Simulation of Fuzzy Logic Based Energy Management for the Home with Grid Connected PV-Battery System

Soetedjo, Aryuanto; Nakhoda, Yusuf Ismail; Saleh, Choirul

doi:10.1109/icsgsc.2018.8541271

Cited by 9 publications

(6 citation statements)

References 10 publications

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“…The nonlinear prediction model is a popular research direction, especially the combination of neural networks [57]. As for producers, it is evident that climate impacts the output of producers; however, the mechanism governing this phenomenon remains unclear [58] [59]. Combinatorial methods are also gaining increasing attention, such as the method combining machine learning and numerical weather prediction, which represents the integration of approximate physical models and statistical analyses.…”

Section: Predictionmentioning

confidence: 99%

Energy Management in Microgrid and Multi-microgrid

Xing¹,

Jia²

2023

Preprint

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<p>Considered as basic structures of next-generation energy system, environment-friendly and flexible microgrid (MG) systems are potential solutions to deal with stochastic renewable energy sources (RESs). The essence of management is to maximize the benefits of available resources through coordination and optimization, which also applies to energy systems. Adaptable energy management approaches provide the possibility to construct effective and various energy interaction. The purpose of this paper is to present a systematic review of energy management in MG systems, and further to propose an energy management model. Therefore, this paper first comprehensively reviews recent research studies on MG, particularly in the interconnected mode (called as multi-microgrid, MMG). Based on the analysis of relative literatures, a STEER (stability, touch, efficiency, evenness, and resilience) strategy is proposed and formulated from the perspective of energy utilization for energy management. Then, critical technologies in energy management are reviewed and summarized from the aspects of control, communication, optimization, prediction, and evaluation. Furthermore, energy management system is illustrated from the perspectives of system function, management framework and operation logic, based on a four-layer hierarchical architecture. Eight main prospects on the future trend of energy management in MG and MMG are also presented.</p>

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Section: Predictionmentioning

confidence: 99%

Energy Management in Microgrid and Multi-microgrid

Xing¹,

Jia²

2023

Preprint

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“…This paper primarily reviews the components of the single phase PV system so as to develop a new modified system which is cost effective and efficient. In paper Simulation of Fuzzy Logic based energy management for the home with grid connected PV-Battery System is presented [20]. The Fuzzy Logic Controller is an effective method to handle the optimization problem such as for minimizing the electricity cost in the grid connected PV-battery system.…”

Section: Stability and Power Quality Improvement In Grid Connectementioning

confidence: 99%

Stability and Power Quality Improvement in Grid Connected System - A Review

Chittora¹

2020

IJRASET

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The modern power system is a complex network which contains of numerous generators, transmission lines, loads and transformers etc. There has been exponential increase in the power demand over the last few decades, transmission lines are loaded more than was planned. With the increased loading of long transmission lines, the problem of transient stability, after a major fault becomes a transmission limiting factor. This leads to FACTs devices being installed in the system. The scarcity of power can be met by using renewable sources like Solar Energy, Wind energy etc. The Solar Energy systems also called PV system can be used in isolated or grid connected mode. PV system has numerous benefits and its use has been increasing in recent years. Beside electricity shortage, a more vulnerable issue in modern power system is related to quality of power delivered. A good quality of power ensures smooth operations of electrical utilities. Harmonic distortion in the grid is produced by non-linear loads connected to grid. This reduces the power quality in the system.

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“…Rule-based optimization approaches are based on pre-defined rules, attempting to achieve an optimized solution to a mathematical problem [26]. Rule-based optimization approaches include Action Dependent Heuristic Dynamic Programming (ADHDP) [27], [38], [39], static and dynamic rule-based approaches [28]- [31], Particle Swarm Optimization (PSO) [35]- [37], and Fuzzy Logic [32], [33]. The primary objectives of rule-based optimization approaches are minimizing the energy costs, reducing energy consumption from the electric grid and emission, optimizing the energy consumption and flow, and maximizing the comforts of consumers [27]- [33], [35]- [38].…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Cluster-Based Energy Management System Using Deep Reinforcement Learning Without Affecting Prosumer Comfort: V2X Technologies and Peer-to-Peer Energy Trading

Yavuz,

Kivanç

2024

IEEE Access

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The concept of Prosumer has enabled consumers to actively participate in Peer-to-Peer (P2P) energy trading, particularly as Renewable Energy Source (RES)s and Electric Vehicle (EV)s have become more accessible and cost-effective. In addition to the P2P energy trading, prosumers benefit from the relatively high energy capacity of EVs through the integration of Vehicle-to-X (V2X) technologies, such as Vehicle-to-Home (V2H), Vehicle-to-Load (V2L), and Vehicle-to-Grid (V2G). Optimization of an Energy Management System (EMS) is required to allocate the required energy efficiently within the cluster, due to the complex pricing and energy exchange mechanism of P2P energy trading and multiple EVs with V2X technologies. In this paper, Deep Reinforcement Learning (DRL) based EMS optimization method is proposed to optimize the pricing and energy exchanging mechanisms of the P2P energy trading without affecting the comfort of prosumers. The proposed EMS is applied to a small-scale cluster-based environment, including multiple (6) prosumers, P2P energy trading with novel hybrid pricing and energy exchanging mechanisms, and V2X technologies (V2H, V2L, and V2G) to reduce the overall energy costs and increase the Self-Sufficiency Ratio (SSR)s. Multi Double Deep Q-Network (DDQN) agents based DRL algorithm is implemented and the environment is formulated as a Markov Decision Process (MDP) to optimize the decision-making process. Numerical results show that the proposed EMS reduces the overall energy costs by 19.18%, increases the SSRs by 9.39%, and achieves an overall 65.87% SSR. Additionally, numerical results indicates that model-free DRL, such as DDQN agent based Deep Q-Network (DQN) Reinforcement Learning (RL) algorithm, promise to eliminate the energy management complexities with multiple uncertainties.INDEX TERMS Energy management system, peer-to-peer energy trading, vehicle-to-home, multi-agent reinforcement learning, deep reinforcement learning, smart grids.

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Simulation of Fuzzy Logic Based Energy Management for the Home with Grid Connected PV-Battery System

Cited by 9 publications

References 10 publications

Energy Management in Microgrid and Multi-microgrid

Energy Management in Microgrid and Multi-microgrid

Stability and Power Quality Improvement in Grid Connected System - A Review

Optimization of a Cluster-Based Energy Management System Using Deep Reinforcement Learning Without Affecting Prosumer Comfort: V2X Technologies and Peer-to-Peer Energy Trading

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