Problems In Distribution Networks Research Articles

Salp swarm algorithm applied to optimal capacitor allocation problem in distribution network for annual cost savings

Salp swarm algorithm applied to optimal capacitor allocation problem in distribution network for annual cost savings

A Search Group Algorithm for Optimal Distributed Generation Placement

The integration of renewable-based distributed generation (DG) in distribution networks is increasingly common worldwide due to fossil fuel depletion, environmental pollution, and global warming. This paper proposes a Search Group Algorithm (SGA) method to determine the optimal location, capacity, and quantity of DGs in distribution networks. The SGA method has a good ability to find optimal solutions thanks to the ability to balance between the global search and local search in the optimization process. The objective of the problem is to minimize the power loss of the distribution networks while meeting the constraints of the networks, such as power balance, bus voltage limit, branch current limit, DG power limit, and DG penetration limit. The proposed method is applied to the IEEE 33-node and 69-node distribution networks with two different cases. After integrating DGs into distribution networks, the power loss are reduced by 70.14% and 70.23% for the 33-node and 69-node distribution networks, respectively. The results obtained from the SGA method were compared with other methods in the literature. The simulation results have proved that the SGA method has good performance for the optimal DG placement problem in distribution networks.

On the Message Complexity of Fault-Tolerant Computation: Leader Election and Agreement

This paper investigates the message complexity of two fundamental problems, leader election and agreement in the crash-fault synchronous and fully-connected distributed network. We present randomized (Monte Carlo) algorithms for both the problems and also show non-trivial lower bounds on the message complexity. Our algorithms achieve sublinear message complexity in the so-called implicit version of the two problems when tolerating more than a constant fraction of the faulty nodes. In comparison to the state-of-art, our results improved and extended the works of [Gilbert-Kowalski, SODA'10] (which studied only the agreement problem) in several directions. Specifically, our algorithms tolerate any number of faulty nodes up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(n -\operatorname{polylog}n)$</tex-math></inline-formula> . The message complexity (and also the time complexity) of our algorithms is optimal (up to a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\operatorname{polylog}n$</tex-math></inline-formula> factor). Further, our algorithm works in anonymous networks, where nodes do not know each other. To the best of our knowledge, these are the first sub-linear results for both the leader election and the agreement problem in the crash-fault distributed networks.

Distributed Subspace Projection Graph Signal Estimation with Anomaly Interference

The estimation of graph signal is a vital problem in many distributed networks, such as vehicular networks, smart grids, unmanned aerial vehicles (UAVs), and the Internet of Things. In those networks, anomaly interference widely exists, such as network attack, noise, device fault, which will hazard the healthy of the entire system. In the paper, the estimation of graph signals with anomaly interference is investigated. We show that the graph signal estimation problem can be treated as a bandlimited subspace optimization problem, and propose a distributed subspace projection graph signal estimation algorithm based on the graph filter (DispGF), which can achieve better performance with less communication burden. In addition, a graph filter matrix that produces subspace projection is proposed to replace the nonsparse projection matrix, which guarantees distributed implementation and projection accuracy. Different from previous work, here, graph signal estimation is studied with no prior anomaly information. To this end, for FDI attack, random attack, noise interference, we propose anomaly detection and node localization scheme based on smoothness, that can achieve similar performance compared with the case of prior anomaly information known. Numerical experiments verify the effectiveness of the proposed DispGF algorithm. The convergence of the algorithm is theoretically analyzed.

Centralized Control Method for Voltage Coordination Challenges With OLTC and D-STATCOM in Smart Distribution Networks Based IoT Communication Protocol

