Primary Distribution Networks Research Articles

Simultaneous optimisation of tie switches placement and reserve capacity margin of sub-transmission substations considering the conflict between short-term and long-term plannings

Outage management has a crucial role in improving reliability level of distribution networks, and it is profoundly dependent on the reserve capacity margin of sub-transmission (ST) substations and load transferring capability between them. These factors enable distribution system operator (DSOs) to restore interrupted load points in out-of-service areas. In this study, the connection of reserve capacity in ST substations and load transferring capability, made by tie switches, is studied. The mentioned correlation is addressed via costumer interruption cost derived from outage management in primary distribution networks. Then, a mathematical model is developed in which optimal reserve capacity of ST substations and optimal placement of tie switches are determined simultaneously. The resulted optimisation model is formulated as a mixed-integer non-linear problem, and the genetic algorithm is used to solve the problem. Finally, the effectiveness of the proposed model is evaluated in a test distribution network.

Optimal configuration of power distribution networks with variable renewable energy resources

An attractive way to reduce losses in electric power distribution networks is addressing the network reconfiguration problem, which should give a topology for the primary distribution network that minimizes the total losses due to the electrical resistances in the lines and complementary equipment’s (technical losses). Distributed energy resources and additional innovations associated to smart grids allow enhancing the benefits of finding better network topologies. On the other hand, the integration of renewable energy sources with variable random outputs requires expanding the perspective in modeling the network reconfiguration problem and in the shaping of appropriate solution techniques. These issues are the object of this paper. The main new features of the problem are explored with a maquette designed to highlight the consequences of random generation sources in the networks. Following, the paper proposes a formulation for the problem that explicitly considers random energy sources. A state of the art genetic algorithm build under the biased random-key evolution framework (BRKGA) is developed to address this hard combinatorial optimization problem. Case studies with benchmark networks put into perspective the proposed methodology. Results show that random energy values should be explicitly modeled in contemporary approaches to the network reconfiguration problem. The paper provides the grounds for addressing this new problem and points to additional research paths in the area.

Optimal hourly scheduling of distributed generation and capacitors for minimisation of energy loss and reduction in capacitors switching operations

Now-a-days electric power systems all over the world are undergoing a phase of increased size and complexity due to rising load demand expansion. This situation leads to excessive burden on the power distribution systems posing many challenges before the distribution system utilities. Minimisation of real power loss and maintaining voltage profile are the major requirements for a distribution system apart from cost reduction. Capacitors and tap changing transformers are being conventionally used for maintaining voltage profile along a primary distribution network. When a distributed generation (DG) is incorporated in a distribution system, the problem of efficiently controlling a system becomes more complex. In this study, a novel multi-objective optimisation problem of hourly scheduling of voltage control devices in coordination with a DG is attempted. An efficient particle swarm optimisation technique is used to solve the problem. Another novelty of this work is the use of Newton-based power flow method for evaluation of function value. The algorithm proposed is validated on IEEE standard 33 bus and 69 bus radial distribution systems. The results are encouraging.

Consideration of uncertainty of information on load and generation values under daily reconfiguration of primary distribution network

Intelligent electric networks make it possible to remotely control switchings, thus preventing overloads and enhancing reliability of power supply to consumers. In the paper, the daily reconfiguration of a primary distribution network is considered as a means to reduce energy losses in the network. An algorithm applied for the reconfiguration is based on the methods of the theory of graphs and includes a high-speed program for load flow calculation. The research is focused on the impact of renewable generation and active demand with loads changing depending on daily variation in electricity price on the reduction in losses at reconfiguration. An algorithm is proposed to optimize load curves of load-controlled consumers. The calculation of probabilistic load flows is applied to assess the impact of the uncertainty of nodal power forecast on energy loss reduction at reconfiguration. The results of the research demonstrate the effectiveness of the proposed approaches, and are illustrated by a 33-node test distribution network.

Design of optimization model for a hydrogen supply chain under emission constraints - A case study of Germany

The increasing global demand for petroleum-based fuels, mainly driven by the economic growth in emerging markets imposes significant challenges in terms of energy supply and environmental mitigation strategies. This work introduces an approach for the design and decision making of primary energy source, production, storage, and distribution networks for hydrogen supply in regions (or countries) under emission constraints. The problem was mathematically represented using a source-sink system approach to determine the most suitable hydrogen supply chain (HSC) network. The optimization problem was formulated as a Mixed Integer Linear Programming (MILP) model using GAMS® modeling system. The optimization objective consists of the minimization of the total network cost, both in terms of capital and operating expenditures, subject to: supply, demand, mass conservation, technical performance, economic, and environmental constraints. The model was used to plan the future hydrogen supply chain network for Germany in the year 2030 under emission constraints. The optimization results show that the model is a valuable tool for planning the optimal hydrogen supply chain network of a particular region or country.

