3,564 research outputs found
A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization
In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is
proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization
problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy
with a satisfactory trade-off between exploration and exploitation capabilities was added to the
model predictive control. The proposed strategy was evaluated using a representative microgrid that
includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage
system. The achieved results demonstrate the validity of the proposed approach, outperforming
a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost.
In addition, the proposed approach also better manages the use of the energy storage system.Ministerio de Economía y Competitividad DPI2016-75294-C2-2-RUnión Europea (Programa Horizonte 2020) 76409
Robust optimization based energy dispatch in smart grids considering demand uncertainty
In this study we discuss the application of robust optimization to the problem of economic energy dispatch in smart grids. Robust optimization based MPC strategies for tackling uncertain load demands are developed. Unexpected additive disturbances are modelled by defining an affine dependence between the control inputs and the uncertain load demands.
The developed strategies were applied to a hybrid power system connected to an electrical power grid. Furthermore, to demonstrate the superiority of the standard Economic MPC over the MPC tracking, a comparison (e.g average daily cost) between the standard MPC tracking, the standard Economic MPC, and the integration of both in one-layer and two-layer approaches was carried out. The goal of this research is to design a controller based on Economic MPC
strategies, that tackles uncertainties, in order to minimise economic costs and guarantee service reliability of the system.Postprint (author's final draft
Achieving the Dispatchability of Distribution Feeders through Prosumers Data Driven Forecasting and Model Predictive Control of Electrochemical Storage
We propose and experimentally validate a control strategy to dispatch the
operation of a distribution feeder interfacing heterogeneous prosumers by using
a grid-connected battery energy storage system (BESS) as a controllable element
coupled with a minimally invasive monitoring infrastructure. It consists in a
two-stage procedure: day-ahead dispatch planning, where the feeder 5-minute
average power consumption trajectory for the next day of operation (called
\emph{dispatch plan}) is determined, and intra-day/real-time operation, where
the mismatch with respect to the \emph{dispatch plan} is corrected by applying
receding horizon model predictive control (MPC) to decide the BESS
charging/discharging profile while accounting for operational constraints. The
consumption forecast necessary to compute the \emph{dispatch plan} and the
battery model for the MPC algorithm are built by applying adaptive data driven
methodologies. The discussed control framework currently operates on a daily
basis to dispatch the operation of a 20~kV feeder of the EPFL university campus
using a 750~kW/500~kWh lithium titanate BESS.Comment: Submitted for publication, 201
Distributed MPC for coordinated energy efficiency utilization in microgrid systems
To improve the renewable energy utilization of distributed microgrid systems, this paper presents an optimal distributed model predictive control strategy to coordinate energy management among microgrid systems. In particular, through information exchange among systems, each microgrid in the network, which includes renewable generation, storage systems, and some controllable loads, can maintain its own systemwide supply and demand balance. With our mechanism, the closed-loop stability of the distributed microgrid systems can be guaranteed. In addition, we provide evaluation criteria of renewable energy utilization to validate our proposed method. Simulations show that the supply demand balance in each microgrid is achieved while, at the same time, the system operation cost is reduced, which demonstrates the effectiveness and efficiency of our proposed policy.Accepted manuscrip
Reliability of Dynamic Load Scheduling with Solar Forecast Scenarios
This paper presents and evaluates the performance of an optimal scheduling
algorithm that selects the on/off combinations and timing of a finite set of
dynamic electric loads on the basis of short term predictions of the power
delivery from a photovoltaic source. In the algorithm for optimal scheduling,
each load is modeled with a dynamic power profile that may be different for on
and off switching. Optimal scheduling is achieved by the evaluation of a
user-specified criterion function with possible power constraints. The
scheduling algorithm exploits the use of a moving finite time horizon and the
resulting finite number of scheduling combinations to achieve real-time
computation of the optimal timing and switching of loads. The moving time
horizon in the proposed optimal scheduling algorithm provides an opportunity to
use short term (time moving) predictions of solar power based on advection of
clouds detected in sky images. Advection, persistence, and perfect forecast
scenarios are used as input to the load scheduling algorithm to elucidate the
effect of forecast errors on mis-scheduling. The advection forecast creates
less events where the load demand is greater than the available solar energy,
as compared to persistence. Increasing the decision horizon leads to increasing
error and decreased efficiency of the system, measured as the amount of power
consumed by the aggregate loads normalized by total solar power. For a
standalone system with a real forecast, energy reserves are necessary to
provide the excess energy required by mis-scheduled loads. A method for battery
sizing is proposed for future work.Comment: 6 pager, 4 figures, Syscon 201
Advanced Discrete-Time Control Methods for Industrial Applications
This thesis focuses on developing advanced control methods for two industrial
systems in discrete-time aiming to enhance their performance in delivering the
control objectives as well as considering the practical aspects. The first part
addresses wind power dispatch into the electricity network using a battery
energy storage system (BESS). To manage the amount of energy sold to the
electricity market, a novel control scheme is developed based on discrete-time
model predictive control (MPC) to ensure the optimal operation of the BESS in
the presence of practical constraints. The control scheme follows a decision
policy to sell more energy at peak demand times and store it at off-peaks in
compliance with the Australian National Electricity Market rules. The
performance of the control system is assessed under different scenarios using
actual wind farm and electricity price data in simulation environment. The
second part considers the control of overhead crane systems for automatic
operation. To achieve high-speed load transportation with high-precision and
minimum load swings, a new modeling approach is developed based on independent
joint control strategy which considers actuators as the main plant. The
nonlinearities of overhead crane dynamics are treated as disturbances acting on
each actuator. The resulting model enables us to estimate the unknown
parameters of the system including coulomb friction constants. A novel load
swing control is also designed based on passivity-based control to suppress
load swings. Two discrete-time controllers are then developed based on MPC and
state feedback control to track reference trajectories along with a feedforward
control to compensate for disturbances using computed torque control and a
novel disturbance observer. The practical results on an experimental overhead
crane setup demonstrate the high performance of the designed control systems.Comment: PhD Thesis, 230 page
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