Model Predictive Control Method of Bi-level Multi-objective Fuzzy Optimization for Regional Integrated Energy System

doi:10.12204/j.issn.1000-7229.2020.12.012

Abstract

Abstract:

Aimed at the characteristics that the regional integrated energy system needs to meet the requirements of power grid dispatch and the system optimization problem, a model predictive control method of bi-level multi-objective fuzzy optimization for the coordination of the regional integrated energy system and the power grid is proposed. Firstly, on the basis of the regional integrated energy "source-load-storage" characteristics model and its constraints, a bi-level multi-objective optimization model is established. The upper-level model takes the multi-objective maximum of comprehensive energy efficiency and maximum clean energy accommodation, which both are concerned by grid dispatching, as the optimization objective. The lower-level model takes the multi-objective minimum of operation and energy cost as the optimization objective. Solving the single-level model by model predictive control, the upper and lower objective function intervals are obtained. Then the intervals are blurred to establish a model predictive control method with maximum satisfaction as the new objective, so as to get an optimization result that takes both the upper and lower level into account. Finally, a simulation example is used to verify that the method in the paper is correct and effective.

Key words: regional integrated energy system, comprehensive energy efficiency, renewable energy accommodation, bi-level multi-objective optimization, model predictive control

CLC Number:

TM73

Lü Zhenhua, LI Qiang, HAN Huachun, WANG Dashuo, MA Rui. Model Predictive Control Method of Bi-level Multi-objective Fuzzy Optimization for Regional Integrated Energy System[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(12): 123-132.

Figures/Tables 12

Fig.1 Schematic diagram of the structure of a regional integrated energy system

Fig.2 Schematic diagram of rolling optimization solution for model predictive control

Fig.3 Multi-energy load demand of regional integrated energy system

Fig.4 Time-of-use time electricity price curve

Fig.5 Prediction results of wind power output in the prediction link

Fig.6 Comparison of optimal dispatch decisions of power equipment

Fig.7 Comparison of optimal dispatch decisions for natural gas equipment

Fig.8 Comparison of optimal dispatch decisions for thermal equipment

Table 1 Comparison of the output strategies of part of devices in scenarios 1 and 2

Table 2 Comparison of comprehensive energy efficiency in scenarios 1 and 2

Fig.9 Comparison of wind power output accommodation

Fig.10 Analysis of cost components of regional integrated energy system in scenario 2

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