This paper proposes a coordination method for the voltage control devices based on optimal settings of the D-STATCOMs, on the load tap changer (OLTC) transformer and the distributed generations (DGs)-based renewable energy resources (RERs). The central controller is introduced to handle the active smart distribution network (SDN) problems to maintain the voltage profile within its permissible limits, minimize power losses in different operating conditions, and minimize the energy wastage from the distributed renewable energy resources. These problems are formulated as a multi-objective optimization problem. With increasing load demand and RERs in the distribution system, voltage coordination threatens real-time efficiency. In this research work, central controller-based Gorilla Troops optimization (GTO) algorithm is proposed to detect the optimum solutions for the voltage coordination problem. The load demand uncertainty and the stochastic nature of power generated from RERs (PV panels and wind turbines) are considered in the voltage coordination problem due to their significant effects on the operation and planning of the SDN. The proposed SDN has been represented based on the Internet of Things (IoT) communication protocol. It enhances the data and information transfer between the system-connected agents. A practical test system, NDEDC-24 bus radial distribution network from the North Delta Electrical Distribution Company and IEEE-33 node system are used to test and evaluate the proposed method. The results are compared with other well-known evolutionary methods. The proposed method obtains more accurate results than the other methods.

Research on Volt/Var Control of Distribution Networks Based on PPO Algorithm

In this paper, a model free volt/var control (VVC) algorithm is developed by using deep reinforcement learning (DRL). We transform the VVC problem of distribution networks into the network framework of PPO algorithm, in order to avoid directly solving a large-scale nonlinear optimization problem. We select photovoltaic inverters as agents to adjust system voltage in a distribution network, taking the reactive power output of inverters as action variables. An appropriate reward function is designed to guide the interaction between photovoltaic inverters and the distribution network environment. OPENDSS is used to output system node voltage and network loss. This method realizes the goal of optimal VVC in distribution network. The IEEE 13-bus three phase unbalanced distribution system is used to verify the effectiveness of the proposed algorithm. Simulation results demonstrate that the proposed method has excellent performance in voltage and reactive power regulation of a distribution network.

A Comparative Study of Wind Energy Conversion System incorporating the Doubly Fed Induction Generator and the Permanent Magnet Synchronous Generator

The renewable energy power producer (RPP) sector is growing rapidly to become an important source of power in South Africa and in nations across the globe. Companies within this sector provide a variety of clean energy sources. Despite its ability to support the power system and conserve the environment that sustains life, the rising usage of renewable distributed generators (RDGs) poses power quality problems in the overall distribution network, such as the voltage instability at buses, the increase in voltage/current harmonic distortions, etc. Furthermore, RDGs are required to remain connected to the electrical power network during grid faults and provide system support by injecting reactive current when required, depending on the nature of the fault. This study provides a dynamic performance analysis of a doubly fed induction generator (DFIG) based wind energy conversion system (WECS), and permanent magnet synchronous generator (PMSG) based WECS. The potential fault ride-through capability solutions in grid code power generation and injection of reactive power are taken into account, and the application results of both systems are shown under similar working conditions. From the simulation results, it is found that the DFIG based WECS topology performed well compared to the PMSG based WECS topology.

Solving the capacitor placement problem in radial distribution networks

The optimal capacitor allocation problem is suggested in this article. This study purposes to maximize the voltage stability, minimize the power losses, and consolidate the annual net savings. Calculating the optimal locations and sizes of fixed and switched capacitors is done in two steps. The first step is using the fuzzy expert rules in calculating the most candidate buses for capacitor allocation. While the second step is using a nondominated sorting genetic algorithm II (NSGA-II) in determining the list of Pareto optimal solutions and then applying a fuzzy decision maker to pick the most compromise. To emphasize the effectiveness of the proposed method, radial distribution systems are proposed; IEEE 33-bus system and the actual Portuguese IEEE 94-bus system. To demonstrate the strength and applicability of this method, a multiobjective water cycle algorithm (MOWCA), multiobjective grey wolf optimizer (MOGWO), and other optimizers used in the published papers are used. From simulation and analysis, the proposed NSGA-II outperforms other optimizers considered for comparison in achieving the maximum percentages of minimization in a real power loss of 32.369% and 31.1011% for the 33-bus system and 25.6296% and 25.3027% for the 94-bus system, the maximum percentages of minimization in a reactive power loss of 31.7916% and 30.4948% for 33-bus system and 25.9457% and 25.8001% for the 94-bus system, the maximum annual net savings of 23,612 $ and 23,131 $ for the 33-bus system for fixed and switched capacitors, respectively, and boosting the total voltage stability, which show its superior ability to give high-grade solutions.

