

Static security classification and evaluation classifier design in electric power grid with presence of PV power plants using C-4.5

Energy suppliers all over the world must expand energy in a way that is secure, clean, affordable, and environmentally responsible. Photovoltaic (PV) has been a competitive renewable-energy source for the power generation mix in the world. With the presence of solar PV technology, this paper proposes C4.5 approach for static security evaluation and classification (SSE). This paper proposes PV generators connected to the grid when bilateral energy transactions with the loads are implemented to see their impacts on the system security. To build a classifier in binary class, the process is divided into four components: data collection, pre-processing and feature selection, comparison of the techniques, best classifier selection and performance evaluation. A comprehensive comparison of four of Decision Tree׳s Algorithms for SSE is conducted. The study is (accomplished using) conducted on IEEE 30 bus system, which comprises 5 PV power generators deliver a total power of 40 MW. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to train and test the classifiers. Empirically, with the presence of PV power generators, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, C4.5 is an efficient and effective approach for real-time evaluation and classification classifier desig

Economic and environment analysis of a grid connected solar PV system in Malaysia

Grid-connected Photovoltaic (GCPV) system can play a vital role in lowering electricity demand, shifting peak load and mitigating the greenhouse-gas effect. This study investigates the potential implementation and the economic analysis of a GCPV system for a residential house located in Johor Bahru, the southern city of Malaysia. Hybrid Optimization Model for Electric Renewable (HOMER) simulation software was used to determine the technical feasibility of the system and to perform the economic analysis of the system. The impact of the PV system on the environment is also discussed. The PV module was modelled based on the parameters obtained from PV module manufacturer's data sheet to supply 4 kW total demand of a typical house. The economic simulations for the studied GC system are focused on the net present costs, cost of energy, excess electricity produced and the reduction of CO2 emissions. As a result, a critical study on a grid-connected PV system economically and environmentally is obtained. Results show that solar energy is one of the best renewable energy sources with the least negative impacts on the environment. However, the reliance on conventional energy resources will continue as long as the cost of fuel is reasonable

Artificial neural network-based photovoltaic module temperature estimation for tropical climate of Malaysia and its impact on photovoltaic system energy yield

This article presents an artificial neural network (ANN)-based approach for predicting photovoltaic (PV) module temperature using meteorological variables. The proposed approach utilizes actual hourly records of various meteorological parameters, such as ambient temperature Ta, solar irradiation G, relative humidity RH, and wind speed Ws as input variables. The hourly meteorological data were collected over 9?months in the year 2009 from a 92-kWp installed PV system in Selangor, Malaysia. The data were divided into two sets: training data, which are a set of 1849 (April–October) hourly data, and 578 (November–December) hourly records of working as test data. Four ANN models have been developed by using different combination of meteorological parameters as inputs, and, for each model, the output is the PV module temperature Tm. It was found that the model using all parameters, including RH and Ws as inputs, gave the most accurate results with correlation coefficient (r) 95.9%, and 0.41, 0.1, and 4.5% for MBE, RMSE, and MPE, respectively. To show the superiority and applicability of the developed ANN model, results from the proposed ANN model have been compared with the conventional model adopted by Malaysia Energy Center and another mathematical model based on regression. With the model's simplicity, the proposed approach can be used as an effective tool for predicting the PV module temperature, for any type of PV systems, in remote or rural locations with no direct measurement equipments. The developed model also will be very useful in studying PV system performance and estimating its energy output

Examination of Low Voltage Grid- Connected PV Generation Under Different Penetration Levels

The existence of abundant and free solar energy has contributed to the rapid development of PV systems, particularly rooftop installations and solar plants construction at transmission and distribution levels. Nevertheless, high penetration level of PV systems at distribution network level could potentially lead to abnormal operation of network. At steady state, the impacts are experienced on voltage (level, profile, stability and drop), line losses, equipment loading, etc. In this paper, a part of Benghazi distribution network is simulated by NEPLAN software. A real data for load and weather were used to simulate the real condition. The paper studies the impacts by comparing the behavior of the distribution grid without PV installations and with PV systems installed with different penetration levels (0%, 50%, 65% and 75%). The results generally showed that the installation of rooftop PV systems caused improvement in voltage profile, decreasing equipment’s (transformers and lines/feeders) loading and line losses. On the other hand, extensive deployment of PV capacity will cause an increase in equipment’s loading. The high penetration of PV systems will also affect the voltage profile, voltage level, and losses. From the results, the allowable penetration percentage (Hosting capacity) for the simulated network must not exceed 65%

Photovoltaic technology in Malaysia: past, present, and future plan

This article presents solar energy or specifically the solar photovoltaic (PV) development outlook in Malaysia. The paper first introduces the massive potential of solar energy in the country, the key players in the solar energy development and the early solar energy policies, and programmes in the country. The most important to the PV development is the Malaysia Building Integrated Photovoltaic initiative, which is presented in this paper followed by an explanation on the Feed-in Tariff recently introduced in the country to encourage new solar PV projects. The outlook for solar PV in Malaysia is optimistic and as the uptake of solar PV increases, the unit cost is coming down rapidly. Solar PV is expected to be the most competitive Renewable Energy (RE) source, with the potential to achieve grid parity for electrical power in the country in the near future, and surpassing all other REs combined by 2050