Robust Optimization of Building Microgrid Considering the Uncertainty of the Aggregated Power of Thermostatically Controlled Loads

doi:10.12204/j.issn.1000-7229.2021.04.007

Abstract

Abstract:

With the rapid development of microgrid systems with multiple intelligent buildings integrated, thermostatically controlled loads (TCLs) have gradually become an important demand-response resource. However, its distribution dispersion and response uncertainty also put forward higher requirements for the regulation. To address the distribution dispersion of TCLs, this paper puts forward a TCLs aggregation scheme based on the building's resistance-capacitance (RC) model. Specifically, the RC heat transfer model is linearized at first, and then the TCLs are aggregated according to the law of large numbers, thence the building's physical structural parameters can be taken into account. This TCLs aggregation scheme not only adapts to the differentiated modeling requirements of buildings but also has preferable calculation efficiency. On the basis of the proposed TCLs aggregation scheme, a two-stage robust optimization model applicable to the integrated building microgrid system is established, which considers the uncertainties of aggregated TCLs and distributed renewable generators, and is solved by the column and constraint generation algorithm. Results show that the proposed TCLs aggregation scheme can reach the accuracy requirements of microgrid scheduling operation under diverse outdoor temperatures and solar radiation. Meanwhile, the established two-stage robust optimization model can effectively reduce the power regulation of tie lines under real circumstances, thence enhancing the stability and robustness of the microgrid system.

Key words: microgrid with buildings, aggregated thermostatically controlled load, uncertainty, robust optimization

CLC Number:

TM73

HUA Guanghui, LI Chen, ZHANG Yong, LI Dan, LIU Chuang, WANG Cheng. Robust Optimization of Building Microgrid Considering the Uncertainty of the Aggregated Power of Thermostatically Controlled Loads[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(4): 59-68.

Figures/Tables 12

Fig.1 Single-user RC network model for building

Fig.2 Schematic diagram of the operation states of thermostatically controlled loads

Fig.3 Schematic diagram of the interaction between the two stages of the robust scheduling model

Fig.4 Integrative operation mode of the microgrid system with multiple smart buildings integrated

Table 1 Ranges of parameters of thermostatically controlled loads and Intelligent building

Fig.5 Estimated aggregated power of TCLs and its upper and lower limits

Fig.6 Distribution of relative error of integrated power

Fig.7 Aggregated power under different weather conditions

Fig.8 Day-ahead forecasting value of the distributed renewable generators in the microgrid

Table 2 Operation parameters of the dispatchable DGs

Fig.9 Scheduling strategy of microgrid under different methods

Table 3 Simulation results with different methods under 1 000 sampled scenarios

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