System Power Loss Research Articles

Multi-Objective Optimal Power Flow With Integration of Renewable Energy Sources Using Fuzzy Membership Function

In recent past, to meet the growing energy demand of electricity, integration of renewable energy resources (RESs) in an electrical network is a center of attention. Furthermore, optimal integration of these RESs make this task more challenging because of their intermittent nature. Therefore, in the present study power flow problem is treated as a multi-constraint, multi-objective optimal power flow (MOOPF) problem along with optimal integration of RESs. Whereas, the objectives of MOOPF are threefold: overall generation cost, real power loss of system and carbon emission reduction of thermal sources. In this work, a computationally efficient technique is presented to find the most feasible values of different control variables of the power system having distributed RESs. Whereas, the constraint satisfaction is achieved by using penalty function approach (PFA) and to further develop true Pareto front (PF), Pareto dominance method is used to categorize Pareto dominate solution. Moreover, to deal with intermittent nature of RES, probability density function (PDF) and stochastic power models of RES are used to calculate available power from RESs. Since, objectives of the MOOPF problem are conflicting in nature, after having the set of non-dominating solutions fuzzy membership function (FMF) approach has been used to extract the best compromise solution (BCS). To test the validity of developed technique, the IEEE-30 bus system has been modified with integration of RESs and final optimization problem is solved by using particle swarm optimization (PSO) algorithm. Simulation results show the achievement of proposed technique managing fuel cost value long with the optimal values of other objectives.

Coyote Optimization Algorithm-Based Approach for Strategic Planning of Photovoltaic Distributed Generation

The optimal planning for distributed generations (DGs) associated with photovoltaics (PVs) in the utility-owned distribution system is crucial for increasing high penetration of renewables while against practical system operation constraints. Such PV-DG planning is categorized as a complicated mixed-integer nonlinear programming (MINLP) problem and is extremely difficult to solve by using conventional methods. In recent years, several bio-inspired metaheuristic algorithms have been proposed to tackle various complicated real-parameter optimization problems. This paper proposes a two-stage approach including a new bio-inspired algorithm, Coyote Optimization Algorithm (COA), to solve the large-scale MINLP PV-DG sizing problem considering different load levels. The objective function terms under consideration include the total system power loss and voltage regulator tap changes at different load levels while against limits of rms bus voltages, tap changes, and PV-DG constraints at each candidate bus. The proposed method is tested using the IEEE 123-bus unbalanced benchmark system and an actual utility distribution network. Results obtained are then compared with those obtained by a classic MINLP solver-based and four other bio-inspired methods. Moreover, results also show that the proposed method leads to lower loss, a minimum number of regulator tap changes, and higher PV penetration capacity among the compared methods and is suitable for solving the large-scale PV-DG planning problem in distribution systems.

Distributed Reactive Power Regulation Considering Load Voltage and Power Loss

In this study, optimal reactive power regulation in distribution networks is achieved through the use of distributed reactive power regulators that can 1) perceive their own voltage magnitude and the P/Q flows in the connected branches, 2) communicate with nearby regulators, and 3) adjust the reactive power injections into the grid to minimize system power losses and maintain the bus voltages of nearby loads. Compared with many existing distributed reactive power regulation strategies, the proposed method can estimate and maintain the bus voltage of unmeasurable load buses within the limitations. Furthermore, this method releases the hardly achieved bus voltage angle requirement, which makes it practical for real-world.

