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Demand Response in Smart Grids

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 35242

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Special Issue Editors

Prof. Dr. Pedro Faria

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Guest Editor

GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal
Interests: demand response; electricity markets; energy communities; renewable energy integration; real-time simulation; smart grids; virtual power players
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Prof. Dr. Zita Vale

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Co-Guest Editor

GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-465 Porto, Portugal
Interests: artificial intelligence; demand response; electric vehicles; electricity markets; power and energy systems; renewable and sustainable energy; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The concepts of demand response and smart grids are two rather wide-scope and key topics in the operation of power and energy systems. Although new demand response approaches appear every day, more work is needed to catch its full potential, bringing advantages for all the involved players. The successful implementation of smart grids requires the widespread use of demand response not only by gathering the flexibility of large and medium consumers but also targeting small-size consumers. Effective approaches are needed to put in place adequate strategies and methods to design and manage demand response. As part of the power and energy ecosystem, demand response is a very valuable resource which, when coordinated with the increasing penetration of renewable energy and market-driven business models, can significantly increase the system efficiency while keeping energy costs at reasonable levels.

This Special Issue will address all aspects related to demand flexibility, demand response, and their importance for efficient smart grids.

Prof. Dr. Pedro Faria
Prof. Dr. Zita Vale
Guest Editors

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Keywords

active consumers
business models
consumer profiling
consumption baseline
demand flexibility
demand response
demand response programs
demand side management
distributed energy resources
distributed energy storage
distributed generation
electric and hybrid vehicles
energy efficiency
energy efficient buildings
energy management
energy markets
energy policy
energy resource optimization
energy tariffs
explicit and implicit demand response
incentive-based and price-based demand response
intelligent resource management
load balancing in smart grids
load flexibility
load forecasting
regulatory aspects
renewable energy
smart cities
smart grids
smart homes and smart buildings
transactive energy

Published Papers (11 papers)

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Editorial

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3 pages, 187 KiB

Open AccessEditorial

Demand Response in Smart Grids

by Pedro Faria and Zita Vale

Energies 2023, 16(2), 863; https://doi.org/10.3390/en16020863 - 12 Jan 2023

Cited by 3 | Viewed by 1415

Abstract

The Special Issue “Demand Response in Smart Grids” includes 10 papers [...] Full article

(This article belongs to the Special Issue Demand Response in Smart Grids)

Research

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27 pages, 6814 KiB

Open AccessFeature PaperArticle

Demand Response Coupled with Dynamic Thermal Rating for Increased Transformer Reserve and Lifetime

by Ildar Daminov, Rémy Rigo-Mariani, Raphael Caire, Anton Prokhorov and Marie-Cécile Alvarez-Hérault

Energies 2021, 14(5), 1378; https://doi.org/10.3390/en14051378 - 03 Mar 2021

Cited by 7 | Viewed by 2985

Abstract

(1) Background: This paper proposes a strategy coupling Demand Response Program with Dynamic Thermal Rating to ensure a transformer reserve for the load connection. This solution is an alternative to expensive grid reinforcements. (2) Methods: The proposed methodology firstly considers the N-1 mode under strict assumptions on load and ambient temperature and then identifies critical periods of the year when transformer constraints are violated. For each critical period, the integrated management/sizing problem is solved in YALMIP to find the minimal Demand Response needed to ensure a load connection. However, due to the nonlinear thermal model of transformers, the optimization problem becomes intractable at long periods. To overcome this problem, a validated piece-wise linearization is applied here. (3) Results: It is possible to increase reserve margins significantly compared to conventional approaches. These high reserve margins could be achieved for relatively small Demand Response volumes. For instance, a reserve margin of 75% (of transformer nominal rating) can be ensured if only 1% of the annual energy is curtailed. Moreover, the maximal amplitude of Demand Response (in kW) should be activated only 2–3 h during a year. (4) Conclusions: Improvements for combining Demand Response with Dynamic Thermal Rating are suggested. Results could be used to develop consumer connection agreements with variable network access. Full article

(This article belongs to the Special Issue Demand Response in Smart Grids)

