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Relevant bibliographies by topics / Incentive-based demand response strategies

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Academic literature on the topic 'Incentive-based demand response strategies'

Author: Grafiati

Published: 6 September 2023

Last updated: 7 September 2023

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Journal articles on the topic "Incentive-based demand response strategies"

1

Deng, Chunyu, and Kehe Wu. "Residential Demand Response Strategy Based on Deep Deterministic Policy Gradient." Processes 9, no. 4 (2021): 660. http://dx.doi.org/10.3390/pr9040660.

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With the continuous improvement of the power system and the deepening of electricity market reform, the trend of users’ active participation in power distribution is more and more significant. Demand response has become the promising focus of smart grid research. Providing reasonable incentive strategies for power grid companies and demand response strategies for customers plays a crucial role in maximizing the benefits of different participants. To meet different expectations of multiple agents in the same environment, deep reinforcement learning was adopted. The generative model of residenti

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2

Alasseri, Rajeev, Ashish Tripathi, T. Joji Rao, and K. J. Sreekanth. "A review on implementation strategies for demand side management (DSM) in Kuwait through incentive-based demand response programs." Renewable and Sustainable Energy Reviews 77 (September 2017): 617–35. http://dx.doi.org/10.1016/j.rser.2017.04.023.

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3

Domínguez-Garabitos, Máximo A., Víctor S. Ocaña-Guevara, Félix Santos-García, Adriana Arango-Manrique, and Miguel Aybar-Mejía. "A Methodological Proposal for Implementing Demand-Shifting Strategies in the Wholesale Electricity Market." Energies 15, no. 4 (2022): 1307. http://dx.doi.org/10.3390/en15041307.

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The energy transition has shown that fossil generation can be complemented with renewable energy and other resources capable of providing flexibility to the energy system’s operation, in compliance with the wholesale electricity market’s rules. This paper proposes a market-based methodology for introducing flexible demand in the energy dispatch, optimizing the scheduling of electricity system operation in the short-term, and considers the challenge of implementing an incentive scheme for participants in demand-response programs. The scheme includes the criteria of the elasticity of substitutio

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4

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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5

Leobner, Ines, Peter Smolek, Bernhard Heinzl, et al. "Simulation-based Strategies for Smart Demand Response." Journal of Sustainable Development of Energy, Water and Environment Systems 6, no. 1 (2017): 33–46. http://dx.doi.org/10.13044/j.sdewes.d5.0168.

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6

Deltetto, Davide, Davide Coraci, Giuseppe Pinto, Marco Savino Piscitelli, and Alfonso Capozzoli. "Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings." Energies 14, no. 10 (2021): 2933. http://dx.doi.org/10.3390/en14102933.

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Demand Response (DR) programs represent an effective way to optimally manage building energy demand while increasing Renewable Energy Sources (RES) integration and grid reliability, helping the decarbonization of the electricity sector. To fully exploit such opportunities, buildings are required to become sources of energy flexibility, adapting their energy demand to meet specific grid requirements. However, in most cases, the energy flexibility of a single building is typically too small to be exploited in the flexibility market, highlighting the necessity to perform analysis at a multiple-bu

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7

Chen, Tang, Sun, Zhou, Wang, and Mao. "Reliability Evaluation Method Considering Demand Response (DR) of Household Electrical Equipment in Distribution Networks." Processes 7, no. 11 (2019): 799. http://dx.doi.org/10.3390/pr7110799.

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The load characteristic of typical household electrical equipment is elaborately analyzed. Considering the electric vehicles’ (EVs’) charging behavior and air conditioning’s thermodynamic property, an electricity price-based demand response (DR) model and an incentive-based DR model for two kinds of typical high-power electrical equipment are proposed to obtain the load curve considering two different kinds of DR mechanisms. Afterwards, a load shedding strategy is introduced to improve the traditional reliability evaluation method for distribution networks, with the capacity constraints of tie

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8

Liu, Shuxin, Jing Xu, Chaojian Xing, et al. "Study on Dynamic Pricing Strategy for Industrial Power Users Considering Demand Response Differences in Master–Slave Game." Sustainability 15, no. 16 (2023): 12265. http://dx.doi.org/10.3390/su151612265.

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With the deepening of power market reform, further study on power trading mechanisms has become the core issue of power market study. The development stage of the industrial electricity market requires efficient and flexible pricing mechanisms. Currently available pricing strategies are inadequate for demand response management. Therefore, this paper provides an in-depth study of the pricing mechanism in the industrial electricity market in the context of electricity market reform. It proposes a demand–response-based dynamic pricing strategy for industrial parks. The method proposes a dynamic

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9

Muraña-Silvera, Jonathan, Sergio Enrique Nesmachnow-Cánovas, Santiago Damián Iturriaga-Fabra, et al. "Smart grid demand response strategies for datacenters." Proceedings of the Institute for System Programming of the RAS 33, no. 2 (2021): 125–36. http://dx.doi.org/10.15514/ispras-2021-33(2)-7.

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This article presents demand response techniques for the participation of datacenters in smart electricity markets under the smart grid paradigm. The proposed approach includes a datacenter model based on empirical information to determine the power consumption of CPU-intensive and memory-intensive tasks. A negotiation approach between the datacenter and clients and a heuristic planning method for energy reduction optimization are proposed. The experimental evaluation is performed over realistic problem instances modeling different types of clients. Results indicate that the proposed approach

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10

Lahrsen, Inga-Marie, Mathias Hofmann, and Robert Müller. "Flexibility of Epichlorohydrin Production—Increasing Profitability by Demand Response for Electricity and Balancing Market." Processes 10, no. 4 (2022): 761. http://dx.doi.org/10.3390/pr10040761.

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The increasing share of variable renewable energies in the power grid is an incentive to explore demand response strategies. Chlor-alkali processes are high potential candidates, according to previous publications. Within Germany’s chemical industry, chlorine production accounts for approximately 20% of electricity use and could play a significant role in power grid stabilisation on the consumer end. This study focuses on the feasibility of load flexibilisation in epichlorohydrin plants, with the second biggest estimated demand response potential for chlorine-based products in Germany. A plant

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