A multiyear DG-incorporated framework for expansion planning of distribution networks using binary chaotic shark smell optimization algorithm

In this paper, a new model for MEPDN (multiyear expansion planning of distribution networks) is proposed. By solving this model, the optimal expansion scheme of primary (i.e. medium voltage) distribution network including the reinforcement pattern of primary feeders as well as location and size of DG (distributed generators) during an ascertained planning period is determined. Furthermore, the time-based feature of proposed model allows it to specify the investments/reinforcements time (i.e. year). Moreover, a minimum load shedding-based analytical approach for optimizing the network's reliability is introduced. The associated objective function of proposed model is minimizing the total investment and operation costs. To solve the formulated MEPDN model as a complex multi-dimensional optimization problem, a new evolutionary algorithm-based solution method called BCSSO (Binary Chaotic Shark Smell Optimization) is presented. The effectiveness of the proposed MEPDN model and solution approach is illustrated by applying them on two widely-used test cases including 12-bus and 33-bus distribution network and comparing the acquired results with the results of other solution methods.

Kruskal's Maximal Spanning Tree Algorithm for Optimizing Distribution Network Topology to Improve Voltage Stability

—Alteration of power network topology is often required to meet important objectives, such as restoring connectivity, minimizing power losses, maintaining stability, maximizing power transfer capability etc., and is achieved by switching of circuit breakers and other switching devices in the power network. Primary power distribution networks are often interconnected and meshed but should be transformed to radial topology to achieve various operational advantages. Distribution networks also need to be reconfigured after faults to restore power at all the load points. Reconfiguring a power network, however, is a complicated multi-constrained optimization problem, as there may exist many feasible switching combinations in a large power network. This article proposes a novel application of graph theory, supported by Kruskal's maximal spanning tree algorithm, to search for the optimal network topology and to optimally convert an interconnected meshed network into a radial system to achieve best operational characteristics, cost, and control. The proposed technique has been demonstrated on a 30-node primary distribution network originally having a mesh topology, and the results indicate significant performance improvement after transformation into optimal radial topology.

Dynamic model of a storage system with real-time simulation for power system reliability

SUMMARY The paper deals with the upgrading of the primary distribution networks applying the smart grid strategies. In particular, a new research field is devoted to the application of storage systems for the power quality improvement and the better exploitation of renewable sources. In this context, this work proposes the use of a storage based on lithium-ion batteries for the black start and island operation of a portion of the medium voltage network. The innovation of the idea proposed is related to a soft start of the system that reduces transient overvoltages and transient inrush current components in order to reduce the stress of the system and allow the supply in island through direct current/alternating current converters. Simulations carried out on a dedicated model implemented in real-time digital simulator are reported and analyzed. These tests are the basis for a full-scale prototype installation of the storage in a real Italian network as a part of a big project founded by the Italian Energy Authority. Copyright © 2014 John Wiley & Sons, Ltd.

Developing “Extra Distance” as a measure for the evaluation of road freight transport performance

Purpose– The purpose of this paper is to develop a measure that links the causes and consequences of disruptions in freight transport operations. Such a measure is needed to quantify the scale of impact and identify the root causes of disruptions.Design/methodology/approach– In order to develop this measure, an inductive approach was adopted, using four primary case studies to test the measure in an industrial environment. The case studies are from the fast moving consumer goods sector with primary and secondary distribution networks included. The “Extra Distance” measure has been evaluated against established generic criteria that define the quality of any performance measure.Findings– The research indicates good compliance with the criteria used to evaluate the “Extra Distance” measure. The measure is also found to be useful for practitioners who are able to directly relate the measure to their distribution network operations.Research limitations/implications– Further research should see the “Extra Distance” measure further tested in other freight transport operations and industrial sectors.Practical implications– The measure is directly related to a number of causes of uncertainty which helps freight transport managers to quickly identify potential solutions. The “Extra Distance” measure can be used to quantify the effects of disruptions which can occur in road freight transport networks generate unnecessary cost within distribution networks, potentially eroding profit margins which are known to be very low in the road freight transport industry.Originality/value– This paper presents a novel approach to the assessment of the impact caused by uncertainty within freight transport operations.

