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Day-ahead unit commitment (UC) solution methods seek to determine the status and output of all available generators. In a world with an increasing integration of renewable energy sources such as wind electric generators (WEG), the UC solution process must model and include WEGs too in the decision-making process. Power output of WEGs in a day-ahead decision-making process are usually modeled using a short-term probabilistic forecast. Inclusion of WEGs introduces uncertainty in the solution that may be quantified through an imbalance risk function. Thus a UC solution method should seek a solution to minimize cost and risk in schedule. Two strategies are proposed in this paper to minimize costs and handle risks introduced due to WEGs. These formulations are posed as fuzzy optimization models and are solved using the mixed integer linear programming (MILP) technique. The proposed strategies are tested on a 26- and a 100-generator systems with associated transmission networks that have three and six wind generators each. The results with two strategies of handling WEGs are discussed. Index Terms--Power systems, unit commitment, wind energy.

A probabilistic method for a pool-based combined energy and reserve market, which considers both generators and interruptible loads (ILs), is proposed in this paper. We minimize generation production cost together with the cost associated with expected energy not supplied (EENS) due to outages of generating units and failures of ILs to supply the reserve when required. The proposed settlement scheme allocates equitably cost of reserve among generators and ILs. Given that the method takes into account reliability of individual generators and ILs, the proposed scheme provides incentives for all market participants to improve their performance. A number of case studies are carried out to illustrate the impact of participant's characteristics on the reserve cost allocation. Index Terms--Expected energy not supplied (EENS), interruptible load, probabilistic reserve, system risk.

A probabilistic reserve scheme with iterated linear programming is proposed to maintain a uniform system reliability level in each dispatch period. Besides, a new zero-sum settlement scheme is introduced. It maintains a net zero sum of payment (loss) and compensation (gain) among the online generators by considering the expected energy not served (EENS) or reserve responsibility share of each unit. Solution results of the IEEE 26-unit system have revealed that the combined probabilistic reserve and zero-sum settlement scheme gives the Gencos stimuli to enhance their performance. Index Terms--EENS, probabilistic reserve, system reliability level, zero-sum.

This paper presents a new method for optimal reconfiguration of radial distribution systems (RDS). Optimal reconfiguration involves selection of the best set of branches to be opened, one each from each loop, such that the resulting RDS has the desired performance. Amongst the several performance criteria considered for optimal network reconfiguration, maximizing loadability is an important one. Owing to the discrete nature of the solution space, a fuzzy adaptation of the evolutionary programming algorithm for optimal reconfiguration of RDS to maximize loadability is proposed in this paper. This method maximizes a fuzzy index developed using a maximum loadability index. A 33-bus RDS is optimally reconfigured by the proposed method and the results are presented. Index Terms--Power distribution, power distribution control, power flow control.

Two new ordering methods that can be used to reduce the elements in the inverse factors of a sparse matrix are proposed. Compared with all other commonly used ordering methods, the new methods will produce less fill-in elements. The proposed methods are based on the diagonalization of A via the use of a transformation matrix, C. A new node sequence for the power network and all the elements of the C matrix are generated in only a single stage instead of the conventional LDU decomposition followed by a series of multiplications for W-matrix. The methods may be used for the parallel solution of sparse matrix equations. Test results show that the proposed methods can reduce the computation burden effectively.

Effective control of spinning reserve can provide substantial cost reductions in large power systems. This paper suggests that this goal can be achieved by tightly integrating a probabilistic reserve assessment with the unit commitment (UC) function. This enables the reliability of individual units and the uncertainty of the load forecast to be considered for short-term scheduling. The generation can then be scheduled to meet a given risk index. It is further argued that the optimal value of this risk index should be selected on the basis of a tradeoff between the total schedule cost obtained from the UC solution and the expected cost of energy not served which can be derived from the probabilistic reserve assessment. Finally, the problem of over-commitment of reserve in Lagrangian Relaxation based UC is reduced by means of a new unit decommitment technique incorporating a look-ahead feature. Keywords--Unit commitment, probabilistic reserve assessment, Lagrangian Relaxation, value of lost load, cost/benefit analysis.