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35 pages, 3563 KiB

Open AccessArticle

A Methodology to Systematically Identify and Characterize Energy Flexibility Measures in Industrial Systems

by Alejandro Tristán, Flurina Heuberger and Alexander Sauer

Energies 2020, 13(22), 5887; https://doi.org/10.3390/en13225887 - 11 Nov 2020

Cited by 19 | Viewed by 3837

Abstract

Industrial energy flexibility enables companies to optimize their energy-associated production costs and support the energy transition towards renewable energy sources. The first step towards achieving energy flexible operation in a production facility is to identify and characterize the energy flexibility measures available in the industrial systems that comprise it. These industrial systems are both the manufacturing systems that directly execute the production tasks and the systems performing supporting tasks or tasks necessary for the operation of these manufacturing systems. Energy flexibility measures are conscious and quantifiable actions to carry out a defined change of operative state in an industrial system. This work proposes a methodology to identify and characterize the available energy flexibility measures in industrial systems regardless of the task they perform in the facility. This methodology is the basis of energy flexibility-oriented industrial energy audits, in juxtaposition with the current industrial energy audits that focus on energy efficiency. This audit will provide industrial enterprises with a qualitative and quantitative understanding of the capabilities of their industrial systems, and hence their production facilities, for energy flexible operation. The audit results facilitate a company’s decision making towards the implementation, evaluation and management of these capabilities. Full article

(This article belongs to the Special Issue Demand Response in Smart Grids)

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21 pages, 5162 KiB

Open AccessArticle

Quantifying the Flexibility of Electric Vehicles in Germany and California—A Case Study

by Michel Zade, Zhengjie You, Babu Kumaran Nalini, Peter Tzscheutschler and Ulrich Wagner

Energies 2020, 13(21), 5617; https://doi.org/10.3390/en13215617 - 27 Oct 2020

Cited by 16 | Viewed by 2878

Abstract

The adoption of electric vehicles is incentivized by governments around the world to decarbonize the mobility sector. Simultaneously, the continuously increasing amount of renewable energy sources and electric devices such as heat pumps and electric vehicles leads to congested grids. To meet this challenge, several forms of flexibility markets are currently being researched. So far, no analysis has calculated the actual flexibility potential of electric vehicles with different operating strategies, electricity tariffs and charging power levels while taking into account realistic user behavior. Therefore, this paper presents a detailed case study of the flexibility potential of electric vehicles for fixed and dynamic prices, for three charging power levels in consideration of Californian and German user behavior. The model developed uses vehicle and mobility data that is publicly available from field trials in the USA and Germany, cost-optimizes the charging process of the vehicles, and then calculates the flexibility of each electric vehicle for every 15 min. The results show that positive flexibility is mostly available during either the evening or early morning hours. Negative flexibility follows the periodic vehicle availability at home if the user chooses to charge the vehicle as late as possible. Increased charging power levels lead to increased amounts of flexibility. Future research will focus on the integration of stochastic forecasts for vehicle availability and electricity tariffs. Full article

(This article belongs to the Special Issue Demand Response in Smart Grids)

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15 pages, 2648 KiB

Open AccessArticle

Non-Intrusive Identification of Load Patterns in Smart Homes Using Percentage Total Harmonic Distortion

by Hari Prasad Devarapalli, V. S. S. Siva Sarma Dhanikonda and Sitarama Brahmam Gunturi

Energies 2020, 13(18), 4628; https://doi.org/10.3390/en13184628 - 06 Sep 2020

Cited by 14 | Viewed by 2205

Abstract

Demand Response (DR) plays a vital role in a smart grid, helping consumers plan their usage patterns and optimize electricity consumption and also reduce harmonic pollution in a distribution grid without compromising on their needs. The first step of DR is the disaggregation of loads and identifying them individually. The literature suggests that this is accomplished through electric features. Present-day households are using modern power electronic-based nonlinear loads such as LED (Light Emitting Diode) lamps, electronic regulators and digital controllers to reduce the electricity consumption. Furthermore, usage of SMPS (Switched-Mode Power Supply) for computing and mobile phone chargers is increasing in every home. These nonlinear loads, while reducing electricity consumption, also introduce harmonic pollution into the distribution grid. This article presents a deterministic approach to the non-intrusive identification of load patterns using percentage Total Harmonic Distortion (THD) for DR management from a Power Quality perspective. The percentage THD of various combinations of loads is estimated by enhanced dual-spectrum line interpolated FFT (Fast Fourier Transform) with a four-term minimal side-lobe window using a LabVIEW-based hardware setup in real time. The results demonstrate that percentage THD identifies a different combination of loads effectively and advocates alternate load combinations for recommending to the consumer to reduce harmonic pollution in the distribution grid. Full article

