Power Electronics Platforms for Grid-Tied Smart Buildings

Renewable energy sources (such as sun, wind, water, or fuel cells) are attracting great interest for either grid-tied or off-grid arrangements in smart green buildings. It must be either used when generated, stored for future use on-site, delivered to the power grid, or shared among combination of these. Grid-tied buildings are connected to the utility grid service lines. Off-grid buildings have no connection to utility service lines. Both types employ inverters to convert power from direct current (DC) to alternating current (AC), and most off-grid systems have batteries to store energy for use when needed. Accordingly, power electronics systems are playing an important role as the enabling technology for smart grid. In addition, smart meter represents the interface part between the green building and the utility grid. In order to realize the interaction between both systems, a bidirectional power conditioning module is needed. This chapter introduces the different power electronics platforms suitable for grid-tied smart green buildings (such as residential homes, commercial, and industrial) as well as its integrative functionality with advanced metering infrastructure (AMI). In order to show the superiority of these platforms in conjunction with smart meters, a hardware case study with one of the most popular power electronics topologies is presented

Ten questions concerning integrating smart buildings into the smart grid

Recent advances in information and communications technology (ICT) have initiated development of a smart electrical grid and smart buildings. Buildings consume a large portion of the total electricity production worldwide, and to fully develop a smart grid they must be integrated with that grid. Buildings can now be ‘prosumers’ on the grid (both producers and consumers), and the continued growth of distributed renewable energy generation is raising new challenges in terms of grid stability over various time scales. Buildings can contribute to grid stability by managing their overall electrical demand in response to current conditions. Facility managers must balance demand response requests by grid operators with energy needed to maintain smooth building operations. For example, maintaining thermal comfort within an occupied building requires energy and, thus an optimized solution balancing energy use with indoor environmental quality (adequate thermal comfort, lighting, etc.) is needed. Successful integration of buildings and their systems with the grid also requires interoperable data exchange. However, the adoption and integration of newer control and communication technologies into buildings can be problematic with older legacy HVAC and building control systems. Public policy and economic structures have not kept up with the technical developments that have given rise to the budding smart grid, and further developments are needed in both technical and non-technical areas

MODEL PREDICTIVE CONTROL OF BUILDING ENERGY MANAGEMENT SYSTEMS IN A SMART GRID ENVIRONMENT

Buildings are a major source of energy consumption. In the United States, buildings are responsible for more than 70% of all power consumption. Over 40% of this building power consumption is from the Heating, Ventilation, and Air Conditioning (HVAC) systems. Modern technologies such as building Energy Storage Systems (ESS), renewable energy sources, and advanced control algorithms allow for so-called Smart Buildings to increase energy efficiency. Smart Buildings further benefit from existing in a Smart Grid environment, where information such as pricing and anticipated power load is sent over two way communitcation between the grid operator and the power consumer. The traditional control systems for these HVAC systems are often simple and do not exploit the principles of optimal control. This study applies Model Predictive Control (MPC) and ESS to the problem of controlling a Smart Building in a Smart Grid environment. Simulations are performed for various optimal control objective functions. These objectives include price minimization, energy minimization, and an introduced Building to Grid (B2G) index optimization. The B2G optimization aims to both decrease the price of power for the consumer while avoiding large spikes in power consumption to maintain a steady load profile which benefits the grid operator. The results show that MPC has potential for large performance increases in Building Energy Management, while meeting the constraints for B2G integration

Performance analysis of blockchain-based smart grid with Ethereum and Hyperledger implementations

Abstract. Smart grids lay the foundation for future communities. Smart homes, smart buildings, smart streets, and smart offices are built when intelligent devices piles on intelligent devices. To reach the maximum capacity, they all must be supported by an intelligent power supply. For optimal and real-time electricity consumption, monitoring and trading, blockchain possess number of potential benefits in its application to electricity infrastructure. A comprehensive system architecture of blockchain-based smart grid is proposed and peer-to-peer (P2P) energy trading is implemented between Distribution System Operators (DSO), Local energy providers and Consumers. This thesis presents a virtual smart grid equipped with smart contracts capable of virtual activities like market payment function and the comparison and the performance of the blockchain-based smart grid by using Ethereum and Hyperledger Fabric-based implementations. The challenges faced during the implementation of blockchain protocols are discussed and evaluation in the light of finding sustainable solutions to develop secure and reliable smart grid operations, is the major objective of the thesis

IoT and information processing in smart energy applications.

The articles in this special section address smart energy applications from the perspective of the Internet of Things (IoT). For smart grid applications, we need to predict the electrical load so that the underlying smart grid can effectively balance the power supply and demand. In general, predictions are made based on the data obtained using IoT and smart meter technologies. The (IoT) could accelerate establishment of such infrastructures. With IoT technologies, many more devices could be controlled and managed through the Internet; data pertaining to the grid, commercial buildings, and residential premises can readily be collected and utilized. To derive valuable information from the data, further information and data processing become essential

USING SMART GRID TECHNOLOGY IN ENERGY DISTRIBUTION SYSTEMS

Using smart grid technology in today energy distribution systems will reduce cost, reach manageability, provide safety of energy supply chain to end customer and provide new innovative energy service delivery. Term “smart grid” can be explained with following words – intelligent, self-sustained, with management based on IP (Internet Protocol) telecommunication network for transportation of critical data in real-time from customer site (smart meters, smart homes, smart buildings) and distributed power plants to central management station (energy service provider operations). Main function of the central management station is to acquire and evaluate stored data in real time and based on this stored and evaluated data, in case of emergency, power outage on some subsystem or increased need for power on specific location, to apply necessary steps in real-time. Therefore data conformity and security in smart grid technology is an important function concept to implement. Nevertheless primary goal of smart grid technology is to improve the efficiency, reliability and safety of power delivery by modernizing both the transmission and the distribution grids. This article has a goal to provide a high-end top-level view of a modern telecommunication infrastructure needed to implement a smart grid technology into an energy transmission and distribution grid

Buildings-to-Grid Integration Framework

This paper puts forth a mathematical framework for Buildings-to-Grid (BtG) integration in smart cities. The framework explicitly couples power grid and building's control actions and operational decisions, and can be utilized by buildings and power grids operators to simultaneously optimize their performance. Simplified dynamics of building clusters and building-integrated power networks with algebraic equations are presented---both operating at different time-scales. A model predictive control (MPC)-based algorithm that formulates the BtG integration and accounts for the time-scale discrepancy is developed. The formulation captures dynamic and algebraic power flow constraints of power networks and is shown to be numerically advantageous. The paper analytically establishes that the BtG integration yields a reduced total system cost in comparison with decoupled designs where grid and building operators determine their controls separately. The developed framework is tested on standard power networks that include thousands of buildings modeled using industrial data. Case studies demonstrate building energy savings and significant frequency regulation, while these findings carry over in network simulations with nonlinear power flows and mismatch in building model parameters. Finally, simulations indicate that the performance does not significantly worsen when there is uncertainty in the forecasted weather and base load conditions.Comment: In Press, IEEE Transactions on Smart Gri