Simulation and the monte carlo method

Review of energy storage technologies for sustainable power networks

Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm

A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources

To extract the maximum potential advantages in light of environmental, economical and technical aspects, the optimum installation and sizing of Distributed Generation (DG) in distribution network has always been challenging for utilities as well as customers. The installation of DG would be of maximum benefit where setting up of central power generating units are not practical, or in remote and small areas where the installation of transmission lines or availability of unused land is out of question. The objective of optimal installation of DG in distribution system is to achieve proper operation of distribution networks with minimization of the system losses, improvement of the voltage profile, enhanced system reliability, stability and loadability etc. In this respect analytical (classical) methods, although well-matched for small systems, perform adversely for large and complex objective functions. Unlike the analytical (classical) methods, the intelligent techniques for optimal sizing and siting of DGs are speedy, possess good convergence characteristics, and are well suited for large and complex systems. However, to find a global optimal solution of complex multi-objective problems, a hybrid of two or more meta-heuristic optimization techniques give more effective and reliable solution. This paper presents the fundamentals of DG and DG technologies review the classical and heuristic approaches for optimal sizing and placement of DG units in distribution networks and study their impacts on utilities and customers. An attempt has also been made to compare the analytical (classical) and meta-heuristic techniques for optimal sizing and siting of DG in distribution networks. The present study can contribute meaningful knowledge and assist as a reference for investigators and utility engineers on issues to be considered for optimal sizing and siting of DG units in distribution systems.

Optimal sizing and siting techniques for distributed generation in distribution systems: A review

Probabilistic load flow for photovoltaic distributed generation using the Cornish–Fisher expansion

In this study, analytical techniques and the Monte Carlo method were both applied to solve a probabilistic load flow in radial distribution networks with photovoltaic-distributed generation, but considering the technical constraints that apply to the networks (e.g. voltage regulation). The analytical technique used in this study combined the method of cumulants with the Gram-Charlier expansion to resolve probabilistic load flow. This was performed by modelling the loads and the photovoltaic (PV) distributed generation as random variables. For this purpose, the authors developed a new probabilistic model that took into account the random nature of solar irradiance and load. The results obtained demonstrate that this new analytical technique can be applied to keep voltages within standard limits at all load nodes of radial distribution networks with photovoltaic-distributed generation. A computational cost reduction has demonstrated that the analytical technique used in this study performed better than the Monte Carlo method. Acceptable solutions were reached with a smaller number of iterations. Convergence was thus rapidly attained with a lower computational cost than that needed with the Monte Carlo method.

Probabilistic load flow for radial distribution networks with photovoltaic generators

Photovoltaic (PV) systems are gradually replacing conventional synchronous generators. However, reduced system inertia and lack of dynamic grid support from PV are the main issues that could have a detrimental impact on the transient response in power systems when critical contingencies arise. In this study, the authors modelled and analysed transient and small-signal stability for a representative transmission system with realistic loading scenarios and high PV penetration levels. First, system eigenvalues were calculated to identify critical modes. Thereafter, the results of the small-signal analysis were further expanded by performing transient simulations after critical contingencies. Such contingencies detrimentally excited the critical modes of the system. To carry out this analysis, they implemented a positive-sequence dynamic model of a utility-scale PV unit (USPVU) in the open programming environment MATLAB/Simulink. This dynamic model is based on a Western Electricity Coordinating Council (WECC) generic model (full converter model), which is suitable for electromechanical transient studies. Also included was the model of the PV array, dc-dc converter, and associated control systems. The most critical factors pertaining to the detrimental or beneficial impact of USPVUs on stability were the unit commitment and dispatch strategy and the protection/control strategy during voltage swell and dip events for equivalent PV penetration and loading scenarios.

F.J. Ruiz-Rodriguez

Papers

Probabilistic load flow for photovoltaic distributed generation using the Cornish–Fisher expansion

Probabilistic load flow for radial distribution networks with photovoltaic generators

Stability assessment for transmission systems with large utility-scale photovoltaic units

Voltage unbalance assessment in secondary radial distribution networks with single-phase photovoltaic systems

Modelling and assessment of the combined technical impact of electric vehicles and photovoltaic generation in radial distribution systems