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Статті в журналах з теми "Demand response (DR) programs"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Zhang, You You, Rui Ma, and Zi Heng Xu. "The Review of Demand Response Programs in Smart Grid." Advanced Materials Research 614-615 (December 2012): 1800–1803. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.1800.

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Demand response (DR) has become an important part of smart grid, mainly for modulating load curve, emergency treatment, balancing the demand and supply of power system. The features of smart grid-self healing and interactive-give the new meaning to DR. In smart grid, the promotion strategies of ISO/RTO in energy and auxiliary service markets implement DR programs in the project of auxiliary services and can reduce energy consumption. This paper summarizes the global related researches and practices, including the classification, progress of each subclass, the future development potential and D

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2

Shekari, Mohammadreza, Hamidreza Arasteh, Alireza Sheikhi Fini, and Vahid Vahidinasab. "Demand Response Requirements from the Cultural, Social, and Behavioral Perspectives." Applied Sciences 11, no. 23 (2021): 11456. http://dx.doi.org/10.3390/app112311456.

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Demand-side response programs, commonly known as demand response (DR), are interesting ways to attract consumers’ participation to improve electric consumption patterns. Customers are encouraged to modify their usage patterns in reaction to price increases through DR programs. When wholesale market prices are high or network reliability is at risk, DR can help to establish a balance between electricity generation and consumption by providing incentives or considering penalties. The overall objective of adopting DR programs is to increase network reliability and decrease operational costs. Neve

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3

H. Al-Kharsan, Ibrahim, Ali Z. Ghazi Zahid, Ali F.Marhoon, and Jawad Radhi Mahmood. "Demand response programs in smart grids â€“ survey." International Journal of Engineering & Technology 7, no. 4 (2019): 5090–99. http://dx.doi.org/10.14419/ijet.v7i4.23797.

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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 i

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4

Faria, Pedro, and Zita Vale. "A Demand Response Approach to Scheduling Constrained Load Shifting." Energies 12, no. 9 (2019): 1752. http://dx.doi.org/10.3390/en12091752.

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Demand response (DR) and its advantages are nowadays unquestionable due to the success of several recent implementations of DR programs. Improved methodologies and approaches are needed for the adequate consumers’ schedule in DR events, taking the consumers’ behaviour and preferences into account. In this paper, a virtual power player manages DR programs, minimizing operation costs, respecting the consumption shifting constraints. The impact of the consumption shifting in the target periods is taken into consideration. The advantages of the DR use in comparison with distributed generation (DG)

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5

Vahid-Ghavidel, Morteza, Mohammad Sadegh Javadi, Matthew Gough, Sérgio F. Santos, Miadreza Shafie-khah, and João P. S. Catalão. "Demand Response Programs in Multi-Energy Systems: A Review." Energies 13, no. 17 (2020): 4332. http://dx.doi.org/10.3390/en13174332.

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A key challenge for future energy systems is how to minimize the effects of employing demand response (DR) programs on the consumer. There exists a diverse range of consumers with a variety of types of loads, such as must-run loads, and this can reduce the impact of consumer participation in DR programs. Multi-energy systems (MES) can solve this issue and have the capability to reduce any discomfort faced by all types of consumers who are willing to participate in the DRPs. In this paper, the most recent implementations of DR frameworks in the MESs are comprehensively reviewed. The DR modellin

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6

Vardakas, John S., Nizar Zorba, and Christos Verikoukis. "A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms." IEEE Communications Surveys & Tutorials 17, no. 1 (2015): 152–78. https://doi.org/10.1109/COMST.2014.2341586.

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The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the con

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7

Safdar, Madia, Ghulam Amjad Hussain, and Matti Lehtonen. "Costs of Demand Response from Residential Customers’ Perspective." Energies 12, no. 9 (2019): 1617. http://dx.doi.org/10.3390/en12091617.

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Анотація:

Electricity demand in a certain locality varies during the day, depending on weather conditions, daily life routines, or a social event in a town. During high/peak demands, expensive power plants are put into operation, which affects electricity prices. Moreover, power lines are overloaded. If generation capacity is insufficient, a blackout may result. Demand response (DR) programs are widely proposed in energy research to tackle these problems. Although the benefits of DR programs are well known, customer response levels to these programs is low. This is due to the small fraction of benefits

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8

Park, Herie. "Human Comfort-Based-Home Energy Management for Demand Response Participation." Energies 13, no. 10 (2020): 2463. http://dx.doi.org/10.3390/en13102463.

