A Typical Distributed Generation Scenario Reduction Method Based on an Improved Clustering Algorithm

Lv, Sitong; Li, Jianguo; Guo, Yongxin; Shi, Zhong

doi:10.3390/app9204262

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…However, stochastic variables, such as SPG, rarely follow a specific distribution, which leads to the generation of simplified scenarios that do not share the same statistical properties as the real observations. The most commonly used sampling technique is Monte Carlo sampling (MCS) [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41]. In [33], MCS was used to sample the assumed error distribution of PV power curtailment forecasts generated by gated recurrent units (GRUs).…”

Section: Parametric Sampling-based Methodsmentioning

confidence: 99%

A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models

Kousounadis-Knousen,

Bazionis,

Georgilaki

et al. 2023

Energies

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Scenario generation has attracted wide attention in recent years owing to the high penetration of uncertainty sources in modern power systems and the introduction of stochastic optimization for handling decision-making problems. These include unit commitment, optimal bidding, online supply–demand management, and long-term planning of integrated renewable energy systems. Simultaneously, the installed capacity of solar power is increasing due to its availability and periodical characteristics, as well as the flexibility and cost reduction of photovoltaic (PV) technologies. This paper evaluates scenario generation methods in the context of solar power and highlights their advantages and limitations. Furthermore, it introduces taxonomies based on weather classification techniques and temporal horizons. Fine-grained weather classifications can significantly improve the overall quality of the generated scenario sets. The performance of different scenario generation methods is strongly related to the temporal horizon of the target domain. This paper also conducts a systematic review of the currently trending deep generative models to assess introduced improvements, as well as to identify their limitations. Finally, several research directions are proposed based on the findings and drawn conclusions to address current challenges and adapt to future advancements in modern power systems.

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Section: Parametric Sampling-based Methodsmentioning

confidence: 99%

A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models

Kousounadis-Knousen,

Bazionis,

Georgilaki

et al. 2023

Energies

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“…In [30], the clustering method k-means clustering based on numerical weather prediction (K-means-NWP) was adopted to describe the uncertainty of wind power output. Reference [31] proposed an improved k-means clustering method to reduce the annual scenario set in the planning of distributed generation. In [32], fuzzy c-mean-clustering comprehensive quality (FCM-CCQ) clustering method was used to describe typical output scenarios of wind and solar power.…”

Section: Stochastic Optimization Methods Based On Scenario Setmentioning

confidence: 99%

Scenario-Set-Based Economic Dispatch of Power System With Wind Power and Energy Storage System

Zeng

Hong-mei

2020

IEEE Access

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In view of uncertainties caused by large-scale wind power integration, energy storage system (ESS) is being considered to stabilize the fluctuation of wind power. In this paper, the influence of ESS on power system operation with wind power is analyzed in detail, and an economic dispatch (ED) model with wind power and ESS is proposed based on scenario set. First, the initial scenario set of wind power output is generated by the Monte Carlo sampling. To overcome the shortcoming of heavy dependence on the initial clustering centers, which usually leads to unstable clustering results, the k-means clustering is improved by combining self-organizing feature map neural network and particle swarm optimization (PSO). Then, the initial scenario set is reduced based on this improved k-means clustering method. Finally, an ED model solved by PSO is used to minimize the comprehensive power generation cost based on the reduced scenario set. Taking IEEE-39 bus system as an example, the scenario-set-based ED model is implemented in this paper. The simulation results show that, when solving the ED problem with wind power and ESS, the proposed method considering scenario reduction makes not only the clustering index better, but also the results of ED more reasonable.

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“…The choice of the appropriate model for calculating wind power is of great importance. The most simplified model that expresses the relationship that links wind power (𝑃 𝑊𝑇 ) to wind speed (𝑣 𝑊 ) [26] is formalized in (5).…”

Section: The Wind Turbine Modelingmentioning

confidence: 99%

An intelligent energy management system for optimum design and real-time operation

Zedak¹,

Belfqih²,

Boukherouaa³

et al. 2023

IJPEDS

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<span lang="EN-US">Planning and management of distribution networks has become a very difficult task, especially with the strong expansion of renewable energy sources (RES) which are intermittent in nature. Maintaining fluidity and reliability of real-time decisions while taking into consideration uncertainties related to production and increasing the profit of distribution network operators is the objective of the system proposed in this work. It is an intelligent energy management system dedicated to the management of grid-integrated RES and battery energy storage systems (BESS), composed of: i) a real-time control and data acquisition model, ii) a model for forecasting the intermittent parameters of RES based on neural networks, iii) a long-term planning model based on the optimal placement and size of RES and BESS, and iv) an hourly planning model for scheduling the energy distribution between energy sources. The non-dominated sorting genetic algorithm and the entropy-TOPSIS method (technique for order of preference by similarity to ideal solution) form the basic block of this model. To evaluate it, a modified IEEE 33 bus network was used for testing and the results, for short-term scheduling, proved that the system succeeds in maximizing profits and significantly minimizing CO<sub>2</sub> emissions, in addition to power losses and voltage drops.</span>

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A Typical Distributed Generation Scenario Reduction Method Based on an Improved Clustering Algorithm

Cited by 11 publications

References 22 publications

A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models

A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models

Scenario-Set-Based Economic Dispatch of Power System With Wind Power and Energy Storage System

An intelligent energy management system for optimum design and real-time operation

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