Active Currents, Power Factor, and Apparent Power for Practical Power Delivery Systems

Concepts of apparent power and power factor as measures of a system's power delivery capability are over a century old but have not been defined in one general, rigorous and acceptable way. Instantaneous power is defined precisely, and average power measured over a selected period is widely accepted. The many ways of defining and measuring reactive and apparent power in single and three phase systems are based on different assumptions and give different results in real cases. Building on definitions in the IEEE Standard 1459-2010, this paper formulates in vector space linear algebra and the frequency domain, the active wire currents as those that cause the minimum losses in a network for the power delivered. Power factor measures the relative efficiency of power delivery as defined by the losses. Apparent power consistent with early terminology is the maximum power that can be sourced for the same original line losses and has the unit of power: Watt. It is identified without requiring the contentious concept of reactive and non-active power components. Measurements based on this approach are independent of assumptions about sinusoidal waveform, voltage and current balance, and frequency-dependent wire resistances, and apply to power delivery systems with any number of wires. The rigor of this novel general formulation is important for technical design of compensators and inverters; analyzing power system losses, delivery efficiency and voltage stability; and electricity cost allocation and pricing.

Optimal PV Penetration for Power Losses Subject to Transient Stability and Harmonics

Abstract Meeting the load growth and limiting the pollution level of fossil fuel based power generation are global challenges. One solution is to integrate clean and renewable energy sources to the power system. However high penetration of renewable energy sources can affect transmission line thermal limits and harmonics injection in the system. This paper illustrates the optimal location of Photovoltaic (PV) plant penetration in power system. The objective is to minimize real power system losses. This analysis is subjected to transient stability as well as individual and total harmonic distortion level constraints. IEEE 30 bus system is considered for such investigation by using Electrical Transient and Analysis Program (ETAP 19.5). Multi case capability of ETAP was the base of the analysis. The contribution of this paper is the consideration of the accumulated impact of transmission losses, transient stability, transmission line thermal limits, harmonic level and steady state voltage limits. The results of the investigation for this system within the applied constraints have brought the penetration level (PL) to 50%. This result is satisfactory and encouraging.

CHP Economic Dispatch Considering Prohibited Zones to Sustainable Energy Using Self-Regulating Particle Swarm Optimization Algorithm

Economic dispatch is the optimal scheduling for generating units with technical constraints. Combined heat and power economic dispatch (CHPED) refers to minimization of the total energy cost for generating electricity and heat supply to load demand. This planning model integrates heat and power energy to balance energy supply and demand, mitigate climate change and improve energy efficiency of sustainable cities and green buildings. In this paper for the first time, self-regulating particle swarm optimization (SRPSO) algorithm is utilized for solving the CHPED problem by considering valve point effects and prohibited zones on fuel cost function of pure generation units and electrical power losses in transmission systems. The main advantage of SRPSO algorithm to PSO algorithm is the inertia weight flexibility with respect to search conditions. In this algorithm, unlike PSO algorithm that inertia weight reduces in each iteration, this value increases or reduces proportional to particles’ positions, which will lead particles to achieve optimal value with higher speed. The capability and effectiveness of the proposed algorithm are evaluated on a large-scale energy system using MATLAB environment. The results obtained by SRPSO algorithm are outperformed by other optimization methods from the economic, sustainable energy and time consumption point of view.

System Design of Management of Energy Resources in the Field of Housing

The main areas of work connected with improvement of the sphere of housing are development of the regulating,managing configurations of the equipment, change parameters of real-time operation, remote data collection from metering devices, adjustment of volumes of the provided utilities. It will allow to improve monitoring of functioning of a data collection system about the received utilities and to increase efficiency of identification of power losses in system. Automatic data acquisition from the metering devices of water and the electric power transferred directly to financial settlements center will allow to reduce influence on them of a human factor, to exclude errors at removal and information transfer.

Impact of Static Var Compensator (SVC) Devices on Power System Losses

Abstract The aim of this paper is to investigate the effect of the location of the SVC installation on the amount of power losses in the power system. The IEEE modified system with 3 wind turbines and 24 nodes was used as the test system. For the purpose of discovering the optimal location of the SVC device, GAMS programme was used. Comparing the results for losses before and after setting SVC to the optimum position in order to minimize losses, it was concluded that the position and power of the SVC device greatly influence the amount of losses in the system.

