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Optimization Models to Foster Demand Response in Power Systems
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Optimization Models to Foster Demand Response in Power Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: closed (10 December 2021) | Viewed by 21107

Special Issue Editor

Prof. Dr. Carlos Henggeler Antunes

E-Mail Website
Guest Editor
INESC Coimbra, Department of Electrical and Computer Engineering, University of Coimbra, Polo 2, 3030-290 Coimbra, Portugal
Interests: energy efficiency; demand side management; demand response; optimization models and methods in energy systems; multi-objective optimization; multi-criteria decision analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

The management of demand-side resources, by making the most of the flexibility of end-users in the operation of some loads, is increasingly becoming a relevant issue in face of the penetration of distributed renewable generation and the need to ensure a high level of system efficiency in the evolution to smart(er) grids. For this purpose, adequate optimization models and algorithms embedded in automated energy management systems are required for the integrated optimization of all available energy resources (grid, loads, local generation and storage) considering dynamic time-of-use tariff schemes, with potential benefits for the end-users, retailers, and grid operators. Moreover, encompassing the participation of end-users in emerging energy, capacity, and ancillary service markets conveys an additional challenge for those models. Also, new entities as aggregators and energy communities are being developed, with potential impact on the reconfiguration of the relationships between the stakeholders (including generators, grid operators, prosumers) and market architectures, leading to the empowerment of formerly passive consumers who will gain a more proactive role through demand response and market participation mechanisms. The ultimate aim is to develop more sustainable, reliable, and efficient grids, with potential benefits for all players globally.

Contributions to this Special Issue are expected to cover novel models and optimization tools for addressing a wide range of topics in demand-side management and demand response, namely concerning load scheduling, the integrated optimization of energy resources, issues associated with the location of equipment, as well as communications, system reliability and provision of ancillary services, and market design and operation in the realm of the evolution to smart grids. Contributions reporting real-world case studies are also welcome.

All papers will undergo a stringent peer review procedure in accordance with the quality standards of Energies. Papers must contain original research results including comprehensive mathematical models, algorithmic advances, and extensive numerical experiments. Numerical illustrations cannot be toy examples, but must be real or realistic case studies for which all data should be provided (in the paper or as supplementary material) to ensure the replicability of results. The research reported in contributed papers should convey novel and significant work with respect to the relevant literature.

Prof. Dr. Carlos Henggeler Antunes
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optimization models and methods
  • power systems
  • smart grids
  • demand response
  • demand-side management

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Published Papers (5 papers)

