Optimized dispatching of city-scale integrated energy system considering the flexibilities of city gas gate station and line packing

Zhuang, Wennan; Zhou, Suyang; Gu, Wei; Chen, Xiaogang

doi:10.1016/j.apenergy.2021.116689

Cited by 26 publications

(2 citation statements)

References 47 publications

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“…In [14] and [15], a novel mixed-integer linear programming (MILP) optimization model was introduced for multi-energy networks, in which electricity, heating, and cooling loads and sources were considered in the IES. The authors of [16] proposed an optimized dispatching model for city-scale IES that realized the flexibilities of energy utilization. Pan et al [17] proposed a reasonable power to heat and hydrogen model with start-up or shut-down constraints and a novel seasonal hydrogen storage model for an electricity-hydrogen IES.…”

Section: B Literature Review and Research Gapmentioning

confidence: 99%

Bi-Level Decentralized Optimal Economic Dispatch for Urban Regional Integrated Energy System Under Carbon Emission Constraints

Gu¹,

Shen

et al. 2022

IEEE Access

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Based on the real-time environmental constraints in urban regional construction, this paper constructed a bi-level decentralized low-carbon optimal dispatching model of the urban regional integrated energy system (RIES), including the park integrated energy systems (IESs). In this model, a bi-level optimal allocation model of carbon emission constraints between the urban and the park is proposed for the first time. The upper urban will formulate the real-time carbon emission constraints based on real-time environmental monitoring, decomposing the historical carbon emissions to the lower park IESs; the lower park will meet the real-time carbon emission constraints during optimization. We through the upper urban with the lower park between the bi-level decentralized optimization to ensure that the objective function's upper urban power, natural gas, and heat distribution network system is minimum total network loss. In addition, it is necessary to ensure the minimum operating cost of each park IESs and focus on how to meet the requirements of the overall environment of urban RIES. Furthermore, we study the influence of optimal allocation strategy of carbon emission constraints on network loss, and operating cost of urban RIES under different scenarios. Then, an improved analytical target cascading (ATC) method is applied to solve the bilevel decentralized optimal dispatching model of urban RIES. Finally, an example under three different scenarios is given to verify the superiority and effectiveness of the proposed model and the improved method.

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Section: B Literature Review and Research Gapmentioning

confidence: 99%

Bi-Level Decentralized Optimal Economic Dispatch for Urban Regional Integrated Energy System Under Carbon Emission Constraints

Gu¹,

Shen

et al. 2022

IEEE Access

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“…It can be said that it is the most revolutionary science and technology in this era [1]. Learning, as its name implies, is to let the machine realize people's learning behavior, thus improving the performance of the system through continuous self-learning, and finally giving a relatively correct judgment according to the new conditions [2]. It integrates knowledge of many disciplines, including cross probability theory, statistics, cybernetics, algorithm complexity, computational theory in complex environment and other interdisciplinary fields.…”

Section: Introductionmentioning

confidence: 99%

Construction of demand response model of integrated energy system based on machine learning algorithm

Hou

2022

E3S Web Conf.

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The “multi-energy era”, which is complementary to new energy and fossil energy, has arrived. Demand-side management (DSM) has gradually gained worldwide attention because of its advantages of high flexibility and great response potential. However, the appearance of integrated energy system (IES) has broken the existing mode of independent operation of traditional energy systems, and made different forms of multi-energy flow more and more closely coupled. Demand response is the key measure to stimulate demand-side resources to participate in scheduling. IES can integrate various forms of energy, which brings new development to demand response. This paper studies the demand response project of IES, introduces the basic concept and popularization value of integrated demand response, and builds the demand response model of IES based on machine learning algorithm.

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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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Optimized dispatching of city-scale integrated energy system considering the flexibilities of city gas gate station and line packing

Cited by 26 publications

References 47 publications

Bi-Level Decentralized Optimal Economic Dispatch for Urban Regional Integrated Energy System Under Carbon Emission Constraints

Bi-Level Decentralized Optimal Economic Dispatch for Urban Regional Integrated Energy System Under Carbon Emission Constraints

Construction of demand response model of integrated energy system based on machine learning algorithm

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

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