Network Reconfiguration for an Electric Distribution System with Distributed Generators based on Symbiotic Organisms Search

This paper proposes a method of network reconfiguration based on symbiotic organisms search (SOS) algorithm for reducing power loss of the electric distribution system. The SOS is a recent developed meta-heuristic algorithm inspired from the symbiotic interaction strategies of organisms for surviving and propagating in the ecosystem. Compared to other algorithms, SOS does not need any control parameters during the searching process. The advantages of the proposed SOS method have been validated in two electric distribution systems. Three network cases have been considered for each system, consisting of performing network reconfiguration on the system without distributed generator (DG) placement, the system installed type-P DGs and the system installed type-PQ DGs. The comparison results with particle swarm optimization and other previous methods show that the proposed SOS can be a promising technique for the problem of electric network reconfiguration.

Multi-objective Evolutionary Algorithms for Power Distribution System Optimal Reconfiguration

In this paper two reconfiguration methodologies for three-phase electric power distribution systems based on multi-objective optimization algorithms are developed in order to simultaneously optimize two objective functions, (1) power losses and (2) three-phase unbalanced voltage minimization. The proposed optimization involves only radial topology configurations which is the most common configuration in electric distribution systems. The formulation of the problem considers the radiality as a constraint, increasing the computational complexity. The Prim and Kruskal algorithms are tested to fix infeasible configurations. In distribution systems, the three-phase unbalanced voltage and power losses limit the power supply to the loads and may even cause overheating in distribution lines, transformers and other equipment. An alternative to solve this problem is through a reconfiguration process, by opening and/or closing switches altering the distribution system configuration under operation. Hence, in this work the three-phase unbalanced voltage and power losses in radial distribution systems are addressed as a multi-objective optimization problem, firstly, using a method based on weighted sum; and, secondly, implementing NSGA-II algorithm. An example of distribution system is presented to prove the effectiveness of the proposed method.

Optimal Placement and Sizing of Distributed Generation in Distribution System using Analytical Method

The reliability of distribution network can be improved with the penetration of small scale distributed generation (DG) unit to the distribution grid. Nevertheless, the location and sizing of the DG in the distribution network have always become a topic of debate. This problem arises as different capacity of DG at various location can affect the performance of the entire system. The main objective of this study is to recommend a suitable size of DG to be placed at the most appropriate location for better voltage profile and minimum power loss. Therefore, this paper presents an analytical approach with a fixed DG step size of 500 kW up to 4500 kW DG to analyses the effect of a single P-type DG in IEEE 33 bus system with consideration of system power loss and voltage profile. Four scenarios have been selected for discussions where Scenario 1: 3500 kW DG placed at node 3; Scenario 2: 2500 kW DG placed at node 6; Scenario 3: 1000 kW DG placed at node 18 and Scenario 4: 3000 kW DG placed at node 7. Results show that all the four scenarios are able to reduce the power loss and improve the voltage profile however Scenario 4 has better performance where it complies with minimum voltage requirement and minimizing the system power loss.

Application of Voltage and Lines Stability Index for Optimal Placement of Wind Energy with a System Load Increase Scenario

Background: Modern power system operations often faced with very unsafe conditions caused by voltage stability problems. If the problem cannot be controlled with the right method, then a cascade system can occur. This condition can cause a voltage reduction drastically and leads to a power outage. Objective: The Fast Voltage Stability Index (FVSI) and Line Stability Factor (LQP) implemented in this study. Both indices can determine the optimal location and capacity of Wind Energy (WE) into the grid to anticipate a sustained increase in load. Method: One type of optimization method, a new variant of Genetic Algorithms, is used to solve the multi-objective optimization problem known as Genetic Algorithm Sorting Non-Domination Sorting II (NSGA-II). This algorithm can determine the optimal location and WE's capacity into the grid by minimizing line power loss (Ploss) of power system with a system load increase scenario. Bus voltage security, thermal line limits, and stability system are used as obstacles to maintain the system in a safe condition due to the increasing the maximum load. Results: The method suggested in this paper has been adequately tested on modification of the IEEE 14-bus standard test system connected to the WE. The WE integrated into the grid modeled using the Power System Analysis Toolbox (PSAT). Based on the multi-objective manner, the method developed can determine the best location and capacity of the WE simultaneously by minimizing Ploss with SLI and satisfied all the system's security and stability constraints. Conclusion: The technique provides well-distributed non-dominated solutions and well exploration of the research space.

