Article

Performance evaluation of power demand scheduling scenarios in a smart grid environment

March 2015
Applied Energy 142:164-178

March 2015
142:164-178

DOI:10.1016/j.apenergy.2014.12.060

Authors:

John Vardakas

University of Western Macedonia

Christos Verikoukis

University of Patras

Smart grid technology is considered as the ultimate solution to challenges that emerge from the increasing power demands, the subsequent increase in pollution, and the outmoded power grid infrastructure. The successful implementation of the smart grid is mainly driven by the utilization of modern communication technologies, which aim at the provision of advanced demand side management mechanisms, such as demand response. In this paper, we present and analyze four power-demand scheduling scenarios that aim to reduce the peak demand in a smart grid infrastructure. The proposed scenarios consider that each consumer is equipped with a certain number of appliances of different power demands and different operational times, while the percentage of consumers that agree to participate in the demand scheduling program is also incorporated in our models. We provide the analysis for the determination of the peak demand in a residential area, based on recursive formulas. The proposed analysis is validated through simulations; the accuracy of the analytical models is found to be quite satisfactory. Moreover, we unveil the consistency and necessity of the proposed scenarios and corresponding analytical models.

Demand Side Management for Smart Houses: A Survey

Article

Full-text available

Jun 2021

Continuous advancements in Information and Communication Technology and the emergence of the Big Data era have altered how traditional power systems function. Such developments have led to increased reliability and efficiency, in turn contributing to operational, economic, and environmental improvements and leading to the development of a new technique known as Demand Side Management or DSM. In essence, DSM is a management activity that encourages users to optimize their electricity consumption by controlling the operation of their electrical appliances to reduce utility bills and their use during peak times. While users may save money on electricity costs by rescheduling their power consumption, they may also experience inconvenience due to the inflexibility of getting power on demand. Hence, several challenges must be considered to achieve a successful DSM. In this work, we analyze the power scheduling techniques in Smart Houses as proposed in most cited papers. We then examine the advantages and drawbacks of such methods and compare their contributions based on operational, economic, and environmental aspects.

Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of Green Energy Resources in Smart Cities Signature of Student

Research Proposal

Full-text available

Oct 2019

In the recent era, the energy management problem considering peak load is solved using demand side rather than supply side management. However, various electricity consumers have different interests and willingness when considering peak load management. In this regard, efficient energy management solutions are required where the priority of an appliance is considered according to user's interest. Shifting of the load based on priorities will be beneficial for consumers during time slots where electricity prices are high and also to a utility to control the peak load demand. However, it is difficult for consumers to respond manually to demand response incentives like time of use (ToU) and real-time pricing (RTP) signals due to the lack of interest, busy schedules or their unwillingness. Therefore, in order to take full benefits of such incentives, the need for energy management controller (EMC) capable of making smart decisions in response to the price signals, without jeopardizing user comfort is need of the hour. The researchers have been focused on designing autonomous EMC for energy management based on shifting the residential load from on-peak to off-peak hours without considering appliances' priorities and threshold limits. This causes in the creation of rebound peaks, that is also a risk to the grid sustainability. When consumers shift the operation load of their smart home appliances to off-peak hours, it also results in an increased peak-to-average ratio (PAR). Demand side management (DSM) in smart grid (SG) authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. This results in reduced carbon emission, consumer electricity cost, and increased grid sustainability. Most of the existing DSM techniques ignore priority defined by consumers. In this work, we present a DSM strategy based on the load shifting technique, which considers shifting of various energy cycles of an appliance according to the consumer defined priority. The proposed day-ahead load shifting technique is mathematically formulated and mapped as multiple knapsack problem (MKP). This reduces the rebound-effect caused by load shifting to off-peak time slots and also minimize the PAR. The autonomous EMC proposed embeds three meta-heuristics optimization techniques; genetic algorithm, enhanced differential evolution, and binary particle swarm optimization along with optimal stopping rule (OSR), which is used for solving the load shifting problem. Simulations are carried out using three different appliances and the preliminary results validate that the proposed DSM strategy successfully shifts the appliances' operational time to off-peak time slots, which consequently leads to substantial electricity cost savings in reasonable waiting time, and also help in reducing the peak load demand from the SG through knapsack capacity limit. In addition, we calculate the feasible regions to show the relationship between cost, energy consumption, and delay. The simulation section presents the initial results that we have got so far. Next, we will extend our work by considering multiple appliances and homes along with hybridization of meta-heuristics schemes for better exploratory and exploitive search space. The integration of green energy resources obtained from the natural resources like sun, wind etc. will also be considered in the proposed model. We will also evaluate microgrids energy trading using fogs in various regions of the smart city to minimize energy losses as compared to the main grid.

Peak Demand Reduction Through Demand Control: A Mathematical Analysis

Conference Paper

Full-text available

Jul 2016

The utilization of information and communication technologies in power systems is a vital tool for the transformation of the entire infrastructure, from generation, distribution, to electricity consumption, into an intelligent, reliable and energy-efficient smart grid. The concept of Demand Response (DR) includes all the activities that target the alteration of the electricity consumers' demand profile, which will benefit not only themselves, but also the power grid. In this paper, we propose new and more effective DR-based power-demand control scenarios that target the peak demand reduction is a smart grid infrastructure. The proposed scenarios and corresponding analytical models are applied to a residential area, where each residence is equipped with a specific number of appliances with diverse power demands. Moreover, the proposed scenarios take into account the fact that some appliances are able to contribute to the peak demand reduction more effectively, compared to other, by incorporating different power thresholds per appliance for the activation of the power control mechanisms. The accuracy of the proposed models is verified through simulation and found to be quite satisfactory.

Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of Green Energy Resources in Smart Cities

Thesis

Full-text available

Oct 2018

A smart city is an eﬃcient, reliable, and sustainable urban center that facilitates its inhabitants with a high quality of life standards via optimal management of its resources. Energy management of smart homes (SHs) is one of the most challenging and demanding issues which needs signiﬁcant eﬀort and attention. Demand side management (DSM) in smart grid (SG) authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. In DSM, scheduling of appliances based on consumer-deﬁned priorities is an important task performed by a home energy management controller (HEMC). However, user discomfort is caused by the scheduling of home appliances based on the demand response or limiting its time of use. Further, rebound peaks that are regenerated in the oﬀ-peak hours is also a major challenge in DSM. In addition, an increase in the world population is resulting in high energy demand; thus, causing a huge consumption of fossil fuels. This ultimately results in severe environmental problems for mankind and nature. Renewable energy sources (RESs) emerge as an alternative to the fossil sources. These RESs have the advantages of environmental friendliness and sustainability, which are incorporated in SHs via two modes: grid-connected (GC) or stand-alone (SA). The reliability concerns in RESs are usually met with the usage of hybrid RESs along with the integration of energy storage systems (ESS). The eﬃcient usage of these components in the hybrid RESs requires an optimum unit sizing that achieves the objectives pertaining to cost minimization and reliability in SA mode. These are some of the main concerns of a decision maker. This thesis focuses on employing meta-heuristic techniques for eﬃcient utilization of energy and RESs in SH. At ﬁrst, an evolutionary accretive comfort algorithm (EACA) is developed based on four postulations which allows the time-varying priorities to be quantiﬁed in time and device-based features. Based on the input data, considering the appliances’ power ratings, its time of use, and absolute comfort derived from priorities, the EACA is able to generate an optimal energy consumption pattern which would give maximum satisfaction at a predetermined user budget. A cost per unit comfort index (χ) which relates the consumer expenditure to the achievable comfort is also demonstrated. To test the applicability of the proposed EACA, three budget scenarios of 1.5 $/day, 2.0 $/day, and 2.5$/day are performed. Secondly, a priority-induced DSM strategy based on the load shifting technique considering various energy cycles of an appliance is presented. The day-ahead load shifting technique is mathematically formulated and mapped with multiple knapsack problem (MKP) to mitigate the rebound peaks. The proposed autonomous HEMC embeds three meta-heuristic optimization techniques: genetic algorithm (GA), enhanced diﬀerential evolution (EDE), and binary particle swarm optimization (BPSO) along with the optimal stopping rule, which is used for solving the load shifting problem. Next, we integrate the RESs and ESS in a residential sector considering GC mode. The proposed optimized home energy management system minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of the electricity market. Here the appliances are classiﬁed into shiftable and non-shiftable categories, and a hybrid GA-BPSO (HGPO) scheme outperforms to other algorithms in terms of cost and a peak-to-average ratio (PAR). Finally, meta-heuristic schemes that do not depend on algorithmic-speciﬁc parameters are focused for RESs and ESS integration in a SA system. Preliminary, Jaya algorithm is used for ﬁnding an optimal unit sizing of RESs components, including photovoltaic (PV) panels, wind turbines (WTs), and fuel cell (FC) with an objective to reduce the consumer total annual cost. The methodology is applied to real solar irradiation and wind speed data taken for Hawksbay, Pakistan. Next, an improved Jaya and the learning phase as depicted in teaching learningbased optimization (TLBO), named JLBO algorithm for optimal unit sizing of a PV-WT-Battery hybrid system is also demonstrated for another site located in Rafsanjan, Iran. The system reliability is considered using the maximum allowable loss of power supply probability (LPSPmax) provided by the consumer. Thus, the thesis objectives achieved are to have a green, reliable, economical, and sustainable power supply in the SH.

