Capacity configuration optimization for island microgrid with wind/solar/pumped storage considering demand response

Hu, Rongxing; He, Xiaoming; Jing, Zhaoxia; Yuan, Zheng

doi:10.1109/isgt-asia.2015.7387085

Cited by 13 publications

(1 citation statement)

References 29 publications

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“…Masaud and El-Saadany (2020) established the optimal size and service life of energy storage capacity, but did not consider the uncertainty of renewable energy. Hu et al (2015) proposed a capacity configuration optimization model of island microgrid considering demand response and uses the particle swarm algorithm to minimize system life cycle cost. Zhu et al (2019) comprehensively considered the electricity cost, photovoltaic power generation, and energy storage cost, and established an optimization model for photovoltaic power generation and ESCC.…”

Section: Introductionmentioning

confidence: 99%

Distributionally Robust Capacity Configuration for Energy Storage in Microgrid Considering Renewable Utilization

Ding

Zheng

et al. 2022

Front. Energy Res.

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The energy storage plays an important role in the operation safety of the microgrid system. Appropriate capacity configuration of energy storage can improve the economy, safety, and renewable energy utilization of the microgrid. This study considers the uncertainty of renewable energy, and builds an energy storage capacity configuration (ESCC) in microgrid by using the distributionally robust optimization (DRO). This model co-optimizes energy storage planning, day-ahead scheduling, and renewable energy utilization of the microgrid, which derives the energy storage configuration strategy, balancing renewable energy utilization and operation economics of microgrid. The proposed model is a two-stage model with distributionally robust chance constrains. By applying decision rules, variable substitution, and duality techniques, this model is approximately transformed into a mixed integer programming problem with a second-order cone constraint, which can be directly solved. Experiments on the IEEE 33-bus system are carried out to verify the effectiveness and advantages of the proposed model.

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Section: Introductionmentioning

confidence: 99%