Penetration Of Distributed Generation Research Articles

Multistage AC transmission expansion planning including fault current‐limiting high‐temperature superconducting cables and multiple distributed generations to improve short‐circuit level and grid‐scale flexibility

AbstractThis paper proposes a new multistage AC model for transmission expansion planning that finds an optimal combination of transmission lines, fault current‐limiting high‐temperature superconducting cables, and multiple distributed generations (DGs). On this basis, the proposed model, from a new perspective, allows for simultaneous improvement of the short‐circuit level and grid‐scale flexibility (GFLX) under both normal and fault conditions. The objective function to be minimized includes not only the net present worth of the total investment and operation costs but also the congestion‐induced GFLX degradation measure. This model also takes the AC power balance and flow relationships, equipment capacity limits, nodal voltage bounds, DG penetration level limit, as well as discrete logical and financial restrictions together into account with the short‐circuit level constraint. To overcome the complexity of solving the resultant non‐convex mixed‐integer non‐linear optimization problem, a multi‐objective integer‐coded melody search algorithm is employed, followed by a fuzzy satisfying decision‐making mechanism to obtain the final optimal solution. The exhaustive case studies conducted on the IEEE 24‐ and 118‐bus test systems verify the efficacy of the newly developed model in terms of cost‐effectiveness, flexibility, and short‐circuit level suppression when facing different normal and fault conditions.

Dynamic impact of hybrid wind-solar photovoltaic power injection on small signal stability of Nigerian 11kV power system using Self Organizing Map neural network

Power industry reorganization, legislation on greenhouse gas emissions and inadequate conventional generation capacity encourage the idea of incorporating Distributed Generation (DG) into the distribution networks as unconventional sources of power generation. High penetration of DG has further increased the complexity of the analysis of small signal stability because the variability of renewable energy sources affects the stability of power system. Therefore, this research work investigates the effect of wind and solar photovoltaic power injection on small signal stability of Nigerian 11 kV power network. Wind and solar photovoltaic were modelled using probabilistic methods and output power at twenty-one different locations within Nigeria was computed. Using the real and reactive power data of the network, Self-Organizing Map (SOM) neural network was trained and tested for the assessment of the small signal stability of the distribution network before and after the injection of renewable power in python virtual environment. The integration of hybrid wind-solar PV distributed generation at 30 %, 60 %, 90 % and 100 % into the studied 11 kV power distribution network improved its small signal stability by 1.4 %, 2.1 %, 2.5 % and 3.2 % respectively. Moreover, increase in the injection of power gotten from wind and solar sources reduces the oscillation frequency of the system from 1.39 Hz to 1.01 Hz and increases the damping from 4.6 % to 13.2 %. Reactive power increment in the range of 5 %-20 % improved the small signal stability of the system by the range 2.4 %-7 %. This improvement will translate to better power transfer capability of the network.

Oppositional based artificial rabbits optimization applied for optimal allocation of nonlinear DG in distribution networks considering total harmonic distortion limit

This paper proposes a new optimization approach to identify the optimal placement and sizes of distributed generation (DG) units in a radial distribution network (RDN). Inverter-based DG also known as nonlinear DG (NLDG) units inject nonlinear current into the system, which can cause harmonic distortion. This harmonic distortion can limit the penetration of DG in the system by affecting the stability and reliability of the power system. Therefore, it is essential to consider harmonic distortion when identifying the optimal placement and sizes of DG units in RDNs to ensure DG’s safe and effective integration into the power system. The aim of the proposed work is to minimize real power loss, voltage deviation (VD), and total harmonic distortion limit (THD), and improve the voltage stability index (VSI) by integrating the search strategies of opposition-based learning and artificial rabbits optimization (ARO) to achieve better performance. The proposed algorithm, called opposition-based artificial rabbits optimization (OARO), considers different types of DG units like DG TYPE I and DG TYPE III and is suggested for two familiar RDNs: IEEE 33-bus, and 118-bus systems. The Pareto optimality concept has been introduced to solve the multi-objective problems of the systems. The simulation outcomes have been analyzed by comparing several optimization techniques in various cases. OARO has been shown to produce high-quality results with minimal iterations, reducing the time needed to solve the issue and conserving energy. Overall, the proposed approach offers a promising solution for identifying DG units’ optimal placement and sizes in RDNs while considering various operating scenarios.

