The power system restoration control has a higher uncertainty level than the preventive control of cascading failures. In order to ensure the feasibility of the decision support system of restoration control, a decisi...The power system restoration control has a higher uncertainty level than the preventive control of cascading failures. In order to ensure the feasibility of the decision support system of restoration control, a decision support framework for adaptive restoration control of transmission system is proposed, which can support the coordinated restoration of multiple partitions, coordinated restoration of units and loads, and coordination of multi-partition decision-making process and actual restoration process. The proposed framework is divided into two layers, global coordination layer and partition optimization layer. The upper layer partitions the transmission system according to the power outage scenario, constantly and dynamically adjusts the partitions during the restoration process, and optimizes the time-space decision-making of inter-partition connectivity. For each partition, the lower layer pre-selects restoration targets according to the estimated restoration income, optimizes the corresponding restoration paths, and evaluates the restoration plans according to the expected net income per unit of power consumption. During the restoration process, if the restoration operation such as energizing the outage branch fails, the current restoration plan will be adaptively switched to the sub-optimal one or re-optimized if necessary. The framework includes two operation modes, i.e., the on-line operation mode and training simulation mode, and provides an information interaction interface for collaborative restoration with related distribution systems. The effectiveness and adaptability of the proposed framework is demonstrated by simulations using the modified IEEE 118-bus system.展开更多
The urban power grid(UPG)combines transmission and distribution networks.Past studies on UPG congestion mitigation have primarily focused on relieving local congestion while ignoring large-scale energy transfer with s...The urban power grid(UPG)combines transmission and distribution networks.Past studies on UPG congestion mitigation have primarily focused on relieving local congestion while ignoring large-scale energy transfer with safety margins and load balancing.This situation is expected to worsen with the proliferation of renewable energy and electric vehicles.In this paper,a two-layer congestion mitigation framework is proposed,one which considers the congestion of the UPG with flexible topologies.In the upper-layer,the particle swarm optimization algorithm is employed to optimize the power supply distribution(PSD)of substation transformers.This is known as the upper-layer PSD.The lower-layer model recalculates the new PSD,known as the lower-layer PSD,based on the topology candidates.A candidate topology is at an optimum when the Euclidean distance mismatch between the upper-and lower-layer PSDs is the smallest.This optimum topology is tested by standard power flow to ascertain its feasibility.The optimum transitioning sequence between the initial and optimum topologies is also determined by the two-layer framework to minimize voltage deviation and line overloading of the UPG considering dynamic thermal rating.The proposed framework is tested on a 56-node test system.Results show that the proposed framework can significantly reduce congestion,maintain safety margins,and determine the optimum transitioning sequence.展开更多
基金This work was supported in part by the China State Grid Corporation project of the Key Technologies of Power Grid Proactive Support for Energy Transition (No. 5100-202040325A-0-0-00).
文摘The power system restoration control has a higher uncertainty level than the preventive control of cascading failures. In order to ensure the feasibility of the decision support system of restoration control, a decision support framework for adaptive restoration control of transmission system is proposed, which can support the coordinated restoration of multiple partitions, coordinated restoration of units and loads, and coordination of multi-partition decision-making process and actual restoration process. The proposed framework is divided into two layers, global coordination layer and partition optimization layer. The upper layer partitions the transmission system according to the power outage scenario, constantly and dynamically adjusts the partitions during the restoration process, and optimizes the time-space decision-making of inter-partition connectivity. For each partition, the lower layer pre-selects restoration targets according to the estimated restoration income, optimizes the corresponding restoration paths, and evaluates the restoration plans according to the expected net income per unit of power consumption. During the restoration process, if the restoration operation such as energizing the outage branch fails, the current restoration plan will be adaptively switched to the sub-optimal one or re-optimized if necessary. The framework includes two operation modes, i.e., the on-line operation mode and training simulation mode, and provides an information interaction interface for collaborative restoration with related distribution systems. The effectiveness and adaptability of the proposed framework is demonstrated by simulations using the modified IEEE 118-bus system.
基金supported by the Universiti Sains Malaysia,Research University Team(RUTeam)Grant Scheme(No.1001/PELECT/8580011).
文摘The urban power grid(UPG)combines transmission and distribution networks.Past studies on UPG congestion mitigation have primarily focused on relieving local congestion while ignoring large-scale energy transfer with safety margins and load balancing.This situation is expected to worsen with the proliferation of renewable energy and electric vehicles.In this paper,a two-layer congestion mitigation framework is proposed,one which considers the congestion of the UPG with flexible topologies.In the upper-layer,the particle swarm optimization algorithm is employed to optimize the power supply distribution(PSD)of substation transformers.This is known as the upper-layer PSD.The lower-layer model recalculates the new PSD,known as the lower-layer PSD,based on the topology candidates.A candidate topology is at an optimum when the Euclidean distance mismatch between the upper-and lower-layer PSDs is the smallest.This optimum topology is tested by standard power flow to ascertain its feasibility.The optimum transitioning sequence between the initial and optimum topologies is also determined by the two-layer framework to minimize voltage deviation and line overloading of the UPG considering dynamic thermal rating.The proposed framework is tested on a 56-node test system.Results show that the proposed framework can significantly reduce congestion,maintain safety margins,and determine the optimum transitioning sequence.
