AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
Desirable objectives for smart grids are12/20/2023 ![]() ![]() However, an intelligent optimization mechanism is extremely necessary in order to prevent peak formation during low hours of the day ( Hafeez et al., 2020a). This increases the possibility for electricity consumers to alter their load patterns in accordance with tariffs. Therefore, users can modify the load at their own pace. Due to smart grids, electric utility companies (EUCs) are able to dispatch price signals to consumers using day-ahead pricing (DAP) signals, time of use (TOU), and real-time pricing (RTP) signals. With the emergence of liberality in the electricity market, efficiency is improved due to better economic solutions provided by the power companies ( Ribeiro et al., 2018). Offering incentives in the form of prices revolutionized the traditional power grid and enabled utility companies to change the behavior of consumers in terms of energy consumption ( Ma et al., 2016). Figure 1 represents a model of a smart grid. Due to the elastic nature of loads, the success of smart grids lies in the availability of resources like distributed generation ( Ribeiro et al., 2020). Passive customers have become active consumers because of the smart grid. Conventional networks have become a smart grid due to advances in communication technologies and their integration into the electricity infrastructure. The concept of smart grids has been developed as a result of reduced effectiveness, environmental concerns ( Yu et al., 2023), distributed economic dispatch ( Li et al., 2022), distributed grounding layout ( Xiao et al., 2022), harmonic power flow ( Xie and Sun, 2022), diverse maintenance needs, and reliability issues in the traditional power network ( Li et al., 2022). Traditional power grids cannot cope with the current world’s needs because of the enormous increase in energy demand. The United States Department of Energy projects that energy use will increase by 56% in 2040 ( United States Department of Energy, 1225). Energy consumption from buildings accounts for approximately one-third of the energy that is generated worldwide ( Gul and Sandhya, 2015). The development of technology, substantial use in industry, and introduction of electric vehicles on the road have led to an increased demand for electricity. Over the last decade, the energy needs of consumers have risen at an exponential rate ( Hafeez et al., 2019 Alzahrani et al., 2023). The results demonstrate that the HGBFOA algorithm outperforms existing algorithms in terms of scheduling, energy consumption, power costs, PAR, and user comfort. To evaluate the performance of HGBFOA, comparisons were made with other well-known algorithms, including the particle swarm optimization algorithm (PSO), GA, BFOA, and hybrid genetic particle optimization algorithm (HGPO). By leveraging the characteristics of GA and BFOA, the HGBFOA algorithm achieves an efficient appliance scheduling mechanism, reduced energy consumption, minimized peak-to-average ratio (PAR), cost optimization, and improved user comfort level. The proposed HGBFOA-based EMC effectively solves energy management problems for four categories of residential loads: time elastic, power elastic, critical, and hybrid. This study introduces a novel hybrid algorithm called the hybrid genetic bacteria foraging optimization algorithm (HGBFOA), which combines the desirable features of the genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) in its design and implementation. To address this issue, energy management controllers (EMCs) have emerged as automated solutions for energy management problems using DR signals. However, users’ decreased awareness poses a challenge in responding to signals from DR programs. The development of smart grids has revolutionized modern energy markets, enabling users to participate in demand response (DR) programs and maintain a balance between power generation and demand. ![]() 4School of Engineering and the Built Environment, Edinburgh Napier University, Edinburgh, United Kingdom.3Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. ![]()
0 Comments
Read More
Leave a Reply. |