Leandro T. Marques

Validation of a methodology for service restoration on a real Brazilian distribution system

Recently a practical and efficient methodology for service restoration in distribution systems was developed. This methodology combines Multi-objective Evolutionary Algorithms with the tree encoding named Node-Depth Encoding. In comparison with other methodologies already proposed for service restoration, the novel features of this methodology are: (i) to generate adequate service restoration plans for large scale distribution networks (networks modeling distribution systems with thousand of buses and switchers) with relatively soft computing without requiring any network simplification; and (ii) to generate service restoration plans for multiple-faults as good as for a single fault. This paper reports the experience in using that methodology to generate service restoration plans in one real distribution system of COPEL, a company of the Brazilian Electricity sector. More specifically, this paper reports: the main points of that methodology, the analysis of the service restoration plans generated by it performed by engineers of COPEL, and also some suggestions of these engineers in order to improve the methodology.

Service Restoration With Prioritization of Customers and Switches and Determination of Switching Sequence

Distribution system (DS) service restoration (SR) in contingency situations is one of the most complex and challenging problems in DS operation. It is usually formulated as a multi-objective and multi-constraint optimization problem that must be quickly solved. Several methods have been proposed for its solution, however, most of them still have limitations. Some demand long running time when applied to large-scale DSs modeled with no simplification, whereas others disregard some important aspects of the SR problem. This paper proposes a methodology based on multi-objective evolutionary algorithms for the SR problem and overcoming of such limitations. In contrast to methods reported in the literature, the methodology: 1) deals with large-scale DSs with relatively soft computing time and requires no network topology simplification; 2) prioritizes the operation of remotely controlled switches; 3) prioritizes supply to three levels of priority customers; and 4) provides switching sequences. A mathematical formulation of the problem is also proposed. Several tests were conducted for the evaluation of the methodology and single and multiple fault cases in large-scale DSs (from 631 to 5158 switches) were considered.

Determination of switching sequence of Service Restoration in Distribution Systems: Application and analysis on a real and large-scale radial system

It is computationally hard to solve the Service Restoration (SR) problem for large-scale Distribution Systems (DSs) without any system simplification, since this problem is combinatorial and non-linear, involving several constraints and objectives. The methodology named MEAN-MH+ES has proved able to generate feasible solutions (radial configuration attending all the operational constraints) with relatively soft computing and without requiring any network simplification in several tests performed on the real and large-scale DS of Londrina city (Brazil). The MEAN-MH+ES combines Multi-objective Evolutionary Algorithm with Node-Depth Encoding, Multiple-criteria tables, alarming Heuristic and an Exhaustive search. However, as the majority of the methodologies for solving the SR problem, the MEAN-MH+ES does not provide a feasible sequence of switching operations to reach the final configuration (the feasible solution) from the initial configuration (the configuration with the faulted areas identified and isolated). This paper proposes to incorporate a heuristic procedure into MEAN-MH+ES, which enable to provide a Feasible Sequence of Switching Operations (FSSO), that is, a switching operation sequence that generates only intermediate configurations that respect the operational constraints. The proposed heuristic procedure is confirmed on tests performed on the real and large-scale DS of Londrina city.

Service restoration in large-scale distribution systems considering three levels of priority customers

Service restoration in contingency situations is one of the most critical problems in the context of distribution system operation. It is a problem with multiple objectives and multiple constraints whose solution must be gotten as soon as possible. Besides, due to the existence of customers with higher priority of supply (e.g., big industries and hospitals), it is necessary to prioritize service restoration to these loads. However, it is important to highlight that these priority customers have different priority levels, that also must be considered during the determination of a feasible service restoration plan. In this paper, a methodology based on multi-objective evolutionary algorithm is proposed for solving service restoration problem in large-scale distribution system taking into account the existence of three levels of priority customers. Simulations results have shown the proposed methodology is able to find suitable service restoration plans for large-scale real distribution systems (from 3,860 to 30,880 buses) with relatively soft computing without requiring any network simplification.

Modeling issues on load flow calculation for meshed distribution systems

Load flow solution is required for determining a steady-state condition of electric power systems for various load demands. This motivated the development of several methods for load flow calculation in transmission and distribution systems. However, there are some scenarios of meshed operation in distribution systems that pose additional challenges for the direct application of load flow calculation methods. This paper reports a detailed study of these scenarios, some without a detailed analysis in the literature, highlighting the modeling issues of each one to perform a load flow calculation in meshed distribution systems. The paper also proposes some alternatives to overcome these modeling issues, which are related to the substation model and the reference bus of the network. To illustrate the main theoretical conclusions, computational simulations based on real distribution feeders from a Brazilian utility are presented and analyzed.

A Multi-objective Evolutionary Algorithm with Efficient Data Structure and Heuristic Initialization for Fault Service Restoration

Service restoration in energy distribution systems is a complex optimization problem with many restrictions. After a fault occurrence, the challenge is obtain a service restoration plan reconnecting all the healthy out-of-service areas satisfying all the operational and technical constraints. Recent works have study the use of meta-heuristics in order to find a sub-optimal solution with low computational complexity. One of these works include the use of a multi-objective algorithm with a new data structure called node depth encoding. This paper proposes and analyses the use of a new heuristic initialization procedure to be used with node depth encoding which guarantees the analysis of all possible solutions considering only a restricted number of switches incident to the out-of-service areas. The proposed methodology is evaluated by applying it to the real and large-scale distribution system of Londrina city (Brazil). The results showed that the new heuristic improves the overall performance reducing the number of switches operations to reconfigure the distribution system.