Photovoltaic self-consumption in buildings : A review

Buildings all around the world consume a significant amount of energy, which is more or less one-third of the total primary energy resources. This has raised concerns over energy supplies, rapid energy resource depletion, rising building service demands, improved comfort life styles along with the increased time spent in buildings; consequently, this has shown a rising energy demand in the near future. However, contemporary buildings’ energy efficiency has been fast tracked solution to cope/limit the rising energy demand of this sector. Building energy efficiency has turned out to be a multi-faceted problem, when provided with the limitation for the satisfaction of the indoor comfort index. However, the comfort level for occupants and their behavior have a significant effect on the energy consumption pattern. It is generally perceived that energy unaware activities can also add one-third to the building’s energy performance. Researchers and investigators have been working with this issue for over a decade; yet it remains a challenge. This review paper presents a comprehensive and significant research conducted on state-of-the-art intelligent control systems for energy and comfort management in smart energy buildings (SEB’s). It also aims at providing a building research community for better understanding and up-to-date knowledge for energy and comfort related trends and future directions. The main table summarizes 121 works closely related to the mentioned issue. Key areas focused on include comfort parameters, control systems, intelligent computational methods, simulation tools, occupants’ behavior and preferences, building types, supply source considerations and countries research interest in this sector. Trends for future developments and existing research in this area have been broadly studied and depicted in a graphical layout. In addition, prospective future advancements and gaps have also been discussed comprehensively.

A review on optimized control systems for building energy and comfort management of smart sustainable buildings

https://pure.rug.nl/ws/files/2357318/2013EnergyBuildingsNguyen.pdf

Energy intelligent buildings based on user activity: A survey

Energy sustainability and environmental preservation have become worldwide concerns with the many manifestations of climate change and the continually increasing demand for energy. As cities and nations become more technologically advanced, electricity consumption rises to levels that may no longer be manageable if left unattended. The Smart Grid offers an answer to the shift to more sustainable technologies such as distributed generation and microgrids. A general public awareness and adequate attention from potential researchers and policy makers is crucial. This paper presents an overview of the Smart Grid with its general features, functionalities and characteristics. It presents the Smart Grid fundamental and related technologies and have identified the research activities, challenges and issues. It demonstrates how these technologies have shaped the modern electricity grid and continued to evolve and strengthen its role in the better alignment of energy demand and supply. Smart Grid implementation and practices in various locations are also unveiled. Concrete energy policies facilitate Smart Grid initiatives across the nations. Interestingly, Smart Grid practices in different regions barely indicate competition but rather an unbordered community of similar aspirations and shared lessons.

A review of the development of Smart Grid technologies

Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.

[ACM Press the 2nd ACM Workshop - Zurich, Switzerland (2010.11.02-2010.11.02)] Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys \'10 - Occupancy-driven energy management for smart building automation

The residential sector is currently one of the major contributors to the global energy balance. However, the energy demand of residential users has been so far largely uncontrollable and inelastic with respect to the power grid conditions. With the massive introduction of renewable energy sources and the large variations in energy flows, also the residential sector is required to provide some flexibility in energy use so as to contribute to the stability and efficiency of the electric system. To address this issue, demand management mechanisms can be used to optimally manage the energy resources of customers and their energy demand profiles. A very promising technique is represented by demand-side management (DSM), which consists in a proactive method aimed at making users energy-efficient in the long term. In this paper, we survey the most relevant studies on optimization methods for DSM of residential consumers. Specifically, we review the related literature according to three axes defining contrasting characteristics of the schemes proposed: DSM for individual users versus DSM for cooperative consumers, deterministic DSM versus stochastic DSM and day-ahead DSM versus real-time DSM. Based on this classification, we provide a big picture of the key features of different approaches and techniques and discuss future research directions.

Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey

With the Smart Grid revolution and the increasing interest in renewable distributed sources, house energy consumption will play a significant role in the energy system: the whole energy generation and distribution system performance can be improved by optimizing the house energy management. Beside the energy bill reduction for single users, another advantage can be obtained for the overall system by jointly managing the energy consumption of a set of users, thus reducing their peak absorption. In this paper we propose optimization models which allow to manage every day energy load for both single and multi-users cases, taking into account distributed energy sources and batteries. Computational results, obtained applying models on real life data, are provided and discussed.

House energy demand optimization in single and multi-user scenarios

Electricity demand management mechanisms are expected to play a key role in smart grid infrastructures to reduce buildings power demand at peak hours, by means of dynamic pricing strategies. Unfortunately these kinds of mechanisms require the users to manually set a lot of configuration parameters, thereby reducing the usability of these solutions. In this paper we propose a system, developed within the BEE Project, for predicting the usage of household appliances in order to automatically provide inputs to electricity management mechanism, exactly in the same way a user could do. In our architecture we use a wireless power meter sensor network to monitor home appliances consumption. Data provided by sensors are then processed every 24 hours to forecast which devices will be used on the next day, at what time and for how long. This information represents just the input parameters required by load demand management systems, hence avoiding complex manual settings by the user.

Forecasting the usage of household appliances through power meter sensors for demand management in the smart grid

The high energy required by home appliances (like white goods, audio/video devices and communication equipments) and air conditioning systems (heating and cooling), makes our homes one of the most critical areas for the impact of energy consumption on natural environment. In this paper we present a work in progress within the European project AIM for the design of a system that can minimize energy waste in home environments efficiently managing devices operation modes. In our architecture we use a wireless sensor network to monitor physical parameters (like light and temperature) as well as the presence of users at home and in each of its rooms. With gathered data our system creates profiles of the behavior of house inhabitants and through a prediction algorithm is able to automatically set system parameters in order to optimize energy consumption and cost while guaranteeing the required comfort level. When users change their habits due to unpredictable events, the system is able to detect wrong predictions analyzing in real time information from sensors and to modify system behavior accordingly. By the automatic control of energy management system it is possible to avoid complex manual settings of system parameters that would prevent the introduction of home automation systems for energy saving into the mass market.

Home energy saving through a user profiling system based on wireless sensors

This paper proposes a fully distributed Demand-Side Management system for Smart Grid infrastructures, especially tailored to reduce the peak demand of residential users. In particular, we use a dynamic pricing strategy, where energy tariffs are function of the overall power demand of customers. We consider two practical cases: (1) a fully distributed approach, where each appliance decides autonomously its own scheduling, and (2) a hybrid approach, where each user must schedule all his appliances. We analyze numerically these two approaches, showing that they are characterized practically by the same performance level in all the considered grid scenarios. We model the proposed system using a non-cooperative game theoretical approach, and demonstrate that our game is a generalized ordinal potential one under general conditions. Furthermore, we propose a simple yet effective best response strategy that is proved to converge in a few steps to a pure Nash Equilibrium, thus demonstrating the robustness of the power scheduling plan obtained without any central coordination of the operator or the customers. Numerical results, obtained using real load profiles and appliance models, show that the system-wide peak absorption achieved in a completely distributed fashion can be reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to meet the growing energy demand.

Antimo Barbato

Papers

Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey

House energy demand optimization in single and multi-user scenarios

Forecasting the usage of household appliances through power meter sensors for demand management in the smart grid

Home energy saving through a user profiling system based on wireless sensors

A Distributed Demand-Side Management Framework for the Smart Grid