Blockchain-based solution for energy demand-side management of residential buildings

Abstract Smart homes, connected through a network, can optimize the energy consumption and general load shape of their area. In this work, a blockchain-based smart solution is presented for demand-side management of residential buildings in a neighborhood to improve Peaks to Average Ratios (PAR) of power load, reduce energy consumption, and increase the thermal comfort of occupants by modeling heating, illumination, and appliance systems. For real-time power and temperature monitoring of the neighborhood, a transient numerical physical model has been developed. The simulator has been validated with data measured from a building in Northern Italy. Then, a neighborhood with 2,000 households has been modeled for different occupancy patterns, initial values, and boundary conditions. Two different control scenarios, namely basic and smart, have been considered. In the basic scenario, everything is managed by occupants except the boiler, which is controlled by the indoor temperature of the home. Instead, in the smart scenario, a blockchain-based network has been introduced for buildings to exchange a parameter called the Probability of the Next Hour (PNH). Ethereum Solidity has been deployed for smart contract development in the blockchain. The results show that using blockchain-connected smart controllers aimed at demand-side management can improve PAR, comfort level, and energy efficiency of buildings, which can bring about CO2 reduction on an urban and even global scale

Artificial Intelligent Based Energy and Demand Side Management for Microgrids and Smart Homes Considering Customer Privacy

The rapid development of various power electronics applications facilitates the integration of many smart grid applications in recent years. However, integration of intermittent renewable energy sources, highly stochastic electric vehicles (EVs) activities on the grid and time-varying smart loads have increased the level of grid vulnerability to unusual and high complexity and quality-related problems. Among these problems is to accurately estimate the real contribution and consumption of household loads, in the era of smart appliances and interoperability operation, and its relative impact to the grid’s operation. Specifically, household loads represent a significant percentage of electrical energy consumption and, therefore, could offer great prosperity to the rise of the demand-side management (DSM) programs, which subsequently improve the stability of the grid’s operation. As a result, our main focus in this dissertation is to develop DSM strategies based on Artificial Intelligence (AI) techniques to properly model and estimate the amount of support smart homes could offer to the smart grids and microgrid’s operation. Throughout the way to achieve our goals, we develop an energy management framework for smart homes that operate in efficient and reliable microgrids with multiple energy sources and energy storage applications to meet the demands at a stable voltage and frequency limits. Furthermore, we develop a precise short-term load forecasting (STLF), which is a critical tool needed to manage a DSM program for residential loads that have very high uncertainty and volatility in load consumption. We also develop an energy exchange portal with communication sources, demands, and connectivity information between each consumer and the local power utility at the distribution level. Finally, creative AI methodologies have been developed throughout the way to facilitate the integration, control, and management of the DSM programs taking into account the consumers’ own privacy and security. The security of the DSM is provided by preserving the indoor privacy of the smart homes by sharing limited and encoded data among household appliances controllers

Novel paradigms for advanced distribution grid energy management

The electricity distribution grid was not designed to cope with load dynamics imposed by high penetration of electric vehicles, neither to deal with the increasing deployment of distributed Renewable Energy Sources. Distribution System Operators (DSO) will increasingly rely on flexible Distributed Energy Resources (flexible loads, controllable generation and storage) to keep the grid stable and to ensure quality of supply. In order to properly integrate demand-side flexibility, DSOs need new energy management architectures, capable of fostering collaboration with wholesale market actors and pro-sumers. We propose the creation of Virtual Distribution Grids (VDG) over a common physical infrastructure , to cope with heterogeneity of resources and actors, and with the increasing complexity of distribution grid management and related resources allocation problems. Focusing on residential VDG, we propose an agent-based hierarchical architecture for providing Demand-Side Management services through a market-based approach, where households transact their surplus/lack of energy and their flexibility with neighbours, aggregators, utilities and DSOs. For implementing the overall solution, we consider fine-grained control of smart homes based on Inter-net of Things technology. Homes seamlessly transact self-enforcing smart contracts over a blockchain-based generic platform. Finally, we extend the architecture to solve existing problems on smart home control, beyond energy management

