A Demand Response Approach to Scheduling Constrained Load Shifting

Faria, Pedro; Vale, Zita

doi:10.3390/en12091752

Cited by 29 publications

(15 citation statements)

References 24 publications

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“…Khalid et al [18] presented a pricing model to delineate the rates for on-peak and shoulder-peak hours having the goal of charging a per-unit price-taking into account the consumed energy and the extra generation cost. Faria and Vale [19] proposed a method to minimize the operation costs, where the virtual power player manages DR programs and respects the consumption shifting constraints making a comparison between the advantages of DR use and DG. Proving the benefits of DR in the operation of distributed energy resources, namely when considering the lack of supply.…”

Section: Related Literaturementioning

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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Section: Related Literaturementioning

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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“…Typically, short and real-time DR is the most applicable programs that are used by the aggregator or any other network operator. In this context, incentive-based DR programs are is a nice target to focus on, as they are mostly being applied near to real-time (from a month in advance to 15 minutes [14]). Also, as the focus of this paper is to study the economic behavior and financial survey of aggregator during the ramp period, incentive-based DR programs are interesting to be discussed and applied.…”

Section: B 22 Dr Program Characteristicsmentioning

confidence: 99%

Aggregation of Consumers Participation in the Ramping of a Demand Response Event

Abrishambaf

Faria

Vale

2020

2020 17th International Conference on the European Energy Market (EEM)

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As the global population is daily soaring, the need for electrical energy is also increasing. This makes the role of the power distribution network more tangible, as the efficiency of all sectors should be increased. The need for smart management and strategic planning, such as demand response programs are obvious in the context. This paper proposes an aggregator model that employs DR programs for managing the network balance. In this model, a specific analysis has been provided for the ramp period and demand response timeline to show the financial behaviors of the aggregator. In the case study of the paper, two demand response events are proposed using actual consumption profiles and a cost comparison has been presented using various pricing schemes. The results remark the costs related to the ramp period before the event and show how such costs are important in daily electricity expenses of the aggregator model.

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“…The electricity sector is facing several challenges due to concerns about environmental issues [ 1 , 2 ]. The efficient use of electricity can be improved with the support of smart grids [ 3 ]. In fact, in the context of smart grids, consumers can receive incentives for reducing electricity consumption in certain periods [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Use of Sensors and Analyzers Data for Load Forecasting: A Two Stage Approach

Ramos

Teixeira

Faria

et al. 2020

Sensors

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The increase in sensors in buildings and home automation bring potential information to improve buildings’ energy management. One promissory field is load forecasting, where the inclusion of other sensors’ data in addition to load consumption may improve the forecasting results. However, an adequate selection of sensor parameters to use as input to the load forecasting should be done. In this paper, a methodology is proposed that includes a two-stage approach to improve the use of sensor data for a specific building. As an innovation, in the first stage, the relevant sensor data is selected for each specific building, while in the second stage, the load forecast is updated according to the actual forecast error. When a certain error is reached, the forecasting algorithm (Artificial Neural Network or K-Nearest Neighbors) is trained with the most recent data instead of training the algorithm every time. Data collection is provided by a prototype of agent-based sensors developed by the authors in order to support the proposed methodology. In this case study, data over a period of six months with five-minute time intervals regarding eight types of sensors are used. These data have been adapted from an office building to illustrate the advantages of the proposed methodology.

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A Demand Response Approach to Scheduling Constrained Load Shifting

Cited by 29 publications

References 24 publications

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

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

Aggregation of Consumers Participation in the Ramping of a Demand Response Event

Use of Sensors and Analyzers Data for Load Forecasting: A Two Stage Approach

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