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Microgrid (MG) could allow renewable and clean resources to penetrate into a controllable utility and achieve maximum utilisation of existing energy and demand-side management. This study proposes a new paradigm for distribution system operation considering MG conception. This study is focused on probabilistic analysis of optimal power dispatch considering economic aspects in MGs environment with technical constraints. In this study the economic operation of small scale energy zones is formulated and solved as an optimisation problem. A typical MG consists wind turbine (WT), photo voltaic (PV), micro turbine, fuel cell, combined heat and power and electric loads. Fluctuation behaviour of loads and generated power by WTs and PVs are caused complexity in proposed problem. Cost function includes generated powers by units, power transaction between MGs and main grid, operation and maintenance cost of resources and cost of pollutants emission. Considering MG concept in smart grids, the balance between supply-demand is secured through power exchanging between MGs and main grid, so that the value of objective function be minimised. The imperialist competitive algorithm is applied to solve proposed problem and obtained results are compared with Monte Carlo simulation method.

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Summary A framework for interconnected multi‐microgrids (MMGs) optimal power dispatch at smart distribution grids is introduced with network uncertainties. In proposed work, microgrids (MGs) not only exchange power with main grid, but also operate in interconnected mode to increase network reliability. For each small scale energy resources (SSER) and load, a probabilistic model is applied to determine the optimal power sharing of interconnected MGs with minimum operating cost. The power interchange between MGs and the main grid is regulated based on the total operating cost regarding the sold or purchased power either by MGs or by main grid. The stochastic analysis of network variables is done considering optimization constraints and explained as probability distribution function (PDF) and cumulative distribution function (CDF). The imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) algorithms are applied to optimize the object function, and obtained results are compared with Monte Carlo simulation (MCS) method. The results confirm that the optimal power sharing between MGs and main grid can decrease the operating cost of the smart distribution networks. Copyright © 2015 John Wiley & Sons, Ltd.

One of the basic components of future distribution networks is renewable energy resources (RER). The uncertainty in power production of renewable resources such as wind and solar as well as load is another characteristic of such networks. Conventional power flow methods may not be suitable for active distribution networks such as microgrids. In this study, a heuristic load flow method considering the effects of intermittent behaviour of RERs and load is modelled in probabilistic load flow (PLF) algorithm. The method is suitable for both radial and weakly meshed distribution networks with RER for operation and planning of microgrids. Imperialist competitive algorithm (ICA) as heuristic-based optimisation algorithm is applied to solve the PLF. Based on PLF technique, calculated parameters of the system such as bus voltages and feeders' current are extracted as random variables. A modified version of IEEE 33-bus test system with RER is used to evaluate efficiency and capability of the algorithm. Results are compared with Monte Carlo simulation method. The probability density function and cumulative distribution function (CDF) of some network variable are compared. Based on the results, the presented approach can solve the PLF problem regardless of the type of distribution network.

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