A Hybrid Microenergy Storage System for Power Supply of Forest Wireless Sensor Nodes

Wireless sensor nodes (WSNs) are widely used in the field of environmental detection; however, they face serious power supply problems caused by the complexity of the environmental layout. In this study, a new ultra-low-power hybrid energy harvesting (HEH) system for two types of microenergy collection (photovoltaic (PV) and soil-temperature-difference thermoelectric (TE)) was designed to provide stable power to WSNs. The power supply capabilities of two microenergy sources were assessed by analyzing the electrical characteristics and performing continuous energy data collection. The HEH system consisted of two separated power converters and an electronic multiplexer circuit to avoid impedance mismatch and improve efficiency. The feasibility of the self-powered HEH system was verified by consumption analysis of the HEH system, the WSNs, and the data analysis of the collected microenergy. Taking the summation of PV and TEG input power of 1.26 mW (PPV:PTEG was about 3:1) as an example, the power loss of the HEH system accounted for 33.8% of the total input power. Furthermore, the ratio decreased as the value of the input power increased.

Multiple distributed generations placement and sizing based on voltage stabilityindex and power loss minimization

This paper proposes a rapid analytical method to determine the locations and sizes of multiple distributed generations (DGs) inside a distribution network. DGs locations are chosen with the aim of enhancing voltage stability and their sizes are picked so as to minimize system power losses. To evaluate the effect of the DG's nature on the system's performance, the proposed DG allocation method is tested for all DG types and the impact of the combination of different types of DGs is equally investigated, which offers a guide to designing an optimal hybrid network.

Risk early warning method for distribution system with sources-networks-loads-vehicles based on fuzzy C-mean clustering

A risk probability classification model of distribution system with source-network-load-vehicle is established based on fuzzy C-means clustering and fuzzy mathematics in this paper. Monte Carlo simulation method is used to simulate various scenarios of operation and failure of lines, transformers, generators in distribution systems. The data needed for probability classification of voltage fluctuation, line overload, power loss and load loss of distribution system are obtained. For the severity of the accident, the membership functions of voltage fluctuation, line overload, power loss and load loss are constructed by using fuzzy distribution functions based on fuzzy classification method, and the corresponding risk severity classification model is constructed. Mamdani fuzzy reasoning is used to synthesize the risk probability and risk severity for determining the risk level. A risk early warning method for distribution system based on fuzzy C-means clustering is proposed. Taking IEEE-33, IEEE-39 and IEEE-118 systems as studying examples, the risk levels of voltage fluctuation, line overload, power loss and load loss are determined with the cases of normal operation and anticipated fault in distribution system.

An Autonomous Charge Controller for Electric Vehicles Using Online Sensitivity Estimation

Despite their sustainable benefits, large-scale adoption of electric vehicles (EVs) into the distribution system is challenging. Uncontrolled charging of EVs could increase voltage violations, system power losses, and feeder overloads. In this article, an autonomous EV charge control strategy is proposed to control EV charging in a way that mitigates their negative impacts. To ensure fair contribution from EVs connected to different nodes, both the local nodal voltage and sensitivity of voltage to load changes are used as inputs to the proposed controller. This is so because these two inputs have a complementary relationship. That is, nodes with lower voltages are generally more sensitive to load changes than those with higher voltages. In addition, a novel approach for locally estimating the voltage sensitivities online is proposed. This ensures the robustness of the proposed control strategy to changes in loading conditions and system configurations without the need for communication. An EV-rich test distribution system is modeled in the DIgSILENT PowerFactory environment and used to validate the proposed control strategy. The test results demonstrate the effectiveness and robustness of the proposed strategy considering different loading conditions, system reconfiguration, distributed generators, and voltage control devices.

