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Register a new userA Spanning Tree-based Genetic Algorithm for Distribution Network Reconfiguration
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This paper presents a spanning tree-based genetic algorithm (GA) for the reconfiguration of electrical distribution systems with the objective of minimizing active power losses. Due to low voltage levels at distribution systems, power losses are high and sensitive to system configuration. Therefore, optimal reconfiguration is an important factor in the operation of distribution systems to minimize active power losses. Smart and automated electric distribution systems are able to reconfigure as a response to changes in load levels to minimize active power losses. The proposed method searches spanning trees of potential configurations and finds the optimal spanning tree using a genetic algorithm in two steps. In the first step, all invalid combinations of branches and tie-lines (i.e., switching combinations that do not provide power to some of loads or violate the radiality and connectivity conditions) generated by initial population of GA are filtered out with the help of spanning-tree search algorithm. In the second step, power flow analyses are performed only for combinations that form spanning trees. The optimal configuration is then determined based on the amount of active power losses (optimal configuration is the one that results in minimum power losses). The proposed method is implemented on several systems including the well-known 33-node and 69-node systems. The results show that the proposed method is accurate and efficient in comparison with existing methods.
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Radiality constraints are involved in both distribution system restoration and reconfiguration problems. However, a set of widely used radiality constraints, i.e., the spanning tree (ST) constraints, has its limitations which have not been well recognized. In this letter, the limitation of the ST constraints is analyzed and an effective set of constraints, referred to as the single-commodity flow constraints, is presented. Furthermore, a combined set of constraints is proposed and case studies indicate that the combined constraints can gain computational efficiency in the reconfiguration problem. Recommendations on the use of radiality constraints are also provided.
Massive adoptions of combined heat and power (CHP) units necessitate the coordinated operation of power system and district heating system (DHS). Exploiting the reconfigurable property of district heating networks (DHNs) provides a cost-effective solution to enhance the flexibility of the power system by redistributing heat loads in DHS. In this paper, a unit commitment considering combined electricity and reconfigurable heating network (UC-CERHN) is proposed to coordinate the day-ahead scheduling of power system and DHS. The DHS is formulated as a nonlinear and mixed-integer model with considering the reconfigurable DHN. Also, an auxiliary energy flow variable is introduced in the formed DHS model to make the commitment problem tractable, where the computational burdens are significantly reduced. Extensive case studies are presented to validate the effectiveness of the approximated model and illustrate the potential benefits of the proposed method with respect to congestion management and wind power accommodation. (Corresponding author:Hongbin Sun)
This paper provides an optimized cable path planning solution for a tree-topology network in an irregular 2D manifold in a 3D Euclidean space, with an application to the planning of submarine cable networks. Our solution method is based on total cost minimization, where the individual cable costs are assumed to be linear to the length of the corresponding submarine cables subject to latency constraints between pairs of nodes. These latency constraints limit the cable length and number of hops between any pair of nodes. Our method combines the Fast Marching Method (FMM) and a new Integer Linear Programming (ILP) formulation for Minimum Spanning Tree (MST) where there are constraints between pairs of nodes. We note that this problem of MST with constraints is NP-complete. Nevertheless, we demonstrate that ILP running time is adequate for the great majority of existing cable systems. For cable systems for which ILP is not able to find the optimal solution within an acceptable time, we propose an alternative heuristic algorithm based on Prims algorithm. In addition, we apply our FMM/ILP-based algorithm to a real-world cable path planning example and demonstrate that it can effectively find an MST with latency constraints between pairs of nodes.
When providing bulk power system services, a third-party aggregator could inadvertently cause operational issues at the distribution level. We propose a coordination architecture in which an aggregator and distribution operator coordinate to avoid distribution network constraint violations, while preserving private information. The aggregator controls thermostatic loads to provide frequency regulation, while the distribution operator overrides the aggregators control actions when necessary to ensure safe network operation. Using this architecture, we propose two control strategies, which differ in terms of measurement and communication requirements, as well as model complexity and scalability. The first uses an aggregate model and blocking controller, while the second uses individual load models and a mode-count controller. Both outperform a benchmark strategy in terms of tracking accuracy. Furthermore, the second strategy performs better than the first, with only 0.10% average RMS error (compared to 0.70%). The second is also able to maintain safe operation of the distribution network while overriding less than 1% of the aggregators control actions (compared to approximately 15% by the first strategy). However, the second strategy has significantly more measurement, communication, and computational requirements, and therefore would be more complex and expensive to implement than the first strategy.
Given an undirected, weighted graph, the minimum spanning tree (MST) is a tree that connects all of the vertices of the graph with minimum sum of edge weights. In real world applications, network designers often seek to quickly find a replacement edge for each edge in the MST. For example, when a traffic accident closes a road in a transportation network, or a line goes down in a communication network, the replacement edge may reconnect the MST at lowest cost. In the paper, we consider the case of finding the lowest cost replacement edge for each edge of the MST. A previous algorithm by Tarjan takes $O(m alpha(m, n))$ time, where $alpha(m, n)$ is the inverse Ackermanns function. Given the MST and sorted non-tree edges, our algorithm is the first that runs in $O(m+n)$ time and $O(m+n)$ space to find all replacement edges. Moreover, it is easy to implement and our experimental study demonstrates fast performance on several types of graphs. Additionally, since the most vital edge is the tree edge whose removal causes the highest cost, our algorithm finds it in linear time.
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