The simulation in this paper shows how the renewable energy in the hybrid system cooperate with the load shifting in the energy management method optimize the system and the stability of the grid. The flexibility of postponed able demand in scheduling method that applied to load shifts giving the renewable energy in hybrid energy to contribute optimally. Giving the power generation estimation that obtained from probability function is able to forecast the generation output (both of PV and Wind) to determine the power generation scale which is able to match with load demand variance.
Having larger of storage system charging and discahrging will have better efficiency of storage system. The 70-80% rate of charging and discarging would be better than only optimiza the storage in 30-50%. However, The various rate of storage system charging or discharging is not given significant different to scale the component sizing, it only shows little different amount storage sizing capacity. In the other hand, the various rate of the load shifting from scheduling energy management show have huge influence to determine the component sizing of generating power scale and the storage capacity. More load shifting allowable is less power generating scale and storage system will be installed, with the 70% storage discharging rate and 50% Load Shifting amount the generation of the wind is around 2800 scale. Comparing with around 3000 wind generation scale if implement 30% Load Shifting, of course the total expense also increases around 21900 M$.
Apart from the minimum generation scale to install and minimum expense, implement high load shifting will be a drawback having a lot number or moving load demand. Therefore, the real optimal solution of the problem cannot judge by the higher cost much better or lowest cost is worst. It is depending on the user’s objectives of each setting, if we want the less expensive cost to install the component there will be a huge amount of energy to manage, and having extra expenses will allow the system stability better.
The additional equations of balancing generating scale make the component sizing more efficient to implement in the places which are have the potential of both energies. Without the balancing equation the PV is always resulted 0 generation scale because the optimal solution is always try to find the minimum solution from the available generation and in this research PV estimation less than the Wind. This could implement the true component of the hybrid system, more than one of renewable generating power together supply the system, resulted in 50% LS and 70% storage discharging rate the wind has 2830 scale generation and 3232 for PV generation scale.
However, the less output of the solar (which could make expensive to install larger capacity) make the component sizing of wind power dominant, it shown on 10% LS and 70% storage discharging rate the wind scale increase 679 scale but the PV only 31 scale.
Comparing the performance of classical Mixed Integer Non-Linear Programming, Genetic Algorithm, and Differential Evolution, the Mixed Integer Non-Linear Programming cannot take into account the real stochastic variable into the optimization method. By using the estimation point of the output generation the Mixed
guess, in the 50% LS and 70% storage discharging rate it resulted 2128 scale or the wind and 2509 for the PV. This not include the random variation of the load demand, then it is difficult to judge it is global optima or just local optima consider the random variance of generating output and the load demand.
The both of Genetic Algorithm and Differential Evolution is show quite good approach to solve stochastic method. In the objective value to solve minimum expense with the feasible result, the Genetic Algorithm showed better in lower allowable load shifting rate value. The other advantages of the Genetic Algorithm are the convergence speed is faster than Differential Evolution, in my analysis, it could be the structure of crossover and mutation that proceed in the beginning than selection operator. In my opinion, the Differential Evolution would have a better result in the larger population area or generation times, however, it would be time consume and sometimes the result yields not in the feasible area.
In the future work, this approach could be expanding into real time simulation with additional installed generating power in the system. Therefore, the installation of hybrid energy would be less with the combination of existed power plant. Besides, it also could plan installation of the new hybrid system while optimize the existing power plant (both renewable energy and conventional fossil fuel energy). Together we could also reduce the use of fossil fueled power plant operation.
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