Transactions on Energy Systems and Engineering Applications

Economic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regions

Sri Suresh Mavuri — 2024-08-21

This paper presents a comprehensive framework for the economic scheduling and dispatching of Distributed Generators (DGs) in modified 33-bus and 69-bus systems across multi-microgrid regions. The framework introduces two key techniques: a novel dispatch strategy for optimizing the charging and discharging of Electric Vehicle (EV) batteries, and a robust power dispatch method for islanded distribution systems. The EV dispatch strategy uses a multi-criteria decision analysis method, Probabilistic Elimination and Choice Expressing Reality (p-ELECTRE), to maximize profits for EV owners while meeting power system requirements. This strategy is tested on fleets of 100 and 200 EVs with random travel plans within the modified 33-bus and 69-bus systems, and employs the BAT Optimization Algorithm (BOA) for optimal power dispatch. The second technique addresses the power dispatch in islanded systems by sectionalizing them into self-supplied microgrids, aiming to minimize operational costs, system losses, and voltage deviation using the Jaya algorithm. Additionally, a multi-objective cost-effective emission dispatch is evaluated using Whale Optimization Algorithm (WOA), showing superior performance over Differential Evolution (DE), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). Comparative analysis highlights the scalability and adaptability of the proposed approach, making it a valuable tool for efficient microgrid management. Simulation results confirm significant improvements in cost savings, system reliability, and operational efficiency under various uncertainty scenarios.

Design of PV fed single-switch transformer less topology powered electric vehicle

Jeetender Vemula — 2024-07-31

As a result of an increase in the availability of resources that were not harmful to the environment, solar energy applications shot to popularity. Photovoltaic cells power systems that necessitate DC-DC converters because of their low voltage output. This investigation uses photovoltaic cells (PV) to power a high-voltage gain design with just one switch and no transformer. The proposed circuit utilizes a single regulated switch, which contributes to a reduction in switching losses. It requires fundamental pulse regulation. The network used a switched capacitor cell and an LC passive filter to provide an accurate step-up voltage. We can obtain the equation for the step-up voltage gain from the steady-state continuous conduction mode. The equations used for the theoretical design of converters include energy. To show that the topology is comparable with other modern converters that have been published, a comparison was made between it and other converters. In order to validate the converter's effectiveness, simulations built in MATLAB and Simulink are used.

Risk assessment of electric power generation systems using modified jellyfish search algorithm

Archana Chittari — 2024-07-31

An electric utility's main goal is to fulfil the requirements and expectations of its customers by providing power. When there are uncertainties, like equipment failures, system reliability evaluation offers a framework to guarantee that the system will still function properly. A modified Jellyfish Search Algorithm (JFSA) has been proposed for estimation of Electric power generation system reliability indices. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and other modified versions of algorithms have been used in algorithms that use optimization methods for the assessment of reliability indices. Jelly Fish Search Algorithm has been used in power systems to find the economic load dispatch of generating units, for integration of Distributed Generation (DG) units, Maximum Power tracking of PV system and Optimal Power Flow solutions etc. However, JFSA has not been implemented for the evaluation of reliability indices for electric power generation system. In this context a modified JFSA algorithm is developed for evaluation of certain reliability indices such as Loss of Load Expectation (LOLE), and Expected Demand Not Supplied (EDNS), Loss of Load Probability (LOLP). The algorithm presented is implemented on two test system which are RBTS 6 bus system and IEEE 24 bus Reliability Test System. The Results obtained are compared for different models of Generation and Load and are analysed.

A battery energy storage system as an alternative for mitigating issues in the distribution network

Thiago Luiz Caretta — 2024-07-31

distribution network to boost energy efficacy. The investigation specifically scrutinizes the integration of BESS into the distribution network feeder, both in the presence and absence of distributed photovoltaic penetration, with a focus on enhancing load factor and power factor. Utilizing the OpenDSS software with a Python interface, simulations were conducted to assess a range of scenarios involving the injection and absorption of active and reactive power. The outcomes underscore noteworthy enhancements in both load factor and power factor at the feeder output. Additionally, the integration of BESS exhibits a reduction in power losses along the feeder. These findings offer valuable perspectives for advancing energy efficiency in distribution networks, and have implications for future research and practical implementation.