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The residential building sector is encouraged to participate in demand response (DR) programs owing to its flexible and effective energy resources during peak hours with the help of a home energy management system (HEMS). Although the HEMS contributes to reducing energy consumption of the building and the participation of occupants in energy saving programs, unwanted interruptions and strict guidance from the system cause inconvenience to the occupants further leading to their limited participation in the DR programs. This paper presents a human comfort-based control approach for home energy m

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9

Ustundag Soykan, Elif, and Mustafa Bagriyanik. "The Effect of SMiShing Attack on Security of Demand Response Programs." Energies 13, no. 17 (2020): 4542. http://dx.doi.org/10.3390/en13174542.

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Demand response (DR) is a vital element for a reliable and sustainable power grid. Consumer behavior is a key factor in the success of DR programs. In this study, we focus on how consumer reaction to Short Messaging Service (SMS) messages can disturb the demand response. We present a new type of threat to DR programs using SMS phishing attacks. We follow a holistic approach starting from a risk assessment focusing on DR programs’ notification message security following the Smart Grid Information Security (SGIS) risk methodology. We identify threats, conduct impact analysis, and estimate the li

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10

Mohammad, Mirzaei Talabari. "Optimization of grid-connected MicroGrid demand considering demand response." National Security and Strategic Planning 2024, no. 1 (2024): 60–65. http://dx.doi.org/10.37468/2307-1400-2024-1-60-65.

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Electricity grid is a product of urbanization expansion and rapid development of various infrastructures worldwide and over the past centuries. Although power companies are located in diverse regions, they typically use the same technologies to generate and distribute electricity. Proper implementation of the demand response (DR) program should be provided with some equipment to make subscribers aware of electricity price at any time and accordingly provide a proper response to the grid to reduce costs. This, in turn, reduces demand during peak hours. The intelligent grid, using the two-way co

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11

Gutiérrez Caballero, Susana María, José Luis Hernández, Marzia Mammina, Luca Vitale, and Alessandro Rossi. "Interoperability in demand response services for building systems interaction." Open Research Europe 4 (November 28, 2024): 256. http://dx.doi.org/10.12688/openreseurope.18826.1.

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Background The increasing complexity of building systems, such as HVAC (Heating, Ventilation, and Air Conditioning), lighting, and energy management, has exacerbated interoperability challenges. Diverse communication protocols and data formats hinder seamless integration, affecting optimal building performance and energy efficiency. Demand Response (DR) programs, which adjust energy use based on grid signals, are particularly impacted by these issues. Methods This study proposes an ontology-based approach to address interoperability in DR programs, using the DEDALUS project as a case study. Th

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12

Mohammad, AliFotouhi Ghazvini, Soares Joao, Abrishambaf Omid, Castro Rui, and Vale Zita. "Demand response implementation in smart households." Energy and Buidlings 143, no. 15 May 2017 (2017): 129–48. https://doi.org/10.1016/j.enbuild.2017.03.020.

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Home energy management system (HEMS) is essential for residential electricity consumers to participate actively in demand response (DR) programs. Dynamic pricing schemes are not sufficiently effective for end-users without utilizing a HEMS for consumption management. In this paper, an intelligent HEMS algorithm is proposed to schedule the consumption of controllable appliances in a smart household. Electric vehicle (EV) and electric water heater (EWH) are incorporated in the HEMS. They are controllable appliances with storage capability. EVs are flexible energy-intensive loads, which can provi

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13

Vesa, Andreea Valeria, Tudor Cioara, Ionut Anghel, et al. "Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs." Sustainability 12, no. 4 (2020): 1417. http://dx.doi.org/10.3390/su12041417.

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In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network

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14

Tang, Cheng Jen, and Miau Ru Dai. "Identifying Constraints Regarding Power Usage Effectiveness for Data Centers Engaging in Demand Response Programs." Applied Mechanics and Materials 284-287 (January 2013): 3597–603. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3597.

