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Peak Load Shaving Model Based on Individual's Habit
Abstract
Smart Grid is supposed to play an important role in future energy management. In smart grid, Demand Side Management (DSM) is one of the main areas which is under focus of researchers in order to solve the classical problem of peak demand management. In this paper, we have proposed a Habit Based DSM (HBDSM) model for peak load shaving. Proposed method is based on individuals habit which is modeled using Markov Chain. The main focus of the work is to minimize the cost by optimizing battery consumption using Equal Interval Search algorithm in order to minimize energy consumption from the grid and so as to shave the demand curve. Simulation results prove the effectiveness of the proposed model. Keyword– HEMS, Demand Side Management,Habit Model HBDSM, Markov Chain, Equal Interval Search.
... Peak shaving through the use of DSM and multi-agent system was presented in [20] indicating a demand reduction of 20%. The peak load shaving and cost minimisation were the objectives of [24] where modelling habits through Markov chain and user location are considered. Load management in [25] was guaranteed through an optimal allocation of energy storage system. ...
... That negligence also applies for [34,44]. The work in [24,25] did not present appliance operational models; Aside from water pumps in [49,50], no other appliances were modelled. The problem in [24] was subject to the utilisation of energy storage system and reducing the cost by minimising energy purchase from the grid. ...
... The work in [24,25] did not present appliance operational models; Aside from water pumps in [49,50], no other appliances were modelled. The problem in [24] was subject to the utilisation of energy storage system and reducing the cost by minimising energy purchase from the grid. However, such a concept does not fit the work here where power generation is in deficiency and consumers' benefits from accessing electricity services are to be maximised regardless the cost. ...
Meeting electricity demand in remote communities and non-electrified regions in the poor developing world is a challenge. Power generation is in shortage compared to electricity demand. Electric utilities either would enforce grid’s zonal load curtailment or not electrify regions. Controlling electricity demand can play a vital role in enabling electricity access; however, weather uncertainty drives electricity demand variability. This paper provides an overview of current demand side management research, identify research gaps and propose a more promising approach to enable electricity access. Also, it proposes manipulating appliances models to fit their operation in applications where power supply shortage is an issue such. The proposed work considers the effect of the probabilistic nature of weather and meeting AC grid codes of operation.
... The techniques [13]- [19] are also developed for the energy management in different perspectives. By following all the aforementioned techniques, we have proposed a new technique in order to overcome their trade-offs. ...
High price fluctuations have a direct impact on electricity market. Thus, accurate and plausible price forecasts have been implemented to mitigate the consequences of price dynamics. This paper proposes two techniques to deal with the Electricity Price Forecasting (EPF) problem. Firstly, Convolutional Neural Network (CNN) model is used to predict the EPF. Secondly, a principle component analysis model is used for the feature extraction. We have conducted simulations to prove the effectiveness of the proposed approach, which show that CNN based approach outperforms the multilayer perceptron model.
... Because location is time dependent, random transitions between different locations can be modeled as a Markov process. An extension of this work is proposed in [25]. It is based on Markov chain as a probabilistic habit model to minimize the power purchased from the grid. ...
In this paper, the different machine learning and data mining approaches used for Residential Energy Smart Management (RESM) will be discussed and classified according to some meaningful criteria. The proposed classification is an attempt to highlight the advantages and limitations of each category. Moreover, we emphasize the complementarity between approaches belonging to different categories and we point out the main challenges that still face RESM.
... The ALS by FRGFAS may occur as a result of load-demand imbalance leading to inadequate power supply. The controller performs autonomous control for load management and supports the system stability via load shaving [17] with little or no end user inconvenience. The design aim of the FRGFAS is to provide a frequency response decentralized controller that operates based on myRIO-1900 FPGA embedded system control algorithms [18]. ...
