Centralized optimal management of a smart distribution system considering the importance of load reduction based on prioritizing smart home appliances

Abstract

The distribution system's economic operation is significantly impacted by the management of distributed generation (DG) resources, energy storage (ES), and controllable loads. The paper employs a smart distribution system that incorporates dispatchable and non-dispatchable DG resources, as well as battery storage, in addition to the demand response (DR) scheme. New modelling was performed in hourly steps to achieve the optimal unit commitment. In smart homes, appliances are prioritized and classified into four types: adjustable, interruptible, shiftable, and uncontrollable loads. Load reduction in smart homes is also considered based on load prioritization and customer participation in the DR scheme to achieve the proposed scheme's objectives. The proposed method considers costs associated with microturbines (MTs), including manufacturing, start-up, shutdown, and pollution. Additionally, planning is conducted to purchase or sell electricity to the upstream network. The simulation is run on an IEEE 33-bus system to demonstrate the proposed method's effectiveness. The system is assumed to be capable of operation in both island and grid-connected modes. The results demonstrate the proposed approach's efficacy in load reduction, operation cost, and execution time.

Providing a New Multiobjective Two-Layer Approach for Developing Service Restoration of a Smart Distribution System by Islanding of Faulty Area

“…In addition, x is an array of decision variables, which in this problem are the generation power of units. By changing the e 2 , Pareto solutions to the problem are generated (in this problem, there are 10 Pareto solutions) [45]. To solve the epsilonconstraint method, the problem is solved as a single objective by considering the main objective function (cost).…”

“…To choose the best answer for the decision-maker, it is suggested to use the fuzzy method with a linear membership function [43]. Te proposed fuzzy decision-making method and its membership functions are defned as (44) and (45), used for maximization and minimization, respectively. Te output of objective functions in case of worst and best answers is arranged in the order of the nadir point f SN n and ideal point f u n .…”

“…Te assumptions for optimizing the problem according to the study of references [19,22,23,45] are mentioned as follows:…”

“…For home appliances, uncontrollable appliances are considered 0.8 kW. Also, 3.5 kW of the total power of equipment with frst priority, 3.1 kW of equipment with second priority, and 2.6 kW of equipment with third priority are assumed for each house [45]. Table 1 shows the MT information.…”

Optimal demand response programs selection using CNN‐LSTM algorithm with big data analysis of load curves

“…This project develops an integrated scheduling model of hot rolling shop scheduling and electricity DR, and a multi‐objective production scheduling algorithm is designed. In [27], this paper uses an intelligent distribution system that includes electricity resources in addition to a DR scheme. Load reduction in smart homes is also considered based on load prioritization and customer participation in the DR plan to achieve the goals of the proposed plan.…”