Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management

Boaro, Matteo; Fuselli, Danilo; Angelis, Francesco De; Liu, Derong; Wei, Qinglai; Piazza, Francesco

doi:10.1007/s12559-012-9191-y

Cited by 82 publications

(41 citation statements)

References 31 publications

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“…Therefore, the following equation was used. In this equation, V R is the measured reference wind speed and V T is the wind velocity, calculated theoretically, α represents the coefficient of friction [8] due to grounds roughness.…”

Section: Presentation and Installation Of The Employed Wind Turbinementioning

confidence: 99%

“…• the solar beams impact with the solar panels under a fairly right angle [8,13]. The platform is also designed to get into different configurations by connecting the PV panels in series and in parallel.…”

Section: Solar Panel Installation and Measurementsmentioning

confidence: 99%

“…One of the most important considerations for installation is to select the appropriate panels and turbine for required power [8]. For this purpose, as a preliminary study, measurements were taken from the solar panels which would be used as the main energy source and the amount of energy that could be received in the worst conditions was calculated [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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The Design Steps of a Hybrid Energy System

Şahin

Okumuş

2017

Acta Phys. Pol. A

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In this paper the design stages of a mixed photovoltaic-wind energy system, supplied with solar and wind energy was discussed. First, general information about the employed wind turbines and solar panels is given and their basic characteristics are addressed. Then, essential information for the system setup and measurements was given. The system setup, operation of the system, the ways to store energy and how energy is consumed were described and the results were interpreted. This study serves as a background for the experimental studies in the future.

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Section: Presentation and Installation Of The Employed Wind Turbinementioning

confidence: 99%

Section: Solar Panel Installation and Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Design Steps of a Hybrid Energy System

Şahin

Okumuş

2017

Acta Phys. Pol. A

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“…Moreover, a self-adaptive window that is autonomously adjusted through a statistical hypothesis test was presented for handling complex concept drift in [10]. Furthermore, an adaptive dynamic programmingbased algorithm is introduced in [11], to manage load demand and minimise the entire energy cost using renewable energy in smart grid. The previous work is significant and can be used for reference in establishing an optimal thermal model in our work.…”

Section: Introductionmentioning

confidence: 99%

A Semi-blind Model with Parameter Identification for Building Temperature Estimation

et al. 2017

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An accurate thermal model for building enables the heating system (HS) to work efficiently as well as save energy. Thermal modelling often requires physical parameters of the building, which are difficult to be accurately determined. The aim of this work is to develop an optimal thermal model for better understanding of thermal dynamics with the goal of using this to estimate temperature variation in a few hours ahead within building. Based on the characteristics of thermal motion, a conventional physics-based (PB) model for building temperature estimation is introduced first. Afterwards, in order to refine the model and improve the actual performance, we propose an innovative semi-blind (SB) model based on data-driven approaches. Additionally, the methodologies including self-adaptive algorithms (SAAs) and grey prediction technique (GPT) have been applied in dealing with the integrated parameters estimation (IPE) process to ensure the practicability of the implemented model. The proposed model schema is validated by testing in a laboratory. The results indicate that the proposed approach achieves much higher accuracy in estimating temperature variation than the conventional PB model, with only limited knowledge of the building characteristics. The root mean square deviation (RMSD) of SB model and PB model are 0.18 and 0.43, respectively. According to the results, it can be conclu-

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“…In [7], Q-learning algorithm was proposed to solve the optimal battery control for residential energy systems. In [3,6], optimal controls of renewable resources were obtained by Q-learning for smart micro-grid systems. In [8], optimal collaboration problems for sensor network systems were solved by distributed Q-learning.…”

Section: Introductionmentioning

confidence: 99%