An analytical formula for multiple DGs allocations to reduce distribution system losses

Due to the rapid increase in electricity consumption and the need to increase the share of renewable sources of energy, there is a tendency to install distributed generations (DGs) at the level of distribution systems. The allocation of DGs has to be optimally determined to reduce system power losses. In this paper, an analytical formula (AF) is developed for loss reduction due to inserting multiple DG units. This analytical formula achieves the optimal placement of multiple DGs. The derived analytical formula considers the effects of the DGs during the placement of each unit. Based on this analytical formula, an iterative method is proposed for the sizing of the multiple DGs. In addition, mathematical proofs are provided to estimate approximately the effects of the load level, along with branch resistance, substation transformer tapping and DG power factor on the DG allocation. Applications on IEEE 33 and 69-bus distribution test systems are implemented for their original and simultaneous reconfiguration with DGs. The results of AF are compared with that given in the literature. Accordingly, the superiority of achieving the proved optimal solution is validated along with reducing the computational steps.

Improving Power Quality of Power System Using D-STATCOM

In power system there are several types of losses which disturb whole of the electrical equipment installed in it. Therefore, it is required to pay more attention towards the improvement of power quality by minimizing the losses of power system. The present invention relates to the improvement of power quality of power system using distribution static compensator. For improving the power quality, the power system along with the distribution static compensator is designed in the MATLAB simulation and the result obtained after compensating the losses is shown in this paper.

Distribution System Reconfiguration for Loss Minimization and Voltage Profile Enhancement by using Discrete – Improved Binary Particle Swarm Optimization Algorithm

Distribution system reconfiguration is done by altering the open / close position of two kinds of switches: usually open tie switches and sectionalizing switches usually closed. Its main purpose is restoration of supply via other route to improve reliability, sometimes for load balancing by relieving overloads. Feeder reconfiguration is very good alternative to reduce power losses and improve voltage profile to improve overall performance. Distribution system reconfiguration is a very cost effective way to reduce the distribution system power losses, enhance voltage profile and system reliability. This paper presents application of novel Discrete - improved binary particle swarm optimization (D-IBPSO) algorithm for distribution system reconfiguration for minimization of real power loss and improvement of voltage profile. The algorithm is implemented to a 16-bus, 33-bus system and a 69-bus system considering different loading conditions. The simulation results indicate that the suggested technique can accomplish optimal reconfiguration and significantly reduce power losses on the supply scheme and enhance the voltage profile.

Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm

This paper presents a Genetic Algorithm (GA) for optimal location and sizing of multiple distributed generation (DG) for loss minimization. The study is implemented on a 33-bus radial distribution system to optimally allocate different numbers of DGs through the minimization of total active power losses and voltage deviation at power constraints of 0 – 2 MW and 0 – 3 MW respectively. The study proposed a PQ model of DG and Direct Load Flow (DLF) technique that uses Bus Incidence to Branch current (BIBC) and Branch Current to Bus Voltage (BCBV) matrices. The result obtained a minimum base case voltage level of 0.9898 p.u at bus 18 with variations of voltage improvements at other buses after single and multiple DG allocations in the system. Besides, the total power loss before DG allocation is observed as 0.2243 MW, and total power loss after DG allocation was determined based on the power constraints. Various optimal locations were seen depending on the power limits of different DG sizes. The results have shown that the impact of optimal allocation and sizing of three DG is more advantageous concerning voltage improvement, reduction of the voltage deviation and also total power loss in the distribution system. The results obtained in the 0 – 2 MW power limit is consistent to the 0 – 3 MW power limits regarding the influence of allocating DG to the network and minimization of total power losses.