A Joint Scheduling Strategy for Wind and Solar Photovoltaic Systems to Grasp Imbalance Cost in Competitive Market

Das, Subrata; Das, Amlan; Dawn, Subhojit; Gope, Sadhan; Ustun, Taha Selim

doi:10.3390/su14095005

Cited by 18 publications

(8 citation statements)

References 31 publications

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“…As the power system becomes congested, the system operator uses an MPC ancillary service. It helps regulate electric power quality, manage realtime constraints, and avoid power deviations [84]. Figure 9 shows the cascade interconnection of tertiary MPC controllers in a microgrid.…”

Section: Microgrids Tertiary Control: Mpc-based Economic Optimizationmentioning

confidence: 99%

Model Predictive Control Strategies in Microgrids: A Concise Revisit

et al. 2022

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The world is rapidly integrating renewable energy resources into the existing grid systems. However, the unpredictable nature of renewables and uncertain load profiles cause issues such as poor power quality, lower system reliability, complex power management, battery degradation, high operating costs, and lower efficiency. Microgrids can help smart grid technology overcome several problems associated with renewable energy integration. Distant locations can obtain electricity without building extensive transmission infrastructure, cutting development costs, or transmission losses. The intermittent nature of renewable energy sources contributes to microgrid problems such as poor power quality, decreased reliability, and high operating costs. Model predictive control (MPC) is an effective method to address challenging industrial and scientific issues. Advancements in MPC that accept different system constraints have solved multiple concerns in uncertain microgrid systems. MPC applied to three hierarchal control layers in a microgrid resolves the problems of power quality, power sharing, energy management, and economic optimization. This study demonstrates that MPC microgrid control is suitable for low-cost operation, improved management, and reliable control. The shortcomings of recent model predictive control techniques for microgrids are reviewed, and future research directions for MPC microgrids are identified.INDEX TERMS Microgrids, renewable energy resources, model predictive control, power quality enhancement, energy management system, hybrid energy storage system, demand side management, demand response, distributed systems

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Section: Microgrids Tertiary Control: Mpc-based Economic Optimizationmentioning

confidence: 99%

Model Predictive Control Strategies in Microgrids: A Concise Revisit

et al. 2022

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“…A magnetotactic-based optimization technique (MBO) is applied for controller gain tuning in [42][43][44][45][46][47]. New optimization techniques such as flower pollination algorithm [48], satin bowerbird optimization [49], butterfly optimization algorithm [50], artificial gorilla troops [51],honey badger algorithm [52] are also used in the literature.…”

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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“…In [29], the authors minimize the imbalance cost with the help of a Smart Flower Optimization Algorithm (SFOA) and Honey Badger Algorithm (HBA) for a wind and solar PV integrated power system. Here, imbalance arises due to the variations of predicted wind speed and actual wind speed of the system.…”

Section: Introductionmentioning

confidence: 99%

A Strategy for System Risk Mitigation Using FACTS Devices in a Wind Incorporated Competitive Power System

et al. 2022

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Electricity demand is sharply increasing with the growing population of human beings. Due to financial, social, and political barriers, there are lots of difficulties when building new thermal power plants and transmission lines. To solve this problem, renewable energy sources and flexible AC transmission systems (FACTS) can operate together in a power network. Renewable energy sources can provide additional power to the grid, whereas FACTS devices can increase the thermal limit of existing transmission lines. It is always desirable for an electrical network to operate under stable and secure conditions. The system runs at risk if any abnormality occurs in the generation, transmission, or distribution sections. This paper outlines a strategy for reducing system risks via the optimal operation of wind farms and FACTS devices. Here, a thyristor-controlled series compensator (TCSC) and a unified power flow controller (UPFC) have both been considered for differing the thermal limit of transmission lines. The impact of the wind farm, as well as the combined effect of the wind farm and FACTS devices on system economy, were investigated in this work. Both regulated and deregulated environments have been chosen to verify the proposed approach. Value at risk (VaR) and cumulative value at risk (CVaR) calculations were used to evaluate the system risk. The work was performed on modified IEEE 14 bus and modified IEEE 30-bus systems. A comparative study was carried out using different optimization techniques, i.e., Artificial Gorilla Troops Optimizer Algorithm (AGTO), Honey Badger Algorithm (HBA), and Sequential Quadratic Programming (SQP) to check the effect of renewable integration in the regulated and deregulated power systems in terms of system risk and operating cost.

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A Joint Scheduling Strategy for Wind and Solar Photovoltaic Systems to Grasp Imbalance Cost in Competitive Market

Cited by 18 publications

References 31 publications

Model Predictive Control Strategies in Microgrids: A Concise Revisit

Model Predictive Control Strategies in Microgrids: A Concise Revisit

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

A Strategy for System Risk Mitigation Using FACTS Devices in a Wind Incorporated Competitive Power System

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