Distributed Wind Generation Research Articles

A Novel Framework to Determine the Impact of Time Varying Load Models on Wind DG Planning

Distributed Generation (DG) based on Renewable Energy Sources (RES) are considered as an effective and economical technology for the advancement of an Electric Power System (EPS) to fulfill the load demand. Mostly, studies pertaining to DG planning are performed while considering constant load demand and DG generation. However, these considerations may provide misleading and inconsistent values for loss reduction, voltage profile, power quality, and other operational parameters. Therefore, this paper proposes a novel framework to determine the impact of different Time Varying Voltage Dependent (TVVD) load models on wind DG planning study. Firstly, wind DG optimal allocation is performed using Salp Swarm Algorithm (SSA) for different TVVD load models. Afterwards, impact of different TVVD load models on wind DG planning is investigated. Comparative evaluation of various impact indices, real and reactive power (losses and intakes), penetration level, and apparent power support provided due to integration of wind DG are discussed for various TVVD loads. The analysis of results indicates that TVVD loads have a significant impact on performance of distribution system and DG planning studies.

Reactive coordinated optimal operation of distributed wind generation

Large-scale distributed wind generation (DWG) integration brings new challenges to the optimal operation of the distribution network. The reactive supports from wind turbines (WTs) and reactive power resources can improve both the operation economy and renewable energy consumption. In this paper, a multi-period reactive coordinated optimal operation model for DWG in the distribution network is established. The active-reactive power coordination characteristics of two typical types of WTs are considered and the operating strategy of reactive power resources is integrated in the model. The second-order cone programming (SOCP) is developed to transform the original nonlinear power flow model into a linear and convex model, which would significantly improve the power flow calculation efficiency for DWG penetrated distribution network. The simulation results show that the integration of reactive power resources can further promote the consumption of DWG and improve the operating profits of the distribution network.

Optimal placement of battery swap stations in microgrids with micro pumped hydro storage systems, photovoltaic, wind and geothermal distributed generators

The penetration of electric vehicles (EVs) in vehicle markets is increasing; however long charging time in battery charging stations is an obstacle for larger adoption of EVs. In order to address this problem, battery swap stations (BSSs) have been introduced to exchange near-empty EV batteries with fully charged batteries. Refilling an EV in BSS takes only a few minutes. With decentralization of power systems, BSSs are typically connected to the microgrid (MG) in their neighborhood. Although the location of BSS in MG affects MG operation cost, to the best knowledge of the author, optimal placement of BSS has not been done from the perspective of MG. Therefore, in this paper, the objective is to find optimal location of BSSs in a MG with micro pumped hydro storage (PHS), photovoltaic, wind and geothermal units, while reactive power dispatch and all network constraints are considered by AC optimal power flow. The effect of BSS capacity and maximum charging/discharging power, BSS to MG link capacity, PHS capacity and maximum power of PHS unit on MG operation and optimal BSS location are investigated. DICOPT solver in general algebraic mathematical system (GAMS) is used to solve the formulated mixed-integer nonlinear optimisation problem.

Optimization of distribution network reconfiguration by a novel RCA integrated with genetic algorithm

This paper proposes a novel structure for distribution network reconfiguration (DNR) to improve reliability indices and decrease the operational cost of power systems. In order to increase the applicability of the problem in the smart grid, a distribution network involving plug-in hybrid electric vehicles (PHEV) and distributed wind generation (RDG) is addressed. The aim is to determine the optimized configuration of the distribution network considering different scenarios of PHEV’s charging/discharging and power realization of RDGs. To hedge against the nonlinearity of the problem, an improved genetic algorithm (IGA) is proposed. The suggested IGA decreases the time burden calculation of the problem, which is necessary for DNR problems. Moreover, a fast radial checking algorithm (RCA) is suggested to avoid tedious radial checking procedures. Numerical studies are implemented on IEEE 33-bus test system. The results show that the suggested approach optimizes the configuration of the distribution network when a renewable power shortage occurs or PHEVs’ operation is rescheduled.

