An integrated multiple layer perceptron-genetic algorithm decision support system for photovoltaic power plant site selection

There is a need for non-renewable energy sources in generation of power for almost every domestic and commercial purposes. This source of energy helps in the development of a country. Because of the increasing usage of the fossil fuels and depletion of these resources, our focus has been shifted towards the renewable sources of energy like solar, water and wind. Therefore, in the present scenario, the usage of renewable sources has been increasing rapidly. Selection of a solar power plant (SPP) requires environmental factor, local terrain, and local weather issues. Thus, a large amount of investment is required for installation. Multi-criteria decision making (MCDM) is a method that identifies one in choosing the best sites among the other proposed options. This paper gives a detailed study of optimal ranking of SPP site using analytical hierarchy process (AHP), multiple layer perceptron (MLP) neural network trained with back propagation (BP) algorithm and genetic algorithm (GA). Three SPP sites of India were considered and various important criteria like local weather, geographical location, and environmental factors are included in our study as SPP site selection is a multi-criteria problem. A precise comparison of these three methods is listed in this paper.

Hybrid Renewable Energy Generation Through Incremental Conductance Mppt

The relevance of electricity generation from renewable energy sources is growing every day in the current global energy environment. The scarcity of fossil fuels and the environmental risks connected with traditional power producing methods are the main reasons behind this. The major sources of non-conventional energy are wind and solar which can be harnessed easily. A new system design for hybrid photovoltaic and wind-power generation is introduced within this study. A Modified M.P.P.T. has been proposed to strengthen productivity of this system. The proposed approach employs the Incremental Conductance (IC) MPPT technique. Under varied climatic conditions (Solar irradiance & Temperature), IC is utilized to determine the optimum voltage output of a photo voltaic generator (P.V.G.) within the photo voltaic system (P.V.) structure. The Incremental Conductance is utilized to manage the converter’s technology having boosting function. The P.M.S.G. is used to determine the maximum voltage output for varied wind flow rates in wind turbine system. Simulations are conducted in Matlab2019b to test efficacy of the proposed MPPT. The proposed scheme’s effectiveness can be supported with simulation results.

Hybrid Renewable Energy Generation Through Incremental Conductance Mppt

The relevance of electricity generation from renewable energy sources is growing every day in the current global energy environment. The scarcity of fossil fuels and the environmental risks connected with traditional power producing methods are the main reasons behind this. The major sources of non-conventional energy are wind and solar which can be harnessed easily. A new system design for hybrid photovoltaic and wind-power generation is introduced within this study. A Modified M.P.P.T. has been proposed to strengthen productivity of this system. The proposed approach employs the Incremental Conductance (IC) MPPT technique. Under varied climatic conditions (Solar irradiance & Temperature), IC is utilized to determine the optimum voltage output of a photo voltaic generator (P.V.G.) within the photo voltaic system (P.V.) structure. The Incremental Conductance is utilized to manage the converter’s technology having boosting function. The P.M.S.G. is used to determine the maximum voltage output for varied wind flow rates in wind turbine system. Simulations are conducted in Matlab2019b to test efficacy of the proposed MPPT. The proposed scheme's effectiveness can be supported with simulation results.

REA: Resource Exergy Analysis - A basic guideline for application focusing on energy systems evaluation

This publication provides a basic guideline to the application of Resource Exergy Analysis (REA) with a focus on energy systems evaluation. REA is a proven application of exergy analysis to the field of technology comparison.REA aims to help decision makers to obtain an indicator in addition to GHG emissions, that is grounded in science, namely Resource Consumption.Even if an energy system uses GHG-free energy increased Resource Consumption likely increases the need for fossil fuels and thus GHG emissions of the global economy. Resource Consumption can replace the less comprehensive Primary Energy Consumption as an indictor and reduce the risk of suboptimal decisions.Evaluating energy systems using REA is key to ensure that climate targets are reached in time.

