Short-Term Forecasting of Uncertain Parameters for Virtual Power Plants

The paper proposes a coordinated frequency control strategy for Virtual Power Plant (VPPs), with the inclusion of Distributed Energy Resource (DERs), e.g., Solar Photo-Voltaic Generation (SPVG), Wind Generator (WG) as well as Energy Storage System (ESS). The objective is to improve the short-term dynamic response of the overall power system. The robustness of the proposed control is evaluated through a Monte Carlo analysis and a detailed modeling of stochastic disturbances of loads, wind speed, and solar irradiance. The impact of communication delays of a variety of realistic communication networks with different bandwidths is also discussed and evaluated. The case study is based on a modified version of the WSCC 9-bus test system with inclusion of a VPP. This is modeled as a distribution network with inclusion of a variety of DERs.

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Short-term operational planning framework for virtual power plants with high renewable penetrations

IET Renewable Power Generation ◽

10.1049/iet-rpg.2015.0358 ◽

2016 ◽

Vol 10 (5) ◽

pp. 623-633 ◽

Cited By ~ 51

Author(s):

Fengji Luo ◽

Jiajia Yang ◽

Zhao Yang Dong ◽

Ke Meng ◽

Kit Po Wong ◽

...

Keyword(s):

Power Plants ◽

Operational Planning ◽

Virtual Power ◽

Short Term ◽

Planning Framework ◽

Virtual Power Plants

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A stochastic short-term scheduling of virtual power plants with electric vehicles under competitive markets

International Journal of Electrical Power & Energy Systems ◽

10.1016/j.ijepes.2020.106343 ◽

2021 ◽

Vol 124 ◽

pp. 106343

Author(s):

Homa Rashidizadeh-Kermani ◽

Mostafa Vahedipour-Dahraie ◽

Miadreza Shafie-khah ◽

Pierluigi Siano

Keyword(s):

Electric Vehicles ◽

Power Plants ◽

Competitive Markets ◽

Virtual Power ◽

Short Term ◽

Virtual Power Plants

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Short-term economics of virtual power plants

10.1049/cp.2009.1139 ◽

2009 ◽

Cited By ~ 4

Author(s):

Koen Kok

Keyword(s):

Power Plants ◽

Virtual Power ◽

Short Term ◽

Virtual Power Plants

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Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks

Frontiers in Energy Research ◽

10.3389/fenrg.2021.722212 ◽

2021 ◽

Vol 9 ◽

Author(s):

Dilantha Haputhanthri ◽

Daswin De Silva ◽

Seppo Sierla ◽

Damminda Alahakoon ◽

Rashmika Nawaratne ◽

...

Keyword(s):

Solar Irradiance ◽

Cloud Cover ◽

Power Plants ◽

Short Term Memory ◽

Virtual Power ◽

Short Term ◽

Photovoltaic Generation ◽

Term Memory ◽

Virtual Power Plants ◽

Long Short Term Memory

The rapid penetration of photovoltaic generation reduces power grid inertia and increases the need for intelligent energy resources that can cope in real time with the imbalance between power generation and consumption. Virtual power plants are a technology for coordinating such resources and monetizing them, for example on electricity markets with real-time pricing or on frequency reserves markets. Accurate short-term photovoltaic generation forecasts are essential for such virtual power plants. Although significant research has been done on medium- and long-term photovoltaic generation forecasting, the short-term forecasting problem requires special attention to sudden fluctuations due to the high variability of cloud cover and related weather events. Solar irradiance nowcasting aims to resolve this variability by providing reliable short-term forecasts of the expected power generation capacity. Sky images captured in proximity to the photovoltaic panels are used to determine cloud behavior and solar intensity. This is a computationally challenging task for conventional computer vision techniques and only a handful of Artificial Intelligence (AI) methods have been proposed. In this paper, a novel multimodal approach is proposed based on two Long Short-Term Memory Networks (LSTM) that receives a temporal image modality of a stream of sky images, a temporal numerical modality of a time-series of past solar irradiance readings and cloud cover readings as inputs for irradiance nowcasting. The proposed nowcasting pipeline consists of a preprocessing module and an irradiance augmentation module that implements methods for cloud detection, Sun localization and mask generation. The complete approach was empirically evaluated on a real-world solar irradiance case study across the four seasons of the northern hemisphere, resulting in a mean improvement of 39% for multimodality.

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