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Register a new userAn Ecological Robustness-Oriented Approach for Power System Network Expansion
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Electric power grids are critical infrastructure that support modern society by supplying electric energy to critical infrastructure systems. Incidents are increasing that range from natural disasters to cyber attacks. These incidents threaten the reliability of power systems and create disturbances that affect the whole society. While existing standards and technologies are being applied to proactively improve power system reliability and resilience, there are still widespread electricity outages that cause billions of dollars in economic loss annually and threaten societal function and safety. Improving resilience in preparation for such events warrants strategic network design to harden the system. This paper presents an approach to strengthen power system security and reliability against disturbances by expanding the network structure from an ecosystems perspective. Ecosystems have survived a wide range of disturbances over a long time period, and an ecosystems robust structure has been identified as the key element for its survivability. In this paper, we first present a study of the correlation of ecological robustness and power system structures. Then, we present a mixed-integer nonlinear programming problem (MINLP) that expands the transmission network structure to maximize ecological robustness with power system constraints for an improved ability to absorb disturbances. We solve the MINLP problem for the IEEE 24 Bus Reliability Test System and three synthetic power grids with 200-, 500- and 2000-buses, respectively. Our evaluation results show the optimized power systems have increased the networks robustness, more equally distributed power flows, and less violations under different levels of contingencies.
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This paper develops a grey-box approach to small-signal stability analysis of complex power systems that facilitates root-cause tracing without requiring disclosure of the full details of the internal control structure of apparatus connected to the system. The grey-box enables participation analysis in impedance models, which is popular in power electronics and increasingly accepted in power systems for stability analysis. The Impedance participation factor is proposed and defined in terms of the residue of the whole-system admittance matrix. It is proved that, the so defined impedance participation factor equals the sensitivity of the whole-system eigenvalue with respect to apparatus impedance. The classic state participation factor is related to the impedance participation factor via a chain-rule. Based on the chain-rule, a three-layer grey-box approach, with three degrees of transparency, is proposed for root-cause tracing to different depths, i.e. apparatus, states, and parameters, according to the available information. The association of impedance participation factor with eigenvalue sensitivity points to the re-tuning that would stabilize the system. The impedance participation factor can be measured in the field or calculated from the black-box impedance spectra with little prior knowledge required.
This paper presents a network hardware-in-the-loop (HIL) simulation system for modeling large-scale power systems. Researchers have developed many HIL test systems for power systems in recent years. Those test systems can model both microsecond-level dynamic responses of power electronic systems and millisecond-level transients of transmission and distribution grids. By integrating individual HIL test systems into a network of HIL test systems, we can create large-scale power grid digital twins with flexible structures at required modeling resolution that fits for a wide range of system operating conditions. This will not only significantly reduce the need for field tests when developing new technologies but also greatly shorten the model development cycle. In this paper, we present a networked OPAL-RT based HIL test system for developing transmission-distribution coordinative Volt-VAR regulation technologies as an example to illustrate system setups, communication requirements among different HIL simulation systems, and system connection mechanisms. Impacts of communication delays, information exchange cycles, and computing delays are illustrated. Simulation results show that the performance of a networked HIL test system is satisfactory.
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)
The existence of multiple solutions to AC optimal power flow (ACOPF) problems has been noted for decades. Existing solvers are generally successful in finding local solutions, which satisfy first and second order optimality conditions, but may not be globally optimal. In this paper, we propose a simple iterative approach to improve the quality of solutions to ACOPF problems. First, we call an existing solver for the ACOPF problem. From the solution and the associated dual variables, we form a partial Lagrangian. Then we optimize this partial Lagrangian and use its solution as a warm start to call the solver again for the ACOPF problem. By repeating this process, we can iteratively improve the solution quality, moving from local solutions to global ones. We show the effectiveness of our algorithm on standard IEEE networks. The simulation results show that our algorithm can escape from local solutions to achieve global optimums within a few iterations.
This paper explores whether graph embedding methods can be used as a tool for analysing the robustness of power-grids within the framework of network science. The paper focuses on the strain elevation tension spring embedding (SETSe) algorithm and compares it to node2vec and Deep Graph Infomax, and the measures mean edge capacity and line load. These five methods are tested on how well they can predict the collapse point of the giant component of a network under random attack. The analysis uses seven power-grid networks, ranging from 14 to 2000 nodes. In total, 3456 load profiles are created for each network by loading the edges of the network to have a range of tolerances and concentrating network capacity into fewer edges. One hundred random attack sequences are generated for each load profile, and the mean number of attacks required for the giant component to collapse for each profile is recorded. The relationship between the embedding values for each load profile and the mean collapse point is then compared across all five methods. It is found that only SETSe and line load perform well as proxies for robustness with $R^2 = 0.89$ for both measures. When tested on a time series normal operating conditions line load performs exceptionally well ($R=0.99$). However, the SETSe algorithm provides valuable qualitative insight into the state of the power-grid by leveraging its method local smoothing and global weighting of node features to provide an interpretable geographical embedding. This paper shows that graph representation algorithms can be used to analyse network properties such as robustness to cascading failure attacks, even when the network is embedded at node level.
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