Predicting the Creation of Smart Grids considering the Role of Big Data in North America's Electricity Market

Article

Full-text available

Apr 2022

Reza Tajik

Using dual-bandwidth digital technology, the smart grid delivers energy from manufacturers to customers, saving consumers energy by controlling their home appliances, reducing costs and enhancing reliability and transparency. The smart grid on the one hand and the shaping of the electricity market, on the other hand, have created a huge amount of data every day through the daily interactions of electricity and to decide which market players to include manufacturers, transmitters, distributors and in the near future on smart grids for electricity consumers, storage and then be analyzed. This huge amount of data, despite the many benefits it can bring, can cause problems in data storage, display and analysis. For example, one of the most important tasks of North America's electricity market management is to forecast electricity prices for future periods based on stored historical data and because of the type of data produced in this area of the big data typ. Data has problems with different fields, especially in data analysis. In this paper, the role of modern data mining methods for big data analysis is compared by comparing two traditional and new data mining methods and performing statistical experiments. The results of this paper show that a slight improvement in electricity price forecasts due to high volume of electricity exchanges can bring incredible savings to the players in this field. Therefore, the use of modern data mining methods in this field is very important and practical.

A multi-objective energy optimization in smart grid with high penetration of renewable energy sources

Article

Oct 2021
APPL ENERG

Energy optimization plays a vital role in energy management, economic savings, effective planning, reliable and secure power grid operation. However, energy optimization is challenging due to the uncertain and intermittent nature of renewable energy sources (RES) and consumer’s behavior. A rigid energy optimization model with assertive intermittent, stochastic, and non-linear behavior capturing abilities is needed in this context. Thus, a novel energy optimization model is developed to optimize the smart microgrid’s performance by reducing the operating cost, pollution emission and maximizing availability using RES. To predict the behavior of RES like solar and wind probability density function (PDF) and cumulative density function (CDF) are proposed. Contrarily, to resolve uncertainty and non-linearity of RES, a hybrid scheme of demand response programs (DRPS) and incline block tariff (IBT) with the participation of industrial, commercial, and residential consumers is introduced. For the developed model, an energy optimization strategy based on multi-objective wind-driven optimization (MOWDO) algorithm and multi-objective genetic algorithm (MOGA) is utilized to optimize the operation cost, pollution emission, and availability with/without the involvement in hybrid DRPS and IBT. Simulation results are considered in two different cases: operating cost and pollution emission, and operating cost and availability with/without participating in the hybrid scheme of DRPS and IBT. Simulation results illustrate that the proposed energy optimization model optimizes the performance of smart microgrid in aspects of operation cost, pollution emission, and availability compared to the existing models with/without involvement in hybrid scheme of DRPS and IBT. Thus, results validate that the proposed energy optimization model’s performance is outstanding compared to the existing models.

Electrical Energy Consumption Forecasting for Efficient Energy Management in Smart Grid

Thesis

Full-text available

May 2021

Accurate, fast, and stable electrical energy consumption forecasting plays a vital role in decision making, energy management, effective planning, reliable and secure power system operation. Inaccurate forecasting can lead to the electricity shortage, wastage of energy resources, power outage, and in the worst case, power grid collapse. Contrarily, accurate forecasting enables policymakers and public agencies to make real-time decisions imperative for the energy management and power system’s secure and reliable operation. However, accurate, fast, and stable forecasting is challenging due to consumers’ uncertain and intermittent electrical energy consumption behavior. In this context, a rigid forecasting model with assertive stochastic and non-linear behavior capturing abilities is needed. Thus, several forecasting strategies have been emerged in state-of-the-art work, starting from conventional time series to modern data analytic methods to solve the non-linear electrical consumption prediction problems. The individual techniques partially resolved the forecasting problem by improving forecast accuracy. However, the improvement in accuracy is not up to the mark. Besides, individual techniques (conventional or modern) suffer from their inherent limitations. Due to inherent limitations, forecasting results of individual methods (conventional or modern) are no longer as accurate as required. To solve such problems, hybrid models developed, which fully utilize individual methods’ advantages and have comparatively improved performance. Only some models are commendable that improve accuracy, while others perform better in convergence rate. However, considering only one aspect (accuracy or convergence rate) is insufficient. Thus, accuracy and convergence rate both are of prime importance and can be improved simultaneously. Therefore, scholars and industries have the primary goal of developing a forecasting model, which provides robust, stable, and accurate electrical energy consumption forecasting for efficient energy management. With this motivation, a novel two-stage hybrid model is developed by integrating the electrical energy consumption forecasting stage with the energy management stage. The first stage is for electrical energy consumption forecasting, and the second stage is for efficient energy management. The first stage composed of four modules: (i) a novel cascaded framework based on factored conditional deep belief network (FCDBN), (ii) deep learning based forecaster cascaded with a heuristic algorithm based optimizer framework, (iii) support vector machine (SVM) based forecaster integrated with modified enhanced differential evolution (mEDE) algorithm framework, and (iv) accurate and fast converging deep learning based forecaster framework. The first module framework consists of data preprocessing phase, FCDBN training phase, and FCDBN based forecasting phase. The first module framework is developed in a cascaded manner, where each former phase’s output is fed into the later phase as input, namely cascaded framework. The second module is a hybrid framework composed of data preprocessing and feature selection, training and forecasting, and optimization phases. The third module is an integrated framework of data preprocessing and features engineering, SVM based forecaster, and mEDE based optimizer, namely FA-HELF. The fourth module is a cascaded framework of feature selector, FCRBM based forecaster, and GWDO based optimizer, namely FS-FCRBM-GWDO. The purpose of the modules in the first stage is to provide fast, accurate, and stable electrical energy consumption forecasting. To accomplish this goal first, the data is preprocessed to convert it into a usable format. Secondly, clean and prepared data is passed through feature selection and extraction phases to select the most relevant and desired features from the data. Thirdly, the feature engineering phase’s output is fed to the training phase to empower the forecaster through training and learning processes to accurately forecast electrical energy consumption. Finally, the forecasted electrical energy consumption is given as an input to the optimization phase to further minimize the error in predicted results by optimizing the model’s hyperparameters. The first stage results are fed to the second stage of the proposed model for efficient energy management. The second stage is based on optimization strategies that utilize the forecasted electrical energy consumption pattern for efficient energy management. The second stage comprises three modules: (i) day ahead genetic modified enhanced differential evolution algorithm based module, (ii) genetic modified enhanced differential evolution algorithm based scheduling module, and (iii) genetic wind driven optimization algorithm based energy management controlling module. The purpose of the second stage is to reduce the bill of electricity, mitigate peaks in demand, and acquire the desired tradeoff between electricity bill and user discomfort by utilizing forecasted electrical energy consumption. The proposed model is favorable for both consumers and power companies because it fulfills the need both parties. For consumers, the proposed model minimizes electricity bill and discomfort in terms of waiting time simultaneously. In contrast, the proposed model rewards power companies by alleviating peaks in demand to increase power system stability. Simulation results confirmed the effectiveness and productiveness of the proposed model by comparing it with benchmark models.

Residential Load Scheduling Based Analytical Optimization Method

Article

Full-text available

Feb 2021

Peak load periods in smart grids significantly affect the energy stability produced by energy suppliers. One of the important factors that distinctly affects the load during these periods is the household energy consumption. Thus, managing and improving energy demand for smart home appliances can effectively reduce the peak loads which represents a major challenge. This paper introduces a dynamic Analytical optimization Method (AM) to find the optimum managing for residential energy load. The results showed that the maximum load of total demand is decreased by 35%, as well as, the energy consumption cost bill is decreased by 44%. The results of proposed method are compared with two widely used optimization methods; Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Although the results of the proposed method showed a superior time saving for achieving the final results.

Desain Sistem Interkoneksi Micro-grid di Indonesia dan Analisis Kestabilan Transient menggunakan Software ETAP

Article

Aug 2020

Dynamic residential load scheduling based on an adaptive consumption level pricing scheme

Preprint

Jul 2020

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). The proposed load scheduling system encourages customers to manage their energy consumption within the allowable consumption allowance (CA) of the proposed DR pricing scheme to achieve lower energy bills. Simulation results show that employing the proposed DRLS system benefits the customers by reducing their energy bill and the utility companies by decreasing the peak load of the aggregated load demand. For a given case study, the proposed residential load scheduling system based on ACLPS allows customers to reduce their energy bills by up to 53% and to decrease the peak load by up to 35%.

A Modular Framework for Optimal Load Scheduling under Price-Based Demand Response Scheme in Smart Grid

Article

Full-text available

Aug 2019

With the emergence of the smart grid (SG), real-time interaction is favorable for both residents and power companies in optimal load scheduling to alleviate electricity cost and peaks in demand. In this paper, a modular framework is introduced for efficient load scheduling. The proposed framework is comprised of four modules: power company module, forecaster module, home energy management controller (HEMC) module, and resident module. The forecaster module receives a demand response (DR), information (real-time pricing scheme (RTPS) and critical peak pricing scheme (CPPS)), and load from the power company module to forecast pricing signals and load. The HEMC module is based on our proposed hybrid gray wolf-modified enhanced differential evolutionary (HGWmEDE) algorithm using the output of the forecaster module to schedule the household load. Each appliance of the resident module receives the schedule from the HEMC module. In a smart home, all the appliances operate according to the schedule to reduce electricity cost and peaks in demand with the affordable waiting time. The simulation results validated that the proposed framework handled the uncertainties in load and supply and provided optimal load scheduling, which facilitates both residents and power companies.

