Optimization Of Distribution Networks Research Articles

Hybrid particle swarm optimization and tabu search for the design of large-scale water distribution networks

ABSTRACT Water distribution network (WDN) optimization has received special attention from various technicians and researchers, mainly due to its high costs of implementation, operation and maintenance. However, the low computational efficiency of most developed algorithms makes them difficult to apply in large-scale WDN design problems. This article presents a hybrid particle swarm optimization and tabu search (H-PSOTS) algorithm for WDN design. Incorporating tabu search (TS) as a local improvement procedure enables the H-PSOTS algorithm to avoid local optima and show satisfactory performance. Pure particle swarm optimization (PSO) and H-PSOTS algorithms were applied to three benchmark networks proposed in the literature: the Balerma irrigation network, the ZJ network and the Rural network. The hybrid methodology obtained good results when seeking an optimal solution and revealed high computational performance, making it a new option for the optimal design of real water distribution networks.

Optimization Design Method for Photovoltaic and Energy Storage Grid-Connected Microgrid Considering Power Optimization of Distribution Network

Optimization Design Method for Photovoltaic and Energy Storage Grid-Connected Microgrid Considering Power Optimization of Distribution Network

Multi-Scenario and Multi-Objective Collaborative Optimization of Distribution Network Considering Electric Vehicles and Mobile Energy Storage Systems

Due to the short-term large-scale access of renewable energy and residential electric vehicles in residential communities, the voltage limit in the distribution network will be exceeded, and the quality of power supply will be seriously reduced. Therefore, this paper introduces the mobile energy storage system (MESS), which effectively solves the problem of overvoltage limit caused by the large number of distributed power sources and household electric vehicles in the distribution network. This paper proposes an optimal scheduling model for distribution network based on mobile energy storage system. First, the space-time energy transfer model of mobile energy storage is established, and the transfer cost of MESS and the income of peak shaving and valley filling are considered. According to daily driving data of household electric vehicle (EV), a prosumer group electric vehicle charging and discharging model is established; After that, by establishing a target model for maximizing MESS operating income and penalizing voltage overshoot under different scenarios such as low peak load of electric vehicles and different initial capacities of MESS, a multi-scenario multi-objective collaborative optimization model for the distribution network is established; Finally, an improved IEEE33-bus system is used to analyze the calculation example. The results of the calculation example show that the optimal scheduling model in this paper can improve the photovoltaic consumption capacity and improve the voltage limit problem, which verifies the effectiveness and economy of the scheduling model.

Optimal Operation of Automated Distribution Networks Based-MRFO Algorithm

Nowadays, distribution utilities expend large investments on Distributed System Automation (DSA) based on smart secondary substations at load, capacitor, and distributed generator points with installed automatic sectionalizing switches on their branches. This article addresses the optimal control and operation of distribution systems that minimize the wasted energy and introducing quantitative and qualitative power services to meet consumers’ satisfaction. Simultaneous allocations of Distributed Generators (DGs) and Capacitor Banks (CBs) are handled at peak loading condition. Then, the DSA is optimally activated for optimal Distribution Network Reconfiguration (DNR), optimal DGs commitment, and optimal CBs switching for losses minimization in coordination with different loading conditions. Practical daily load variation is applied to simulate the dynamic operation of automated distribution systems. For achieving these targets, the Manta Ray Foraging Optimization Algorithm (MRFOA) is adopted. MRFOA is an effective and simple structure optimizer that emulates three various individual manta rays foraging organizations. The capability of the MRFOA is applied to the IEEE 33-bus, 69-bus and practical distribution network of 84-bus due to the Taiwan Power Company (TPC). A comparison with recent techniques has been conducted to prove the effectiveness of MRFOA. The accomplished results demonstrate that the proposed MRFOA has great effectiveness and robustness among other optimization techniques.