A Radial Path Building Algorithm for Optimal Feeder Planning of Primary Distribution Networks Considering Reliability Assessment

Optimal feeder routing is an important part of general optimal distribution network planning. This article proposes a new algorithm for optimal feeder routing using an easy step-by-step, radial path building algorithm, ensuring minimization of the total system planning cost. Furthermore, reliability assessment is carried out to obtain the most reliable feeder routing to acquire less interrupted network structure with different feeder configurations. The proposed approach is also tested for optimal feeder routing with variations in the number of substations, which provides information on the trade-off between optimality and reliability of the system configuration. Moreover, the concept of principle of optimality is effectively used to make the proposed approach computationally more efficient and useful. The extensive test results reflect the potential ability of the proposed approach for optimizing the network structure in power distribution networks.

A new evolutionary solution method for dynamic expansion planning of DG-integrated primary distribution networks

Reconstruction in the power system and appearing of new technologies for generation capacity of electrical energy has led to significant innovation in Distribution Network Expansion Planning (DNEP). Distributed Generation (DG) includes the application of small/medium generation units located in power distribution networks and/or near the load centers. Appropriate utilization of DG can affect the various technical and operational indices of the distribution network such as the feeder loading, energy losses and voltage profile. In addition, application of DG in proper size is an essential tool to achieve the DG maximum potential benefits. In this paper, a time-based (dynamic) model for DNEP is proposed to determine the optimal size, location and installation year of DG in distribution system. Also, in this model, the Optimal Power Flow (OPF) is exerted to determine the optimal generation of DGs for every potential solution in order to minimize the investment and operation costs following the load growth in a specified planning period. Besides, the reinforcement requirements of existing distribution feeders are considered, simultaneously. The proposed optimization problem is solved by the combination of evolutionary methods of a new Binary Enhanced Particle Swarm Optimization (BEPSO) and Modified Differential Evolution (MDE) to find the optimal expansion strategy and solve OPF, respectively. The proposed planning approach is applied to two typical primary distribution networks and compared with several other methods. These comparisons illustrate the effectiveness of the proposed DNEP approach.

Optimal distributed energy resources planning in a competitive electricity market: Multiobjective optimization and probabilistic design

This paper presents a probabilistic multiobjective framework for optimal distributed energy resources (DERs) planning in the distribution electricity networks. The proposed model is from the distribution company (DISCO) viewpoint. The projected formulation is based on nonlinear programming (NLP) computation. The proposed design attempts to achieve a trade-off between minimizing the monetary cost and minimizing the emission of pollutants in presence of the electrical load as well as electricity market prices uncertainties. The monetary cost objective function consists of distributed generation (DG) investment and operation cost, payment toward loss compensation as well as payment for purchased power from the network. A hybrid fuzzy C-mean/Monte-Carlo simulation (FCM/MCS) model is used for scenario based modeling of the electricity prices and a combined roulette-wheel/Monte-Carlo simulation (RW/MCS) model is used for generation of the load scenarios. The proposed planning model considers six different types of DERs including wind turbine, photovoltaic, fuel cell, micro turbine, gas turbine and diesel engine. In order to demonstrate the performance of the proposed methodology, it is applied to a primary distribution network and using a fuzzified decision making approach, the best compromised solution among the Pareto optimal solutions is found.

Reliability-constrained Based Optimal Placement and Sizing of Multiple Distributed Generators in Power Distribution Network Using Cat Swarm Optimization

This article presents optimal placement and sizing of multiple distributed generators to achieve higher overall system reliability in large-scale primary distribution networks using a novel random search algorithm known as cat swarm optimization. A composite reliability index is used as the objective function in the optimization process. Furthermore, the effect of multiple distributed generator units on power transfer capacity and power loss reduction has been observed. Extensive simulations are carried out based on three practical distribution systems to demonstrate the effectiveness of the proposed method. Further, qualitative comparisons are made with adaptive weight particle swarm optimization, particle swarm optimization with constriction factor, and binary-coded genetic algorithm to show the efficacy of the proposed method for optimal placement and sizing of distributed generators in power distribution networks.

Proposal of a New Sensitive Ground Overcurrent Protection for High-Impedance Faults

This paper presents the development of a sensitive ground overcurrent protection algorithm. The proposed algorithm is able to detect high-impedance faults produced by downed conductors in solidly grounded primary distribution networks. It presents a higher level of accuracy when compared to conventional overcurrent protection functions. The algorithm is based only on current measurements and uses additional conditions to generate the trip signal, in order to prevent false tripping due to its high level of sensitivity. This paper presents a detailed description of the proposed algorithm, the hardware prototype developed during the research and its performance in laboratory tests.

