This research
addresses the energy cost optimization problem in the smart grid from the
users' perspective. We first propose a unified model which integrates the
partial aspects of previous research in a single cost optimization model. It
considers the components which have significant impact on cost optimization,
e.g., storage, renewables, microgrid, etc. The model
utilizes load and source scheduling, and energy trading strategies for cost
optimization. It also addresses the inconvenience created to the users by
delaying certain tasks. The model enables Peer-to-Peer (P2P) energy trading
among the participating households in the microgrid.
In P2P trading, the households determine the microgrid
energy price and quantity to minimize the total cost. On the other hand, P2P
trading potentially results in an unfair cost distribution among the
participating households. We address this unfair cost distribution problem by
employing Pareto optimality, ensuring that no households will be worse off to
improve the cost of others. However, the optimal solution approach of the
unified model is a non-convex Mixed Integer Nonlinear Programming (MINLP)
problem.
Our results show
that, even for small problem sizes, the solution time increases exponentially.
Hence, it cannot be utilized to solve practical scenarios. To address this
problem, we also propose a bi-linear model which provides an approximate
solution within a realistic timeframe. Its complexity is less than the unified
model because it works with multiple lower dimensional convex solution spaces.
Our results show that the solution time of the bi-linear model is very low
(mostly less than a minute) compared to the optimal model. Moreover, for real
datasets, 99% of the solutions generated by the bi-linear model are optimal
solutions. Finally, we used real datasets of Ottawa to evaluate the impact of
renewables and storage in the microgrid. Our results
show that P2P energy trading is beneficial if the households have both storage
and renewables. In the presence of renewables, increased storage capacity
increases cost savings until it reaches a saturation point. Our findings could
be helpful for policy makers to design programs and initiatives for the
households to accelerate the adoption of storage and renewables in the smart
grid.