Feeder Power Loss Research Articles

Planning and Research of Distribution Feeder Automation with Decentralized Power Supply

The high penetration of distributed generation in distributed energy systems causes the variation of power loss and makes the power grid become more complicated, so this paper takes various types of optimal algorithms into account and simulates the feeder reconfiguration on the IEEE-33 system as well as the Taiwan power system. The simulation verifies linear population size reduction of successful history-based adaptive differential evolution (L-SHADE) and particle swarm optimization (PSO) fitness in different systems and provides the recommended location of distributed energy. The proposed method keeps the voltage bound of 0.95 to 1.03 p.u. of Taiwan regulation. In the IEEE-33 system, we achieved a 52.57% power loss reduction after feeder reconfiguration, and a 70.55% power loss reduction after the distributed generator was implemented and feeder reconfiguration. Under the variation of load demand and power generation of the Taiwan power system, we establish the system models by forecasting one-day load demand. Then, we propose a one-day feeder switch operation strategy by considering the switches’ operation frequency with the reduction of 83.3% manual operation and recommend feeder automation to achieve feeder power loss reduction, voltage profile improvement and get regional power grid resilient configuration.

Day-Ahead Optimal Scheduling of Distributed Resources and Network Reconfiguration Under Uncertain Environment

Future distribution systems can be seen with very high penetration of renewable energy sources (RESs) such as solar photovoltaics and wind turbines on account of diverse techno-economic and social concerns. The uncertainty and variability associated with these RESs along with the stochastic nature of load demand imposes real challenges to system operators. More realistic formulations and suitably tailored methodologies can coordinate well-known operational strategies to achieve optimum performance of distribution systems. This article presents a new methodology to optimally coordinate day-ahead scheduling of distributed resources (DRs) with distribution network reconfiguration (DNR). The scheduling problem optimizes economic operation by considering O&M charges of DRs, emission charges of micro turbines, and sale/purchase of electricity to the customers/grid whereas feeder power losses are minimized by solving DNR problem. Proposed methodology coordinates these two key strategies by coarse and fine tuning to optimize several techno-economic and social objectives while duly addressing more realistic scenario of distribution systems. Application results on a modified standard 33-bus distribution system demonstrate the effectiveness of the proposed methodology.

Reliability analysis and power quality improvement model using enthalpy based grey wolf optimizer

With the modern era of a restructured power system environment, there are various power quality issues faced when it is being transferred within different utilities. To overcome these issues and deliver maximum effective power maintaining its quality in terms of Voltage Stability, Losses, distortion in the signal received etc. is the major challenge. This paper presents the enthalpy based Grey Wolf Optimisation (GWO) model and aims to solve the issues of the node voltage deviation power system, feeder power losses, power factor, and total harmonic distortion in a power system. The results of the model presented is compared with different conventional algorithms and found to be most suitable due to its simplicity, faster convergence and high search precision. The analysis of the presented method is performed on IEEE 30 and 57 test bus systems in MATLAB/SIMULINK environment. When compared with the conventional models, our proposed method shows 0.49, 0.69 and 1.41% better fitness accuracy for IEEE 30 and 3.23, 2.37 and 1.45% better for IEEE 57 than PSO, ABC and GA respectively. The proposed method has proved to be effective in terms of all the objectives stated above and attaining the maximum quality of power.

Data Analytics for Substation Overloading Assessment of Solar Integrated Distribution System

It is an important concern to supply uninterrupted power to consumer under competitive environment. Therefore, available reserve margin of substation transformer is utmost concerns for power utilities due to its direct effects on power quality, reliability and revenue loss. This research work focus on data analytics application to predict substation overloading assessment for integrated distribution system. This paper has developed three adaptive neural fuzzy interface system (ANFIS) to predict the available substation reserve margin by considering base case and different solar integration scenarios. The ANFIS models were trained and tested using stochastic data obtained from load flow computation for a modified IEEE 37 feeder. To train ANFIS models, input variables are taken as “voltage unbalance factor, feeder power loss to load ratio, maximum branch loading, voltage deviation, and minimum power factor”, while target variable is taken as substation reserve margin”. All predications of ANFIS models are compared with those values obtained from load flow computation. It was found that predicated results have good agreement with load flow computational data and R-squared value fall in the range of 0.9182 to 0.9173. The proposed research work is useful to predict operational performance of integrated solar distribution system in smart grid environment.

