Federico Milano

Multiobjective optimization for pricing system security in electricity markets

This paper proposes a novel technique for representing system security in the operations of decentralized electricity markets, with special emphasis on voltage stability. An interior point method is used to solve the optimal power flow problem with a multiobjective function for maximizing both social benefit and the distance to maximum loading conditions. A six-bus system with both supply and demand-side bidding is used to illustrate the proposed technique for both elastic and inelastic demand, and a 129-bus test system that models the Italian HV transmission network is used for testing the practical applicability of the proposed method. The results obtained show that the proposed technique is able to improve system security while yielding better market conditions through increased transaction levels and improved locational marginal prices throughout the system.

Securing Transient Stability Using Time-Domain Simulations Within an Optimal Power Flow

This paper provides a methodology to restore transient stability. It relies on a well-behaved optimal power flow model with embedded transient stability constraints. The proposed methodology can be used for both dispatching and redispatching. In addition to power flow constraints and limits, the resulting optimal power flow model includes discrete time equations describing the time evolution of all machines in the system. Transient stability constraints are formulated by reducing the initial multi-machine model to a one-machine infinite-bus equivalent. This equivalent allows imposing angle bounds that ensure transient stability. The proposed optimal power flow model is tested and analyzed using an illustrative nine-bus system, the well-known New England 39-bus system, a ten-machine system, and a real-world 1228-bus system with 292 synchronous machines.

Locational marginal price sensitivities

Within an optimal power flow market clearing framework, this paper provides expressions to compute the sensitivities of locational marginal prices with respect to power demands. Sensitivities with respect to other parameters can also be obtained. An example and a case study are used to illustrate the expressions derived.

Optimal Network Placement of SVC Devices

This paper addresses the optimal placement of static Var compensators (SVCs) in a transmission network in such a manner that its loading margin is maximized. A multi scenario framework that includes contingencies is considered. This problem is formulated as a nonlinear programming problem that includes binary decisions, i.e., variables to decide the actual placement of the SVCs. Given the mixed-integer nonconvex nature of this problem, a Benders decomposition technique within a restart framework is used. Detailed numerical simulations on realistic electric energy systems demonstrate the appropriate behavior of the proposed technique. Conclusions are duly drawn.

Linear performance indices to predict oscillatory stability problems in power systems

Various indices are proposed and studied to detect and predict oscillatory instabilities associated with Hopf bifurcations (HBs) in power systems. A methodology is also presented to produce a linear profile for these indices. They are based on eigenvalue and singular values of the state and extended system matrices. Their application to several test power systems is presented to demonstrate their usefulness, particularly for online applications.

Continuous Newton's Method for Power Flow Analysis

This paper describes the application of the continuous Newtons method to the power flow problem. This method basically consists in formulating the power flow problem as a set of autonomous ordinary differential equations. Based on this formal analogy, we propose an entire family of numerically efficient algorithms for solving ill-conditioned or badly-initialized power flow cases. The paper also shows that the classical Newton-Raphsons method and most robust power flow techniques proposed in the literature are particular cases of the proposed formulation. An example based on a 1254-bus model of the UCTE system is presented and discussed.

Impact of Time Delays on Power System Stability

The paper describes the impact of time-delays on small-signal angle stability of power systems. With this aim, the paper presents a power system model based on delay differential algebraic equations (DDAE) and describes a general technique for computing the spectrum of DDAE. The paper focuses in particular on delays due to the terminal voltage measurements and transducers of automatic voltage regulators and power system stabilizers of synchronous machines. The proposed technique is applied to a benchmark system, namely the IEEE 14-bus test system, as well as to a real-world system. Time domain simulations are also presented to confirm the results of the DDAE spectral analysis.

Sensitivity-based security-constrained OPF market clearing model

Summary form only given. This paper proposes a novel technique for representing system security constraints that properly include voltage stability limits in the operation of competitive electricity markets. The market clearing algorithm is modeled as a voltage stability constrained optimal power flow (OPF) problem, while the distance to the closest critical power flow solution is represented by means of a loading parameter and evaluated using a continuation power flow (CPF) technique. Sensitivities obtained at the OPF step are used to estimate power directions for the CPF method, while the CPF analysis provides the loading parameter to be used in the OPF problem based on an N-1 contingency criterion. The OPF and the CPF steps are repeated until the maximum loading parameter is found, thus providing optimal solutions considering both proper market conditions and security margins. Two benchmark systems with both supply and demand bidding are used to illustrate and test the proposed technique

A python-based software tool for power system analysis

This paper presents a power system analysis tool, called DOME, entirely based on Python scripting language as well as on public domain efficient C and Fortran libraries. The objects of the paper are twofold. First, the paper discusses the features that makes the Python language an adequate tool for research, massive numerical simulations and education. Then the paper describes the architecture of the developed software tool and provides a variety of examples to show the advanced features and the performance of the developed tool.

A Systematic Method to Model Power Systems as Stochastic Differential Algebraic Equations

This paper proposes a systematic and general approach to model power systems as continuous stochastic differential-algebraic equations. With this aim, the paper provides a theoretical background on stochastic differential-algebraic equations and justifies the need for stochastic models in power system analysis. Then, the paper describes a general procedure to define stochastic dynamic models. Practical issues related to the numerical integration of the resulting power system model are also discussed. A case study illustrating the proposed approach is provided based on the IEEE 145-bus 50-machine system. The case study also illustrates and compares the reliability of the results obtained using stochastic and conventional probabilistic models.