Chance-Constrained Optimal Distribution Network Partitioning to Enhance Power Grid Resilience

This article proposes a method for identifying potential self-adequate sub-networks in the existing power distribution grid. These sub-networks can be equipped with control and protection schemes to form microgrids capable of sustaining local loads during power systems contingencies, thereby mitigating disasters. Towards identifying the best microgrid candidates, this work formulates a chance-constrained optimal distribution network partitioning (ODNP) problem addressing uncertainties in load and distributed energy resources; and presents a solution methodology using the sample average approximation (SAA) technique. Practical constraints like ensuring network radiality and availability of grid-forming generators are considered. Quality of the obtained solution is evaluated by comparison with- a) an upper bound on the probability that the identified microgrids are supply-deficient, and b) a lower bound on the objective value for the true optimization problem. Performance of the ODNP formulation is illustrated through case-studies on a modified IEEE 37-bus feeder. It is shown that the network flexibility is well utilized; the partitioning changes with risk budget; and that the SAA method is able to yield good quality solutions with modest computation cost.

Optimisasi Multi-objektif pada Rekonfigurasi Jaringan Distribusi Tenaga Listrik dengan Integrasi Pembangkit Terdistribusi Menggunakan Metode Sistem Kekebalan Buatan

This study proposes a multi-objective optimization for power distribution network reconfiguration by integrating distributed generators using an artificial immune system (AIS) method. The most effective and inexpensive technique in reducing power losses in distribution networks is optimizing the network reconfiguration. On the other hand, small to medium scale renewable energy power plant applications are growing rapidly. These power plants are operated on-grid to a distribution network, known as distributed generation (DG). The presence of DG in this distribution network poses new challenges in distribution network operations. In this study, the distribution network optimization was carried out using the AIS method. In optimization, the goal to be achieved is not only one objective but should be multiple objectives. Multi-objective optimization aims to reduce power losses, improve the voltage profile, and maintain a maintained network load balance. The AIS method has the advantage of fast convergence and avoids local minima. To test the superiority of the AIS method, the distribution network optimization with and without DG integration was carried out for the 33-bus and 71-bus models of the IEEE standard distribution networks. The results show that the AIS method can produce better system operating conditions than before the optimization. The parameters for the success of the optimization are minimal active power losses, suitable voltage profiles, and maintained load balance. This optimization has successfully increased the efficiency of the distribution network by an average of 0.61%.

Functional Feasibility in Optimal Evaluation of Water Distribution Network Performances

The traditional approach for the optimization of water distribution networks (WDNs) does not always lead to consistent solutions from an operational point of view. The latest optimization algorithms identify solutions that are “the best solutions” in mathematical terms but that can be less than robust against changes in operating conditions, resulting in the worst case in hydraulically infeasible configurations. Thus, this paper aims to provide a methodology that can synthesize the network performance capabilities under the change in operating conditions with two convergent strategies. The first consists of the implementation of new performance indices (PIs), the demand deficit and the pressure range, and the evaluation of their ability to criticality highlight in operating conditions. The second is the introduction of a new approach to weight the infeasible solutions in the final result, which are those inconsistent with the real hydraulic network performances. The analysis shows that the use of these new indices makes it easier to understand the behavior of the network and to identify any weaknesses. This is true if these indices consider the hydraulically inconsistent solutions that may arise from the simulations of different operation conditions; otherwise, results that poorly represent the real behavior of the network would be obtained.

Slime Mold Inspired Distribution Network Initial Solution

Electricity distribution network optimisation has attracted attention in recent years due to the widespread penetration of distributed generation. A considerable portion of network optimisation algorithms rely on an initial solution that is supposed to bypass the time-consuming steps of optimisation routines. The aim of this paper is to present a nature inspired algorithm for initial network generation. Based on slime mold behaviour, the algorithm can generate a large-scale network in a reasonable computation time. A mathematical formulation and parameter exploration of the slime mold algorithm are presented. Slime mold networks resemble a relaxed minimum spanning tree with better balance between the investment and loss costs of a distribution network. Results indicate lower total costs for suburban and urban networks.

