A Distributed MPC to Exploit Reactive Power V2G for Real-Time Voltage Regulation in Distribution Networks

Hu, Jindi; Ye, Chengjin; Ding, Yi; Tang, Jinjiang; Liu, Si

doi:10.1109/tsg.2021.3109453

Cited by 105 publications

(49 citation statements)

References 35 publications

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“…To determine the optimal location and capacity, one SVC and TCSC are installed, which are considered as a sequence with three cells for the optimization problem, where the first two cells represent the location (SVC bus number and line number for the TCSC) and the third cell represents capacity. According to the generator bus model for the SVC, its optimal reactive capacity is calculated using the power flow [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. The capacitance range of the SCV is between −50 to 50 MVAr.…”

Section: Simulation Resultsmentioning

confidence: 99%

Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

et al. 2021

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Reactive power compensation is one of the practical tools that can be used to improve power systems and reduce costs. These benefits are achieved when the compensators are installed in a suitable place with optimal capacity. This study solves the issues of optimal supply and the purchase of reactive power in the IEEE 30-bus power system, especially when considering voltage stability and reducing total generation and operational costs, including generation costs, reserves, and the installation of reactive power control devices. The modified version of the artificial bee colony (MABC) algorithm is proposed to solve optimization problems and its results are compared with the artificial bee colony (ABC) algorithm, the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA). The simulation results showed that the minimum losses in the power system requires further costs for reactive power compensation. Also, optimization results proved that the proposed MABC algorithm has a lower active power loss, reactive power costs, a better voltage profile and greater stability than the other three algorithms.

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Section: Simulation Resultsmentioning

confidence: 99%

Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

et al. 2021

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“…Control techniques emerged as a novel solution to solve problems accompanied by stochastic methods [69][70][71][72][73][74][75][76][77]. For example, in [78], the authors presented a method for power usage scheduling in SG using Model Predictive Control (MPC).…”

Section: Control Techniquesmentioning

confidence: 99%

Optimal Demand-Side Management Using Flat Pricing Scheme in Smart Grid

Albogamy

Ashfaq

Hafeez³

et al. 2022

Processes

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This work proposes a framework to solve demand-side management (DSM) problem by systematically scheduling energy consumption using flat pricing scheme (FPS) in smart grid (SG). The framework includes microgrid with renewable energy sources (solar and wind), energy storage systems, electric vehicles (EVs), and building appliances like time flexible, power flexible, and base/critical appliances. For the proposed framework, we develop an ant colony optimization (ACO) algorithm, which efficiently schedules smart appliances, and EVs batteries charging/discharging with microgrid and without (W/O) microgrid under FPS to minimize energy cost, carbon emission, and peak to average ratio (PAR). An integrated technique of enhanced differential evolution (EDE) algorithm and artificial neural network (ANN) is devised to predict solar irradiance and wind speed for accurate microgrid energy estimation. To endorse the applicability of the proposed framework, simulations are conducted. Moreover, the proposed framework based on the ACO algorithm is compared to mixed-integer linear programming (MILP) and W/O scheduling energy management frameworks in terms of energy cost, carbon emission, and PAR. The developed ACO algorithm reduces energy cost, PAR, and carbon emission by 23.69%, 26.20%, and 15.35% in scenario I, and 25.09%, 31.45%, and 18.50% in scenario II, respectively, as compared to W/O scheduling case. The results affirm the applicability of the proposed framework in aspects of the desired objectives.

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“…Consider a time-varying discrete-time system as follows : 𝑥 𝑘+1 = 𝐴 𝑘 𝑥 𝑘 + 𝐵 𝑘 𝑢 𝑘 + 𝐸𝑑 𝑘 (16) 𝑦 𝑘 = 𝐶𝑥 𝑘 + 𝐷𝑢 𝑘 (17) where, 𝑥 𝑘 ∈ ℝ 𝑛 , 𝑢 𝑘 ∈ ℝ 𝑛 𝑢 , 𝑑 𝑘 ∈ ℝ 𝑛 𝑑 and 𝑦 𝑘 ∈ ℝ 𝑛 𝑦 are system states, control inputs, disturbances, and outputs of the system, respectively [41]. Also, 𝐴 𝑘 ∈ ℝ 𝑛×𝑛 and 𝐵 𝑘 ∈ ℝ 𝑛×𝑛 𝑢 are time-varying matrices.…”

Section: Model Flywheel and Battery Energy Storage Systemsmentioning

confidence: 99%

A Novel Control Strategy Based on an Adaptive Fuzzy Model Predictive Control for Frequency Regulation of a Microgrid With Uncertain and Time-Varying Parameters

et al. 2022

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The intermittent and uncertain behavior of renewable energy sources; moreover, the increase in penetration of these resources causes some drawbacks in power grids, particularly in low-inertia microgrids. In addition to supply-side challenges, changing load rate has a considerable negative effect on small-size microgrids. Therefore, according to these challenges, the lack of balance between generation and demand is a vital issue in microgrids. One way to face these challenges would be using a suitable control strategy. In this research, an adaptive fuzzy model predictive control has been proposed as a novel control approach and has been compared with conventional controllers such as an optimal PI, and an adaptive optimal model predictive control. It is important to mention that, various types of load changing and time-varying parameters have been considered for a new model of small size microgrids in this study. The comparison and simulation results obviously indicate the effectiveness of the proposed control strategy.

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A Distributed MPC to Exploit Reactive Power V2G for Real-Time Voltage Regulation in Distribution Networks

Cited by 105 publications

References 35 publications

Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

Optimal Demand-Side Management Using Flat Pricing Scheme in Smart Grid

A Novel Control Strategy Based on an Adaptive Fuzzy Model Predictive Control for Frequency Regulation of a Microgrid With Uncertain and Time-Varying Parameters

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