Energy Management in High RER Multi-Microgrid System via Energy Trading and Storage Optimization

Ali, Sajid; Kazmi, Syed Ali Abbas; Malik, M. Afzaal; Bhatti, Ali Hussain Umar; Haseeb, Muhammad; Kazmi, Syed Muhammad Raza; Shin, Dong Ryeol

doi:10.1109/access.2021.3132505

Cited by 16 publications

(6 citation statements)

References 34 publications

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“…The author in [16] utilized the MILP technique to schedule energy consumption-related problems to minimize electricity costs. In the literature [17], energy trading between the neighbouring MGs and optimal storage scheduling reduces grid dependency. In multi-microgrid (MMG), energy is managed through the cooperation of networked microgrids in distributed systems has been proposed in [18].…”

Section: Related Work and Motivationmentioning

confidence: 99%

Multi-Micro Grid System Reinforcement Across Deregulated Markets, Energy Resources Scheduling and Demand Side Management Using a Multi-Agent-Based Optimization in Smart Grid Paradigm

Sajid,

Altamimi,

Kazmi

et al. 2024

IEEE Access

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Multi-agent based small scaled smart grid reinforcement scheme is proposed to manage energy resources by enhancing resilience to supply power to critical loads in peak demand by leveraging demand side management (DSM) for smoothing load profile and optimal energy storage system (ESS) scheduling in response to grid cost. Interconnected microgrids comprise diverse energy resources that allow sharing energy to balance fluctuation by deploying a market that rewards cheap energy. Three stages are defined in this proposed scheme. In the first stage, without DER, consumption charges are reduced to 7.1% through DSM, but customer comfort is compromised. In the second stage, microgrids comprise RER; energy consumptionrelated charges are reduced to 92.2% by the feed-in tariff (FIT) Program. In the final stage, the inclusion of energy market for trading among microgrids (ESS, diesel and RER) resulting in reduced charges greater than charges incurred from the grid is defined by 122.1%. The findings show that the average power drawn from utility grid increased from 134.7kW to 205.3kW, and peak demand decreased from 591kW to 495kW. Integer linear programming optimization technique is applied to allow the optimal dispatch of microgrids generation to reduce the operation cost to 42.64%. Less reliance on the grid, flexible energy use, and avoiding costly electricity purchases are achieved through DSM, effective resources scheduling, and energy trading.INDEX TERMS Inter-connected multi-microgrids, multi-agent system, demand response program, market system, optimized scheduling, reverse power flow, real-time energy pricing

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Section: Related Work and Motivationmentioning

confidence: 99%

Multi-Micro Grid System Reinforcement Across Deregulated Markets, Energy Resources Scheduling and Demand Side Management Using a Multi-Agent-Based Optimization in Smart Grid Paradigm

Sajid,

Altamimi,

Kazmi

et al. 2024

IEEE Access

Self Cite

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“…According to the IEEE Standard 1547.4 [6], the operation and reliability of a distribution system can be improved by splitting it into multiple MGs. MMG's technical advantages are mainly reflected in the following aspects: 1) in MMG, the energy sharing among the MGs allows the MGs to satisfy their power demand with their own cheaper renewable energy resource which can reduce the cost of fossil fueled generation [7]; 2) the interconnected MGs will effectively improve the utilization of renewable energy, reduce the burden on the main grid, and can also improve the reliability not only for themselves but also for the main grid [8]; 3) in MMG, each microgrid has high self-healing feature and it can also operate in a stable way by sharing extra energy and spare resources with other connected MGs [9][10][11][12]; 4) in MMG, the interconnected MGs can support each other with local generation capacities to achieve the overall reliability and minimize the total load curtailment in the system [13][14].…”

Section: Figure1 the Global Microgrid Market Size (2021-2027)mentioning

confidence: 99%

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

Zhang

2022

IEEE Access

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Microgrid (MG) is one of the most effective solution to integrate distributed renewable energy into power system. However, modern MG has several technical challenges such as migration to multi vector energy system, increasing renewable energy penetration, and the uncertainties that arise from the large-scale incorporation of renewable energy. Recent development of multi-microgrid (MMG) could potentially mitigate these challenges. MMG has obvious advantages in improving the system stability, reliability, economy, and energy efficiency of power grid operation through autonomous management and coordinated control among networked MGs. In the meantime, uncertainties from many elements and operators in the MMG system also pose huge challenges in modeling. This paper will present and review typical architecture of MMG, including physical layer, information layer and application layer. Moreover, this paper will critically review and analyze challenges in uncertainty modelling and solution in MMGs. Finally, the paper will also discuss future research areas and development trends of MMG.

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“…In [15], an energy management scheme suitable for interconnected multi-microgrids was presented to address the challenges posed by increased DER integration and high energy storage system costs. The project aims to reduce operational costs for each microgrid while maximizing the use of renewable energy sources (RERs) without disrupting loads.…”

Section: Introductionmentioning

confidence: 99%

Demand Forecasting and Resource Scheduling of Independent Energy Storage Market in Power Grid with Deep Learning

Zhiqiang Wang

2024

jes

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The power grid presents several obstacles for demand forecasting and resource scheduling, such as a substantial amount of data, a growing number of factors influencing the demand profile, uncertainty in the generation profile of distributed energy from renewable sources, and a shortage of historical data. Here, we provide a unique market-oriented energy storage method based on artificial intelligence (AI) that aims to optimize operational profit in the electricity market between consumers, energy storage, and grid service providers. The approach is divided into two parts; the first is the Residual Network-50 (GR-ResNet-50) algorithm, which is used to overcome future uncertainties related to load needs and power pricing. The second uses deep learning based on the Radial Heap Basis Algorithm (RHBA) to determine the most effective charging or discharging operation considering the grid peak states, load demand, and the state of the batteries. The performance of the proposed method in this study is compared with the conventional method in terms of 0.007% of low MAPE and 20 seconds of less execution time. The research shows that the proposed strategy significantly lowers on-peak power, improves operational profit, and improves effective performance.

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Energy Management in High RER Multi-Microgrid System via Energy Trading and Storage Optimization

Cited by 16 publications

References 34 publications

Multi-Micro Grid System Reinforcement Across Deregulated Markets, Energy Resources Scheduling and Demand Side Management Using a Multi-Agent-Based Optimization in Smart Grid Paradigm

Multi-Micro Grid System Reinforcement Across Deregulated Markets, Energy Resources Scheduling and Demand Side Management Using a Multi-Agent-Based Optimization in Smart Grid Paradigm

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

Demand Forecasting and Resource Scheduling of Independent Energy Storage Market in Power Grid with Deep Learning

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