Modeling Carbon Release of Brazilian Highest Economic Pole and Major Urban Emitter: Comparing Classical Methods and Artificial Neural Networks

Despite the concern about climate change and the associated negative impacts, fossil fuels continue to prevail in the global energy consumption. This paper aimed to propose the first model that relates CO2 emissions of Sao Paulo, the main urban center emitter in Brazil, with gross national product and energy consumption. Thus, we investigated the accuracy of three different methods: multivariate linear regression, elastic-net regression, and multilayer perceptron artificial neural networks. Comparing the results, we clearly demonstrated the superiority of artificial neural networks when compared with the other models. They presented better results of mean absolute percentage error (MAPE = 0.76%) and the highest possible coefficient of determination (R2 = 1.00). This investigation provides an innovative integrated climate-economic approach for the accurate prediction of carbon emissions. Therefore, it can be considered as a potential valuable decision-support tool for policymakers to design and implement effective environmental policies.

Utilization of Electric Vehicles for Vehicle-to-Grid Services: Progress and Perspectives

With every passing second, we witness the effect of the global environmental impact of fossil fuels and carbon emissions, to which nations across the globe respond by coming up with ambitious goals to become carbon-free and energy-efficient. At the same time, electric vehicles (EVs) are developed as a possible solution to reach this ambitious goal of making a cleaner environment and facilitating smarter transportation modes. This excellent idea of shifting towards an entirely EV-based mobility industry and economy results in a range of issues that need to be addressed. The issues range from ramping up the electricity generation for the projected increase in consumption to developing an infrastructure that is large enough to support the higher demand for electricity that arises due to the market penetration of EVs. Vehicle to grid (V2G) is a concept that is largely in a testing phase in the current scenario. However, it appears to offer a solution to the issues created by a mobility sector that the constantly growing EV fleet will dominate. Furthermore, the integration of EVs with the grid seems to offer various cost-wise and environment-wise benefits while assisting the grid by tapping into the idle energy of parked EVs during peak hours. This review aims to present some of the possible ancillary service potentials of such a system while also discussing the potential challenges, impacts, and future market penetration capabilities of V2G technology.

Mathematical modelling and simulation of the thermo-catalytic decomposition of methane for economically improved hydrogen production

The demand for low-emission hydrogen is set to grow as the world transitions to a future hydrogen economy. Unlike current methods of hydrogen production, which largely derive from fossil fuels with unabated emissions, the thermo-catalytic methane decomposition (TCMD) process is a promising intermediate solution that generates no direct carbon dioxide emissions and can bridge the transition to green hydrogen whilst utilising existing gas infrastructure. This process is yet to see widespread adoption, however, due to the high catalyst turnover costs resulting from the inevitable deactivation of the catalyst, which plays a decisive role in the feasibility of the process. In this study, a feasible TCMD process was identified and a simplified mathematical model was developed, which provides a dynamic estimation for the hydrogen production rate and catalyst turnover costs over various process conditions. The work consisted of a parametric study as well as an investigation into the different process modes. Based on the numerous simulation results it was possible to find the optimal process parameters that maximise the hydrogen pro- duction rate and minimise the catalyst turnover costs, therefore increasing the economic potential of the process and hence its commercial viability.

Sizing, Optimization, and Financial Analysis of a Green Hydrogen Refueling Station in Remote Regions

Hydrogen (H2) can be a promising energy carrier for decarbonizing the economy and especially the transport sector, which is considered as one of the sectors with high carbon emissions due to the extensive use of fossil fuels. H2 is a nontoxic energy carrier that could replace fossil fuels. Fuel Cell Electric Vehicles (FCEVs) can decrease air pollution and reduce greenhouse gases when H2 is produced from Renewable Energy Sources (RES) and at the same time being accessible through a widespread network of Hydrogen Refueling Stations (HRSs). In this study, both the sizing of the equipment and financial analysis were performed for an HRS supplied with H2 from the excess electrical energy of a 10 MW wind park. The aim was to determine the optimum configuration of an HRS under the investigation of six different scenarios with various numbers of FCEVs and monthly demands, as well as ascertaining the economic viability of each examined scenario. The effect of the number of vehicles that the installation can refuel to balance the initial cost of the investment and the fuel cost in remote regions was investigated. The results showed that a wind-powered HRS could be a viable solution when sized appropriately and H2 can be used as a storage mean for the rejected wind energy. It was concluded that scenarios with low FCEVs penetration have low economic performance since the payback period presented significantly high values.