Optimization of combustion characteristics on a diesel engine fueled by Mahua biodiesel with dispersion of graphene oxide and zinc oxide nanoparticles as additives using design of experiment

P srinivas reddy — 2024-07-31

The current research investigates the effects of adding metallic graphene oxide (GO) and non-metallic zinc oxide (ZnO) nanoparticles to Mahua biodiesel blend (B20) on the combustion parameters of a diesel engine. GO and ZnO nanoparticles were utilized at a concentration of 75 mg/L, combined with a 1:1 mixture of the surfactant CTAB and the dispersant TWEEN 80. When nanoparticles were introduced to blended biofuel, combustion parameters such as cumulative heart rate, mean gas temperature, mass percent burnt, and rise of pressure increase (RoPR) greatly improved at higher injection pressures. When compared to clean diesel, utilizing B20+ZnO Nanoparticles+ NIS dispersant at 250 bar resulted in 6%, 15%, 7%, and 7.6% improvements in CHRR, MGT, MFB, and RoPR, respectively. The correlation coefficient (R²) for B20+ZnO NPs+ NIS (1:1) for CHRR, MGT, MFB and RoPR is 0.975, 0.978, 0.966 and 0.9883 when compared to GO nanoparticle inclusions, considering it as optimum combination and an efficient fuel. When compared to other fuel samples, the CHRR, MGT, MFB and RoPR for B20+ZnO NPs+ NIS are 2.484%, 3.2%, 2.6% and 1.25% higher, respectively, according to a statistical analysis conducted by design expert.

Impact of high blends of Madhuca Logifolia biodiesel on the performance, combustion and emission parameters in a CRDI diesel engine at variable compression ratio

Himani Parekh — 2024-09-02

The country today uses a variety of industrial and transportation facilities that are fueled by diesel fuel. However, because of its non-sustainable and polluting nature, there is an urgent need for a more environmentally acceptable substitute that can be utilized in existing engines with no or little modification. Madhucalongifolia (Mahua) was considered a main source for biodiesel production based on its availability and its nature to not impact the food chain. The raw oil was converted to biodiesel using the process of transesterification. The higher blends of B80 (80% mahua biodiesel, 20% diesel by vol.) and B90 (90% mahua biodiesel, 10% diesel by vol.) were prepared. The experiment was carried out using an eddy current dynamometer and involved a Kirloskar 4-stroke single-cylinder which was water-cooled, CRDI diesel engine. The base run was generated using 18:1 compression ratio diesel fuel. These outcomes were contrasted with identical engine conditions using blends of B80 and B90 biodiesel as fuel. The most favourable results in terms of the engine parameters ie. BTE, SFC, cylinder pressure, HC, NOx and CO were as stated here. There was an increase of 8.87% in BTE for the B90 blend. A minor increase of 2.77% in SFC was observed with the B90 blend. The cylinder pressure for B90 was decreased by 0.024%. The emissions for B80 and diesel were lesser in comparison to B90. Diesel showed the lowest CO (7.9%) emissions whereas HC and NOx for B80 decreased by 24.39% and 3.42% respectively. The engine was made to run at two lower compression ratios of 16 and 17. When using a fuel blend of B80 at a compression ratio of 16, the performance metrics were significantly better. It could be concluded that, the compatible results were found with B80 biodiesel blend with compression ratio of 16. The BTE, SFC, cylinder pressure, HC, NOx and CO were quantified as 25.61%, 0.34kg/kWh, 30.27 bar, 50ppm, 1204 ppm and 0.24% by volume respectively. In comparison to the base run (diesel fuel and compression ratio of 18), there was 15.98% increase in the BTE, 5.55% decrease in the SFC, 16.07% decrease in the cylinder pressure, 21.95% decrease in the emission of HC, 23.55% decrease in NOx and 9.09% increase in CO emissions.