Daily increasing use of tidal power generation proves its outstanding features as a renewable source. Due to environmental concerns, tidal current energy which has no greenhouse emission attracted researchers’ attention in the last decade. Additionally, the significant potential of tidal technologies to economically benefit the utility in long-term periods is substantial. Tidal energy can be highly forecasted based on short-time given data and hence it will be a reliable renewable resource which can be fitted into power systems. In this paper, investigations of effects of a practical stream tidal turbine in Lake Saroma in the eastern area of Hokkaido, Japan, allocated in a real microgrid (MG), is considered in order to solve an environmental/economic bi-objective optimization problem. For this purpose, an intelligent evolutionary multi-objective modified bird mating optimizer (MMOBMO) algorithm is proposed. Additionally, a detailed economic model of storage devices is considered in the problem. Results show the efficiency of the suggested algorithm in satisfying economic/environmental objectives. The effectiveness of the proposed approach is validated by making comparison with original BMO and PSO on a practical MG.
Tides, i.e. ocean water oscillations, occur due to gravity principles among the moon, the sun and the earth. Therefore, tidal energy can be assumed as a class of renewable energies. In comparison with other kinds of renewable resources such as wind and solar, tidal energy can be claimed to be better predictable according to the short time data categories . With the possibility of acquiring accurate tidal prediction, researchers can rely confidently on tidal currents to produce electricity for long term periods . From the emission standpoint, tidal energy does not result in any air, water or thermal pollution. However, from the economic standpoint, although the primary investment of tidal power is approximately high, its subsequent benefits are highly considerable. Tidal energy technologies are categorized into three major groups, namely tidal range, tidal current or tidal stream turbines (TST) and hybrid technologies .
Inadequate amounts of fossil fuel along with global warming concerns give rise to the appearance of renewable energy sources (RES) among which tidal turbines are of great importance according to the above mentioned advantages. Different RESs including tidal, photovoltaic (PV) and wind generators beside energy storage systems are used in microgrids (MGs) in order to take their benefits as power generators to increase the reliability of the power system and to yield better performance . In order to supply the load while minimum levels of cost and emission are satisfied under considered constraints, the economic⧹environmental dispatch of MGs shall be taken into account as a multi-objective problem.
The effects of implementing the practical tidal stream turbine (TST) of Lake Saroma green microgrid (SLMG) in a typical MG were dealt with in this paper. The optimal energy management of the system was investigated using the proposed MMOBMO algorithm, while a new economic/environmental model for the practical TST along with a detailed economic model for storage devices were presented. It was observed that since PV and tidal generators are complementary, an MG consisting of these two generators is more proper in sustainably supplying load. In order to demonstrate the effective performance of the proposed algorithm, two different cases were studied. Moreover, the ON/OFF states of dispatchable DGs were taken into account. Consequently, the superiority of the algorithm in dealing with mixedinteger problems in comparison with PSO and the original BMO was verified. In addition to economic advantages, since tidal energy is well predictable, it was suggested that in regions with high potential of harnessing tidal energies, other renewable units shall be replaced by tidal generators. Future works can include the following:
(i) Considering the uncertainties of renewable resources including tidal and PV generators and solving the probabilistic MG’s energy management.
(ii) Investigating demand response and electric vehicles’ effects along with other elements of the future smart grid in the considered MG’s energy management.
(iii) Inspecting reliability subject as an objective function in the MG’s optimal operation.