Valuation of Flexible Transmission Investment Options Under Uncertainty

Abstract

Abstract--Significant uncertainty surrounds the future development of electricity systems, primarily in terms of size, location and type of new renewable generation to be connected. In this paper we assess the potential for flexible network technologies, such as phase-shifting transformers, and nonnetwork solutions, such as energy storage and demand-side management, to constitute valuable interim measures within a long-term planning strategy. The benefit of such flexible assets lies not only in the transmission services provided but also in the way they can facilitate and de-risk subsequent decisions by deferring commitment to capital-intensive projects until more information on generation development becomes available. A novel stochastic formulation for transmission expansion planning is presented that includes consideration of investment in these flexible solutions. The proposed framework is demonstrated with a case study on the IEEE-RTS where flexible technologies are shown to constitute valuable investment options when facing uncertainties in future renewable generation development.

Stochastic optimisation-based valuation of smart grid options under firm DG contracts

“…In addition, the inadequacy of deterministic approaches is shown to incorporate smart technologies in their investment plans and their preference for immediate investments in conventional assets. Note that Heuristic rules based on deterministic decisions, such as identifying common elements across scenarios, also underperform; this fact that has been demonstrated in the context of congestion-driven transmission investment in [20].…”

“…Line thermal limitation is defined in (16) while (17) sets the limits on voltage magnitudes for all buses, except for the substation which is fixed to a pre-determined value V set (18). Constraints (19) and (20) model the CVC deployment, after which the substation voltage is no longer fixed, but can be controlled to take values in the domain [V min cvc , V max cvc ]. In more detail, the voltage magnitude at the substation is defined in (18) as follows: when the CVC scheme has not been deployed (̃=0) a fixed voltage-target policy is assumed, where , ,1 = , = , , = 0 according to (18)(19)(20).…”

“…Constraints (19) and (20) model the CVC deployment, after which the substation voltage is no longer fixed, but can be controlled to take values in the domain [V min cvc , V max cvc ]. In more detail, the voltage magnitude at the substation is defined in (18) as follows: when the CVC scheme has not been deployed (̃=0) a fixed voltage-target policy is assumed, where , ,1 = , = , , = 0 according to (18)(19)(20). If a CVC scheme has been deployed (̃=1), then the substation voltage target no longer follows a fixed voltage-target policy.…”

“…The CVC technology can measure the actual voltage values at all buses in the network, enabling the optimal regulation of the substation voltage by ±5% of the nominal voltage setpoint (set at 1.01 pu). This technology is modelled by (18), (19), and (20) in the previous section. The SOP technology allows optimal control of active power flow through its two terminals; 95% efficiency and 50 kW capacity are used in this case study.…”

Option value of Soft Open Points in distribution networks

“…In reality, however, a very large number of competing strategic opportunities can arise in distribution system design due to the planner's ability to dynamically adjust his strategy according to the uncertainty evolution across a wide range of investment technologies, locations and timings [9]. To this end, the use of an optimization framework is essential for the identification of the optimal investment strategy across all candidate solutions [10].…”