Redundant binary codes in genetic algorithms: multi-objective design optimization of water distribution networks

Abstract Genetic algorithms have been shown to be highly effective for optimization problems in various disciplines, and binary coding is generally adopted as it is straightforward to implement and lends itself to problems with discrete-valued decision variables. However, a difficulty associated with binary coding is the existence of redundant codes that do not correspond to any element in the finite discrete set that the encoded parameter belongs to. A common technique used to address redundant binary codes is to discard the chromosomes in which they occur. Effective alternatives to the outright removal of redundant codes are lacking in the literature. This article presents illustrative examples based on the problem of optimizing the design of water distribution networks. Two benchmark networks in the literature and two different multi-objective design optimization models were considered. Different fixed mapping schemes gave significantly different solutions in the search space. The main inference from the results is that mapping schemes that improved diversity in the population of solutions achieved better results, which may pave the way for the development of practical and effective mapping schemes.

An Integrated Approach to Optimal Charging Scheduling of Electric Vehicles Integrated with Improved Medium-Voltage Network Reconfiguration for Power Loss Minimization

The uncoordinated integration of electric vehicles (EVs) severely deteriorates the operational performance of a distribution network. To optimize distribution network performance in an EV charging environment, this paper presents a two-stage optimization approach, which integrates coordinated EV charging with network reconfiguration. A formulation to minimize system power loss is presented, and an optimal solution is obtained using a binary particle swarm optimization algorithm. The proposed approach is tested on a modified IEEE 33-bus medium-voltage node test system, coupled with a low voltage distribution network. Results of the coordinated and uncoordinated EV charging are compared with those of the developed integrated approach, and the operational performance of the system is studied. The results show that the integration of network reconfiguration with coordinated EV charging significantly decreases network power losses and fairly improves voltage profile. Thus, the proposed strategy can lead to improved operational performance of the system while dealing with the growing penetration of EVs in the network.

Use of Energy-Based Domain Knowledge as Feedback to Evolutionary Algorithms for the Optimization of Water Distribution Networks

The optimization of water distribution networks (WDN) has evolved, requiring approaches that seek to reduce capital costs and maximize the reliability of the system simultaneously. Hence, several evolutionary algorithms, such as the non-dominated sorting-based multi-objective evolutionary algorithm (NSGA-II), have been widely used despite the high computational costs required to achieve an acceptable solution. Alternatively, energy-based methods have been used to reach near-optimal solutions with reduced computational requirements. This paper presents a method to combine the domain knowledge given by energy-based methods with an evolutionary algorithm, in a way that improves the convergence rate and reduces the overall computational requirements to find near-optimal Pareto fronts (PFs). This method is divided into three steps: parameters calibration, preprocessing of the optimal power use surface (OPUS) results, and periodic feedback using OPUS in NSGA II. The method was tested in four benchmark networks with different characteristics, seeking to minimize the costs of the WDN and maximizing its reliability. Then the results were compared with a generic implementation of NSGA-II, and the performance and quality of the solutions were evaluated using two metrics: hypervolume (HV) and modified inverted generational distance (IGD+). The results showed that the feedback procedure increases the efficiency of the algorithm, particularly the first time the algorithm is retrofitted.

