Camille Hamon

Experience and Challenges With Short-Term Balancing in European Systems With Large Share of Wind Power

The amount of wind power in the world is quickly increasing. The background for this development is improved technology, decreased costs for the units, and increased concern regarding environmental problems of competing technologies such as fossil fuels. Some areas are starting to experience very high penetration levels of wind and there have been many instances when wind power has exceeded 50% of the electrical energy production in some balancing areas. The aims of this paper are to show the increased need for balancing, caused by wind power in the minutes to hourly time scale, and to show how this balancing has been performed in some systems when the wind share was higher than 50%. Experience has shown that this is possible, but that there are some challenges that have to be solved as the amount of wind power increases.

A Stochastic Optimal Power Flow Problem With Stability Constraints—Part II: The Optimization Problem

Stochastic optimal power flow can provide the system operator with adequate strategies for controlling the power flow to maintain secure operation under stochastic parameter variations. One limitation of stochastic optimal power flow has been that only limits on line flows have been used as stability constraints. In many systems voltage stability and small-signal stability also play an important role in constraining the operation. In this paper we aim to extend the stochastic optimal power flow problem to include constraints for voltage stability as well as small-signal stability. This is done by approximating the voltage stability and small-signal stability constraint surfaces with second-order approximations in parameter space. Then we refine methods from mathematical finance to be able to estimate the probability of violating the constraints. In this, the second part of the paper, we look at how Cornish-Fisher expansion combined with a method of excluding sets that are counted twice, can be used to estimate the probability of violating the stability constraints. We then show in a numerical example how this leads to an efficient solution method for the stochastic optimal power flow problem.

Doubly-fed induction generator modeling and control in DigSilent PowerFactory

Several computer programs exist to carry out dynamic simulations and this study will focus on one of them, namely DigSilent PowerFactory. It offers two built-in models of doubly-fed induction generator. A new model has also been developed, based upon a controllable voltage source. These three models are compared, in terms of dynamic behavior and simulation time. One of them is then used to study the impact of an input control signal based on the single machine equivalent method. This signal provides power oscillation damping.

Closure of “applying stochastic optimal power flow to power systems with large amounts of wind power and detailed stability limits”

Increasing wind power penetration levels bring about new challenges for power systems operation and planning, because wind power forecast errors increase the uncertainty faced by the different actors. One specific problem is generation re-dispatch during the operation period, a problem in which the system operator seeks the cheapest way of re-dispatching generators while maintaining an acceptable level of system security. Stochastic optimal power flows are re-dispatch algorithms which account for the uncertainty in the optimization problem itself. In this article, an existing stochastic optimal power flow (SOPF) formulation is extended to include the case of non-Gaussian distributed forecast errors. This is an important case when considering wind power, since it has been shown that wind power forecast errors are in general not normally distributed. Approximations are necessary for solving this SOPF formulation. The method is illustrated in a small power system in which the accuracy of these approximations is also assessed for different probability distributions of the load and wind power.

The value of using chance-constrained optimal power flows for generation re-dispatch under uncertainty with detailed security constraints

The uncertainty faced in the operation of power systems increases as larger amounts of intermittent sources, such as wind and solar power, are being installed. Traditionally, an optimal generation re-dispatch is obtained by solving security-constrained optimal power flows (SCOPF). The resulting system operation is then optimal for given values of the uncertain parameters. New methods have been developed to consider the uncertainty directly in the generation re-dispatch optimization problem. Chance-constrained optimal power flows (CCOPF) are such methods. In this paper, SCOPF and CCOPF are compared and the benefits of using CCOPF for power systems operation under uncertainty are discussed. The discussion is illustrated by a case study in the IEEE 39 bus system, in which the generation re-dispatch obtained by CCOPF is shown to always be cheaper than that obtained by SCOPF.

Review paper on wind power impact on operation of reserves

This paper reviews studies concerning new challenges for European transmission system operators (TSOs) when operating primary, secondary and tertiary reserves in a system with large amounts of wind power. The review adopts three perspectives. First, the impact on existing markets is discussed and it is shown that need for additional reserve requirements does not necessarily mean need for new reserve capacity. Secondly, possible designs of improved load-frequency control schemes are presented. The proposed solutions exhibit a trend towards market-based procurement mechanisms and automation of reserve operations. Finally, participation of wind power in load-frequency control is examined. Technical designs are presented for participation in primary control.

On improving data and models on corrective control failures for use in probabilistic reliability management

One of the most pressing concerns in the investigation of new probabilistic reliability criteria pertains to the data required as input to the evolving probabilistic models. This paper discusses an area of failure data collection that has been overlooked in power system reliability studies: corrective control actions. Background information is provided on the need for evolution of data collection systems in this context. Further, steps that can be taken to build a database of parameters necessary for modelling corrective actions are provided, to be useful in assessing system behaviour from a probabilistic reliability management perspective. Modelling of corrective control actions using event trees is illustrated. Throughout, the various challenges foreseen in the building of corresponding databases and models are outlined.

Stochastic optimal power flow problem with stability constraints

Summary form only given. Stochastic optimal power flow can provide the system operator with adequate strategies for controlling the power flow to maintain secure operation under stochastic parameter variations. One limitation of stochastic optimal power flow has been that only limits on line flows have been used as stability constraints. In many systems voltage stability and small-signal stability also play an important role in constraining the operation. In this article we aim to extend the stochastic optimal power flow problem to include constraints for voltage stability as well as small-signal stability. This is done by approximating the voltage stability and small-signal stability constraint boundaries with second order approximations in parameter space. Then we refine methods from mathematical finance to be able to estimate the probability of violating the constraints. In the first part of the article, we derive second-order approximations of stability boundaries in parameter space. In the second part of the article, we look at how Cornish-Fisher expansion combined with a method of excluding sets that are counted twice, can be used to estimate the probability of violating the stability constraints. We then show in a numerical example how this leads to an efficient solution method for the stochastic optimal power flow problem.