Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach

The market penetration of Plug-in Electric Vehicles (PEVs) is escalating due to their energy saving and environmental benefits. In order to address PEVs impact on the electric networks, the aggregators need to accurately predict the PEV Travel Behavior (PEV-TB) since the addition of a great number of PEVs to the current distribution network poses serious challenges to the power system. Forecasting PEV-TB is critical because of the high degree of uncertainties in drivers’ behavior. Existing studies mostly simplified the PEV-TB by mapping travel behavior from conventional vehicles. This could cause bias in power estimation considering the differences in PEV-TB because of charging pattern which consequently could bungle economic analysis of aggregators. In this study, to forecast PEV-TB an artificial intelligence-based method -feedforward and recurrent Artificial Neural Networks (ANN) with Levenberg Marquardt (LM) training method based on Rough structure - is developed. The method is based on historical data including arrival time, departure time and trip length. In this study, the correlation among arrival time, departure time and trip length is also considered. The forecasted PEV-TB is then compared with Monte Carlo Simulation (MCS) which is the main benchmarking method in this field. The results comparison depicted the robustness of the proposed methodology. The proposed method reduces the aggregators’ financial loss approximately by 16 $/PEV per year compared to the conventional methods. The findings underline the importance of applying more accurate methods to forecast PEV-TB to gain the most benefit of vehicle electrification in the years to come.Peer ReviewedPostprint (author's final draft

Loss calculation and reduction in radial distribution systems

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A Novel Method for Load Flow Analysis of Unbalanced Three-Phase Radial Distribution Networks

This paper presents a novel method for load flow analysis in radially operated 3-phase distribution networks without solving the well-known conventional load flow equations. The method can be applied for distribution systems in which the loads are unbalanced. As the size of matrix used is very small compared to those in conventional methods,the amount of memory used is very small,the speed is very high,and the relative speed of calculation increases with the size of the system. The method was applied to some practical networks and the results show the superiority of this method over the conventional ones. As this method is significantly faster than any other method developed to date,it has great potential for on-line operations. Key Words: Radial load flow,distribution,three-phase load-flow. Load flow analysis forms an essential prerequisite for power system studies. Considerable research has already been carried out in the development of computer programs for load flow analysis of large power systems. However, these general purpose programs may encounter convergence difficulties when a radial distribution system with a large number of buses is to be solved and, hence, development of a special program for radia

Distribution Network Expansion Using Hybrid SA/TS Algorithm

Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing total cost of the objective function with technical constraints and reliability limits, make it a combinatorial problem which should be solved by optimization algorithms. This paper presents a new hybrid simulated annealing and tabu search algorithm for distribution network expansion problem. Proposed hybrid algorithm is based on tabu search and an auxiliary simulated annealing algorithm controls the tabu list of the main algorithm. Also, another auxiliary simulated annealing based algorithm has been added to local searches of the main algorithm to make it more efficient. The numerical results show that the method is very accurate and fast comparing with the other algorithms

Regulator prigušenja za statički sinkroni kompenzator temeljen na HBM optimizaciji

The aim of this paper is to investigate a novel approach for output feedback damping controller design of the static synchronous compensator (STATCOM) in order to enhance the damping of power system low frequency oscillations (LFO). The design of output feedback controller is considered as an optimization problem according to the time domain-based objective function solved by a honey bee mating optimization (HBMO) algorithm that has a strong ability to find the most optimistic results. To validate the accuracy of results a comparison with genetic algorithm (GA) has been made. The effectiveness of the proposed controller are tested and demonstrated through eigenvalue analysis and nonlinear time-domain simulation studies over a wide range of loading conditions. The simulation study shows that the designed controller by HBMO performs better than GA in finding the solution.U ovome radu istražene su metode za sintezu regulatora prigušenja za statički sinkroni kompenzator (STATCOM) u svrhu povećanja prigušenja nisko frekvencijskih oscilacija u energetskim sustavima. Sinteza regulatora je razmatrana kao optimizacijski problem u vremenskoj domeni koji je riješen pomoću HBM algoritma optimizacije (eng. Honey bee mating) koji ima svojstvo pronalaska najoptimističnijeg rezultata. U svrhu provjere rezultata napravljena je usporedba s rješenjem koje daje genetski algoritam. Efikasnost predloženog regulatora testirana je uz pomoć analize svojstvenih vrijednosti i nelinearnih simulacija u vremenskoj domeni za različite uvjete. Simulacijski rezultati pokazuju da se korištenjem regulatora s HBM optimizacijom postižu bolji rezultati nego korištenjem genetskog algoritma