Day-Ahead DSM-Integrated Hybrid-Power-Management-Incorporated CEED of Solar Thermal/Wind/Wave/BESS System Using HFPSO

Kumar, Kothalanka Kameswara Pavan; Soren, Nirmala; Latif, Abdul; Das, Dulal Chandra; Hussain, Shoeb; Al‐Durra, Ahmed; Ustun, Taha Selim

doi:10.3390/su14031169

Cited by 18 publications

(5 citation statements)

References 27 publications

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“…The proposed energy management controller delivered better daily advantages and maximized consumer benefits in energy utilization and cost, with the potential for long-term projections and further optimization algorithms. Kothalanka Kameswara Pavan Kumar et al introduced a unique hybrid power management algorithm (PMA) that integrated DSM to optimize economic and emission load dispatch while taking losses into account [29]. The technique decreased thermal energy consumption and emissions on an IEEE 30-bus system with renewables and storage by using a hybrid firefly particle swarm optimization (HFPSO) approach.…”

Section: Literature Review On Peak Load Management In Indiamentioning

confidence: 99%

Evolution of a Summer Peak Intelligent Controller (SPIC) for Residential Distribution Networks

Parangusam,

Lekshmana,

Gono

et al. 2023

Energies

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Electricity demand has increased tremendously in recent years, due to the fact that all sectors require energy for their operation. Due to the increased amount of modern home appliances on the market, residential areas consume a significant amount of energy. This article focuses on the residential community to reduce peak load on residential distribution networks. Mostly, the residential consumer’s power demand increases more during the summer season due to many air conditioners (AC) operating in residential homes. This paper proposes a novel summer peak intelligent controller (SPIC) algorithm to reduce summer peak load in residential distribution transformers (RDT). This proposed SPIC algorithm is implemented in a multi-home energy management system (MHEMS) with a four-home hardware prototype and a real-time TNEB system. This hardware prototype is divided into two different cases, one with and one without taking user comfort into account. When considering consumer comfort, all residential homes reduce their peak load almost equally. The maximum and minimum contribution percentages in Case 2 are 29.82% and 19.30%, respectively. Additionally, the real-time TNEB system is addressed in two different cases: with and without incentive-based programs. In the real-time TNEB system during peak hours, the novel SPIC algorithm reduces peak demand in Case 1 by 113.70 kW, and Case 2 further reduces it to 118.80 kW. The peak load decrease in Case 2 during peak hours is 4.5% greater than in Case 1. In addition, we conducted a residential consumer opinion survey to validate the acceptance rate of the proposed design and algorithm.

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Section: Literature Review On Peak Load Management In Indiamentioning

confidence: 99%

Evolution of a Summer Peak Intelligent Controller (SPIC) for Residential Distribution Networks

Parangusam,

Lekshmana,

Gono

et al. 2023

Energies

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“…BBO base optimization studies are reported in [29][30][31]. The authors in in [32][33][34][35] have employed the PSO technique for controller gain tuning. The authors in [36,37] have used the ABC.…”

Section: Related Workmentioning

confidence: 99%

Novel COVID-19 Based Optimization Algorithm (C-19BOA) for Performance Improvement of Power Systems

et al. 2022

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The ongoing pandemic due to novel coronavirus disease-2019 (COVID-19) has rapidly unsettled the health sector with a considerable fatality rate. The main factors that help minimize the spread of this deadly virus are the proper use of masks, social distancing and antibody growth rate in a person. Based on these factors, we propose a new nature-inspired meta-heuristic algorithm named COVID-19 Based Optimization Algorithm (C-19BOA). The proposed C-19BOA mimics the spread and control behavior of coronavirus disease centered on three containment factors: (1) social distancing, (2) use of masks, and (3) antibody rate. Initially, the mathematical models of containment factors are presented, and further, the proposed C-19BOA is developed. To ascertain the effectiveness of the developed C-19BOA, its performance is verified on standard IEEE mathematical benchmark functions for the minimization of these benchmark functions and convergence to the optimal values. These performances are compared with established bio-inspired optimization algorithms available in the literature. Finally, the developed C-19BOA is applied on an electrical power system load–frequency–control model to test its effectiveness in optimizing the power system parameters and to check its applicability in solving modern engineering problems. A performance comparison of the proposed C-19BOA and other optimization algorithms is validated based on optimizing the controller gains for reducing the steady-state errors by comparing the effective frequency and tie-line power regulation ability of an industrially applied Proportional–Integral–Derivative controller (PID) and Active Disturbance Rejection controller (ADRC). Moreover, the robustness of C-19BOA optimized PID and ADRC gains is tested by varying the system parameters from their nominal values.

