Majority of electricity networks have growing number of ageing elements. Critical network components, such as ageing underground cables, are very expensive to install and disruptive to replace. On the other hand, global climate changes have made connection of new low carbon technologies (LCT) into the grids increasingly necessary. These factors are contributing to the increasing complexity of the planning and management of power systems. Numerous techniques published on this subject tend to ignore the impact of LCT integration and the anchoring ꞌSmartꞌ solutions on ageing network assets, such as underground cables and transformers. This thesis presents the development procedures of an ageing underground cable reliability model (IEC-Arrhenius-Weibull model) and cable ranking models for replacement based on system wide effects and thermal loss-of-life metrics. In addition, a new concept of LCT integration and distribution network management was proposed using two optimization models. The first optimizes connection of new wind sources by minimizing the connection cost and the cost of cable thermal loss-of-lives in the planning period. In the second stage, the network is optimally reconfigured in such a way to minimize thermal-loss-of-life of ageing cable. Both optimization models are formulated as mixed integer non-linear programming (MINLP) problems applicable to radially operated medium voltage networks. To quantify the reliability benefits of the proposed approach, Sequential Monte Carlo Simulation (SMCS) procedure was formulated. Some of the main features of the SMCS procedure are the IEC-Arrhenius-Weibull model for ageing cable, optimal network reconfiguration, wind generation modelling using ARMA models and real time thermal ratings. The final outputs are reliability metrics, cable ranking lists for replacement, savings due to 'non-spend' cable thermal lives, etc. These studies have proven to be important in formulating an effective strategy for extending the lives of network cables, managing overall network reliability and planning cables replacement in power distribution networks.

Distribution system plays a significant role in the overall electrical power system due to its impact on electricity costs, reliability as well as security of supplied energy. Optimal development planning of modern distribution system is mainly required to satisfy continuous change in customer demands and generations in a cost-effective manner, utilizing the available smart solutions. All these aspects need to be addressed in modern distribution planning methodology that can be applied today in real-life. Review has shown that there are no distributions planning models that adequately model security of supply of radially operated networks. Moreover, the optimal development planning models still do not consider multiple operating regimes, which has become a necessity due to connection of low carbon technologies. Numerous techniques published on this subject tend to ignore the regulations and planning standards that must be complied during system development, resulting in methodology that is not in-tuned with business practices. Furthermore, a comprehensive model that integrates all major components of today’s real-life distribution planning is still lacking, even though many of them have been addressed individually. In this thesis, integrated planning methodology for development of distribution system is proposed, incorporating utility practices in the UK. The overall methodology built on two independent stages, investment stage and operation stage. The operation stage is further cast into two sub-stages, quality of supply planning and minimization of operation costs planning. The overall planning methodology incorporates the novel probabilistic decision tree concept for distribution system planning to consider probable network uncertainties. The first model which is the investment stage determines the new construction and reinforcement of circuits and switchgear, along with circuit decommissioning. Multiple operating regimes due to fluctuation in generation and load profiles are considered, in addition to explicit modelling of N-1 security constraint according to P2/6 planning standards. The quality of supply planning determines the allocation of switchgear and its automation to maximise the reliability benefits from the regulatory incentive regime. Finally, the operation model determines the optimal network configuration that minimises the total operation costs of distribution system. The final outputs are list of cables and switchgear for construction, reinforcement, and decommission, benefits harvested due to quality of supply investments on switchgear, optimal network running arrangement, etc. These studies have proven to be important in formulating effective strategies for development of distribution system, in compliance to the planning standards and resulted in higher network operation capabilities.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-01-19T10:47:34Z No. of bitstreams: 1 felipedasilvaseta.pdf: 1053075 bytes, checksum: 8a24a576cad55e9b46efe4bde9405104 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-01-25T17:44:56Z (GMT) No. of bitstreams: 1 felipedasilvaseta.pdf: 1053075 bytes, checksum: 8a24a576cad55e9b46efe4bde9405104 (MD5) / Made available in DSpace on 2016-01-25T17:44:56Z (GMT). No. of bitstreams: 1 felipedasilvaseta.pdf: 1053075 bytes, checksum: 8a24a576cad55e9b46efe4bde9405104 (MD5) Previous issue date: 2015-08-10 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O presente trabalho propõe uma metodologia para a resolução do problema de reconfiguração ótima de sistemas de distribuição de energia elétrica utilizando uma representação mais realista de parâmetros com incertezas. O objetivo é avaliar o impacto de se representar incertezas dos sistemas no problema de reconfiguração em relação a modelos tradicionais determinísticos. O modelo de reconfiguração probabilística proposto visa minimizar as perdas totais de energia considerando incertezas sobre a demanda e sobre a geração distribuída a partir da energia eólica, além de diferentes níveis de carregamento dos sistemas. A metodologia proposta é baseada na técnica meta-heurística Sistema Imunológico Artificial. Os fundamentos da matemática intervalar são incorporados em um fluxo de potência intervalar que modela as incertezas da demanda provenientes principalmente de erros de previsão e medição, bem como incertezas na geração por fontes eólicas devido a intermitências nos regimes de ventos. Desta forma, as variáveis de entrada intervalares são as demandas ativas e reativas das barras do sistema e os valores de velocidade de vento nas regiões das usinas eólicas. As incertezas da entrada são propagadas para as variáveis de saída do fluxo de potência, como as tensões nodais. Como resultado, as perdas totais de energia a serem minimizadas também são determinadas na forma intervalar. Uma metodologia para comparação de intervalos baseada na média e no raio dos intervalos é utilizada para determinar a topologia ótima. Restrições de tensão, radialidade e conectividade da rede são consideradas. O algoritmo proposto é testado em sistemas conhecidos da literatura. / The present work proposes a methodology to solve the problem of optimal reconfiguration of power distribution systems by using a more realistic representation of uncertain parameters. The objective is to evaluate the impact of representing uncertainties in the reconfiguration problem in relation to traditional deterministic models. The proposed probabilistic reconfiguration model aims at minimizing the total energy loss considering uncertainties on the load demand and the distributed generation from wind energy, as well as different load levels. The proposed methodology is based on the meta-heuristic technique Artificial Immune System. The interval mathematics fundamentals are embedded in an interval power flow that models the uncertainties of load forecast and measurements, as well as uncertainties due to the intermittences of the wind. Therefore, the input interval variables are the active and reactive loads at the network nodes and the wind speed in the regions where the wind farms are installed. The input uncertainties are thus propagated to the output power flow variables as the nodal voltages. As a result, the total energy losses to be minimized are also given in interval form. A methodology for comparing intervals that is based on the interval average and size is used to determine the best topology. Voltage constraints, radial configuration and network connectivity are considered. The proposed algorithm is tested in systems known in the literature.