Simulation-Supported Testing of Smart Energy Product Prototypes

Article

Full-text available

May 2019

Smart energy products and services (SEPS) have a key role in the development of smart grids, and testing methods such as co-simulation and scenario-based simulations can be useful tools for evaluating the potential of new SEPS concepts during their early development stages. Three innovative conceptual designs for home energy management products (HEMPs)—a specific category of SEPS—were successfully tested using a simulation environment, validating their operation using simulated production and load profiles. For comparison with reality, end user tests were carried out on two of the HEMP concepts and showed mixed results for achieving more efficient energy use, with one of the concepts reducing energy consumption by 27% and the other increasing it by 25%. The scenario-based simulations provided additional insights on the performance of these products, matching some of the general trends observed during end user tests but failing to sufficiently approximate the observed results. Overall, the presented testing methods successfully evaluated the performance of HEMPs under various use conditions and identified bottlenecks, which could be improved in future designs. It is recommended that in addition to HEMPs, these tests are repeated with different SEPS and energy systems to enhance the robustness of the methods.

A Methodology for Dependability Evaluation of Smart Grids

Article

Full-text available

May 2019

Smart grids are a new trend in electric power distribution, which has been guiding the digitization of electric ecosystems. These smart networks are continually being introduced in order to improve the dependability (reliability, availability) and efficiency of power grid systems. However, smart grids are often complex, composed of heterogeneous components (intelligent automation systems, Information and Communication Technologies (ICT) control systems, power systems, smart metering systems, and others). Additionally, they are organized under a hierarchical topology infrastructure demanded by priority-based services, resulting in a costly modeling and evaluation of their dependability requirements. This work explores smart grid modeling as a graph in order to propose a methodology for dependability evaluation. The methodology is based on Fault Tree formalism, where the top event is generated automatically and encompasses the hierarchical infrastructure, redundant features, load priorities, and failure and repair distribution rates of all components of a smart grid. The methodology is suitable to be applied in early design stages, making possible to evaluate instantaneous and average measurements of reliability and availability, as well as to identify eventual critical regions and components of smart grid. The study of a specific use-case of low-voltage distribution network is used for validation purposes.

Optimizing the management of smart home energy resources under different power cost scenarios

Article

May 2019
APPL ENERG

Demand response programs in smart grids-survey

Article

Full-text available

Jan 2018

The smart grid in this century has an essential role in changing the philosophy of the electrical power engineering. In the past, the generation must be equal to the demand under any situation but with the introduction of the non-conventional grids everything is changed, and the customers should consume energy in the same amount to what already generated from the generation units. The tool to achieve all that is the demand response (DR) strategy. DR can alter the consumption pattern of the consumers to make it flatting instead of the sharp curves that lead to additional costs coming from the increasing generating in the periods of the peaks in the load curve. In this paper, the demand response programs listed and discussed with an indication to the documents that deal with each type.

Performance Analysis Methods in Smart Grids: An Overview

Article

Aug 2018

Surender Reddy Salkuti

This paper presents an overview of the performance analysis methods available for the Smart Grid (SG). Increased energy demand, volatile energy costs, uncertain power generation from the renewable energy resources (RERs), electric vehicles, and environmental concerns are coming together to change the nature of the traditional power grid. Many utility companies are now moving towards the smart metering and the Smart Grid solutions to address these challenges. Smart Grid is inclusive of advance tools, latest communication technologies and storage devices, which makes the Smart Grid vulnerable and complex. This paper aims to review the performance analysis of Smart Grid. It also presents various models of the Smart Grid performance indices. It presents the methods available for stability, reliability and resilience assessment in Smart Grid. It also describes the implementation approach using the real time tools and techniques.

PhD-Dissertation-Mahdi-Motalleb (1)

Thesis

Full-text available

Dec 2017

Mahdi Motalleb

The increased presence of variable renewable generation drives a greater need for authorities to procure more Ancillary Services (AS) for grid balance. One of these services is Contingency Reserve (CR), which is used to regulate the grid frequency in contingencies. Many Independent System Operators (ISOs) are structuring the rules of AS markets such that DR can participate alongside traditional supply-side resources. The available capacity of the generators can be used more efficiently for power production which they were designed for and not CR; cutting costs, and reducing pollution. As the ratio of inverter-based generation compared to conventional generation increases, the mechanical inertia used to stabilize frequency decreases. When coupled with the sensitivity of inverter-based generation to transient frequencies, the provision of AS from other sources than generators becomes increasingly important. This chapter provides a method to use AS for providing CR using DR to ensure system stability for a set of credible contingencies, while also satisfying economic and market goals. In the AS market, Optimal Power Flow (OPF) is used to find the optimal offers/bids and transient behavior of frequency is considered. The proposed model separates DR into two categories—faster and slower—based on the deviation from the normal frequency of grid power. In a standard numerical example, it has been shown that the proposed model can clear energy and AS’ bids simultaneously while minimizing the total operating cost and satisfying transient frequency requirements.

Compression of smart meter big data: A survey

Article

Aug 2018
RENEW SUST ENERG REV

A Novel Meta-Heuristic Hybrid Enhanced Differential Harmony Wind Driven (EDHWDO) Optimization Technique for Demand Side Management in Smart Grid

Conference Paper

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May 2018

Appliance Scheduling in Smart Homes with Harmony Search Algorithm for Different Operation Time Intervals

Conference Paper

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May 2018

EDHBPSO: Enhanced Differential Harmony Binary Particle Swarm Optimization for Demand Side Management in Smart Grid

Conference Paper

Full-text available

May 2018

Implementing Elephant Herding Optimization Algorithm with different Operation Time Intervals for Appliance Scheduling.... Home Energy Managment System using Meta-Heuristic Techniques View project Building energy and comfort management View project 682 PUBLICATIONS 4,045 CITATIONS SEE PROFILE Implementing Elephant Herding Optimization Algorithm with different Operation Time Intervals for Appliance Scheduling in Smart Grid

Conference Paper

Feb 2018

Implementing Elephant Herding Optimization Algorithm with different Operation Time Intervals for Appliance Scheduling in Smart Grid

Conference Paper

Full-text available

Feb 2018

Demand Side Management (DSM) is the strategy applied in smart grid domain for Home Energy Management (HEM) and balancing of electricity demand and supply. Numerous optimization techniques have been proposed by research community for HEM. In this study, we have implemented Elephant Herding Optimization (EHO) technique to achieve four objectives: cost, PAR and waiting time minimization with user comfort maximization. EHO has been implemented with three different Operation Time Intervals (OTIs) i.e., 05, 30 and 60 minutes. Simulation results showed that EHO performed much better than un-schedule case and EHO efficiency for shorter OTI is also better than longer OTIs.

Harnessing business intelligence in smart grids: A case of the electricity market

Article

Apr 2018
COMPUT IND

This paper discusses analytical aspects of smart grids and offers insights into the development of a business intelligence solution for the electricity market. The goal is to design a system that provides an emerging electricity market with the necessary data flows and information for forecasting, data analysis and decision making, leading to better business results and more control over the market. By employing a methodology specifically suited to the electricity market domain, we designed a business intelligence solution for the Serbian electricity market operator “Elektromreža Srbije”. The research results show that the proposed approach leads to more effective market management in data-rich smart grid environments, while still being dynamic enough to adapt to frequent rule changes in the still developing grids and their markets.

A Noval Meta-Heuristic Hybrid Enhanced Differential Harmony Wind Driven (EDHWDO) Optimization Technique For Demand Side Management in Smart Grid

Conference Paper

Feb 2018

In recent decades numerous Demand Side Management (DSM) techniques have been merged for Smart Grid (SG). These techniques have been very effective in minimizing cost. However, lack in other characteristics like effective control over Peak Average Ratio (PAR) that why they are not equally beneficial for the utility. Due to different characteristic of different operations some techniques are really work well for cost and some for PAR. We have evaluated and extracted different operations from Harmony Search Algorithm (HSA), Enhanced Differential Evolution (EDE) and Wind Driven Optimization (WDO) for controlling cost along with PAR. A hybrid Enhanced Differential Harmony Wind Driven Optimization (EDHWADO) is formed that combines the cost effectiveness of WDO, HSA and PAR controlling operation through integration of EDE. The proposed hybrid engine proved to be most cost effective in comparison to Genetic Algorithm (GA), WDO along effective PAR control and User Comfort (UC) maximization. Real Time Pricing (RTP) signals are used to evaluate real time behavior of appliances. Three categories of appliances are used; fixed, elastic and shiftable. On basis of performances metrics, simulations proved that EDHWDO leads substantial cost saving along with enhancing user comfort and also helps in significant cost reduction.

