Optimal Scheduling of an Isolated Wind-Diesel-Energy Storage System Considering Fast Frequency Response and Forecast Error

Nguyen-Hong, Nhung; Nakanishi, Yoshinobu

doi:10.3390/en12050843

Cited by 10 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the integration of distributed renewable energy sources and the consequent bidirectionality of the electricity grid, the planning and scheduling strategy becomes a topic of primary importance within the research field, for local distributors, and for infrastructure management [164]. In this context, several studies proposed coordinated scheduling of generators in order to relieve the grid from the randomness and volatility of renewables, such as wind and solar [67], [165], [166], [167], [168]. Moreover, the significance of energy hubs in the field is steadily increasing, as they embody a multi-carrier energy system catering to diverse types of energy demands.…”

Section: Power System Planning and Control 1) Schedulingmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

View full text Add to dashboard Cite

As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies.INDEX TERMS Clustering algorithms, K-means clustering, power systems. FIGURE 3. K-means clustering and power system bibliometric trend per country.

show abstract

Section: Power System Planning and Control 1) Schedulingmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…(3) Establishing objective functions with the objective of minimizing the cost. In [45][46][47], the minimum sums of the investment costs, maintenance costs, and pollution costs were used as the objective functions. The authors of [48][49][50] aimed at the lowest cost and considered the power output and SOC constraints of a hybrid energy storage.…”

Section: Of 20mentioning

confidence: 99%

Research on Energy Storage Optimization for Large-Scale PV Power Stations under Given Long-Distance Delivery Mode

Yang

Lian

et al. 2019

Energies

View full text Add to dashboard Cite

Western China has good conditions for constructing large-scale photovoltaic (PV) power stations; however, such power plants with large fluctuations and strong randomness suffer from the long-distance power transmission problem, which needs to be solved. For large-scale PV power stations that do not have the conditions for simultaneous hydropower and PV power, this study examined long-distance delivery mode and energy storage optimization. The objective was to realize the long-distance transmission of electrical energy and maximize the economic value of the energy storage and PV power storage. For a large-scale PV power station, the energy storage optimization was modelled under a given long-distance delivery mode, and the economic evaluation system quantified using the net present value (NPV) of the battery was based on the energy dispatch optimization model. By contrast, a lithium battery performance model was developed. Therefore, further analysis of the economics of the energy storage and obtaining the best capacity of the energy storage battery and corresponding replacement cycle considered battery degradation. The case study of Qinghai Gonghe 100 MWp demonstration base PV power station showed that the optimal energy storage capacity was 5 MWh, and the optimal replacement period was 2 years. Therefore, the annual abandoned electricity was reduced by 3.051 × 10 4 MWh compared with no energy storage. The utilization rate of both the PV power station and quality of the delivered electricity were modelled to realize a long-distance transmission to the grid net. This will have an important guiding significance to develop and construct large-scale single PV power stations.

show abstract

“…Therefore, research on the participation of incident standby in the grid auxiliary service market has received extensive attention [10][11][12] . Refs.…”

Section: Introductionmentioning

confidence: 99%

An Economic Optimization Method for Demand-side Energy-storage Accident Backup Assisted Deep Peaking of Thermal Power Units

Wen

Wang

et al. 2022

Chin. J. Electr. Eng.

View full text Add to dashboard Cite

With the large-scale penetration of new energy such as wind power, its anti-modulation peak characteristics have increased challenges in power systems. Therefore, an economic optimization method for depth peak regulation and the depth of the emergency of the Energy storage (ES) accident on the demand side is proposed. First, a quantitative model of unplanned disconnection risk caused by weather state, load level, and fault type is constructed to obtain the spare and available ES capacity.Therefore, a deep peak regulation (DPR) economic optimization model is established to minimize the fuel injection cost of thermal power units, including ES accident standby, unit damage, and fuel demand. The particle swarm optimization algorithm is used to simulate the modified IEEE 30-node system. Based on the results, the DPR of ES accident standby can reduce the wind abandonment rate by 1.1% and the total peak adjustment cost by 33.5% under class-A weather. In class-C weather, the wind abandonment ratio can be reduced by 4.19%, reducing the cost of the total adjustment peak by 31.4%. The multiple purposes of improving the power grid modulation, wind power, and the standby utilization of ES accidents can be achieved.

show abstract

Optimal Scheduling of an Isolated Wind-Diesel-Energy Storage System Considering Fast Frequency Response and Forecast Error

Cited by 10 publications

References 25 publications

K-Means and Alternative Clustering Methods in Modern Power Systems

K-Means and Alternative Clustering Methods in Modern Power Systems

Research on Energy Storage Optimization for Large-Scale PV Power Stations under Given Long-Distance Delivery Mode

An Economic Optimization Method for Demand-side Energy-storage Accident Backup Assisted Deep Peaking of Thermal Power Units

Contact Info

Product

Resources

About