Analysis of Key Performance Indicators for Local Electricity Markets’ Design

Okwuibe, Godwin C.; Wadhwa, Mukund; Brenner, Thomas; Tzscheutschler, Peter; Hamacher, Thomas

doi:10.1109/icjece.2021.3091718

Cited by 7 publications

(4 citation statements)

References 34 publications

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“…The local market trader may incur some loss from the market if they do not use intelligent agents for the bidding/offering. Additionally, comparing our key findings in this paper with a previous work [20] that has similar data and bidding/offering strategy, our proposed model shows better performance indicators, especially with the SMS and the SS.…”

Section: Discussionsupporting

confidence: 53%

“…Share of individual savings: The share of individual savings (SIS) is the percentage of savings made by an individual prosumer or consumer for trading in the LEM as compared to trading without the LEM [20]. SIS is presented in (23), where Π j,t and Π * j,t are the net costs for trading electricity without and with the LEM, respectively, for agent j at time t.…”

Section: Performance Indicatorsmentioning

confidence: 99%

“…The authors of [19] implemented the uniform pricing, pay-as-bid, generalized second-price, and Vickrey-Clark-Groves clearing methods and evaluated the benefits and the efficiencies of the mechanisms. In [20], the authors suggested that the bidding strategy of the LEM participants have more impact on the performance of community-based LEMs as compared to increasing the production-toconsumption ratio of the local community. In [21], the authors proposed the advantages of a hierarchical approach for energy and flexibility trading in the LEM considering distribution network constraints and the offering/bidding strategies of prosumers.…”

Section: Introduction 1motivation and Literature Reviewmentioning

confidence: 99%

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Evaluation of Hierarchical, Multi-Agent, Community-Based, Local Energy Markets Based on Key Performance Indicators

Okwuibe

Gazafroudi²,

Hambridge

et al. 2022

Energies

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In recent years, local energy markets (LEMs) have been introduced to empower end-customers within energy communities at the distribution level of the power system, in order to be able to trade their energy locally in a competitive and fair environment. However, there is still some challenge with regard to the most efficient approach in organising the LEMs for the electricity exchange between consumers and prosumers while ensuring that they are responsible for their electricity-related choices, and concerning which LEM model is suitable for which prosumer or consumer type. This paper presents a hierarchical model for the organisation of agent-based local energy markets. According to the proposed model, prosumers and consumers are enabled to transact electricity within the local energy community and with the grid in a coordinated manner to ensure technical and economic benefits for the LEM’s agents. The model is implemented in a software tool called Grid Singularity Exchange (GSyE), and it is verified in a real German energy community case study. The simulation results demonstrate that trading electricity within the LEM offers economic and technical benefits compared to transacting with the up-stream grid. This can further lead to the decarbonization of the power system sector. Furthermore, we propose two models for LEMs consisting of multi-layer and single-layer hierarchical agent-based structures. According to our study, the multi-layer hierarchical model is more profitable for household prosumers as compared to trading within the single-layer hierarchical LEM. However, the single-layer LEM is more be beneficial for industrial prosumers.

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Section: Discussionsupporting

confidence: 53%

Section: Performance Indicatorsmentioning

confidence: 99%

Section: Introduction 1motivation and Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Hierarchical, Multi-Agent, Community-Based, Local Energy Markets Based on Key Performance Indicators

Okwuibe

Gazafroudi²,

Hambridge

et al. 2022

Energies

Self Cite

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“…We consider a community consisting of multiple residential consumers and a spot market aggregator. The spot market process consists of five to fifteen minutes slot and takes place before the real-time energy consumption [19]. The spot market aggregator's objective is to maximize profits.…”

Section: Model Of the Systemmentioning

confidence: 99%

A Computationally Efficient Method for Energy Allocation in Spot Markets With Application to Transactive Energy Systems

et al. 2022

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Spot markets provide an interesting opportunity for profit maximization by energy trading based on immediate decisions on participant bids. However, their short market-clearing time can affect computational efficiency, search space, and reliability of price-energy allocation to bidding participants. Accordingly, developing a prompt and effective decision-making process plays a vital role in smooth energy delivery in these markets. This paper proposes an approach to alleviate the computational cost of the spot market aggregator in order to decide price-energy bids. The proposed bidding model is developed for the transactive energy systems, where the spot market aggregator utilizes the proposed method to maximize profit by choosing participants' demand-side bids. The proposed method can efficiently manage participants' combined energy and price information and avoid a highly complicated search space. It takes advantage of the multi-variable Taylor series approximation to create users' individual cost functions. The approximated cost functions lead to user-specific bids that expedite the spot market transaction while maintaining aggregator profit. The resultant system is able to exercise profit maximization with high performance within milliseconds. The efficiency of this scheme is also demonstrated through a comparative study by using the particle swarm optimization method.INDEX TERMS Transactive energy, spot markets, combinatorial auction, smart grids, computational efficiency, demand response. NOMENCLATURE INDICES FUNCTIONS xi Average energy demand. C(y) Original cost function. C aprx Approximation of cost function. U i k+1 Utility function.

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Survey and Analysis of Local Energy Markets Based on Distributed Ledger Technologies

Okwuibe

Brenner²,

Tzscheutschler

et al. 2023

IEEE Access

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Analysis of Key Performance Indicators for Local Electricity Markets’ Design

Cited by 7 publications

References 34 publications

Evaluation of Hierarchical, Multi-Agent, Community-Based, Local Energy Markets Based on Key Performance Indicators

Evaluation of Hierarchical, Multi-Agent, Community-Based, Local Energy Markets Based on Key Performance Indicators

A Computationally Efficient Method for Energy Allocation in Spot Markets With Application to Transactive Energy Systems

Survey and Analysis of Local Energy Markets Based on Distributed Ledger Technologies

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