A Novel Algorithm for Optimal Operation of Hydrothermal Power Systems under Considering the Constraints in Transmission Networks

Nguyen, Thang Trung; Dinh, Bach Hoang; Quynh, Nguyen Vu; Duong, Minh Quan; Đại, Lê Văn

doi:10.3390/en11010188

Cited by 9 publications

(6 citation statements)

References 54 publications

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“…In our paper, there are only 22 data columns for each industry. For this reason, after considering other methods for uncertain qualitative systems [57,58], we decided to adopt the GM (1, 1) model. The software environment in which we use this approach is the Data Processing System.…”

Section: Gm (1 1) Forecasting Modelmentioning

confidence: 99%

Analyzing and Forecasting Energy Consumption in China’s Manufacturing Industry and Its Subindustries

2018

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In the context of new industrialization, the energy problem being experienced by the manufacturing industry has aroused social concerns. This paper focuses on the energy use of 27 subindustries in China’s manufacturing industry and it develops an energy consumption index for 1994–2015. Subsequently, the method of grey relational analysis is used, with the full period divided according to years in which change points occur. The empirical analysis indicates that the energy consumption indexes generally exhibit a declining trend. Using the grey model (GM (1,1)) to forecast the index indicates a continued downward trend up to 2025 for energy-intensive industries, which is a more optimistic scenario than the trend forecast for the whole manufacturing sector. Thus, these energy-intensive industries do not drag down the performance of the whole manufacturing industry in regard to energy intensity. In future, more attention should be paid to energy-saving efforts by nontraditional high-energy-consuming industries. Although the results show that energy efficiency is improving in China, total annual consumption is rising rapidly. Therefore, the industry needs to continue to strengthen independent innovation and improve the efficiency of new energy use. The Chinese government should formulate feasible long-term plans to encourage enterprises to save energy.

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Section: Gm (1 1) Forecasting Modelmentioning

confidence: 99%

Analyzing and Forecasting Energy Consumption in China’s Manufacturing Industry and Its Subindustries

2018

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“…Update Q-value matrix and probability distribution matrix as Equations (11) and (12), respectively 10:…”

Section: Algorithmmentioning

confidence: 99%

“…Calculate indices of ACE, CPS1, CPS2 by (1),(6), (7) Calculate the reward function by (15) Predict the next systemic state with deep forest by (8)- (10) Select Q learning from the next systemic state Update Q value matrix and probability distribution matrix by (11), (12) Select an action at the predicted state by (13) DFRL based controller AGC units…”

Section: Emergency Situationsmentioning

confidence: 99%

“…For example: an optimized sliding mode controller (SMC) [4] was proposed for the AGC of interconnected multi-area power systems in deregulated environments [5]; a two-layer active disturbance rejection controller (ADRC) was designed for the load frequency control (LFC) of interconnected power systems [6]; a fractional-order proportional-integral-derivative (FOPID) controller with two or three degrees of freedom was employed for AGC [7,8]; and optimized fuzzy logic control (FLC) was utilized for LFC in hydrothermal systems [9]. A modified cuckoo search algorithm [10] and an efficient and new modified differential evolution algorithm [11] were also proposed for hydrothermal power systems. Furthermore, many reinforcement learning algorithms which can update their control strategies online have been utilized for AGC: a relaxed Q learning-based controller was proposed for relaxed AGC [12,13]; a Q(λ) learning-based controller was applied to smart generation control in multi-agent systems (MASs) [14]; and an R(λ) imitation learning-based controller was designed for AGC [15].…”

Section: Introductionmentioning

confidence: 99%

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Deep Forest Reinforcement Learning for Preventive Strategy Considering Automatic Generation Control in Large-Scale Interconnected Power Systems

Yin

Zhao

et al. 2018

Applied Sciences

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To reduce occurrences of emergency situations in large-scale interconnected power systems with large continuous disturbances, a preventive strategy for the automatic generation control (AGC) of power systems is proposed. To mitigate the curse of dimensionality that arises in conventional reinforcement learning algorithms, deep forest is applied to reinforcement learning. Therefore, deep forest reinforcement learning (DFRL) as a preventive strategy for AGC is proposed in this paper. The DFRL method consists of deep forest and multiple subsidiary reinforcement learning. The deep forest component of the DFRL is applied to predict the next systemic state of a power system, including emergency states and normal states. The multiple subsidiary reinforcement learning component, which includes reinforcement learning for emergency states and reinforcement learning for normal states, is applied to learn the features of the power system. The performance of the DFRL algorithm was compared to that of 10 other conventional AGC algorithms on a two-area load frequency control power system, a three-area power system, and the China Southern Power Grid. The DFRL method achieved the highest control performance. With this new method, both the occurrences of emergency situations and the curse of dimensionality can be simultaneously reduced.

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“…A multi-objective reactive power scheduling model was proposed in [11], where the hybrid fuzzy multi-objective evolutionary algorithm was utilized to obtain the Pareto front. A novel cuckoo search algorithm was used in [12] to overcome optimal power flow problems and enhance the operation capacity of hydrothermal power systems. However, the obvious drawbacks of the heuristic algorithm are that they often require long calculation time and it is easy for them to fall into local optimum.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Coordinated Active–Reactive Power Optimization for Active Distribution Network with Energy Storage Systems

Wang

Jiang

et al. 2019

Applied Sciences

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This paper proposes a coordinated active–reactive power optimization model for an active distribution network with energy storage systems, where the active and reactive resources are handled simultaneously. The model aims to minimize the power losses, the operation cost, and the voltage deviation of the distribution network. In particular, the reactive power capabilities of distributed generators and energy storage systems are fully utilized to minimize power losses and improve voltage profiles. The uncertainties pertaining to the forecasted values of renewable energy sources are modelled by scenario-based stochastic programming. The second-order cone programming relaxation method is used to deal with the nonlinear power flow constraints and transform the original mixed integer nonlinear programming problem into a tractable mixed integer second-order cone programming model, thus the difficulty of problem solving is significantly reduced. The 33-bus and 69-bus distribution networks are used to demonstrate the effectiveness of the proposed approach. Simulation results show that the proposed coordinated optimization approach helps improve the economic operation for active distribution network while improving the system security significantly.

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A Novel Algorithm for Optimal Operation of Hydrothermal Power Systems under Considering the Constraints in Transmission Networks

Cited by 9 publications

References 54 publications

Analyzing and Forecasting Energy Consumption in China’s Manufacturing Industry and Its Subindustries

Analyzing and Forecasting Energy Consumption in China’s Manufacturing Industry and Its Subindustries

Deep Forest Reinforcement Learning for Preventive Strategy Considering Automatic Generation Control in Large-Scale Interconnected Power Systems

Dynamic Coordinated Active–Reactive Power Optimization for Active Distribution Network with Energy Storage Systems

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