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Demand response (DR) is an important ingredient and regarded as the killer application of the emerging smart grid. The continuously growing energy consumption of data centers makes data centers promising candidates with significant potential for DR. Participating in DR programs makes data centers have another finical resource in addition to service income. On the other hand, some government organizations also offer considerable incentives to promote energy saving actions for facilities with some certain certifications. Leadership in Energy and Environmental Design (LEED) rating system develope

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15

Lee, Eunjung, Jinho Kim, and Dongsik Jang. "Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study." Energies 13, no. 6 (2020): 1348. http://dx.doi.org/10.3390/en13061348.

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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 segmen

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16

Stanelyte, Daiva, Neringa Radziukyniene, and Virginijus Radziukynas. "Overview of Demand-Response Services: A Review." Energies 15, no. 5 (2022): 1659. http://dx.doi.org/10.3390/en15051659.

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It is essential for the electricity sector to analyze and determine the distribution capacity throughput and apply new methods aimed at increasing the capacity of the transmission system. Consequently, the transition to modern electricity networks is two-sided, i.e., involving technological and social modifications. The demand response (DR) redistributes consumption away from peak times when grid load and costs are the highest. It incentivizes customers to use electricity when supply is high and inexpensive due to various market mechanisms. The present DR policy proposals stress the importance

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17

Alharbi, Walied. "Integrating Internet-of-Things-Based Houses into Demand Response Programs in Smart Grid." Energies 16, no. 9 (2023): 3699. http://dx.doi.org/10.3390/en16093699.

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This paper presents a novel framework that mathematically and optimally quantifies demand response (DR) provisions, considering the power availability of Internet of Things (IoT)-based house load management for the provision of flexibility in the smart grid. The proposed framework first models house loads using IoT windows and occupant behavior, and then integrates IoT-based house loads into DR programs based on a novel mathematical optimization model to provide the optimal power flexibility considering the penetration of IoT-based houses in distribution systems. Numerical results that conside

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18

Zhang, Rong, and Bing Qi. "Research of Effect of Battery Participating in Demand Response." Advanced Materials Research 986-987 (July 2014): 1240–44. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1240.

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Batteries have been widely used in the residential electric systems. This paper firstly analyzes the characteristics and feasibility of using the battery to participate in Demand Response (DR) programs. Secondly, the load characteristics in the two different kinds of battery systems are studied, including the power battery of electric bicycles and the battery which are used as the standby power for communication systems and computer systems. Thirdly, this paper studies the DR implementation method of reducing peak and filling valley and related impact on power grid load.

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19

Ojo, Oluwafemi Tayo, Muhammad Bolakale Salman, Ijeoma Lilian Agbanusi, et al. "Enhancing Power Grid Resilience Through Energy Storage And Demand Response." Path of Science 11, no. 1 (2025): 8023. https://doi.org/10.22178/pos.113-25.

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The resilience of power grids is increasingly essential in the face of climate change, extreme weather events, and the growing complexity of energy systems. To ensure continuous electricity supply during outages and stress events, utilities and grid operators are exploring innovative solutions. This paper examines two key strategies — energy storage systems (ESS) and demand response (DR) — for enhancing grid resilience. Energy storage technologies allow grid operators to store excess electricity during periods of low demand and release it during peak usage or disturbances.Meanwhile, demand res

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20

Sangeeth L R, Silpa, Saji K. Mathew, and Vidyasagar Potdar. "Information Processing view of Electricity Demand Response Systems: A Comparative Study Between India and Australia." Pacific Asia Journal of the Association for Information Systems 12 (June 30, 2020): 27–63. http://dx.doi.org/10.17705/1thci.12402.

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Abstract Background: In recent years, demand response (DR) has gained increased attention from utilities, regulators, and market aggregators to meet the growing demands of electricity. The key aspect of a successful DR program is the effective processing of data and information to gain critical insights. This study aims to identify information processing needs and capacity that interact to improve energy DR effectiveness. To this end, organizational information processing theory (OIPT) is employed to understand the role of Information Systems (IS) resources in achieving desired DR program perf

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21

Sangeeth L R, Silpa, Saji K. Mathew, and Vidyasagar Potdar. "Information Processing view of Electricity Demand Response Systems: A Comparative Study Between India and Australia." Pacific Asia Journal of the Association for Information Systems 12 (June 2020): 27–63. http://dx.doi.org/10.17705/1pais.12402.