Feedback Power Cost Optimization in Power Distribution Networks With Prosumers

In this article, we propose a feedback control approach for solving optimal power flow (OPF) problems in power distribution networks (DNs) based on a projected gradient scheme, where the cost is given by the sum of functions of local power injections. This approach does not require detailed knowledge of the grid model and enables real-time tracking of the optimal generation configuration. Moreover, the communication burden is minimal: only a broadcast from a central entity, called the Network Supervisor (NS), to the agents in the network is needed. Agents do not have to share local information that can be sensitive, e.g., their power injections or the value of the local costs. Our approach is used to solve a particular OPF problem targeting DNs with high penetration of distributed energy resources (DERs) in which the energy cost is given by the remuneration that the DERs’ owners receive for the service they provide.

Optimization of Switch Allocation Problems in Power Distribution Networks

This paper presents the implementation of the mono-objective Switch Allocation Problem (SAP) optimization model for electric power distribution networks, considering the equivalent interruption duration per consumer unit EIDCU and non-distributed energy END reliability indexes. We use the current summation algorithm to solve the power flow, and we employ an intelligent bee colony algorithm to solve the model. Two network topologies, one with 43 and another with 136 bars, adapted from the literature, are used to illustrate the solution. Results show a significant reduction in the financial cost of planning a power distribution network.

Research on optimization strategy of urban medium voltage direct current distribution network construction considering application scenarios

In the distribution network planning work, taking into account the constraints of funds and construction annual plan, there is always the prioritization problem of multiple MVDC distribution networks. Aiming at the problem, this paper proposes a method for the optimization analysis of MVDC distribution network planning objects. Firstly, a comprehensive evaluation index system of urban MVDC distribution network planning and optimization is established from three aspects of improving power supply reliability and the quality of voltage, MVDC distribution network scenario types and distribution network social benefits. Then, the qualitative indexes are quantified based on the cloud model, and the comprehensive evaluation values of each distribution network are calculated and sorted by the hierarchical additive weighting of Delphi entropy weight method. Taking the maximum comprehensive evaluation value of the scheme as objective function and the annual total investment as constraint, the construction priority of multiple MVDC distribution networks is determined. Finally, taking several distribution networks in Guangzhou, Guangdong Province, China as examples, the simulation results show that the optimal objects of distribution network based on the urban scenario demands are X4, X8 and X1, with the lowest total investment cost of 19,180 yuan, thus verifying the effectiveness of the proposed method.

Distributed Cooperation for Voltage Regulation in Future Distribution Networks

As the penetration of distributed energy resources (DERs) increases significantly in the distribution networks, their integration reshapes distribution system power flows and leads to serious voltage quality problems in distribution networks. In this paper, a novel distributed cooperation voltage regulation method is proposed for future distribution networks. The proposed method can minimize the number of agents involved in the voltage regulation and it minimizes the change of required power for voltage regulation, which together minimizes the need for re-dispatching, i.e., the impact of voltage regulation on exchange of energy. Moreover, the proposed method performs online optimization, i.e., the value of the decision variable is physically implemented as a controller set-point at each iteration which reduces the response time. The presented algorithm is benchmarked against ADMM and centralized optimization. The results show the voltage regulation effectiveness. Compared to the centralized method, the proposed method performs better in the case of a single agent failure, and compared to the ADMM method, the proposed method greatly reduces the action time and does not require retuning if network topology or agent participation changes.

Trilevel Optimization Model for Competitive Pricing of Electric Vehicle Charging Station Considering Distribution Locational Marginal Price

We study the competitive pricing problem of electric vehicle charging station (EVCS). The EVCS determines its optimal pricing strategy considering the competition from the other EVCS and the impact of charging loads on the distribution locational marginal price (DLMP) of the power network. A trilevel optimization model is proposed, where the upper and middle level capture the profit maximization problems of this EVCS and its competitor respectively, while the lower level represents the optimal power flow problem of distribution network. Due to its computational challenge, this model is reformulated into a single level one, and their equivalence is rigorously proved. Furthermore, we develop an exact algorithm to achieve the global optimal solution, and also prove its finite convergence. Finally, numerical results validate our trilevel model and exact algorithm, and demonstrate their merits in increasing profits for the EVCS.

