Multi-Regional Optimal Power Flow Using Marine Predators Algorithm Considering Load and Generation Variability

Swief, R.A.; Hassan, Nada Mamdouh; Hasanien, Hany M.; Abdelaziz, Almoataz Y.; Kamh, Mohamed Zakaria

doi:10.1109/access.2021.3081374

Cited by 26 publications

(10 citation statements)

References 43 publications

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“…In future work, we will consider the application of the SAC algorithm in OPF [40,41]. Moreover, the high proportion of renewable energy is a development trend of the current power system.…”

Section: Discussionmentioning

confidence: 99%

Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic

et al. 2021

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To ensure the safe operation of an interconnected power system, it is necessary to maintain the stability of the frequency and the tie-line exchanged power. This is one of the hottest issues in the power system field and is usually called load frequency control. To overcome the influences of load disturbances on multi-source power systems containing thermal power plants, hydropower plants, and gas turbine plants, we design a linear active disturbance rejection control (LADRC) based on the tie-line bias control mode. For LADRC, the parameter selection of the controller directly affects the response performance of the entire system, and it is usually not feasible to manually adjust parameters. Therefore, to obtain the optimal controller parameters, we use the Soft Actor-Critic algorithm in reinforcement learning to obtain the controller parameters in real time, and we design the reward function according to the needs of the power system. We carry out simulation experiments to verify the effectiveness of the proposed method. Compared with the results of other proportional–integral–derivative control techniques using optimization algorithms and LADRC with constant parameters, the proposed method shows significant advantages in terms of overshoot, undershoot, and settling time. In addition, by adding different disturbances to different areas of the multi-source power system, we demonstrate the robustness of the proposed control strategy.

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“…In future work, we will consider the application of the SAC algorithm in OPF [40,41]. Moreover, the high proportion of renewable energy is a development trend of the current power system.…”

Section: Discussionmentioning

confidence: 99%

Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic

et al. 2021

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“…In the proposed optimization model (equations ( 1)-( 22)), because of considering power flow-related constraints, the problem is nonlinear and non-convex. In such problems in the area of power systems [49], Heuristic algorithms are promising approaches for finding the optimal solution. Hence, we decided to use a heuristic algorithm in our study.…”

Section: B Evolutionary-based Tlbo Approachmentioning

confidence: 99%

Cloud Energy Storage Investment by Collaboration of Microgrids for Profit and Reliability Enhancement Considering a TSO-DSO Yearly Reward

Haghighi

Jalalzad²,

Salehizadeh

et al. 2023

IEEE Access

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A shared pool of grid-scale storage resources called Cloud Energy Storage (CES) can bring substantial benefits to the economical and reliable operation of MGs. However, the investment cost of CES may not be affordable for a single microgrid (MG). As a solution, we propose an approach in which neighboring microgrids in a distribution network collaborate and form a multi-microgrid (multi-MG) to install a shared CES to increase their profit and improve their reliability. Different investment scenarios are evaluated by considering the yearly reward from TSO and DSO. For each of the investment scenarios, TSO and DSO give a yearly reward based on the contribution of CES in peak-shaving and distribution network operation yearly cost reduction. Afterward, a decision table is provided in which, for all investment scenarios, profit, reliability index based on expected energy not supplied (ENS), and TSO-DSO yearly reward are determined. Finally, the microgrids select one of the investment scenarios using a multi-attribute decisionmaking approach. Simulation results of a case study validate the effectiveness of the proposed collaborative decision-making framework in increasing the economic value of CES investment, reliability enhancement in multi-MG, and peak-shaving.

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“…Reference [24] proposed several corrector techniques based on efficient Newton-like methods to improve the computational performance of conventional continuation power flow analysis. Reference [25] studied some relevant aspects related with the power flow solution of ill-conditioned cases using the current injection formulation. Reference [26] solve the OPF problem using meta-heuristic MPA algorithm.…”

Section: Case Studymentioning

confidence: 99%

Adaptive Volt–Var Control in Smart PV Inverter for Mitigating Voltage Unbalance at PCC Using Multiagent Deep Reinforcement Learning

et al. 2021

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Modern distribution networks face an increasing number of challenges in maintaining balanced grid voltages because of the rapid increase in single-phase distributed generators. Because of the proliferation of inverter-based resources, such as photovoltaic (PV) resources, in distribution networks, a novel method is proposed for mitigating voltage unbalance at the point of common coupling by tuning the volt–var curve of each PV inverter through a day-ahead deep reinforcement learning training platform with forecast data in a digital twin grid. The proposed strategy uses proximal policy optimization, which can effectively search for a global optimal solution. Deep reinforcement learning has a major advantage in that the calculation time required to derive an optimal action in the smart inverter can be significantly reduced. In the proposed framework, multiple agents with multiple inverters require information on the load consumption and active power output of each PV inverter. The results demonstrate the effectiveness of the proposed control strategy on the modified IEEE 13 standard bus systems with time-varying load and PV profiles. A comparison of the effect on voltage unbalance mitigation shows that the proposed inverter can address voltage unbalance issues more efficiently than a fixed droop inverter.

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Multi-Regional Optimal Power Flow Using Marine Predators Algorithm Considering Load and Generation Variability

Cited by 26 publications

References 43 publications

Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic

Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic

Cloud Energy Storage Investment by Collaboration of Microgrids for Profit and Reliability Enhancement Considering a TSO-DSO Yearly Reward

Adaptive Volt–Var Control in Smart PV Inverter for Mitigating Voltage Unbalance at PCC Using Multiagent Deep Reinforcement Learning

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