Additions and corrections to “Terminal coalgebras in well-founded set theory”

This note is to correct certain mistaken impressions of the author's that were in the original paper, “Terminal coalgebras in well-founded set theory”, which appeared in Theoretical Computer Science 114 (1993) 299–315.

Some additions and other records new to the ornithology of South Carolina

This document contains notes on several species of birds observed by Arthur T. Wayne in South Carolina including the Sooty Shearwater, the Harlequin Duck, the Snow Goose, the Blue Goose, the Egret, the Yellow-crowned Night Heron, the Hudsonian Curlew, the Turnstone, the Pigeon Hawk, the Crested Flycatcher, the Bronzed Grackle, the Carolina Grackle, the Ipswich Sparrow, Leconte’s Sparrow, the Mountain Solitary Vireo, Bachman’s Warbler, the Magnolia Warbler, the Black-throated Green Warbler, and the Connecticut Warbler.

Dispatch of distributed Energy Resources to Provide Energy and Reserve in Smart Grids using a Particle Swarm Optimization Approach

The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized. The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation. Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.