Application of Reinforcement Learning Algorithm for Scheduling of Microgrid

Jayaraj, Saritha; P, Imthias Ahamed T; K, Bijuna Kunju

doi:10.1109/gcat47503.2019.8978453

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…However, only fixed tariffs have been considered in their work. Other optimization methods include dynamic programming [22], quadratic programming [23,24], genetic algorithm [25,26], particle swarming [26][27][28][29], honey bee mating [30], as well as machine learning techniques, most notably reinforcement learning [31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm

et al. 2023

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Given the fundamental role of renewable energy assets in achieving global temperature control targets, new energy management methods are required to efficiently match intermittent renewable generation and demand. Based on analysing various designed cases, this paper explores a number of heuristics for a smart battery scheduling algorithm that efficiently matches available power supply and demand. The core of improvement of the proposed smart battery scheduling algorithm is exploiting future knowledge, which can be realized by current state-of-the-art forecasting techniques, to effectively store and trade energy. The performance of the developed heuristic battery scheduling algorithm using forecast data of demands, generation, and energy prices is compared to a heuristic baseline algorithm, where decisions are made solely on the current state of the battery, demand, and generation. The battery scheduling algorithms are tested using real data from two large-scale smart energy trials in the UK, in addition to various types and levels of simulated uncertainty in forecasts. The results show that when using a battery to store generated energy, on average, the newly proposed algorithm outperforms the baseline algorithm, obtaining up to 20–60% more profit for the prosumer from their energy assets, in cases where the battery is optimally sized and high-quality forecasts are available. Crucially, the proposed algorithm generates greater profit than the baseline method even with large uncertainty on the forecast, showing the robustness of the proposed solution. On average, only 2–12% of profit is lost on generation and demand uncertainty compared to perfect forecasts. Furthermore, the performance of the proposed algorithm increases as the uncertainty decreases, showing great promise for the algorithm as the quality of forecasting keeps improving.

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Section: Related Workmentioning

confidence: 99%

Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm

et al. 2023

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“…The CFFNN produced by this connection architecture is called cascade forward. One option for the CFFNN model is in equation (6). The job in issue is the stimulation task between the contribution and production layers, and the stimulation function from the contribution layer to the production layer is represented by the weight in the contribution layer's stimulation function.…”

Section: Cascaded Feed Forward Neural Network Controllermentioning

confidence: 99%

“…Varying asymmetrical inverters have different dc-link voltages [4] [5]. The design of the PV controller, the inverter, the interface, and the scheduling of microgrids [6][7] all play significant roles in enhancing the performance of grid-connected PV systems. Energy trading in microgrids is improved by the application of various learning algorithms [8].…”

Section: Introductionmentioning

confidence: 99%

Cascade feedforward neural network and deep neural network controller on photovoltaic system with cascaded multilevel inverters: Comparison on standalone and grid integrated system

Mailugundla¹,

Tegampure²

2022

J. Mechatron. Electr. Power Veh. Technol.

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The introduction of a micro-grid-based power generation network will help to meet the demands of consumers while reducing environmental impact. Several industrialized and emerging countries allocate considerable resources to renewable energy-based power generation and invest significant sums of money in this area. This study examines the challenges involved with electricity generation through photovoltaic (PV) systems and the integration of the same with the grid to mitigate power quality issues and improve the power factor for various loading conditions. An innovative multilayer inverter for grid-connected PV systems has been developed to enhance the voltage profile and resulted in a drop in total harmonic distortion (THD). A cascade multilevel inverter (associated with a grid-integrated PV system and managed using multiple innovative artificial intelligence controllers) was developed in this research project. Various advanced intelligent controllers, such as cascade feedforward neural networks (CFFNN) and deep neural networks (DNN), have been analyzed under various operating situations and observed that the THD of voltage, current at the grid, and the load is less than 3 % as per the IEEE 519 standards along with this power factor is maintained nearly unity for the grid-connected system. The quality of power in terms of voltage, frequency, total harmonics distortion, and power factor are improved by using a novel deep neural network algorithm in a cascaded multilevel inverter and verified according to IEEE 1547 and IEEE 519 standards to determine the efficacy of the proposed system.

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“…The validity of the approach is verified by experiments with read data. Jayaraj employed Q learning algorithm, a variant of reinforcement learning, to carry out economic scheduling of a microgrid with photovoltaic cells and accumulators [12]. The experiments results show that the proposed method is effective and can reduce net transaction cost.…”

Section: Related Workmentioning

confidence: 99%

Power Flow Adjustment for Smart Microgrid Based on Edge Computing and Deep Reinforcement Learning

Jiao

Cao

et al. 2021

Preprint

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As one of the core components that improve transportation, generation, delivery, and electricity consumption in terms of protection and reliability, smart grid can provide full visibility and universal control of power assets and services, provide resilience to system anomalies and enable new ways to supply and trade resources in a coordinated manner. In current power grids, a large number of power supply and demand components, sensing and control devices generate lots of requirements, e.g., data perception, information transmission, business processing and real-time control, while existing centralized cloud computing paradigm is hard to address issues and challenges such as rapid response and local autonomy. Specifically, the trend of micro grid computing is one of the key challenges in smart grid, because a lot of in the power grid, diverse, adjustable supply components and more complex, optimization of difficulty is also relatively large, whereas traditional, manual, centralized methods are often dependent on expert experience, and requires a lot of manpower. Furthermore, the application of edge intelligence to power flow adjustment in smart grid is still in its infancy. In order to meet this challenge, we propose a power control framework combining edge computing and machine learning, which makes full use of edge nodes to sense network state and power control, so as to achieve the goal of fast response and local autonomy. Furthermore, we design and implement parameters such as state, action and reward by using deep reinforcement learning to make intelligent control decisions, aiming at the problem that flow calculation often does not converge. The simulation results demonstrate the effectiveness of our method with successful dynamic power flow calculating and stable operation under various power conditions.

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Application of Reinforcement Learning Algorithm for Scheduling of Microgrid

Cited by 4 publications

References 11 publications

Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm

Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm

Cascade feedforward neural network and deep neural network controller on photovoltaic system with cascaded multilevel inverters: Comparison on standalone and grid integrated system

Power Flow Adjustment for Smart Microgrid Based on Edge Computing and Deep Reinforcement Learning

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