Load shedding is a powerful scheme used for corrective and preventive measures; corrective to restore system's stability and preventive to avoid catastrophic failure. However, the affected end users are deprived of power supply absolutely with no choice. This paper presents the design, development, feasibility and merits of Frequency-response Grid -Friendly Appliance System (FRGFAS) in a smart home. FRGFAS is a decentralized Adaptive Load Shaving(ALS) device that supports grid's system stability by sensing grid's frequency deterioration level and turns ON/OFF loads accordingly. The FRGFAS permits end users to carry out load shaving at their scale of preference in smart homes via flexible demand responses and automates outdoor lighting to optimum operational hours. FRGFAS obviate load shedding by shaving loads whenever the system is in distress and reset loads supply to the normal state when it stabilizes, this Consequently increases the end user comfort zone and averts a blackout.
... The ALS by FRGFAS may occur as a result of load-demand imbalance leading to inadequate power supply. The controller performs autonomous control for load management and supports the system stability via load shaving [17] with little or no end user inconvenience. The design aim of the FRGFAS is to provide a frequency response decentralized controller that operates based on myRIO-1900 FPGA embedded system control algorithms [18]. ...
High price fluctuations have a direct impact on electricity market. Thus, accurate and plausible price forecasts have been implemented to mitigate the consequences of price dynamics. This paper proposes two techniques to deal with the Electricity Price Forecasting (EPF) problem. Firstly, Convolutional Neural Network (CNN) model is used to predict the EPF. Secondly, a principle component analysis model is used for the feature extraction. We have conducted simulations to prove the effectiveness of the proposed approach, which show that CNN based approach outperforms the multilayer perceptron model.
As population grows and energy consumption increases, generation, transmission, and energy distribution costs also increase. Sudden and unpredicted demand increase at peak periods might lead to failure and even damage the power grid. This is a challenge for stability and reliability of the grid. Peak load shaving is considered as an effective approach while transition from peak load periods. In this paper, peak load shaving is modeled mathematically through storing energy on demand side and solved using optimization method. Using the results obtained from solving the optimization problem, a simple effective algorithm is proposed for peak load shaving via real-time scheduling of distributed battery storage systems without complicated calculations. All characteristics required for systemic design of peak load shaving for residential, commercial, and industrial loads are presented. This method can be used in the presence of photovoltaic arrays or other renewable or nonrenewable distributed energy resources simultaneously, and it can be adapted to different conditions and demands. Here, real measured data of a residential state, an office building with photovoltaics, a hotel, and a small office are used for simulation, and GAMS is used for analysis.
The state of the art of old-age grids into smart grids provides residents the opportunity to schedule their appliances to consume the energy optimally that leads to potentially balance the demand side as well as the supply side more effectively and minimizes the power Peak-to-Average Ratio (PAR), which ultimately provides benefit to the residents in the form of reduction cost and expense. The Energy Management System (EMS) in the home receives the market and system signals and controls the loads, Heating, Ventilation and Air Conditioning systems (HVAC), storages and local generation units according to the user preferences. This survey encompasses novel home energy management techniques including different shiftable and non-shiftable load scheduling methods and peak shaving strategies. Several Pricing strategies have been suggested for smart grid such as, Real-Time Pricing (RTP), Time of Use (ToU), Inclining Block Rates (IBR), Critical Peak Pricing (CPP), etc. Moreover, this paper discusses the HEM architecture and reveals that the different energy management techniques intelligently schedule the appliances in order to satisfy the maximum resident's comfort level and consume the energy optimally.