OPTIMASI PENEMPATAN DAN KAPASITAS DISTRIBUTED GENERATION (DG) UNTUK MEMINIMALKAN RUGI-RUGI DAYA PADA PENYULANG ABANG MENGGUNAKAN METODE QUANTUM GENETIC ALGORITHM (QGA)

This research was conducted to obtain the optimal DG location with the aim of minimizing power losses in feeders. Penyulang Abang has a length of 207,946 kms with power losses of 1,105 MW. One way to reduce power losses is to install distributed generation. Distributed generation is installed in four Abang feeder locations. Optimization is done by using the Quantum Genetic Algorithm (QGA) method. Quantum Genetic Algorithm is an evolutionary algorithm. Genetic Quantum Method Algorithms are based on the concept of quantum bits (qubit) and the superposition of quantum mechanical forms. The purpose of this method is to select the best results from the results obtained. The final result of the QGA method is the most efficient distributed generation and capacity placed in several Abang feeder locations, namely buses 1, 5, 7 and 302 with capacities of 0.374 MW, 1,894 MW, 1,988 MW and 0.500 MW, respectively. So that the power losses are reduced to 0.234 MW.

OPTIMASI PENEMPATAN KAPASITOR BANK UNTUK PERBAIKAN RUGI DAYA PADA PENYULANG SABA MENGGUNAKAN ALGORITMA GENETIKA

Saba feeder is a feeder who supplies 78 distribution transformers with feeder length 38,959 kms, through this Saba feeder electrical energy is channeled radially to each distribution substation. In 2017 the voltage shrinkage at Saba feeder was 9.88% (18,024 kV) while the total power loss was 445.5 kW. In this study an attempt was made to overcome the voltage losses and power losses using the method of optimizing bank capacitors with genetic algorithms and network reconfiguration. The best solution obtained from this study will be selected for repair of voltage losses and power losses in Saba feeders. The results showed that by optimizing bank capacitors using genetic algorithms, the placement of capacitor banks was placed on bus 23 (the channel leading to the BB0024 transformer) and successfully reduced the power loss to 331.7 kW. The network reconfiguration succeeded in fixing the voltage on the Saba feeder with a voltage drop of 4.75% and a total power loss of 182.7 kW. With the combined method, reconfiguration and optimization of bank capacitors with genetic algorithms were obtained on bus 27 (channel to transformer BB0047) and managed to reduce power losses to 143 kW.

Determination of Optimal PV Locations and Capacity in Radial Distribution System To Reduce Power Losses

Photovoltaic (PV ) penetration has increased significantly in the last decade. PV Penetration with optimal capacity and location can reduce feeder power losses. The aims are to investigate degradation characteristics of feeder power loss value due to PV penetration in some radial distribution system and to obtain the smallest losses based on its characteristics to determine optimal PV capacity and installation location on radial distribution system. The simulation used DIgSILENT Powerfactory 14.1 to obtain losses data of the feeder. PV penetration location is varied from the nearest substation to the farthest bus from substation. The losses characteristics as the shift of the installation location of the PV to the end of the feeder produces the second order polynomial graph (y = ax2 - bx + c, a> 0) and the function graph of x with the negative (y = ax-c). This characteristic is used in calculation with C programming to determine the optimal location and capacity of PV, where the result of determining the optimum point is in accordance with the result of simulation, but it has different value of feeder power loss of 11.18 %. By simulation, optimal PV location with range 42.1% - 89.47% or average 67.25% feeder length from substation with 80% - 90% penetration.

