Adaptive Dynamic Programming-Based Optimal Control Scheme for Energy Storage Systems With Solar Renewable Energy

Wei, Qinglai; Shi, Guang; Song, Ruizhuo; Liu, Yu

doi:10.1109/tie.2017.2674581

Cited by 129 publications

(42 citation statements)

References 37 publications

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“…Thus, the condition (12) holds, and the function V is an input-to-state stability Lyapunov function. Additionally, substituting the triggering condition (14) into (24), then we will get…”

Section: The Triggering Condition Is Dissatisfiedmentioning

confidence: 99%

“…Remark 1. It can be seen that the discrete-time event-triggered system (4) is asymptotically stable with triggering condition (14) under Assumption 1. Compared with the existing work, 37 the new triggering condition needs fewer assumptions to stabilize the discrete-time systems.…”

Section: The Triggering Condition Is Satisfiedmentioning

confidence: 99%

“…Iterative methods are widely used in ADP to obtain solutions of the Bellman equation indirectly . Adaptive dynamic programming can be divided into many categories, such as heuristic dynamic programming (HDP) dual heuristic dynamic programming (DHP), action dependent DHP (also called Q‐learning), globalized DHP, and so on. Adaptive dynamic programming has been widely used in real‐world applications …”

Section: Introductionmentioning

confidence: 99%

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A novel triggering condition of event‐triggered control based on heuristic dynamic programming for discrete‐time systems

Wang

Wei

Liu

2018

Optim Control Appl Methods

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Summary In this paper, an event‐triggered heuristic dynamic programming algorithm for discrete‐time nonlinear systems with a novel triggering condition is studied. Different from traditional heuristic dynamic programming algorithms, the control law in this algorithm will only be updated when the triggering condition is satisfied to reduce the computational burden. Three neural networks are employed, which are model network, action network, and critic network. Model functions, control laws, and value functions are estimated using neural networks, respectively. The main contribution of this algorithm is the novel triggering condition with simpler form and fewer assumptions. Additionally, a proof of the stability for discrete‐time systems using Lyapunov technique is given. Finally, two simulations are shown to verify the effectiveness of the developed algorithm.

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“…Thus, the condition (12) holds, and the function V is an input-to-state stability Lyapunov function. Additionally, substituting the triggering condition (14) into (24), then we will get…”

Section: The Triggering Condition Is Dissatisfiedmentioning

confidence: 99%

Section: The Triggering Condition Is Satisfiedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel triggering condition of event‐triggered control based on heuristic dynamic programming for discrete‐time systems

Wang

Wei

Liu

2018

Optim Control Appl Methods

Self Cite

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“…46 The optimal performance index function, which minimizes the total electricity cost and simultaneously extends the battery's lifetime, is established based on the data of the real-time electricity price, the load demand, and the solar renewable energy. 46 The optimal performance index function, which minimizes the total electricity cost and simultaneously extends the battery's lifetime, is established based on the data of the real-time electricity price, the load demand, and the solar renewable energy.…”

Section: Energy Efficiency and Renewable Energymentioning

confidence: 99%

A review on the features and technologies for energy efficiency of smart grid

Anjana¹,

Shaji

2017

Int J Energy Res

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Summary In today's world striving for efficiency in every sector, especially power generation and distribution, smart grids emerge as the solution for efficiently meeting the increasing demand. They adjust themselves to optimally deliver energy at the lowest cost and highest quality possible. The grid successfully makes use of renewable energy resources, electric vehicles, and smart pricing techniques in its attempt to achieve energy efficiency. It also promotes a greener environment by striving to reduce greenhouse gas emissions. Information communication technology (ICT) helps the grid in collecting consumption data from the consumers and in sharing tariff information. ICT also helps to gather information about the status of the grid with regard to aspects like power quality, faults etc. The purpose of this paper is to review recent literature with a view to comprehensively present the technologies employed in the smart grid for achieving energy efficiency and the challenges involved therein.

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“…In recent several decades, considerable works have been devoted into developing methods to get the approximate solution of the HJB equation, and relevant results can be found in the literature. [22][23][24][25][26][27][28][29] On the basis of reinforcement learning and dynamic programming, ADP was proposed by Werbos. 30,31 Instead of solving the equation directly, the ADP algorithm estimates the value (cost) function in the HJB equation to achieve the optimal control by the means of iteration, which is inspired by the idea of dynamic programming.…”

Section: Introductionmentioning

confidence: 99%

Online reinforcement learning for a class of partially unknown continuous‐time nonlinear systems via value iteration

Zhang

et al. 2017

Optim Control Appl Methods

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Summary In this paper, a modified value iteration–based approximate dynamic programming method is proposed for a class of affine nonlinear continuous‐time systems, whose dynamics are partially unknown. The value iteration algorithm is established in an online fashion, and the convergence proof is given. To attenuate the effect caused by the unascertained characteristics of the system dynamics, the integral reinforcement learning scheme is also used. In the proposed approximate dynamic programming method, it is emphasized that the single‐network structure is utilized to estimate the value functions and the control policies. That is, the iteration process is implemented on the actor/critic structure, in which case only the critic NN is required to be identified. Then, the least‐squares scheme is derived for the NN weights updating. Finally, a linear system and a nonlinear system are tested to evaluate the performance of the proposed online value iteration algorithm. Both of the examples show the feasibility and effectiveness of the proposed algorithms.

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Adaptive Dynamic Programming-Based Optimal Control Scheme for Energy Storage Systems With Solar Renewable Energy

Cited by 129 publications

References 37 publications

A novel triggering condition of event‐triggered control based on heuristic dynamic programming for discrete‐time systems

A novel triggering condition of event‐triggered control based on heuristic dynamic programming for discrete‐time systems

A review on the features and technologies for energy efficiency of smart grid

Online reinforcement learning for a class of partially unknown continuous‐time nonlinear systems via value iteration

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