Probabilistic generation model for optimal allocation of wind DG in distribution systems with time varying load models

Renewable energy-based Distributed Generation (DG) is the most imperative part of modern power system and offers many potential benefits. To attain maximum benefits offered by DG integration, it is important to model the time varying characteristics of both load and generation. Therefore, this paper presents a new Weibull distribution-based time-coupled Probabilistic Generation model for optimal placement and sizing of wind DG with time varying voltage dependent (TVVD) loads. At first, Probabilistic model is proposed for wind speed uncertainty modeling to calculate the hourly output power from wind DG. Afterwards, the values of output power are considered for determining optimal allocation and penetration of wind DG in distribution network to minimize the Average Multi-Objective Index (AIMO) using Particle Swarm Optimization (PSO). The strength of the proposed methodology is validated on IEEE 33 and 69-bus systems. Results depict that, proposed methodology is appropriate for wind speed modeling and is suitable for implementing in power system planning. • Time-varying characteristics of load and generation are assumed for DG allocation. • Weibull distribution-based PG model is proposed to determine hourly wind DG output. • Hourly output power values are considered for the optimal allocation of DG. • The penetration level of wind DG is calculated for different TVVD load models.

Reliability Evaluation of Smart Distribution Grids with Renewable Energy Sources and Demand Side Management

Technical advances and reduced technology costs have led to a significant increase in the use of renewable energy-based distributed generators (DGs) in the existing distribution grids. Modern distribution grid operation is also related to the efforts and incentives to influence changes in customers’ behavior and load use, known as demand side management (DSM). The effective implementation of renewable energy source (RES)-based DGs and DSM, particularly in microgrid operating modes, may benefit customers and distribution networks. This changes in the reliability performance of distribution networks owing to the integration of wind and photovoltaic DGs are evaluated in this study along with the implementation of load shifting and peak clipping DSM schemes for residential, commercial, and industrial load sectors. A reliability evaluation is based on energy not supplied and unavailability (hours per year), calculated by considering the seasonal changes in wind speed and solar irradiation, which are represented by their corresponding Weibull distribution functions. Load variations are incorporated using a normal distribution function. The reliability performances of different RES-based DGs and DSM schemes are evaluated using the non-sequential Monte Carlo simulation approach for modeling the random occurrence of faults in the system resulting in customer interruptions with the corresponding repair times required to restore the supply.

Techno-economic evaluation of PEVs energy storage capability in wind distributed generations planning

Plug-in electric vehicles (PEVs) can be used for renewable energy dispatching in both operation and planning of distribution networks. In this paper, wind distributed generations (WDGs) are planned based on vehicle to grid (V2G) operation mode of PEVs. The optimal long-term planning is based on optimal short-term scheduling considering all related uncertainties. A comprehensive model of the batteries degradation is considered in the objective function. A two-layer optimization procedure is proposed in which the optimal location and capacity of WDGs are determined in first layer, while optimal charge and/or discharge of PEVs are scheduled in the second layer, subject to technical constraints. The numerical results delineate the capability of PEVs in V2G operation mode for wind energy dispatching in WDGs planning. The simulation results show the storage capability of PEVs will increase by their number growing. Therefore, the operation and planning costs of WDGs will be reduced if the PEVs penetration to be high. The capacity of installed WDGs in smart charging scenario is less than uncoordinated charging scenario as the PEVs properly dispatch WDGs in smart charging strategy. Moreover, the results show that the PEVs battery degradation cost limit the participation rate of PEVs in WDGs dispatching.

Multi-objective optimal short-term planning of renewable distributed generations and capacitor banks in power system considering different uncertainties including plug-in electric vehicles

The increasing penetration of solar distributed generations (SDGs) and wind distributed generations (WDGs) together with plug-in electric vehicles (PEVs) will lead to a promising amount of reduction in greenhouse gas emissions. Nevertheless, they bring about adversities such as uncertainty in production-load sides, augmented power loss, and voltage instability in the power system, which should be carefully addressed to increase the reliability. In this concern, this paper proposes a multi-objective optimization methodology for sizing and siting of SDGs, WDGs, and capacitor banks (CBs) in the power system considering uncertainties stemmed from PEVs load demand, solar irradiance, wind speed, and the conventional load. The understudy objectives are the voltage stability index, green-house gas emissions, and the total cost. An unconventional point estimate method (PEM) is used to handle the related uncertainties, and chance-constrained programming method is deployed to deal with smooth constraints. The corresponding probability distribution functions of output variables are estimated by the maximum entropy method. Furthermore, robustness analysis is made by Monte Carlo simulation (MCS). The proposed methodology is applied to a typical radial distribution network. The results show that the presence of PEV’s significantly increases the load demand, which results in voltage collapse in the distribution system without the presence of distributed generations. However, the proposed probabilistic method ensures the safe operation of the distribution system with the optimal allocation of renewable distributed generations and CBs. Moreover, the results of deterministic and probabilistic cases are compared under different penetration levels of PEVs. The best tradeoff solution of the Pareto front is selected by the fuzzy satisfying method.