Operation Optimization of DC Distribution Network with BSS Based on GA-WDO Hybrid Algorithm

Background: The high permeability of Distributed Generation (DG) and the development of DC load represented by electric vehicle Battery Swapping Station (BSS) pose new challenges to the reliable and economic operation of DC distribution system. Methods: In order to improve the wind and solar absorption rate and the reliable operation of DC distribution network and coordinate the interests and demands of BSS and DC distribution company, the upper level takes the abandonment rate and the minimum variance of BSS charging and discharging net load as two objective functions, and the lower level takes the minimum operation cost of DC distribution network and BSS as the objective function. Secondly, this paper proposes a method that combines Genetic Algorithm (GA) with Wind-Driven Optimization algorithm (WDO). CPLEX and hybrid GA-WDO are used to solve the upper and lower models, respectively. Results: Finally, an example shows that the proposed optimization model can reduce the operation cost of DC distribution network with BSS and improve the utilization rate of wind and light, which shows the rationality and effectiveness of the optimization model. Conclusion: In this paper, considering the randomness and uncertainty of wind power generation and photovoltaic power generation, this paper establishes the upper objective function with the minimum abandonment rate and load variance and the lower objective function with the minimum operation cost of DC distribution network and BSS operators.

Multi-objective optimization of water distribution networks: An overview

Multi-objective optimization of water distribution networks: An overview

Segregated Linear Decision Rules for Inverter Watt-VAr Control

The modern photovoltaic inverter has an active power-reactive power (Watt-VAr) control curve that is part of its management modes. The Watt-VAr curve, whose settings can be remotely modified, regulates the inverter reactive power output in function of its active power. This paper proposes the optimization of inverter segregated linear decision rules (LDRs), starting from the legacy Volt-VAr optimization in distribution networks. As opposed to classical LDRs that are interfaced with Volt-VAr optimization, the segregated LDRs are piecewise linear continuous curves that directly satisfy the reactive power capability of inverters, and their multiple segments can capture the correlation in solar power variability. The paper presents linear programming formulations for optimizing the segregated linear decision rules using stochastic optimization, robust optimization, and distributionally robust optimization. The optimization schemes are demonstrated on both meshed and radial networks, and their performance is validated via Monte-Carlo simulation.

Strategic planning of renewable distributed generation in radial distribution system using advanced MOPSO method

The integration of wind turbine, solar photovoltaic based distributed generation in low voltage distribution network has many advantages. However, the output power from wind turbine and solar PVs are intermittent in nature. The production of these intermittent power generations may introduce many challenges in distribution network such as increase in power losses, voltage deviation and instability. Therefore, this paper proposes optimal integration of these intermittent renewable DGs along with biomass and capacitor banks to optimize distribution network parameters. The real time wind speed and solar irradiance data are processed in time series mathematical model along with relevant probability distribution function (PDF) for calculating the accurate power output from these renewable resources. Later, an advanced multi-objective particle swarm optimization technique has been used to optimize the distribution network parameters such as power losses, voltage deviation and voltage stability index. The proposed model has been validates and tested on Portuguese 94 bus system. Moreover, the proposed model results revealed the power loss reduction up to 77.82%, voltage deviation 9.68% and voltage stability index 44.25%.

A Propose Model Optimal Supply Chain Distribution Network for Farmer Industrial

A supply chain should be operated in the most efficient way in a highly competitive environment, with the goals of cost minimization, shipment delays, inventories and expenditures, and distribution maximization, gain, return on investment, level of customer support, and efficiency. The development of supply-chain distribution networks is therefore an extremely complex task, due to the large physical production and distribution network flows, the uncertainties associated with external interface customers and suppliers as well as the non-linear dynamics linked to internal information flows. This study aims to address a problem in domestic distribution in a supply chain system that includes manufacturers, distribution centers and consumer zones to determine the optimum configuration of the network. We propose a mixed integer linear programming model to solve the problem.

Preconditioning Water Distribution Network Optimization with Head Loss–Based Design Method

AbstractThis paper develops a new domain knowledge–based initial design method for the optimization of water distribution network design. The new initial water distribution network design method, t...