Appliance Scheduling in Smart Homes with Harmony Search Algorithm for different Operation Time Intervals

Conference Paper

Feb 2018

Rapidly increasing population leads to increased electricity demand which ultimately requires Demand Side Management (DSM) to balance the electricity demand and supply in smart grid. Various techniques have been proposed by research community for Home Energy Management (HEM). In this study, we have used Harmony Search Algorithm (HSA) as an optimization technique to achieve four objectives: cost, PAR and waiting time minimization with user comfort maximization. HSA has been implemented in three different scenarios on the basis of varying Operation Time Intervals (OTIs) i.e., 05, 30 and 60 minutes. Simulation results showed that HSA performed much better as compared to un-schedule case and shorter OTI produced better results as compared to longer OTIs.

Implementing Elephant Herding Optimization Algorithm with different Operation Time Intervals for Appliance Scheduling in Smart Grid

Conference Paper

Feb 2018

EDHBPSO: Enhanced Differential Harmony Binary Particle Swarm Optimization for Demand Side Management in Smart Grid

Conference Paper

Feb 2018

Now a days home energy management system is being used all across the world. Ever increasing energy demands has brought many challenges in aspect of luxury and cost saving. Our work is mainly based on efficient utilization of resources by smart planning. In this aspect scheduling of home electronic devices play vital role. Calculating energy consumption and its timings paved the way to utilize it in most efficient way. In our work we have proposed hybrid of meta heuristic techniques such as HSA, EDE, BPSO. Key feature of state of art techniques has been merged together to evolve a technique that works excellent not only in aspect of cost saving, peak average reduction as well as provision of adequate user comfort. In order to evaluate our proposed technique we have considered set of nine devices includes interruptible, non interruptible and base devices in single home and perform scheduling by advance meter controller (AMC) to further send demand response to utility by smart meters. In response, utility provides real time pricing signals. Two way communication is establish to perform scheduling meticulously.

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

Article

Full-text available

Feb 2018

With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, demand side management (DSM) is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE), is proposed by merging enhanced differential evolution (EDE) and gray wolf optimization (GWO) scheme using real-time pricing (RTP) and critical peak pricing (CPP). Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR) is reduced to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30- and 60-min operational time interval (OTI). On the other hand, the PAR and electricity bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.

Simulation Analysis of Impact of Smart Grid and Renewable Energy on GHG Emission

Article

Full-text available

Sep 2017

China once again promises to manage energy consumption and peak CO2 emission around 2030 in Paris Agreement in 2016 that expresses her ambition of mitigating emission. Using renewable energy to optimize energy structure is recognized as effective countermeasure to reduce GHG emission. Additionally, it is inevitable that improving energy efficiency is still core issue in energy usage. Smart Grid (SG) and renewable energy are collectively introduced in this research. Power supply and demand model is constructed to analyze the effect of SG and renewable energy on energy usage. Input-Output (I-O) simulation model is applied to make dynamic analysis based on extended I-O framework. Comprehensive model is constructed to evaluate the impact of SG and renewable energy on economic growth, energy usage and environmental improvement under different emission limitation. The proper policies covering carbon tax and subsidy are proposed to mitigate GHG emission, improve energy usage and optimize economic structure. Trade-off among economic growth, energy conversation and environmental improvement is realized in the study area.

An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes

Article

Full-text available

Aug 2017

Smart grid is an emerging technology which is considered to be an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of smart grid (SG), which aims at provision of demand side management mechanisms (DSM), such as demand response (DR). In this paper, we propose a hybrid technique named as teacher learning genetic optimization (TLGO) by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspects which must be addressed along with cost minimization. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power-flexible appliances on consumers’ bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort.

User Comfort Oriented Residential Power Scheduling in Smart Homes

Conference Paper

Jun 2017

Smart grid is an emerging technology which is considered as an ultimate solution to meet the increasing power demand challenges. Modern communication technologies has enabled the successful implementation of smart grid, which aims at provision of demand side management mechanisms, such as demand response. In this paper, we propose residential load scheduling model for demand side management. It is assumed that electric prices are announced on day-ahead basis. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem, and an optimal schedule is achieved by solving the minimization problem. Simulation results validate that teacher learning based optimization performs better as compared to genetic algorithm, showing comparable results with linear programming with less computational efforts. TLBO is able to obtain the desired trade-off between consumer electric bill and user discomfort.

User Comfort Oriented Residential Power Scheduling for Smart Homes

Thesis

Full-text available

Jul 2017

Smart grid (SG) is an emerging technology which is considered as an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of SG, which aims at provision of demand side management (DSM) mechanisms, such as demand response (DR). In this thesis, we propose teacher learning genetic optimization (TLGO) technique by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspect which must be addressed along with cost minimization. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power flexible appliances on consumers' bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort.

User Comfort Oriented Residential Power Scheduling in Smart Homes

Chapter

Jul 2018

Multi-objective cost-load optimization for demand side management of a residential area in smart grids

Article

Mar 2017

Demand side management (DSM) is one of the most interesting areas in smart grids, and presents households with numerous opportunities to lower their electricity bills. There are many recent works on DSM and smart homes discussing how to keep control on electricity consumption. However, systems that consider minimization of peak load and cost simultaneously for a residential area with multiple households have not received sufficient attention. This study, therefore, proposes an intelligent energy management framework that can be used to minimize both electrical peak load and electricity cost. Constraints, including daily energy requirements and consumer preferences are considered in the framework and the proposed model is a multi-objective mixed integer linear programming (MOMILP). Simulation results for different scenarios with different objectives verified the effectiveness of the proposed model in significantly reducing the electricity cost and the electrical peak load.

Demand side management in a smart micro-grid in the presence of renewable generation and demand response

Article

May 2017

In this study, a stochastic programming model is proposed to optimize the performance of a smart micro-grid in a short term to minimize operating costs and emissions with renewable sources. In order to achieve an accurate model, the use of a probability density function to predict the wind speed and solar irradiance is proposed. On the other hand, in order to resolve the power produced from the wind and the solar renewable uncertainty of sources, the use of demand response programs with the participation of residential, commercial and industrial consumers is proposed. In this paper, we recommend the use of incentive-based payments as price offer packages in order to implement demand response programs. Results of the simulation are considered in three different cases for the optimization of operational costs and emissions with/without the involvement of demand response. The multi-objective particle swarm optimization method is utilized to solve this problem. In order to validate the proposed model, it is employed on a sample smart micro-grid, and the obtained numerical results clearly indicate the impact of demand side management on reducing the effect of uncertainty induced by the predicted power generation using wind turbines and solar cells.

Optimal planning and design of a microgrid with integration of energy storage and electric vehicles considering cost savings and emissions reduction

Article

Nov 2023

Optimal Battery Scheduling with and without Renewable Energy Sources for Efficient Home Energy Management

Conference Paper

Nov 2022

Modelling Critical Success Factors for Smart Grid Development in India

Chapter

Jan 2022

In the last few decades, technological advancement in the energy sector has accelerated the evolution of the smart grid, leading to the need for interdisciplinary research in power system and management. India, the third-largest country in the production and consumption of electricity, is facing numerous challenges related to electricity like high transmission and distribution loss, electricity theft, and pollution concerns. Due to these challenges, the energy sector is looking to adopt new technologies to make the grid more efficient, sustainable, and secure. In this regard, this research aims to identify factors that can be considered enablers for developing smart grid technology in India. The present work has explored a systematic and scientific approach that includes content analysis, exploratory factor analysis, and total interpretive structural modelling. This paper primarily contributes to developing a hierarchical model of the identified enabling factors, which will help the industry persons visualise the roadmap for implementing smart grid technology, especially in a developing country like India.KeywordsSmart gridData analysisTISMEFAStatistical analysis

Interconnection System Simulation Analysis of Transient Micro-grid Stability in Indonesia

Conference Paper

Aug 2021

Appliance Scheduling Towards Energy Management in IoT Networks Using Bacteria Foraging Optimization (BFO) Algorithm

Chapter

Sep 2020

Arome Junior Gabriel

Modern life is almost impossible without electricity, and there is an explosive growth in the daily demand for electric energy. Furthermore, the explosive increase in the number of Internet of Things (IoT) devices today has led to corresponding growth in the demand for electricity by these devices. Serious energy crisis arises as a consequence of these high energy demand. One good solution to this problem could be Demand Side Management (DSM) which involves scheduling of consumers’ appliances in a fashion that will ensure peak load reduction. This ultimately will ensure stability of the Smart Grid (SG) networks, minimization of electricity cost, as well as maximization of user comfort. In this work, we adopted Bacteria Foraging (BFA) Optimization technique for the scheduling of IoT appliances. Here, the load is shifted from peak hours toward the off peak hours. The results show that BFA optimisation based scheduling technique caused a reduction in the total electricity cost and peak average ratio.