Economic Analysis of PV Distributed Generation Investment Based on Optimum Capacity for Power Losses Reducing

This paper presents an economic evaluation to choose the most benefecial investment of photovoltaic (PV) distributed generation based on technical calculation of optimum PV location and capacity to reduce power losses. Input of economical evaluation is using the output calculation of technical analyses which has been calculate in previous study. The technical evaluation calculate the optimal PV location to reduce feeder power loss by using sample of 7 type of radial distribution, 5 feedersfrom Indonesia State Electricity Company (PLN) and 2 from IEEE, which calculated by DIgSILENT 14.1. The optimal location that has been calculated is used as a based location to find out the optimum PV capacity to be installed by using some economical parameter. The annual savings from feeder loss reduction, the annual revenue of PV power sales, the initial capital investment cost for a PV and the operating and maintenance (O&M) cost are then taken into account to evaluate the benefit and cost of the PV investment to covers analyses of net present value (NPV), Internal rate of return (IRR), and payback period (PP). All of economic methods calculation is compared to get the most beneficial investment. Based on calculation, with proper selection of PV and capacities, the investment can be profitable by choosing NPV based-decision. Mathematical equation characteristic of the results by these indicators is also presented.

Enhanced Bat Algorithm for Analyzing the Reliability and Contingency Issue in Radial Distribution Power System

The paper proposes the hybrid technique to observe the contingency and improve the reliability of radial distribution system (RDS). The hybrid algorithm used is interface of Bat algorithm and particle swarm optimization that is used to examine the contingency and reliability indices in the RDS. The feeder power loss, system node voltage deviation, system average interruption frequency index, system average interruption unavailability index and the energy not supplied at every load position in a sharing scheme are used to resolve the contingencies to minimize the power quality and reliability indices. The proposed techniques are implemented on MATLAB/Simulink platform and experienced on the IEEE 33 bus RDS. To establish the effectiveness of the proposed algorithm, results are compared with the reported Bat and Firefly Algorithm.

Branch current decomposition method for loss allocation in contemporary distribution systems

The presence of distributed generation (DG) units in reconfigured distribution systems introduces complexity in loss allocation (LA). DG integration alters feeder power losses so DG owners (DGOs) may be incentivized or penalized accordingly, whereas network reconfiguration alters feeding path of network user’s thus further increases complexity in LA. The LA method should judiciously allocate losses among load points and simultaneously reward DGOs and distribution network operator (DNO) for their contribution towards loss reduction. Moreover, the LA method should take care of the reactive power transactions of network users. This paper proposes a branch current decomposition method (BCDM) based upon circuit theory for allocating losses in active reconfigured distribution system while giving due consideration to reactive power transactions of network users. BCDM considers virtual branch voltage drops which is well supported by analytical treatment. In addition, Superposition is utilized to incentivize/penalize DGOs. A new LA strategy is proposed for fair allocation of loss/loss incentives among network users and DNO. Proposed method is thoroughly investigated under varying loading, load power factors and under varying network topologies. The comparison results obtained on standard test distribution system highlights the importance of proposed method.

Cross-term Decomposition Method for Loss Allocation in Distribution Systems Considering Load Power Factor

The customers of distribution system are generally encouraged to maintain better power factor through suitable tariff structures. The feeder power loss allocation is a part of tariff. The feeder power losses also depend upon power factor of loads. Therefore, loss allocation method should incorporate suitable rewarding and penalizing strategy for better and poor factors loads. This paper proposes a new distribution loss allocation method that considers load power factor to allocate losses in a more practical way. The proposed method employs bifurcation of cross terms of branch power loss among contributing nodes. The method provides loss allocation to system nodes in such a way that it provides rebates/penalties against variation in load power factor only to the concerned nodes. The proposed method is applied to 30-bus and 33-bus test distribution system and compared with other established methods. The analysis of the application results highlights the importance of the proposed method.