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Abstract Background: In recent years, demand response (DR) has gained increased attention from utilities, regulators, and market aggregators to meet the growing demands of electricity. The key aspect of a successful DR program is the effective processing of data and information to gain critical insights. This study aims to identify information processing needs and capacity that interact to improve energy DR effectiveness. To this end, organizational information processing theory (OIPT) is employed to understand the role of Information Systems (IS) resources in achieving desired DR program perf

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22

Krishnadas, Gautham, and Aristides Kiprakis. "A Machine Learning Pipeline for Demand Response Capacity Scheduling." Energies 13, no. 7 (2020): 1848. http://dx.doi.org/10.3390/en13071848.

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Demand response (DR) is an integral component of smart grid operations that offers the necessary flexibility to support its decarbonisation. In incentive-based DR programs, deviations from the scheduled DR capacity affect the grid’s energy balance and result in revenue losses for the DR participants. This issue aggravates with increasing DR delivery from participants such as large consumer buildings who have limited standard methods to follow for DR capacity scheduling. Load curtailment based DR capacity availability from such consumers can be forecasted reliably with the help of supervised ma

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23

Takano, Hirotaka, Naohiro Yoshida, Hiroshi Asano, Aya Hagishima, and Nguyen Duc Tuyen. "Calculation Method for Electricity Price and Rebate Level in Demand Response Programs." Applied Sciences 11, no. 15 (2021): 6871. http://dx.doi.org/10.3390/app11156871.

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Demand response programs (DRs) can be implemented with less investment costs than those in power plants or facilities and enable us to control power demand. Therefore, they are highly expected as an efficient option for power supply–demand-balancing operations. On the other hand, DRs bring new difficulties on how to evaluate the cooperation of consumers and to decide electricity prices or rebate levels with reflecting its results. This paper presents a theoretical approach that calculates electricity prices and rebate levels in DRs based on the framework of social welfare maximization. In the

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24

Clark, Quentin, Fatih Acun, Ioannis C. Paschalidis, and Ayse Coskun. "Learning a Data Center Model for Efficient Demand Response." ACM SIGEnergy Energy Informatics Review 4, no. 5 (2024): 98–105. https://doi.org/10.1145/3727200.3727215.

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Data center demand is projected to increase dramatically over the coming decades, creating concerns about their carbon footprint and motivating the design of methods that can scale data center capabilities sustainably. One such method is Demand Response (DR), which provides incentives for power consumers to regulate their consumption in compliance with sustainability and capacity needs in the grid. One limitation of existing work in data center DR is the computational expense of generating accurate average power estimates and flexibility forecasts for a data center, given knowledge about the d

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25

Conteh, Abdul, Mohammed Elsayed Lotfy, Oludamilare Bode Adewuyi, Paras Mandal, Hiroshi Takahashi, and Tomonobu Senjyu. "Demand Response Economic Assessment with the Integration of Renewable Energy for Developing Electricity Markets." Sustainability 12, no. 7 (2020): 2653. http://dx.doi.org/10.3390/su12072653.

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Electricity disparity in sub-Saharan Africa is a multi-dimensional challenge that has significant implications on the current socio-economic predicament of the region. Strategic implementation of demand response (DR) programs and renewable energy (RE) integration can provide efficient solutions with several benefits such as peak load reduction, grid congestion mitigation, load profile modification, and greenhouse gas emissions reduction. In this research, an incentive and price-based DR programs model using the price elasticity concepts is proposed. Economic analysis of the customer benefit, u

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26

Joao, Soares, Morais Hugo, Sousa Tiago, Vale Zita, and Faria Pedro. "Day-ahead resource scheduling including demand response for electric vehicles." IEEE Transactions on Smart Grid 4, no. 1 (2013): 596–605. https://doi.org/10.1109/TSG.2012.2235865.

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The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs in the V2G approach. Three different DR programs are designed and tested

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27

Pedro, Faria, Spinola João, and Vale Zita. "Aggregation and Remuneration of Electricity Consumers and Producers for the Definition of Demand-Response Programs." IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 12, no. 3 (2017): 952–61. https://doi.org/10.1109/TII.2016.2541542.

