Optimal energy management of microgrid using advanced multi-objective particle swarm optimization

Ánh, Hồ Phạm Huy; Kien, Cao Van

doi:10.1108/ec-05-2019-0194

Cited by 14 publications

(10 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, it can be assumed that there are n EVs in the station at time t, including i vehicle in a state of nonchargeable and dischargeable (SOC = SOC max ), including j vehicle in a state of rechargeable and non-dischargeable (SOC < SOC m ), and the remaining vehicles in both charging and discharging states (SOC m < SOC < SOC max ). It can be obtained that at time t, the boundary of the overall charging power of the EV station is shown in (7):…”

Section: The V2g Model Of Evsmentioning

confidence: 99%

“…In [6], a genetic algorithm based on a memory mechanism is proposed to solve the problem of minimizing the operating cost of a microgrid. In [7], for a microgrid system combining renewable energy and traditional power generation, an energy management strategy for a hybrid thermoelectric island microgrid is proposed based on a multi-objective particle swarm optimization (MOPSO) algorithm. In [8], based on the chaotic search particle swarm optimization algorithm, with the goal of minimizing the total cost, the economic operation optimization model of the microgrid is constructed from three aspects: operating cost, environmental impact and system safety, so as to effectively reduce the operating cost of the microgrid and ensure the safety and stability of the power supply and electricity consumption.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning

Wen

Fan

et al. 2022

Sustainability

View full text Add to dashboard Cite

In recent years, the access of various distributed power sources and electric vehicles (EVs) has brought more and more randomness and uncertainty to the operation and regulation of microgrids. Therefore, an optimal scheduling strategy for microgrids with EVs based on Deep Q-learning is proposed in this paper. Firstly, a vehicle-to-grid (V2G) model considering the mobility of EVs and the randomness of user charging behavior is proposed. The charging time distribution model, charging demand model, state-of-charge (SOC) dynamic model and the model of travel location are comprehensively established, thereby realizing the construction of the mathematical model of the microgrid with EVs：it can obtain the charging/discharging situation in the EV station, so as to obtain the overall output power of the EV station. Secondly, based on Deep Q-learning, the state space and action space are set up according to the actual microgrid system, and the design of the optimal scheduling reward function is completed with the goal of economy. Finally, the calculation example results show that compared with the traditional optimization algorithm, the strategy proposed in this paper has the ability of online learning and can cope with the randomness of renewable resources better. Meanwhile, the agent with experience replay ability can be trained to complete the evolution process, so as to adapt to the nonlinear influence caused by the mobility of EVs and the periodicity of user behavior, which is feasible and superior in the field of optimal scheduling of microgrids with renewable resources and EVs.

show abstract

Section: The V2g Model Of Evsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning

Wen

Fan

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…The algorithm will virtually never miss a chance to locate the global optimum solution in the optimization method if this approach is used. The location update method of each moth relative to the flame may be described by Equation (5) in order to develop a mathematical model of moth flying behavior in response to the flame…”

Section: Moth-flame Optimizationmentioning

confidence: 99%

“…Motevasel et al [2] used the Modified Bacterial Foraging Optimization Algorithm. Then, there are the ant colony optimization algorithm and simulated annealing algorithm [3], the Non-dominated Sorting Genetic Algorithms-II [4], multi-objective particle swarm optimization [5], distributed proximal strategy optimization [6], genetic algorithm [7], and so on.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Regional Combined Heat and Power System Based on Improved MFO Algorithm

et al. 2022

View full text Add to dashboard Cite

Due to the inflexibility of cogeneration power plants and the uncertainty of wind power production, the excess power of the distribution network brings challenges to the power grid operation. This paper introduced an improved moth-flame optimization algorithm to meet the challenge of energy complementary dispatching. The proposed algorithm adopts three effective strategies, namely inertia weight, unified initialization, and the spiral position update strategy, which maintains a strong global search ability and a potent compromise between global and local search. The effectiveness of the proposed method was evaluated by benchmark functions. Furthermore, the proposed method was applied to combine heat and power system operation problems and economic dispatch in light load and wind power unpredictability. In order to verify the robustness of the algorithm and solve the complex constraints of power systems under extreme conditions, three different cases had been discussed. The experimental findings indicate that the proposed algorithm shows better performances in terms of convergence speed, ability to escape from a local optimum solution, and population diversity maintenance under different complexity conditions of engineering problems.

show abstract

“…Beruvides et al [25] proposes an improved crossentropy PSO method for parameter optimization of a microscaled manufacturing process. Anh and Kien [26] suggested a new way for optimal energy management of hybrid micro-grid system using advanced multi-objective PSO. Author in [27] introduced an approach for identifying parameters using modified PSO of a Fuzzy NARX IMC structure for MISO dynamic system control.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Fuzzy Sliding Mode Control for Uncertain PAM Robot Arm Plant Enhanced with Evolutionary Technique

Ánh¹,

Kien²

2021

IJCIS

Self Cite

View full text Add to dashboard Cite

This paper introduces an enhanced fuzzy sliding mode control (EFSMC) algorithm used for controlling the uncertain pneumatic artificial muscle (PAM) robot arm containing external disturbances. The nonlinear features of investigated PAM robot arm system are approximated using fuzzy logic. Moreover, the coefficients of fuzzy model are optimum selected by evolutionary differential eveloution (DE) technique. The new EFSMC algorithm is designed based on the traditional sliding mode controller in which the adaptive fuzzy rule is developed based on the Lyapunov stability theory and is fuzzified with Mandani fuzzy scheme. As a consequent, the closed-loop stability of nonlinear uncertain PAM robot arm system is guaranteed to follow the global asymptotic stability. Experimental results are shown. It is evident that the proposed adaptive fuzzy rule suitable with the EFSMC controller which ensures an outperforming method in comparison with other advanced control approaches.

show abstract

Optimal energy management of microgrid using advanced multi-objective particle swarm optimization

Cited by 14 publications

References 21 publications

An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning

An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning

Optimal Scheduling of Regional Combined Heat and Power System Based on Improved MFO Algorithm

Hybrid Fuzzy Sliding Mode Control for Uncertain PAM Robot Arm Plant Enhanced with Evolutionary Technique

Contact Info

Product

Resources

About