Increase of capacity in electric arc-furnace steel mill factories by means of a demand-side management strategy and ampacity techniques

Article

Jan 2021
INT J ELEC POWER

An electric arc-furnace is a complex industry which demands high levels of electrical energy in order to heat iron materials and other additives needed for the production of cast iron and/or steelmaking. The cost of the electrical energy demanded by the factory during the production can be greater than 20% of the overall cost. This kind of arc-furnace allows the production of steel with levels of scrap metal feedstock up to 100%. From an electrical point of view, the factory size in terms of its maximum apparent power demanded from the grid is designed to make use of the static capacity of the transmission line that supplies the energy. In that case, it is not possible to increase the power of the factory above the static rating by adding new facilites without installing new transmission infrastructures. This paper presents a methodology that allows an increase in net power of an arc-furnace factory without installing new transmission lines. The novelty of the proposed solution is based on a mix strategy that combines Demand-Side Management (DSM) methodologies and the use of ampacity techniques according IEEE 738 and CIGRE TB601. The application of DSM methodologies provides an improvement in the sustainability of not only the industrial customer but also in the overall grid. As a secondary effect, it reduces operational costs and the greenhouse gas emissions. The proposed methodology has been tested in an arc-furnace factory located in the North of Spain.

A novel billing approach for fair and effective demand side management: Appliance level billing (AppLeBill)

Article

Oct 2020
INT J ELEC POWER

Demand side management (DSM) is an important part of smart electrical grid studies due to high energy efficiency it provides. Effective DSM techniques are hinged on participation level of the customers and control manner of their loads so if there is no fair and appropriate management, customers will not embrace demand side management applications. Besides, the customers might not participate to the demand response process willingly if their comforts are not considered. Therefore, the DSM systems must consider fairness and customer’s comfort as well as distribution system objectives. Multifarious DSM systems have been proposed in the literature, however, the fair billing has not included as a parameter in most of these studies. This paper introduces a novel billing approach based upon appliance level billing (AppLeBill) for fair and effective demand side management. The proposed AppLeBill idea offers a billing strategy such that time-shiftable appliances (TSAs) in a residence may have a separate billing rate rather than a single billing rate for total electricity usage. The proposed AppLeBill concept includes three different schemes which are explained and simulated in order to compare each other in terms of their potential benefits.

ELM-Fuzzy Method for Automated Decision-Making in Price Directed Electricity Markets

Conference Paper

Sep 2019

The Erlang Multirate Loss Model

Article

Feb 2019

This chapter starts with random arriving calls of fixed bandwidth requirements and fixed bandwidth allocation during service. Before the study of multirate teletraffic loss models where multiple service‐classes of different bandwidth per call requirements are accommodated in a service system, the chapter presents the simpler case where all calls belong to just one service‐class, and considers the multi‐service system. When aiming at QoS equalization among service‐classes, the recursive CBP calculation of the Erlang multirate loss model under the BR policy according to the Roberts method can be improved by the following method proposed by Stasiak and Glabowski. According to ITU‐T, a fixed routing network is a network in which a route providing a connection between an originating node and a destination node is fixed for every service‐class (or for every traffic flow of the same service‐class). The chapter also considers that a fixed routing network consists of L links.

Optimal scheduling of time shiftable loads in a task scheduling based demand response program by symbiotic organisms search algorithm

Conference Paper

Dec 2017

Demand side management is one of the key issues in smart electrical grid studies. Power demand of consumers can be managed via demand response(DR) programs. Task scheduling (TS) based demand response is one of the DR schemes. Objective of the TS approach is to schedule activation time of the appropriate time-shiftable loads to reduce power consumption in peak-demand hours. Heuristic optimization methods can be used to provide this objective. Symbiotic Organisms Search (SOS) and Cuckoo Search (CS) algorithms are two of the most recent heuristic methods. In this paper, scheduling of the time-shiftable home appliances executed by SOS and CS algorithms. Time-shiftable home appliances of the consumers and run time tolerances of them aggregated by mini public survey to achieve results which close to reality. Obtained results are indicated the superiority of the SOS algorithm and demonstrated the positive effects of the demand side management on the electrical grid.

A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack

Article

Full-text available

Apr 2019

Demand side management (DSM) in smart grid authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. This results in reduced carbon emission, consumer electricity cost, and increased grid sustainability. Most of the existing DSM techniques ignore priority defined by consumers. In this paper, we present priority-induced DSM strategy based on the load shifting tech-nique considering various energy cycles of an appliance. The day-ahead load shifting technique proposed is mathematically formulated and mapped to multiple knapsack problem to mitigate the rebound peaks. The autonomous energy management controller proposed embeds three meta-heuristic optimization techniques; genetic algorithm, enhanced differential evolu-tion, and binary particle swarm optimization along with optimal stopping rule, which is used for solving the load shifting problem. Simulations are carried out using three different appliances and the results validate that the proposed DSM strategy successfully shifts the appliance operations to off-peak time slots, which consequently leads to substantial electricity cost savings in reasonable waiting time, and also helps in reducing the peak load demand from the smart grid. In addition, we calculate the feasible regions to show the relationship between cost, energy consumption, and delay. A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack. Available from: https://www.researchgate.net/publication/323945280_A_priority-induced_demand_side_management_system_to_mitigate_rebound_peaks_using_multiple_knapsack [accessed Mar 26 2018].

Reduction of energy intensity in a hospital after implementation of an energy management system respecting consumer fuzzy preferences (in Persian)

Article

Full-text available

Mar 2018

Energy management is one of the most essential steps to reduce the energy intensity. Sectors that provide public services such as public hospitals which benefit from low electricity prices are the major consumers of electrical energy. Therefore, they should have specific plans for energy management. In this study, it has been tried to present a proposed plan to optimize electrical energy consumption in hospitals by modified consumption patterns and respecting consumer preferences. Data for a set of devices utilize by a sample consumer have been gathered. After determining device specifications and consumer preferences, a scheduling model has been proposed. Three scenarios consist of peak energy minimization (load management), minimization of energy costs and combining the first and the second scenarios have been designed, investigated, run and compared to each other, as the objectives of energy management systems implementation and finally, the energy intensity index has been assessed before and after optimization. The results showed that in the combined scenario that has considered both objectives simultaneously, greater reduction in energy intensity is observed. The proposed model is a practical one for intelligent management of electrical consumption. Thus, performing the model in hospitals is recommended.

A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack

Article

Feb 2018

Demand side management (DSM) in smart grid authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. This results in reduced carbon emission , consumer electricity cost, and increased grid sustainability. Most of the existing DSM techniques ignore priority defined by consumers. In this paper, we present priority-induced DSM strategy based on the load shifting technique considering various energy cycles of an appliance. The day-ahead load shifting technique proposed is mathematically formulated and mapped to multiple knapsack problem (MKP) to mitigate the rebound peaks. The autonomous energy management controller (EMC) proposed embeds three meta-heuristic optimization techniques; genetic algorithm, enhanced differential evolution, and binary particle swarm optimization along with optimal stopping rule (OSR), which is used for solving the load shifting problem. Simulations are carried out using three different appliances and the results validate that the proposed DSM strategy successfully shifts the appliance operations to off-peak time slots, which consequently leads to substantial electricity cost savings in reasonable waiting time, and also helps in reducing the peak load demand from the smart grid. In addition, we calculate the feasible regions to show the relationship between cost, energy consumption, and delay.

Efficient Utilization of HEM Controller Using Heuristic Optimization Techniques

Conference Paper

May 2018

The performance and comparative analysis of home energy management controller using three optimization techniques; genetic algorithm (GA), enhanced differential evolution (EDE) and optimal stopping rule (OSR) has been evaluated in this paper. In this regard, a generic system model consisting of home area network, advanced metering infrastructure, home energy management controller, and smart appliances has been proposed. Price threshold policy and priority of appliance have also been considered to depict monthly and yearly average electricity bill savings and appliance delay using day-ahead real-time pricing (DA-RTP). Simulation results validate that all our proposed schemes successfully shifts the appliance operations to off-peak times and results in reduced electricity bill with reasonable waiting time.

Updated Miscellaneous Electricity Loads and Appliance Energy Usage Profiles for Use in Home Energy Ratings, the Building America Benchmark Procedures and Related Calculations

Article

Full-text available

Scheduling policies for two-state smart-home appliances in dynamic electricity pricing environments

Article

Full-text available

May 2014
ENERGY

In this paper we present and analyze online and offline scheduling models for the determination of the maximum power consumption in a smart grid environment. The proposed load models consider that each consumer's residence is equipped with a certain number of appliances of different power demands and different operational times, while the appliances' feature of alternating between ON and OFF states is also incorporated. Each load model is correlated with a scheduling policy that aims to the reduction of the power consumption through the compression of power demands or the postponement of power requests. Furthermore, we associate each load model with a proper dynamic pricing process in order to provide consumers with incentives to contribute to the overall power consumption reduction. The evaluation of the load models through simulation reveals the consistency and the accuracy of the proposed analysis.

A Communication-Based Appliance Scheduling Scheme for Consumer-Premise Energy Management Systems

Article

Full-text available

Mar 2013

In this paper, a communication-based load scheduling protocol is proposed for in-home appliances connected over a home area network. Specifically, a joint access and scheduling approach for appliances is developed to enable in-home appliances to coordinate power usage so that the total energy demand for the home is kept below a target value. The proposed protocol considers both “schedulable” appliances which have delay flexibility, and “critical” appliances which consume power as they desire. An optimization problem is formulated for the energy management controller to decide the target values for each time slot, by incorporating the variation of electricity prices and distributed wind power uncertainty. We model the evolution of the protocol as a two-dimensional Markov chain, and derive the steady-state distribution, by which the average delay of an appliance is then obtained. Simulation results verify the analysis and show cost saving to customers using the proposed scheme.