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The use of distributed generation and demand-response (DR) programs is needed for improving business models, namely concerning the remuneration of these resources in the context of smart grids. In this paper, a methodology is proposed in which a virtual power player aggregates several small-sized resources, including consumers participating in DR programs. The global operation costs resulting from the resource scheduling are minimized. After scheduling the resources in several operation scenarios, clustering tools are applied in order to obtain distinct resources’ groups. The remuneratio

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28

Shibily, Joseph, and A. Jasmin E. "Demand response program for smart grid through real time pricing and home energy management system." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 4558–67. https://doi.org/10.11591/ijece.v11i5.pp4558-4567.

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Aim of demand response (DR) programs are to change the usage pattern of electricity in such a way that, beneficial to the consumers as well as to the distributors by applying some methods or technology. This way additional cost to erect new energy sources can be postponed in power grid. Best method to implement demand response (DR) program is by influencing consumer through the implementation of real time pricing scheme. To harness the benefit of DR, automated home energy management system is essential. This paper presents a comprehensive demand response system with real time pricing. The real

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29

Ganesan, Kamalanathan, João Tomé Saraiva, and Ricardo J. Bessa. "On the Use of Causality Inference in Designing Tariffs to Implement More Effective Behavioral Demand Response Programs." Energies 12, no. 14 (2019): 2666. http://dx.doi.org/10.3390/en12142666.

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Анотація:

Providing a price tariff that matches the randomized behavior of residential consumers is one of the major barriers to demand response (DR) implementation. The current trend of DR products provided by aggregators or retailers are not consumer-specific, which poses additional barriers for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causality inference between DR tariffs and observed residential electricity consumption to estimate consumers’ consumption elasticity. It determines the flexibility of each client under th

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30

Siahchehre Kholerdi, Somayeh, and Ali Ghasemi-Marzbali. "Effect of Demand Response Programs on Industrial Specific Energy Consumption: Study at Three Cement Plants." International Transactions on Electrical Energy Systems 2022 (June 24, 2022): 1–15. http://dx.doi.org/10.1155/2022/8550927.

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Анотація:

Demand-side management (DSM) is the modification of consumer utilization manner for energy through various methods to smooth their power consumption curve and increase their energy efficiency. Although the DSM improves the electricity grid stability and protects the environment, it allows customers to reduce their costs. The available literature classifies DSM into two different areas: (i) energy efficiency (EE) and (ii) demand response (DR). In other words, cement plants are the largest customers of electricity, so the implementation of DSM programs in these heavy industries is very important

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31

Conteh, Abdul, Mohammed Elsayed Lotfy, Kiptoo Mark Kipngetich, Tomonobu Senjyu, Paras Mandal, and Shantanu Chakraborty. "An Economic Analysis of Demand Side Management Considering Interruptible Load and Renewable Energy Integration: A Case Study of Freetown Sierra Leone." Sustainability 11, no. 10 (2019): 2828. http://dx.doi.org/10.3390/su11102828.

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Анотація:

Like in most developing countries, meeting the load demand and reduction in transmission grid bottlenecks remains a significant challenge for the power sector in Sierra Leone. In recent years, research attention has shifted to demand response (DR) programs geared towards improving the supply availability and quality of energy markets in developed countries. However, very few studies have discussed the implementation of suitable DR programs for developing countries, especially when utilizing renewable energy (RE) resources. In this paper, using the Freetown’s peak load demand data and the price

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32

Hajibandeh, Neda, Miadreza Shafie-khah, Sobhan Badakhshan, Jamshid Aghaei, Sílvio Mariano, and João Catalão. "Multi-Objective Market Clearing Model with an Autonomous Demand Response Scheme." Energies 12, no. 7 (2019): 1261. http://dx.doi.org/10.3390/en12071261.

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Demand response (DR) is known as a key solution in modern power systems and electricity markets for mitigating wind power uncertainties. However, effective incorporation of DR into power system operation scheduling needs knowledge of the price–elastic demand curve that relies on several factors such as estimation of a customer’s elasticity as well as their participation level in DR programs. To overcome this challenge, this paper proposes a novel autonomous DR scheme without prediction of the price–elastic demand curve so that the DR providers apply their selected load profiles ranked in the h

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33

Amewornu, Ernestina M., and Nnamdi I. Nwulu. "Assessing the impact of demand response programs on the reliability of the Ghanian distribution network." PLOS ONE 16, no. 3 (2021): e0248012. http://dx.doi.org/10.1371/journal.pone.0248012.

