Design of cost-based sizing and energy management framework for standalone microgrid using reinforcement learning

Khawaja, Yara; Qiqieh, Issa; Alzubi, Jafar A.; Alzubi, Omar A.; Allahham, Adib; Giaouris, Damian

doi:10.1016/j.solener.2023.01.027

Cited by 33 publications

(10 citation statements)

References 32 publications

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“…In [75], reinforcement learning was proposed to optimize the electric management system (EMS) and size of a PV/BESS microgrid. The Q-learning algorithm is used to determine the next actions for the EMS.…”

Section: K Strategies Using Machine Learningmentioning

confidence: 99%

Survey of Strategies to Optimize Battery Operation to Minimize the Electricity Cost in a Microgrid With Renewable Energy Sources and Electric Vehicles

Colucci,

Mahgoub,

Yousefizadeh

et al. 2024

IEEE Access

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There is a rapid increase in the utilization of renewable sources such as solar and wind to provide power and electricity. The reason for this trend is to reduce costs and preserve the environment. However, the challenge is to efficiently use and store the energy from these sources. One approach is to optimize the decision when to charge or discharge a battery. The objective is to generate the greatest monetary gain. More charge/discharge cycles will reduce the life of a battery, thereby increasing the cost. A strategy to reduce the number of charge cycles while maintaining the effectiveness of electricity distribution from battery storage will improve battery life. With the inevitable proliferation of electric vehicles (EVs) in the market, strategies specific to electric vehicle battery profitability will be explored. An additional concern which threatens the financial feasibility of battery energy storage systems (BESSs) is the requirement of secure operation. There is currently little research study on strategies to detect cyberattacks on such systems. In this paper, we have presented a novel taxonomy for battery optimization, survey representative BESS utilization strategies, and classify these schemes within the taxonomy. Within our classification, we outline the battery optimization methods that have been discussed, analyze their ability to address issues that arise when implementing a BESS, and describe alternative research that could be explored. Future research could refer to this information to create unique battery optimization schemes to provide more efficiency and optimal revenue for a BESS when compared to current strategies.

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Section: K Strategies Using Machine Learningmentioning

confidence: 99%

Survey of Strategies to Optimize Battery Operation to Minimize the Electricity Cost in a Microgrid With Renewable Energy Sources and Electric Vehicles

Colucci,

Mahgoub,

Yousefizadeh

et al. 2024

IEEE Access

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“…Various energy management strategies for optimal sizing of PV-BES in hybrid power systems have been outlined in [39][40][41]. It is important to ensure that each energy source functions within its parameters when designing an EMS while optimizing the system's performance.…”

Section: Existing Work and Their Limitationsmentioning

confidence: 99%

A Grid-Connected Optimal Hybrid PV-BES System Sizing for Malaysian Commercial Buildings

et al. 2023

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In this article, the optimal sizing of hybrid solar photovoltaic and battery energy storage systems is evaluated with respect to rooftop space and feed-in tariff rates. The battery scheduling is performed using a proposed rule-based energy management strategy. The rules are formulated based on the demand limit, PV export power limit, and state of charge of the battery. Furthermore, optimization modeling with initial choices of parameters and constraints in terms of solar photovoltaic and battery energy storage capabilities is developed to minimize the total net present cost. The hourly values of solar irradiance, air temperature, electrical loads, and electricity rates are considered the inputs of the optimization process. The optimization results are achieved using particle swarm optimization and validated through an uncertainty analysis. It is observed that an optimal photovoltaic and battery energy storage system can reduce the cost of electricity by 12.33%, including the sale of 5944.029 kWh of electricity to the grid. Furthermore, energy consumption, peak demand, and greenhouse gas emissions are reduced by 13.71%, 5.85%, and 62.59%, respectively. A comprehensive analysis between the variable and fixed data for the load, energy from PV, batteries, and the grid, and costs demonstrates that the optimal sizing of photovoltaic and battery energy storage systems with the best mix of energy from PV, batteries, and the grid provides the optimal solution for the proposed configuration.

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“…In order to enable more adaptive and clever control decisions, deep learning models with the ability to comprehend intricate patterns and relationships from historical data include gated recurrent units (GRUs), long short-term memory (LSTM), and recurrent neural networks (RNNs) [12]. Hybrid control approaches can potentially improve machine learning systems' efficiency, stability, and resilience by fusing the interpretability of rule-based systems with the learning power of deep neural networks [13].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

Akbulut,

Cavus,

Cengiz

et al. 2024

Energies

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Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R2 value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831.

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Design of cost-based sizing and energy management framework for standalone microgrid using reinforcement learning

Cited by 33 publications

References 32 publications

Survey of Strategies to Optimize Battery Operation to Minimize the Electricity Cost in a Microgrid With Renewable Energy Sources and Electric Vehicles

Survey of Strategies to Optimize Battery Operation to Minimize the Electricity Cost in a Microgrid With Renewable Energy Sources and Electric Vehicles

A Grid-Connected Optimal Hybrid PV-BES System Sizing for Malaysian Commercial Buildings

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

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