Cogeneration units use heat and electricity as the main operation mode in the heating season. It suffers the lack of peak shaving ability, resulting in intense wind curtailment. This study presents a coordinated scheduling strategy of heat storage thermoelectric units considering the uncertainty of wind power. The developed method performs a wind power data measurement and process system based on edge computing and deep learning to acquire operation data quickly and independently and adopts a wind power prediction method based on interval scenario. Furthermore, the coordinated control model of the thermal storage unit is established after the analysis of the characteristics of coordinated scheduling strategy of heat storage thermoelectric units based on wind heat complementation, and then the improved particle swarm optimization (PSO) is adopted to the solving process. The proposed method is verified by applying it to two typical thermal power units.

A weighted double-loop network can be modeled by a directed graph G(n; h/sub 1/, h/sub 2/; w/sub 1/, w/sub 2/) with vertex set Z/sub n/={0, 1, ..., n-1} and edge set E=E/sub 1//spl cup/E/sub 2/, where E/sub 1/={(u, u+h/sub 1/)|u/spl isin/Z/sub n/}, E/sub 2/={(u, u+h/sub 2/)|u/spl isin/Z/sub n/}. Assume that the weight of each edge in E/sub 1/ is w/sub 1/ and the weight of each edge in E/sub 2/ is w/sub 2/. In this paper, we present an optimal routing algorithm on double-loop networks under the case where there is at most one faulty element. Our algorithm is based on the fact that the shortest path from a vertex to any other vertex in a double-loop network is in the L-shape region.

Consider the problem of transporting a set of objects between the vertices of a simple graph by a vehicle that traverses the edges of the graph. The problem of finding a shortest tour for the vehicle to transport all objects from their initial vertices to their destination vertices is called the vehicle routing problem. The problem is multiple capacity if the vehicle can handle more than one objects at a time. The problem is preemptive if objects can be unloaded at the intermediate vertices. In this paper, we present an O(kn+n2) time algorithm for multiple capacity preemptive vehicle routing problem on paths, where k is the number of objects to be moved and n is the number of vertices in the path.

Consider the problem of finding a minimum cost tour to transport a set of objects between the vertices of a tree by a vehicle that travels along the edges of the tree. The vehicle can carry only one object at a time, and it starts and finishes at the same vertex of the tree. It is shown that if objects can be dropped at intermediate vertices along its route and picked up later, then the problem can be solved in polynomial time. Two efficient algorithms are presented for this problem. The first algorithm runs in $O(k + qn)$ time, where n is the number of vertices in the tree, k is the number of objects to be moved, and $q \leq \min \{ k,n \}$ is the number of nontrivial connected components in a related directed graph. The second algorithm runs in $O(k + n\log n)$ time.

The problem of transporting a set of objects between the vertices of a tree by a unit capacity vehicle that travels along the edges of the tree is considered. For the case in which any object can be dropped at intermediate vertices along its route and picked up later, the authors present algorithms for finding a minimum cost transportation that run in O(k+qn) time and in O(k+n log n) time, where n is the number of vertices in the tree, k is the number of objects to be moved, and q>