Designing an Energy Management System for smart houses

Acone, Mariano; Romano, Roberto; Piccolo, Antonio; Siano, Pierluigi; Loia, Francesca; Ippolito, Mariano Giuseppe; Zizzo, Gaetano

doi:10.1109/eeeic.2015.7165424

Cited by 20 publications

(15 citation statements)

References 30 publications

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“…The objective of the shiftable loads function is to plan the use of shiftable loads in order to minimize their energy cost [24,26]. The decision about the period of operation related to shiftable loads requires the knowledge of the following information: the list of priorities by which the user requests the operation; the electric characteristics such as absorbed electrical power and operating time; the hourly price of the energy that will be purchased and absorbed from the grid.…”

Section: Emsmentioning

confidence: 99%

“…A thermal model of the smart-home is considered, able to calculate the electric energy required by air conditioning [26]. In consideration of the operating mode (summer or winter) and of the temperature set point chosen by the user, the control system adjusts the electrical power necessary for the air conditioning system so that the temperature inside the house reaches the desired value.…”

Section: Modeling Of the Smart House With The Photovoltaic-battery Symentioning

confidence: 99%

“…DR encourages the interaction and responsiveness of the customers by offering a broad range of benefits on system operation and on market efficiency. In [20][21][22][23][24][25][26][27][28][29][30][31][32], these issues are analyzed and different solutions are proposed, with particular reference to the benefits in a smart grid and to the market efficiency.A probabilistic methodology for evaluating the impact of residential DR choices is proposed, by using a combination of Monte Carlo Simulation (MCS) and Real Time (RT) distribution market simulation based on Distribution Locational Marginal Price (D-LMPs) in [20,25]. Real-time pricing (RTP) at the residential level is considered in combination with small-scale Distributed Generation (DG), in order to suggest an energy scheduling solution [23].…”

mentioning

confidence: 99%

“…Real-time pricing (RTP) at the residential level is considered in combination with small-scale Distributed Generation (DG), in order to suggest an energy scheduling solution [23]. In [26,30,31], electrical and thermal appliances are jointly scheduled, considering the residents' consumption behavior, seasonal probability and other aspects. The economic aspects related to the installation of distributed energy storage systems designed to reduce the electricity cost for a customer-side application, are discussed in [32], by considering flexible electricity tariffs.…”

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Combined Operation of Electrical Loads, Air Conditioning and Photovoltaic-Battery Systems in Smart Houses

et al. 2017

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In this paper, a novel Energy Management System (EMS) is proposed for a hybrid energy system with photovoltaic (PV) and energy storage system for a smart house. The EMS is designed to control the shiftable loads, the air conditioning and the electric storage system. The aim is to reduce the electrical energy consumption cost without compromising the end-user comfort. Monte Carlo Simulation (MCS) is used to estimate the optimal size of the hybrid system considering energy saving and investment costs. Simulations results confirm the effectiveness of the proposed EMS in decreasing the electrical energy consumption and costs. The proposed method for the sizing of the hybrid system is also able to select the best size of the PV-battery system in smart houses. Some studies focus on the characteristics of the converters in the micro-grid systems with the aim of improving the conversion efficiency. In [7], a hybrid system consisting of a PV array and rechargeable battery integrated to the distribution grid is presented. The system performs load sharing with the distribution grid by controlling the voltage source inverter (VSI), the boost and the buck-boost converters. The cost-benefits problem of solar PV technology is analyzed in [8,9]. In [8], the authors propose a system with given PV modules and EES based on bank capacities to solve the daily energy flow control problem. Furthermore, in order to facilitate intensive penetration of PV production into the grid, a method is proposed in [9]. Peak shaving is realized with the minimal cost and power fluctuations on the grid are reduced by using a power supervisor based on a predictive power scheduling algorithm. A basic analysis study of stand-alone solar PV micro-grid power system is presented in [10]. The system is designed on the basis of residential load profile and solar exposure level in a particular area. In order to improve the integration of PV and energy storage systems, a power management strategy is designed to manage power flows between PV systems and an ultra-capacitor (UC) [11].A numerical model that computes an optimized capacity of energy consumption is proposed in [12]. This model is based on weekly forecast with the aim of providing a stable energy that is insulated from the instability of weather change. The system is able to increase the energy consumption in normal days while maintaining minimum stable energy in bad weather conditions.To increase the use of PV energy in the residential applications, some researchers suggest a control of power flow and a selection of MPPT parameters for grid-connected PV systems with battery storage [13]. Other works discuss these issues in terms of innovative converter features for PV-battery systems [14]; a reconfigurable solar converter (RSC) is presented to perform dc/ac and dc/dc operations, by using a single-stage three phase grid-tie solar PV converter.A control method for the power electronic converters is discussed in [15]: the potential of utilizing battery energy storage for participating in primary frequenc...

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Section: Emsmentioning

confidence: 99%

Section: Modeling Of the Smart House With The Photovoltaic-battery Symentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Combined Operation of Electrical Loads, Air Conditioning and Photovoltaic-Battery Systems in Smart Houses

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UCB1 Based Reinforcement Learning Model for Adaptive Energy Management in Buildings

Andrade

Pinto

Praça

et al. 2019

Advances in Intelligent Systems and Computing

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This paper proposes a reinforcement learning model for intelligent energy management in buildings, using a UCB1 based approach. Energy management in buildings has become a critical task in recent years, due to the incentives to the increase of energy efficiency and renewable energy sources penetration. Managing the energy consumption, generation and storage in this domain, becomes, however, an arduous task, due to the large uncertainty of the different resources, adjacent to the dynamic characteristics of this environment. In this scope, reinforcement learning is a promising solution to provide adaptiveness to the energy management methods, by learning with the on-going changes in the environment. The model proposed in this paper aims at supporting decisions on the best actions to take in each moment, regarding buildings energy management. A UCB1 based algorithm is applied, and the results are compared to those of an EXP3 approach and a simple reinforcement learning algorithm. Results show that the proposed approach is able to achieve a higher quality of results, by reaching a higher rate of successful actions identification, when compared to the other considered reference approaches.

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Energy Storage Systems

Schmiegel¹

2023

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This book introduces different storage technologies. It starts with mechanical storage, then continues with electrical, electrochemical, and chemical storage. Furthermore, demand side management is discussed as a special form of storage technology. Furthermore, the book describes how storage systems are designed. For this purpose, the book gives an introduction to requirement management and systems engineering—both important tools for the design of storage systems. Since knowledge of power electronics and drive technology is usually required, the book also introduces the reader to the topic. Various application examples of different storage systems allow the reader to become familiar with the introduced toolbox and to use it themselves. Examples include opportunity chargers, solar power storage systems, vehicles with hybrid drive trains, and the supply of an island with wind, storage, and power to gas.

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Designing an Energy Management System for smart houses

Cited by 20 publications

References 30 publications

Combined Operation of Electrical Loads, Air Conditioning and Photovoltaic-Battery Systems in Smart Houses

Combined Operation of Electrical Loads, Air Conditioning and Photovoltaic-Battery Systems in Smart Houses

UCB1 Based Reinforcement Learning Model for Adaptive Energy Management in Buildings

Energy Storage Systems

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