A stochastic formulation for distribution automation planning incorporating emergency demand response programs

Abstract

Summary Advanced distribution automation (ADA) is a vital basis of the smart grid, which leads to enhancement of reliability level. Moreover, demand response programs are highly proposed to be employed in the smart grid. In this paper, a cross‐sectional benefit of smart grid maturity model is proposed in which the emergency demand response programs (EDRPs) are considered in the ADA planning problem. In fact, the role of EDRPs is considered in the service restoration process that affects the ADA plan. The service restoration process possesses several uncertain parameters including the demanded power of customers during service restoration, which are considered in the proposed formulation for ADA planning. The objective function of the optimization problem includes various costs imposed to the distribution system operator due to contingency occurrence including the expected total customer interruption cost, total investment cost of automatic sectionalizing switches, circuit breakers, and fuses deployment, and the total expected cost of implementing EDRP programs in the service restoration. The presented ADA planning methodology is tested on the fourth bus number of Roy Bilinton test system (RBTS4) to investigate its effectiveness.

Optimal allocation of protection and control devices in smart distribution systems: Models, methods, and future research

“…The following computational intelligence based optimisation methods (also called metaheuristic optimisation methods) have been used for the solution of the OAPCD problem: � Alliance algorithm [62]; � Ant colony system (ACS) [33,[46][47][48]; � Artificial bee colony (ABC) [76,81,93]; � Differential evolution (DE) [84]; � Differential search [80]; � Genetic algorithm (GA) [24,25,35,36,39,43,53,56,57,61,78,86,88,89,91,95,99,100,108,110,116,117,119]; � Greedy randomized adaptive search procedure [82]; � Immune algorithm [42,51]; � Memetic algorithm [71,74]; [26]; � Shuffled frog leaping algorithm [60]; � Tabu search [45,50]. These optimisation algorithms are generally nature-inspired methods.…”

“…In case an overcurrent passes through the fuse, it is heated and, depending on time, it may melt. The allocation of fuse is considered in the following reviewed works [27,29,31,35,41,45,[48][49][50]63,64,77,85,89,94,[98][99][100]104,108,110,113], and [117].…”

“…Single objective. The most common single objective is the minimisation of the total cost, which is the objective function of the OAPCD problems of the works [24][25][26]28,30,36,[40][41][42]45,50,51,[56][57][58]69,71,72,75,76,82,85,87,90,91,[97][98][99][101][102][103][104][105][106][107]112,113,[115][116][117][118]120], and [121]. The objective is to minimise the total cost, which is the sum of GEORGILAKIS ET AL.…”

A hybrid machine learning and meta‐heuristic algorithm based service restoration scheme for radial power distribution system

“…Consequently, the number of RCS operations carries more weightage than no. of MCS operation in objective function given in Equation (1). The tabular results show that with different conditions, the best fitness value, that is, minimum f agg is provided by different algorithms.…”

“…Smart grid or modern power system must have advanced distribution automation strategies which can be deployed in less, and consequently improving the overall reliability of the system. 1 Machine Learning (ML) based prediction methods can be applied for various applications in smart grid, such as load forecasting, demand forecasting and resilience studies etc. 2 In Reference 3, the authors have proposed an ML based method for reliability evaluation of power distribution network.…”