With the fast-paced changing technologies in the power industry, new power references addressing new technologies are coming to the market. So there is an urgent need to keep track of international experiences and activities taking place in the field of modern unit-commitment (UC) problem. This paper gives a bibliographical survey, mathematical formulations, and general backgrounds of research and developments in the field of UC problem for past 35 years based on more than 150 published articles. The collected literature has been divided into many sections, so that new researchers do not face any difficulty in carrying out research in the area of next-generation UC problem under both the regulated and deregulated power industry.

Unit commitment-a bibliographical survey

This paper addresses the bidding problem faced by a virtual power plant (VPP) in a joint market of energy and spinning reserve service. The proposed bidding strategy is a non-equilibrium model based on the deterministic price-based unit commitment (PBUC) which takes the supply-demand balancing constraint and security constraints of VPP itself into account. The presented model creates a single operating profile from a composite of the parameters characterizing each distributed energy resources (DER), which is a component of VPP, and incorporates network constraints into its description of the capabilities of the portfolio. The presented model is a nonlinear mixed-integer programming with inter-temporal constraints and solved by genetic algorithm (GA).

Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part I: Problem Formulation

Electric power systems have experienced continuous growth in all the three major sectors of the power system namely, generation, transmission and distribution. Electricity cannot be stored economically, but there has to be continuous balance between demand and supply. The increase in load sizes and operational complexity such as generation allocation, non-utility generation planning, and pricing brought about by the widespread interconnection of transmission systems and inter-utility power transaction contracts, has introduced major difficulties into the operation of power system. Allocation of customers' load demands among the available thermal power generating units in an economic, secure and reliable way has been a subject of interest since 1920 or even earlier. However practically, the generating units have non-convex input-output characteristics due to prohibited operating zones, valve-point loadings and multi-fuel effects considered as heavy equality and inequality constraints, which cannot be directly solved by mathematical programming methods. Dynamic programming can treat such types of problems, but it suffers from the curse of dimensionality. Over the past decade, many prominent methods have been developed to solve these problems, such as the hierarchical numerical methods, tabu search, neural network approaches, genetic algorithm, evolutionary programming, swarm optimisation, differential evolution and hybrid search methods. Review of evolutionary method has been presented.

Power System Optimization

An induction motor is the most frequently used electric machine in high-performance drive applications. Control schemes of such drives require an exact knowledge of at least some of the induction motor parameters. Any mismatch between the parameter values used within the controller and actual parameter values in the motor leads to a deterioration in the drive performance. Numerous methods for induction machine on-line and off-line parameter estimation have been developed exclusively for application in high-performance drives. This paper aims at providing a review of the major techniques used for the induction motor parameter estimation. The paper is illustrated throughout with experimental and simulation examples related to various parameter estimation techniques.

A review of RFO induction motor parameter estimation techniques

A new simulated annealing (SA) algorithm combined with a dynamic economic dispatch method has been developed for solving the short-term unit commitment (UC) problem. SA is used for the scheduling of the generating units, while a dynamic economic dispatch method is applied incorporating the ramp rate constraints in the solution of the UC problem. New rules concerning the tuning of the control parameters of the SA algorithm are proposed. Three alternative mechanisms for generating feasible trial solutions in the neighborhood of the current one, contributing to the reduction of the required CPU time, are also presented. The ramp rates are taken into account by performing either a backward or a forward sequence of conventional economic dispatches with modified limits on the generating units. The proposed algorithm is considerably fast and provides feasible near-optimal solutions. Numerical simulations have proved the effectiveness of the proposed algorithm in solving large UC problems within a reasonable execution time.

Unit commitment by an enhanced simulated annealing algorithm

The authors presents a novel evolutionary programming (EP) based algorithm for the short-term hydrothermal scheduling problem. To more realistically represent the relationship between the generation and amount of water discharge for hydroaggregates, the generation models of the hydro plants as well as thermal plants are often expressed as nonlinear and nonsmooth curves with prohibited areas. The advantage of the proposed algorithm is that it is capable of determining the global or near global optimal solutions to such an optimisation problem with multiple local minima. The developed algorithm is illustrated and tested on two model systems. The test results are compared with those obtained using gradient search, simulated annealing and genetic algorithm methods. Numerical results show that the proposed EP-based algorithm can provide accurate solutions within a reasonable time.

Ching Lien Huang

Papers

Scheduling short-term hydrothermal generation using evolutionary programming techniques

Solving the unit commitment problem with a genetic algorithm through a constraint satisfaction technique

Power frequency harmonic measurement using integer periodic extension method

Sensorless speed identification of vector—controlled induction drives via neural network based estimation

Hybrid filter application for power quality improvement