Risk-constrained multi-level framework for coordination of generation and transmission expansion planning in liberalised environments – part II: method and case studies

Investment options of transmission expansion planning (TEP) involve different lead times according to their length, technology, and environmental and social impacts. TEP planners can utilize the various lead times to deal with the risk of uncertainty. This paper proposes a novel framework for TEP under an uncertain environment, which includes investment options with various lead times. A multi-stage model is developed to reflect the different lead times in the planning method. The level of demand uncertainty is represented using a relative standard deviation. Demand uncertainty in the presented multi-stage model and its influence on the optimal decision are studied. The problem is formulated as a mixed integer linear problem to which stochastic programming is applied, and the proposed framework is illustrated from case studies on a modified Garver’s six-bus system. The case studies verify the effectiveness of the framework for TEP problems with a mathematically tractable model and demonstrates that the proposed method achieves better performance than other methods when the problems involve investment candidates with various lead times under uncertain conditions.

Download Full-text

A Decentralized Solution for Transmission Expansion Planning: Getting Inspiration from Nature

Energies ◽

10.3390/en12234427 ◽

2019 ◽

Vol 12 (23) ◽

pp. 4427

Author(s):

Lumbreras ◽

Wogrin ◽

Navarro ◽

Bertazzi ◽

Pereda

Keyword(s):

Global Optimization ◽

Network Design ◽

Case Studies ◽

Road Network ◽

Optimization Techniques ◽

Food Sources ◽

Computational Time ◽

Expansion Planning ◽

Transmission Expansion Planning ◽

Transmission Expansion

Transmission expansion planning is a problem of considerable complexity where classical optimization techniques are unable to handle large case studies. Decomposition and divide-and-conquer strategies have been applied to this problem. We propose an alternative approach based on agent-based modeling (ABM) and inspired by the behavior of the Plasmodium mold, which builds efficient transportation networks as result of its search for food sources. Algorithms inspired by this mold have already been applied to road-network design. We modify an existing ABM for road-network design to include the idiosyncratic features of power systems and their related physics, and test it over an array of case studies. Our results show that the ABM can provide near-optimal designs in all the instances studied, possibly with some further interesting properties with respect to the robustness of the developed design. In addition, the model works in a decentralized manner, using mostly local information. This means that computational time will scale with size in a more benign way than global optimization approaches. Our work shows promise in applying ABMs to solve similarly complex global optimization problems in the energy landscape.

Download Full-text