The SOS algorithm

The recent tendencies in the power systems has been toward
the socioeconomic growth and avoiding environmental concerns,
aiming at higher power quality, more reliable services and
increased energy efficiency especially in the distribution level
[1e3]. The response to these issues necessitates the use of alternative
energy sources particularly the energy which can be
renewed including PVs (photovoltaics) and WTs (wind turbines).
Moreover, other types of DGs (distributed generations) such as FCs
(fuel cells) and MTs (micro turbines) have opened new insights for
having active power systems with higher efficiencies in the near
future [4,5]. Considering these into account, MG which consists of
several alternative sources can be considered as a principal device
to attain the favourable targets while distributing electricity more
efficiently, successfully and securely. The MG concept undertakes a
cluster of loads and Micro-Sources operating as a single controllable
module that can offer either power or heat locally [6]. In other
words, MG idea considers and manages part of the challenges and
benefits generated by the application of DGs in the new smart grids.
As a result, in recent years, the investigations on MG have widely
and rapidly appeared in the literature. Hafez et al. investigated a
renewable MG from the planning, design and operation perspective
in order to minimise the expenses during the MG lifetime [7].
Pipattanasomporn et al. studied the practical application of an
agent-based technique for flexible operation of a MG that has PV
system joined with battery storage [8]. In Ref. [9], Morais et al.
implemented an optimal operation technique for a renewable MG
to optimise the performance of the supply using a mix-integer
linear programming by applying the right timing. In Ref. [10] an
optimal energy management of a renewable MG laboratory over a
seven day period was investigated and an intelligent deterministic
minimisation method was implemented. In Ref. [11] MG in interconnected
mode of operation was studied to optimize the local
production and exchanged power under various market policies.
Chen et al. minimised the MG cost by planning forecast, storage and
optimisation modules and application of a real-coded genetic algorithm
[12]. Dukpa et al. proposed a contribution policy to
participate in the day-ahead unit commitment in a MG based on