La innovación disruptiva de la generación de electricidad distribuida: un reto para la administración pública en México

Distributed generation (DG) is the generation of electricity, mostly photovoltaic, close to consumption centers by individual users in residences or companies, whose main objective is self-consumption. It is considered disruptive by breaking with the paradigm of centralized generation. The objective of this study was to analyse the trend, benefits and challenges that the penetration of this technology in Mexico implies for the public administration. A study with a quantitative approach, based on empirical data published by international organizations and the Mexican government, with the study subjects being the country and its 32 States. A significant penetration of DG was found in Mexico where it has had a compound annual growth rate of 45% in recent years, reaching an installed capacity of around 3 GW. Its geographical distribution in Mexico is heterogeneous and depends on the economic level of the State. The contribution that DG makes to the energy transition is important in environmental terms, it represents a saving of more than one million tons of CO2 per year and in economic terms a significant saving for the public administration in the area of electrical energy generation. The Mexican public administration faces a double challenge: on the one hand, stimulating DG to increase the generation of clean energy to comply with international commitments and on the other, achieving the flexibility required by the National Electric System for the integration of intermittent clean energies. such as photovoltaics.

DERs-Load Flow Convergence Sensitivity Analysis Using Topological Reconfiguration

During the electric power system (EPS) modelling with massive use of distributed energy resources (DERs) - distributed generation (DG), storage and other distributed technologies such as electric vehicles - simplified and ideal conditions are assumed for the active distribution network. From the grid side, these elements are modelled as absorption and injection of power and/or current. In this paper, using the model MV-Benchmarck System CIGRE Task Force C6.04, a comparative analytical straightforward algorithm of convergence limits on load flow based on sum of powers and sum of currents along the topological matrix has been simulated. The convergence sensitivity analysis was examined for 3 system characteristics: radial and meshed Configuration, DG penetration and R/X ratio, finding percentage differences of up to 6% convergence sensitivity by power hosting capacity between two -non-linear- methods used for load flow.

Impact on on-load TAP-changer transformers in industries due to high penetration of photovoltaic distributed generation

Impact on on-load TAP-changer transformers in industries due to high penetration of photovoltaic distributed generation

Stochastic optimal allocation for a battery energy storage system in high renewable-penetrated distribution networks

As the penetration of renewable distributed generation (RDG) continues to grow, the stochastic and intermittent nature of its output imposes significant challenges on distribution networks (DNs), such as source–load mismatch and voltage fluctuations, which seriously affects the safety and reliability of the system. Thus, this paper presents a stochastic optimal allocation method for a battery energy storage system (BESS) in the DN, with the consideration of annual load growth, BESS degradation, and DN operation, aiming to minimize the overall cost of DNs and harvest more renewable energy. Based on the rainflow-counting concept, BESS degradation is efficiently modeled and linearized to improve solvability. Additionally, to address the uncertainties of RDG outputs and loads, a stochastic optimization (SO) method is adopted. Furthermore, considering that a large number of integer variables of the BESS allocation model may cause a heavy computational burden, a feasibility pump-based solution algorithm is introduced to accelerate the solving speed. Finally, the effectiveness of the proposed BESS allocation method and the solution algorithm is verified on a 33-bus DN system through comparative analyses, showing high efficiency and performance.

DG Compensation Using Optimal Distribution in Microgrids with High DG Penetration

This study developed a model to compensate for both active and passive power in an electric microgrid with an optimal power flow to allow a detailed analysis of its power flow, which encourages the integration of renewable energy sources (RESs). Mixture integer linear programming (MILP) is proposed, with distributed generation (DG) integrated into the existing bars. It is developed in the GAMS tool to minimize operating costs in the microgrid as an objective function, to guarantee reliability, and quality in the microgrid. The algorithm calculates an optimal solution that meets all requirements for reliability and quality in the microgrid. Finally, a comparison of data between the two systems without and with DG was performed, showing an improvement in voltage profiles, reduced active power losses in the lines, a better power factor (PF), and improved angular behavior; that also adheres to the restrictions set forth at a minimum operating cost. A nine-bar model has been used to model the problem by IEEE standards.

