Browsing by Author "Lopez Quizhpi, Julio Cesar"

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a stochastic mixed-integer conic programming model for distribution system expansion planning considering wind generation
(2018) Lopez Quizhpi, Julio Cesar
This paper presents a stochastic scenario-based approach to finding an efficient plan for the electrical power distribution systems. In this paper the stochasticity for the distribution system expansion planning (DSEP) problem refers to the loads and wind speed behavior. The proposed DSEP model consist the expansion and/or construction of new substations, installation of new primary feeders and/or reinforcement the existing, installation of wind-distributed generation based, reconfiguration of existing network, and the proposed DSEP is solved considering uncertainty in electric demand and distributed generation. In this regard, a two-stage stochastic programming model is used, wherein the first stage the investment decision is made and the second stage calculates the expected operating value which depends on the stochastic scenarios. The mathematical approach is based on a mixed integer conic programming (MICP) model. By using this MICP model and a commercial optimization solver, finding the optimal global solution is guaranteed. Moreover, in this paper by using the Tabu Search algorithm and take the advantages of a stochastic conic optimal power flow model, an efficient hybrid algorithm is developed. With the aim of comparing the performance of the optimization techniques based on solution of MICP model directly and using a hybrid proposed methodology, they are tested in a 24-node distribution system and the results are compared in detail. Â© 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
Optimal distribution network reconfiguration with distributed generation using a genetic algorithm
(Institute of Electrical and Electronics Engineers Inc., 2019) Peñaloza Moran, John Eugenio; Yumbla Romero, Jairo Gonzalo; Lopez Quizhpi, Julio Cesar; Padilha Feltrin, Antonio
This paper presents the develop of a computational tool for solve the distribution network reconfiguration problem considering the power losses minimization and distributed generation. The proposed tool satisfies the operational constraints of systems, e.g., voltage limits in nodes and the current capacity of lines. The use of a MINLP and a Genetic Algorithm guarantees convergence to good quality solutions. This is a searching adaptive method - based on natural selection and natural genetic. It improves the solutions in combinatorial problems, in each iteration through their operators (selection, crossover and mutation). 14-Bus, 33-Bus and 69-bus test systems and 880-Bus real system were employed to show the effectiveness and satisfactory results of the proposed tool.
Optimal plug-in electric vehicles charging coordination in electrical distribution networks
(Institute of Electrical and Electronics Engineers Inc., 2018) Lopez Quizhpi, Julio Cesar; Cordero Moreno, Daniel Guillermo; Lopez Quizhpi, Julio Cesar
Abstract: Electric vehicles are shifting the paradigm of electrical distribution networks operation. Thus, the operation of electrical distribution networks is becoming more complex and challenging for ensuring quality, security and reliability. In this paper, the plug-in electric vehicles charging coordination on electrical distribution networks is to carried out. The plug-in electric vehicles batteries equations are modeled and embedded into a charging coordination mixed-integer second-order cone programming model. The proposed mathematical model is formulated to minimize the total operational costs of the electrical distribution networks by determining an optimal charging coordination for the plug-in electric vehicles batteries. Results of a case study based on the 136-node test system show the effectiveness of the proposed approach.
Robust microgrid operation under renewable generation
(Institute of Electrical and Electronics Engineers Inc., 2018) Lopez Quizhpi, Julio Cesar; Pozo, David; Lopez Quizhpi, Julio Cesar
Renewable generation is shifting conventional generation in microgrids. Thus, the operation of microgrids is becoming more complex and challenging for ensuring stability and reliability. In this paper, we introduce a risk-averse approach for optimal operation of microgrids in the presence of renewable generation (RG) and Battery Energy Storage System (BESS). These sources are modeled and embedded into an AC optimal convex power flow equations. A Two-Stage Adaptive Robust Optimization mathematical framework is presented to address uncertainties in the load demand and renewable resources. Uncertainty is depicted with a polyhedron instead of probability distribution function. The resulting model is a three-level min-max-min problem. The robust model is formulated to minimize the total cost of energy purchased from the distribution substation and energy cost of distributed generation. Results of an illustrative example and a case study based on the 11-node test system show the effectiveness of the proposed approach.
Static and dynamic convex distribution network expansion planning
(Springer Verlag, 2018) Lopez Quizhpi, Julio Cesar; Pozo, David
This chapter presents static and dynamic optimization-based models for planning the electric distribution network. Based on a branch flow model, two Mixed-Integer Conic Quadratic Programming (MICQP) convex formulations are proposed to solve the network expansion planning models including high modeling fidelity of the intrinsic interaction of the manifold elements of the networks. The objective of the presented models is to minimize investment and operation costs by optimally deciding on installing new feeders and/or changing existing ones for others with larger capacities, installing new substations or expanding existing ones and, finally, installing capacitor banks and voltage regulators, modifying the network topology. In addition, discrete tap settings of voltage regulators are modeled as a set of mixed-integer linear equations, which are embedded in an ac optimal power flow. The presented MICQP models are convex optimization problems. Therefore globality and convergence are guaranteed. Computational results to verify the efficiency of the proposed methodology are obtained for a 24-node test system. Finally, conclusions are duly drawn