A Dynamic Peer-to-Peer Electricity Market Model for a Community Microgrid With Price-Based Demand Response

Alfaverh, Fayiz; Denaï, Mouloud; Sun, Yichuang

doi:10.1109/tsg.2023.3246083

Cited by 10 publications

(2 citation statements)

References 21 publications

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“…The use of electric vehicles for covering the uncertainties related to RERs is also addressed in a number of works through different methods. In [30], a peer-to-peer energy sharing method is proposed in which the vehicle owners trade their excessive stored energy with others at a lower electricity price that financially benefits both the energy buyer and seller. Although the existence of electric vehicles able to discharge to the grid is a random variable, its probability distribution is quite different than that of wind energy.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Economic Integration of Renewable Energy Resources through Incentive-Based Demand Response Programs

Jalilzadeh Hamidi,

Asadinejad

2024

Energies

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The integration of renewable generation presents a promising venue for displacing fossil fuels, yet integration remains a challenge. This paper investigates Demand Response (DR) as a means of economically integrating Renewable Energy Resources (RERs). We propose Incentive-Based DR (IBDR) programs, particularly suitable for small customers. The uncertainties in the electricity market price pose a challenge to IBDR programs, which is addressed in this paper through a novel and robust IBDR approach that considers both the electricity market price uncertainties and customer responses to incentives. In this paper, scenarios are simulated premised on the Western Electricity Coordinating Council (WECC) 240-bus system in which coal-fired power plants become inactivated, while the RER contribution increases in the span of one year. The simulation results indicate that the proposed IBDR program mitigates the issues associated with renewable expansion, such as utility benefit loss and market price volatility. In addition, the proposed IBDR effectively manages up to 30% of errors in day-ahead wind forecasts that significantly reduce financial risks linked to IBDR programs.

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Section: Introductionmentioning

confidence: 99%

Improvement of Economic Integration of Renewable Energy Resources through Incentive-Based Demand Response Programs

Jalilzadeh Hamidi,

Asadinejad

2024

Energies

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“…The active involvement of smart homes in operation scheduling relies on the time-of-use electricity price strategy, prompting scholars to study optimal time-of-use electricity price strategies [21]. Furthermore, Literature [22,23] explores the formulation of optimal time-of-use electricity prices considering reliability loss and power loss, respectively. Smart homes can also passively and directly participate in operation scheduling through Internet of Things (IoT) technology.…”

Section: Introductionmentioning

confidence: 99%

Low-Voltage Distribution Network Loss-Reduction Method Based on Load-Timing Characteristics and Adjustment Capabilities

Huangfu,

Wang,

et al. 2024

Energies

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The primary contributors to elevated line losses in low-voltage distribution networks are three-phase load imbalances and variations in load peak–valley differentials. The conventional manual phase sequence adjustment fails to capitalize on the temporal characteristics of the load, and the proliferation of smart homes has opened up new scheduling possibilities for managing the load. Consequently, this paper introduces a loss-reduction method for low-voltage distribution networks that leverages load-timing characteristics and adjustment capabilities. This method combines dynamic and static methods to reduce energy consumption from different time scales. To commence, this paper introduced a hierarchical fuzzy C-means algorithm (H-FCM), taking into account the distance and similarity of load curves. Subsequently, a phase sequence adjustment method, grounded in load-timing characteristics, was developed. The typical user load curve, derived from the classification of user loads, serves as the foundation for constructing a long-term commutation model, therefore mitigating the impact of load fluctuations on artificial commutation. Following this, this paper addressed the interruptible and transferable characteristics of various smart homes. This paper proposed a multi-objective transferable load (TL) optimal timing task adjustment model and a peak-shaving control strategy specifically designed for maximum sustainable power reduction of temperature-controlled loads (TCL). These strategies aim to achieve real-time load adjustment, correct static commutation errors, and reduce peak-to-valley differences. Finally, a simulation verification model was established in MATLAB (R2022a). The results show that the proposed method mainly solves the problems of three-phase imbalance and large load peak–valley difference in low-voltage distribution networks and reduces the line loss of low-voltage distribution networks through manual commutation and load adjustment.

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A three-layer game theoretic-based strategy for optimal scheduling of microgrids by leveraging a dynamic demand response program designer to unlock the potential of smart buildings and electric vehicle fleets

et al. 2023

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A Dynamic Peer-to-Peer Electricity Market Model for a Community Microgrid With Price-Based Demand Response

Cited by 10 publications

References 21 publications

Improvement of Economic Integration of Renewable Energy Resources through Incentive-Based Demand Response Programs

Improvement of Economic Integration of Renewable Energy Resources through Incentive-Based Demand Response Programs

Low-Voltage Distribution Network Loss-Reduction Method Based on Load-Timing Characteristics and Adjustment Capabilities

A three-layer game theoretic-based strategy for optimal scheduling of microgrids by leveraging a dynamic demand response program designer to unlock the potential of smart buildings and electric vehicle fleets

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