Demand side management (DSM) is one of the main functionalities of the smart grid as it allows the consumer to adjust its energy consumption for an efficient energy management. Most of the existing DSM techniques aim at minimizing the energy cost while not considering the comfort of consumers. Therefore, maintaining a trade-off between these two conflicting objectives is still a challenging task. This paper proposes a novel DSM approach for residential consumers based on a non-cooperative game theoretic approach, where each player is encouraged to reshape its electricity consumption pattern through the dynamic pricing policy applied by the smart grid operator. The players are guided to select the best strategy that consists of scheduling their electric appliances in order to minimize the daily energy cost and their discomfort level. The Nash Equilibrium of the energy management game is achieved using Non-Sorting Genetic Algorithm NSGA-II. Simulation results show the effectiveness of the distributed non cooperative game approach for the residential energy management problem where an appreciable energy cost reduction is reached while maintaining the discomfort in an acceptable level.
This paper presents an extensive multi-period optimal power flow framework, with new modelling elements, for smart LV distribution systems that rely on residential flexibility for combating operational issues. A detailed performance assessment of different setups is performed, including: ZIP flexible loads (FLs), varying degrees of controllability of conventional residential devices, such as electric vehicles (EVs) or photovoltaics (PVs), by the distribution system operator (DSO) (adhering to customer-dependent restrictions) and full exploitation of the capabilities offered by state-of-the-art…
This paper proposes a peer to peer (P2P), blockchain based energy trading market platform for residential communities with the objective of reducing overall community peak demand and household electricity bills. Smart homes within the community place energy bids for its available distributed energy resources (DERs) for each discrete trading period during a day, and a double auction mechanism is used to clear the market and compute the market clearing price (MCP). The marketplace is implemented on a permissioned blockchain infrastructure, where bids are stored to the immutable ledger and smart …
In smart grid, the demand side management (DSM) techniques need to be designed to process a large number of controllable loads of several types. In this paper, we proposed a framework to study the demand side management in smart grid which contains a variety of loads in two service areas, one with multiple residential households, and one bus with commercial customers. Specifically, each household may have renewable generation as well as interruptible and uninterruptible appliances to make individual scheduling to optimize the electric energy cost by making the best time of the electricity usag…
University of Central Florida（中佛罗里达大学）；University of Ottawa（渥太华大学）
Appliance-level load forecasting plays a critical role in residential energy management, besides having significant importance for ancillary services performed by the utilities. In this paper, we propose to use an LSTM-based sequence-to-sequence (seq2seq) learning model that can capture the load profiles of appliances. We use a real dataset collected fromfour residential buildings and compare our proposed schemewith three other techniques, namely VARMA, Dilated One Dimensional Convolutional Neural Network, and an LSTM model.The results show that the proposed LSTM-based seq2seq model outperform…
Wide-scale electrification of the transportation sector will require careful planning and coordination with the power grid. Left unmanaged, uncoordinated charging of electric vehicles (EVs) at increased levels of penetration will amplify existing peak loads, potentially outstripping the grid's capacity to reliably meet demand. In this paper, we report findings from the OptimizEV Project - a real-world pilot study in Upstate New York exploring a novel approach to coordinated residential EV charging. The proposed coordination mechanism seeks to harness the latent flexibility in EV charging by of…
A network of observers is considered, where through asynchronous (with bounded delay) communications, they cooperatively estimate the states of a Linear Time-Invariant (LTI) system. In such a setting, a new type of adversary might affect the observation process by impersonating the identity of the regular node, which is a violation of communication authenticity. These adversaries also inherit the capabilities of Byzantine nodes, making them more powerful threats called smart spoofers. We show how asynchronous networks are vulnerable to smart spoofing attack. In the estimation scheme considered…
Demand response (DR) for smart grids, which intends to balance the required power demand with the available supply resources, has been gaining widespread attention. The growing demand for electricity has presented new opportunities for residential load scheduling systems to improve energy consumption by shifting or curtailing the demand required with respect to price change or emergency cases. In this paper, a dynamic residential load scheduling system (DRLS) is proposed for optimal scheduling of household appliances on the basis of an adaptive consumption level (CL) pricing scheme (ACLPS). Th…
Energy Storage Sharing in Smart Grid: A Modified Auction Based Approach
Singapore University of Technology and Design（新加坡科技设计大学）；State Grid Smart Grid Research Institute（国家电网智能电网研究院）；Ministry of Home Affairs, Singapore（新加坡内政部）；National ICT Australia（澳大利亚国家信息技术中心）；Australian National University（澳大利亚国立大学）；Princeton University（普林斯顿大学）；Zhejiang University（浙江大学）
In modern smart grids, charging of local energy storage devices is coordinated on a residential level to compensate the volatile aggregated power demand on the time interval of interest. However, this results in a perpetual usage of all batteries which reduces their lifetime. We enforce group sparsity by using an $\ell_{p,q}$-regularization on the control to counteract this phenomenon. This leads to a non-smooth convex optimization problem, for which we propose a tailored Alternating Direction Method of Multipliers algorithm. We elaborate further how to embed it in a Model Predictive Control f…
