Transmission expansion planning using multivariate interpolation

• We propose a new methodology to solve the transmission expansion planning problem.
• We use multivariate interpolation to speed up the processing time.
• We use binary particle swarm optimization.
• We compare the results to traditional methods.
• We run our experiments on the Graver's 6-bus and IEEE 24-bus test systems.

The total cost of the transmission expansion planning (TEP) problem consists of investment and operation costs. The former is the required capital investment cost for new circuits throughout the network, and the latter is the cost of optimal generation dispatch to meet the demand at each hour. Traditionally, due to computational limits and long-term planning, the operation cost is not computed for hourly demand in the TEP problem. It is typically computed for the peak demand occurring during each year. In addition, the price of fuel used in the operation problem is considered fixed rather than variable over time. In this paper, we use a multivariate interpolation method to compute the operation cost for the TEP problem in which the demand changes from hour to hour and the fuel price from day to day. A binary particle swarm optimization (BPSO) is proposed to solve the TEP problem. We apply our method to the Garver's 6-bus system and the IEEE 24-bus system for a planning horizon of ten years. By using the multivariate interpolation, the computational time of the solving algorithm is reduced. We compare our method with traditional methods based on the total cost of the obtained expansion plans. Experimental results show that the proposed method is an enhancement to solving the multi-year security-constrained TEP problem.