Application of Gradient Descent Continuous Actor-Critic Algorithm for Bilateral Spot Electricity Market Modeling Considering Renewable Power Penetration

Zhao, Huiru; Wang, Yuwei; Zhao, Meiling; Sun, Chuyu; Tan, Qian

doi:10.3390/a10020053

Cited by 5 publications

(6 citation statements)

References 37 publications

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“…This study employs the MA technique to derive the optimal switching characteristics of CMOS inverters. By analyzing the switching operation of the CMOS inverter, the fall and rise time (t f and t r ) and propagation delay times (t PHL and t PLH ) are determined [11][12][13][14][15][16]18]. Figure 2 shows the CMOS inverter, while Figure 3 displays the voltage waveforms.…”

Section: Switching Characteristics Of the Cmos Invertermentioning

confidence: 99%

“…On the other hand, the low to high propagation delay pertains to the time delay between the 50% transition of the falling input voltage and the 50% transition of the rising output voltage. These values can be determined using Equations ( 11) and (12), respectively [11][12][13][14][15][16]18]. To determine the fall time, one must measure the duration necessary for the output voltage to decrease from 90% to 10% levels.…”

Section: Switching Characteristics Of the Cmos Invertermentioning

confidence: 99%

“…There are various optimization algorithms available in the literature. Gradient descent [15,16], genetic algorithms [17][18][19], RMSProp [20], and many other optimization algorithms have been used to improve the efficiency and accuracy of solving complex problems. In the published literature, different evolutionary optimization methods have been used to design inverters with optimal switching characteristics.…”

Section: Introductionmentioning

confidence: 99%

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A Mayfly-Based Approach for CMOS Inverter Design with Symmetrical Switching

2023

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This paper presents a novel approach to designing a CMOS inverter using the Mayfly Optimization Algorithm (MA). The MA is utilized in this paper to obtain symmetrical switching of the inverter, which is crucial in many digital electronic circuits. The MA method is found to have a fast convergence rate compared to other optimization methods, such as the Symbiotic Organisms Search (SOS), Particle Swarm Optimization (PSO), and Differential Evolution (DE). A total of eight different sets of design parameters and criteria were analyzed in Case I, and the results confirmed compatibility between the MA and Spice techniques. The maximum discrepancy in fall time across all design sets was found to be 2.075711 ns. In Case II, the objective was to create a symmetrical inverter with identical fall and rise times. The difference in fall and rise times was minimized based on Spice simulations, with the maximum difference measuring 0.9784731 ns. In Case III, the CMOS inverter was designed to achieve symmetrical fall and rise times as well as propagation delays. The Spice simulation results demonstrated that symmetry had been successfully achieved, with the minimum difference measuring 0.312893 ns and the maximum difference measuring 1.076540 ns. These Spice simulation results are consistent with the MA results. The results conclude that the MA is a reliable and simple optimization technique and can be used in similar electronic topologies.

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Section: Switching Characteristics Of the Cmos Invertermentioning

confidence: 99%

Section: Switching Characteristics Of the Cmos Invertermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Mayfly-Based Approach for CMOS Inverter Design with Symmetrical Switching

2023

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“…Yu, Shi, and Huang et al [16] proposed an optimal tracking control of an underwater vehicle based on the DDPG algorithm and achieved better control accuracy than that of the traditional PID algorithm. Other studies such as [17,18] adopted the reinforcement learning of continuous state space to energy management.…”

Section: Introductionmentioning

confidence: 99%

Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning

Chen

et al. 2018

Algorithms

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We developed a novel control strategy of speed servo systems based on deep reinforcement learning. The control parameters of speed servo systems are difficult to regulate for practical applications, and problems of moment disturbance and inertia mutation occur during the operation process. A class of reinforcement learning agents for speed servo systems is designed based on the deep deterministic policy gradient algorithm. The agents are trained by a significant number of system data. After learning completion, they can automatically adjust the control parameters of servo systems and compensate for current online. Consequently, a servo system can always maintain good control performance. Numerous experiments are conducted to verify the proposed control strategy. Results show that the proposed method can achieve proportional-integral-derivative automatic tuning and effectively overcome the effects of inertia mutation and torque disturbance.

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“…Diagram of the test system. Note.Figure 1is reproduced from[41] (under the Creative Commons Attribution License/public domain). For the sake of simplicity, here it is assumed that the minimum and maximum congestion constraints in all transmission lines are ± 40 MW.…”

mentioning

confidence: 99%

Continuous Reinforcement Algorithm and Robust Economic Dispatching-Based Spot Electricity Market Modeling considering Strategic Behaviors of Wind Power Producers and Other Participants

Yuan

Nie

Guo

2019

Journal of Electrical and Computer Engineering

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In a spot wholesale electricity market containing strategic bidding interactions among wind power producers and other participants such as fossil generation companies and distribution companies, the randomly fluctuating natures of wind power hinders not only the modeling and simulating of the dynamic bidding process and equilibrium of the electricity market but also the effectiveness about keeping economy and reliability in market clearing (economic dispatching) corresponding to the independent system operator. Because the gradient descent continuous actor-critic algorithm is demonstrated as an effective method in dealing with Markov’s decision-making problems with continuous state and action spaces and the robust economic dispatch model can optimize the permitted real-time wind power deviation intervals based on wind power producers’ bidding power output, in this paper, considering bidding interactions among wind power producers and other participants, we propose a gradient descent continuous actor-critic algorithm-based hour-ahead electricity market modeling approach with the robust economic dispatch model embedded. Simulations are implemented on the IEEE 30-bus test system, which, to some extent, verifies the market operation economy and the robustness against wind power fluctuations by using our proposed modeling approach.

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Application of Gradient Descent Continuous Actor-Critic Algorithm for Bilateral Spot Electricity Market Modeling Considering Renewable Power Penetration

Cited by 5 publications

References 37 publications

A Mayfly-Based Approach for CMOS Inverter Design with Symmetrical Switching

A Mayfly-Based Approach for CMOS Inverter Design with Symmetrical Switching

Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning

Continuous Reinforcement Algorithm and Robust Economic Dispatching-Based Spot Electricity Market Modeling considering Strategic Behaviors of Wind Power Producers and Other Participants

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