Application of effective gravitational search algorithm with constraint priority and expert experience in optimal allocation problems of distribution network

Optimal allocation problem of distribution network (OAPDN), which attracts much attention of electric enterprise, contributes to the flexible and environmental-friendly power supply by rationally introducing distributed generations (DGs) and shunt capacitors (SCs). To smoothly solve OAPDN problems, several effective measures such as advantageous schemes guidance (ASG) mechanism are presented and integrated into the proposed modified gravitational search algorithm with expert experience (MGSA-EE). Multiple OAPDN experiments essentially indicate that the MGSA-EE with higher efficiency and stronger exploration capability reduces the power loss on 33, 69 and 119 node networks by 94.15%, 98.10% and 84.59%, which is superior to most published technologies.Furthermore, multi-objective OAPDN problem which simultaneously considers two or more goals is also studied. Compared with the single-objective one, it is more in line with the diverse demands of actual electricity market, but the difficulty is greatly increased as well. On this basis, this paper extends MGSA-EE to an innovative multi-objective MGSA-EE (MMGSA-EE) algorithm by the suggested non-inferior sorting strategy with constraints-prior.Most typically in multi-objective OAPDN experiments on large scale networks, MMGSA-EE achieves high-quality scheme that concurrently reduces power loss and voltage deviation of 69-node network by 94.07% and 98.69%. Meanwhile, several quantitative indicators also prove that the proposed MMGSA-EE has competitive advantages over the original algorithm in terms of Pareto fronts, node voltage profiles and execution time.In general, MGSA-EE and MMGSA-EE algorithms provide efficient tools for exploring superior DG/SC configuration schemes, and are of great significance to fill research gaps in multi-objective optimizations of distribution network.

Research on voltage coupling mechanism of MV-LV distribution network under high-proportion distributed PV penetration

With the rapid growth of distributed photovoltaic(PV) installed capacity, the access of large-scale distributed PV has brought the distribution network problems such as power flow return and voltage exceeding. At present, there have been abundant researches on the impact of high proportion of PV on the voltage of distribution network, but most of them only consider the situation of a single voltage level distribution network, instead of the coupling effect between different voltage levels. Therefore, this paper took the 10kV/380V distribution network as the research object, firstly analyzed the mechanism of voltage interaction between medium and low voltage distribution networks, then established the voltage evaluation indexes of medium voltage-low voltage(MV-LV) distribution networks, finally used an actual distribution network in Chengde as case to verify the coupling mechanism through the MATPOWER platform, as well as evaluated its voltage characteristics. The work done in this paper makes the evaluation of the voltage characteristics of the distribution network more realistic and has certain engineering significance.

A novel Hybrid Harris hawk sine cosine optimization algorithm for reactive power optimization problem

ABSTRACT Reactive power optimisation can effectively reduce active power loss and improve voltage quality, which is a great significance for power system planning. When the reactive power optimisation problem is solved by Harris Hawk optimisation (HHO) algorithm, there are slow convergence and falling into local optima. This is caused by the multiple random parameters in HHO’s exploration phase. To solve this problem, the Improved Logistic Chaotic mapping, Sine and Cosine Algorithm (SCA), the dynamic adaptive inertia weights and greedy strategy are introduced; the aim is to speed up convergence, reduce blind spots and improve the search capability. The improved algorithm was tested on the classical 23 benchmark functions; Wilcoxon’s signed-rank test and Friedman test were tested, the results show that the improved algorithm can obtain better performance. The improved algorithm is applied to the reactive power optimisation problem in distribution networks with distributed generators (DG). When the reactive power optimisation problem is solved by the improved algorithms HHO, WOA, CSO, CS and PSO, respectively, the improved algorithm can obtain the lowest active power loss. Compared with no optimisation, active power loss is reduced by 33.19%. Finally, the node voltage quality ensures the safe operation of the system.

