Modernized Planning of Smart Grid Based on Distributed Power Generations and Energy Storage Systems Using Soft Computing Methods

Abstract

The modest objective is to check the integrated effect of energy storage systems (ESSs) and distributed generations (DGs) and compare the optimization of the size and location of ESS and DG to explore its challenges for smart grids (SGs) modernization. The research enlisted different algorithms for cost-effectiveness, security, voltage control, and less power losses. From this perspective, optimization of the distribution network’s energy storage and capacity are being performed using a variety of methods, including the particle swarm, ant-lion optimization, genetic, and flower pollination algorithms. The experimental outcomes demonstrate the effectiveness of these techniques in lowering distribution network operating costs and controlling system load fluctuations. The efficiency and dependability of the distribution network (DN) are both maximized by these strategies.

Optimizing Building Energy Management with Deep Reinforcement Learning for Smart and Sustainable Infrastructure

“…The building has a covering area of 10,000 square meters over five floors. The building was built using energy-efficient materials such as low-emissivity (low-E) glass and high R-value insulation [21][22][23][24]. The occupancy rate in the building varies, with a typical daily peak of 800 occupants during standard office hours (9 AM to 5 PM) and decreasing to less than 100 occupants during off-hours and weekends.…”

“…More precisely, data from April to June are utilized for the neural network models' training phase, while July data is reserved for evaluating the model's effectiveness. To accurately represent the indoor environment's thermal dynamics within proposed BEMS optimization simulations, this work implements a simplified model for indoor temperature changes, structured as follows: EQU (22).…”

Solar-Powered LVDC Nano-Grid with Smart FPGA-based Residential Power Switching Algorithm

“…The controller continuously detects the bus voltage and switches the bi-directional converter between buck and boost modes based on the bus voltage level. The bus voltage is set to the saturation level of 50 v to protect the system from overvoltage occurrences in the bus under no-load or open circuit conditions [24,25,26].…”

Review on the Optimal Configuration of Distributed Energy Storage

“…Therefore, intelligent algorithms such as GA [8], PSO [34], SA [40] and their improved algorithms have been rapidly applied and developed in the field of energy storage configuration. Generally, the intelligent optimization algorithm aims at the universal description of the problem and usually pays little attention to the structure information.…”

“…Distributed energy storage with the characteristics of fast response, easy control and bidirectional regulation is becoming an important part of improving the flexibility of a power system, absorbing a high proportion of renewable energy and satisfying the dynamic balance between supply and demand of a power system [6,7]. Moreover, distributed energy storage is also a solution to the costly infrastructure construction of delayed power systems, and it plays a key role in improving energy efficiency and reducing carbon emissions, gradually becoming an important mainstay for the development of distributed generation, smart grid and microgrid [8][9][10]. Therefore, it is clear that distributed energy storage has become an indispensable part of all aspects of a power system, as shown in Figure 1.…”