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Chen Nanming

Bio: Chen Nanming is an academic researcher. The author has contributed to research in topics: Automatic Generation Control. The author has an hindex of 1, co-authored 1 publications receiving 133 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article, a technique that combines multipass dynamic programming (MPDP) technique with successive approximations to solve the daily hydrothermal coordination problem is presented, where the long computation time and the large storage memory requirement are reduced.
Abstract: A technique that combines multipass dynamic programming (MPDP) technique with successive approximations to solve the daily hydrothermal coordination problem is presented. The long computation time and the large storage memory requirement are reduced. Due to the characteristics of hydrothermal coordination, the resulting load-following capability is much better than that achieved by applying MPDP to area automatic generation control. The generation can follow any shape load curve instead of only constant loads and ramps as in previous applications. >

141 citations


Cited by
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Book ChapterDOI
05 Mar 2007
TL;DR: This systematic study is able to find a minimum frequency of change allowed in a problem for two dynamic EMO procedures to adequately track Pareto-optimal frontiers on-line and suggest an automatic decision-making procedure for arriving at a dynamic single optimal solution on- line.
Abstract: Most real-world optimization problems involve objectives, constraints, and parameters which constantly change with time Treating such problems as a stationary optimization problem demand the knowledge of the pattern of change a priori and even then the procedure can be computationally expensive Although dynamic consideration using evolutionary algorithms has been made for single-objective optimization problems, there has been a lukewarm interest in formulating and solving dynamic multi-objective optimization problems In this paper, we modify the commonly-used NSGA-II procedure in tracking a new Pareto-optimal front, as soon as there is a change in the problem Introduction of a few random solutions or a few mutated solutions are investigated in detail The approaches are tested and compared on a test problem and a real-world optimization of a hydro-thermal power scheduling problem This systematic study is able to find a minimum frequency of change allowed in a problem for two dynamic EMO procedures to adequately track Pareto-optimal frontiers on-line Based on these results, this paper also suggests an automatic decision-making procedure for arriving at a dynamic single optimal solution on-line

434 citations

Journal ArticleDOI
TL;DR: Numerical experiences show that the solution technique is computationally efficient, simple, and suitable for decision support of short-term hydro operations planning and can be easily extended for scheduling applications in deregulated environments.
Abstract: This paper describes experiences with mixed integer linear programming (MILP) based approaches on the short-term hydro scheduling (STHS) function. The STHS is used to determine the optimal or near-optimal schedules for the dispatchable hydro units in a hydro-dominant system for a user-definable study period at each time step while respecting all system and hydraulic constraints. The problem can be modeled in detail for a hydro system that contains both conventional and pumped-storage units. Discrete and dynamic constraints such as unit startup/shutdown and minimum-up/minimum-down time limits are also included in the model for hydro unit commitment (HUC). The STHS problem is solved with a state-of-the-art package which includes an algebraic modeling language and a MILP solver. The usefulness of the proposed solution algorithm is illustrated by testing the problem with actual hydraulic system data. Numerical experiences show that the solution technique is computationally efficient, simple, and suitable for decision support of short-term hydro operations planning. In addition, the proposed approaches can be easily extended for scheduling applications in a deregulated environment.

251 citations

Journal ArticleDOI
TL;DR: In this article, a teaching learning based optimization (TLBO) algorithm is proposed to solve short-term hydrothermal scheduling (HTS) problem considering nonlinearities like valve point loading effects of the thermal unit and prohibited discharge zone of water reservoir of the hydro plants.

190 citations

Journal ArticleDOI
TL;DR: In this article, a detailed survey on load frequency control (LFC) mechanism is presented, which explores the depth study issues related to LFC mechanism based on different sources of power system models and reveals the investigation of soft computing based optimization technique and application of ESS and HVDC-link in LFC.
Abstract: Over the past few decades, many publications have been made in the area of Load frequency control (LFC) of interconnected power systems. Load frequency control is necessary to develop better control in order to achieve less effect on the frequency and tie line power deviations after a load perturbation. However, number of control strategies has been employed in the design of load frequency controllers in order to achieve a better dynamic response and the exact choice of the LFC controller in a particular case requires sufficient expertise because each controller has its own merits and demerits. Due to this, an appropriate review of load frequency control (LFC) mechanism is essential and a few attempts have been made in this concern. This paper presents a detailed survey on load frequency control (LFC) mechanism. The overall study explores the depth study issues related to LFC mechanism based on different sources of power system models. This paper focused on different control techniques of LFC, which also includes all the recent application of FACTS devices. This review reveals the investigation of soft computing based optimization technique and application of Energy Storage System (ESS) and HVDC-link in LFC. These studies also illustrates conventional power system, deregulated of power environment as well as distributed generation and micro grids. This paper is designed in order to highlight the major traits of Load forecasting and some critical case studies on LFC.

170 citations

Journal ArticleDOI
TL;DR: The MIPSO algorithm is used to solve the optimal operating schedule of a battery energy storage system for an industrial time-of-use rate user with wind turbine generators and reaches the minimum electricity charge of TOU rates users with WTGs.
Abstract: This paper presents a new algorithm for the solution of nonlinear optimal scheduling problems. This algorithm is called ldquomultipass iteration particle swarm optimizationrdquo (MIPSO). A new index called ldquoiteration bestrdquo is incorporated into ldquoparticle swarm optimizationrdquo (PSO) to improve solution quality. The concept of multipass dynamic programming is applied to further modify the PSO to improve computation efficiency. The MIPSO algorithm is used to solve the optimal operating schedule of a battery energy storage system (BESS) for an industrial time-of-use (TOU) rate user with wind turbine generators (WTGs). The effects of wind speed uncertainty and load are considered in this paper, and the resulting optimal operating schedule of the BESS reaches the minimum electricity charge of TOU rates users with WTGs. The feasibility of the new algorithm is demonstrated by a numerical example, and MIPSO solution quality and computation efficiency are compared to those of other algorithms.

158 citations