Swarm Intelligence Based Multi-phase OPF For Peak Power Loss Reduction In A Smart Grid

Recently there has been increasing interest in improving smart grids efficiency using computational intelligence. A key challenge in future smart grid is designing Optimal Power Flow tool to solve important planning problems including optimal DG capacities. Although, a number of OPF tools exists for balanced networks there is a lack of research for unbalanced multi-phase distribution networks. In this paper, a new OPF technique has been proposed for the DG capacity planning of a smart grid. During the formulation of the proposed algorithm, multi-phase power distribution system is considered which has unbalanced loadings, voltage control and reactive power compensation devices. The proposed algorithm is built upon a co-simulation framework that optimizes the objective by adapting a constriction factor Particle Swarm optimization. The proposed multi-phase OPF technique is validated using IEEE 8500-node benchmark distribution system.Comment: IEEE PES GM 2014, Washington DC, US

Progresses in analytical design of distribution grids and energy storage

none4noIn the last years, a change in the power generation paradigm has been promoted by the increasing use of renewable energy sources combined with the need to reduce CO2 emissions. Small and distributed power generators are preferred to the classical centralized and sizeable ones. Accordingly, this fact led to a new way to think and design distributions grids. One of the challenges is to handle bidirectional power flow at the distribution substations transformer from and to the national transportation grid. The aim of this paper is to review and analyze the different mathematical methods to design the architecture of a distribution grid and the state of the art of the technologies used to produce and eventually store or convert, in different energy carriers, electricity produced by renewable energy sources, coping with the aleatory of these sources.openColangelo G.; Spirto G.; Milanese M.; de Risi A.Colangelo, G.; Spirto, G.; Milanese, M.; de Risi, A

Review on distribution network optimization under uncertainty

With the increase of renewable energy in electricity generation and increased engagement from demand sides, distribution network planning and operation face great challenges in the provision of stable, secure and dedicated service under a high level of uncertainty in network behaviors. Distribution network planning and operation, at the same time, also benefit from the changes of current and future distribution networks in terms of the availability of increased resources, diversity, smartness, controllability and flexibility of the distribution networks. This paper reviews the critical optimization problems faced by distribution planning and operation, including how to cope with these changes, how to integrate an optimization process in a problem-solving framework to efficiently search for optimal strategy and how to optimize sources and flexibilities properly in order to achieve cost-effective operation and provide quality of services as required, among other factors. This paper also discusses the approaches to reduce the heavy computation load when solving large-scale network optimization problems, for instance by integrating the prior knowledge of network configuration in optimization search space. A number of optimization techniques have been reviewed and discussed in the paper. This paper also discusses the changes, challenges and opportunities in future distribution networks, analyzes the possible problems that will be faced by future network planning and operations and discusses the potential strategies to solve these optimization problems

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems

Optimal allocation of distributed generation for power loss reduction and voltage profile improvement

Distributed generation (DG) integration in a distribution system has increased to high penetration levels. There is a need to improve technical benefits of DG integration by optimal allocation in a power system network. These benefits include electrical power losses reduction and voltage profile improvement. Optimal DG location and sizing in a power system distribution network with the aim of reducing system power losses and improving the voltage profile still remain a major problem. Though much research has been done on optimal DG location and sizing in a power system distribution network with the aim of reducing system power losses and improving the voltage profile, most of the existing works in the literature use several techniques such as computation, artificial intelligence and an analytical approach, but they still suffer from several drawbacks. As a result, much can still be done in coming up with new algorithms to improve the already existing ones so as to address this important issue more efficiently and effectively. The majority of the proposed algorithms emphasize real power losses only in their formulations. They ignore the reactive power losses which are the key to the operation of the power systems. Hence, there is an urgent need for an approach that will incorporate reactive power and voltage profile in the optimization process, such that the effect of high power losses and poor voltage profile can be mitigated. This research used Genetic Algorithm and Improved Particle Swarm Optimization (GA-IPSO) for optimal placement and sizing of DG for power loss reduction and improvement of voltage profile. GA-IPSO is used to optimize DG location and size while considering both real and reactive power losses. The real and reactive power as well as power loss sensitivity factors were utilized in identifying the candidate buses for DG allocation. The GA-IPSO algorithm was programmed in Matlab. This algorithm reduces the search space for the search process, increases its rate of convergence and also eliminates the possibility of being trapped in local minima. Also, the new approach will help in reducing power loss and improve the voltage profile via placement and sizing