Optimal Wind DG Integration for Security Risk-Based Line Overload Enhancement: A Two Stage Approach

Line congestion margin is the available line capacity before the line becomes fully loaded. It is a quantity to measure the transmission lines security level. Placing of large scale distributed generation (DGs) units can be a key technique to alleviate line congestion, hence enhance the transmission line congestion margin, and grid security levels. However, the influence of DG integration on line congestion margin is effective at locations where transmission lines operate near to their maximum capacity. In addition, determining the required penetration level of DG (DG size) is crucial for maximizing the DG system support benefits in transmission system. A two stage approach is presented in this paper for optimal integration of large-scale wind DG for improving line congestion risk based on the congestion margin level. In stage one, a probabilistic approach is developed to predict lines with the highest probability to be congested considering the uncertainty of the line congestion margin. Once lines with a highest risk to be congested are determined at the end of the first stage, the result from stage one is employed to place DG at the node bus to which the predicted most congested line is delivering power. A Mixed Integer Linear Programing (MILP) optimization model is developed in the second stage to determine the optimal DG penetration level (DG size) for improving transmission line congestion margin considering transmission line investment deferral.

Commercialization of a Diffuser Augmented Wind Turbine for Distributed Generation

Small-scale distributed wind generation faces challenges in being cost competitive due to recent advances in solar photovoltaic and battery storage technology. Reductions in levelized cost of energy (LCOE) can be achieved by improvements in aerodynamic efficiency, generator controller design, or reducing cost of manufacture. In this paper we present a case study detailing the commercialization of a novel 200 W high-efficiency diffuser augmented wind turbine (DAWT). Results include increased rotor efficiency, bespoke controller design, and the novel use of manufacturing processes. Findings and conclusions are of direct interest to small wind turbine designers as they seek to reduce LCOE.

Improved Amplitude Differential Protection Scheme Based on the Frequency Spectrum Index for Distribution Networks With DFIG-Based Wind DGs

With the ever-increasing integration of wind power into the distribution network (DN) in the form of distributed generations (DGs), the optimal application environment for the conventional three-stage overcurrent protection no longer exists. Meanwhile, a majority of advanced protection schemes cannot be implemented due to the lack of voltage information and weak communication conditions in DN. As the most typical wind-based DG used in the DN, the doubly fed induction generator (DFIG)-based DG is focused in this paper. First, the frequency and amplitude characteristics of the fault current supplied by a DFIG-based DG are studied. Then, the amplitude differential protection (ADP) scheme is described using the weak-infeed characteristics of the DFIG-based DG. Furthermore, the frequency spectrum index, which is constructed based on the different frequency characteristics of DFIG-based DGs from synchronous generators, is introduced as a regulator factor to further improve the sensitivity of the ADP. Since only the current amplitude information is needed and the strict synchronization of the sampling data at the two ends of the line is not required, the proposed scheme is easy to implement under the limited hardware conditions in present DN. The simulation results show that the proposed scheme has a high sensitivity to the internal faults and has absolute selectivity for external faults, especially under the existing DG permeability rules.

Effect of Distributed Wind Generation on the Voltage Sag of Distribution Networks

As the issue of global warming is worsening, the shift towards using renewable energy resources is becoming more of an obligation rather than an option. With the continual decline in the cost of distributed small and medium-scale renewables and government sponsored programs, the outlook of growth of these converter-based resources remain high. Renewable energy resources are connected at the end-user terminals, in close proximity to the load at the distribution network. Such connection in the locale brings perceived benefits of transmission loss reduction, increased energy efficiency and improved voltage regulation. Yet, distributed renewable generation have noticeable effects on system’s power quality. This paper investigates the impacts of distributed wind generation on the voltage sag of distribution systems. A systematic approach is constructed to capture voltage sag occurrence incidents, due to wind generation connected at distribution nodes, and trigger the dynamic voltage restorer (DVR) into active operation mode to rectify the voltage sag problem. A test feeder system is represented using MATLAB/Simulink with wind turbines connected at several nodes of the system. A model for the DVR is developed in Simulink. It was then integrated with the test feeder system. Simulation results show that the incorporation of increased proportions of wind generation into the distribution network may give rise to negative operating conflicts as far as the voltage sag is concerned. Results manifest that the DVR is capable of effective correction of the voltage sag, caused by a three phase short-circuit fault, in presence of high penetration levels of variable wind generation connected at disparate locations in the distribution network.

