Day-Ahead Optimized Operation of Micro Energy Grid Considering Integrated Demand Response

doi:10.12204/j.issn.1000-7229.2021.07.002

Abstract

Abstract:

Micro energy grid is the development direction of the terminal energy supply of the energy Internet (EI). The economic optimization of the system urgently needs to tap the potential of multi-energy loads, and a single demand response cannot meet the coordinated optimization of “source-network-load”. In this paper, aiming at the coupling conversion characteristics of multiple energy sources in the micro energy grid, a generalized energy hub model including energy storage and integrated demand response (IDR) is established. Energy operators of micro energy grid are introduced and three comprehensive demand response models, namely, transfer model, transforming model and reduction model are established. A day-ahead optimization scheduling method for micro energy grid considering comprehensive demand response is proposed, in which time-of-use energy prices are formulated, three demand response modes are coordinated and optimized. A mixed integer linear programming model with the goal of profit maximization is established, and the solution is solved by CPLEX software. Combined with a calculation example, the effectiveness of the comprehensive demand response model and the day-ahead optimal dispatching method proposed in this paper is verified. Results show that the multi-energy load coupling and complementary capabilities of the micro energy grid are maximized, and the economic efficiency of system operation is improved.

Key words: energy operator, integrated demand response (IDR), energy hub, micro energy network

CLC Number:

TM73

YANG Huanhong, SHI Bowen, HUANG Wentao, DING Yutao, WANG Jie, YU Wei, ZHU Ziye. Day-Ahead Optimized Operation of Micro Energy Grid Considering Integrated Demand Response[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(7): 11-19.

Figures/Tables 10

Fig.1 Energy hub model of micro energy grid

Fig.2 Function of energy operator

Fig.3 Flow chart of optimized scheduling

Fig.4 Time-of-use energy price

Table 1 Conversion rates of devices

Table 2 Cost comparison among three scenarios

Table 3 Profit comparison among three scenarios

Fig.5 Participation in response in each mode

Fig.6 Load curves of each model before and after optimization

Fig.7 Load curve before and after multi-energy optimization

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