‘To be or not to be modern’ that is the question for automatic generation control (point of view of a utility engineer)

Carpentier, J.

doi:10.1016/0142-0615(85)90013-4

Cited by 53 publications

(19 citation statements)

References 21 publications

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“…The first articles concerning LFC were published in the late 60's [5][6][7] and the original, classical solution applied in practice was, at the same time, the first practical application of the great dynamic system control. The concept applied for this control has been so reliable that practice has absolutely approved it [8,9]. At the level of a power system's interconnection it is presented by the regulation of decentralized type (if any), but at the individual control area (CA) level it is a centralized regulation (control) directed towards individual regulating units outputs.…”

Section: Lfc Basicsmentioning

confidence: 99%

An efficient approach for the load‐frequency control and its role under the conditions of deregulated environment

Stojkovic

2006

Int Trans Elec Energy Syst

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SUMMARYThis paper describes an specific approach to resolving problem of load frequency (LF) control in the power system of Montenegro ( Serbia and Montenegro). The complete LF controller was developed, coded, and implemented on the single PC computer 'from the zero' by the Company's staff in a fast, feasible, and cost-effective manner. Its extremely high 'in-field' efficiency was achieved by: the compact design of the controller, the implementation of the algorithm for efficient filtering of Control Error, the implementation of the algorithm for minimizing of the regulating units movement, as well as the feed-back algorithm for permanent adjustment of the tie-lines power measurements. All details on these algorithms are given. The official field results for the 6 month exploitation have confirmed the top regulating success in the Second UCTE Synchronous Zone. The Electric Power Company of Montenegro (EPCG) was recently subjected to enacting of the new Energy Law, and to the promoting of the free electricity market. So, the additional modifications were made at the AGC controller to meet the requirements of deregulation processes. Thus, the AGC system keeps track of the momentary active power imbalance, detects it, corrects it and communicates an adequate amount of the balance energy service (BES) power, on the 15-minute basis, to the real-time part of the market operating system (MOS), where it will be allocated to the real time market (RTM) balancing resources.

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Section: Lfc Basicsmentioning

confidence: 99%

An efficient approach for the load‐frequency control and its role under the conditions of deregulated environment

Stojkovic

2006

Int Trans Elec Energy Syst

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“…or optimal power flow techniques. The relevant capabilities and desirable properties of these modern proposals are seen as high (summarized from [11]):…”

Section: Motivation For New Methodsmentioning

confidence: 99%

“…not fragile, by avoiding data complexity, and, probably most important, being area-wise decentralized. With all potentials lying in modern control techniques involving optimal control and optimal power flow: one should never forget the engineer's perspective, particularly regarding the objectives, the constraints and, especially, the robustness of the control system [11].…”

Section: Motivation For New Methodsmentioning

confidence: 99%

Model Predictive Frequency Control employing stability constraints

Trabert

Ulbig

Andersson

2015

2015 American Control Conference (ACC)

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In this article a model predictive control (MPC) based frequency control scheme for energy storage units was derived, focusing on the incorporation of stability constraints based on Lyapunov theory and the concept of passivity.The proposed control schemes, guaranteeing closed-loop stability, are applied on a one-area and two-area power system. For the two-area power system, a coordinated (centralized) control and an uncoordinated (decentralized) control approach is conducted.The stability properties of the different MPC setups were derived, implemented and simulated. Furthermore the corresponding control performance was analyzed. Index TermsMPC, Nonlinear MPC, Stability, Control Lyapunov Functions, Passivity, Constraints, Power Systems. I. INTRODUCTION A. MotivationTraditionally, power system operation has essentially been based on the assumption, that electricity is reliably and steadily produced by large power plants, which are fully dispatchable, i.e. controllable and have a high frequency inertia. However, a strong trend towards generation of electrical energy by renewable energy sources (RES), i.e. PV units and wind turbines with no or decoupled rotating masses, exists 1 . How power systems could be adapted in order to accommodate for increasing shares of uncontrolled fluctuating RES as well as power market activities is a highly relevant and still open research question. Options to deal with these challenges might be the expansion of transmission capacities, the extensive integration of storage capacities as well as a better exploitation of the inherent flexibility within the power system. Model predictive control (MPC) as an optimal control scheme for regulating grid frequency receives rising attention due to rapidly growing shares of variable RES and thereby arising challenges for power system operation. The choice of MPC as a control approach is especially motivated by its ability to incorporate operational constraints of power systems, which cannot be handled by conventional P/PI-controllers. It enables the provision of frequency control using a generic power system storage unit, e.g. a battery, with given operational constraints, such as the power ramp rate, power rating and storage capacity. Additionally, the recently growing interest in using MPC for control purposes of power systems emerges due to the availability of faster and cheaper computing resources, as a significant computational effort comes with the use of such a receding horizon control scheme.

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“…Several studies have explored the cost of providing AGC and tertiary reserve incurred by the generating agents in order to develop bidding strategies under a deregulated framework [5][6][7][8][9][10]. In addition, the design and optimization of the AGC area regulator, which calculates generating unit setpoints has been widely addressed in order to maximize the profit obtained from providing the service [11][12][13][14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%