Sequence Generative Adversarial Networks for Wind Power Scenario Generation

With the rapid increase in distributed wind generation, considerable efforts have been devoted to the microgrid day-ahead scheduling. The effectiveness of those methods will highly depend on the selection of the uncertainty sets. We propose a distribution-free approach for wind power scenario generation, using sequence generative adversarial networks. To capture the temporal correlation, the model adopts the long short-term memory architecture and uses generative adversarial networks coupled with reinforcement learning, which, in contrast to the existing methods, avoids manual labeling and captures the complex dynamics of the weather. We conduct case studies based on the data from the Bonneville Power Administration and the National Renewable Energy Laboratory, and show that the generated scenarios can better characterize the variability of wind power and reduce the risk of uncertainties, compared with those produced by Gaussian distribution, vanilla long short-term memory, and multivariate kernel density estimation. Moreover, the proposed method achieves better performance when applied to the day-ahead scheduling of microgrids.

Passive Islanding Detection Technique for Integrated Distributed Generation at Zero Power Balanced Islanding

Renewable power generation systems have more advantages in the integrated power system compared to the generation due to fossil fuels because of their advantages like reliability and power quality. One of the important problems due to such renewable distributed generation (DG) system is an unintentional islanding. Islanding is caused if DG supplies power to load after disconnecting from the grid. As per the DG interconnection standards, it is required to detect the islanding within two seconds after islanding with the equipments connected to it. In this paper a new passive islanding detection method is presented for wind DG integrated power system with rate of change of positive sequence voltage (ROCOPSV) and rate of change of positive sequence current (ROCOPSC). The islanding is detected if both the values of ROCOPSV and ROCONSV are more than a predefined threshold value. The test system results carried on MATLAB shows the performance of the proposed method for various islanding and non islanding events with different power imbalances. The results conclude that, this method can detect islanding even at balanced islanding with zero non detection zone (NDZ).

Methodology for Assessing the Risk of Unintentional Islanding of Distributed Wind Generators Using Passive Schemes

Anti-islanding protection of distributed generation is usually performed by passive schemes, which are based on local measurements of voltage. However, these schemes may fail in detecting the islanding occurrence for some specific operating conditions. For a distributed wind generator connected to a specific grid, these operating conditions depend on the load curve and the wind speed distribution. Thus, this paper proposes a methodology to assess the risk of a passive anti-islanding scheme not being able to detect an islanding occurrence in a distributed wind generator. This risk level may be used in cost–reliability evaluations carried out to select the most suitable protection scheme.

Multi‐objective planning model for multi‐phase distribution system under uncertainty considering reconfiguration

In this study, multi-objective particle swarm optimisation (MOPSO) and preference order (PO) ranking-based multi-objective planning model is presented for placement and sizing of wind and solar-based distributed generators (DGs), and capacitors in the multi-phase distribution network, under uncertainty considering network reconfiguration. Uncertainty in solar irradiance, wind speed, and load are considered using Monte Carlo simulation (MCS) with suitable probabilistic models. The planning problem is formulated considering different scenarios generated using MCS. For objective function formulation with varying demand and generation conditions, a dynamic load generation model is developed. A priority vector is proposed for DG and capacitor placement using the analytic hierarchy process to reduce the search space and computational time. The key benefits of the proposed DG placement algorithm are that it gives a single solution that is nearly optimal for all the possible network topologies and it works well for both unbalanced and balanced conditions. The proposed technique has been applied to IEEE 34-bus and IEEE 123-bus systems. The result shows a significant reduction in power losses, current unbalancing and improvement in reliability after the placement of DGs and capacitors.

