Juan Camilo Lopez

Centralized Self-Healing Scheme for Electrical Distribution Systems

In this paper, a two-stage procedure is proposed in order to solve the centralized self-healing scheme for electrical distribution systems. The considered self-healing actions are the reconfiguration of the distribution grid and, if needed, node and zone load-shedding. Thus, the proposed procedure determines the status of the switching devices in order to effectively isolate a faulty zone and minimize the number of de-energized nodes and zones, while ensuring that the operative and electrical constraints of the system are not violated. The proposed method is comprised of two stages. The first stage solves a mixed integer linear programming (MILP) problem in order to obtain the binary decision variables for the self-healing scheme (i.e., the switching device status and energized zones). In the second stage, a nonlinear programming (NLP) problem is solved in order to adjust the steady-state operating point of the topology found in the first stage (i.e., correction of the continuous electrical variables and load-shedding optimization). Commercial optimization solvers are used in the first stage to solve the MILP problem and in the second stage to solve the NLP problem. A 44-node test system and a real Brazilian distribution system with 964-nodes were used to test and verify the proposed methodology.

Optimal Restoration/Maintenance Switching Sequence of Unbalanced Three-Phase Distribution Systems

This paper presents a mixed-integer non-linear programming (MINLP) model for the optimal restoration/maintenance switching sequence of unbalanced three-phase electrical distribution systems. Once the protection coordination has identified and cleared a faulty zone, the proposed MINLP model determines the status of remotely controlled switches and the dispatchable distributed generation (DG) units, used to de-energized the troubled section of the network and supply as much load as possible. The restoration considers the switching sequence over a discrete horizon, guaranteeing that the operational constraints of the distribution system are not violated in every step of the sequence. Furthermore, a set of linearization strategies are presented to transform the proposed MINLP model into a mixed-integer linear programming (MILP) model. The use of MILP models guarantees convergence to optimality by applying convex optimization techniques. Tests are performed on an unbalanced three-phase radial distribution system consisting of 123 nodes, 12 switches, and three dispatchable DG units. The obtained results show that the proposed optimization model is a holistic procedure that can be used to efficiently manage power restoration or to minimize isolated areas in case of scheduled maintenance in modern electrical distribution systems.

Microgrids Energy Management Using Robust Convex Programming

This paper presents an energy management system (EMS) for single-phase or balanced three-phase microgrids via robust convex optimization. Along a finite planning horizon, the solution provided by the proposed microgrids EMS remains feasible under adverse conditions of random demands and renewable energy resources. The proposed model is represented as a convex mixed-integer second-order cone programming model. Two operation modes are considered: grid-connected and isolated. In grid-connected mode, the proposed EMS minimizes the costs of energy imports, dispatches of distributed generation (DG) units, and the operation of the energy storage systems. In isolated mode, the proposed EMS minimizes the unsupplied demand considering consumer priorities. Global robustness of the proposed mathematical model is adjusted using a single parameter

Optimal Operation of Radial Distribution Systems Using Extended Dynamic Programming

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Optimal Operation of Unbalanced Three-Phase Islanded Droop-Based Microgrids

. The robustness of the solutions provided by the robust EMS are assessed using the Monte Carlo simulation method. In this case, DG units are set to operate in frequency and voltage droop control to support network fluctuations. Simulations are deployed using a microgrid with 136-nodes and several distributed energy resources. Results showed that the proposed model is suitable for the short-term microgrids energy management system. The robustness of the final solution was directly proportional to the operational costs, and it can be effectively controlled by the proposed parameter

Optimal schedule of dispatchable DG in electrical distribution systems with extended dynamic programming

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Optimal reconfiguration of electrical distribution systems considering reliability indices improvement

for both operation modes. When compared to stochastic approaches, the proposed formulation proved to be more flexible and less time-consuming.

Robust optimisation applied to the reconfiguration of distribution systems with reliability constraints

An extended dynamic programming (EDP) approach is developed to optimize the ac steady-state operation of radial electrical distribution systems (EDS). Based on the optimality principle of the recursive Hamilton–Jacobi–Bellman equations, the proposed EDP approach determines the optimal operation of the EDS by setting the values of the controllable variables at each time period. A suitable definition for the stages of the problem makes it possible to represent the optimal ac power flow of radial EDS as a dynamic programming problem, wherein the “curse of dimensionality” is a minor concern, since the number of state and control variables at each stage is low and the time complexity of the algorithm grows linearly with the number of nodes of the EDS. The proposed EDP is applied to solve the economic dispatch of the DG units installed in a radial EDS. The effectiveness and the scalability of the EDP approach is illustrated using real-scale systems and comparisons with commercial programming solvers. Finally, generalizations to consider other EDS operation problems are also discussed.

Security-constrained optimal energy management system for three-phase residential microgrids

This paper presents a new mixed-integer nonlinear programming (MINLP) model for the optimal operation of unbalanced three-phase droop-based microgrids. The proposed MINLP model can be seen as an extension of an optimal power flow for microgrids operating in islanded mode, that aims to minimize the total amount of unsupplied demand and the total distributed generator (DG) generation cost. Since the slack bus concept is not longer valid, the proposed model considers the frequency and voltage magnitude reference as variables. In this case, DGs units operate with droop control to balance the system and provide a frequency and voltage magnitude reference. Additionally, a set of efficient linearizations are introduced in order to approximate the original MINLP problem into a mixed-integer linear programming (MILP) model that can be solved using commercial solvers. The proposed model has been tested in a 25-bus unbalanced three-phase microgrid and a large 124-node grid, considering different operational and time-coupling constraints for the DGs and the battery systems (BSs). Load curtailment and different modes of operation for the wind turbines have also been tested. Finally, an error assessment between the original MINLP and the approximated MILP model has been conducted.

A Generalized Model for the Optimal Operation of Microgrids in Grid-Connected and Islanded Droop-Based Mode

In this paper, the optimal schedule of dispatchable distributed generation (DG) units connected to radial electrical distribution systems (EDS) is solved using an extended dynamic programming approach. The objective of the optimal DG scheduling problem is to determine the hour-by-hour active generation output of each dispatchable DG unit, in order to minimize the total active power losses of the EDS and the generation costs. The proposed extended dynamic programming (EDP) is an advantageous approach because convexity is not required to obtain a global optimal solution, and the “curse of dimensionality” is not a concern since the computational complexity of the algorithm grows linearly with the size of the network. Besides, the state variables have only two dimensions, one to represent the active power flows and the other to represent the nodal voltages. A 56-nodes MV distribution system with two dispatchable DG units is used to evaluate the performance of the proposed EDP approach, considering a deterministic and a stochastic case. A set of Monte Carlo simulations is used to analyze the influence of uncertainties. Results confirm that the proposed methodology is a suitable approach to unveil the best operation schedule for dispatchable DG units.