Nonlinearity-Adaptive Data-Driven Power Flow Constraint for Distribution Network Optimization

Data-driven power flow (PF) analysis methods have higher practical value to deal with the inaccurate branch parameters in medium and low-voltage distribution networks, which can be easily integrated into the classic optimization framework as a constraint. However, the PF mapping relationship of the measurement data shows stronger nonlinearity, with the high penetration of distributed generation (DG), leading to the low accuracy of the linear PF constraint. To this end, we propose an incomplete dimension lifting (IDL) data-driven PF method for distribution network optimization. The state variables are divided into optimization variables and uncontrollable variables, among them, the optimization variables keep the original linear state space or converted into quadratic space to simplify the optimization solution; and the uncontrollable variables is dimension lifted to fit the nonlinearity of PF. In addition, in order to deal with the problem of gradient direction error caused by nonlinear model overfitting, the first-order sensitivity constraint is involved in parameter regression process. The standard IEEE cases with high penetration of DG demonstrate that the proposed method can achieve higher accurate PF constraint and better optimization results, compared to the existing data-driven PF constrain.

AVALIAÇÃO DA INFLUÊNCIA DA INSERÇÃO DA GERAÇÃO DISTRIBUÍDA EM SISTEMAS DE DISTRIBUIÇÃO DE ENERGIA ELÉTRICA

This paper seeks to evaluate the impacts arising from the insertion of distributed generation (DG) in electrical power distribution systems. In this circumstance, using the OpenDSS computational tool, simulations of four different scenarios were carried out, varying the DG penetration levels as a function of the total feeder load, considering a specific point of operation. Finally, the same feeder was simulated with photovoltaic generating sources distributed in bars next to the loads, in order to analyze the temporal characteristic of solar generation as a function of demand. Through the simulations, it was possible to analyze, interpret and highlight the impacts that the insertion of DG produces in the electrical power distribution systems. It was evident that the increase in DG penetration led to an improvement in the feeder voltage profile, power flow inversion and reduction of electrical losses in the feeder. It should be noted that the insertion of DG from renewable sources is a Brazilian reality and a path to be followed, since the diversification of the energy matrix in line with the use of new technologies, such as smart grids, will provide electrical power systems with greater safety, operational efficiency and quality in the product delivered to consumers.

Adaptive overcurrent protection scheme coordination in presence of distributed generation using radial basis neural network

The operational performance of conventional overcurrent protection relay coordination connected to a distribution network is adversely affected by the penetration of distributed generators (DG) at different buses in the network. To address this problem, this paper proposes a novel adaptive protection coordination scheme using a radial basis function neural network (RBFNN), which automatically adjusts the overcurrent relay settings, i.e., time setting multiplier (TSM) and plug setting multiplier (PSM) based on the penetration of DGs. Short circuit currents and voltages measured at different buses are acquired using the remote terminal units (RTU) connected to different buses within the terminal network. Communication between the various remotes and local end station RTUs is through hybrid communication systems of fiber optic and power line communication system modules. The new adaptive overcurrent protection scheme is applied to the IEEE 33-bus distribution network with and without DGs, for single and multi-DG penetration using both the ETAP and MATLAB software. The simulation results show the proposed scheme significantly improves the protection coordination.

A Review of Recent Control Trchniques of Virtual Synchronous Machine for Renewable Energy

Abstract Increased distributed generation (DG) integration into power system via power electronic devices results in a reduction in the total equivalent inertia of power grid utility, which lead to serve frequency stability and performance degradation of the power system. To facilitate the penetration of DG into the power grid utility, improved control schemes of the power electronics converter based DG are required to improve the system stability when it is dominated by DG based inverter. A virtual synchronous generator (VSG ) approach is suggested, which consists in controlling the static power converter to mimic the essential dynamic behavior of the conventional synchronous generator. The main objective of this paper is to review the most recent topologies of virtual inertia control. Along this work, a detail description of each topology is presented. In addition, VSG control equations given voltage and frequency control is explained in detail. The VSG problems and future research on VSG control are discussed.

A resilient self‐healing approach for active distribution networks considering dynamic microgrid formation

AbstractA large number of renewable distributed generation (DG) systems connect to the distribution network, affecting the structure of the traditional distribution network and forming an active distribution network (ADN). Although the accelerating grid penetration of DGs brings significant challenges to the distribution network operation, the islanded operation capability of DGs provides a flexible solution to the self‐healing of ADNs from faults. To ensure that ADN can quickly recover and reconfigure in the event of a fault and continue to maintain safe, economical, and reliable operation, this paper proposes a dynamic microgrid formation method for ADNs combined with the dynamic network reconfiguration and intentional islanding operation of DGs. An optimization model is designed to represent the proposed self‐healing method, maximizing the load restoration and minimizing the DG cost, line network loss, and voltage excursion simultaneously. A binary hybrid optimization solver is applied to pursue the optimal self‐healing schedules from the optimization model. The self‐healing method is evaluated on the Institute of Electrical and Electronics Engineers (IEEE) 33‐node system and the IEEE 123‐node system, which indicate its rationality and effectiveness fully verified. By optimizing the on and off states of the normally open switches and the on‐grid and off‐grid operation states of DGs, ADNs not only get healed with minimum load curtailment, but also achieve minimal DG generation cost, network loss, and node voltage deviation. In addition, compared with traditional solvers, the proposed solver has a slightly higher computational time than the corresponding solver.

