Intent Profile Strategy for Virtual Power Plant Participation in Simultaneous Energy Markets With Dynamic Storage Management

Aguilar, Juan; Bordons, Carlos; Arce, Alicia; Galán, Ramón

doi:10.1109/access.2022.3155170

Cited by 5 publications

(1 citation statement)

References 33 publications

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“…The effective management of an TCLs aggregation often requires a central higher-level control system denoted as a virtual power plant (VPP) [5]. The VPP integrates the TCLs and other resources into energy markets, while also providing essential grid services [5], [6].…”

Section: A Generalmentioning

confidence: 99%

Maximum Reserved Capacity of Aggregated Electric Water Heaters Virtual Battery for Peak Management

Arafat,

Shokouhinejad,

Castillo Guerra

et al. 2024

IEEE Access

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This paper introduces new insights into the integration of thermostatically controlled loads (TCLs) as hybrid energy sources for grid ancillary and demand response services. Leveraging a generalized virtual battery (VB) model emerges as an effective approach to determine maximum reserve capacity of these aggregated devices. Our research extends the VB model and endeavors to establish a pragmatic framework for EWH control for peak shaving and managing the payback effect. The importance of aggregator capacity in mitigating the impacts of TCL external control, such as customer comfort and safety, is emphasized. Electric water heaters (EWHs) are used as the residential TCL device given their extensive availability and thermal capacity. Two TCL control scenarios, OFF control and ON/OFF control are compared using the Model Predictive Control (MPC) method. The ON/OFF control was found to improve peak shaving capability by approximately 47% when compared to the more rudimentary OFF control mechanism. The main contributions of this study are threefold: assessment of maximum reserve capacity using a modified VB model, creation of a reference control signal based on this result, and development of effective control strategies for managing the payback effect when maximal reserves are utilized. The robustness of the maximum capacity estimation is analyzed through a sensitivity analysis mainly driven by variations in hot water consumption and communication loss. Comprehensive and comparative simulation results show improved capabilities in the utilization of the maximum reserved energy of loads, minimizing the expected payback to avoid additional energy peaks and assessing the impacts of external factors that can affect the expected maximum capacity of the VB.INDEX TERMS Demand response, peak management, model predictive control, thermostatically controlled loads, virtual battery.

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Section: A Generalmentioning

confidence: 99%

Maximum Reserved Capacity of Aggregated Electric Water Heaters Virtual Battery for Peak Management

Arafat,

Shokouhinejad,

Castillo Guerra

et al. 2024

IEEE Access

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Low-carbon economic dispatch optimization of a virtual power plant based on deep reinforcement learning in China's carbon market environment

Hua

Niu

2022

Journal of Renewable and Sustainable Energy

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In the context of China's dual carbon targets, reducing carbon emissions has become even more urgent and important. In order to reduce carbon emissions during the operation of a virtual power plant(VPP), a carbon market containing carbon emission allowances (CEA) and the Chinese certified voluntary emission reduction (CCER) project was introduced, and a VPP day-ahead and real-time coordinated scheduling optimization model was developed. This model was optimized to maximize benefits and minimize carbon emissions, further increasing the emphasis on carbon reduction from the VPP. The deep Q network (DQN) in the deep reinforcement learning (DRL) algorithm was introduced to solve for the complexity and non-linearity of the VPP model. Finally, to verify the validity and feasibility of the model and the solution algorithm, a VPP was chosen for the analysis of arithmetic examples. The results showed that: (1) In the calculation example, the VPP obtained more than 30 thousand yuan of carbon revenue per day by participating in carbon trading, which effectively mobilizes interest in carbon emission reduction. (2) Compared to the scheduling result with profit maximization as the goal, the scheduling result considering dual goals reduced carbon emissions by 32.7% at the cost of a 2.6% reduction in revenue, obtaining a better compromise. (3) The two-stage coordinated optimization model obtained 3.6% higher gains than the day-ahead optimization model that took into account deviation penalties. (4) By comparing the scheduling model solution results from the Yalmip toolbox and DQN algorithm, the effectiveness and feasibility of the DQN algorithm were verified.

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Virtual Power Plant for Optimizing Power Flow in Large Scale Distributed Networks

Rajasegharan,

Veena,

Ramesh

et al. 2023

Electric Power Components and Systems

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Intent Profile Strategy for Virtual Power Plant Participation in Simultaneous Energy Markets With Dynamic Storage Management

Cited by 5 publications

References 33 publications

Maximum Reserved Capacity of Aggregated Electric Water Heaters Virtual Battery for Peak Management

Maximum Reserved Capacity of Aggregated Electric Water Heaters Virtual Battery for Peak Management

Low-carbon economic dispatch optimization of a virtual power plant based on deep reinforcement learning in China's carbon market environment

Virtual Power Plant for Optimizing Power Flow in Large Scale Distributed Networks

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