A Smart Home provides integrating and electronic information services to help residential users manage their energy usage and bill cost but also exposes users to significant privacy risks due to fine-grained information collected by smart meters. Taking account of users' privacy concerns, this paper focuses on cost-effective runtime scheduling designed for schedulable and non-schedulable appliances. To alleviate the influence of operation uncertainties introduced by non-schedulable appliances, we formulate the problem by minimizing the expected sum of electricity cost under the worst privacy s…
In part II, we present a fully distributed nonlinear variable time headway space strategy to ensure the subsequent safe cruising and junction crossing, where the cooperative perception of multiple neighbors stimuli and the cooperative tracking of the follower connected automated vehicles(CAVs) to the leader CAV are developed, which will result in a heterogeneous traffic flow dynamic. Once the proper length of the platoon determined by the ADSCAS characterized in part I is formed, we propose a cooperative observer design to estimate the leader CAV's acceleration adjustment which is affected by …
Beyond Weather Correlation: A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia
School of Computing and Information Technology（计算与信息科技学院）
Scheduling a residential building short-term to optimize the electricity bill can be difficult with the inclusion of capacity-based grid tariffs. Scheduling the building based on a proposed measured-peak (MP) grid tariff, which is a cost based on the highest peak power over a period, requires the user to consider the impact the current decision-making has in the future. Therefore, the authors propose a mathematical model using stochastic dynamic programming (SDP) that tries to represent the long-term impact of current decision-making. The SDP algorithm calculates non-linear expected future cos…
MERLIN: Multi-agent offline and transfer learning for occupant-centric energy flexible operation of grid-interactive communities using smart meter data and CityLearn
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Electric Power Research Institute（电力科学研究院）
The cost of the power distribution infrastructures is driven by the peak power encountered in the system. Therefore, the distribution network operators consider billing consumers behind a common transformer in the function of their peak demand and leave it to the consumers to manage their collective costs. This management problem is, however, not trivial. In this paper, we consider a multi-agent residential smart grid system, where each agent has local renewable energy production and energy storage, and all agents are connected to a local transformer. The objective is to develop an optimal pol…
Heating, ventilating, and air-conditioning (HVAC) systems consume a large amount of energy in residential houses and buildings. Effective energy management of HVAC is a cost-effective way to improve energy efficiency and reduce the energy cost of residential users. This work develops a novel distributed method for the residential transactive energy system that enables multiple users to interactively optimize their energy management of HVAC systems and behind-the-meter batteries. Specifically, this method effectively reduces the cost of smart homes by employing energy trading among users to lev…
University of California, Berkeley（加州大学伯克利分校）；Xi’an Jiaotong University（西安交通大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；National University of Singapore（新加坡国立大学）
Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data a…
Residential smart meters have been widely installed in urban houses nationwide to provide efficient and responsive monitoring and billing for consumers. Studies have shown that providing customers with device-level usage information can lead consumers to economize significant amounts of energy, while modern smart meters can only provide informative whole-home data with low resolution. Thus, energy disaggregation research which aims to decompose the aggregated energy consumption data into its component appliances has attracted broad attention. In this paper, a discriminative disaggregation mode…
Energy Management for a User Interactive Smart Community: A Stackelberg Game Approach
Singapore University of Technology and Design（新加坡科技设计大学）；State Key Lab. of Industrial Control Technology, Zhejiang University（浙江大学工业控制技术国家重点实验室）；NICTA（澳大利亚国家信息与通信技术学院）；School of Engineering and Applied Science, Princeton University（普林斯顿大学工程与应用科学学院）
Participation in residential energy demand response programs requires an active role by the consumers. They contribute flexibility in how they use their appliances as the means to adjust energy consumption, and reduce demand peaks, possibly at the expense of their own comfort (e.g., thermal). Understanding the collective potential of appliance-level flexibility for reducing demand peaks is challenging and complex. For instance, physical characteristics of appliances, usage preferences, and comfort requirements all influence consumer flexibility, adoption, and effectiveness of demand response p…
University of Kansas（堪萨斯大学）；Amazon Web Services（亚马逊云科技）
This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users. DR has a widely recognized potential for improving power grid stability and reliability, while at the same time reducing end-users energy bills. However, the conventional DR techniques come with several shortcomings, such as the inability to handle operational uncertainties whi…
Korea University（高丽大学）；Mofl Inc.（Mofl公司）；Hallym University（翰林大学）；University of Central Florida（中佛罗里达大学）；Ajou University（亚洲大学）
CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a key technology for security in smart city environments. This paper creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services. UAVs should be deployed to cover a large area while minimize overlapping and shadow areas for a reliable surveillance system. However, the operation of UAVs is subject to high uncertainty, necessitating autonomous recovery systems. This work develops a multi-agent deep reinforcement learning-based management scheme for reliable indus…