(This article belongs to the Special Issue Demand Response in Smart Grids)

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19 pages, 418 KiB

Open AccessArticle

Impact of Social Welfare Metrics on Energy Allocation in Multi-Objective Optimization

by Anders Clausen, Aisha Umair, Yves Demazeau and Bo Nørregaard Jørgensen

Energies 2020, 13(11), 2961; https://doi.org/10.3390/en13112961 - 09 Jun 2020

Cited by 4 | Viewed by 1617

Abstract

Resource allocation problems are at the core of the smart grid where energy supply and demand must match. Multi-objective optimization can be applied in such cases to find the optimal allocation of energy resources among consumers considering energy domain factors such as variable and intermittent production, market prices, or demand response events. In this regard, this paper considers consumer energy demand and system-wide energy constraints to be individual objectives and optimization variables to be the allocation of energy over time to each of the consumers. This paper considers a case in which multi-objective optimization is used to generate Pareto sets of solutions containing possible allocations for multiple energy intensive consumers constituted by commercial greenhouse growers. We consider the problem of selecting a final solution from these Pareto sets, one of maximizing the social welfare between objectives. Social welfare is a set of metrics often applied to multi-agent systems to evaluate the overall system performance. We introduce and apply social welfare ordering using different social welfare metrics to select solutions from these sets to investigate the impact of the type of social welfare metric on the optimization outcome. The results of our experiments indicate how different social welfare metrics affect the optimization outcome and how that translates to general resource allocation strategies. Full article

(This article belongs to the Special Issue Demand Response in Smart Grids)

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18 pages, 32508 KiB

Open AccessArticle

Ramping of Demand Response Event with Deploying Distinct Programs by an Aggregator

by Omid Abrishambaf, Pedro Faria and Zita Vale

Energies 2020, 13(6), 1389; https://doi.org/10.3390/en13061389 - 16 Mar 2020

Cited by 13 | Viewed by 2570

Abstract

System operators have moved towards the integration of renewable resources. However, these resources make network management unstable as they have variations in produced energy. Thus, some strategic plans, like demand response programs, are required to overcome these concerns. This paper develops an aggregator model with a precise vision of the demand response timeline. The model at first discusses the role of an aggregator, and thereafter is presented an innovative approach to how the aggregator deals with short and real-time demand response programs. A case study is developed for the model using real-time simulator and laboratory resources to survey the performance of the model under practical challenges. The real-time simulation uses an OP5600 machine that controls six laboratory resistive loads. Furthermore, the actual consumption profiles are adapted from the loads with a small-time step to precisely survey the behavior of each load. Also, remuneration costs of the event during the case study have been calculated and compared using both actual and simulated demand reduction profiles in the periods prior to event, such as the ramp period. Full article

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18 pages, 4181 KiB

Open AccessArticle

Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study

by Eunjung Lee, Jinho Kim and Dongsik Jang

Energies 2020, 13(6), 1348; https://doi.org/10.3390/en13061348 - 13 Mar 2020

Cited by 15 | Viewed by 3815

Abstract

Due to the heterogeneity of demand response behaviors among customers, selecting a suitable segment is one of the key factors for the efficient and stable operation of the demand response (DR) program. Most utilities recognize the importance of targeted enrollment. Customer targeting in DR programs is normally implemented based on customer segmentation. Residential customers are characterized by low electricity consumption and large variability across times of consumption. These factors are considered to be the primary challenges in household load profile segmentation. Existing customer segmentation methods have limitations in reflecting daily consumption of electricity, peak demand timings, and load patterns. In this study, we propose a new clustering method to segment customers more effectively in residential demand response programs and thereby, identify suitable customer targets in DR. The approach can be described as a two-stage k-means procedure including consumption features and load patterns. We provide evidence of the outstanding performance of the proposed method compared to existing k-means, Self-Organizing Map (SOM) and Fuzzy C-Means (FCM) models. Segmentation results are also analyzed to identify appropriate groups participating in DR, and the DR effect of targeted groups was estimated in comparison with customers without load profile segmentation. We applied the proposed method to residential customers who participated in a peak-time rebate pilot DR program in Korea. The result proves that the proposed method shows outstanding performance: demand reduction increased by 33.44% compared with the opt-in case and the utility saving cost in DR operation was 437,256 KRW. Furthermore, our study shows that organizations applying DR programs, such as retail utilities or independent system operators, can more economically manage incentive-based DR programs by selecting targeted customers. Full article

