Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids

Salazar, Eduardo J.; Jurado, Mauro; Samper, Mauricio E.

doi:10.3390/en16031466

Cited by 17 publications

(5 citation statements)

References 68 publications

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“…Typically, elasticity is negative, indicating an inverse relationship between electricity demand and electricity prices [39]. Several studies have investigated the price elasticity of demand for ADS [40][41][42]. In [43], the authors determined that price elasticities of demand can vary between −0.2 and −0.8, based on data and surveys conducted in the U.S.…”

Section: Demand Response Managementmentioning

confidence: 99%

“…In [43], the authors determined that price elasticities of demand can vary between −0.2 and −0.8, based on data and surveys conducted in the U.S. Furthermore, in [40,42], it was concluded that electricity demand is more elastic during peak hours compared to off-peak hours, making DR an efficient resource for enhancing system safety and quality during times of high stress.…”

Section: Demand Response Managementmentioning

confidence: 99%

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Day-Ahead Operational Planning for DisCos Based on Demand Response Flexibility and Volt/Var Control

Jurado,

Salazar,

Samper

et al. 2023

Energies

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Considering the integration of distributed energy resources (DER) such as distributed generation, demand response, and electric vehicles, day-ahead scheduling plays a significant role in the operation of active distribution systems. Therefore, this article proposes a comprehensive methodology for the short-term operational planning of a distribution company (DisCo), aiming to minimize the total daily operational cost. The proposed methodology integrates on-load tap changers, capacitor banks, and flexible loads participating in demand response (DR) to reduce losses and manage congestion and voltage violations, while considering the costs associated with the operation and use of controllable resources. Furthermore, to forecast PV output and load demand behind the meter at the MV/LV distribution transformer level, a short-term net load forecasting model using deep learning techniques has been incorporated. The proposed scheme is solved through an efficient two-stage strategy based on genetic algorithms and dynamic programming. Numerical results based on the modified IEEE 13-node distribution system and a typical 37-node Latin American system validate the effectiveness of the proposed methodology. The obtained results verify that, through the proposed methodology, the DisCo can effectively schedule its installations and DR to minimize the total operational cost while reducing losses and robustly managing voltage and congestion issues.

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Section: Demand Response Managementmentioning

confidence: 99%

Section: Demand Response Managementmentioning

confidence: 99%

Day-Ahead Operational Planning for DisCos Based on Demand Response Flexibility and Volt/Var Control

Jurado,

Salazar,

Samper

et al. 2023

Energies

Self Cite

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“…RL's application in smart grids has been growing steadily. Researchers [1][2][3][4][5] have used RL to improve various aspects of smart grid management, including demand response, load balancing, and energy storage. Despite these advancements, there are still limitations in current approaches that hinder optimal energy efficiency: ➢ Limited Flexibility: Traditional smart grid control systems often use static rules or models, which are not easily adaptable to changing grid conditions.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Energy Efficiency in Smart Grids through Reinforcement Learning-Based Control Strategies

Nilam B. Panchal

2024

jes

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The rapid growth of smart grids has ushered in new opportunities for enhancing energy efficiency through advanced control strategies. This paper explores the potential of reinforcement learning (RL) to improve the energy efficiency of smart grids, focusing on RL-based control strategies. We begin with a comprehensive review of existing technologies, examining a range of architectures and methods used to implement RL in smart grid environments. This review highlights both the benefits and limitations of these approaches, offering a balanced analysis of their effectiveness in addressing the unique challenges of smart grid management. Following this review, we propose a new RL-based control strategy designed to optimize energy efficiency. Our approach leverages the strengths of state-of-the-art RL algorithms while addressing common shortcomings identified in previous work. We evaluate our strategy using a detailed simulation that reflects real-world smart grid scenarios. The results demonstrate significant improvements in energy efficiency compared to traditional control methods. Finally, we discuss best practices for applying RL in smart grids, providing guidelines for researchers and practitioners seeking to implement these strategies. Our recommendations focus on maximizing energy efficiency while ensuring stability and scalability in smart grid systems. Through this work, we aim to contribute to the ongoing development of sustainable and efficient smart grid technologies.

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“…The architectural flexibility of SPP allows for the development of financial incentives tailored to unique contexts, maximizing profit margins for both MNOs, the one that signed a contract with the end user and the one that will act as a temporary provider. The SPP module utilizes the reverse auction theory from the game theory family implemented with reinforcement learning [4], in order to return the maximum incentive profit for both the original and the new alternative provider. The choice of deep reinforcement learning [5] to implement the reverse auction theory boosts the scalability and reliability of the system and make it as fair as it can be for all the participants.…”

mentioning

confidence: 99%

A Reinforcement Learning-Based Reverse Auction Enforcing Smart Pricing Policies towards B5G Offloading Strategies

Kaltakis,

Dimos,

Giannoulakis

et al. 2024

Electronics

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In this paper, we present our work on developing a Smart Pricing Policies module specifically designed for individual users and Mobile Network Operators (MNOs). Our framework will operate in a multi-MNO blockchain radio access network (B-RAN) and is tasked with determining prices for resource sharing among users and MNOs. Our sophisticated adaptive pricing system can adjust to situations where User Equipment (UE) shifts out of the coverage area of their MNO by immediately sealing a contract with a different MNO to cover the users’ needs. This way, we aim to provide financial incentives to MNOs while ensuring continuous network optimization for all parties involved. Our system accomplishes that by utilizing deep reinforcement learning (DLR) to implement a reverse auction model. In our reinforcement learning scenario, the MNOs, acting as agents, enter a competition and try to bid the most appealing price based on the user’s request, and based on the reward system, agents that do not win in the current round will adjust their strategies in an attempt to secure a win in subsequent rounds. The findings indicated that combining DRL with reverse auction theory offers a more appropriate method for addressing the pricing and bid challenges, and additionally, administrators can utilize this strategy to gain a notable edge by dynamically selecting and adjusting their methods according to the individual network conditions and requirements.

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Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids

Cited by 17 publications

References 68 publications

Day-Ahead Operational Planning for DisCos Based on Demand Response Flexibility and Volt/Var Control

Day-Ahead Operational Planning for DisCos Based on Demand Response Flexibility and Volt/Var Control

Enhancing Energy Efficiency in Smart Grids through Reinforcement Learning-Based Control Strategies

A Reinforcement Learning-Based Reverse Auction Enforcing Smart Pricing Policies towards B5G Offloading Strategies

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