Study of improved load sharing methodologies for distributed generation units connected in a microgrid

In this paper, an improved load sharing strategy is proposed for distributed generation units (DGs) connected in a microgrid. Conventional frequency and voltage droop control result in unacceptable active and reactive power sharing. The proposed method formulates a suitable algorithm for load sharing in the islanded microgrid. The feeder power loss and the line impedance voltage drops are minimized so as to regulate the voltage at the point of common coupling (PCC) at its nominal value. The desired DG output voltages are calculated and a linear relationship is obtained between the shared active and reactive powers and the DG output voltages. A master DG controller sets the frequency which is followed by other DG units. The reference powers for the DG units are adjusted so as to maintain the rated PCC voltage. The proposed strategy is verified taking into account the DG ratings, unequal line impedance drops, feeder losses, change in system impedance and effect of DG local loads and formulates an improved power sharing strategy that also facilitates PCC voltage regulation under variable loading conditions. Simulation and experimental results are presented to verify the effectiveness of the proposed method.

Current decomposition method for loss allocation in distribution systems

Distribution system customers are consistently encouraged to maintain better power factor. The power factor is crucial in deciding the feeder power losses so the loss allocation method must incorporate this fact thereby rewarding or penalising the concerned customers. Therefore, it is imperative to have a judicious reflection of load power factor while designing a loss allocation method. This study proposes a new branch current decomposition method for distribution loss allocation that inherently considers load power factor. The proposed BCDM is investigated on 30-bus and 33-bus test distribution systems. The simulation results reveal the significance of the proposed method.

Fuzzy Logic Controller Based Distributed Generation Integration Strategy for Stochastic Performance Improvement

In the restructured environment, distributed generation (DG) is considered as a very promising option due to a high initial capital cost of conventional plants, environmental concerns, and power shortage. Apart from the above, distributed generation (DG) has also abilities to improve performance of feeder. Most of the distribution feeders have radial structure, which compel to observe the impact of distributed generations on feeder performance, having different characteristics and composition of time varying static ZIP load models. Two fuzzy-based expert system is proposed for selecting and ranking the most appropriated periods to an integration of distributed generations with a feeder. Madami type fuzzy logic controller was developed for sizing of distributed generation, whereas Sugeno type fuzzy logic controller was developed for the DG location. Input parameters for Madami fuzzy logic controller are substation reserve capacity, feeder power loss to load ratio, voltage unbalance, and apparent power imbalances. DG output, survivability index, and node distance from substation are chosen as input to Sugeno type fuzzy logic controller. The stochastic performance of proposed fuzzy expert systems was evaluated on a modified IEEE 37 node test feeder with 15 minutes characteristics time interval varying static ZIP load models.

Stochastic Distribution System Operation Considering Voltage Regulation Risks in the Presence of PV Generation

Variable over voltage, excessive tap counts, and voltage regulator (VR) runaway condition are major operational challenges in distribution network while accommodating generation from photovoltaics (PVs). The conventional approach to achieve voltage control based on offline simulation for voltage set point calculation does not consider forecast errors. In this work, a stochastic optimal voltage control strategy is proposed while considering load and irradiance forecast errors. Stochastic operational risks such as overvoltage and VR runaway are defined through a chance constrained optimization (CCO) problem. This classical formulation to mitigate runaway is further improved by introducing a stochastic index called the Tap Tail Expectation . Operational objectives such as power losses and excessive tap count minimization are considered in the formulation. A sampling approach is proposed to solve the CCO. Along with other voltage control devices, the PV inverter voltage support features are coordinated. The simulation study is performed using a realistic distribution system model and practically measured irradiance to demonstrate the effectiveness of the proposed technique. The proposed approach is a useful operational procedure for distribution system operators. The approach can minimize feeder power losses, avoid voltage violations, and alleviate VR runaway.

Smart DER control for minimizing power losses in distribution feeders

Distributed energy resources, such as distributed generators and storage systems, are generally considered as active power sources. Indeed, they can also be used as reactive power resources, which contribute to reduce the power losses in a distribution feeder. Adopting a decentralized approach, in this paper an on-line Optimization Strategy is introduced to define the optimal set point of a classical reactive power control system for distributed energy resources so as to minimize the power losses along the feeder. Using only local measurements, firstly the actual operating conditions of the distribution system are estimated and, then, a constraint minimization problem is solved so as to calculate the optimal set point. A suitable change of coordinates yields to evaluate the optimal solution in an analytic closed form. Numerical simulation results are also presented to give evidence of the reduction of power losses and of the performance of the proposed smart control scheme.