(This article belongs to the Special Issue Demand Response in Smart Grids)

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18 pages, 2775 KiB

Open AccessArticle

Electricity Consumption Prediction of Solid Electric Thermal Storage with a Cyber–Physical Approach

by Huichao Ji, Junyou Yang, Haixin Wang, Kun Tian, Martin Onyeka Okoye and Jiawei Feng

Energies 2019, 12(24), 4744; https://doi.org/10.3390/en12244744 - 12 Dec 2019

Cited by 6 | Viewed by 2200

Abstract

This paper proposes a cyber–physical approach to enhance the prediction accuracy of electricity consumption of solid electric thermal storage (SETS) system, which integrates a physical model and a data-based cyber model. In the cyber–physical model, the prediction error of the physical model is used as an input of the cyber model to further calibrate the prediction error. Firstly, customers’ behavior characteristics are extracted by the integration of K-means and one-versus-one support vector machine. Secondly, based on the behavior characteristics and ambient temperature, the physical model is developed to predict daily electricity consumption. Finally, the error levels of physical model are classified, together with the temperature and prediction values of the physical model, are selected as the inputs of the cyber model using the back propagation (BP) neural network to calibrate the results of the physical model. The effectiveness of the proposed cyber–physical model (CPM) is verified by a 1 MW SETS system. The simulation results show that, compared with the physical model (PM) and cyber model (CM), the maximum relative errors (MRE) with the CPM are reduced to 25.4% and 4.8%, respectively. Full article

(This article belongs to the Special Issue Demand Response in Smart Grids)

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19 pages, 2070 KiB

Open AccessArticle

Evolutionary Analysis for Residential Consumer Participating in Demand Response Considering Irrational Behavior

by Xiaofeng Liu, Qi Wang and Wenting Wang

Energies 2019, 12(19), 3727; https://doi.org/10.3390/en12193727 - 29 Sep 2019

Cited by 8 | Viewed by 1879

Abstract

Demand response (DR) has been recognized as a powerful tool to relieve energy imbalance in the smart grid. Most previous works have ignored the irrational behavior of energy consumers in DR project implementation. Accordingly, in this paper, we focus on solving two questions during the execution of DR. Firstly, considering the bounded rationality of residential users, a population dynamic model is proposed to describe the decision behavior on whether to participate in the DR project, and then the evolutionary process of consumers participating in DR is analyzed. Secondly, for the DR participants, they have to compete dispatching amounts for maximal profit in a day-ahead bidding market, hence, a non-cooperative game model is proposed to describe the competition behavior, and the uniqueness of the Nash equilibrium is analyzed with mathematical proof. Then, the distributed algorithm is designed to search the evolutionary result and the Nash equilibrium. Finally, a case study is performed to show the effectiveness of the formulated models. Full article

(This article belongs to the Special Issue Demand Response in Smart Grids)

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Review

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30 pages, 4377 KiB

Open AccessFeature PaperReview

Review of Control and Energy Management Approaches in Micro-Grid Systems

by Abdellatif Elmouatamid, Radouane Ouladsine, Mohamed Bakhouya, Najib El Kamoun, Mohammed Khaidar and Khalid Zine-Dine

Energies 2021, 14(1), 168; https://doi.org/10.3390/en14010168 - 31 Dec 2020

Cited by 100 | Viewed by 8256

Abstract

The demand for electricity is increased due to the development of the industry, the electrification of transport, the rise of household demand, and the increase in demand for digitally connected devices and air conditioning systems. For that, solutions and actions should be developed for greater consumers of electricity. For instance, MG (Micro-grid) buildings are one of the main consumers of electricity, and if they are correctly constructed, controlled, and operated, a significant energy saving can be attained. As a solution, hybrid RES (renewable energy source) systems are proposed, offering the possibility for simple consumers to be producers of electricity. This hybrid system contains different renewable generators connected to energy storage systems, making it possible to locally produce a part of energy in order to minimize the consumption from the utility grid. This work gives a concise state-of-the-art overview of the main control approaches for energy management in MG systems. Principally, this study is carried out in order to define the suitable control approach for MGs for energy management in buildings. A classification of approaches is also given in order to shed more light on the need for predictive control for energy management in MGs. Full article

(This article belongs to the Special Issue Demand Response in Smart Grids)

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