Demand Response and Distributed Generation Remuneration Approach Considering Planning and Operation Stages

Silva, Cátia; Faria, Pedro; Vale, Zita

doi:10.3390/en12142721

Cited by 11 publications

(6 citation statements)

References 20 publications

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“…A series of parameters are set by the Principal Component Analysis (PCA) agent in which optimization decisions are done and the parameters are obtained. The agent of this layer implements AI techniques and a case-based reasoning system (CBR) [28]. The previous optimization cases are collected by the system that allows us to obtain the consumption optimization.…”

Section: Decision Makingmentioning

confidence: 99%

Research on Multi-Agent Systems in a Smart Small Grid for Resource Apportionment and Planning

Yang

Paúl

2021

SCPE

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With the advancement in the technology, deployment of sensors in the industrial or public building is increasing rapidly. The basic aim is to obtain the data from the environment and decision making to the energy saving. The activities caused by the human results the undergoing negative change in the environment. There are many techniques available for decision making and consider the environmental factors solely which cause the energy consumption. However, user’s preferences are not adapted by the systems, but at energy consumption optimization, these systems are very successful. The end-users use the system which considers the factors and their wellbeing are get affected. The distributed generation is incorporated by the Smart Small Grid (SSG), communication network and the sensors for the more reliable, flexible and efficient grid. The energy saving system is presented in this paper which also adapts to the inhabitants preferences apart from environmental conditions consideration. The architecture of Multi-Agent System (MAS) and the agents are utilized for negotiation process performance between the users comfort preferences and optimization degree that according to these preferences, achievement of system is done. The energy consumption of 40% is obtained and in the inhabitants' behavior pattern, the algorithm was specialized. The 16.89% of reduction is obtained by the existing system and it was focused to obtain the agreement between the system and users for user preference satisfaction and the energy optimization is also performed at the same time.

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Section: Decision Makingmentioning

confidence: 99%

Research on Multi-Agent Systems in a Smart Small Grid for Resource Apportionment and Planning

Yang

Paúl

2021

SCPE

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“…The authors of the present paper designed a methodology that deals with the uncertainty of the small resources, focusing on the consumers with the assignment of a Reliability Rate to optimally manage the local community in cases of DR events. Previous works [12][13][14] already proposed a business model to help Aggregators, although the doubt associated with the actual response of the consumers was never considered. The action described in the paper is relevant to the operation being a simulation of a real-time reaction by the resources to DR events and how the Aggregator should manage the uncertainties associated.…”

Section: Background and Motivationmentioning

confidence: 99%

Rating the Participation in Demand Response Programs for a More Accurate Aggregated Schedule of Consumers after Enrolment Period

2020

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Aggregation of small size consumers and Distributed Generation (DG) units have a considerable impact to catch the full flexibility potential, in the context of Demand Response programs. New incentive mechanisms are needed to remunerate consumers adequately and to recognize the ones that have more reliable participation. The authors propose an innovative approach to be used in the operation phase, to deal with the uncertainty to Demand Response events, where a certain target is requested for an energy community managed by the Aggregator. The innovative content deals with assigning and updating a Reliability Rate to each consumer according to the actual response in a reduction request. Three distinct methods have been implemented and compared. The initial rates assigned according to participation in the Demand Response events after one month of the enrolment period and the ones with higher reliability follow scheduling, performed using linear optimization. The results prove that using the proposed approach, the energy community manager finds the more reliable consumers in each period, and the reduction target achieved in DR events. A clustering algorithm is implemented to determine the final consumer rate for one month considering the centroid value.

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“…In order to effectively merge the large scale of DRERs in the network, further tactical services are needed, such as Demand Response (DR) program [2]. In fact, DR brings flexibility in the current trend of power systems, enabling the network operator to relieve congestion of the grid and reduce the peak periods [3] [4]. DR program is defined as the modification in the consumption patterns of the end-users in order to respond to the incentives paid by DR entity [5].…”

Section: Introductionmentioning

confidence: 99%

An Optimization Based Community Model of Consumers and Prosumers: A Real-Time Simulation and Emulation Approach

et al. 2021

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The electricity consumption pattern is being increased day by day. Currently, network operators are moving towards renewable energy resources and applying demand response programs. However, the small and medium scale consumers and producers are needed to be aggregated and participate in the electricity markets as a unique resource. This paper proposes an optimization-based community model for aggregating the small scales consumers and producers. The model includes a central controller, which is considered as an aggregator, and several local community managers to keep the network balanced locally. Furthermore. real-time simulation approach and several real devices as hardware-in-the-loop are used to validate the system under practical challenges. The results of the paper reveal a gap between the simulation and experimental results and prove the performance of system in real-time mode using actual devices.

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Demand Response and Distributed Generation Remuneration Approach Considering Planning and Operation Stages

Cited by 11 publications

References 20 publications

Research on Multi-Agent Systems in a Smart Small Grid for Resource Apportionment and Planning

Research on Multi-Agent Systems in a Smart Small Grid for Resource Apportionment and Planning

Rating the Participation in Demand Response Programs for a More Accurate Aggregated Schedule of Consumers after Enrolment Period

An Optimization Based Community Model of Consumers and Prosumers: A Real-Time Simulation and Emulation Approach

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