Imbalance Reduction of P2P Energy Market by Closed-Loop Clustering and Forecasting

Zhang, Chi; Li, Ran; Zhang, Zhong; Li, Canbing; Bian, Yuankai; Li, Furong

doi:10.1109/tsg.2022.3199245

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…A mechanism to support demand forecast error assessment in the LEM is proposed in [36]. Another model is developed in [37] to mitigate imbalances in the LEM through accurate forecasting. Further, the application of artificial intelligence (AI)-based approaches is also noticeable in the existing literature, which may contribute to improving forecasting services.…”

Section: ) Forecasting and Blockchain Platformsmentioning

confidence: 99%

Integrating Forecasting Service and Gen2 Blockchain Into a Local Energy Trading Platform to Promote Sustainability Goals

Ali,

Azim,

Ojha

et al. 2024

IEEE Access

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Peer-to-peer (P2P) trading in a local energy market (LEM) offers various participants the opportunity to negotiate and strike energy deals among themselves using a distributed ledger technology called blockchain. In this paper, a new local model is presented using a layer-2 scalability solution for secondgeneration (Gen2) blockchain technology to enable P2P trading among four types of participants: consumers, prosumers with solar photovoltaic (PV) systems, prosumers with solar PV systems and battery energy storage systems (BESSs), and electric vehicles (EVs). The proposed LEM trading platform involves several critical steps, including the creation of typical forecasting profiles for load consumption, solar generation, and battery state-of-charge (SOC) through a forecasting solution. Next, the LEM participants place their pricing bids using a trading agent service, and the trading engine collects the profiles data and bid prices, which performs matchmaking in a forward-facing market. The output of the trading engine consists of dispatch signals for prices and energy values that are sent to each participant to execute actual trading. Furthermore, the trading engines store the accepted and past bidding data and energy values of P2P trades for each participant in blockchain technology, which can be retrieved and displayed on the LEM user interface screens of participants and administrators using their blockchain addresses at any time during the trading process. This study focuses on simulating proposed LEM models, incorporating functional limitations and market rules. These rules aim to reduce energy costs, enhance margins for utilities and retailers, and mitigate grid congestion through BESSs, resulting in reduced operational and capital expenditure. LEM outcomes are analysed and compared with a Business-as-usual (BAU) model. Participants' energy trading behaviour, costrevenue dynamics, grid impact, and blockchain implementation costs are explored. The study highlights LEM benefits in terms of reduced CO2 emissions by 984 kg CO2, increased self-sufficiency by 2.2%, and improved financial benefits of all participants by 21.6%. The use of modern blockchain technology guarantees secure data storage and rapid, cost-effective energy trading, thereby making the proposed LEM platform a viable solution in the distribution market.INDEX TERMS Blockchain technology, forecasting service, LEM, P2P energy trading, smart contracts.

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Section: ) Forecasting and Blockchain Platformsmentioning

confidence: 99%

Integrating Forecasting Service and Gen2 Blockchain Into a Local Energy Trading Platform to Promote Sustainability Goals

Ali,

Azim,

Ojha

et al. 2024

IEEE Access

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Sky-Image-Derived Deep Decomposition for Ultra-Short-Term Photovoltaic Power Forecasting

Liu,

Zang,

Ding

et al. 2024

IEEE Trans. Sustain. Energy

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Prediction Model-Assisted Optimization Scheduling Strategy for Renewable Energy in the Microgrid

Cao,

Yang,

et al. 2024

Processes

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As the global reliance on renewable energy sources grows, wind and photovoltaic power, as pivotal components, pose significant challenges to power system dispatch due to their volatility and uncertainty. To effectively address this challenge, this paper proposes a renewable energy optimization dispatch strategy based on a prediction model. First, this paper constructs a prediction model combining functional data analysis and recurrent neural networks (RNNs) to achieve an accurate prediction of renewable energy output. On this basis, considering the economic and environmental benefits of system operation, an optimal multi-objective dispatch model for renewable energy is established, and the multi-objective optimization problem is transformed into a single-objective optimization problem using weighting methods to reduce the complexity of the solution. Finally, a typical microgrid test system is used to verify the effectiveness and feasibility of the proposed method. The results of the numerical example show that the proposed model can achieve an accurate prediction of renewable energy sources, reduce the conservatism of traditional dispatch decisions, and balance economic and environmental benefits.

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Imbalance Reduction of P2P Energy Market by Closed-Loop Clustering and Forecasting

Cited by 3 publications

References 28 publications

Integrating Forecasting Service and Gen2 Blockchain Into a Local Energy Trading Platform to Promote Sustainability Goals

Integrating Forecasting Service and Gen2 Blockchain Into a Local Energy Trading Platform to Promote Sustainability Goals

Sky-Image-Derived Deep Decomposition for Ultra-Short-Term Photovoltaic Power Forecasting

Prediction Model-Assisted Optimization Scheduling Strategy for Renewable Energy in the Microgrid

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