Statistical modeling and optimal energy distribution of cogeneration units by genetic algorithms

Our main objective is to evaluate the performance of a new method to optimize the energy management of a production system composed of six cogeneration units using artificial intelligence. The optimization criterion is economic and environmental in order to minimize the total fuel cost, as well as the reduction of polluting gas emissions such as COx, NOx and SOx. First, a statistical model has been developed to determine the power that the cogeneration units can provide. Then, an economic model of operation was developed: fuel consumption and pollutant gas emissions as a function of the power produced. Finally, we studied the energy optimization of the system using genetic algorithms (GA), and contribute to the research on improving the efficiency of the studied power system. The GA has a better optimization performance, it can easily choose satisfactory solutions according to the optimization objectives, and compensate for these defects using its own characteristics. These characteristics make GA have outstanding advantages in iterative optimization. The robustness of the proposed algorithm is validated by testing six cogeneration units, and the obtained simulation results of the proposed system prove the value and effectiveness of GA for efficiency improvement as well as operating cost minimization.

Cockpit crew pairing Pareto optimisation in a budget airline

Crew pairing is the primary cost checkpoint in airline crew scheduling. Because the crew cost comes second after the fuel cost, a substantial cost saving can be gained from effective crew pairing. In this paper, the cockpit crew pairing problem (CCPP) of a budget airline was studied. Unlike the conventional CCPP that focuses solely on the cost component, many more objectives deemed to be no less important than cost minimisation were also taken into consideration. The adaptive non-dominated sorting differential algorithm III (ANSDE III) was proposed to optimise the CCPP against many objectives simultaneously. The performance of ANSDE III was compared against the NSGA III, MOEA/D, and MODE algorithms under several Pareto optimal measurements, where ANSDE III outperformed the others in every metric.

Dynamic Combined Economic Emission Dispatch Including Wind Generators by Real Coded Genetic Algorithm

With the growing environmental depletion, the shift in the focus towards minimizing the emissions of gases released in the conventional generators and further incorporation of a cleaner alternate renewable source of energy such as wind or solar to the existing system is of utmost importance. The research paper aims to build an environmentally resilient electric power system. Real coded genetic algorithm- powerful optimization technique is employed to solve the dynamic combined economic emission dispatch i.e. DCEED strategy for two proposed algorithm. The first proposed DCEED algorithm includes fuel cost of only conventional generators while in the second algorithm along with conventional generators, wind powered generators with varying power output characteristic is added. A comparative analysis of both the algorithms in terms of total combined cost, emission level and fuel cost is taken into account and it is observed that in spite of wind uncertainty the proposed method is more economical.

Effect of Increase in the Price of Petroleum and Increase in the Sales of Electric Scooter

Our paper is among the first to measure the potential effect of increase in the price of petroleum vs increase in the sales of electric scooter. With the prices of petrol inching closer to Rs 100 per liter, the demand for battery operated scooters is on the rise as people want to save on fuel cost. As a result, the sale of e-scooters has increased considerably in the city. buyers of two-wheelers are also evincing interest in purchasing e-scooters and making enquiries about vehicles in the local showrooms Okinawa, a large scooter manufacturer in India, recently reported a 30% sales jump in the last quarter alone. The company attributes this largely to the rise in gasoline prices. Without being tied to a pump, electric motorcycle riders are insulated from fluctuating gasoline prices, and can instead “fill up” on much less expensive electricity from any wall outlet.

Impact of parameter control on the performance of APSO and PSO algorithms for the CSTHTS problem: An improvement in algorithmic structure and results

Cascaded Short Term Hydro-Thermal Scheduling problem (CSTHTS) is a single objective, non-linear multi-modal or convex (depending upon the cost function of thermal generation) type of Short Term Hydro-Thermal Scheduling (STHTS), having complex hydel constraints. It has been solved by many metaheuristic optimization algorithms, as found in the literature. Recently, the authors have published the best-achieved results of the CSTHTS problem having quadratic fuel cost function of thermal generation using an improved variant of the Accelerated PSO (APSO) algorithm, as compared to the other previously implemented algorithms. This article discusses and presents further improvement in the results obtained by both improved variants of APSO and PSO algorithms, implemented on the CSTHTS problem.

