Demand Side Management (DSM) strategies are of-ten associated with the objectives of smoothing the load curve and reducing peak load. Although the future of demand side manage-ment is technically dependent on remote and automatic control of residential loads, the end-users play a significant role by shifting the use of appliances to the off-peak hours when they are exposed to Day-ahead market price. This paper proposes an optimum so-lution to the problem of scheduling of household demand side management in the presence of PV generation under a set of tech-nical constraints such as dynamic electricity pricing and voltage deviation. The proposed solution is implemented based on the Clonal Selection Algorithm (CSA). This solution is evaluated through a set of scenarios and simulation results show that the proposed approach results in the reduction of electricity bills and the import of energy from the grid.
Nowadays the emerging smart grid technology opens up the possibility of two-way communication between customers and energy utilities. Demand Response Management (DRM) offers the promise of saving money for commercial customers and households while helps utilities operate more efficiently. In this paper, an Incentive-based Demand Response Optimization (IDRO) model is proposed to efficiently schedule household appliances for minimum usage during peak hours. The proposed method is a multi-objective optimization technique based on Nonlinear Auto-Regressive Neural Network (NAR-NN) which considers e…
EcoNet: Multiagent Planning and Control Of Household Energy Resources Using Active Inference
VERSES, Los Angeles, CA, USA（VERSES, 美国洛杉矶州）；Spatial Web Foundation, Los Angeles, CA, USA（空间网络基金会, 美国洛杉矶州）；Dept. of Engineering and Informatics, Univ. of Sussex, Brighton, UK（工程与信息学系, 英国苏塞克斯大学）；Institut Sante et societe, UQAM, Montreal, CAN（健康与社会研究所, 加拿大蒙特利尔）；Macquarie University, Sydney, AUS（麦考瑞大学, 澳大利亚悉尼）；LearnableLoopAI.com, Custer, WA, USA（LearnableLoopAI.com, 美国华盛顿州）；Queen Square Institute of Neurology, UCL, London, UK（queen square 神经学研究所, 英国伦敦大学学院）
Smart home appliances can time-shift and curtail their power demand to assist demand side management or allow operation with limited power, as in an off-grid application. This paper proposes a scheduling process to start appliances with time-varying deterministic load profiles. Self-triggered model predictive control is used to limit the household net power demand below a given threshold. Meanwhile, deterministic load profiles are more difficult to schedule compared to variable charging or thermal loads because system failure will occur once power demand is not satisfied. The proposed scheme f…
The problem of household waste management is becoming more and more acute with the growth of economic development that the country of Morocco has experienced. Moreover, the management of this waste is a burden for the municipalities, in view of its cost, which is increasing with time. Therefore, in this work we present the design, the mechanical and photovoltaic study of a new autonomous solar composter intended mainly for households. It allows to transform organic waste in situ, into a good quality compost that serves as a soil conditioner, in a short time compared to other composting systems…
With the increasing popularity of electric vehicles, distributed energy generation and storage facilities in smart grid systems, an efficient Demand-Side Management (DSM) is urgent for energy savings and peak loads reduction. Traditional DSM works focusing on optimizing the energy activities for a single household can not scale up to large-scale home energy management problems. Multi-agent Deep Reinforcement Learning (MA-DRL) shows a potential way to solve the problem of scalability, where modern homes interact together to reduce energy consumers consumption while striking a balance between en…
In this paper, we investigate the management of energy storage control and load scheduling in scenarios considering a grid-connected photovoltaic (PV) system using packetized energy management. The aim is to reduce an average aggregated system cost through the proposed \textit{packetized energy management controller} considering household energy consumption, procurement price, load scheduling delays, PV self-sufficiency via generated renewable energy and battery degradation. The proposed approach solves the joint optimization problem using established heuristics, namely genetic algorithm (GA),…
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…
This paper presents a multi-agent control architecture and an online optimization method based on dynamic average consensus to coordinate the power consumption of a large population of Thermostatically Controlled Loads (TCLs). Our objective is to penalize peaks of power demand, smooth the load profile and enable Demand Side Management (DSM). The proposed architecture and methods exploit only local measurements of power consumption via Smart Power Sockets (SPSs) with no access to their internal temperature. No centralized aggregator of information is exploited and agents preserve their privacy …
Load points are one of the most vital parts of power systems. Due to the new load forms and programs introduced in the demand side, the load-serving entities (LSEs) no longer deal with lump loads, but rather with more dynamic, rational and price elastic loads. The high inter-temporal and behavioral variability of the load profile makes it almost impossible for utilities and system operators to expect the demand curve with the needed accuracy. A sound granularity of the load compositions and consumption percentages and patterns throughout the year is essential for avoiding energy losses, design…
Future energy systems rely mostly on supply-dependent resources like wind and solar energy. Since most of the produced energy from distributed renewable sources arises as electricity, electrification of energy consumers and producers, called prosumers, expands. In this context, multi-modal distribution grids make use of the storage capabilities of other energy sectors like heat, and abolish the supply-dependency and uncertainty of the feed-in of decentralized generators. This provides flexibility in the energy system, which increases security of supply and at the same time ensures economical e…
Management of Renewable Energy for A Shared Facility Controller in Smart Grid
Singapore University of Technology and Design（新加坡科技设计大学）；University of Technology Sydney（悉尼科技大学）；Data61 (NICTA), CSIRO（数据六十一（国家信息技术中心），联邦科学与工业研究组织）；Australian National University (ANU)（澳大利亚国立大学）；Lahore University of Management Sciences（拉合尔管理科学大学）
The increasing penetration of renewable generation requires new tools to achieve a high matching between demand and renewable generation at building and community levels. Large commercial and public buildings with parking lots have a considerable potential to provide energy flexibility by controlling the charging of Electric Vehicles (EVs) and injecting part of the stored energy into the building, using Building-to-Vehicle (B2V) and Vehicle-to-Building (V2B) systems. However, EVs and buildings do not often belong to the same entity and in Portugal the existing regulation does not allow financi…
School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
We consider the problem of demand-side energy management, where each household is equipped with a smart meter that is able to schedule home appliances online. The goal is to minimize the overall cost under a real-time pricing scheme. While previous works have introduced centralized approaches in which the scheduling algorithm has full observability, we propose the formulation of a smart grid environment as a Markov game. Each household is a decentralized agent with partial observability, which allows scalability and privacy-preservation in a realistic setting. The grid operator produces a pric…
The need for resiliency of electricity supply is increasing due to increasing frequency of natural disasters---such as hurricanes---that disrupt supply from the power grid. Rooftop solar photovoltaic (PV) panels together with batteries can provide resiliency in many scenarios. Without intelligent and automated decision making that can trade off conflicting requirements, a large PV system and a large battery is needed to provide meaningful resiliency. By using forecast of solar generation and household demand, an intelligent decision maker can operate the equipment (battery and critical loads) …
