Distribution network reconfiguration using time-varying acceleration coefficient assisted binary particle swarm optimization

Devi²,

et al. 2023

Biomimetics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.

Section: Metaheuristicsmentioning

confidence: 99%

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

Devi²,

et al. 2023

Biomimetics

“…From the perspective of the solution procedure, literature [14] employed an efficient Genetic Algorithm (GA) to accelerate the calculation and show higher accuracy in DNR. In [15], researchers proposed an improved Particle Swarm Optimization (PSO) which involved a new acceleration coefficient. Although the solutions of two algorithms above had indicated both a rapid convergence and a good robustness of these approaches, they mainly focused on a single optimization problem.…”

Section: B Development and Research Status Of Dnrmentioning

confidence: 99%

“…However, there will be some invalid solutions in optimization of DNR because of the different islands [30]. The invalid solutions that can not meet the constraint of distribution network topology will greatly reduce the search efficiency [15]. Therefore, the topologies of different systems need to be simplified via the method presented below.…”

Section: Mathematical Model Of Distribution Network Reconfiguration A...mentioning

confidence: 99%

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Multi-Objective Optimization for Distribution Network Reconfiguration With Reactive Power Optimization of New Energy and EVs

2023

IEEE Access

The rise of new energy and the wide application of electric vehicles (EVs) have led to the substantial expansion of distribution network in recent years. The problems such as the decline of transmission reliability and the rise of power loss in distribution network are becoming increasingly serious. Therefore, distribution network can greatly improve its reliability and quality of power supply voltage through changing topology. This work built a distribution network reconfiguration (DNR) model with new energy and EVs firstly. Then, the position of bus tie switches and the reactive power regulation range of new energy and EVs were proposed as decision variables. A multi-objective evolutionary algorithm would be applied to this DNR model and the optimization results will be obtained when considering the line loss and voltage deviation as the objective function. In order to get different optimal compromise solutions with the changes of actual environment, this work employed a new decision-making method named Prevalence Effect Method (PEM). Finally, a high-quality strategy of DNR and reactive power regulation will be obtained.

International Transactions on Electrical Energy Systems

“…Moreover, the GA methodology has given smaller standard deviation values than DE in all scenarios. Te near-optimal outcome represents the one with the lower standard deviation [45,46]. In that manner, the GA-based optimization is more successful than DE since it has the smaller standard deviation.…”

Section: Comparison Between Ga and De For 10 Runs Of Stochastic Optim...mentioning

confidence: 99%

Chance-Constrained Optimization of Photovoltaic System Allocation considering Power Loss, Voltage Level, and Line Current

Barutcu

2023

Changes are emerging that will significantly alter structure and operation of this century’s distribution networks, and photovoltaic (PV) systems will play greater role in energy sector, with implications for power system reliability. Considering uncertainties in solar irradiance and electrical loads and incorporating them into the optimization problem within an appropriate methodology is becoming increasingly important in reshaping distribution networks. In this paper, uncertainty scenarios are handled with Monte Carlo Simulation (MCS) under genetic algorithm (GA) and differential evolution- (DE-) based optimization, and probability distribution functions (pdf) of bus voltages and line current are obtained to be used in chance-constrained stochastic programming. This present study focuses on investigating impact of uncertainties in PV system operating under different irradiance scenarios on power loss with probabilistic constraints in distribution networks instead of precise deterministic limits to contribute more efficient and reliable use of energy. By combining meta-heuristic optimization and MCS technique under one framework, this paper contributes to knowledge base of how to allocate PV plants within distribution networks under chance-constrained strategy. In order to show the effectiveness of the proposed methodology, obtained optimization results are tested using MCS under set of uncertainty conditions and network constraints are evaluated for limit violation probabilities. The effectiveness of this method is investigated based on comparative results of two different optimization methods through probabilistic analysis and simulation.