Reschedule of Distributed Energy Resources by an Aggregator for Market Participation

Faria, Pedro; Spínola, João; Vale, Zita

doi:10.3390/en11040713

Cited by 19 publications

(11 citation statements)

References 28 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Six clustering methods such as k-means algorithm, density-based algorithm, hierarchical clustering (agglomerative nesting and divisive analysis), standard AP clustering algorithm, and improved AP clustering algorithm were used to cluster analysis. The k-means algorithm is a classical and extensive clustering method, which can be used to explore data structure [23]. The density-based algorithm is a method of high-density connected regions that is insensitive to noise and can find classes of arbitrary shapes [24].…”

Section: Comparison Of Different Algorithmsmentioning

confidence: 99%

Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data

Han

et al. 2019

Energies

View full text Add to dashboard Cite

The electric power industry is an essential part of the energy industry as it strengthens the monitoring and control management of household electricity for the construction of an economic power system. In this paper, a non-intrusive affinity propagation (AP) clustering algorithm is improved according to the factor graph model and the belief propagation theory. The energy data of non-intrusive monitoring consists of the actual energy consumption data of each electronic appliance. The experimental results show that this improved algorithm identifies the basic and combined class of home appliances. According to the possibility of conversion between different classes, the combination of classes is broken down into different basic classes. This method provides the basis for power management companies to allocate electricity scientifically and rationally.Energies 2019, 12, 992 2 of 20 power of different electrical appliances. The power company can obtain the usual information about the user's electricity, to make a scientific and reasonable decision. This method can reduce the cost of installation and reduce the level of interference in measurement [6]. Commonly used clustering methods for identifying appliances include the following: Hart first proposed non-intrusive household appliance load monitoring and analyzed the algorithm and characteristics, in this case, the essence of this method is to decompose the aggregated load data of household appliance [7]. After decades of research, many methods have been applied to pattern recognition of NILM. Some representative methods are K-means, K-nearest neighbor, enhanced ISODATA and artificial neural network [8][9][10][11]. These methods provide some ideas for identifying the operating mode of electrical appliances, but when there is a large number of home appliances, the recognition results are often not ideal. Frey and Dueck first proposed a standard AP clustering algorithm to solve the clustering problem [12]. The standard AP clustering algorithm can be derived from the factor graph model and the belief propagation theory [12,13]. The factor graph model, proposed by Kschischang and Frey, is a graph description method where the global function is decomposed into the product of the local function [14]. The theory of belief propagation was proposed by Pearl and is a kind of message transfer algorithm for inference in the graph model [15]. Since its publication, AP clustering algorithm has been applied in many fields because of its unique clustering characteristics.

show abstract

Section: Comparison Of Different Algorithmsmentioning

confidence: 99%

Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data

Han

et al. 2019

Energies

View full text Add to dashboard Cite

show abstract

“…This paper provides a summary of the Energies Special Issue covering the published articles [1][2][3][4][5][6][7][8][9][10][11][12][13], which address several topics related to distributed energy resources management. Table 1 identifies the most relevant topics in each publication; most of them cover three or more topics.…”

Section: Contributionsmentioning

confidence: 99%

“…Finally, reference [13] presented a methodology addressing the rescheduling of resources in a sequence of the definition of a new aggregation and remuneration process. A representative tariff for each group of distributed energy resources is obtained.…”

Section: Contributionsmentioning

confidence: 99%

Distributed Energy Resources Management

Faria¹

2019

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…Demand-response aggregators were developed and deployed around the world, and more in Europe and the United States. Aggregator involvement in energy markets increases the access of a small resource to them, enabling case studies to be presented for flexibility of demand [19,20]. Real-time simulations [21,22] have applications to control theory.…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Temperature Control Algorithm for IoT Networks in Smart Buildings

et al. 2018

Self Cite

View full text Add to dashboard Cite

The monitoring of the Internet of things networks depends to a great extent on the availability and correct functioning of all the network nodes that collect data. This network nodes all of which must correctly satisfy their purpose to ensure the efficiency and high quality of monitoring and control of the internet of things networks. This paper focuses on the problem of fault-tolerant maintenance of a networked environment in the domain of the internet of things. Based on continuous-time Markov chains, together with a cooperative control algorithm, a novel feedback model-based predictive hybrid control algorithm is proposed to improve the maintenance and reliability of the internet of things network. Virtual sensors are substituted for the sensors that the algorithm predicts will not function properly in future time intervals; this allows for maintaining reliable monitoring and control of the internet of things network. In this way, the internet of things network improves its robustness since our fault tolerant control algorithm finds the malfunction nodes that are collecting incorrect data and self-correct this issue replacing malfunctioning sensors with new ones. In addition, the proposed model is capable of optimising sensor positioning. As a result, data collection from the environment can be kept stable. The developed continuous-time control model is applied to guarantee reliable monitoring and control of temperature in a smart supermarket. Finally, the efficiency of the presented approach is verified with the results obtained in the conducted case study.

show abstract

Reschedule of Distributed Energy Resources by an Aggregator for Market Participation

Cited by 19 publications

References 28 publications

Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data

Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data

Distributed Energy Resources Management

Fault-Tolerant Temperature Control Algorithm for IoT Networks in Smart Buildings

Contact Info

Product

Resources

About