This paper proposes a multi-objective phasor measurement units (PMUs) placement method in electric transmission grids. Further consideration is devoted to the early PMU placement formulations, to simultaneously determine minimum number of PMUs, as well as maximum measurement redundancy. Moreover, a new methodology is presented for valuation of observability under contingencies, including line outages and loss of PMUs. Furthermore, a generalized observability function is introduced to allocate the PMUs in presence of conventional non-synchronous measurements. The resultant optimization problem is solved using Cellular Learning Automata (CLA), introducing new CLA local rules to improve the optimization process. The developed method is conducted on IEEE standard test systems as well as the Iranian 230- and 400-kV transmission grids, followed by a discussion on results.

This paper introduces a multi-objective fault indicator (FI) placement method in electric distribution systems. The prevalent FI placement problem formulation is extended by considering effects of existing protection and control devices on customers' restoration time. Moreover, the customers' average restoration time index (CARTI) is proposed, as a new technical objective function with respect to uncertainties of automatic switching. Furthermore, a multi-objective solution approach is developed to simultaneously minimize indispensable economic and technical objectives. The resultant optimization problem is solved through a multi-objective particle swarm optimization (MOPSO) based algorithm, accompanied by a fuzzy decision making method to select the best result among the obtained Pareto optimal set of solutions. Assuming SAIDI and CARTI as technical objectives, the proposed method is applied to bus number four of the Roy Billinton test system (RBTS4), as well as a real-life distribution network with about 5500 customers, followed by a discussion on results.

Demand forecasting plays an important role as a decision support tool in power system management, especially in smart grid and liberalized power market. In this paper, a demand forecasting method is presented by using support vector regression (SVR). The proposed method is applied to practical hourly data of the Greater Tehran Electricity Distribution Company. The SVR parameters are selected by using a grid optimization process and an investigation on different kernel functions. Moreover, correlation analysis is used to find exogenous variables. Acceptable accuracy of load prediction is shown by comparing the result of SVR model to that of the artificial neural networks and the actual data, concluding that the method is applicable to day-ahead spot pricing of electricity in the liberalized power market. Copyright © 2013 John Wiley & Sons, Ltd.

Maintenance management, as a key part of the asset management practices, plays a vital role in enhancing the reliability of the electricity distribution systems (EDS) where realizing a highly reliable EDS is being attributed higher and higher criticality in modern society. In this paper, a new approach is proposed to improve the reliability of EDS through optimal scheduling of preventive maintenance (PM) tasks and allocation of automatic switches. The suggested objective is to minimize a combined customer-based (SAIDI and SAIFI) and cost-based reliability indices. The total reliability cost (TRC) includes those associated with the corrective maintenance (CM) actions, PM tasks, and automatic switch investments. The proposed approach is implemented in three different scenarios: (1) switch placement, (2) PM tasks scheduling and budget management, as well as (3) a joint switch and PM tasks decision-making. The aforementioned scenarios are applied on a standard reliability test system (RBTS4) followed by multiple sensitivity analysis to further demonstrate the efficacy and performance of the proposed framework.

This paper presents a new solution approach to determine the optimal number and locations of both manual and automatic switches, with respect to probabilities of all feasible control sequences under contingencies. Furthermore, both transformer and bus failure rates are incorporated in the proposed problem formulation to reach more practical results. An artificial bee colony based algorithm is also introduced to solve the optimization problem. Besides, prevalent cost function formulation of switch placement problem is extended by incorporating operation probabilities of switches and the affiliated infrastructures. Bus number four of the Roy Billinton test system is employed to illustrate the effects of the proposed approach on distribution networks reliability. Moreover, performance of the developed algorithm is studied in several scenarios, and the obtained results are compared with those of previous methods. Detailed numerical results and comparisons presented in the paper show that the proposed solution approach could noticeably improve the obtained results with low computational burden; thus, it can be used as an effective tool for joint automatic and manual switch placement within practical distribution networks. Copyright © 2014 John Wiley & Sons, Ltd.