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To level the fluctuations in electric power sourced from renewable energy, the transmission network can be spread over a wide area, but this is expected to dramatically increase the renewable energy rate. Therefore, this paper proposes an algorithm that analyzes the maximum amount of renewable energy in the network, and hence optimizes the type of electric power source connected to the transmission network, and the arrangement and capacity of each power source. The proposed algorithm is based on a genetic algorithm, which effectively processes many nonlinear variables concurrently. Accounting for the power interchange in the transmission network and the energy storage in electric heat pumps and heat storage tanks, the objective function plans the arrangement of the electric power sources that maximizes the economic efficiency of the system. The developed algorithm is applied to a renewable-energy network in Hokkaido, Japan. In this area, the introductory rate of renewable energy was 39.5% of the total electricity production. Moreover, the cost of a distributed power-supply network was 9.99 × 1010 USD. The proposed system is equivalent to 1.88 years of Hokkaido’s energy consumption.

Electric and heat power supply network of Hokkaido in consideration of the leveling effect by a wide-area interconnection of wind-farm and solar-farm