D3A energy exchange for a transactive grid

Trbovich, Ana S.; Hambridge, Sarah; Biggelaar, Dirk van den; Hesse, Ewald; Sioshansi, Fereidoon P.

doi:10.1016/b978-0-12-819951-0.00013-x

Cited by 6 publications

(5 citation statements)

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“…Eqs. ( 24) and (25) shows that the model places 70% value on monetary reward while the remaining 30% is on accuracy. The general reward function is the sum of the monetary and accuracy reward function.…”

Section: ) Reward Functionmentioning

confidence: 97%

“…The proposed models use information/data initially acquired from the LEM and the participants to formulate the most optimal bidding/offering prices for consumers and prosumers within an LEM for them to make optimal benefits from their consumption's/productions. The models were implemented on an interface and open source code-base of the Grid Singularity bidding application programming interface [25]. Furthermore, we evaluate the models using performance indicators such as self-sufficiency, share of market savings and traded energy quantity of the prosumers/consumers in the LEM.…”

Section: Contribution and Organizationmentioning

confidence: 99%

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Intelligent Bidding Strategies for Prosumers in Local Energy Markets Based on Reinforcement Learning

Okwuibe

Bhalodia²,

Gazafroudi³

et al. 2022

IEEE Access

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Local energy markets (LEMs) are proposed in recent years as a way to enable local prosumers and community to trade their electricity and have control over their electrical related resources by ensuring that electricity is traded closer to where it is produced. However, literature is still scarce with the most optimal and effective trading strategies for LEM design. In this work, we propose two reinforcement learning based intelligent bidding strategies for prosumers and consumers trading within an LEM. Our proposed models were evaluated of their performance by testing them in a German real case scenario. The simulation results show that intelligent bidding strategies create additional self sufficiency and market savings to the local community compared to the baseline strategy where the agents make their trading decision randomly without an intelligent agent. Moreover, modelling the intelligent agents to perform towards a common goal creates more share of individual savings for the prosumers and consumers compared to the classical intelligent bidding strategies employed in this work.INDEX TERMS Bidding strategy, energy community, local energy markets, Markov decision process, peerto-peer, reinforcement learning.

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Section: ) Reward Functionmentioning

confidence: 97%

Section: Contribution and Organizationmentioning

confidence: 99%

Intelligent Bidding Strategies for Prosumers in Local Energy Markets Based on Reinforcement Learning

Okwuibe

Bhalodia²,

Gazafroudi³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…By doing so, the model will perform the clustering while considering that a certain variable is of more importance to the model compared to others and therefore perform according to this instruction. The model was implemented on an interface and open source code-base of the Grid Singularity Exchange to simulate, and optimize energy trading in local communities [35]. Furthermore, we evaluate the model using performance indicators such as selfsufficiency, self-consumption ratio, share of market savings and traded energy quantity.…”

Section: Contribution and Organizationmentioning

confidence: 99%

“…The proposed LEM advanced clustering model is developed as a Python code and implemented by integrating with the open-source Grid Singularity Exchange (GSy-E) [35], [48], [49] and bidding agent application programming interface (API) as represented in Fig. 5.…”

Section: A Simulation Frameworkmentioning

confidence: 99%

Advanced Clustering Approach for Peer-to-Peer Local Energy Markets Considering Prosumers’ Preference Vectors

Okwuibe

Gazafroudi²,

Mengelkamp³

et al. 2023

IEEE Access

Self Cite

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Local energy markets (LEMs) are utilized in a bottom-up power systems approach for reducing the complexity of the traditional, centralized power system and to enable better integration of decentralized renewable energy resources (RES). Peer-to-peer (P2P) energy trading creates opportunities for prosumers to trade their RES with other prosumers in the LEM. Although several scenarios were proposed in the literature for modelling P2P energy trading, there is still a gap in the literature considering the heterogeneous characteristics of prosumers' bidding preferences during P2P matching in the LEM. In this paper, we present heterogeneous characteristics of bidding preferences for prosumers considering energy quantity, bid/offer price, geographic location, location of agents on the local community and cluster welfare. Moreover, this paper proposes an advanced clustering model for P2P matching in the energy community considering the heterogeneous characteristics of bidding preferences for prosumers. For evaluating our proposed model performance, two German real case scenarios of a small and large communities were studied. The simulations results show that using price preference, as the criterion for clustering, offers more technical and economic benefits to energy communities compared to other clustering scenarios. On the other hand, clustering scenarios based on location of prosumers ensure that energy is traded among prosumers who are closer to each other.INDEX TERMS Energy community, advanced clustering, local energy market, matching mechanism, Peerto-peer trading.

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“…Enexis [53], [192] Prototyping IOTA-based cryptocurrency transactions for EV charging (currently with IBM consulting) ✓ Energy Web Chain (EWC) [193], [194] Public and enterprise-grade blockchain-based platform designed for enabling any energy asset owned by any customer to participate in any energy market. Also includes a Cryptoasset (energy web token)…”

Section: ✓ ✓mentioning

confidence: 99%

Blockchain for Distributed Energy Resources Management and Integration

et al. 2022

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Power grids all over the world are transitioning towards a decentralized structure. Under such a transition, blockchain technology is emerging as a potential solution for technical, deployment and decentralization issues, given its security, integrity, decentralized nature and required infrastructure. Moreover, blockchain technology offers excellent features like non-repudiation and immutability which makes it a promising application for DER integration and management on reliability factors. In this paper, a comprehensive review of blockchain applications for DER management and integration is presented. First, a blockchain-based literature review of research activities in the DER integration area and related tasks including entrepreneurial efforts is carried out. Next, the different opportunities and challenges of DER integration and management in power grids, i.e., centralization, regulatory support, development costs are discussed. Finally, some key research challenges and opportunities of including blockchain technology to DER integration and management issues are presented.INDEX TERMS Blockchain, Distributed energy resources (DER), Distributed ledger technologies, Consensus algorithms.

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D3A energy exchange for a transactive grid

Cited by 6 publications

References 0 publications

Intelligent Bidding Strategies for Prosumers in Local Energy Markets Based on Reinforcement Learning

Intelligent Bidding Strategies for Prosumers in Local Energy Markets Based on Reinforcement Learning

Advanced Clustering Approach for Peer-to-Peer Local Energy Markets Considering Prosumers’ Preference Vectors

Blockchain for Distributed Energy Resources Management and Integration

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