Analysis of voltage and frequency stability of electric power system network with photovoltaic-based generation penetration

The operation of Distributed Generation (DG) with renewable energy sources integrated with distribution networks through microgrids poses challenges in terms of operation and control. If this is left unchecked, it can have a negative impact on system security and reliability in terms of voltage and frequency stability that will be disrupted because of frequent variations in power production and loading levels. This study investigates voltage and frequency stability in microgrids because of the penetration of DG with photovoltaic (PV) renewable energy sources in the power system using the Virtual Synchronous Generator (VSG) control technique. The VSG is a control alteration that enhances the capabilities of the power system so that voltage and frequency stability can be preserved and improved. The VSG control method with additional damping controllers that increase inertia with additional virtual inertia is used to simulate the speed of restoration of voltage and frequency stability of the power system due to the penetration of PV-based power plants. The simulation results show that at the time of penetration of PV-based power plants in the power system, there is a momentary instability in voltage and frequency, but it is immediately dampened by VSG control and can be quickly restored so that the stability of voltage and frequency is maintained.

A PSO-TVAC for optimal installation of multiple distributed generations in a radial distribution system

<span>The integration of distributed generations (DGs) into distribution system networks has seen a significant increase owing to the depletion of conventional energy resources and the growing power demand. However, a high penetration of DGs can adversely affect the stability of the distribution networks due to the intermittent nature of their generation capabilities. Hence, it is crucial to design DGs optimally to support grid voltage regulation and improve distribution networks performance. This study utilizes a particle swarm optimization (PSO) with time-varying acceleration coefficients (PSO-TVAC) technique to optimize the location and size of various distributed generation units while minimizing the total active power loss. The initial system power loss was determined using a distribution load flow analysis based on the backward-forward technique. The PSO-TVAC algorithm was then employed to identify the optimal placement and sizing of DGs within the standard IEEE 33-bus radial distribution network. To assess the proposed algorithm's effectiveness, photovoltaic (PV) and wind turbine (WT) were considered as the DGs. In comparison with other algorithms, PSO-TVAC achieved the lowest power loss, measuring 72.79 [kW] and 12.14 [kW] for <br /> 3-PV and 3-WT installations, respectively. Furthermore, the optimal installation of 3-PV and 3-WT improved the distribution system performance by 65.49% and 94.25%, respectively.</span>

Optimal Allocation of Storage Capacity in Distribution Network for Renewable Energy Expansion

Today the integration of renewable energy production technologies into power systems brings a new challenge in terms of optimal usage of renewable. It is known that due to the discontinuous nature of renewable energy, the challenge can be associated with the presence of unwanted voltage fluctuations and power losses in power grids. This study performs optimization of the calculation of hosting capacity to determine the maximum amount of renewable energy that can be further expanded. This can be possible by energy storage deployment on the same power grids. The battery storages are expected to have power injection and absorption associated with discharging and charging of energy storages, respectively, to solve voltage fluctuations and power disturbances. Thus, to enhance the probability of distributed generation penetration, it is necessary to have optimal allocation and size during the renewable energy penetration in low-voltage distribution networks. Global optimization as a genetic algorithm (GA) method is used during hosting capacity assessment. The results show that using GA-based optimization, total cost and loss can be reduced by 38.8 and 73%, respectively. The practical results are verified by the IEEE-33 bus system.

