Dynamic voltage support with the rector SVC in California’s San Joaquin Valley

Maximizing Firm Wind Connection to Security Constrained Transmission Networks

“…Other important network operational characteristics such as steady-state and dynamic voltage behavior, short-circuit levels as well as transient stability are more suited to detailed in-depth analysis [20], may not be fully amenable to a manageable optimization process across such a range of power flow conditions, and are typically assessed once the grid is known to be thermally secure. The algorithm outlined in this paper can be viewed as an optimistic total wind capacity allocation from which to carry out such analyses, with due consideration of advanced technology solutions [21]. If desired, spare line capacity can be set aside for related reactive power flows or known system stability constraints.…”

Optimal firm wind capacity allocation to power systems with security constraints

“…Important network operational issues involving steady-state and dynamic voltage concerns, shortcircuit levels as well as transient stability behaviour are more suited to detailed in-depth analysis, may not be fully amenable to a manageable optimization process across such a range of many operating points, and are often assessed only once the grid is known to be thermally secure. The algorithm outlined in this paper could be viewed as the best possible wind capacity allocation from which to carry out such analyses, considering all possible solutions [13]. The DC load flow model is linear, and thus linear constraints can be formulated to represent network security criteria in the optimization stage.…”

“…In contrast this paper uses the product of the 'rank' (i.e. its ordered position with respect to the other constraint coefficients for that optimization variable (a term unrelated to linear algebra matrix rank)) of each constraint coefficient to determine the ordered position of the data-point p as in (13). This is a superior preprocessing scheme as coefficient ranks are a more natural measure of each data-point's dominance in the overall dataset.…”

“…rank order (13) Constraints are swept through the total constraint set in order of this rank product, creating an efficient preprocessing scheme with no associated simplification error. The constraints with the highest coefficient rank products will quickly remove a large number of redundant constraints, but progress will slow down as constraints with lower overall rank product are tested for dominance.…”