Achieving Optimality and Fairness in Autonomous Demand Response: Benchmarks and Billing Mechanisms

Article

Full-text available

Jun 2013

Autonomous demand response (DR) programs are scalable and result in a minimal control overhead on utilities. The idea is to equip each user with an energy consumption scheduling (ECS) device to automatically control the user's flexible load to minimize his energy expenditure, based on the updated electricity pricing information. While most prior works on autonomous DR have focused on coordinating the operation of ECS devices in order to achieve various system-wide goals, such as minimizing the total cost of generation or minimizing the peak-to-average ratio in the load demand, they fall short addressing the important issue of fairness. That is, while they usually guarantee optimality, they do not assure that the participating users are rewarded according to their contributions in achieving the overall system's design objectives. Similarly, they do not address the important problem of co-existence when only a sub-set of users participate in a deployed autonomous DR program. In this paper, we seek to tackle these shortcomings and design new autonomous DR systems that can achieve both optimality and fairness. In this regard, we first develop a centralized DR system to serve as a benchmark. Then, we develop a smart electricity billing mechanism that can enforce both optimality and fairness in autonomous DR systems in a decentralized fashion.

Demand-Side Management for Regulation Service Provisioning Through Internal Pricing

Article

Full-text available

Aug 2012

We develop a market-based mechanism that enables a building smart microgrid operator (SMO) to offer regulation service reserves and meet the associated obligation of fast response to commands issued by the wholesale market independent system operator (ISO) who provides energy and purchases reserves. The proposed market-based mechanism allows the SMO to control the behavior of internal loads through price signals and to provide feedback to the ISO. A regulation service reserves quantity is transacted between the SMO and the ISO for a relatively long period of time (e.g., a one-hour-long time-scale). During this period the ISO follows shorter time-scale stochastic dynamics to repeatedly request from the SMO to decrease or increase its consumption. We model the operational task of selecting an optimal short time-scale dynamic pricing policy as a stochastic dynamic program that maximizes average SMO and ISO utility. We then formulate an associated nonlinear programming static problem that provides an upper bound on the optimal utility. We study an asymptotic regime in which this upper bound is shown to be tight and the static policy provides an efficient approximation of the dynamic pricing policy. Equally importantly, this framework allows us to optimize the long time-scale decision of determining the optimal regulation service reserve quantity. We demonstrate, verify and validate the proposed approach through a series of Monte Carlo simulations of the controlled system time trajectories.

Residential Demand Response model and impact on voltage profile and losses of an electric distribution network

Article

Full-text available

Aug 2012
APPL ENERG

This paper develops a model for Demand Response (DR) by utilizing consumer behavior modeling considering different scenarios and levels of consumer rationality. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices for different types of consumers. These price elasticity matrices (PEMs) are utilized to calculate the level of Demand Response for a given consumer considering a day-ahead real time pricing scenario. DR models are applied to the IEEE 8500-node test feeder which is a real world large radial distribution network. A comprehensive analysis has been performed on the effects of demand reduction and redistribution on system voltages and losses. Results show that considerable DR can boost in system voltage due for further demand curtailment through demand side management techniques like Volt/Var Control (VVC).

An analysis of peak demand reductions due to elasticity of domestic appliances

Article

Full-text available

May 2012

Unlike prior work on demand management, which typically requires industrial loads to be turned off during peak times, this paper studies the potential to carry out demand response by modifying the elastic load components of common household appliances. Such a component can decrease its instantaneous power draw at the expense of increasing its duration of operation with no impact on the appliance's lifetime. We identify the elastic components of ten common household appliances. Assuming separate control of an appliance's elastic component, we quantify the relationship between the potential reduction in aggregate peak and the duration required to complete the operation of appliances in four geographic regions: Ontario, Quebec, France and India. We find that even with a small extension to the operation duration of appliances, peak demand can be significantly reduced in all four regions both during winter and summer. For example, during winter in Quebec, a nearly 125 MW reduction in peak demand can be obtained with just a 10% increase in appliance operation duration. We conclude that exploiting appliance elasticity to reduce peak power demand should be an important consideration for appliance manufacturers. From a policy perspective, our study gives regulators the ability to quantitatively assess the impact of requiring manufacturers to conform to "smart appliance" standards.

Demand-Side Load Scheduling Incentivized by Dynamic Energy Prices

Article

Full-text available

Oct 2011

Demand response is an important part of the smart grid technologies. This is a particularly interesting problem with the availability of dynamic energy pricing models. Electricity consumers are encouraged to consume electricity more prudently in order to minimize their electric bill, which is in turn calculated based on dynamic energy prices. In this paper, task scheduling policies that help consumers minimize their electrical energy cost by setting the time of use (TOU) of energy in the facility. Moreover, the utility companies can reasonably expect that their customers reduce their consumption at critical times in response to higher energy prices during those times. These policies target two different scenarios: (i) scheduling with a TOU-dependent energy pricing function subject to a constraint on total power consumption; and (ii) scheduling with a TOU and total power consumption-dependent pricing function for electricity consumption. Exact solutions (based on Branch and Bound) are presented for these task scheduling problems. In addition, a rank-based heuristic and a force directed-based heuristic are presented to efficiently solve the aforesaid problems. The proposed heuristic solutions are demonstrated to have very high quality and competitive performance compared to the exact solutions. Moreover, ability of demand shaping utilizing the aforementioned pricing schemes is demonstrated by the simulation results.

Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities

Article

Full-text available

Jan 2012

Optimization of energy consumption in future in-telligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key com-munications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.

Demand response modeling considering Interruptible/Curtailable loads and capacity market programs

Article

Full-text available

Aug 2009
APPL ENERG

Recently, a massive focus has been made on demand response (DR) programs, aimed to electricity price reduction, transmission lines congestion resolving, security enhancement and improvement of market liquidity. Basically, demand response programs are divided into two main categories namely, incentive-based programs and time-based programs. The focus of this paper is on Interruptible/Curtailable service (I/C) and capacity market programs (CAP), which are incentive-based demand response programs including penalties for customers in case of no responding to load reduction. First, by using the concept of price elasticity of demand and customer benefit function, economic model of above mentioned programs is developed. The proposed model helps the independent system operator (ISO) to identify and employ relevant DR program which both improves the characteristics of the load curve and also be welcome by customers. To evaluate the performance of the model, simulation study has been conducted using the load curve of the peak day of the Iranian power system grid in 2007. In the numerical study section, the impact of these programs on load shape and load level, and benefit of customers as well as reduction of energy consumption are shown. In addition, by using strategy success indices the results of simulation studies for different scenarios are analyzed and investigated for determination of the scenarios priority.

From Packet to Power Switching: Digital Direct Load Scheduling

Article

Full-text available

Feb 2012

At present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making price-aware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.

An Experimental Investigation of Cooking, Refrigeration and Drying End-uses in 100 Households

Article

This paper presents the findings of an experimental study performed in 100 French households on the end-use power demand and energy consumption of domestic appliances focusing on cooking appliances, but also covering domestic cold appliances 1 and clothes-dryers (Sidler and Waide 1999). The study centred on the analysis of a database containing metered results for 517 cases of 32 types of domestic electrical appliance covering the main forms of electric cooking, as well as auxiliary uses, such as coffee-makers, kettles, etc… The households, all located in central France, were metered for a period of one month between January and July 1998. The power demand and energy consumption of each appliance was recorded every 10 minutes over the whole month using the DIACE monitoring system (Gilbert 1998). Annual energy consumption estimates were made from interpretation of this data in most cases by assuming that the usage in the second half of the year would be identical to that in the first. This enabled a hierarchy of cooking appliance energy consumption to be established wherein electric cookers (combined hob 2 and oven) used the most at 457 kWh/year, ahead of induction hobs 3 (337 kWh/year), ceramic hobs (281 kWh/year), and ovens (224 kWh/year). The combined cooking-related energy consumption accounted for 14 % of the total electricity-specific energy consumption of the households surveyed. The average annual energy consumption of all electric cooking appliances was 568 kWh/year. A pronounced and regular monthly variation in cooking-related electricity consumption is found, with the energy consumption in January being 1.25 times the annual average monthly value and 0.72 times in June. Some 96% of all cooking related energy consumption was attributable to loads of less than 3 kW. Interestingly, the standby power consumption of induction hobs was found to be very significant (30% of their total annual energy use) and to offset all their energy savings in the cooking mode compared to conventional hobs. However, this conclusion is not generic and would not apply to many newer induction hobs with low standby power levels. 1 Domestic refrigerators, freezers and their combinations 2 hobs are also called ranges or cook-tops 3 Induction hobs have induction coils embedded beneath a glass ceramic cooking surface. The induction coils use a frequency converter to raise the frequency of the mains AC, which causes electromagnetic eddy currents to be induced in the bottom of the cooking pans and thereby heats the food. This process is ordinarily more efficient than conventional electric resitance hobs.