Smart grid technology in the developing world

A smart grid is the integration of communication and information technologies with contemporary power infrastructure to enhance load service and to incorporate continually evolving end-use applications. It is the latest advancement in the areas of power generation, transmission and distribution. It has advanced beyond the traditional grid structure at every stage; a smart grid is capable of incorporating distributed generation (DG) renewable sources and has improved transmission capabilities through implementation of technologies such as Flexible AC Transmission Systems (FACTS). Through the addition of control technology in the distribution network a smart grid is able to implement “self-healing” and other methods to improve reliability of power supply. Enhanced interconnectivity also offers the option of microgrid development which can be accomplished more quickly and affordably than a large scale grid. The ultimate goal of this approach is to then connect various microgrids to establish a robust network. On the consumer’s side, smart devices are being developed which can practice load shifting to reduce demand on the grid at peak hours. One facet of this technology network is the smart meter, an enhanced metering device used by the consumer to practice demand side management through control technology and informed decision making. All of these characteristics make the smart grid more reliable, efficient, versatile, cost effective, interactive and environmentally beneficial than other systems. The goal of this paper is to first explore the characteristics of a smart grid system and to report on current work that is being done implementing these systems, particularly in developing countries. The latter half of the paper will then present a test for smart grid compatibility on a national level based on the necessary and beneficial preconditions for smart grid development. That test will then be applied to nations that lack a significant or reliable power generation and transmission system. The results of this test will determine specific regions which meet the criteria for both a high compatibility for smart grid development and a high demand for the solutions it offers. Those results will be synthesized into a proposal for future work, with the goal of broadening the global focus of smart grid development to include countries where millions of people still lack access to electricity in their cities and homes

Agent-based control for decentralised demand side management in the smart grid

Central to the vision of the smart grid is the deployment of smart meters that will allow autonomous software agents, representing the consumers, to optimise their use of devices and heating in the smart home while interacting with the grid. However, without some form of coordination, the population of agents may end up with overly-homogeneous optimised consumption patterns that may generate significant peaks in demand in the grid. These peaks, in turn, reduce the efficiency of the overall system, increase carbon emissions, and may even, in the worst case, cause blackouts. Hence, in this paper, we introduce a novel model of a Decentralised Demand Side Management (DDSM) mechanism that allows agents, by adapting the deferment of their loads based on grid prices, to coordinate in a decentralised manner. Specifically, using average UK consumption profiles for 26M homes, we demonstrate that, through an emergent coordination of the agents, the peak demand of domestic consumers in the grid can be reduced by up to 17% and carbon emissions by up to 6%. We also show that our DDSM mechanism is robust to the increasing electrification of heating in UK homes (i.e. it exhibits a similar efficiency)

Decentralized Demand Side Management with Rooftop PV in Residential Distribution Network

In the past extensive researches have been conducted on demand side management (DSM) program which aims at reducing peak loads and saving electricity cost. In this paper, we propose a framework to study decentralized household demand side management in a residential distribution network which consists of multiple smart homes with schedulable electrical appliances and some rooftop photovoltaic generation units. Each smart home makes individual appliance scheduling to optimize the electric energy cost according to the day-ahead forecast of electricity prices and its willingness for convenience sacrifice. Using the developed simulation model, we examine the performance of decentralized household DSM and study their impacts on the distribution network operation and renewable integration, in terms of utilization efficiency of rooftop PV generation, overall voltage deviation, real power loss, and possible reverse power flows.Comment: 5 pages, 7 figures, ISGT 2018 conferenc

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Simplified Algorithm for Dynamic Demand Response in Smart Homes Under Smart Grid Environment

Under Smart Grid environment, the consumers may respond to incentive--based smart energy tariffs for a particular consumption pattern. Demand Response (DR) is a portfolio of signaling schemes from the utility to the consumers for load shifting/shedding with a given deadline. The signaling schemes include Time--of--Use (ToU) pricing, Maximum Demand Limit (MDL) signals etc. This paper proposes a DR algorithm which schedules the operation of home appliances/loads through a minimization problem. The category of loads and their operational timings in a day have been considered as the operational parameters of the system. These operational parameters determine the dynamic priority of a load, which is an intermediate step of this algorithm. The ToU pricing, MDL signals, and the dynamic priority of loads are the constraints in this formulated minimization problem, which yields an optimal schedule of operation for each participating load within the consumer provided duration. The objective is to flatten the daily load curve of a smart home by distributing the operation of its appliances in possible low--price intervals without violating the MDL constraint. This proposed algorithm is simulated in MATLAB environment against various test cases. The obtained results are plotted to depict significant monetary savings and flattened load curves.Comment: This paper was accepted and presented in 2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia). Furthermore, the conference proceedings has been published in IEEE Xplor