Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm

Application of the hybrid Big Bang-Big Crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems

Distribution system optimisation is defined as satisfying the demand of the system in the most economical, reliable and environment-friendly way while all the related operational or geographical constraints are met. In the case that the future load of the system is intended to be met, the problem is also called “expansion planning” problem. In this paper, the existing researches on distribution system optimisation problem are reviewed from the viewpoint of their used optimisation algorithm, used objectives, used decision variables, load model, case study, planning type and planning period. This review found that although diverse optimisation algorithms have already been applied to distribution system optimisation problem, developing efficient algorithms with the ability of escaping from local optima and finding near-global solutions is required. In particular, development of diversity enhancement strategies for metaheuristics and applying them to this problem seems to be fruitful. This review also found that some aspects of distribution systems such as deregulation and demand side management have not been taken into account in modelling of distribution system optimisation problem.

Optimisation of electric distribution systems: A review

Unpredictable nature of renewable energy sources puts power system operators in different conditions in terms of maintaining the system reliability. Microgrids (MGs) can provide an effective solution for the problems of grid-connected renewable energy sources due to their tuning capability and flexibility. In this study, a stochastic optimization strategy was proposed for participating in energy market operations considering demand response (DR). The results indicated the operating cost decreased when the MG implemented DR programs. Also, DR program could shift energy consumption from on- to off-peak hours and flatten the load curve. Therefore, a scheduling model was presented for the operation of energy carriers and reserves considering security constraints of power and natural gas grids in interconnected hubs as well as responsive load participation using the developed water wave optimization (WWO) algorithm. The objective function of this model aimed to minimize the operating cost of sources to supply electrical and thermal loads applied to the proposed MG. WWO is a meta-heuristic algorithm inspired by the behavior of water waves. The waves formed on the sea surface have complex, but interesting, relationships that can be used to solve optimization problems. In this algorithm, each problem solution is encoded as a water wave and undergoes changes in the problem search space based on three behaviors of propagation, refraction, and decay of water waves or problem solutions. The simplicity of the structure of this method, elitism and the ability to escape from local points due to the existence of different search operators and the mutation of generations have led to the use of this method. The results indicated a decline in operating costs through electrical and thermal responsive load participation and thermal energy storage system. Results of the proposed model revealed a correlation between the electricity price and natural gas consumption, indicating multi-carrier energy grids should be examined and optimized simultaneously. 

Optimal economic scheduling of microgrids considering renewable energy sources based on energy hub model using demand response and improved water wave optimization algorithm

This paper proposes a novel adaptive optimization algorithm to solve the network reconfiguration and distributed generation (DG) placement problems with objective functions including power loss minimization and voltage stability index (VSI) improvement. The proposed technique called Adaptive Shuffled Frogs Leaping Algorithm (ASFLA) was performed for solving network reconfiguration and DG installation in IEEE 33- and 69-bus distribution systems with seven different scenarios. The performance of ASFLA was compared to that of other algorithms such as Fireworks Algorithm (FWA), Adaptive Cuckoo Search Algorithm (ACSA) and Shuffled Frogs Leaping Algorithm (SFLA). It was found that the power loss and VSI provided by ASFLA were better than those given by FWA, ACSA and SFLA in both 33- and 69-bus systems. The best solution of power loss reduction and VSI improvement of both 33- and 69-bus systems was achieved when the network reconfiguration with optimal sizing and the location DG were simultaneously implemented. From our analysis, it was indicated that the ASFLA could provide better solutions than other methods since the generating process, local and global searching of this algorithm were significantly improved from a conventional method. Hence, the ASFLA becomes another effective algorithm for solving network reconfiguration and DG placement problems in electrical distribution systems.

https://www.mdpi.com/1996-1073/12/3/553/pdf

Power Loss Minimization and Voltage Stability Improvement in Electrical Distribution System via Network Reconfiguration and Distributed Generation Placement Using Novel Adaptive Shuffled Frogs Leaping Algorithm

A flexible-reliable operation optimization model of the networked energy hubs with distributed generations, energy storage systems and demand response

Decreasing activity cost of a distribution system company by reconfiguration and power generation control of DGs based on shuffled frog leaping algorithm

An innovative technique for optimization and sensitivity analysis of a PV/DG/BESS based on converged Henry gas solubility optimizer: A case study

This paper presents a new approach for the coordinated design of a power system stabilizer- (PSS-) and static VAR compensator- (SVC-) based stabilizer. For this purpose, the design problem is considered as an optimization problem, while the decision variables are the controllers’ parameters. This paper proposes an effective optimization algorithm based on a rat swarm optimizer, namely, adaptive rat swarm optimization (ARSO), for solving complex optimization problems as well as coordinated design of controllers. In the proposed ARSO, instead of a random initial population, the algorithm starts the search process with fitter solutions using the concept of the opposite number. In addition, in each iteration of the optimization, the new algorithm replaces the worst solution with its opposite or a random part of the best solution to avoid getting trapped in local optima and increase the global search ability of the algorithm. The performance of the new ARSO is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. In addition, a case study from the literature is considered to evaluate the efficiency of the proposed ARSO for coordinated design of controllers in a power system. PSSs and additional SVC controllers are being considered to demonstrate the feasibility of the new technique. The numerical investigations show that the new approach may provide better optimal damping and outperform previous methods.

/pdf/adaptive-rat-swarm-optimization-for-optimum-tuning-of-svc-7va7gkf4.pdf

Adaptive Rat Swarm Optimization for Optimum Tuning of SVC and PSS in a Power System

Energy has been one of the most important topics of political and social discussion in recent decades. A significant proportion of the country’s revenues is derived from energy resources, making it one of the most important and strategic macro policy and sustainable development areas. Energy demand modeling is one of the essential strategies for better managing the energy sector and developing appropriate policies to increase productivity. With the increasing global demand for energy, it is necessary to develop intelligent forecasting methods and algorithms. Different economic and non-economic indicators can be used to estimate the energy demand, including linear and non-linear statistical methods, mathematics, and simulation models. This non-linear relationship between these indicators and energy demand has led researchers to search for intelligent solutions, such as artificial neural networks for non-linear modeling and prediction. The purpose of this study was to use a deep neural network with fuzzy wavelets to predict energy demand in Iran. For the training of the presented components, a hybrid training method incorporating both an inline PSO and a gradient-based algorithm is presented. The provided technique predicts energy consumption in Tehran, Mashhad, Ahvaz, and Urmia from 2010 to 2021. This study shows that the presented method provides high-performance prediction at a lower level of complexity.

https://www.mdpi.com/2227-7390/10/8/1270/pdf?version=1649747868

Behdad Arandian

Papers

A flexible-reliable operation optimization model of the networked energy hubs with distributed generations, energy storage systems and demand response

Decreasing activity cost of a distribution system company by reconfiguration and power generation control of DGs based on shuffled frog leaping algorithm

An innovative technique for optimization and sensitivity analysis of a PV/DG/BESS based on converged Henry gas solubility optimizer: A case study

Adaptive Rat Swarm Optimization for Optimum Tuning of SVC and PSS in a Power System

Developing a Deep Neural Network with Fuzzy Wavelets and Integrating an Inline PSO to Predict Energy Consumption Patterns in Urban Buildings