Abstract
The interdependency of electric and natural gas systems is becoming stronger. The challenge of how to meet various energy demands in an integrated energy system (IES) with minimal cost has drawn considerable attention. The optimal scheduling of IESs is an ideal method to solve this problem. In this study, a day-ahead optimal scheduling model for IES that included an electrical system, a natural gas system, and an energy hub (EH), was established. The proposed EH contained detailed models of the fuel cell (FC) and power to gas (P2G) system. Considering that the optimal scheduling of an IES is a non-convex complex optimal problem, a piecewise self-adaptive particle swarm optimization (PCAPSO) algorithm based on multistage chaotic mapping was proposed to solve it. The objective was to minimize the operating cost of the IES. Three operation scenarios were designed to analyze the operation characteristics of the system under different coupling conditions. The simulation results showed that the PCAPSO algorithm improved the convergence rate and stability compared to the original PSO. An analysis of the results demonstrated the economics of an IES with the proposed EHs and the advantage of cooperation between the FC and P2G system.
Highlights
An integrated energy system (IES) can couple various forms of energy, such as electricity and natural gas, to meet the demands of users for multiple energy sources
Based on the above discussion, we developed a piecewise self-adaptive particle swarm optimization (PCAPSO) algorithm based on chaotic mapping, which updates the inertia weight factor utilizing the random numbers generated by different types of chaotic map
The results show that the fuel cell (FC) is capable of peak load shifting if it cooperates with the hydrogen storage tank (HST), and its operation cost can be offset by selling heat
Summary
An integrated energy system (IES) can couple various forms of energy, such as electricity and natural gas, to meet the demands of users for multiple energy sources. A literature review shows that the research on EHs mainly focuses on the elaboration and enrichment of the model, which can improve the energy utilization rate of the IES and reduce its operation cost. The above work indicates that the modification of the PSO algorithm or its combination with other algorithms can feasibly solve the OEF problem of IESs. Little of the above literature jointly considered detailed models of the FC and P2G system, and none of it mentioned the effect of their operation on the energy flow distribution, renewable energy consumption, and economics of IESs. In the literature, the deeply modified PSO algorithm and the mathematical optimization algorithm are improved in efficiency; their complexity or computation effort is increased .
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