Optimal Reactive Power Compensation by Shunt Capacitor Sizing Using Harmony Search Algorithm in Unbalanced Radial Distribution System for Power loss Minimization

Electricity demand exceeds the supply leading to regional blackout. The power generated is transmitted through a large network and significant power losses in transmission and distribution feeders. Loss reduction through various methods has the potential to yield huge savings in economic growth and environmental impact. One such effective loss minimization method is to inject reactive power by placing shunt capacitors at appropriate places with proper size. A Harmony Search Algorithm methodology is proposed in this work to identify the appropriate size of shunt capacitors by taking the objective as minimizing the cost associated with real power losses along with installation cost of shunt capacitors in an unbalanced radial distribution network. The bus voltage limits, number/size of installed capacitors at each node are taken as constraints. A two stage methodology is adopted in this work to get the solution of the optimization problem. In the first stage, the voltage stability index values at each bus is computed and the node with minimum value of voltage stability index is identified as the most receptive node to voltage slump and such a node is considered as candidate node for installation of shunt capacitors. In the second stage the HSA methodology is used to ascertain the suitable capacitor size. The backward / forward sweep algorithm based Radial distribution load flow algorithm is utilized for power flow simulation. The proposed Harmony Search Algorithm is implemented on the modified IEEE 13 and 37 bus URDN.

Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms

This paper presents an efficient Genetic Algorithms (GAs) based method to improve the reliability and power quality of distribution systems using network reconfiguration. Two new objective functions are formulated to address power quality and reliability issues for the reconfiguration problem. Various power quality and reliability objectives such as feeder power loss, system’s node voltage deviation, system’s average interruption frequency index, system’s average interruption unavailability index and energy not supplied are transformed into a single objective function. This single objective problem is then solved using the GA-based method. The effectiveness of the proposed objective functions has been investigated on two different standard test distribution systems. Application results are promising when compared with other existing method.

Stability-index based method for optimal Var planning in distribution feeders

The problem of the reactive energy optimal planning can be solved in a fast and efficient way using heuristic techniques. The latter reduce the number of the control variables to be determined and lead to a near global optimal solution. The capacitor appropriate locations are firstly determined by decisive indices then, their optimal sizes are calculated. In this paper a stability-index based method is presented. The nodes stability-indices are calculated for identifying the most sensitive nodes to be candidate for receiving near optimal standard capacitors that, reduce the feeder power losses, improve the voltage profile and maximise the economic saving (objective function). In this multi-objective optimisation problem, the commonly used voltage constraint is substituted by a new constraint on the branch reactive currents. This new constraint, allows overcoming the over compensation phenomenon by setting positive branch reactive currents. The solution is further improved by regulating the source node voltage. The proposed approach has been tested on several feeder examples and its effectiveness has been demonstrated through comparative studies. The obtained results have shown that the proposed approach leads to a promising and feasible solution.

APPLICATION OF OPTIMIZATION TO THE SIZE AND CONTROL SETTINGS OF SHUNT CAPACITORS ON DISTRIBUTION FEEDERS

Abstract This paper presents new contributions to the optimization of size and control setting of continuous shunt capacitors on distribution feeders to reduce power and energy losses in the feeders. The current profile is assumed to be a nonlinear function of the distance from the substation, we assume a quadratic function for the current profile. As a result, the loss reduction function to be maximized is a highly nonlinear function, we apply the minimum norm theorem in the frame work of functional analysis optimization technique to maximize this function. A set of linear simultaneous equations is obtained. These equations are solved iteratively using the conjugate-gradient methods. We compare our results with that obtained for a uniform current profile that has been done in the literature. Our contributions show a saving in the size of the capacitors and great loss reductions in the feeder.