Adaptive robust optimization framework for day-ahead microgrid scheduling

This paper proposes a framework to address day-ahead scheduling of a microgrid (MG) with wind, solar and micro-turbine distributed generators as well as batteries, considering the associated uncertainties and AC power flow constraints. The problem uncertainties are wind and solar generations, demand, and grid electricity price. The uncertainties are modelled using bounded intervals within adaptive robust optimization method. Unlike previous robust models presented for MG scheduling, our proposed adaptive robust model optimizes both wait-and-see and here-and-now decisions simultaneously which leads to obtaining a better operating point. In order to make wait-and-see and here-and-now decisions and immunize the model against worst case realizations, the proposed MG scheduling model has been formulated as a tri-level optimization framework. In the first level, here-and-now commitment decisions are made (these are decisions made before realization of uncertainties). In the second stage, the worst case realization of the uncertain parameters is determined. In the third level, the wait-and-see recourse decisions are made (these are decisions made after realization of uncertainties). Since there are before uncertainty-realization decisions, worst uncertainty-realization determination, and after uncertainty-realization decisions, the proposed model becomes a tri-level optimization framework. The proposed adaptive robust tri-level MG scheduling model, the proposed solution approach including AdptRob and ConvAC problems, and the proposed out-of-sample analysis are the main contributions of this paper. A convexified AC power flow model is used in the proposed framework for modeling the distribution network. The 69-bus distribution test system is used to test the proposed adaptive robust model as well as the proposed solution approach which converges very fast. Moreover, the results of the out-of-sample analysis shows that the proposed adaptive robust model results in lower expected cost and lower cost variance.

Unified Planning of Wind Generators and Switched Capacitor Banks: A Multiagent Clustering-Based Distributed Approach

In this paper, a multiagent clustering-based distributed approach for the optimal planning of wind-distributed generators (DGs) and switched capacitor banks (SCBs) is proposed. First, electrical distance matrices for the power systems are constructed. Additionally, a constrained optimization problem, which includes several indices and a few constraints, for the optimal clustering of distribution networks is formulated and solved. After obtaining optimal clusters, agents are assigned to the clusters, and a second multiobjective optimization problem (MOOP) for the distributed planning of wind DGs and SCBs is formulated and assigned to a head agent. The number of objective functions in the MOOP is equal to the number of agents. The objective function of an agent consists of three indices: annual energy losses, investment costs, and voltage enhancement. Moreover, a deep neural network architecture is designed, and four independent networks are trained with six years of wind speed data for the seasonal wind speed forecasting. Two IEEE unbalanced test feeders, one with 37 nodes and the other with 123 nodes, and eight test cases are considered for simulations. The results show that losses and costs are optimized, and the voltage unbalance of the system is reduced.

A Review on Solar PV Based Grid Connected Microinverter Control Schemes and Topologies

From the last decade, there is an increase in the demand of electricity, this will causing depletion in the fossil fuels which results increase in cost. So the focus is shifted to use of renewable energy sources along with the only utility grid but it is not sufficient to supply the power different loads. To overcome these problems, micro-grid (MG) is introduced and it is powered by renewable distributed generation (DG) systems, such as, micro turbines, fuel cells, PV and wind generation due to the limited fossil fuel. Out of the above sources, solar energy provides extraordinary benefits including environmental friendly, surplus availability and low installation cost due to the advanced technology and mass production. The solar grid connected micro inverters gain lot of intention in past few years due to its simple construction, reliability and endurability. Moreover, the grid connected micro inverter has high reliability and it can operate in abnormal conditions also like variations in voltage and current. The micro-inverter has attracted recent market success due to unique features such as lower installation cost, improved energy harvesting, and improved system efficiency. This article gives detailed review on different topologies for grid connected solar PV micro-inverter and suggests the reliable, suitable and efficient topology for micro-inverter.Article History: Received Dec 16th 2017; Received in revised form May 14th 2018; Accepted June 1st 2018; Available onlineHow to Cite This Article: Premkumar, M., Karthick, K and Sowmya, R. (2018) A Review on Solar PV Based Grid Connected Microinverter Control Schemes and Topologies. Int. Journal of Renewable Energy Development, 7(2), 171-182.https://doi.org/10.14710/ijred.7.2.171-182

Determining the sizes of renewable DGs considering seasonal variation of generation and load and their impact on system load growth

In this study, a methodology for determining the solar and wind DG (distributed generation) capacity is proposed by using sequential optimisation method considering a seasonal variation of load demand, seasonal solar, and wind variations. The load demand profile is collected from state load dispatch centre; whereas solar data are collected from Indian Institute of Technology Kharagpur and wind data are collected from a weather station. Along with the DGs, the shunt capacitors are also placed to improve the voltage profile and energy loss reduction with different scenarios. For this purpose, minimisation of a multi-objective function is considered. The proposed methodology is applied to a 69-bus distribution network. The results for different scenarios show the substantial reduction in the annual energy loss and improvement of voltage profile. The result also shows that maximum amount of profit is gained with both renewable DGs and shunt capacitors. The impact of load growth on the distribution network with and without renewable DGs and shunt capacitors are compared. The analysis reveals that with integrating the renewable DGs and shunt capacitors in the system, the distribution network can take load growth for few more years without violating the system constraints.