Distributed Demand and Response Algorithm for Optimizing Social-Welfare in Smart Grid

Conference Paper

May 2012

This paper presents a distributed Demand and Response algorithm for smart grid with the objective of optimizing social-welfare. Assuming the power demand range is known or predictable ahead of time, our proposed distributed algorithm will calculate demand and response of all participating energy demanders and suppliers, as well as energy flow routes, in a fully distributed fashion, such that the social-welfare is optimized. During the computation, each node (e.g., demander or supplier) only needs to exchange limited rounds of messages with its neighboring nodes. It provides a potential scheme for energy trade among participants in the smart grids. Our theoretical analysis proves that the algorithm converges even if there is some random noise induced in the process of our distributed Lagrange-Newton based solution. The simulation also shows that the result is close to that of centralized solution.

A taxonomy for modeling flexibility and a computationally efficient algorithm for dispatch in Smart Grids

Conference Paper

Jun 2013

The word flexibility is central to Smart Grid literature, but to this day a formal definition of flexibility is still pending. This paper present a taxonomy for modeling flexibility in Smart Grids, denoted Buckets, Batteries and Bakeries. We consider a direct control Virtual Power Plant (VPP), which is given the task of servicing a portfolio of flexible consumers by use of a fluctuating power supply. Based on the developed taxonomy we first prove that no causal optimal dispatch strategies exist for the considered problem. We then present two heuristic algorithms for solving the balancing task: Predictive Balancing and Agile Balancing. Predictive Balancing, is a traditional moving horizon algorithm, where power is dispatched based on perfect predictions of the power supply. Agile Balancing, on the other hand, is strictly non-predictive. It is, however, explicitly designed to exploit the heterogeneity of the flexible consumers. Simulation results show that in spite of being non-predictive Agile Balancing can actually out-perform Predictive Balancing even when Predictive Balancing has perfect prediction over a relatively long horizon. This is due to the flexibility-synergy-effects, which Agile Balancing generates. As a further advantage it is demonstrated, that Agile Balancing is extremely computationally efficient since it is based on sorting rather than linear programming.

Scheduling heterogeneous delay tolerant tasks in smart grid with renewable energy

Conference Paper

Dec 2012

The smart grid is the new generation of electricity grid that can efficiently facilitate new distributed sources of energy (e.g., harvested renewable energy), and allow for dynamic electricity price. In this paper, we investigate the cost minimization problem for an end-user, such as a home, community, or a business, which is equipped with renewable energy devices when electrical appliances allow different levels of delay tolerance. The varying price of electricity presents an opportunity to reduce the electricity bill from an end-user's point of view by leveraging the flexibility to schedule operations of various appliances and HVAC systems. We assume that the end user has an energy storage battery as well as an energy harvesting device so that harvested renewable energy can be stored and later used when the price is high. The energy storage battery can also draw energy from the external grid. The problem we formulate here is to minimize the cost of the energy from the external grid while usage of appliances are subject to individual delay constraints and a longterm average delay constraint. The resulting algorithm requires some future information regarding electricity prices, but achieves provable performance without requiring future knowledge of either the power demands or the task arrival process.

Reliability modeling of demand response considering uncertainty of customer behavior

Article

Jun 2014
APPL ENERG

Demand response (DR) has been considered as a generation alternative to improve the reliability indices of the system and load point. However, when the demand resources scheduled in the DR market fail to result in demand reductions, it can potentially bring new problems associated with maintaining a reliable supply. In this paper, a reliability model of the demand resource is constructed considering customers’ behaviors in the same form as conventional generation units, where the availability and unavailability are associated with the simple two-state model. The reliability model is generalized by a multi-state model. In the integrated power market with DR, market players provide the demand reduction and generation, which are represented by an equivalent multi-state demand response and generation, respectively. The reliability indices of the system and load point are evaluated using the optimal power flow by minimizing the summation of load curtailments with various constraints.

Load commitment in a smart home

Article

Aug 2012
APPL ENERG

An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management

Article

Aug 2012
APPL ENERG

This paper proposes the design of a Smart Home Controller strategy providing efficient management of electric energy in a domestic environment. The problem is formalized as an event driven binary linear programming problem, the output of which specifies the best time to run of smart household appliances, under a virtual power threshold constraint, taking into account the real power threshold and the forecast of consumption from not plannable loads. The optimization is performed each time the system is triggered by proper events, in order to tailor the controller action to the real life dynamics of an household. This problem formulation allows to analyze relevant scenarios from consumer and energy retailer point of view: here overload management, optimization of economic saving in case of Time of Use Tariff and Demand Side Management have been discussed and simulated. Simulations have been performed on relevant test cases, based on real load profiles provided by the smart appliance manufacturer Electrolux S.p.A. and on energy tariffs suggested by the energy retailer Edison. Results provide a proof of concept about the consumers benefits coming from the use of local energy management systems and the relevance of automated Demand Side Management for the general target of efficient and cost effective operation of electric networks.

Optimal combined scheduling of generation and demand response with demand resource constraints

Article

Aug 2012
APPL ENERG

Demand response (DR) extends customer participation to power systems and results in a paradigm shift from simplex to interactive operation in power systems due to the advancement of smart grid technology. Therefore, it is important to model the customer characteristics in DR. This paper proposes customer information as the registration and participation information of DR, thus providing indices for evaluating customer response, such as DR magnitude, duration, frequency and marginal cost. The customer response characteristics are modeled from this information. This paper also introduces the new concept of virtual generation resources, whose marginal costs are calculated in the same manner as conventional generation marginal costs, according to customer information. Finally, some of the DR constraints are manipulated and expressed using the information modeled in this paper with various status flags. Optimal scheduling, combined with generation and DR, is proposed by minimizing the system operation cost, including generation and DR costs, with the generation and DR constraints developed in this paper.

Demand response models with correlated price data: A robust optimization approach

Article

Aug 2012
APPL ENERG

In the electricity industry, the processes through which consumers respond to price signals embedded in tariffs by changing their consumption patterns is generally referred to as demand response. In such a context, consumers are offered an opportunity to maximize the surplus derived from electricity usage by actively scheduling consumption over time periods with potentially different energy prices. The objective of this work is to analyze the role of correlation in prices of successive periods over which consumption is to be scheduled, in a demand response context. We use robust optimization techniques to propose an optimization model for consumption scheduling when prices of different periods are highly correlated, and also suggest approaches to correctly incorporate real-world correlated price data to this model. Positive results from quantitative case studies indicate that it is of great importance to employ a solution approach that correctly models correlation among prices when scheduling consumption.

Modeling and simulation of consumer response to dynamic pricing with enabled technologies

Article

Aug 2012
APPL ENERG

Assessing the impacts of dynamic-pricing under the smart grid concept is becoming extremely important for deciding its full deployment. In this paper, we develop a model that represents the response of consumers to dynamic pricing. In the model, consumers use forecasted day-ahead prices to shift daily energy consumption from hours when the price is expected to be high to hours when the price is expected to be low while maintaining the total energy consumption as unchanged. We integrate the consumer response model into the Electricity Market Complex Adaptive System (EMCAS). EMCAS is an agent-based model that simulates restructured electricity markets. We explore the impacts of dynamic-pricing on price spikes, peak demand, consumer energy bills, power supplier profits, and congestion costs. A simulation of an 11-node test network that includes eight generation companies and five aggregated consumers is performed for a period of 1 month. In addition, we simulate the Korean power system.

Optimal Control Policies for Power Demand Scheduling in the Smart Grid

Article

Jul 2012

We study the problem of minimizing the long-term average power grid operational cost through power demand scheduling. A controller at the operator side receives consumer power demand requests with different power requirements, durations and time flexibilities for their satisfaction. Flexibility is modeled as a deadline by which a demand is to be activated. The cost is a convex function of total power consumption, which reflects the fact that each additional unit of power needed to serve demands is more expensive to provision, as demand load increases. We develop a stochastic model and introduce two online demand scheduling policies. In the first one, the Threshold Postponement (TP), the controller serves a new demand request immediately or postpones it to the end of its deadline, depending on current power consumption. In the second one, the Controlled Release (CR), a new request is activated immediately if power consumption is lower than a threshold, else it is queued. Queued demands are activated when deadlines expire or when consumption drops below the threshold. These policies admit an optimal control with switching curve threshold structure, which involves active and postponed demand. The CR policy is asymptotically optimal as deadlines increase, namely it achieves a lower bound on average cost, and the threshold depends only on active demand. Numerical results validate the benefit of our policies compared to the default one of serving demands upon arrival.

Putting the 'Smarts' into the Smart Grid: A Grand Challenge for Artificial Intelligence

Article

Apr 2012

Delivering a decentralized, autonomous, and intelligent system for smart grids represents a grand challenge for computer science and artificial intelligence (AI) research. A key requirement for a safe and efficient electricity grid is that supply and demand are always in perfect balance. The design of such intelligent systems is challenged by the complexity of the domains in which they are deployed. Automated decentralized coordination between such entities must factor in both the individual properties of all actors involved and the incentives given to them to behave in certain ways. It will be important to design simulation systems that can accurately represent both the grid and the reaction of consumers, in order to predict the emergent properties of the system under a range of different conditions and worst-case scenarios.

So What's So Smart about the Smart Grid?

