Energy system and scheduling strategies of electric–gas networks deeply coupled under dual electricity prices

Li, Bo; Li, Xu; Liu, Jingzhu

doi:10.1016/j.ijepes.2023.109132

Cited by 2 publications

(3 citation statements)

References 31 publications

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“…The time of use electricity price mechanism 26 is a common price-based demand response mechanism. The basic concept is to divide a day into multiple time periods and set different electricity price standards based on the needs of electricity users, including peak, flat, and valley prices.…”

Section: Price-based Demand Responsementioning

confidence: 99%

“…The optimization objective function is as follows in Equation ( 25): (2) Constraint condition The operation constraints of the equipment dispatched in the day ahead mainly include the operation constraints (26) of each piece of equipment and the power heat gas balance constraints ( 27), (28), and (29). Because the demand response of the transferable load is considered at this time, the power balance curve changes.…”

Section: ) Objective Functionmentioning

confidence: 99%

“…The equipment constraints include the upper and lower limits of equipment output power constraints (26), gas turbine start stop constraints (32), electricity market constraints (33)-( 35), and natural gas market constraints (36). (c) Determine parameters…”

Section: (B) Equipment Constraintsmentioning

confidence: 99%

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Strategic optimization operations in the integrated energy system through multitime scale comprehensive demand response

Zheng,

Hou,

et al. 2024

Energy Science & Engineering

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In the context of the dual carbon goal strategy, the proportion of new energy generation has increased annually, large‐scale renewable energy integration has been achieved, and the intermittent and uncertain operating characteristics pose an enormous challenge to the complete and stable operation of an integrated energy system (IES), promoting the complexity of IES optimization models. To increase the stability and accuracy of the system and improve the operation efficiency of the system, a Gaussian mixture model is used to fit the probability distribution of wind power and the prediction errors of the photovoltaic output. In addition, the expected maximization method is used to solve a model with hidden variables, the results show that the expectation maximization algorithm can improve the fitting accuracy, reduce the error caused by the subjective setting of the initial value, and make the fitting accuracy of the wind‐solar power prediction error greater. Then, to solve the problem in which the low resolution of the day‐ahead scheduling time leads to large errors, day‐ahead, day‐to‐day multitime scale operation optimization model takes into account the comprehensive demand response according to the differences in wind and solar output and load forecasting accuracy. Finally, simulation validation is conducted in multiple scenarios, and a comparative analysis is performed for single and multitime scale comprehensive demand response scenarios. The simulation results show that compared with the optimization results of no demand response day, the optimization results of this model reduce the total cost by 23.21%, carbon emissions by 13.98%, purchased electricity by 13.87%, and purchased gas by 19.31%, effectively improving the use of gas turbines in the system. The multitime scale scheduling strategy increases the overall operating cost of the IES, modifies the scheduling results, and agrees with real operating scenarios.

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Section: Price-based Demand Responsementioning

confidence: 99%

Section: ) Objective Functionmentioning

confidence: 99%

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Strategic optimization operations in the integrated energy system through multitime scale comprehensive demand response

Zheng,

Hou,

et al. 2024

Energy Science & Engineering

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Microgrid Operation Optimization Method Considering Power-to-Gas Equipment: An Improved Gazelle Optimization Algorithm

Wu,

Wen

et al. 2024

Symmetry

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With the increasingly prominent defects of traditional fossil energy, large-scale renewable energy access to power grids has become a trend. In this study, a microgrid operation optimization method, including power-to-gas equipment and a hybrid energy storage system, is proposed. Firstly, this study constructs a microgrid system structure including P2G equipment and a hybrid energy storage system of electricity and hydrogen. Secondly, aiming at minimizing the system operation cost and carbon emission penalty cost, a low-carbon economic operation optimization model of the proposed microgrid is established. Then, this study proposes a microgrid optimization method based on an improved gazelle optimization algorithm to symmetrically improve economic and environmental performance. Finally, the practicability and superiority of the above model and optimization method are verified using a real microgrid case. The results show that considering the participation of P2G equipment and a hybrid energy storage system in the optimal operation, the carbon emission of the microgrid is reduced to 33.56% of the microgrid considering only a single electric energy storage system, and the comprehensive operation cost is reduced by 6.54%. This study is conducive to promoting the development of clean energy and symmetrically enhancing the economic and environmental performance of the microgrid.

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Energy system and scheduling strategies of electric–gas networks deeply coupled under dual electricity prices

Cited by 2 publications

References 31 publications

Strategic optimization operations in the integrated energy system through multitime scale comprehensive demand response

Strategic optimization operations in the integrated energy system through multitime scale comprehensive demand response

Microgrid Operation Optimization Method Considering Power-to-Gas Equipment: An Improved Gazelle Optimization Algorithm

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