A fuzzy reinforcement learning approach to thermal unit commitment problem

Navin, Nandan Kumar; Sharma, Rajneesh

doi:10.1007/s00521-017-3106-5

Cited by 31 publications

(14 citation statements)

References 50 publications

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“…In [ 28 ], the authors also leverage Fuzzy logic and Q-learning but in a cooperative multi-agent system for controlling the energy of a microgrid. In [ 29 ], Fuzzy and Q-learning are combined to address the problem of thermal unit commitment. Specifically, each input state vector is mapped with the Fuzzy rules to determine all the possible actions with the corresponding Q-values.…”

Section: Related Workmentioning

confidence: 99%

An On-Demand Charging for Connected Target Coverage in WRSNs Using Fuzzy Logic and Q-Learning

Nguyen

et al. 2021

Sensors

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In wireless rechargeable sensor networks (WRSNs), a mobile charger (MC) moves around to compensate for sensor nodes’ energy via a wireless medium. In such a context, designing a charging strategy that optimally prolongs the network lifetime is challenging. This work aims to solve the challenges by introducing a novel, on-demand charging algorithm for MC that attempts to maximize the network lifetime, where the term “network lifetime” is defined by the interval from when the network starts till the first target is not monitored by any sensor. The algorithm, named Fuzzy Q-charging, optimizes both the time and location in which the MC performs its charging tasks. Fuzzy Q-charging uses Fuzzy logic to determine the optimal charging-energy amounts for sensors. From that, we propose a method to find the optimal charging time at each charging location. Fuzzy Q-charging leverages Q-learning to determine the next charging location for maximizing the network lifetime. To this end, Q-charging prioritizes the sensor nodes following their roles and selects a suitable charging location where MC provides sufficient power for the prioritized sensors. We have extensively evaluated the effectiveness of Fuzzy Q-charging in comparison to the related works. The evaluation results show that Fuzzy Q-charging outperforms the others. First, Fuzzy Q-charging can guarantee an infinite lifetime in the WSRNs, which have a sufficient large sensor number or a commensurate target number. Second, in other cases, Fuzzy Q-charging can extend the time until the first target is not monitored by 6.8 times on average and 33.9 times in the best case, compared to existing algorithms.

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Section: Related Workmentioning

confidence: 99%

An On-Demand Charging for Connected Target Coverage in WRSNs Using Fuzzy Logic and Q-Learning

Nguyen

et al. 2021

Sensors

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“…The standard deviation, CPU time, FE are also shown in Table 6. At the same time, the results of AMKMTOA are compared to those of other algorithms including GA 6 , DE 10 , PSO 6 , ICA-PSO 13 , SA-PSO 14 , IA-EDP 20 , MAFRL 21 and KMTOA. The comparative statistics results are summarized in Table 6.…”

Section: Economic Dispatch Problem Solved By Amkmtoamentioning

confidence: 99%

“…27. To verify the proposed algorithm for the 13-unit system, the experimental results are compared with methods which include TSARGA 8 , DE 10 , DECDM 27 , HMAPSO 15 , ICA-PSO 13 , SOMA 28 , IA-EDP 20 , MAFRL 21 and KMTOA. The comparative results are presented in Table 7.…”

Section: Economic Dispatch Problem Solved By Amkmtoamentioning

confidence: 99%

An Optimal Algorithm Based on Kinetic-Molecular Theory with Artificial Memory to Solving Economic Dispatch Problem

Fan

et al. 2018

Current Science

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Economic dispatch (ED) problem exhibits highly nonlinear characteristics, such as prohibited operating zone, ramp rate limits and non-smooth property. Due to its nonlinear characteristics, it is hard to achieve the expected solution by classical methods. To overcome the challenging difficulty, an improved optimization algorithm based on kinetic-molecular theory (KMTOA) was proposed to solve the ED problem in this article. Memory principle is employed into the improved algorithm. By accepting strengthened or weakened stimulus strength, the memory is divided into four states; instant-term, short-term, long-term and forgotten states to update the memory value iteratively. In this way, more and more elites appear in the long-term memory library. Simultaneously, the improved KMTOA, according to the elite population-based guide on the other population, enhances the search ability and avoids the premature convergence which usually suffered in traditional KMTOA. The designs are able to enhance the performance of KMTOA, which has been demonstrated on 12 benchmark functions. To validate the proposed algorithm, we also use three different systems to demonstrate its efficiency and feasibility in solving the ED problem. The experimental results show that the improved KMTOA can achieve higher quality solutions in ED problems.

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“…In Reference [11], the whale algorithm, along with using a series of possible indicators and success indicators of the participation program in the performance function, are used to reduce the power system operation cost. In Reference [12], a UC based on fuzzy logic is proposed to smooth the load profile of the network in the presence of distributed generation sources. In this model, the fluctuations of the load profile and peak load are reduced.…”

Section: Introductionmentioning

confidence: 99%

Unit Commitment for Power Generation Systems Based on Prices in Smart Grid Environment Considering Uncertainty

et al. 2021

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With the growing demand for electricity, the inability of governments to provide the necessary resources to invest in the electricity industry and the rising price of fossil fuels, the tendency is to study and pay attention to economic issues in power systems studies. In this paper a new modified version of gray wolf optimization (MGWO) is proposed to solve the unit commitment (UC) problem in a power system in case of uncertainty. Market price variation is the main source of uncertainty in the UC program. Therefore, a model based on normal probability density function (PDF) is present for reducing the market price uncertainty effect in the model. Simulations are done for a standard 10 thermal units power system, and the results of the optimization by the proposed MGWO are compared with the previous version of the GWO algorithm and particle swarm optimization (PSO) algorithm results. The simulation results confirm the superiority of the proposed MGWO algorithm over the two algorithms PSO and GWO.

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A fuzzy reinforcement learning approach to thermal unit commitment problem

Cited by 31 publications

References 50 publications

An On-Demand Charging for Connected Target Coverage in WRSNs Using Fuzzy Logic and Q-Learning

An On-Demand Charging for Connected Target Coverage in WRSNs Using Fuzzy Logic and Q-Learning

An Optimal Algorithm Based on Kinetic-Molecular Theory with Artificial Memory to Solving Economic Dispatch Problem

Unit Commitment for Power Generation Systems Based on Prices in Smart Grid Environment Considering Uncertainty

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