Article

Dec 2011

Fereidoon P. Sioshansi

Fereidoon P. Sioshansi is President of Menlo Energy Economics and the editor and publisher of EEnergy Informer, a monthly newsletter. His professional experience includes working at Southern California Edison Company (SCE), the Electric Power Research Institute (EPRI), National Economic Research Associates (NERA), and most recently, Ventyx, now part of ABB. Since 2006, he has edited five books on electricity market restructuring, and energy sustainability. He has degrees in Engineering and Economics, including an M.S. and Ph.D. in Economics from Purdue University.

Residential peak electricity demand response—Highlights of some behavioural issues

Article

Sep 2013
RENEW SUST ENERG REV

Electricity demand response refers to consumer actions that change the utility load profile in a way that reduces costs or improves grid security. The focus of demand response has mainly been on the commercial and big industrial sectors because of the large demand reduction that they can offer to the utility grid operators. Utilities are showing increasing interest in residential demand response (RDR). RDR can be treated as an energy resource which can be assessed and commercially developed, however, there are still some issues that remain to be addressed for RDR to be successful. These include price unresponsiveness of some residential consumers, equity issues and high cost of the metering infrastructure. The aim of this paper is to investigate and present some of the challenges in achieving effective voluntary demand reduction based on a review of residential demand response literature as well as the general residential energy use behaviour literature. The authors propose the use of a hybrid engineering approach using social psychology and economic behaviour models to overcome these challenges and realize the benefits of supply security and cost management.

Blocking in a Shared Resource Environment

Article

Nov 1981

Joseph S. Kaufman

In recent years, considerable effort has focused on evaluating the blocking experienced by "customers" in contending for a commonly shared "resource." The customers and resource in question have typically been messages and storage space in message storage applications or data streams and bandwidth in data multiplexing applications. The model employed in these studies, a multidimensional generalization of the classical Erlang loss model, has been limited to exponentially distributed storage (or data transmission) times, questions concerning efficient computational schemes have largely been ignored, and the class of resource sharing policies considered has been unnecessarily restricted. The contribution of this paper is threefold. We first show that the state distribution (obtained by previous authors) is valid for the large class of residency time distributions which have rational Laplace transforms. Second, we show that, for the important and commonly implemented policy of complete sharing, a simple one-dimensional recursion can be developed which eliminates all difficulty in computing quantities of interest-regardless of both the size and dimensionality of the underlying model. Third, we show that the state distribution holds for completely arbitrary resource sharing policies.

Cooperative multi-residence demand response scheduling

Conference Paper

Apr 2011

This paper is concerned with scheduling of demand response among different residences and a utility company. The utility company has a cost function representing the cost of providing energy to end-users, and this cost can be varying across the scheduling horizon. Each end-user has a “must-run” load, and two types of adjustable loads. The first type must consume a specified total amount of energy over the scheduling horizon, but the consumption can be adjusted across different slots. The second type of load has adjustable power consumption without a total energy requirement, but operation of the load at reduced power results in dissatisfaction of the end-user. The problem amounts to minimizing the total cost electricity plus the total user dissatisfaction (social welfare), subject to the individual load consumption constraints. The problem is convex and can be solved by a distributed subgradient method. The utility company and the end-users exchange Lagrange multipliers-interpreted as pricing signals-and hourly consumption data through the Advanced Metering Infrastructure, in order to converge to the optimal amount of electricity production and the optimal power consumption schedule.

Achieving Controllability of Electric Loads

Article

Feb 2011

Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

Article

Jan 2011

Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.

Demand Response and Distribution Grid Operations: Opportunities and Challenges

Article

Oct 2010

Demand response (DR) is becoming an integral part of power system and market operations. Smart grid technologies will further increase the use of DR in everyday operations. Once the volume of the DR reaches a certain threshold, the effect of the DR events on the distribution and transmission system operations will be hard to ignore. This paper proposes changing the business process of DR scheduling and implementation by integrating DR with distribution grid topology. Study cases using OATI webDistribute show the potential DR effect on distribution grid operations and the distribution grid changing the effectiveness of the DR. These examples illustrate the need of integrating demand response with the distribution grid.

The Smart Grid – A saucerful of secrets?

Article

Jul 2011
APPL ENERG

Matthias Wissner

To many, a lot of secrets are at the bottom of the often-cited catchphrase “Smart Grid”. This article gives an overview of the options that information and communication technology (ICT) offers for the restructuring and modernisation of the German power system, in particular with a view towards its development into a Smart Grid and thus tries to reveal these secrets. After a short outline on the development of ICT in terms of technology types and their availability, the further analysis highlights upcoming challenges in all parts of the power value chain and possible solutions for these challenges through the intensified usage of ICT applications. They are examined with regard to their effectiveness and efficiency in the fields of generation, transmission, distribution and supply. Finally, potential obstacles that may defer the introduction of ICT into the power system are shown. The analysis suggests that if certain hurdles are taken, the huge potential of ICT can create additional value in various fields of the whole power value chain. This ranges from increased energy efficiency and the more sophisticated integration of decentralised (renewable) energy plants to a higher security of supply and more efficient organisation of market processes. The results are true for the German power market but can in many areas also be transferred to other industrialised nations with liberalised power markets.

Effect of optimal spinning reserve requirement on system pollution emission considering reserve supplying demand response in the electricity market

Article

Jul 2011
APPL ENERG

Pollution emission reduction is becoming an inevitable global goal. Incorporating pollution reduction goals into power system operation affects several different aspects, such as unit scheduling and system reliability. At the same time, changes in the energy scheduling change the required optimal reserve amount. Optimal spinning reserve scheduling also affects the energy market scheduling. Optimal reserve allocation changes the energy scheduling, which affect the amount of pollution emission. Therefore, incorporating pollution emission reduction and optimal spinning reserve scheduling cannot be studied separately. Analysis of the system effects of pollution reduction should be performed considering the ancillary service market, specificity the optimal spinning reserve scheduling. This problem is addressed in this paper by incorporating optimal spinning reserve scheduling in a combined environment economic dispatch (CEED) in one objective function. The framework of this paper enables the study of the effect of optimal reserve scheduling and emission reduction as well as an analysis of the system effects of pollution reduction. With the increased AMI and smart grid realization, the reserve supplying demand response (RSDR) is becoming an important player in the reserve market, and thus, these resources are also taken into account. In this paper, the objective function is social cost minimization, including the costs associated with energy provision, reserve procurement, expected interruptions and environmental pollution. A MIP-based optimization method is developed, which reduces the computational burden considerably while maintaining the ability to reach to the optimal solution. The IEEE RTS 1996 is used as a test case for numerical simulations, and the results are presented. The numerical results show that optimal reserve scheduling and RSDR utilization resources have a considerable impact on environmental–economic cost characteristics.

Connection-dependent threshold model: A generalization of the Erlang multiple rate loss model

Article

May 2002
PERFORM EVALUATION

In this paper first, we review two extensions of the Erlang multi-rate loss model (EMLM), whereby we can assess the call-level quality-of-service (QoS) of ATM networks. The call-level QoS assessment in ATM networks remains an open issue, due to the emerged elastic services. We consider the coexistence of ABR service with QoS guarantee services in a VP link and evaluate the call blocking probability (CBP), based on the EMLM extensions. In the first extension, the retry models, blocked calls can retry with reduced resource requirements and increased arbitrary mean residency requirements. In the second extension, the threshold models, for blocking avoidance, calls can attempt to connect with other than the initial resource and residency requirements which are state dependent. Secondly, we propose the connection-dependent threshold model (CDTM), which resembles the threshold models, but the state dependency is individualized among call-connections. The proposed CDTM not only generalizes the existing threshold models but also covers the EMLM and the retry models by selecting properly the threshold parameters. Thirdly, we provide formulas for CBP calculation that incorporate bandwidth/trunk reservation schemes, whereby we can balance the grade-of-service among the service-classes. Finally, we investigate the effectiveness of the models applicability on ABR service at call set-up. The retry models can hardly model the behavior of ABR service, while the threshold models perform better than the retry models. The CDTM performs much better than the threshold models; therefore we propose it for assessing the call-level performance of ABR service. We evaluate the above-mentioned models by comparing each other according to the resultant CBP in ATM networks. For the models validation, results obtained by the analytical models are compared with simulation results.

A summary of demand response in electricity markets

Article

Nov 2008
ELECTR POW SYST RES

This paper presents a summary of Demand Response (DR) in deregulated electricity markets. The definition and the classification of DR as well as potential benefits and associated cost components are presented. In addition, the most common indices used for DR measurement and evaluation are highlighted, and some utilities’ experiences with different demand response programs are discussed. Finally, the effect of demand response in electricity prices is highlighted using a simulated case study.

Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid.

Article

Jan 2010

Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments

Article

Sep 2010

Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates . Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.

An experimental investigation of cooking, refrigeration and drying end-uses in 100 households

Jan 2000

O Sidler
P Waide
B Lebot

Sidler O, Waide P, Lebot B. An experimental investigation of cooking, refrigeration and drying end-uses in 100 households. In: Proc ACEEE; 2000.

Performance evaluation of power demand scheduling scenarios in a smart grid environment

Abstract

No full-text available

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