Energy Management in Microgrids with Renewable Energy Sources and Demand Response

Dixit, Shishir; Singh, Pushpendra; Ogale, Jyotsna; Bansal, Praveen; Sawle, Yashwant

doi:10.1016/j.compeleceng.2023.108848

Cited by 24 publications

(4 citation statements)

References 23 publications

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“…Their method comprehensively addresses uncertainties in demand response and wind power forecasting, offering a fresh angle to enhance the economic viability and stability of MGs. Dixit et al (Dixit et al, 2023) concentrated on the energy management of MGs, with a thorough consideration of integrating demand response and renewable energy, aiming at enhancing the energy utilization efficiency of MGs through optimal scheduling techniques. Shakti et al (Singh et al, 2016) incorporated renewable energy sources and energy storage systems into the MGs, achieved cooperative operation among various components within the MGs by coordinated scheduling strategy, leading to an enhancement in overall performance of the system.…”

Section: Introductionmentioning

confidence: 99%

Enhanced bi-level optimal scheduling strategy for distribution network with multi-microgrids considering source-load uncertainties

Li,

Lin,

Kong

et al. 2024

Front. Energy Res.

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With the increasing integrations of renewable energy resources into distribution networks (DNs) and microgrids (MGs), the imperative for an effective market scheduling mechanism becomes paramount to enhance the operational safety, reliability, and economic efficiency of distribution grids. Taking advantage of bi-level programming theory, this study meticulously formulates a comprehensive optimization scheduling model for the multi-MGs distribution network. The upper-level optimization objective is to minimize both the operational losses and total costs of the DN. Concurrently, the lower-level optimization pursues the maximization of daily operational revenue for MGs. Recognizing the pervasive impact of the inherent uncertainty associated with renewable energy sources on system safety and reliability, a cutting-edge scenario-based stochastic planning framework is introduced. The methodology integrates a heuristic matrix matching approach to effectively handle the intricate challenges posed by uncertainties from wind and photovoltaic generations. Moreover, in addressing the proposed nonlinear models, a sophisticated method is employed, utilizing the second-order cone relaxation and linearization methods. These methods meticulously transform the upper and lower-level models into second-order cone planning and mixed-integer linear programming issues, respectively. Finally, the proposed methodologies are rigorously scrutinized and validated with intricate case studies, providing a nuanced understanding of their efficacy. The empirical results underscore the theoretical feasibility and superiority of the proposed scheduling scheme. Notably, the operational performance of the DN as well as the economic viability of multiple MGs can also be significantly improved.

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Section: Introductionmentioning

confidence: 99%

Enhanced bi-level optimal scheduling strategy for distribution network with multi-microgrids considering source-load uncertainties

Li,

Lin,

Kong

et al. 2024

Front. Energy Res.

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“…The introduction of renewable energy sources simultaneously promotes the use of microgrids, making major changes in generation, distribution, and energy consumption. As a result, microgrids, which are characterized by their localized and decentralized nature, are emerging as an important component of the evolving energy infrastructure [20]. These energy systems play an important role in providing reliable power in both grid-connected and islanded (off-grid) modes.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Different Methodologies for Wave Energy Conversion Systems Integration into the Power Grid Using Power Hardware-in-Loop Emulation

Vujkov,

Dragić,

Žnidarec

et al. 2024

Energies

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The ocean energy resources hold the promise of a sustainable solution within global efforts to diversify energy sources and mitigate climate change. Wave energy conversion (WEC) systems, as emerging technologies, offer adaptability and the potential to harness predictable wave energy. However, integration of WEC systems into a power grid brings challenges for system operators due to their nature of operation. Addressing these demands is a multilayered process that involves highly efficient power electronic devices, control systems, and efficient energy storage solutions. This paper specifically focuses on the methodologies of the grid integration of a specific wave energy conversion system—a point absorber developed by the company Sigma Energy. Proposed methodologies are experimentally tested using power hardware-in-loop (PHIL) emulation of a fully monitored and controlled small-scale microgrid equipped with a battery energy storage system (BESS), different emulators of loads, and distributed generators (DG).

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“…The role of battery peak load filling is considered in order to reduce the cost of electricity purchase to reduce the total cost of the system. In practical application, wind power and photovoltaics have a very strong randomness [4] . The problem is transformed into a Markov decision process under the framework of the deep reinforcement problem, and the output situation of each distributed power supply with the minimum total operating cost is obtained through the optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Operation optimization of cogeneration microgrid based on deep reinforcement learning

Sun

2024

J. Phys.: Conf. Ser.

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The deep Q learning method is adopted to transform the microgrid model into the Markov decision model to realize the coordinated management and energy optimization of the microgrid. For the problem of the Markov decision model, we cleverly divide the working state of the gas turbine into different gears to meet its requirements. To reduce the fitting difficulty and improve the result accuracy and convergence stability, a linear penalty term was added to the reward function. Considering the operating and environmental costs, in addition to the purchase and sale price of a large power grid, peak and valley electricity price and operation constraints, we further reduce the peak and valley difference and reduce the operating cost. Finally, the output of the battery and the output of the distributed power supply are obtained through simulation verification. Through the peak-shifting and valley-filling function of the battery, it can effectively deal with the fluctuation of peak and valley electricity prices, reducing the peak and valley difference of electric heating load, and providing strong support for sustainable energy management.

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Energy Management in Microgrids with Renewable Energy Sources and Demand Response

Cited by 24 publications

References 23 publications

Enhanced bi-level optimal scheduling strategy for distribution network with multi-microgrids considering source-load uncertainties

Enhanced bi-level optimal scheduling strategy for distribution network with multi-microgrids considering source-load uncertainties

Evaluation of Different Methodologies for Wave Energy Conversion Systems Integration into the Power Grid Using Power Hardware-in-Loop Emulation

Operation optimization of cogeneration microgrid based on deep reinforcement learning

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