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The balancing of supplied energy to energy demand is often very challenging due to unstable power supply and demand load. This challenge causes the level of performance of distribution networks to be lower than expected. Research has however, shown the role of demand response (DR) on the performance of power networks. This work investigates the influence of DR, in the presence of incorporated renewable energy, on technical loss reduction, reliability, environment, energy saved and incentives paid to consumers with the help of PSAT and AIMMS software. Results from simulation have shown that the

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Joseph, Shibily, and E. A. Jasmin. "Demand response program for smart grid through real time pricing and home energy management system." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 4558. http://dx.doi.org/10.11591/ijece.v11i5.pp4558-4567.

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Aim of demand response (DR) programs are to change the usage pattern of electricity in such a way that, beneficial to the consumers as well as to the distributors by applying some methods or technology. This way additional cost to erect new energy sources can be postponed in power grid. Best method to implement demand response (DR) program is by influencing consumer through the implementation of real time pricing scheme. To harness the benefit of DR, automated home energy management system is essential. This paper presents a comprehensive demand response system with real time pricing. The real

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Durvasulu, Venkat, and Timothy Hansen. "Benefits of a Demand Response Exchange Participating in Existing Bulk-Power Markets." Energies 11, no. 12 (2018): 3361. http://dx.doi.org/10.3390/en11123361.

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In most U.S. market sponsored demand response (DR) programs, revenue earned from energy markets has been relatively low compared to DR used for capacity markets and ancillary services. This paper presents an aggregated DR model participating in the bulk-power market as a service through a pool-based entity called demand response exchange (DRX). Using the DRX structure, DR providers can participate in energy markets as a service to benefit bulk-power market entities. The benefits and challenges to each market entity using DR-as-a-service are presented in an extended review. The DRX model in thi

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Charoen, Prasertsak, Nathavuth Kitbutrawat, and Jasada Kudtongngam. "A Demand Response Implementation with Building Energy Management System." Energies 15, no. 3 (2022): 1220. http://dx.doi.org/10.3390/en15031220.

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The demand response (DR) program is one of the most promising components in the development of the Smart Grid. However, there are many challenges in practical operation to improve the existing and outdated system to comply with the DR programs. In Thailand, the major pain point of the office building owner in the DR program is the additional equipment, modification and operation cost of the existing equipment. Moreover, the sophisticated solution and control are other obstacles that need more measurements and data, and they make the operation difficult to work with. In this paper, we implement

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Jalilzadeh Hamidi, Reza, and Ailin Asadinejad. "Improvement of Economic Integration of Renewable Energy Resources through Incentive-Based Demand Response Programs." Energies 17, no. 11 (2024): 2545. http://dx.doi.org/10.3390/en17112545.

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The integration of renewable generation presents a promising venue for displacing fossil fuels, yet integration remains a challenge. This paper investigates Demand Response (DR) as a means of economically integrating Renewable Energy Resources (RERs). We propose Incentive-Based DR (IBDR) programs, particularly suitable for small customers. The uncertainties in the electricity market price pose a challenge to IBDR programs, which is addressed in this paper through a novel and robust IBDR approach that considers both the electricity market price uncertainties and customer responses to incentives

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Das, Punam, Subhojit Dawn, Sadhan Gope, Diptanu Das, and Ferdinando Salata. "Optimal Reactive Power Dispatch and Demand Response in Electricity Market Using Multi-Objective Grasshopper Optimization Algorithm." Processes 12, no. 9 (2024): 2049. http://dx.doi.org/10.3390/pr12092049.

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Optimal Reactive Power Dispatch (ORPD) is a power system optimization tool that modifies system control variables such as bus voltage and transformer tap settings, and it compensates devices’ Volt Ampere Reactive (VAR) output. It is used to decrease real power loss, enhance the voltage profile, and promote stability. Furthermore, several issues have been faced in electricity markets, such as price volatility, transmission line congestion, and an increase in the cost of electricity during peak hours. Programs such as demand response (DR) provide system operators with more control over how small

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Binyet, Emmanuel, Ming-Chuan Chiu, Hsin-Wei Hsu, Meng-Ying Lee, and Chih-Yuan Wen. "Potential of Demand Response for Power Reallocation, a Literature Review." Energies 15, no. 3 (2022): 863. http://dx.doi.org/10.3390/en15030863.

