Pedro M. S. Carvalho

Optimal distribution network expansion planning under uncertainty by evolutionary decision convergence

We address the problem of distribution network expansion planning under uncertainty and present a new methodology capable of dealing with uncertainty in a realistic complex problem. Uncertainty is represented by a set of scenarios. A strategic objective problem is then formulated to fit the availability of reliable first stage information. An evolutionary algorithmic procedure is proposed to find a robust decision policy; by successive adjustments to the scenarios structure, convergence is achieved for the first stage decisions. A comprehensive example is presented to illustrate the convergence process and the robustness of the achieved decision.

On spanning-tree recombination in evolutionary large-scale network problems - application to electrical distribution planning

We report the key algorithms involved in the recombination-based evolutionary software developed for planning electrical distribution networks. We focus on the dimensionality problem of large-scale networks and on the specificities of its search space. We report the difficulties in handling topology constraints and present both the geno-type and the operators to overcome such difficulties. The operators are designed to process meaningful topological information as geno-type substructures and turn the radiality and connectivity into genetic transmissible properties. First, a theoretical example is presented to illustrate important differences between other common approaches and the approach taken. Then, a real electrical industry application is presented to illustrate the ability of the approach to handle large-scale distribution-network problems.

Solving Radial Topology Constrained Problems with Evolutionary Algorithms

We report key algorithmic specific features involved in the evolutionary radial network problem solution. We focus on the dimensionality problem of large-scale networks and on the singularities of the radial topology search space. We (1) report the difficulties of the canonical genetic algorithm in handling network topology constraints, and (2) present both the genotype information structure and the recombination operator to overcome such difficulties. The proposed recombination operator processes genetic information as meaningful topological structures, and turns radiality and connectivity into genetic transmissible properties. Results are presented to illustrate the difference between the canonical approach and the approach taken.

Mitigation of Interruption Reimbursements by Periodic Network Reconfiguration: Risk-Based Versus Expected-Value Optimization

The reimbursement for violation of customer quality standards such as maximal interruption frequency and duration is at the basis of modern quality of service regulation. In order to satisfy quality standards, distribution operators periodically reconfigure the network so as to minimize the risk of reimbursement. Reconfiguration relies upon reliability analysis of an average year and usually neglects the risk induced by failure variance. This is because the assessment of risk represents a considerable burden for real-scale systems. In this paper, we study the decision error induced by analyzing average years and investigate the evolution of this error with the periodicity of reconfiguration. Examples are given for illustrating the main results

Multi-objective distribution planning approach for optimal network investment with EV charging control

The paper proposes a hybrid methodology based on a Gaussian local search and a genetic algorithms to address the multi-objective and multistage optimal distribution expansion planning problem. The methodology is applied to solve the problem of optimal network investment under the new possibilities enabled by the smart grid, namely the new observability and controllability investments that will be available to manage electric vehicles charging in the future. A multi-objective optimization problem is solved for a low-voltage network feeder and a medium-voltage network feeder with a high EV penetration level scenario. The Pareto surface solution is then projected onto the two investment types considered: demand control investments and traditional network asset investments. The projected surface is then analysed to discuss the merit of demand control w.r.t. postponing tradition asset investments.

Optimal Planning of Grid Reinforcement with Demand Response Control

This chapter presents a hybrid methodology based on a local search algorithm and a genetic algorithm, used to address the multi-objective and multistage optimal distribution expansion planning problem. The methodology is conceived to solve optimal network investment problems under the new possibilities enabled by the smart grid, namely the new observability and controllability investments that will be available to enable demand response in the future. The multi-objective methodology is applied to an existing low-voltage electric distribution network under a congestion scenario to yield a Pareto-optimal set of solutions. The solutions are then projected onto the two investment possibilities considered: demand control investments and traditional network asset investments. The projected surface is then analyzed to discuss the merit of demand control with respect to postponing traditional asset investments.

Towards autonomous reconfiguration in distribution networks: An approach based on finite state automaton models

The demand on distribution network reconfiguration abilities is increasing. This paper proposes a Finite State Automaton model of a distribution network that deals with topologically safe reconfiguration. Autonomous operation of distribution networks can be designed, analysed and implemented in day-to-day operation with the aid of these automata: through supervisory control. Two approaches to supervisor design are proposed. One for the normal operating mode and the other for the contingency mode. Simple and comprehensive examples illustrate the approaches.

Application of Distributed Techniques to the Modelling of IN Architectures and Support of Service Execution

The convergence ot telecommunications and information technologies is at last gaining momentum. One manifestation of this convergence is the interest shown amongst the telecommunication fraternity on the application of Open Distributed Processing (ODP) techniques. This line of experimentation was adopted within the RACE BOOST project by CET/Telecom Portugal, via the development of a distributed IN platform which completely models the IN architecture, and upon which IN services can be executed. This platform uses the ANSAware Distributed Computing Software and served to experiment and validate a number of IN architectural issues and also to gain some insight on the application of ODP techniques to a typical telecommunications problem.