Discharge coefficient equation to calculate the leakage from pipe networks

With the increasing of urbanization, water distribution networks play an important role in human life and the effective use of water resources. Therefore, studies have been made for the optimization of water distribution networks in some fields such as pressure management and leakage control. In this context, the discharge coefficient, which is one of the components of the hydraulic calculations, is a very significant parameter in calculating the losses. In this study, a new equation has been proposed to calculate the discharge coefficient. Computer simulations were done by using ANSYS Fluent and discharge coefficient values were determined for round holes. Firstly, the model validated with theoretical Toricelli (orifice) equation and then, the model was run for number of scenarios according to various internal pressure and hole areas. The model results were formulated by means of regression equations. To satisfy the dimensional homogeneity, the ratio of the hole area to the pipe cross-sectional area, area ratio (r), and the ratio of the internal pressure to the external pressure, pressure ratio (p), were used. In this study, easy to use discharge coefficient equation was proposed to calculate the leakage losses in water distribution networks. With the help of this equation, the discharge coefficient can be calculated precisely for different pressure values and leakage areas rather thantaken as a constant value. Thus, the calculation of the leakage flow rate will be more accurate. Furthermore, it is concluded that the dicharge coefficient varies between 0.65 and 0.72. There is also inverse realtionship between discharge coefficient and pressure and discharge coefficient and leakage area.

An efficient numerical scheme for the thermo-hydraulic simulations of thermal grids

Renewable and smart energy systems require district heating and cooling grids able to operate with variable flow rates, to manage different supply temperatures, and to support distributed energy production involving bidirectional flows. Due to these requirements, accurate and effective thermo-hydraulic models are essential to correctly simulate the flow rates, the head drops and the temperature transients for supporting the design, management and optimisation of thermal distribution networks. In this article, an efficient numerical scheme for the simulation of thermal grids based on a thermo-hydraulic model with a quasi-dynamic approach is proposed. Global gradient algorithm of Todini together with a uniformly distributed representation of demand along the pipes is used for steady-state hydraulic simulations of the networks modelled according to the graph theory. The temperature distribution is computed by solving a first order hyperbolic PDE which accounts for heat advection in the flux term and heat dissipation in an algebraic source term. A very efficient second-order Eulerian-Lagrangian finite volume scheme is employed on a staggered mesh, which evolves in time the temperature distribution starting from the velocity distribution given by the hydraulic solver. The usage of a Eulerian-Lagrangian algorithm for the discretisation of the convective terms, allows the proposed model to be unconditionally stable for every time step size. As such, the main advantages of the proposed model are the flexibility due to the admissibility of any spatial-temporal discretisation, the second order accuracy provided by both the demand schematisation and the thermo-hydraulic solver, and the computational efficiency guaranteed by the decoupled modelling approach. The resulting algorithm is extensively validated on four tests consisting of thermal grids of various complexity that have been carefully designed in order to capture different features and behaviours of the model. The accuracy of the results in terms of velocity, pressure and temperature is tested by separately checking the symmetry, the advection term, the heat loss component and, finally, by simulating a complex grid configuration with multiple heat sources. The article aims to present a proof-of-concept concerning a breakthrough numerical scheme for the efficient thermo-hydraulic simulation of pipeline networks, which proves to be suitably implemented in the modelling of district heating and cooling networks.

Topological optimization of hybrid quantum key distribution networks.

With the growing complexity of quantum key distribution (QKD) network structures, aforehand topology design is of great significance to support a large-number of nodes over a large-spatial area. However, the exclusivity of quantum channels, the limitation of key generation capabilities, the variety of QKD protocols and the necessity of untrusted-relay selection, make the optimal topology design a very complicated task. In this research, a hybrid QKD network is studied for the first time from the perspective of topology, by analyzing the topological differences of various QKD protocols. In addition, to make full use of hybrid networking, an analytical model for optimal topology calculation is proposed, to reach the goal of best secure communication service by optimizing the deployment of various QKD devices and the selection of untrusted-relays under a given cost limit. Plentiful simulation results show that hybrid networking and untrusted-relay selection can bring great performance advantages, and then the universality and effectiveness of the proposed analytical model are verified.