Integrating Demand Response and Renewable Energy In Wholesale Market

Li, Chaojie; Liu, Chen; Yu, Xinghuo; Deng, Ke; Huang, Tingwen; Liu, Liangchen

doi:10.24963/ijcai.2018/53

Cited by 15 publications

(9 citation statements)

References 8 publications

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“…Ma et al (2018) modelled interactions between an energy provider and energy consumers with demand response potential as a hierarchical leader-follower game [50]. Li et al (2018) [51] focused on optimal bidding strategies for forward and spot electricity markets for demand response and renewable generators characterised under a Stackelberg-Cournot-Nash game. The majority of the aforementioned works followed an analytical approach for solving the game equilibrium, often based on well-defined (smooth) cost functions.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Strategic Renewable Energy, Grid and Storage Capacity Investments via Stackelberg-Cournot Modelling

et al. 2021

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With increasing decarbonisation and accessibility to our energy systems and markets, there is a need to understand and optimise the value proposition for different stakeholders. Game-theoretic models represent a promising approach to study strategic interactions between self-interested private energy system investors. In this work, we design and evaluate a game-theoretic framework to study strategic interactions between profit-maximising players that invest in network, renewable generation and storage capacity. Specifically, we study the case where grid capacity is developed by a private renewable investor, but line access is shared with competing renewable and storage investors, thus enabling them to export energy and access electricity demand. We model the problem of deducing how much capacity each player should build as a non-cooperative Stackelberg-Cournot game between a dominant player (leader) who builds the power line and renewable generation capacity, and local renewable and storage investors (multiple followers), who react to the installation of the line by increasing their own capacity. Using data-driven analysis and simulations, we developed an empirical search method for estimating the game equilibrium, where the payoffs capture the realistic operation and control of the energy system under study. A practical demonstration of the underlying methodology is shown for a real-world grid reinforcement project in the UK. The methodology provides a realistic mechanism to analyse investor decision-making and investigate feasible tariffs that encourage distributed renewable investment, with sharing of grid access.

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Section: Related Workmentioning

confidence: 99%

Analysis of Strategic Renewable Energy, Grid and Storage Capacity Investments via Stackelberg-Cournot Modelling

et al. 2021

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“…(15)) for group learning and hierarchy modeling. In this experiment, α is varied amongst [0.001,0.01,0.1,1,10], and the group number [5,10,15,20,25]. As illustrated in Fig.6, we present the performance in terms of HR@10 and NDCG@10 across the datasets.…”

Section: Sensitivity Analysismentioning

confidence: 99%

DBRec

Wen

Zhong

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridging recommendation model (DBRec). DBRec performs latent user/item group discovery simultaneously with collaborative filtering, and interacts group information with users/items for bridging similar users/items. Therefore, a user's preference over an unobserved item, in DBRec, can be bridged by the users within the same group who have rated the item, or the user-rated items that share the same group with the unobserved item. In addition, we propose to jointly learn user-user group (itemitem group) hierarchies, so that we can effectively discover latent groups and learn compact user/item representations. We jointly integrate collaborative filtering, latent group discovering and hierarchical modelling into a unified framework, so that all the model parameters can be learned toward the optimization of the objective function. We validate the effectiveness of the proposed model with two real datasets, and demonstrate its advantage over the state-ofthe-art recommendation models with extensive experiments.

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“…Building energy is a primary target of demand response, and both the central administration of signals and building-level response has been thoroughly studied in residential and industrial settings ( [1], [15], [11], [28], [6].) However, while physical infrastructures [2] of office buildings have been studied for demand response ( [7]), there has been no large scale experiment aimed at eliciting a behavioral demand response.…”

Section: Introductionmentioning

confidence: 99%

Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response

Jang

Spangher

Khattar

et al. 2021

Proceedings of the Twelfth ACM International Conference on Future Energy Systems

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Our team is proposing to run a full-scale energy demand response experiment in an office building. Although this is an exciting endeavor which will provide value to the community, collecting training data for the reinforcement learning agent is costly and will be limited. In this work, we apply a meta-learning architecture to warm start the experiment with simulated tasks, to increase sample efficiency. We present results that demonstrate a similar a step up in complexity still corresponds with better learning. CCS CONCEPTS• Hardware → Energy distribution; • Computer systems organization → Neural networks; • Theory of computation → Reinforcement learning.

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Integrating Demand Response and Renewable Energy In Wholesale Market

Cited by 15 publications

References 8 publications

Analysis of Strategic Renewable Energy, Grid and Storage Capacity Investments via Stackelberg-Cournot Modelling

Analysis of Strategic Renewable Energy, Grid and Storage Capacity Investments via Stackelberg-Cournot Modelling

DBRec

Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response

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