Abstract:
Renewable resources are very effective to meet energy needs due to growing cost of fuel
and decline in fossil fuel holds. This shifts the focus on providing affordable and authentic
sources of electricity. For incorporating solar energy into power system, there are two
challenges: First is accurate forecasting of solar irradiance and second is solving economic dispatch issue. This thesis proposes a novel technique which is from RNN class such as LSTM for optimum day ahead scheduling of three conventional generators and solar photovoltaic power generation as a cost minimization problem. Solar irradiance
forecasting is a non-linear time series problem and LSTM has given fine results in solving
non-linear time series problems. Multivariate single-step and multi-step GHI forecasting
models have been established in this work depending upon meteorological variables and
historical data of GHI. Hyper-parameter tuning is being employed for achieving optimal
parameters and in this way best model is evolved for solar irradiance forecasting. The
simulations for GHI prediction are being performed on dataset gathered in Meteorological
Station of NUST Islamabad, Pakistan. ANN has the ability of rapid convergence near the
solution so it is very good AI technique for solving power system optimization problems.
Economic dispatch problem for three conventional generators is solved by lambda
iteration method and LSTM technique. Training and testing patterns of powers, total costs
and power transmission losses for LSTM are obtained by using lambda iteration method,
dataset of load for this research is taken from NUST grid. Univariate multi-step LSTM
models by doing hyper-parameter tuning are developed for doing optimal scheduling of
generators with and without solar energy incorporation into the power system. By
comparing results for one day, it is seen that solar energy integration results in saving of
operating cost and reduction of transmission power loss. Simulations shows the
effectiveness of LSTM in prediction of GHI and solving economic dispatch problem by
comparing results on the basis of different evaluation metrics.