A novel approach for single and combined power quality disturbances detection using fundamental and harmonic phasors estimation model

The growing use of non-linear loads is one of the main reasons for harmonic distortions in distribution systems. Power quality disturbances have caused improper performance of electrical equipment and have created many technical and economic problems for industries and customers. One approach to overcome these problems and also the correct detection of PQ disturbances is power quality monitoring (PQM). Measurement and estimation are two essential tools to understand and investigate PQ problems in distribution networks. Here, a new estimation model is used to simultaneously extract the fundamental and harmonic phasors information of the system. In other words, the proposed estimation model has been used to access system-level data. Also, the recursive variational mode extraction (RVME) approach efficiency has been investigated for detecting power quality disturbances (PQDs). Finally, to validate the effectiveness of the proposed PQDs detection method, six different scenarios of disturbances are investigated. The IEEE 13-bus and IEEE 34-bus standard distribution test networks are used to evaluate the efficiency of the proposed method. Also, the real-time PQ disturbances are used for validating the proposed PQDs detection method. Results obtained demonstrate the efficiency and high accuracy of the proposed approach in correctly diagnosing real-time PQ disturbances.

A Novel Hybrid Method for Short-Term Probabilistic Load Forecasting in Distribution Networks

In recent decades, the evolution of loads and distributed energy resources has added great complexity and uncertainty to distribution networks. In such a context, a reliable characterization of the uncertainty associated with prediction is fundamental to the wide range of the newly emerging applications in the low-voltage level grid. In this regard, this paper proposes a novel method to solve the short-term probabilistic load forecasting (STPLF) problem in distribution networks in which the loads are usually too volatile to be forecasted accurately. The approach developed employs a Dirichlet process mixture model (DPMM) to handle the uncertainty of load patterns, which is inferred by a Markov chain Monte Carlo (MCMC)-based method. Thereafter, the DPMM representation of the load patterns is combined with a tree-based ensemble learning method to address the STPLF by solving a classification problem. The final result is averaged over all MCMC samples. The proposed technique is compared to selected benchmark methods at different aggregation levels using smart meter datasets collected from customers located in Ireland as well as the City of Saskatoon, SK, Canada. The results obtained demonstrate that the proposed approach outperforms the benchmark methods in STPLF at the given aggregation levels.

A fast non-decoupled algorithm to solve the load flow problem in meshed distribution networks

The purpose of this work is to compare the classical methods of power flow resolution (Newton–Raphson and Gauss–Seidel) with a more recent algorithm known as Alternating Search Direction (ASD), for which its equations, the steps to follow and the parameters to consider are described. In addition, a series of tests are carried out in different distribution networks where the reduction of execution time, accuracy, and robustness of the presented algorithm is demonstrated, taking as a reference the behavior of the well-known Newton–Raphson algorithm. Finally, the advantage of selecting certain parameters in the ASD algorithm is studied.

Economic scheduling of mobile energy storage in distribution networks based on equivalent reconfiguration method

Compared with traditional stationary energy storage system (SESS), mobile energy storage system (MESS) has power transfer ability in both spatial and temporal dimensions. Thus, it can provide greater flexibility in power system auxiliary services. To simplify and clarify the optimal scheduling problem, a novel “virtual switch” indicator is defined to describe the scheduling of MESS and an equivalent reconfiguration method is proposed by analogy with distribution network reconfiguration. Based on the method proposed, the dynamic economic scheduling of MESS in distribution networks(DNs) can be formulated as a scenario-based stochastic optimization problem, where the uncertainties of traffic flow and renewable energy output are considered by generating scenarios using Monte Carlo simulation. The objective function is to maximize the profit of distribution network operator. The time and cost of MESS scheduling are transformed into those of virtual switch switching in distribution network reconfiguration. Therefore, the operation scheduling problem of a MESS is equivalent to a distribution network reconfiguration problem with a SESS and several virtual switches. In this way, a grid-traffic network coupling problem is simplified to a pure distribution network problem, which provides a novel way to solve the MESS scheduling problems. Finally, simulation results on the IEEE 33-bus distribution system and simple traffic network verify the validity and feasibility of the method proposed.