Optimal guidance strategy for flexible load based on hybrid direct load control and time of use

The time-of-use(TOU)strategy can effectively improve the energy consumption mode of customers,reduce the peak-valley difference of load curve,and optimize the allocation of energy resources.This study presents an Optimal guidance mechanism of the flexible load based on strategies of direct load control and time-of-use.First,this study proposes a period partitioning model,which is based on a moving boundary technique with constraint factors,and the Dunn Validity Index(DVI)is used as the objective to solve the period partitioning.Second,a control strategy for the curtailable flexible load is investigated,and a TOU strategy is utilized for further modifying load curve.Third,a price demand response strategy for adjusting transferable load is proposed in this paper.Finally,through the case study analysis of typical daily flexible load curve,the efficiency and correctness of the proposed method and model are validated and proved.

Methodology for the disaggregation and forecast of demand flexibility in large consumers with the application of non-intrusive load monitoring techniques

Technological advances,innovation and the new industry 4.0 paradigm guide Distribution System Operators towards a competitive market that requires the articulation of flexible demand response systems.The lack of measurement and standardization systems in the industry process chain in developing countries prevents the penetration of demand management models,generating inefficiency in the analysis and processing of informa-tion to validate the flexibility potential that large consumers can contribute to the network operator.In this sense,the research uses as input variables the energy and power of the load profile provided by the utility energy meter to obtain the disaggregated forecast in quarter-hour intervals in 4-time windows validated through metrics and its results evaluated by the RMS error to get the total error generated by the methodology with the appli-cation of Machine Learning and Big Data techniques in the Python computational tool through Combinatorial Disaggregation Optimization and Factorial Hidden Markov models.

Improved peak load management control technique for nonlinear and dynamic residential energy consumption pattern

Advancements in computational technologies for residential energy management have become widespread due to increased energy usage and peak demand in households.Controlled switching of appliances during peak periods for energy reduction is one of such solutions.However,this technique may constrict the consumer’s flexibility to utilize desired appliances to fulfil their needs.This study proposes a peak load control technique based on an end-use appliance prioritization and event detection algorithm.A noteworthy feature of the technique is the users’preferred appliance(UPA),which provides the occupants with the flexibility to use any electric load to fulfill their needs at any time whether peak demand control has been initiated or not.Furthermore,to address the challenging issue around the uncertain and varying nature of the users’energy usage pattern,an empirical bottom-up approach is adopted to characterize the consumers’diverse end-use behavior.The results obtained show good performance in terms of peak demand and energy consumption reduction,with 3%to 20%and at least 14.05%in demand reduction for time of use(ToU)periods and energy savings,respectively.These offer a further perspective on demand side management,affording load users a new cost-saving energy usage pattern.