As a typical heat energy storage device

the decentralized electric heating load has a large power and contributes to the hysteresis and storability of electric energy output. But since the existing operation control mode is unitary and available only for single equipment

its impossible to achieve collaborative optimization of electric heaters on a regional scale. For this reason

this paper proposes the concept and framework of a smart electric heating network as well as optimization indexes that could be used to measure the running effect of this network. Then a deterministic operation optimization model for this smart electric heating network is put forward. On the premise of ensuring peak-power limitation

the model is able to calculate the achievable maximum comfort level. By taking the maximum comfort level as a constraint

the minimum operating electricity charges can be calculated. Further

by introducing the uncertainty temperature fluctuation into the equivalent thermal parameter(ETP)model

a robust optimization model for the smart electric heating network is developed. The analysis of examples shows that

compared with the existing temperature control strategies

the deterministic optimization strategy is able to effectively reduce the peak load

significantly improve temperature control effect

and lower operating electricity charges. Compared with the deterministic optimization strategy

the robust optimization strategy can better ensure the comfort level

with its operating power charges slightly higher than those with the deterministic optimization strategy. The proposed optimization strategies can fully respond to the time-of-use price

which realizes load shifting indirectly while enhancing users' economic benefits.