This survey Paper is an ingathering of a comprehen-sive study of user comfort in smart grid. It is a compounding of different arenas of engineering like communication, electronics, management and power. Many techniques, like demand response, demand side management, real time pricing, time of use, home energy management, etc, are explained according to their role in Smart Homes (SHs). This is the short discussion about basic units of SH and their interrelationships. These techniques help to improve electricity consumption. In this survey paper, we focus on user comfort; reduction in bill payments, scheduling methods, necessary addition of renewable energy sources, etc. Thus, the user suffers minimum delay in appliance operation and retailer gets minimum peak-to-average ratio. This is a result of evolutionary enhancement of the previous electricity networks schemes towards an improved and maintainable energy schemes. Keywords Smart Grid, Demand side management, smart home, renewable energy resources, demand response I. INTRODUCTION Smart Grid (SG) is a vision of the coming times power structures along with innovative sensing equipments, control methods and communication schemes at distribution points as well as transmission level. So that electricity supply should be smart and user friendly. In other words, we can say that, The included physical power system on SG and its information methods that attach various tools and effects recital to convert into a customer service program. The smart power system will probably contain some novel technologies in communication, broadcast organization, advanced metering, automation, distributed storage, guard, and surety to tolerate a considerable increment in the dependability and validity of the power network, which will in turn lower the energy costs and user ease. In the present era, environmental businesses, oil shortage, and economic facets of consuming the available energy re-sources. These things motivated nations, govt., technology suppliers, and academic societies to supernumerary conven-tional resources by Renewable Energy Resources (RERs). For this purpose SG and Home Energy Management(HEM) and industrial and commercial energy management are necessary. In society, this Real Time Prices (RTP) is provided by the retailer and industrial and commercial area. In residential area,
Now a days, smart grid is an emerging technologyto increase the optimal use of generated power and reducethe electricity cost. Globally, various schemes in this aspecthave been proposed like DR, DSM, AMI, RTP, TOU, etc. Thispaper presents a survey on some scheduling and optimizationtechniques for home appliances to use the energy efficientlywith aminimum cost. The focus of this paper is on experimental resultsand test beds of different schemes to evaluate their performance.This paper also presents critical analysis on the Home EnergyManagement (HEM) scheduling schemes based on their results.
A Home Energy Management (HEM) system plays a crucial role in realizing residential Demand Response (DR) programs in the smart grid environment. It provides a homeowner the ability to automatically perform smart load controls based on utility signals, customer's preference and load priority. This paper presents the hardware demonstration of the proposed HEM system for managing end-use appliances. The HEM's communication time delay to perform load control is analyzed, along with its residual energy consumption.
Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.
The goal of the MavHome (Managing An Intelligent Versatile Home) project is to create a home that acts as an intelligent agent. In this paper we introduce the MavHome architecture. The role of prediction algorithms within the architecture is discussed, and a meta-predictor is presented which combines the strengths of multiple approaches to inhabitant action prediction. We demonstrate the effectiveness of these algorithms on smart home data.
Current energy demand for appliances in smart homes is nowadays becoming a severe challenge, due to economic and environmental reasons; effective automated approaches must take into account basic information about users, such as the prediction of their course of actions. The present proposal consists in recognizing user daily life activities by simply relying on the analysis of environmental sensory data in order to minimize energy consumption by guaranteeing that peak demands do not exceed a given threshold. Our approach is based on information theory in order to convert raw data into high-level events, used to represent recursively structured activities. Experiments based on publicly available datasets and consumption models are provided to show the effectiveness of our proposal.
This paper focuses on the load shifting problem in a household scenario with a large-capacity battery. We propose a novel Model Predictive Control (MPC) framework to control the charge/discharge power of battery, hence to shave the peak load. Being different from other studies, the framework is designed on the base of individual habit of energy consumption, as it is envisioned that the individual habit is critical for choosing the suitable energy services. In this paper, the habit is modeled as a Markov process and gradually learned by an iterative algorithm; thus, the habit can be utilized for the prediction of future energy consumption. Then, the rolling optimization is applied for the optimal control of the charge/discharge power of battery. It is shown by numerical simulations that the proposed approach can significantly reduce the peak load.
