Optimal Schedule for Networked Microgrids Under Deregulated Power Market Environment Using Model Predictive Control

García-Torres, Félix; Bordons, Carlos; Tobajas, Javier; Márquez, Juan J.; Garrido-Zafra, Joaquín; Moreno-Munoz, A.

doi:10.1109/tsg.2020.3018023

Cited by 44 publications

(20 citation statements)

References 25 publications

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“…To further illustrate the paper results, the proposed MPC scheme is compared with the relevant strategy in Ref. [21] by Garcia et al The latter study has been carried out in a Table 1 e Devices parameters.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal operations for hydrogen-based energy storage systems in wind farms via model predictive control

Abdelghany

Shehzad²,

Liuzza³

et al. 2021

International Journal of Hydrogen Energy

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A hydrogen-based ESS model includes several degradation costs of the electrolyzer and fuel cell.Degradation costs concern working cycles and life spans of the electrolyzer and fuel cell.Features, like warm and cold start, are considered through continuous and logical states combined.An MPC controller minimizes the logical state switching between different operating modes.The controller meets local load demand and maximizes revenue in the electricity market.

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Section: Results Analysis and Discussionmentioning

confidence: 99%

“…With the provision of energy market participation, in Ref. [21] Garcia-Torres et al presented the economic schedule of a network of interconnected microgrids for energy market participation with hybrid ESS under failure conditions. The authors developed and validated the ESS using MPC.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal operations for hydrogen-based energy storage systems in wind farms via model predictive control

Abdelghany

Shehzad²,

Liuzza³

et al. 2021

International Journal of Hydrogen Energy

View full text Add to dashboard Cite

show abstract

“…To validate the proposed approach, we train the GRU-based network model using the training set and then evaluate the well-trained model on the testing set. To demonstrate the advantages of the proposed approach, we compare it with three commonly used online scheduling methods: (1) MPC; (2) ADP; and (3) GA. (1) MPC is a widely used model-based online scheduling method [5][6][7][8][9], which addresses the uncertainty via rolling/receding horizon optimization. At each time step, a multi-timestep optimization model is solved based on real-time forecasts over a prediction horizon.…”

Section: Comparison With Commonly Used Online Scheduling Methodsmentioning

confidence: 99%

“…In [7], a chance-constrained MPC method is proposed and integrated into a hierarchical stochastic energy management system for operation management of interconnected microgrids. In [8], an MPC-based optimal energy management system is designed for the economic re-scheduling of a network of interconnected microgrids under failure conditions. In [9], a two-stage stochastic MPC strategy is developed to manage the local operation of individual microgrid in multi-microgrid systems.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Online Energy Scheduling of a Microgrid Based on Deep Reinforcement Learning

Wang

et al. 2021

Energies

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The proliferation of distributed renewable energy resources (RESs) poses major challenges to the operation of microgrids due to uncertainty. Traditional online scheduling approaches relying on accurate forecasts become difficult to implement due to the increase of uncertain RESs. Although several data-driven methods have been proposed recently to overcome the challenge, they generally suffer from a scalability issue due to the limited ability to optimize high-dimensional continuous control variables. To address these issues, we propose a data-driven online scheduling method for microgrid energy optimization based on continuous-control deep reinforcement learning (DRL). We formulate the online scheduling problem as a Markov decision process (MDP). The objective is to minimize the operating cost of the microgrid considering the uncertainty of RESs generation, load demand, and electricity prices. To learn the optimal scheduling strategy, a Gated Recurrent Unit (GRU)-based network is designed to extract temporal features of uncertainty and generate the optimal scheduling decisions in an end-to-end manner. To optimize the policy with high-dimensional and continuous actions, proximal policy optimization (PPO) is employed to train the neural network-based policy in a data-driven fashion. The proposed method does not require any forecasting information on the uncertainty or a prior knowledge of the physical model of the microgrid. Simulation results using realistic power system data of California Independent System Operator (CAISO) demonstrate the effectiveness of the proposed method.

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“…In the centralized context, the work of [97] applies an MPC on a group of interconnected microgrids with the main grid maximizing the benefits for all the agents and using nonlinear models. From an economical point of view, the work proposed in [98] describes an MPC methodology for energy regulation among microgrids with an internal electric market. It minimizes the deviations due to renewable resources and failures.…”

Section: Number Of Integer Variables [103]mentioning

confidence: 99%

Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

et al. 2021

Self Cite

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Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.

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Optimal Schedule for Networked Microgrids Under Deregulated Power Market Environment Using Model Predictive Control

Cited by 44 publications

References 25 publications

Optimal operations for hydrogen-based energy storage systems in wind farms via model predictive control

Optimal operations for hydrogen-based energy storage systems in wind farms via model predictive control

Data-Driven Online Energy Scheduling of a Microgrid Based on Deep Reinforcement Learning

Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

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