Simultaneous placement of control and protective devices in the presence of emergency demand response programs in smart grid

Self Cite

Summary Smart grid has been developing nowadays as an initiative to operate modern electric distribution systems (EDSs) in a more reliable and efficient manner. With the decreasing price of the energy storage systems (ESSs), ESSs are highly recommended to be exploited in the operation of EDSs. In this manuscript, a comprehensive framework is introduced for ESS incorporation in service restoration process which proposes 2 roles for ESSs in the service restoration. As the first role, ESSs are exploited in the service restoration to re‐energize customers in interrupted zones, as their backup units. As the second role, ESSs are used in the service restoration to increase the number of interrupted customers which transferred to the backup feeders through acting as storage units. The proposed objective function of service restoration consists of customer interruption cost, energy not sold cost, cost of electricity production in combined heat and power units, and cost of ESS incorporation in service restoration. Moreover, the stochastic characteristics of ESS contribution in service restoration are considered in the proposed problem formulation. Further, the impacts of the uncertainties of the service restoration on the proposed approach for ESS incorporation are investigated. The effectiveness of the proposed methodology is deliberated using a standard reliability test system (RBTS‐4). The obtained results show that the incorporating ESSs as storage units will lead to substantially improve the reliability level of EDS through increasing the adequacy of the EDS during service restoration process.

Section: Simulation and Analysismentioning

confidence: 99%

Reliability improvement in smart grid through incorporating energy storage systems in service restoration

Abniki

Taghvaei²,

Mohammadi‐Hosseininejad

2018

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“…Finally, some works present methods for appliance management, protection and control, and scheduling for residential customers aiming to help them to maximize the benefits of DR programs.…”

Section: A Brief Overview Of Price‐based Demand Response Techniquesmentioning

confidence: 99%

Customer targeting optimization system for price-based demand response programs

Siebert

Aoki

Fernandes

et al. 2018

Summary In a demand‐side management (DSM) program, a set of activities are designed to influence electricity consumption in a way that may produce desired changes in the utility's load shape. Detailed analysis when planning DSM programs is crucial because the utility intends to achieve technical and economic benefits but does not wish to lose revenue unnecessarily. To assist utilities in planning for price‐based demand response (DR) programs (an approach to DSM), this paper proposes an optimization system focused on the targeting of residential customers to enroll in a new time‐of‐use tariff. The developed system uses optimal power flow to estimate the reduction needed to achieve the proposed objectives of the DR program in each bus, followed by the selection of the customers that will provide this reduction via binary particle swarm optimization. The optimization system has been tested using real data acquired from a Brazilian utility including customers' load curves, radial distribution feeder data, a time‐of‐use tariff, and estimated elasticity matrices from the literature. The main results show that the system can be of great value supporting power utilities' implementation of price‐based DR programs.

“…The critical points of the electricity distribution network for installing and determining the appropriate and optimal number of remote terminal units (RTUs) based on fuzzy hierarchical method is presented in Razi Kazemi and Dehghanian. [22][23][24][25][26][27] Optimization techniques including linear numeric programming 22 and reactive tabu search (RTS) algorithm 23 are suggested to determine the number and location of switches and protective devices considering the outage and maintenance costs, and investments. Modifying the manual switches to RCSs is carried out to maximize the load restoration.…”

Section: Introductionmentioning

confidence: 99%

“…Modifying the manual switches to RCSs is carried out to maximize the load restoration. In Heydari et al, 27 a method for optimal allocation of automated and manual switches as well as protective devices based on emergency demand response programs (EDRPs) in the presence of load flexibility is presented In Aman et al, 15 a method for simultaneous optimal placement of tie-switches and DGs based on the maximization of system loadability has been presented by discrete artificial bee colony algorithm.…”

Section: Introductionmentioning

confidence: 99%

A novel practical method for simultaneous placement of switching and protective devices considering load uncertainty

Yari

Shakarami

Namdari

et al. 2019

Summary A smart distribution system should be able to restore interrupted customers as quickly as possible after outages. By optimal allocation of switching and protective devices, it is possible to enhance the reliability and increase the restoration capacity of the loads after an outage. In this paper, by taking into account uncertainty in the load data, a novel practical method for the simultaneous planning of optimum location of switching devices including tie‐lines and remote‐control switches (RCSs), fault indicators, and cut‐out fuses is proposed. In this paper, a method for minimization of the costs associated with equipment investment and interruption cost has been proposed. Practical parameters such as geographical position and conditions of the understudy network and feeders, and the configuration and possible constraints of the real network from the experts' viewpoint, have been considered in this study. The actual characteristics of the network are extracted by utilizing the analytical hierarchical process (AHP), the geographic information system (GIS) data, and the event recording software. To optimize the problem, the multi‐objective nondominated sorting genetic algorithm II (NSGAII) is employed. The efficacy of the proposed method is proved through simulation of a real medium‐voltage (MV) feeder.