Demand response and dynamic retail pricing of electricity are key factors in a smart grid to reduce peak loads and to increase the efficiency of the power grid. Air-conditioning and heating loads in residential buildings are major contributors to total electricity consumption. In hot climates, such as Austin, Texas, the electricity cooling load of buildings results in critical peak load during the on-peak period. Demand response (DR) is valuable to reduce both electricity loads and energy costs for end users in a residential building. This paper focuses on developing a control strategy for the HVACs to respond to real-time prices for peak load reduction. A proposed dynamic demand response controller (DDRC) changes the set-point temperature to control HVAC loads depending on electricity retail price published each 15 minutes and partially shifts some of this load away from the peak. The advantages of the proposed control strategy are that DDRC has a detailed scheduling function and compares the real-time retail price of electricity with a threshold price that customers set by their preference in order to control HVAC loads considering energy cost. In addition, a detailed single family house model is developed using OpenStudio and Energyplus considering the geometry of a residential building and geographical environment. This HVAC modeling provides simulation of a house. Comfort level is, moreover, reflected into the DDRC to minimize discomfort when DDRC changes the set-point temperature. Our proposed DDRC is implemented in MATLAB/SIMULINK and connected to the EnergyPlus model via building controls virtual test bed (BCVTB). The real-time retail price is based on the real-time wholesale price in the ERCOT market in Texas. The study shows that DDRC applied in residential HVAC systems could significantly reduce peak loads and electricity bills with a modest variation in thermal comfort.
This paper presents a system architecture for load management in smart buildings which enables autonomous demand side load management in the smart grid. Being of a layered structure composed of three main modules for admission control, load balancing, and demand response management, this architecture can encapsulate the system functionality, assure the interoperability between various components, allow the integration of different energy sources, and ease maintenance and upgrading. Hence it is capable of handling autonomous energy consumption management for systems with heterogeneous dynamics in multiple time-scales and allows seamless integration of diverse techniques for online operation control, optimal scheduling, and dynamic pricing. The design of a home energy manager based on this architecture is illustrated and the simulation results with Matlab/Simulink confirm the viability and efficiency of the proposed framework.
This report describes the implementation and results of a field demonstration wherein residential electric water heaters and thermostats, commercial building space conditioning, municipal water pump loads, and several distributed generators were coordinated to manage constrained feeder electrical distribution through the two-way communication of load status and electric price signals. The field demonstration took place in Washington and Oregon and was paid for by the U.S. Department of Energy and several northwest utilities. Price is found to be an effective control signal for managing transmission or distribution congestion. Real-time signals at 5-minute intervals are shown to shift controlled load in time. The behaviors of customers and their responses under fixed, time-of-use, and real-time price contracts are compared. Peak loads are effectively reduced on the experimental feeder. A novel application of portfolio theory is applied to the selection of an optimal mix of customer contract types.
Lifestyle and habits of users have a direct effect on the energy performance of dwellings and facilities. Hence, in the built environment, advanced control strategies must adapt to user behaviors trying to keep a commitment between energy consumption and comfort requirements. In previous works, the suitability of predictive control based on occupancy profiles for the optimization of HVAC systems has been shown. Resting upon this basis, the present work performs a sensitivity analysis for control strategies based on usage profiles, where the input under variation is the level of habit-regularity of users. Therefore, different hypothetical user models are created and tested. The results of this analysis provide a better understanding of how user behavior affects the energy and comfort performance in dwellings under smart control and paves the way for enhanced controller design.
Proactive User Interfaces (PUIs) aim at facili- tating the interaction with a user interface, e.g., by highlighting fields or adapting the interface. For that purpose, they need to be able to pre- dict the next user action from the interaction his- tory. In this paper, we give an overview of se- quence prediction algorithms (SPAs) that are ap- plied in this domain, and build upon them to de- velop two new algorithms that base on combin- ing different order Markov models. We iden- tify the special requirements that PUIs pose on these algorithms, and evaluate the performance of the SPAs in this regard. For that purpose, we use three datasets with real usage-data and syn- thesize further data with specific characteristics. Our relatively simple yet efficient algorithm FxL performs extremely well in the domain of SPAs which make it a prime candidate for integration in a PUI. To facilitate further research in this field, we provide a Perl library that contains all presented algorithms and tools for the evaluation.
