Day‐ahead optimal energy dispatch schedule for integrated energy system based on AC/DC interconnected infrastructure

Zeng, Aidong; Hao, Shuai; Xu, Qingshan; Liu, Yujun; Jiang, Ling

doi:10.1049/joe.2018.8709

Cited by 3 publications

(1 citation statement)

References 12 publications

(11 reference statements)

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“…Wang et al used a rolling time-domain and particle swarm hybrid optimization algorithm to assign system scheduling factors to achieve optimal operation of the combined cooling, heating, and power system under different seasons [2]. Zeng et al proposed an IES day-ahead optimal scheduling model based on AC-DC interconnection infrastructure to reduce the operating cost of the AC-DC interconnection system and the loss of PV, heat, and cold energy during operation [3]. Wang et al studied the RIES day-ahead optimal scheduling method based on time-of-use tariff demand response by considering the indoor environmental comfort of customers and the economics of system operation [4].…”

Section: Introductionmentioning

confidence: 99%

A Multi-timescale Scheduling Strategy for Residential Integrated Energy System

Gao,

Lu,

et al. 2023

J. Phys.: Conf. Ser.

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In recent years, multi-energy complementation has been deepening, and the application rate of new energy sources has been increasing. A multi-timescale optimal scheduling strategy is proposed to mobilize the energy flexibility of residential integrated energy systems (RIES) and to reduce the negative impact of source-load forecast uncertainty on the scheduling plan. Firstly, for the energy supply structure of RIES, the operation constraint model of each energy hub equipment is established. Then, the optimal scheduling is divided into three stages: day-ahead planning, intra-day planning, and real-time planning, each of which will consider the volatility of source-load prediction and determine the operation status of the equipment according to its importance level. The experimental results show that the proposed multi-timescale scheduling strategy can ensure the economy while minimizing the deviation of the main grid power purchase in the real-time and day-ahead phases, providing a basis for the development of new energy capacity in the grid to grow to higher penetration rates.

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Section: Introductionmentioning

confidence: 99%