Multiple Distributed Generator Placement in Primary Distribution Networks for Loss Reduction

This paper investigates the problem of multiple distributed generator (DG units) placement to achieve a high loss reduction in large-scale primary distribution networks. An improved analytical (IA) method is proposed in this paper. This method is based on IA expressions to calculate the optimal size of four different DG types and a methodology to identify the best location for DG allocation. A technique to get the optimal power factor is presented for DG capable of delivering real and reactive power. Moreover, loss sensitivity factor (LSF) and exhaustive load flow (ELF) methods are also introduced. IA method was tested and validated on three distribution test systems with varying sizes and complexity. Results show that IA method is effective as compared with LSF and ELF solutions. Some interesting results are also discussed in this paper.

Power flow for primary distribution networks considering uncertainty in demand and user connection

This paper presents a method for calculating the power flow in distribution networks considering uncertainties in the distribution system. Active and reactive power are used as uncertain variables and probabilistically modeled through probability distribution functions. Uncertainty about the connection of the users with the different feeders is also considered. A Monte Carlo simulation is used to generate the possible load scenarios of the users. The results of the power flow considering uncertainty are the mean values and standard deviations of the variables of interest (voltages in all nodes, active and reactive power flows, etc.), giving the user valuable information about how the network will behave under uncertainty rather than the traditional fixed values at one point in time. The method is tested using real data from a primary feeder system, and results are presented considering uncertainty in demand and also in the connection. To demonstrate the usefulness of the approach, the results are then used in a probabilistic risk analysis to identify potential problems of undervoltage in distribution systems.

Primary power distribution systems planning taking into account reliability, operation and expansion costs

In this work, the planning problem of primary distribution networks is formulated as a multi-objective mixed-integer non-linear programming model (MINLP). The objective functions of this model are the expansion and operation investment costs of primary distribution networks as well as the system's reliability costs in the contingency events. The reliability costs are obtained by means of calculating the non-supplied energy because of the repairing and switching operations in the distribution network carried out to isolate and to redistribute loads in the affected sections by permanent faults. In order to solve the expansion problem of the primary network a Multi-objective Reactive Tabu Search algorithm (MO-RTS) is proposed. The concepts of dominance are used to obtain Pareto's optimal frontier in the MO-RTS. The problem of placement of sectionalising switches (automatic or manual) to restore the distribution network and to reduce non-supplied energy costs when permanent faults occur in the network is solved simultaneously with the network expansion planning problem by means of a dedicated genetic algorithm. For testing, the proposed methodology is used as a primary power distribution system, with 180 points of consumption, where 49 already exist and 131 can be added to the system during the planning process.

Fault Detection in Power Distribution Systems Using Automated Integration of Computational Intelligence Tools

The main purpose of this paper is to present architecture of automated system that allows monitoring and tracking in real time (online) the possible occurrence of faults and electromagnetic transients observed in primary power distribution networks. Through the interconnection of this automated system to the utility operation center, it will be possible to provide an efficient tool that will assist in decision-making by the Operation Center. In short, the desired purpose aims to have all tools necessary to identify, almost instantaneously, the occurrence of faults and transient disturbances in the primary power distribution system, as well as to determine its respective origin and probable location. The compilations of results from the application of this automated system show that the developed techniques provide accurate results, identifying and locating several occurrences of faults observed in the distribution system.

Levantamiento y Actualización del Modelo de una Red Primaria de Distribución

This paper shows a methodology that allows to obtain an up to date model of a primary distribution network. In the first part, the procedure used for the resistance and reactance calculation of each branch from the Geographic Information System data is described. Also, the approximations used in calculating each parameter are mentioned. Then, the methodology for obtaining the information from the GIS to obtain the network configuration is described and finally, the software structure developed for the automatic calculations of the parameters and the configuration is shown. The computational tool was tested in a real distribution system, in a medium size town (Buenaventura) that has about 6000 buses and 1300 customers, which belongs to a regional utility (EPSA:Empresa de Energía del Pacifico ESP).

Location of faults in power distribution laterals using superimposed components and programmable logic controllers

In this study, a digital fault location and monitoring technique using programmable logic controller (PLC) for primary overhead power distribution networks is presented. This technique employs pre- and post-fault current and voltage information along with data from the laterals. By using lateral current data transferred through shielded coaxial cables to the substation, the possibility of multiple fault point locations are eliminated. The effectiveness of this method is verified through Electromagnetic Transients Program (EMTP) simulations.