Coordinated optimal operation of hydro–wind–solar integrated systems

Zhang, Hongxuan; Lu, Zongxiang; Hu, Wei; Wang, Yiting; Dong, Lei; Zhang, Jietan

doi:10.1016/j.apenergy.2019.03.064

Cited by 165 publications

(59 citation statements)

References 38 publications

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“…Lastly, the integral of absolute error (IAE) [40][41][42] of each algorithm in three scenarios are given by Table 6, in which IAE x ¼ R T 0 jx À x � jdt and x � denotes the reference of variable x, respectively [43,44]. In particular, IAE δ of CBAS algorithm is merely 32.28%, 55.41%, 48.81%, and 56.94% of that of manual tuning [45][46][47], PSO, GA, and BAS algorithm, respectively (bold colour indicates the best results in Tables 4-6).…”

Section: Dfig Lossmentioning

confidence: 99%

Optimal coordinated control of hybrid AC/VSC-HVDC system integrated with DFIG via cooperative beetle antennae search algorithm

Jiang¹,

Huang²,

Zhang³

et al. 2020

PLoS ONE

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Nowadays, with the significant integration of various renewable energy, hybrid alternating current/ voltage source converter based high voltage direct current (AC/VSC-HVDC) system integrated with doubly-fed induction generator (DFIG) has achieved rapidly development in smart grid. A proper control system design for hybrid AC/VSC-HVDC system plays a very crucial role for a reliable and effective power transmission. Hence, this paper designs a novel cooperative beetle antenna search (CBAS) algorithm for optimal coordinated control of hybrid AC/VSC-HVDC system integrated with DFIG. Compared with original beetle antennae search (BAS) algorithm, CBAS algorithm can significantly improve searching efficiency via an efficient cooperation with a group of multiple beetles instead of a single beetle. Particularly, CBAS algorithm can effectively escape from local optimums thanks to its dynamic balance mechanism, which can maintain appropriate trade-off between global exploration and local exploitation. Moreover, three case studies are undertaken to validate the effectiveness and superiorities and effectiveness of CBAS algorithm compared against that of other traditional meta-heuristic algorithms. Especially, the average results of fitness function acquired by CBAS algorithm is merely 46.05%, 41.18%, and 47.82% of that of PSO, GA, and BAS algorithm, respectively.

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Section: Dfig Lossmentioning

confidence: 99%

Optimal coordinated control of hybrid AC/VSC-HVDC system integrated with DFIG via cooperative beetle antennae search algorithm

Jiang¹,

Huang²,

Zhang³

et al. 2020

PLoS ONE

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“…In Figure 3, the electricity price at time 3 of day 9, Pr 3,9 , is compared with the average electricity price of the week preceding the current week, M 1 . In this example, the current price satisfies condition (26), which leads to assigning the value 1 to the decision variable which represents pumping water, T 1 3,11 = 1. Thus, the decision to be taken by the power producer is to pump water in the hydropower plant using the electricity generated at the wind farm at the third hour of day 11.…”

Section: Heuristic Methodology To Generate Solutions-day-ahead Approachmentioning

confidence: 99%

Optimal operation scheduling of a pump hydro storage system coupled with a wind farm

Gomes

Telhada

et al. 2021

IET Renewable Power Gen

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The variability in non-dispatchable power generation makes essential the improvement of production management. This study focuses on the development of an optimisation model for a renewable power unit, composed of a wind farm and a hydro-pump storage power plant, to maximise its revenue. The combination of the two technologies allows the mitigation of risks associated with wind production and electricity price variability. The problem is formulated using linear programming and encompasses the selling of electricity in the Iberian day-ahead market and through a bilateral contract. The model is tested and scrutinised with sets of real historical generation and price data. In the day-ahead market scenario, the proposed methodology leads to an average yearly increase of net revenue ranging between 5% and 20%. In the bilateral contract scenario, the coupling of the wind farm and hydropower plant can reduce the imbalances costs substantially. Furthermore, the work identifies a positive correlation between reservoir dimension, turbine capacity and revenue value. The study also detects a revenue reduction tendency related to the decrease in the volatility of electricity prices.

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“…One of the most common partitional clustering algorithms used in energy system optimization is the k-means algorithm, which has been used in a variety of studies [14,15,24,37,57,58,63,69,74,78,[83][84][85][86][87]97,[137][138][139]141,142,[145][146][147][148][153][154][155][156][157][158][159][160][161]. The objective of the k-means algorithm is to minimize the sum of the squared distances between all cluster members of all clusters and the corresponding cluster centers, i.e., min…”

Section: Partitional Clusteringmentioning

confidence: 99%

A Review on Time Series Aggregation Methods for Energy System Models

et al. 2020

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Due to the high degree of intermittency of renewable energy sources (RES) and the growing interdependences amongst formerly separated energy pathways, the modeling of adequate energy systems is crucial to evaluate existing energy systems and to forecast viable future ones. However, this corresponds to the rising complexity of energy system models (ESMs) and often results in computationally intractable programs. To overcome this problem, time series aggregation (TSA) is frequently used to reduce ESM complexity. As these methods aim at the reduction of input data and preserving the main information about the time series, but are not based on mathematically equivalent transformations, the performance of each method depends on the justifiability of its assumptions. This review systematically categorizes the TSA methods applied in 130 different publications to highlight the underlying assumptions and to evaluate the impact of these on the respective case studies. Moreover, the review analyzes current trends in TSA and formulates subjects for future research. This analysis reveals that the future of TSA is clearly feature-based including clustering and other machine learning techniques which are capable of dealing with the growing amount of input data for ESMs. Further, a growing number of publications focus on bounding the TSA induced error of the ESM optimization result. Thus, this study can be used as both an introduction to the topic and for revealing remaining research gaps.

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Coordinated optimal operation of hydro–wind–solar integrated systems

Cited by 165 publications

References 38 publications

Optimal coordinated control of hybrid AC/VSC-HVDC system integrated with DFIG via cooperative beetle antennae search algorithm

Optimal coordinated control of hybrid AC/VSC-HVDC system integrated with DFIG via cooperative beetle antennae search algorithm

Optimal operation scheduling of a pump hydro storage system coupled with a wind farm

A Review on Time Series Aggregation Methods for Energy System Models

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