Wind power dispatch control with battery energy storage using model predictive control

Khatamianfar, Arash; Khalid, Muhammad; Savkin, Andrey V.; Agelidis, Vassilios G.

doi:10.1109/cca.2012.6402350

Cited by 13 publications

(8 citation statements)

References 17 publications

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“…Receding horizon control (RHC) is used for control. In RHC the optimal series of control inputs is calculated for a window of fixed size into the future, based on the estimated future state of the plant and inputs [2], [7], [10], [11], [12]. Linear optimization methods can be extended or modified in order to size the ES [13], [14].…”

Section: Optimization-based Sizingmentioning

confidence: 99%

Sizing and economic assessment of energy Storage with real-time pricing and ancillary services

Barnes

Balda

2013

2013 4th IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG)

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Energy storage units (ESU) can generate profit through providing multiple services. In order to maximize profit while meeting battery lifetime constraints, it is necessary to select the capacity and charge/discharge schedule of the battery. This paper proposes a methodology to calculate the necessary ESU battery capacity for a given power rating and the lifetime profitability of the ESU. Because the price of electricity is not known more than 24 hours in advance, forecasting is necessary when scheduling the ESU. The reduction in profitability of the ESU because of forecasting errors must be taken into account. The methodology applies ridge regression for price forecasting to overcome the problem of having many highly correlated inputs, which lead to poor performance of the forecaster. The methodology demonstrates the encouraging result that the profitability of an ESU is not highly sensitive to forecaster error, as the scheduler is insensitive to the types of errors introduced by the forecaster. This is because of the nature of the variance in electricity market price.

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Section: Optimization-based Sizingmentioning

confidence: 99%

Sizing and economic assessment of energy Storage with real-time pricing and ancillary services

Barnes

Balda

2013

2013 4th IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG)

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“…Technology advancement of wind energy generation, coupled with government policy support, has led to significant increase of wind generation capacity in many countries [1]. By the end of 2012, a total of 76GW wind energy and 5GW solar power had been installed in China.…”

Section: Introductionmentioning

confidence: 99%

“…Many approaches have been proposed to drive the RES with BESS [1,[10][11][12][13]. One simple control scheme is to charge and discharge the BESS when the wind generation is beyond a minimum or maximum threshold [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…One simple control scheme is to charge and discharge the BESS when the wind generation is beyond a minimum or maximum threshold [14,15]. Another widely Model Predictive Control for Energy Storage Systems in a Network with High Penetration of Renewable Energy and Limited Export Capacity studied method is the model predictive control (MPC) to fulfill different control objectives, mainly peak-shaving [1,13,16,17]. In [1,16], the MPC was applied to control the BESS to compensate the intermittency of the wind power and to manage the amount of energy sold to the electricity market.…”

Section: Introductionmentioning

confidence: 99%

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Model predictive control for energy storage systems in a network with high penetration of renewable energy and limited export capacity

Zeng

Zhang

et al. 2014

2014 Power Systems Computation Conference

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This paper considers a novel control strategy for energy storage systems in networks with high penetration of renewable power and limited network capacity based on the combination of model predictive control (MPC) and hierarchical optimization. The objective is to maximize the output, hence the income, from the renewable generation using appropriate charging and discharging control strategy for energy storage systems based on the prediction of renewable power output, demand and network capability in future time horizon. The battery energy storage system can be used to smooth out the variations in renewable energy such as, wind power, and maximize renewable power output whilst meeting the system constraints. Furthermore network interconnection capacity with other systems must be honored. Network interconnection capability depends on many factors including demand and flexible/inflexible generation within the network and also the external systems. In this paper, we show how this problem can be formulated as an optimization problem, leading directly to the design of a model predictive controller. In this scheme, the optimal control for energy storage systems is implemented in a receding time horizon. The method is applied as a case study to the modified IEEE-30 bus test system and northwest power grid of China. Index Terms-model predictive control; renewable energy source; battery storage energy system;

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“…The variability and intermittence of wind power cause fluctuation of the voltage and frequency of the grid, affecting the safety of the power grid [3,4]. Energy storage systems have been verified to be an effective method for overcoming these deficiencies [5][6][7]. The system that coordinates a wind farm with energy storage systems can be dispatched into power grids, which is similar to that of a conventional energy plant [8].…”

Section: Introductionmentioning

confidence: 99%

Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control

et al. 2015

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Abstract:Energy storage devices are expected to be more frequently implemented in wind farms in near future. In this paper, both pumped hydro and fly wheel storage systems are used to assist a wind farm to smooth the power fluctuations. Due to the significant difference in the response speeds of the two storages types, the wind farm coordination with two types of energy storage is a problem. This paper presents two methods for the coordination problem: a two-level hierarchical model predictive control (MPC) method and a single-level MPC method. In the single-level MPC method, only one MPC controller coordinates the wind farm and the two storage systems to follow the grid scheduling. Alternatively, in the two-level MPC method, two MPC controllers are used to coordinate the wind farm and the two storage systems. The structure of two level MPC consists of outer level and inner level MPC. They run alternatively to perform real-time scheduling and then stop, thus obtaining long-term scheduling results and sending some results to the inner level as input. The single-level MPC method performs both long-and short-term scheduling tasks in each interval. The simulation results show that the methods proposed can improve the utilization of wind power and reduce wind power spillage. In addition, the single-level MPC and the two-level MPC are not interchangeable. The single-level MPC has the advantage of following the grid schedule while the two-level MPC can reduce the optimization time by 60%.

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Wind power dispatch control with battery energy storage using model predictive control

Cited by 13 publications

References 17 publications

Sizing and economic assessment of energy Storage with real-time pricing and ancillary services

Sizing and economic assessment of energy Storage with real-time pricing and ancillary services

Model predictive control for energy storage systems in a network with high penetration of renewable energy and limited export capacity

Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control

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