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The power demand on the electric grid varies according to the time of the day following users’ needs and so does the cost of electricity supply because the electricity mix is formed using different generators of varying capacities. Demand response (DR) is the modification of the consumption load curve following a signal from the electricity provider; it is mostly used for peak clipping. By reducing the short-term mismatch between generation and consumption, it helps to integrate intermittent renewables and new low-carbon technologies such as energy storage, electric vehicles, and power-to-gas.

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Barreto, Rúben, Pedro Faria, and Zita Vale. "Demand Response Programs Management in an Energy Community with Diversity of Appliances." E3S Web of Conferences 239 (2021): 00023. http://dx.doi.org/10.1051/e3sconf/202123900023.

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This paper shows the behaviour of a Demand Response program designed to be implemented in Energy Communities, where they take advantage of photovoltaic production. The primary objective is to manage both photovoltaic overproduction and village consumption efficiently. The DR program focuses on looking for consecutive periods that exceed a target peak set by the aggregator after analysing the consumption of the given energy community. The case study includes three villages, where participants are expected to be members of a community. The results are that participants will see a reduction in co

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Wang, Zhanle, Raman Paranjape, Zhikun Chen, and Kai Zeng. "Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing." Energies 12, no. 15 (2019): 2867. http://dx.doi.org/10.3390/en12152867.

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Demand response (DR) programs encourage consumers to adapt the time of using electricity based on certain factors, such as cost of electricity, renewable energy availability, and ancillary request. It is one of the most economical methods to improve power system stability and energy efficiency. Residential electricity consumption occupies approximately one-third of global electricity usage and has great potential in DR applications. In this study, we propose a multi-agent optimization approach to incorporate residential DR flexibility into the power system and electricity market. The agents co

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Silva, Cátia, Pedro Faria, and Zita Vale. "Demand Response Implementation: Overview of Europe and United States Status." Energies 16, no. 10 (2023): 4043. http://dx.doi.org/10.3390/en16104043.

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The authors review the efforts made in the last five years to implement Demand Response (DR) programs, considering and studying several models and countries. As motivation, climate change has been a topic widely discussed in the last decades, namely in the power and energy sectors. Therefore, it is crucial to substitute non-renewable fuels with more environment-friendly solutions. Enabling Distributed Generation (DG), namely using renewable resources such as wind and solar, can be part of the solution to reduce the greenhouse effects. However, their unpredictable behavior might result in sever

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Li, Yuling, Xiaoying Wang, and Peicong Luo. "Strategies for Datacenters Participating in Demand Response by Two-Stage Decisions." Mathematical Problems in Engineering 2020 (July 22, 2020): 1–15. http://dx.doi.org/10.1155/2020/5206082.

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Modern smart grids have proposed a series of demand response (DR) programs and encourage users to participate in them with the purpose of maintaining reliability and efficiency so as to respond to the sustainable development of demand-side management. As a large load of the smart grid, a datacenter could be regarded as a potential demand response participant. Encouraging datacenters to participate in demand response programs can help the grid to achieve better load balancing effect, while the datacenter can also reduce its own power consumption so as to save electricity costs. In this paper, w

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Beyzaee, Cyrous, Sara Marvi, and Mahdi Zarif. "Risk management and participation of electric vehicle considering transmission line congestion in the smart grids for demand response." Facta universitatis - series: Electronics and Energetics 33, no. 4 (2020): 583–603. http://dx.doi.org/10.2298/fuee2004583b.

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Demand response (DR) could serve as an effective tool to further balance the electricity demand and supply in smart grids. It is also defined as the changes in normal electricity usage by end-use customers in response to pricing and incentive payments. Electric cars (EVs) are potentially distributed energy sources, which support the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes, and their participation in time-based (e.g., time of use) and incentive-based (e.g., regulation services) DR programs helps improve the stability and reduce the potential risks to the grid. Moreover, the smart

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Molina, Juan, Luisa Buitrago, Sandra Téllez, Sandra Giraldo, and Jaime Uribe. "Demand Response Program Implementation Methodology: A Colombian Study Case." Transactions on Energy Systems and Engineering Applications 3, no. 1 (2022): 13–19. http://dx.doi.org/10.32397/tesea.vol3.n1.3.

