Wind-photovoltaic co-generation prediction and energy scheduling of low-carbon complex regional integrated energy system with hydrogen industry chain based on copula-MILP

Gao, Chong; Lin, Jianzhong; Zeng, Jianfeng; Han, Fengwu

doi:10.1016/j.apenergy.2022.120205

Cited by 27 publications

(4 citation statements)

References 48 publications

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“…Limitation (4) displays the limit of operation and purchase of power from the upstream grid. Equation (5) shows the energy level in the battery. Equation (6) illustrates the limitation of battery capacity.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The focus primarily centers on distribution networks [1][2][3]. To realize these objectives and usher in enhanced efficiency, the integration of renewable energy resources (RERs), including photovoltaics (PVs), wind turbines (WTs), and distributed generation (DG) technologies like fuel cells (FCs) and microturbines (MTs), becomes imperative [4,5]. The pivotal role of a microgrid (MG), encompassing diverse power sources, comes to fruition as a means of establishing an efficient, resilient, and secure distribution network.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Jokar,

Niknam,

Dehghani

et al. 2023

Energies

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Within microgrids (MGs), the integration of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP) systems, demand response (DR) initiatives, and energy storage solutions poses intricate scheduling challenges. Coordinating these diverse components is pivotal for optimizing MG performance. This study presents an innovative stochastic framework to streamline energy management in MGs, covering proton exchange membrane fuel cell–CHP (PEMFC-CHP) units, RERs, PHEVs, and various storage methods. To tackle uncertainties in PHEV and RER models, we employ the robust Monte Carlo Simulation (MCS) technique. Challenges related to hydrogen storage strategies in PEMFC-CHP units are addressed through a customized mixed-integer nonlinear programming (MINLP) approach. The integration of intelligent charging protocols governing PHEV charging dynamics is emphasized. Our primary goal centers on maximizing market profits, serving as the foundation for our optimization endeavors. At the heart of our approach is the Meerkat Optimization Algorithm (MOA), unraveling optimal MG operation amidst the intermittent nature of uncertain parameters. To amplify its exploratory capabilities and expedite global optima discovery, we enhance the MOA algorithm. The revised summary commences by outlining the overall goal and core algorithm, followed by a detailed explanation of optimization points for each MG component. Rigorous validation is executed using a conventional test system across diverse planning horizons. A comprehensive comparative analysis spanning varied scenarios establishes our proposed method as a benchmark against existing alternatives.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Jokar,

Niknam,

Dehghani

et al. 2023

Energies

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show abstract

“…The collaborative optimization operation model of the integrated energy system, denoted as “source‐grid‐load‐storage,” is developed in this study and formulated as a mixed‐integer linear programming problem. To implement the model, Yalmip in MATLAB is utilized, and the commercial solver Cplex is employed to solve the problem [20].…”

Section: Constraintsmentioning

confidence: 99%

Collaborative and effective scheduling of integrated energy systems with consideration of carbon restrictions

Meng,

Xu,

Luo

et al. 2023

IET Generation Trans & Dist

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To address the balancing challenge of real‐time power demand and supply in power systems caused by unpredictable output fluctuations of renewable energies, collaborative and effective scheduling of integrated energy systems is proposed with consideration for carbon restrictions. The model incorporates various energy resources, including cold, heat, electricity, and gas, and overcomes the barriers among different levels of heterogeneous energy grid systems. This makes it possible to coordinate the vertical complementarity of source‐grid‐load‐storage and the horizontal complementarity of multi‐energy while taking into account investment costs and carbon trading to achieve the economic operation of the system. This analysis shows that the proposed model can dissipate the excess of new energy generation from the grid. and achieve a balance between supply and demand, while effectively reducing the economic operation cost of the whole system.

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“…This approach yielded a joint probability density function, leading to a correlation sample of load and solar radiation. 16 , 17 Based on the Copula function and the probability distribution function of RE, Mi et al. 18 generated RE sequences considering the uncertainties and correlation of WT and PV.…”

Section: Introductionmentioning

confidence: 99%

Optimal scheduling of electricity and hydrogen integrated energy system considering multiple uncertainties

Chang,

Li,

Zhu

et al. 2024

iScience

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Wind-photovoltaic co-generation prediction and energy scheduling of low-carbon complex regional integrated energy system with hydrogen industry chain based on copula-MILP

Cited by 27 publications

References 48 publications

Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Collaborative and effective scheduling of integrated energy systems with consideration of carbon restrictions

Optimal scheduling of electricity and hydrogen integrated energy system considering multiple uncertainties

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