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This paper presents a methodology for optimal location of capacitors in medium voltage distribution systems, considering uncertainty in demand. This uncertainty is simulated using a probabilistic load flow, which was implemented based on the chain's method, used to solve the operational problem in the Montecarlo simulation. As a method of optimizing a modified version of genetic algorithm, called Chu Beasley, is used. In order to enhance the optimization method, sensitivity factors that establish a relationship between reactive power and system losses are used. To verify the validity of the methodology, a test system from the literature is used, which concluded that the stochastic load flow determines a mean value of demand, with similar results to those obtained with versions that use deterministic power flow. [ABSTRACT FROM AUTHOR]

In order to solve the problem of distribution system planning considering demand uncertainty, this work proposes a methodology for its solution using a Chu-Beasley Genetic Algorithm. To involve uncertainty in the methodology a Montecarlo simulation method is employed, where demand is modeled as a normal distribution function. In the mathematical model of the problem are considered costs of new elements (branches and substations), upgrading of existing substations and branches, and operating costs related to technical losses of the system. The set of constraints consists of the nodal balance equations, maximum allowable capacity of the system elements, maximum allowable voltage drop and radiality of the network. The methodology is applied to a test system from the literature, in which the results obtained show the validity of the methodology, providing to state of the art in this area. [ABSTRACT FROM AUTHOR]

To solve the problem of secondary distribution systems planning, this paper proposes a methodology using a tabu search algorithm as a solution technique. The problem is formulated as a nonlinear mixed-integer model, which takes into account the location and capacity of new elements (distribution transformers and primary-secondary distribution networks), relocation of existing distribution transformers, increasing the capacity of existing elements, secondary network reconfiguration, and phase balance. It also considers the costs associated with connections between primary and secondary networks and energy losses in distribution transformers. The methodology is applied to two test cases: in the first, a comparative analysis with the Chu-Beasley genetic algorithm is made to verify the efficiency of the proposed methodology; and in the second, the results are analyzed in a Colombian distribution system. In both cases, the results are of high quality, supporting the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for optimal relocation of transformers in distribution systems. The problem is formulated as an optimization model of multiobjective non-linear integer type, which considers investment and operating costs, and the benefit of the concept of assets to be recognized by usage charges as stipulated in Resolution CREG 097 of 2008. The model is solved using the multiobjective optimization algorithm NSGA II. To check the validity of the methodology, a real distribution system belonging to an electric utility in Colombia is used, which takes into account both single and three phase distribution transformers. The results compared with respect to the case without considering the relocation of distribution transformers, reflect the importance of this methodology and its benefits to the distribution companies, and serves as a support tool for compliance with the standards set by regulatory bodies that define the appropriate chargeability of distribution transformers. [ABSTRACT FROM AUTHOR]

In this paper a methodology to solve the problem of optimal placement of phasor measurement units (Phasor Measurements Units - PMUs) in electric transmission systems, is presented. The problem is formulated as a binary linear optimization model, where the objective function to be minimized is the cost of installing of PMUs, subject to compliance with a level of redundancy in the measurements. The model is solved using the simulated annealing algorithm. The methodology is applied to IEEE 57-bus system, obtaining results that support the validity of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to solve the problem of service restoration in electric distribution systems, which is proposed as a model of mixed integer non-linear optimization where the objective function is to maximize the load that is outside of service, subject to a set of technical and operational constraints. The problem is solved using heuristic techniques, through sensitivity indicators that guide the restoration process. The methodology is validated in distribution systems, finding good-quality results. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for solving secondary distribution systems planning problem as a mixed integer linear programming problem (MILP). The algorithm takes into account several design issues such as the capacity and location of distribution transformers and secondary feeders. Variable costs of secondary feeders are also considered. The problem is solved using branch and bound algorithm. Numerical results show that the mathematical model and the solution technique are effective for this kind of problems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a master–slave methodology to address the problem of optimal integration (location and sizing) of Distributed Generators (DGs) in Direct Current (DC) networks. This proposed methodology employs a parallel version of the Population-Based Incremental Learning (PPBIL) optimization method in the master stage to solve the location problem and the Vortex Search Algorithm (VSA) in the slave stage to solve the sizing problem. In addition, it uses the reduction of power losses as the objective function, considering all the constraints associated with the technical conditions specific to DGs and DC networks. To validate its effectiveness and robustness, we use as comparison methods, different solution methodologies that have been reported in the specialized literature, as well as two test systems (the 21 and 69-bus test systems). All simulations were performed in MATLAB. According to the results, the proposed hybrid (PPBIL–VSA) methodology provides the best trade-off between quality of the solution and processing times and exhibits an adequate repeatability every time it is executed. [ABSTRACT FROM AUTHOR]