Arash Asrari

Pareto Dominance-Based Multiobjective Optimization Method for Distribution Network Reconfiguration

With ever increasing deployment of automation and communication systems in smart grids, distribution network reconfiguration is becoming a viable solution for improving the operation of power grids. A novel hybrid optimization algorithm is proposed in this paper that determines Pareto frontiers, as the candidate solutions, for multiobjective distribution network reconfiguration problem. The proposed hybrid optimization algorithm combines the concept of fuzzy Pareto dominance with shuffled frog leaping algorithm (SFLA) to recognize optimal nondominated solutions identified by SFLA. The local search step of SFLA is also customized for power systems application so that it automatically creates and analyzes only the feasible and radial configurations in its optimization procedure, which significantly increases the convergence speed of the algorithm. Moreover, an adaptive reliability-based frog encoding is introduced that supervises the algorithm to concentrate on more reliable network topologies. The performance of the proposed method is demonstrated on a 136-bus electricity distribution network.

The Impacts of Distributed Energy Sources on Distribution Network Reconfiguration

Thanks to the recent improvements in renewable energy technologies throughout the world, distributed energy sources are now playing an undeniable role in supplying the electricity in distribution networks. This paper studies the impacts of utilizing distributed generation units on the task of network reconfiguration in distribution systems. Considering the importance of reducing voltage drops and voltage sags in distribution systems, network reconfiguration is formulated as a multiobjective optimization problem in this study to minimize these two objective functions. A Pareto-based metaheuristic optimization algorithm is proposed to identify a Pareto frontier representing the alternative high-quality suboptimal configurations. The proposed optimization method is tested on a 69-bus distribution system to demonstrate the performance of the algorithm.

Reconfiguration of Smart Distribution Systems With Time Varying Loads Using Parallel Computing

The problem of finding optimal configuration of automated/smart power distribution systems topology is an NP-hard combinatorial optimization problem. It becomes more complex when the time varying nature of loads is taken into account. In this paper, a systematic approach is proposed to determine an optimal long-term reconfiguration schedule. To solve the optimization problem, a novel adaptive fuzzy-based parallel genetic algorithm (GA) is proposed that employs the concept of parallel computing in identifying the optimal configuration of the network. The integration of fuzzy logic into the proposed method enhances the efficiency of the parallel GA by adaptively modifying the migration rates among different processors during the optimization process. A computationally efficient graph encoding method based on Dandelion coding strategy is developed, which automatically generates radial topologies and prevents the construction of infeasible radial networks in the optimization process. In order to consider the dynamic behavior of the load and reduce the load condition scenarios over the year under study, fuzzy C-mean clustering method is utilized. Finally, the performance of the proposed method is demonstrated on a 119-bus distribution network, and is compared with that of conventional single GA and conventional parallel GA.

A Hybrid Algorithm for Short-Term Solar Power Prediction—Sunshine State Case Study

The growing rate of the integration of photovoltaic (PV) sites into the structure of power systems makes the task of solar power prediction more important in order to control the power quality and improve the reliability of system. In this paper, a hybrid forecasting algorithm is proposed for hour-ahead solar power prediction. A combination of gradient-descent optimization and meta-heuristic optimization approaches are designed in the structure of the presented model to take into account the prediction accuracy as well as the computational burden. At the first step, the gradient-descent optimization technique is employed to provide the initial parameters of a feedforward artificial neural network (ANN). At the next step, the meta-heuristic optimization model, called shuffled frog leaping algorithm (SFLA), is developed to search for the optimal set of parameters of ANN using the initial individuals found by the gradient-descent optimization. Then, the identified parameters by the customized SFLA will be employed by the ANN for short-term solar power prediction. The performance of the proposed forecasting algorithm is demonstrated on the solar power data of three simulated PV sites in Florida for 2006.

A Stochastic Hybrid Method to Forecast Operating Reserve: Comparison of Fuzzy and Classical Set Theory

Abstract Accurate operating reserve forecasting helps the system operator to make decisions contributing to the security of the power system. It also helps market participants to adopt proper strategic bidding for the day-ahead ancillary services market to enhance their financial profit. This article proposes a stochastic hybrid method to forecast the operating reserve requirement in day-ahead electricity markets. At the first stage, based on using a modified Gray model, the day-ahead operating reserve is forecasted. In order to improve the accuracy of the operating reserve forecasting, at the next stage, a Markov chain model is used to predict the forecasting error of the Gray model. These two models are linked to each other using two different approaches—classical and fuzzy. The proposed approach is verified by the historical data of the operating reserve for spring and autumn seasons in the Khorasan Electricity Network located in Khorasan Province, Iran.

An intelligent neural network-based short-term wind power forecasting in PJM Electricity Market

Due to the rapid growth of wind power generation in the recent years, accurate wind power prediction is necessary for reliable power system operation. This paper proposes a novel forecasting algorithm for day-ahead wind power forecasting. In the presented model, instead of relying on the gradient-descent approach, a meta-heuristic optimization method called shuffled frog leaping algorithm (SFLA) is developed to determine the parameters of a feedforward artificial neural network (ANN). The trained ANN by SFLA will be employed in the next step to forecast the day-ahead wind power data. The performance of the proposed model is demonstrated on the wind power data of PJM Electricity Market for 2015.