Journal
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
Volume 12, Issue 2, Pages 920-930Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2020.3025831
Keywords
Biomass; Transportation; Planning; Biological system modeling; Natural gas; Power systems; Investment; Biomass energy; expansion planning of integrated energy system; electric power distribution; stochastic programming
Categories
Funding
- National Natural Science Foundation of China [51722701]
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The paper proposes an integrated expansion planning model to address biomass availability challenges for centralized biogas plants. The model makes investment decisions on biogas production, delivery networks, and biomass availability. By using multi-stage stochastic planning and polyhedral approximation methods, the problem is transformed into a more tractable form.
Centralized biogas plant (CBP) provides an attractive solution to the energy supply for district heating, electric loads, and residential cooking in remote areas via a local biogas delivery network. To overcome the challenges of biomass availability for CBPs, an integrated expansion planning model is proposed in this paper. The model makes investment decisions on the sequential planning of integrated electric power distribution and biogas delivery networks and uses the constrained biomass transportation network to optimally determine CBP locations in the integrated network. The proposed approach also considers the biomass availability and the cost of biomass supply in the integrated model. The proposed multistage stochastic planning problem makes investment decisions considering the gradual realization of uncertainties as loads grow and biomass is made available. The polyhedral approximation method is applied to convert the proposed mixed-integer second-order cone programming (MISOCP) problem to a mixed-integer linear programming (MILP) problem for making the problem more tractable for large-scale cases. The effectiveness of the proposed integrated model is validated using an 8-node and a 24-node test system.
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