Optimal Allocation of Battery Energy Storage Systems to Enhance System Performance and Reliability in Unbalanced Distribution Networks

The continuously increasing renewable distributed generation (DG) penetration rate significantly reduces environmental pollution and power generation cost and satisfies society’s rapid growth in electricity demand. Nevertheless, high penetration of renewable DGs, such as wind power and photovoltaics (PV), might deteriorate the system’s efficiency and reliability due to its intermittent and stochastic natures. Introducing battery energy storage systems (BESSs) to the distribution system provides a practical method to compensate for the above deficiency since it can deliver and absorb power when needed. Hence, it is important to determine the optimal allocation of BESS to achieve maximum assistance in the grid. This study proposes an optimal BESS allocation methodology to improve reliability and economics in unbalanced distribution systems. The optimal BESS allocation problem is solved by simultaneously minimizing the cost of energy interruption, expected energy not supplied, power loss, line loading, voltage deviation, and BESS cost. The proposed technique is implemented and analyzed on a high renewable DG penetrated unbalanced IEEE-33 bus network using DIgSILENT PowerFactory software (version 2020 SP2A). An enhanced grey wolf optimization (EGWO) algorithm is developed to optimize BESS location and size according to the selected objective function. The simulation results show that the proposed optimal BESS optimization technique significantly improves the economics and reliability in unbalanced distribution systems and the EGWO outperforms the gray wolf optimization (GWO) and particle swarm optimization (PSO) algorithms.

Robust Distribution Network Reconfiguration in the Presence of Distributed Generation Under Uncertainty in Demand and Load Variations

In modern power systems, distributed generation (DG) plays an important role in energy losses reduction, but DG penetration in such networks is restricted by technical and investment limitations. Therefore, distribution network reconfiguration (DNR) and DG operation reduce power losses more efficiently than when only DG is utilized. Also, network reconfiguration mitigates improper DG placement in such systems which are subjected to high penetration of variable loads and DG. On the other hand, electrical energy demand is estimated by load forecasting methods and therefore is a time-variant and uncertain parameter. Moreover, DG power generation is uncertain and depends on variable load power. Consequently, models presented for the reconfiguration problem should be adequately robust against load and generation uncertainties. Thus, the present paper proposes an effective robust model for reconfiguring distribution networks with load and generation uncertainties. The proposed model is sufficiently simple for implementation and is accurate enough to find the optimal solutions in acceptable computational times. The main features of the proposed model are its high robustness against uncertain parameters, in which proposed configurations are not changed easily by small variations in DG power and load amounts, and its effectiveness, which means that uncertain parameters may change the obtained solutions.

An Investigation of the Impact of Distributed Generation Penetration on Directional Overcurrent Relay Coordination in a Distribution Network

Distributed generation units (DGs) are rapidly becoming widespread in distribution systems due to their advantages such as power loss reduction, voltage profile improvement, and economic returns. Many researchers seek new ways to maximize their these advantages. However, their impact on the fault current is a problem for the field of power system protection. The changes in the short-circuit currents due to DGs cause the miscoordination of the directional overcurrent relays (DOCRs). In this paper, the impact of distribution generation penetration on DOCR coordination is analyzed and investigated. Besides this negative impact of DGs, their contributions to reducing power loss and improving the voltage profile are also analyzed for different DG penetration levels. The gazelle optimization algorithm is utilized to solve the DOCR coordination problem studied in this paper. The method is performed on the distribution section of the IEEE 14-bus system. It is seen that a significant number of miscoordinations occur when even the DG penetration is increased by about 10%. With the increase in DG penetration, the number of miscoordinations does not increase proportionally, but there is a proportional increase in active and reactive power loss reduction and voltage profile improvement.

A new optimal allocation of DGs in distribution networks by using coot bird optimization method

Energy is one of the most important topics in the world today and is considered as one of the most effective factors for the development of countries. Due to the limitation of non-renewable energy sources and undesirable effects of consuming these resources on the environment, the strategy of countries has changed towards the use of renewable energy. Renewable energy sources do not decrease over time and operate independently of price fluctuations and are more available, thus being able to play a greater role in modern power systems. Therefore, the optimal location and use of these resources will have an impact on modifying the parameters of the power grid. In this paper an analytical approach for optimal placement and sizing of distributed generation (DG) in power distribution networks to minimize the power loss, bus voltage limits, DG capacity limits, current limits, and DG penetration limit. In the first step, determines the DG capacity causing maximum benefit at different buses, and then selects the best location for DG placement which corresponds to highest benefit in the buses. This method is applicable for sizing and siting of single as well as multiple DG units. The coot bird optimization method (CBOM) is proposed for solving optimal placement, size, and power factor (PF) of DG in distribution network. The suggested method is tested on the IEEE 33-bus, 69-bus, Distribution Networks. The proposed CBOM method has good performance to find optimal placement, size, and PF of DG and it can be applied for various distribution system.