Dynamic Modelling and Mutual Coordination of Electricity and Watershed Networks for Spatio-Temporal Operational Flexibility Enhancement Under Rainy Climates

Cao, Yingping; Zhou, Bin; Chung, C. Y.; Shuai, Zhikang; Hua, Zhihao; Sun, Yunfei

doi:10.1109/tsg.2022.3223877

Cited by 31 publications

(26 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1) Network constraints of the DN. The widely used Distflow branch model is adopted to model the DN as described in the following Cao et al, 2022).…”

Section: Optimization Model Of Dnomentioning

confidence: 99%

Distributionally robust transactive control for active distribution systems with SOP-connected multi-microgrids

Yin

Yang²,

Zhang³

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

The increasing demand for distributed energy and active load increases the risk of voltage violations in active distribution systems. This paper proposes a distributionally robust transactive control method for local energy trading and network operation management of active distribution systems with interconnected microgrids. In particular, the emerging SOP technology is used for the flexible connection of the multi-microgrids (MMGs). First, the local energy interactive market between distribution network operator (DNO) and regional microgrids is constructed to simultaneously solve the economic and security problems of distribution networks considering the energy trading scenarios and various operational constraints. Then, the dual relaxation technique is used to transform the two-layer structure model into a single-layer model. Furthermore, the single-layer game model is further transformed into a two-stage distributed robust problem considering the uncertainties of load demand and renewable power outputs, and the second stage of the problem is decomposed into multiple parallel subproblems without dual information. Finally, numerical simulations on IEEE-33 test system verify the advantages and effectiveness of the proposed method.

show abstract

“…1) Network constraints of the DN. The widely used Distflow branch model is adopted to model the DN as described in the following Cao et al, 2022).…”

Section: Optimization Model Of Dnomentioning

confidence: 99%

Distributionally robust transactive control for active distribution systems with SOP-connected multi-microgrids

Yin

Yang²,

Zhang³

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…Second, due to the grid integration of massive electric vehicles with charging behavior uncertainties, electricity demand variations are inevitable (Castillo et al, 2022). Third, there are various weather risks such as hurricanes, cold snaps, rainstorms, heat waves, and thunderstorms battered power grid infrastructure planning (Cao et al, 2022). The occurrence of significant infrastructure project delay may lead to severe financial losses even if the infrastructure project itself does not sustain physical damage.…”

Section: Risk Factors Affecting Power Grid Infrastructure Planningmentioning

confidence: 99%

“…Power grid infrastructure planning schemes are usually affected by various investment risks in the construction duration, such as financial risks (Ammar and Eling, 2015;Zhao et al, 2022), power demand changes (Li et al, 2022;Yang et al, 2022), and extreme climates (Cao et al, 2022). Due to these investment risks, the actual completion date of power grid infrastructure projects may be delayed, and thus the electricity demand for load growth and renewable energy integration cannot be well satisfied (Dang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Opinions on power grid infrastructure planning with investment risks

et al. 2023

View full text Add to dashboard Cite

“…As a new renewable energy power generation method, solar photovoltaic power generation has the advantages of safety, risk-free, environmental protection and no pollution (Sadamoto et al, 2015). However, photovoltaic power generation is susceptible to the spatiotemporal factors such as weather, season and climate (Hu et al, 2021) (Cao et al, 2022), which makes the power generation of photovoltaic power stations unstable. When the grid is connected, its volatility may cause periodic impact on the power grid and lead to the instability of the power grid.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic power prediction based on sliced bidirectional long short term memory and attention mechanism

Chen

Liu

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

Solar photovoltaic power generation has the characteristics of intermittence and randomness, which makes it a challenge to accurately predict solar power generation power, and it is difficult to achieve the desired effect. Therefore, by fully considering the relationship between power generation data and climate factors, a new prediction method is proposed based on sliced bidirectional long short term memory and the attention mechanism. The prediction results show that the presented model has higher accuracy than the common prediction models multi-layer perceptron, convolution neural network, long short term memory and bidirectional long short term memory. The presented sliced bidirectional cyclic network has high prediction accuracy by low root mean square error and mean absolute error of 1.999 and 1.159 respectively. The time cost is only 24.32% of that of long short term memory network and 13.76% of that of bidirectional long short term memory network.

show abstract

Dynamic Modelling and Mutual Coordination of Electricity and Watershed Networks for Spatio-Temporal Operational Flexibility Enhancement Under Rainy Climates

Cited by 31 publications

References 34 publications

Distributionally robust transactive control for active distribution systems with SOP-connected multi-microgrids

Distributionally robust transactive control for active distribution systems with SOP-connected multi-microgrids

Opinions on power grid infrastructure planning with investment risks

Photovoltaic power prediction based on sliced bidirectional long short term memory and attention mechanism

Contact Info

Product

Resources

About