A hierarchical genetic algorithm and mixed-integer linear programming-based stochastic optimization of the configuration of integrated trigeneration energy systems

Facing the growing pressure of climate change and environmental protection, integrated energy systems (IESs), which comprise different energy sources, have become promising candidates for future energy systems. However, the capacity configuration of each source remains challenging due to the various...

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Vydáno v:Clean technologies and environmental policy Ročník 23; číslo 6; s. 1913 - 1927
Hlavní autoři: Zhang, Yisong, Jiang, Jingjing, Zhang, Xian, Sun, Li
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2021
Springer Nature B.V
Témata:
Algorithms
Candidates
Climate change
Configurations
Cooling
Cost reduction
Costs
Couplings
Earth and Environmental Science
Energy
Energy sources
Environment
Environmental Economics
Environmental Engineering/Biotechnology
Environmental policy
Environmental protection
Genetic algorithms
Genetics
Heat storage
Heating
Hierarchies
Industrial and Production Engineering
Industrial Chemistry/Chemical Engineering
Integer programming
Integrated energy systems
Linear programming
Mathematical analysis
Mixed integer
Monte Carlo simulation
Operating costs
Optimization
Original Paper
Randomness
Renewable resources
Resource availability
Sampling
Storage
Sustainable Development
Stochastic optimization
Genetic algorithm
IES
Economic evaluation
MILP problem
ISSN:1618-954X, 1618-9558
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Shrnutí:Facing the growing pressure of climate change and environmental protection, integrated energy systems (IESs), which comprise different energy sources, have become promising candidates for future energy systems. However, the capacity configuration of each source remains challenging due to the various couplings, randomness of renewables and numerical optimization difficulty. In this paper, a hierarchical optimization framework is proposed to determine the component capacities of trigeneration IESs, i.e., systems involving combined cooling, heating and power (CCHP) generation. The potential variation in the demand and renewable resource availability are considered stochastic factors and captured as scenarios generated according to a probability function. In the first level, with the component capacities and scenarios defined, a mixed-integer linear programming (MILP) problem is formulated to minimize the total system cost. Then, in the second level, the Monte Carlo method is applied to calculate the expectation by feeding different scenarios into the MILP and sampling the minimal costs. Finally, as the second level returns the expected value of the system cost considering the given component capacities, a genetic algorithm is adopted in the third level to search the optimal component capacities. Compared to the conventional deterministic optimization method, the proposed stochastic optimization method reduces the annual operational cost while allowing a wider operational range. In addition, it is revealed that the inclusion of heat storage and grid connections yields notable benefits in terms of IES cost reduction. Graphic abstract
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ISSN:1618-954X
1618-9558
DOI:10.1007/s10098-021-02088-x