With the increase of uncertain and intermittent renewable energy supply on the grid, the power system has become more vulnerable to instability. In this paper, we develop a demand response strategy to improve power system small-signal stability. We pose the problem as an optimization problem wherein the total demand-responsive load is held constant at each time instance but shifted between different buses to improve small-signal stability, which is measured by small-signal stability metrics that are functions of subsets of the system’s eigenvalues, such as the smallest damping ratio. To solve the problem, we use iterative linear programming and generalized eigenvalue sensitivities. We demonstrate the approach via a case study that uses the IEEE 14-bus system. Our results show that shifting the load between buses, can improve a small-signal stability margin. We explore the use of models of different fidelity and find that it is important to include models of the automatic voltage regulators and power system stabilizers. In addition, we show that load shifting can achieve similar improvements to generation shifting and better improvement than simply tuning power system stabilizers.
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Towards Scalable Voltage Control in Smart Grid: A Submodular Optimization Approach
Voltage instability occurs when a power system is unable
to meet reactive power demand at one or more buses.
Voltage instability events have caused several major outages
and promise to become more frequent due to increasing
energy demand. The future smart grid may
help to ensure voltage stability by enabling rapid detection
of possible voltage instability and implementation
of corrective action. These corrective actions will only
be effective in restoring stability if they are chosen in a
timely, scalable manner. Current techniques for selecting
control actions, however, rely on exhaustive search,
and hence may choose an inefficient control strategy. In
this paper, we propose a submodular optimization approach
to designing a control strategy to prevent voltage
instability at one or more buses. Our key insight is
that the deviation from the desired voltage is a supermodular
function of the set of reactive power injections
that are employed, leading to computationally efficient
control algorithms with provable optimality guarantees.
Furthermore, we show that the optimality bound of our
approach can be improved from 1/3 to 1/2 when the
power system operates under heavy loading conditions.
We demonstrate our framework through extensive simulation
study on the IEEE 30 bus test case.
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- Award ID(s):
- 1544173
- NSF-PAR ID:
- 10017840
- Date Published:
- Journal Name:
- 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Voltage collapse is a type of blackout-inducing dynamic instability that occurs when the power demand exceeds the maximum power that can be transferred through the network. The traditional (preventive) approach to avoid voltage collapse is based on ensuring that the network never reaches its maximum capacity. However, such an approach leads to inefficiencies as it prevents operators to fully utilize the network resources and does not account for unprescribed events. To overcome this limitation, this paper seeks to initiate the study of voltage collapse stabilization. More precisely, for a DC star network, we formulate the problem of voltage stability as a dynamic problem where each load seeks to achieve a constant power consumption by updating its conductance as the voltage changes. We show that such a system can be interpreted as a game, where each player (load) seeks to myopically maximize their utility using a gradient-based response. Using this framework, we show that voltage collapse is the unique Nash Equilibrium of the induced game and is caused by the lack of cooperation between loads. Finally, we propose a Voltage Collapse Stabilizer (VCS) controller that uses (flexible) loads that are willing to cooperate and provides a fair allocation of the curtailed demand. Our solution stabilizes voltage collapse even in the presence of non-cooperative loads. Numerical simulations validate several features of our controllers.more » « less
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null (Ed.)Contingency Constrained Optimal Power Flow (CCOPF) differs from traditional Optimal Power Flow (OPF) because its generation dispatch is planned to work with state variables between constraint limits, considering a specific contingency. When it is not desired to have changes in the power dispatch after the contingency occurs, the CCOPF is studied with a preventive perspective, whereas when the contingency occurs and the power dispatch needs to change to operate the system between limits in the post-contingency state, the problem is studied with a corrective perspective. As current power system software tools mainly focus on the traditional OPF problem, having the means to solve CCOPF will benefit power systems planning and operation. This paper presents a Quadratically Constrained Quadratic Programming (QCQP) formulation built within the matpower environment as a solution strategy to the preventive CCOPF. Moreover, an extended OPF model that forces the network to meet all constraints under contingency is proposed as a strategy to find the power dispatch solution for the corrective CCOPF. Validation is made on the IEEE 14-bus test system including photovoltaic generation in one simulation case. It was found that in the QCQP formulation, the power dispatch calculated barely differs in both pre- and post-contingency scenarios while in the OPF extended power network, node voltage values in both pre- and post-contingency scenarios are equal in spite of having different power dispatch for each scenario. This suggests that both the QCQP and the extended OPF formulations proposed, could be implemented in power system software tools in order to solve CCOPF problems from a preventive or corrective perspective.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICCPS.2016.7479120
