Demand Side Management challenges in smart grid: A review

Baharlouei, Zahra; Hashemi, Massoud Reza

doi:10.1109/sgc.2013.6733807

Cited by 28 publications

(12 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…69 The cyber security and privacy are considered as key challenges, when the DSM is implemented in SG network. 70 Cyber attackers can access and misinterpret the information, which is vulnerable to the invasion of privacy. Such information includes the software of the DSM algorithm, load data, price signals, and users' personal information.…”

Section: Challenges Of Dsm Implementationmentioning

confidence: 99%

Progress on the demand side management in smart grid and optimization approaches

et al. 2020

View full text Add to dashboard Cite

The integration of demand side management (DSM) with smart grid (SG) can facilitate residents' transfer into smart homes and sustainable cities by reducing the carbon emission. This manuscript reviews the recent works related to the application of DSM in SG through discussing the techniques and algorithms and their associated challenges for effective implementation. This paper also critically discusses the operation mode of DSM, the profile of energy production, storage and consumption, and finally the benefit obtained by the DSM implementation. Previous literature suggested that DSM practice reduced peak-to-average ratio, energy cost and carbon emission by approximately 10% to 65%, 5% to 50%, and 14%, respectively. The implementation of DSM in SG deals with a number of challenges such as security and privacy, tariff regulation, energy transmission, distribution, and effective utilization of energy resources. A number of international organizations have taken various measures and solutions to guarantee the security and privacy of the DSM in SG discussed. So far, a number of algorithms have been used as optimization approach to solve the DSM optimization problems; however hybrid algorithms have showed better performance than single algorithms due to their faster convergence speed. At the end, the paper presents the research gaps and future research directions.

show abstract

Section: Challenges Of Dsm Implementationmentioning

confidence: 99%

Progress on the demand side management in smart grid and optimization approaches

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Despite having several benefits, the DR deployment especially for residential consumers is still at a primitive stage . Several technical issues, such as control architecture and control strategies, need to be designed to effectively deploy the DR for residential consumers . The control architecture is briefly described in the DR control architecture section.…”

Section: Benefits Of Drmentioning

confidence: 99%

Opportunities and challenges of demand response in active distribution networks

Ponnaganti

Pillai

Bak‐Jensen

2017

WIREs Energy & Environment

View full text Add to dashboard Cite

In power systems, the installed generation capacity must exceed the annual peak demand, even though some capacity is kept idle most of the time. However, if it is uneconomical or not feasible to augment a sufficient capacity, the demand might exceed the available capacity. This mandates the system operator to shed the load in order to maintain security of the system. With the advent of advanced smart metering infrastructure, communication between system operator and end-use customers makes it possible to adjust/curtail/shift the demand with respect to the state of the system. The response of the demand commonly termed as demand response (DR) can be attained either by incentive-based or pricebased. With the help of DR, the renewable energy generation capacity can be increased by tuning the demand to match the variable and unpredictable power from renewable generation. It can also bring other benefits such as peak shaving, hosting capacity enhancement, and generation cost reduction. Furthermore, electric vehicles, heat pumps, and electric water heater can also be used as distributed storage resources to contribute to ancillary services, such as frequency/ voltage regulation, peak-shaving power or help to integrate fluctuating renewable resources. All these DR modes of operation need conventional regulatory frameworks and market design for capitalizing the available resources. Therefore, the objective of the study is to discuss the DR classification and their control strategies, DR role in microgrids and integration of renewable energy resources. Also, highlighted the opportunities and challenges along with the insights for the research scope associated with DR.

show abstract

“…The focus is to decrease the peak of system's demand, preventing new investments to raise the capacity of generation, transmission and distribution of energy [14].…”

Section: Demand Side Managementmentioning

confidence: 99%

An evolutionary approach for the demand side management optimization in smart grid

Vidal

Jacobs

Batista

2014

2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG)

View full text Add to dashboard Cite

An important function of a Smart Grid (SG) is the Demand Side Management (DSM), which consists on controlling loads at customers side, aiming to operate the system with major efficiency and sustainability. The main advantages of this technique are (i) the decrease of demand curve's peak, that results on smoother load profile and (ii) the reduction of both operational costs and the requirement of new investments in the system. The customer can save money by using loads on schedules with lower taxes instead of schedules with higher taxes. In this context, this work proposes a simple metaheuristic to solve the problem of DSM on smart grid. The suggested approach is based on the concept of day-ahead load shifting, which implies on the exchange of the use schedules planned for the next day and aims to obtain the lowest possible cost of energy. The demand management is modeled as an optimization problem whose solution is obtained by using an Evolutionary Algorithm (EA). The experimental tests are carried out considering a smart grid with three distinct demand areas, the first with residential clients, other one with commercial clients and a third one with industrial clients, all of them possessing a major number of controllable loads of diverse types. The obtained results were significant in all three areas, pointing substantial cost reductions for the customers, mainly on the industrial area.

show abstract

Demand Side Management challenges in smart grid: A review

Cited by 28 publications

References 51 publications

Progress on the demand side management in smart grid and optimization approaches

Progress on the demand side management in smart grid and optimization approaches

Opportunities and challenges of demand response in active distribution networks

An evolutionary approach for the demand side management optimization in smart grid

Contact Info

Product

Resources

About