Day-ahead Optimal Economic Dispatch of Park Integrated Energy System Considering Uncertainty of Demand Response

doi:10.12204/j.issn.1000-7229.2021.12.002

Abstract

Abstract:

The park integrated energy system can significantly improve its operation economy through multi-energy complementary. However, the uncertainty of the controllable resource response will bring challenges to the formulation of the day-ahead dispatching strategy. Thus, a day-ahead optimal economic dispatching strategy considering the integrated demand response and its uncertainty is proposed for the park integrated energy system. Firstly, the uncertainty of photovoltaic and load forecast power and the uncertainty of flexible load response are described by triangular fuzzy number, and a day-ahead dispatching model considering uncertainty is established. Then, in order to minimize the total operation cost of the system, the controllable resources of the supply and demand sides of the system are optimized. According to the fuzzy scheduling theory, fuzzy expectation constraints and fuzzy chance constraints are equivalently converted into their certainty forms for easy solution. Finally, the case analysis shows that the proposed strategy can further reduce the operating cost through integrated demand response; and the impact of fuzzy chance constraint confidence level on the system operating cost is analyzed.

Key words: park integrated energy system (PIES), day-ahead optimal dispatch, integrated demand response, uncertainty, fuzzy scheduling theory

CLC Number:

TM73

LIN Wenzhi, YANG Ping, CHEN Xinyu, ZUO Guanlin. Day-ahead Optimal Economic Dispatch of Park Integrated Energy System Considering Uncertainty of Demand Response[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(12): 9-20.

Figures/Tables 20

Fig.1 Structure of typical PIES

Fig.2 Day-ahead scheduling structure of PIES

Table 1

Table 2

Fig.3 Generating power of CCHP units under different confidence levels

Table 3 Energy-consumption satisfaction

Fig.4 Comparison of power load curve before and after IDR

Fig. A1 Day ahead load forecasting of User A

Fig. A2 Day ahead load forecasting of User B

Fig. A3 Day ahead load forecasting of User C

Fig. A4 Hot water demand and outdoor temperature curve

Table A1 Operating parameters of energy conversion units

Table A2 Operating parameters of energy storage units

Table A3 Parameters of triangular membership degree of fuzzy variables

Table A4 Purchase and sale price for PIES

Table A5 Clearing and compensation price of DR transaction

Fig.B1 Comparison of optimal balance of PIES cooling load under different cases

Fig.B2 Comparison of optimal balance of PIES thermal load under different cases

Fig.B3 Comparison of optimal balance of PIES electrical load under different cases

Fig.B4 Comparison of optimal balance of PIES gas load under different cases

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