Data-Driven Adjustable Robust Optimization of Day-ahead Economic Dispatch of Integrated Energy System with Combined Cool, Heat and Power System

doi:10.12204/j.issn.1000-7229.2021.01.004

Abstract

Abstract:

Integrated energy system with combined cool, heat and power system (IES-CCHP) is able to help power system to locally consume distributed wind and solar power, while satisfying charge demand of electric vehicles. However, uncertainties in the charge demand, wind and solar power significantly affect the economy of IES-CCHP. Therefore, this paper applies two-stage adjustable robust optimization to present day-ahead economic dispatch strategy for IES-CCHP. Day-ahead stage decides day-ahead dispatch strategy that can withstand the worst-case scenario before observing value of stochastic variables; real-time stage provides strategy for correcting the day-ahead strategy after confirming the stochastic variables. The objective is to minimize the costs of the two stages. Imprecise Dirichlet model is employed to dig historical data for constructing uncertainty set for describing stochastic variables. And then duality theory, big-M method, and column-and-constraint generation (C&CG) and so on, are applied to solve the presented two-stage model. Finally, experimental cases are carried out to demonstrate the effectiveness of model and method.

Key words: integrated energy system with combined cool, heat and power system(IES-CCHP), adjustable robust optimization, data-driven, imprecise Dirichlet model, renewable energy source

CLC Number:

TM73

CHEN Xiaodong, MA Yue, CHEN Xianbang, LIU Yang. Data-Driven Adjustable Robust Optimization of Day-ahead Economic Dispatch of Integrated Energy System with Combined Cool, Heat and Power System[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(1): 28-40.

Figures/Tables 19

Fig.1 Structure of integrated energy system with combined cool, heat, and power system

Fig.2 Relationship between interval of uncertainty set of wind power and number of historical data

Fig.3 Forecast data of load and renewable energy

Table 1 Impact of the number of samples on economy

Fig.4 Relationship between the number of samples and consumption of renewable energy source

Table 2 Impact of adjustable robust parameter on economy 美元

Fig.5 Relationship between adjustable parameter and consumption of renewable energy source

Table 3 Comparison of economy of different methods 美元

Table 4 Comparison of different electricity transaction strategies 美元

Fig.6 Day-ahead economic dispatch based on deterministic optimization

Fig.7 Day-ahead economic dispatch based on stochastic program

Fig.8 Day-ahead economic dispatch based on robust optimization

Fig.9 Day-ahead economic dispatch based on adjustable robust optimization

Table A1 Parameters of controllable generators

Table A2 Penalty coefficients

Table A3 Prices of buying and selling electricity 美元/(kW·h)

Table A4 Parameters of electrical storage system

Table A5 Parameters of heat storage system

Table A6 Coefficients of energy converting

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