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The industrialization and urbanization are responsible for Greenhouse Gas (GHG) emissions and could generate energy shortage problems. The application of Demand Response (DR) programs enables the user to be empowered towards a conscious consumption of energy, allowing the reduction or displacement of the demand for electrical energy, contributing to the sustainable development of the sector and the operational efficiency of the electrical system, among others. A reference framework for this type of program is detailed along with a literature survey applied to the Colombian case. The considerat

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Zhou, Yanglin, Lin Cheng, Song Ci, Yang Yang, and Shiqian Ma. "A User-Oriented Pricing Design for Demand Response in Smart Grid." Wireless Communications and Mobile Computing 2019 (September 10, 2019): 1–12. http://dx.doi.org/10.1155/2019/8694016.

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Demand response (DR) programs are designed to affect the energy consumption behavior of end-users in smart grid. However, most existing pricing designs for DR programs ignore the influence of end-users’s diversity and personal preference. Thus, in this paper, we investigate an incentive pricing design based on the utility maximization rule with consideration of end-users’ preference and appliances’ operational patterns. In particular, the utility company determines the pricing policy by trading off the budget revenue and social obligation, while each end-user aims to maximize their own utility

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Lee, Eunjung, Dongsik Jang, and Jinho Kim. "A Two-Step Methodology for Free Rider Mitigation with an Improved Settlement Algorithm: Regression in CBL Estimation and New Incentive Payment Rule in Residential Demand Response." Energies 11, no. 12 (2018): 3417. http://dx.doi.org/10.3390/en11123417.

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Recent demand response (DR) research efforts have focused on reducing the peak demand, and thereby electricity prices. Load reductions from DR programs can be viewed as equivalent electricity generation by conventional means. Thus, utility companies must pay incentives to customers who reduce their demand accordingly. However, many key variables intrinsic to residential customers are significantly more complicated compared to those of commercial and industrial customers. Thus, residential DR programs are economically difficult to operate, especially because excess incentive settlements can res

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Hafeez, Ghulam, Zahid Wadud, Imran Ullah Khan, et al. "Efficient Energy Management of IoT-Enabled Smart Homes Under Price-Based Demand Response Program in Smart Grid." Sensors 20, no. 11 (2020): 3155. http://dx.doi.org/10.3390/s20113155.

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There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities that actively participate in electricity market via demand response (DR) programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, an energy management strategy using price-based DR program is developed for IoT-enabled residential buildings. We propos

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Sediqi, Mohammad Masih, Akito Nakadomari, Alexey Mikhaylov, et al. "Impact of Time-of-Use Demand Response Program on Optimal Operation of Afghanistan Real Power System." Energies 15, no. 1 (2022): 296. http://dx.doi.org/10.3390/en15010296.

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Like most developing countries, Afghanistan still employs the traditional philosophy of supplying all its load demands whenever they happen. However, to have a reliable and cost-effective system, the new approach proposes to keep the variations of demand at the lowest possible level. The power system infrastructure requires massive capital investment; demand response (DR) is one of the economic options for running the system according to the new scheme. DR has become the intention of many researchers in developed countries. However, very limited works have investigated the employment of approp

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Wu, Yu-Chi, Chao-Shu Chang, and Wei-Han Li. "Development of a Low-Cost Automated Demand Response Controller for Home Energy Management." Applied Sciences 14, no. 23 (2024): 11434. https://doi.org/10.3390/app142311434.

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This research focuses on developing a low-cost automated demand response controller (DRC) with OpenADR 2.0a capability to enable existing infrared-controlled (IR-controlled) air conditioners (ACs) in homes and buildings to participate in automated demand response programs (ADRPs). The DRC consists of four modules: a smart socket module, an infrared module, a temperature sensor, and a voltage/current module. It can receive, analyze, and respond to demand response (DR) events and perform necessary demand and energy control strategies via IR. Power line communication (PLC) is used for communicati

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