Abstract
An integrated energy system (IES) involving a large number of decision-makers causes problems of bad coordination between energy sub-networks and the IES and it is not able to fully consider the multi-energy complementarity among multiple decision-makers. In this context, firstly, this paper constructs an energy optimal dispatching model of an IES based on uncertain bilevel programming. The upper model takes the transformation matrix of energy hubs as the upper decision-maker, taking the minimum operation cost of the IES in the form of confidence as the objective function; the lower model takes each optimal operation plan of the electric power sub-network, the thermal energy sub-network, and the gas energy sub-network as the lower decision-makers, aiming at the operation economy of each sub-network and considering their operation as necessary constraints. Secondly, a firefly algorithm with chaotic search and an improved light intensity coefficient is designed to improve the proposed model. An empirical analysis was conducted on a pilot area of an integrated energy system in Hebei Province. The results show the following: (1) The typical daily operating cost of the integrated energy system in winter is lower than that in summer; (2) under the same load level, the typical winter and summer running costs of the integrated energy system are lower than that of the traditional microgrid; (3) compared with the particle swarm optimization algorithm, the improved firefly algorithm proposed in the paper has obvious advantages both in terms of running cost and solution time; and (4) when the confidence of the objective function and the constraints increases, the operating cost of various schemes also increase. The above results validate the effectiveness of the energy optimal dispatching model of the IES and the economy of the system operation under the multiple decision-maker hierarchy.
Highlights
In the energy optimal dispatching model of the integrated energy system (IES) based on uncertain bilevel programming, random chance constraints are used for the objective function of the upper model, because the smaller the system operation cost, the better
Combining the basic structure of an IES, an energy optimization model of an IES based on uncertain bilevel programming was constructed in this paper
In order to verify the validity of our model, a pilot area in Hebei Province was selected as an IES example
Summary
With the rapid development of the national economy, the energy consumption system mainly based on fossil energy has brought about increasingly serious environmental issues [1]. Traditional energy systems subordinate to different departments for management and operation, such as cooling, heat, and power, cannot play a role in multi-energy coordination and optimization, which reduce the Energies 2020, 13, 477; doi:10.3390/en13020477 www.mdpi.com/journal/energies. Energies 2020, 13, 477 overall efficiency of energy utilization and the consumption of renewable energy [2,3]. With a series of advantages of improving energy efficiency, promoting renewable energy utilization, and improving energy supply security, an integrated energy system (IES) can complement, coordinate, and optimize different energy supply systems in the planning, design, construction, and operation stages of an energy system, so as to realize energy cascade utilization and collaborative optimization [6,7]. An in-depth study of the IES energy optimal dispatching problem is critical
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