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Research

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16 pages, 1221 KiB  
Article
Demand Response as a Real-Time, Physical Hedge for Retail Electricity Providers: The Electric Reliability Council of Texas Market Case Study
by Andrew Blohm, Jaden Crawford and Steven A. Gabriel
Energies 2021, 14(4), 808; https://doi.org/10.3390/en14040808 - 3 Feb 2021
Cited by 7 | Viewed by 2549
Abstract
Residential demand response (DR) programs are generally administered through an electricity distribution utility, or an electric grid operator. These programs typically reduce electricity consumption by inducing behavioral changes in the occupants of participating households. We propose implementing a wholesale-price-sensitive residential DR program through [...] Read more.
Residential demand response (DR) programs are generally administered through an electricity distribution utility, or an electric grid operator. These programs typically reduce electricity consumption by inducing behavioral changes in the occupants of participating households. We propose implementing a wholesale-price-sensitive residential DR program through the retail electricity provider (REP), who has more naturally aligned incentives to avoid high wholesale electricity prices and maintain customer satisfaction, as compared to distribution utilities, grid operators, and the average residential consumer. Retail electricity providers who serve residential consumers are exposed to substantial price risk as they generally have a portion of their portfolio exposed to variable real-time wholesale electricity prices, despite charging their residential customers a fixed retail electricity price. Using Monte Carlo simulations, we demonstrate that demand response, executed through internet-connected thermostats, to shift real-time residential HVAC load in response to real-time prices, can be used as an effective physical hedge, which is both less costly and more effective than relying solely on financial hedging mechanisms. We find that on average a REP can avoid USD 62.07 annually per household using a load-shifting program. Given that REPs operate in a low margin industry, an annual avoided cost of this magnitude is not trivial. Full article
(This article belongs to the Special Issue Optimization Models to Foster Demand Response in Power Systems)
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20 pages, 1530 KiB  
Article
Comparison of the Effects of Industrial Demand Side Management and Other Flexibilities on the Performance of the Energy System
by Arjuna Nebel, Christine Krüger, Tomke Janßen, Mathieu Saurat, Sebastian Kiefer and Karin Arnold
Energies 2020, 13(17), 4448; https://doi.org/10.3390/en13174448 - 27 Aug 2020
Cited by 10 | Viewed by 2889
Abstract
In order to ensure security of supply in a future energy system with a high share of volatile electricity generation, flexibility technologies are needed. Industrial demand-side management ranks as one of the most efficient flexibility options. This paper analyses the effect of the [...] Read more.
In order to ensure security of supply in a future energy system with a high share of volatile electricity generation, flexibility technologies are needed. Industrial demand-side management ranks as one of the most efficient flexibility options. This paper analyses the effect of the integration of industrial demand-side management through the flexibilisation of aluminium electrolysis and other flexibilities of the electricity system and adjacent sectors. The additional flexibility options include electricity storage, heat storage in district heating networks, controlled charging of electric vehicles, and buffer storage in hydrogen electrolysis. The utilisation of the flexibilities is modelled in different settings with an increasing share of renewable energies, applying a dispatch model. This paper compares which contributions the different flexibilities can make to emission reduction, avoidance of curtailment, and reduction of fuel and CO2 costs, and which circumstances contribute to a decrease or increase of overall emissions with additional flexibilities. The analysis stresses the rising importance of flexibilities in an energy system based on increasing shares of renewable electricity generation, and shows that flexibilities are generally suited to reduce carbon emissions. It is presented that the relative contribution towards the reduction of curtailment and costs of flexibilisation of aluminium electrolysis are high, whereby the absolute effect is small compared to the other options due to the limited number of available processes. Full article
(This article belongs to the Special Issue Optimization Models to Foster Demand Response in Power Systems)
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24 pages, 2790 KiB  
Article
Optimal Energy and Reserve Market Management in Renewable Microgrid-PEVs Parking Lot Systems: V2G, Demand Response and Sustainability Costs
by Viviani Caroline Onishi, Carlos Henggeler Antunes and João Pedro Fernandes Trovão
Energies 2020, 13(8), 1884; https://doi.org/10.3390/en13081884 - 13 Apr 2020
Cited by 32 | Viewed by 4281
Abstract
Vehicle-to-grid (V2G) technology heralds great promise as a demand-side resource to contribute to more efficient grid management and promote the use of decentralized renewable energy. In this light, we propose a new optimization model for the sustainable energy and reserve market management in [...] Read more.
Vehicle-to-grid (V2G) technology heralds great promise as a demand-side resource to contribute to more efficient grid management and promote the use of decentralized renewable energy. In this light, we propose a new optimization model for the sustainable energy and reserve market management in renewable-driven microgrid (RMG) plug-in electric vehicles (PEVs) parking lot systems. The RMG is composed of a hybrid photovoltaic/wind/hydrogen energy and storage system, along with local dispatchable generation units and bidirectional grid connection. The RMG is coupled to a smart PEVs parking lot, which is equipped with grid-to-vehicle (G2V) and V2G technologies allowing for not only PEVs aggregation and control but also optimal allocation of energy resources. Time-of-use (TOU) prices are considered in a demand response program (DRP) to enhance both economic and environmental performances by encouraging end-users to shift their energy demands from peak to off-peak time periods. Additionally, the model accounts for an economic incentive to PEVs owners to compensate for battery degradation. The integrated system eco-efficiency is evaluated through the application of the novel life cycle assessment-based Eco-cost indicator. The resulting mixed-integer linear programming model to minimize sustainability costs is implemented in GAMS and solved to global optimality. Different case studies are performed to demonstrate the effectiveness of the proposed modelling approach. Energy analyses results reveal that the optimal G2V-V2G operation, allied to TOU prices in a DRP, and reserve market management can reduce around 42% the energy and environmental costs of the RMG-PEVs parking lot system. Full article
(This article belongs to the Special Issue Optimization Models to Foster Demand Response in Power Systems)
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28 pages, 5761 KiB  
Article
Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response
by Hyung-Joon Kim and Mun-Kyeom Kim
Energies 2019, 12(21), 4142; https://doi.org/10.3390/en12214142 - 30 Oct 2019
Cited by 22 | Viewed by 3290
Abstract
This paper proposes an optimal energy management approach for a grid-connected microgrid (MG) by considering the demand response (DR). The multi-objective optimization framework involves minimizing the operating cost and maximizing the utility benefit. The proposed approach combines confidence-based velocity-controlled particle swarm optimization (CVCPSO) [...] Read more.
This paper proposes an optimal energy management approach for a grid-connected microgrid (MG) by considering the demand response (DR). The multi-objective optimization framework involves minimizing the operating cost and maximizing the utility benefit. The proposed approach combines confidence-based velocity-controlled particle swarm optimization (CVCPSO) (i.e., PSO with an added confidence term and modified inertia weight and acceleration parameters), with a fuzzy-clustering technique to find the best compromise operating solution for the MG operator. Furthermore, a confidence-based incentive DR (CBIDR) strategy was adopted, which pays different incentives in different periods to attract more DR participants during the peak period and thus ensure the reliability of the MG under the peak load. In addition, the peak load shaving factor (PLSF) was employed to show that the reliability of the peak load had improved. The applicability and effectiveness of the proposed approach were verified by conducting simulations at two different scales of MG test systems. The results confirm that the proposed approach not only enhances the MG system peak load reliability, but also facilitates economical operation with better performance in terms of solution quality and diversity. Full article
(This article belongs to the Special Issue Optimization Models to Foster Demand Response in Power Systems)
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Review

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31 pages, 1194 KiB  
Review
An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem
by Amit Shewale, Anil Mokhade, Nitesh Funde and Neeraj Dhanraj Bokde
Energies 2020, 13(16), 4266; https://doi.org/10.3390/en13164266 - 18 Aug 2020
Cited by 61 | Viewed by 6903
Abstract
Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its [...] Read more.
Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer’s flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance scheduling problem (RASP) is the problem of scheduling appliances at appropriate periods considering the pricing schemes. The objectives of RASP are to minimize electricity cost (EC) of users, minimize the peak-to-average ratio (PAR), and improve the user satisfaction (US) level by minimizing waiting times for the appliances. Various methods have been studied for energy management in residential sectors which encourage the users to schedule their appliances efficiently. This paper aims to give an overview of optimization techniques for residential appliance scheduling. The reviewed studies are classified into classical techniques, heuristic approaches, and meta-heuristic algorithms. Based on this overview, the future research directions are proposed. Full article
(This article belongs to the Special Issue Optimization Models to Foster Demand Response in Power Systems)
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