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“…It is thought that multiple generations from different sources, especially with the temporal overlap of wind and waves, will reduce intermittent generation and therefore accelerate energy transmission. Kumar et al [24] examined integrated renewable energy sources such as wave, wind, solar, and battery energy storage systems. They introduced a hybrid power management algorithm (PMA) integrated with day-ahead demand-side management (DSM), aiming at combining economic and emission load distribution (CEED) to reduce losses.…”

Section: Introductionmentioning

confidence: 99%

Investigation and Optimization of Integrated Electricity Generation from Wind, Wave, and Solar Energy Sources

Balta,

Yumurtaci

2024

Energies

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This study investigates the potential for renewable energy-based electricity generation using existing wave, wind, and solar energies in Türkiye. A significant part of Türkiye’s energy needs is still met using fossil fuels. Considering the country’s resources, renewable energy sources appear as an alternative source to meet these needs. The objective of this study is to find an effective, efficient, economical, environmentally friendly, and sustainable way to produce electricity to reach net-zero targets and transition towards low-carbon and carbon-free energy systems. To be able to make a deep investigation about the relevant issue, six provinces from different regions of Türkiye (Antalya, Çanakkale, İstanbul, İzmir, Kırklareli, and Muğla) are assessed in terms of wave, wind, and solar energy potential, including wave data, wind speeds, sunshine duration, and global radiation values. Wind, wave, and solar energy data of the selected regions were taken from the ERA5 database, which is the weather forecast model of the European Center for Medium-Term Weather Forecasts (ECMWF), and the Ministry of Energy and Natural Resources of the Republic of Türkiye and the General Directorate of Meteorology. Calculations were made using monthly data for the last 5 years. Considering the coastal lengths in the determined regions, the annual total electrical power produced from wave, solar, and wind energies was calculated. In these calculations, the coastal length parameter was assumed to be uniform across all cities, and the electrical power potential from these energy sources was analyzed. Within the framework of these analyses, the number of houses in the selected regions whose electricity needs can be met was calculated. As a result, the potential electrical power and the amount of affordable housing units in the selected regions were compared. As an important result of the studies, it was determined that the characteristic features of the selected regions, such as wavelength, wave height, and wind speed, were directly related to the applicable coast length. The power obtained from wave energy was higher than that from other renewable energy sources, considering the determined coast lengths. Wave energy was followed by parabolic solar collector, wind, and photovoltaic solar energy systems. According to the model, the power obtained from renewable energy systems was at the highest level in the Kırklareli/Demirköy province compared to other locations. Kırklareli was followed by İstanbul, Antalya, İzmir, Muğla, and Çanakkale. It was also found that the electricity needs of 763,578 houses were met in the Kırklareli/Demirköy region, and the electricity needs of 470,590 houses were met in the Çanakkale/Ayvacık region. The statistically optimized factors using the Response Surface Methodology (RSM) for wind, photovoltaic, parabolic solar collector, and wave power were reported as 995.278, 4529.743, 2264.546, and 276,495.09, respectively. The optimal factors aim to achieve a total electricity generation rate of 2.491 × 109 (kWh/year), a total number of houses of 682,590.55 (number/year), and a total cost of USD 813,940,876. In line with the results obtained, the Kırklareli/Demirköy region becomes favorable when considering wave and wave-integrated wind and solar energies. The proposed system has the potential to meet the entire electricity demand of the Kırklareli province based on data from the Republic of Türkiye Energy Market Regulatory Authority (EMRA).

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Day-Ahead DSM-Integrated Hybrid-Power-Management-Incorporated CEED of Solar Thermal/Wind/Wave/BESS System Using HFPSO

Cited by 18 publications

References 27 publications

Evolution of a Summer Peak Intelligent Controller (SPIC) for Residential Distribution Networks

Evolution of a Summer Peak Intelligent Controller (SPIC) for Residential Distribution Networks

Novel COVID-19 Based Optimization Algorithm (C-19BOA) for Performance Improvement of Power Systems

Investigation and Optimization of Integrated Electricity Generation from Wind, Wave, and Solar Energy Sources

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