Scarcity of energy resources, rising electricity generating costs, environmental impacts, and ever-increasing demand for electrical power need optimal economic dispatch in the modern scenario. The expense of generator fuel determines the percentage of a power plant's operating costs, which are minimized via optimal load dispatch (OLD). The OLD problem is determining the best and cheapest power generating strategy among a set of on-line producing units to supply the whole power requirements at a particular period. This paper presents bio-inspired meta-heuristic Ant Lion Optimizer (ALO) techniques to solve the economic load dispatch problem. The main objective of this paper is to reduce the generation fuel cost while meeting equality and inequality requirements. The obtained results demonstrate that the proposed approach can significantly reduce generating expenses even while satisfying the constraints (Power Stability as well as Generation Limit).

Optimizing Hybrid Renewable Energy Sources (HRES) is needed in many aspects. As the population and needs continuously increase, HRES becomes much more necessary as the current technologies cannot satisfy the entire demand. This paper uses MATLAB programming and Simulink to perform the optimization and uses the Particle Swarm Optimization (PSO) for optimization, which is considered one of the best algorithms. There are many isolated and remote communities and cities in the world that cannot connect physically or low budget to a grid for the power supply. There is a large amount of electricity demand in these areas, which is being delivered by the small as well as isolated diesel generators, which is not easy to operate. These diesel generators require a very high cost to operate due to the short supply of crude fuels in these areas and problems in fuel delivery with the complex maintenance of the diesel generators. HRES, such as solar Photovoltaic (PV) as well as wind generators, proposed a feasible alternative for power generation in off-grid communities. Many HRES system have been deployed worldwide, as well as they serve a large variety of applications.

This paper is about Energy Management and Optimization solutions that can help to save money on energy while improving operational performance. When it comes to energy management, real-time data from systems such as process monitoring, automation, and production planning may be a huge assistance. This information, coupled with premium and energy availability data from energy providers/markets, is utilised to compute the ideal production and power generating plan, as well as to attain the best energy pricing.

Energy storage systems are becoming more and more widespread. The newest use case is in the power and energy sector, which is increasingly looking towards energy storage as a solution to speed up its shift to renewables while ensuring maximum supply at peak hours. The best-known example is the Tesla plant at Hornsdale, Australia, the largest battery storage system in the world currently. The boost in EV sales and its market share in developed nations demands effective, efficient, and safe battery management. One of the most important aspects of battery management is estimating the battery State of Charge (SOC). There are now a variety of ways for estimating the level of charge of a cell or a battery pack. Traditional approaches have been investigated, along with their shortcomings. Methods to counter sensor errors have been explored, the Kalman Filter, as well as the Extended Kalman Filter algorithm, have both been explored in detail particularly.

There has been a lot of advancement in the automotive industry. With more and more electronic intervention, many previously used mechanical systems have been replaced by electrical and electronic ones. Here, to design something useful for the automotive industry which can ensure a more comfortable and safer journey for passengers riding a 4-wheeler vehicle. With all the new models and rising competition in the 4-wheeler market, it is evident that customers want full-ride comfort ensured to them. To simulate the scenario and optimize the points where the bumps affect the movement of the chassis and, in turn, the thorax and pelvis body parts for the passenger in this paper. Genetic Algorithm (GA) to maximize and minimize the suspension to test the effect on the body (Passenger Seat) for the given scenario. GA is an optimization approach which aims to determine such input values in order to obtain the optimal output values or outcomes. The MATLAB and Simulink are used in this paper to carry out the whole of the simulation, including coding.

Innovations in technologies that rely on electricity have led to an uncontrollable rise in power usage. In order to predict future electricity demand and enhance the power distribution system, analysis and forecasting of energy consumption systems are necessary. Several issues with the present energy consumption prediction methods make it difficult to anticipate actual energy usage with any degree of accuracy. In order to master the energy prediction method, this study examines fourteen years' worth of hourly energy usage data from a Kaggle open source dataset. In addition, a Long Short Term Memory (LSTM) and Convolution Neural Network (CNN) based method for estimating energy consumption based on actual datasets is presented in the research. The empirical findings demonstrate which LSTM and CNN architectures can improve energy consumption forecasting accuracy.

In India preserving food is traditionally done by the effective drying method. The heat of the sun and the air are being used for several years to dry food to preserve it. Due to depleting fossil fuels and high prices lead to the use of non-conventional energy sources. Drying of products using solar energy has gained importance as it is environmentally friendly and has little impact on the environment. Natural flexible solar dryers and mandatory solar dryers are the two main phases of drying. In natural convection solar systems, air flow is stopped by air flow caused by buoyancy while in a solar convection dryer forced air flow is supplied using an operating fan either solar / residual module or residual fuel. In this project we are trying to build a hybrid solar dryer. The hybrid solar dryer is designed and constructed using direct solar power and a temperature changer.

Electric power plants are located at far off places from demand centers. Due to this the transmission losses become considerably high and thereby a penalty is becoming imposed on plants to generate additional power to meet these transmission losses. ELD, or Economic Load Dispatch, is used in this situation. ELD is the phenomenon where electric power generating units are combined such that the load demand and operational constraints, including various losses, are satisfied with minimum operating cost. This is a widely researched area and the solution can be obtained through various techniques and algorithms such as Bat Algorithm. This study introduces the Doppler effect and the movement of bats across various environments using the Novel Bat Algorithm (NBA). Six generating stations are used to analyse this method's performance.

N on-renewable sources of energy and agriculture have always been of utmost importance to mankind. From providing food to meeting industrial requirements like coal, charcoal, crop, manure, forest residues, and firewood, agriculture has been a major need for rural areas and developing countries that depend on non-renewable sources of energy. To help and promote these countries socially, technologically, and economically, decentralization should be emphasized. This would increase their participation in issues on a global level. In this project, the aim is to develop a photovoltaic system and a biomass system using Simulink, to generate energy to meet the above-mentioned requirements.