2,178 research outputs found
Power Management for Energy Systems
The thesis deals with control methods for flexible and efficient power consumption in commercial refrigeration systems that possess thermal storage capabilities, and for facilitation of more environmental sustainable power production technologies such as wind power. We apply economic model predictive control as the overriding control strategy and present novel studies on suitable modeling and problem formulations for the industrial applications, means to handle uncertainty in the control problems, and dedicated optimization routines to solve the problems involved. Along the way, we present careful numerical simulations with simple case studies as well as validated models in realistic scenarios. The thesis consists of a summary report and a collection of 13 research papers written during the period Marts 2010 to February 2013. Four are published in international peer-reviewed scientific journals and 9 are published at international peer-reviewed scientific conferences
MPC for Wind Power Gradients - Utilizing Forecasts, Rotor Inertia, and Central Energy Storage
We consider the control of a wind power plant, possibly consisting of many individual wind turbines. The goal is to maximize the energy delivered to the power grid under very strict grid requirements to power quality. We define an extremely low power output gradient and demonstrate how decentralized energy storage in the turbines ’ inertia combined with a central storage unit or deferrable consumers can be utilized to achieve this goal at a minimum cost. We propose a variation on model predictive control to incorporate predictions of wind speed. Due to the aerodynamics of the turbines the model contains nonconvex terms. To handle this nonconvexity, we propose a sequential convex optimization method, which typically converges in fewer than 10 iterations. We demonstrate our method in simulations with various wind scenarios and prices for energy storage. These simulations show substantial improvements in terms of limiting the power ramp rates (disturbance rejection) at the cost of very little power. This capability is critical to help balance and stabilize the future power grid with a large penetration of intermittent renewable energy sources
An Economic NMPC Formulation for Wind Turbine Control
Model Predictive Control (MPC) is a strong candidate for the control of large Multi-MegaWatt Wind Turbine Generators. Several MPC and some Nonlinear MPC scheme have been proposed in the literature, based on reference-tracking objective functions. While the resulting schemes offer very promising results, the difficulty of tuning a reference-tracking NMPC scheme for performance is likely to be a hindrance to the industrial success of NMPC-based WTG control. Because they directly maximize the system performance, economic NMPC schemes are more straightforward to tune. Economic NMPC schemes present, however, some known difficulties that are a serious obstacle to their real-time deployment. This paper presents an economic NMPC formulation for maximizing the generated power of wind turbine generators, which does not suffer from such difficulties. The relationship between the proposed and more classical reference-tracking approaches is formally established
Convex Model Predictive Control for Down-regulation Strategies in Wind Turbines
Wind turbine (WT) controllers are often geared towards maximum power
extraction, while suitable operating constraints should be guaranteed such that
WT components are protected from failures. Control strategies can be also
devised to reduce the generated power, for instance to track a power reference
provided by the grid operator. They are called down-regulation strategies and
allow to balance power generation and grid loads, as well as to provide
ancillary grid services, such as frequency regulation. Although this balance is
limited by the wind availability and grid demand, the quality of wind energy
can be improved by introducing down-regulation strategies that make use of the
kinetic energy of the turbine dynamics. This paper shows how the kinetic energy
in the rotating components of turbines can be used as an additional
degree-of-freedom by different down-regulation strategies. In particular we
explore the power tracking problem based on convex model predictive control
(MPC) at a single wind turbine. The use of MPC allows us to introduce a further
constraint that guarantees flow stability and avoids stall conditions.
Simulation results are used to illustrate the performance of the developed
down-regulation strategies. Notably, by maximizing rotor speeds, and thus
kinetic energy, the turbine can still temporarily guarantee tracking of a given
power reference even when occasional saturation of the available wind power
occurs. In the study case we proved that our approach can guarantee power
tracking in saturated conditions for 10 times longer than with traditional
down-regulation strategies.Comment: 6 pages, 2 figures, 61st IEEE Conference on Decision and Control 202
Two-Stage Consensus-Based Distributed MPC for Interconnected Microgrids
In this paper, we propose a model predictive control based two-stage energy
management system that aims at increasing the renewable infeed in
interconnected microgrids (MGs). In particular, the proposed approach ensures
that each MG in the network benefits from power exchange. In the first stage,
the optimal islanded operational cost of each MG is obtained. In the second
stage, the power exchange is determined such that the operational cost of each
MG is below the optimal islanded cost from the first stage. In this stage, a
distributed augmented Lagrangian method is used to solve the optimisation
problem and determine the power flow of the network without requiring a central
entity. This algorithm has faster convergence and same information exchange at
each iteration as the dual decomposition algorithm. The properties of the
algorithm are illustrated in a numerical case study
Risk-Averse Model Predictive Operation Control of Islanded Microgrids
In this paper we present a risk-averse model predictive control (MPC) scheme
for the operation of islanded microgrids with very high share of renewable
energy sources. The proposed scheme mitigates the effect of errors in the
determination of the probability distribution of renewable infeed and load.
This allows to use less complex and less accurate forecasting methods and to
formulate low-dimensional scenario-based optimisation problems which are
suitable for control applications. Additionally, the designer may trade
performance for safety by interpolating between the conventional stochastic and
worst-case MPC formulations. The presented risk-averse MPC problem is
formulated as a mixed-integer quadratically-constrained quadratic problem and
its favourable characteristics are demonstrated in a case study. This includes
a sensitivity analysis that illustrates the robustness to load and renewable
power prediction errors
Model predictive controllers for reduction of mechanical fatigue in wind farms
We consider the problem of dispatching WindFarm (WF) power demand to
individual Wind Turbines (WT) with the goal of minimizing mechanical stresses.
We assume wind is strong enough to let each WTs to produce the required power
and propose different closed-loop Model Predictive Control (MPC) dispatching
algorithms. Similarly to existing approaches based on MPC, our methods do not
require changes in WT hardware but only software changes in the SCADA system of
the WF. However, differently from previous MPC schemes, we augment the model of
a WT with an ARMA predictor of the wind turbulence, which reduces uncertainty
in wind predictions over the MPC control horizon. This allows us to develop
both stochastic and deterministic MPC algorithms. In order to compare different
MPC schemes and demonstrate improvements with respect to classic open-loop
schedulers, we performed simulations using the SimWindFarm toolbox for MatLab.
We demonstrate that MPC controllers allow to achieve reduction of stresses even
in the case of large installations such as the 100-WTs Thanet offshore WF
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