System levelized fuel cost of electricity generation in a two-component nuclear energy system with a closed uranium-plutonium NFC

The authors propose an approach to the calculation of the levelized unit fuel cost (LUFC) of electricity generation for a fast reactor in a two-component nuclear energy system (NES) with regard for plutonium production. The approach is based on taking into account the additional economic effect, which can be achieved through the sale at the market price of the natural uranium released due to the substitution of thermal reactors by fast reactors with MOX fuel based on the plutonium bred in a fast reactor. This requires considering simultaneously the reactor parts of the fuel cycle for fast and thermal reactors. Relationships have been obtained which connect the key neutronic and fuel characteristics with the NPP and fuel cycle economic performance. The described methodology was used for the computational study of the LUFC for a fast sodium-cooled reactor. Calculations have shown that, in the considered case, taking into account the plutonium production leads to the LUFC reduction by nearly half and, therefore, to a major decrease in the total unit cost of electricity generation (levelized cost of electricity or LCOE).

Influence of Degradation, Fuel Cost and Electricity Price Factors on Combine Cycle Power Plant Cost Analysis

Gas turbine-based combine cycle (GT-CC) economic evaluation is very important to bring together own equipment manufacturing companies (OEM’s) and power plant owners. The fuel cost & cost of electricity play the major role in economic evaluation which drives the decision during the bidding. The first portion of this paper encompasses the different cost analysis methods like Net Present Value (NPV), Internal Rate of Return (IRR), Levelized Cost of Electricity (LCOE) and Pay Back Period (PBP) for different fuel costs and electricity prices. The second portion of the paper covers the delta cost benefits due to improvement in the combined cycle degradation GT-CC operators or customers are looking for the opportunities to control and minimize the degradation of the gas turbine power plant which directly impact the profitability. The customer or operator always monitor the plant performance to understand the life cost impact on performance degradation. This paper will help the customers & GT-CC OEM companies to focus on different area to reduce the unit cost of generating electricity, decide to move forward with the project during the proposal and improve the business at various regions based on fuel cost and global geographical political situations. Also, the reader can digest the benefits of improved degradation curve over the normal curve.

Prediction of the monthly cost of energy usage by PEMFC at housing in North Sumatra Province, Indonesia

The development of renewable energy is increasing nowadays. Besides, the development of energy conversion systems that can work at high efficiency also increases along with the decreasing availability of fossil energy. The fuel cell is an electrochemical device that converts chemical reaction energy directly into direct current electrical energy. The use of fuel cells as power generating in housing has also increased rapidly, especially in developed countries. This study aims to develop a model and simulation for the Polymer Exchange Membrane Fuel Cell (PEMFC) system with a working temperature of 165 °C) using Aspen Plus simulation. In this analysis, the model and simulation developed are used to predict the amount of fuel needed when used in housing as an electricity generator and obtain a monetary value for the monthly fuel procurement. The PEMFC system is designed to generate power up to 0.60 kW by consuming hydrogen fuel with a current density of 0.02 A/cm2. The hydrogen consumed by the PEMFC system is around 0.030 kg/hour, with a monthly cost of hydrogen consumption by the system is Rp. 2,052,000. Meanwhile, the monthly electricity from the national grid (PLN) bill costs around Rp. 569,261 (in the year 2019). In comparing the energy bill, at the moment, the fuel cost for PEMFC as a power generation system is much more expensive than PLN’s electricity consumption costs due to the high hydrogen fuel cost.

Environment and Economic Assessment of CNG and Gasoline Engines: An Experimental Analysis

The application of alternative fuels in automobile engines is gaining more popularity among the scientific community than ever. Most of the research emphasis is on the performance and combustion aspect of the engine. The environmental and economic evaluation of these fuels is also equally important for sustainability, which is relatively unexplored and needs to be evaluated. The present work compares the environmental and economic aspects of a spark ignition (SI) engine fueled with gasoline and compressed natural gas (CNG). To study the environmental impact, regulated and unregulated emissions coming out from the engine exhaust were compared. For economic assessment, annual fuel consumption and associated fuel cost were compared under similar engine operating conditions. The economic cost associated with the environmental impact was calculated based on carbon dioxide emissions and compared using carbon pricing. Experiments on an SI engine were performed at various engine loads to achieve a range of operating conditions to evaluate fuel consumption and engine-out emissions. Results show that a CNG fueled engine has 12.7% lower brake specific fuel consumption than the gasoline engine, which leads to 56% lower fuel cost. It is due to the cumulative effect of higher calorific value and the lower fuel price of CNG compared to gasoline. Additionally, average environment emission and associated cost due to carbon dioxide (CO2) emission reduced by 29% using CNG over gasoline. This study shows that CNG can lead to lesser fuel consumption and its associated fuel and environment cost compared to gasoline.