A Control Strategy Based on Deep Reinforcement Learning Under the Combined Wind-Solar Storage System

Huang, Shiying; Li, Peng; Yang, Ming; Gao, Yuan; Yun, Jiangyang; Zhang, Changhang

doi:10.1109/tia.2021.3105497

Cited by 28 publications

(6 citation statements)

References 36 publications

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“…[15] introduces an integrated multi-energy complementary coordination scheduling method. A storage system control model (ESSCM) is proposed in [16] for the wind and solar hybrid combined storage system scenario to facilitate the synergistic operation of wind and photovoltaic (PV) power generation in a combined system, thus maximizing the benefits of the combined system in the electricity market. Zhang et al [17] present a day-ahead scheduling model for an industrial power system integrating wind power and multiple types of storage, proving that the introduction of storage devices can reduce the occurrence of wind curtailment and enhance system flexibility.…”

Section: Introductionmentioning

confidence: 99%

“…Reddy et al [19] propose an optimized scheduling strategy for a battery-thermal-wind-photovoltaic generation system considering the impact of uncertainties in wind, solar photovoltaic, and load forecasting. These scheduling schemes in [14][15][16][17][18][19] focus on the combination of single or dual types of energy sources. The authors of [17,18] do not consider the cost issues of storage systems.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Time-Scale Optimal Scheduling Strategy for Marine Renewable Energy Based on Deep Reinforcement Learning Algorithm

Xu,

Lin,

Shao

et al. 2024

Entropy

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Surrounded by the Shandong Peninsula, the Bohai Sea and Yellow Sea possess vast marine energy resources. An analysis of actual meteorological data from these regions indicates significant seasonality and intra-day uncertainty in wind and photovoltaic power generation. The challenge of scheduling to leverage the complementary characteristics of various renewable energy sources for maintaining grid stability is substantial. In response, we have integrated wave energy with offshore photovoltaic and wind power generation and propose a day-ahead and intra-day multi-time-scale rolling optimization scheduling strategy for the complementary dispatch of these three energy sources. Using real meteorological data from this maritime area, we employed a CNN-LSTM neural network to predict the power generation and load demand of the area on both day-ahead 24 h and intra-day 1 h time scales, with the DDPG algorithm applied for refined electricity management through rolling optimization scheduling of the forecast data. Simulation results demonstrate that the proposed strategy effectively meets load demands through complementary scheduling of wave power, wind power, and photovoltaic power generation based on the climatic characteristics of the Bohai and Yellow Sea regions, reducing the negative impacts of the seasonality and intra-day uncertainty of these three energy sources on the grid. Additionally, compared to the day-ahead scheduling strategy alone, the day-ahead and intra-day rolling optimization scheduling strategy achieved a reduction in system costs by 16.1% and 22% for a typical winter day and a typical summer day, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Time-Scale Optimal Scheduling Strategy for Marine Renewable Energy Based on Deep Reinforcement Learning Algorithm

Xu,

Lin,

Shao

et al. 2024

Entropy

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“…(1) The flexible loads embedded in the wind-storage cooperative framework have not been developed sufficiently in the existing literature. In [ 11 , 19 , 20 , 21 ], the authors did not focus on the favorable effect of the flexible loads in the proposed wind-storage model. As an example, flexible loads were considered in [ 22 ], where the benefits from the suitable management of demand-side flexible loads were validated.…”

Section: Introductionmentioning

confidence: 99%

“…(2) The exploration of reinforcement learning methods for wind-storage cooperative decision-making needs to be enhanced. In [ 19 , 20 , 23 , 24 ], a deep Q-learning strategy was considered in wind-storage systems. However, the main mechanism of the deep Q-learning strategy is to select the actions that can obtain the maximum benefits according to the Q values, which are constructed by the state and action.…”

Section: Introductionmentioning

confidence: 99%

An Improved Deep Reinforcement Learning Method for Dispatch Optimization Strategy of Modern Power Systems

Zhai

Qiu

et al. 2023

Entropy

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As a promising information theory, reinforcement learning has gained much attention. This paper researches a wind-storage cooperative decision-making strategy based on dueling double deep Q-network (D3QN). Firstly, a new wind-storage cooperative model is proposed. Besides wind farms, energy storage systems, and external power grids, demand response loads are also considered, including residential price response loads and thermostatically controlled loads (TCLs). Then, a novel wind-storage cooperative decision-making mechanism is proposed, which combines the direct control of TCLs with the indirect control of residential price response loads. In addition, a kind of deep reinforcement learning algorithm called D3QN is utilized to solve the wind-storage cooperative decision-making problem. Finally, the numerical results verify the effectiveness of D3QN for optimizing the decision-making strategy of a wind-storage cooperation system.

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“…Ref. [18] proposed an optimal dispatch method for integrated energy systems by using the proximal policy optimization (PPO) algorithm for considering security constraints.…”

Section: Introductionmentioning

confidence: 99%

Power System Dispatch Based on Improved Scenario Division with Physical and Data-Driven Features

Huang,

Cao,

Liang

et al. 2023

Energies

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In power systems with high penetration of renewable energy, traditional physical model-based optimal dispatch methods suffer from modeling difficulties and poor adaptability, while data-driven dispatch methods, represented by reinforcement learning, have the advantage of fast decision making and reflecting long-term benefits. However, the performances of data-driven methods are much limited by the problem of distribution shift under insufficient power system scenario samples in the training. To address this issue, this paper proposes an improved scenario division method by integrating the power system’s key physical features and the data-driven variational autoencoder (VAE)-generated features. Next, based on the scenario division results, a multi-scenario data-driven dispatch model is established. The effectiveness of the proposed method is verified by a simulation conducted on a real power system model in a province of China.

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A Control Strategy Based on Deep Reinforcement Learning Under the Combined Wind-Solar Storage System

Cited by 28 publications

References 36 publications

Multi-Time-Scale Optimal Scheduling Strategy for Marine Renewable Energy Based on Deep Reinforcement Learning Algorithm

Multi-Time-Scale Optimal Scheduling Strategy for Marine Renewable Energy Based on Deep Reinforcement Learning Algorithm

An Improved Deep Reinforcement Learning Method for Dispatch Optimization Strategy of Modern Power Systems

Power System Dispatch Based on Improved Scenario Division with Physical and Data-Driven Features

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