Integration Of Renewable Distributed Generation Research Articles

Active Distribution Network Expansion Planning With Dynamic Thermal Rating of Underground Cables and Transformers

The rapid development of renewable distributed generation in active distribution networks (ADNs) imposes an increasing burden on the transfer capability of the ADNs, bringing new challenges to the distribution network expansion planning (DNEP) problem. Dynamic thermal rating (DTR), which evaluates the equipment rating based on the actual weather conditions and equipment thermal states, can enhance the equipment transfer capability to support the integration of renewable distributed generation. In this paper, we propose a DNEP model of ADNs incorporating the DTR of cables and transformers, given that underground cable feeder is preferred in the rapid urbanization trends. Then, we derive a linear reformulation of the original DNEP model and propose a modified Benders decomposition (MBD) algorithm to solve the DNEP model. To select more effective representative day scenarios, we propose a cost-based clustering method for representative day selection applicable to solving the DNEP model. Case studies based on the IEEE 33-node system and the PG&E 69-node system show that the implementation of DTR saves 14.7% and 15.1% of the investment costs of the two systems respectively. The effectiveness of the MBD algorithm and the cost-based clustering method is also verified by the case studies.

New typical power curves generation approach for accurate renewable distributed generation placement in the radial distribution system

<span lang="EN-US">This paper investigates, for the first time, the accuracy of normalized power curves (NPCs), often used to incorporate uncertainties related to wind and solar power generation, when integrating renewable distributed generation (RDG), in the radial distribution system (RDS). In this regard, the present study proposes a comprehensive, simple, and more accurate model, for estimating the expected hourly solar and wind power generation, by adopting a purely probabilistic approach. Actually, in the case of solar RDG, the proposed model allows the calculation of the expected power, without going through a specific probability density function (PDF). The validation of this model is performed through a case study comparing between the classical and the proposed model. The results show that the proposed model generates seasonal NPCs in a less complex and more relevant way compared to the discrete classical model. Furthermore, the margin of error of the classical model for estimating the expected supplied energy is about 12.6% for the photovoltaic (PV) system, and 9% for the wind turbine (WT) system. This introduces an offset of about 10% when calculating the total active losses of the RDS after two RDGs integration.</span>

Effective Power Loss Management in the Distribution System by the Hybrid Cuckoo Search Grey Wolf Optimizer

One of the most convoluted parts of an electrical power system is its distribution system. Compared to generation and transmission losses, distribution losses are very high in the vertically integrated power system. A modern smart grid involves bidirectional power flow due to Renewable Distributed Generations (RDGs). Integration of RDGs near the load points results in a reduction in the distribution losses. This paper proposes the new Hybrid Cuckoo Search Grey Wolf Optimizer (HCSGWO) to optimally place the RDGs such as Solar Photo Voltaic and Fuel Cells and Capacitor Banks (CBs) for the improvement of voltage profiles in each bus and minimization of losses. The hybrid algorithm utilizes the advantages of both the Cuckoo Search and Grey Wolf algorithm. The proposed approach is tested with the Standard IEEE 33-bus system and the real-time TN 84-bus distribution system at all load models such as constant current, constant power, and constant impedance. The HCSGWO minimizes the system losses by optimally placing both the RDGs and CBs when compared to other algorithms. Losses are reduced by installing CBs in the real-time system and significant improvement is achieved in the voltage levels of each bus in the system.

Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Algorithm

In the last few years, the integration of renewable distributed generation (RDG) in the electrical distribution network (EDN) has become a favorable solution that guarantees and keeps a satisfying balance between electrical production and consumption of energy. In this work, various metaheuristic algorithms were implemented to perform the validation of their efficiency in delivering the optimal allocation of both RDGs based on multiple photovoltaic distributed generation (PVDG) and wind turbine distributed generation (WTDG) to the EDN while considering the uncertainties of their electrical energy output as well as the load demand’s variation during all the year’s seasons. The convergence characteristics and the results reveal that the marine predator algorithm was effectively the quickest and best technique to attain the best solutions after a small number of iterations compared to the rest of the utilized algorithms, including particle swarm optimization, the whale optimization algorithm, moth flame optimizer algorithms, and the slime mold algorithm. Meanwhile, as an example, the marine predator algorithm minimized the seasonal active losses down to 56.56% and 56.09% for both applied networks of IEEE 33 and 69-bus, respectively. To reach those results, a multi-objective function (MOF) was developed to simultaneously minimize the technical indices of the total active power loss index (APLI) and reactive power loss index (RPLI), voltage deviation index (VDI), operating time index (OTI), and coordination time interval index (CTII) of overcurrent relay in the test system EDNs, in order to approach the practical case, in which there are too many parameters to be optimized, considering different constraints, during the uncertain time and variable data of load and energy production.

Optimal integration of renewable distributed generators and electric vehicles in a radial distributed network

With the rising interest in sustainable transportation efforts, the optimal integration of Electric Vehicles (EVs) in the transportation business has come into existence very drastically. However, because of the increased power consumption, their impact on the electric power market might result in more enormous losses in the active power, a reduced voltage profile, as well as reduced voltage stability limits. In Radial Distribution System (RDS), getting the reduced effect of EVs load is crucial. It is significantly important to build charging stations for EVs and Distributed Generators (DGs) at the best locations. This article presents a combined approach to solve the optimal integration of DGs and EVs problem. First, the desired locations of DGs and EVs are found using the Voltage Stability Index (VSI) method.Further, the resource ratings are obtained by the Firefly algorithm (FA) method. EVs charging dump and smart charging strategies are used, and comparison is done with both systems. Furthermore, the main objective of this work is to reduce the power loss and maintain a good voltage profile at each bus in the RDS. Finally, the developed approach is tested on IEEE 69 bus system.

Analysis of voltage rise phenomena in electrical power network with high concentration of renewable distributed generations

The increasing penetration levels of renewable distributed generation (RDG) into a power system have proven to bring both positive and negative impacts. The occurrence of under voltage at the far end of a conventional electrical distribution network (DN) may not raise concern anymore with RDGs integration into a power system. However, a penetration of RDGs into power system may cause problems such as voltage rise or over-voltage and reverse power flows at the Point of Common Coupling (PCC) between RDG and DN. This research paper presents the impact of voltage rise effect and reverse power flow constraint in power system with high concentration of RDG. The analysis is conducted on a sample DN, i.e., IEEE 13-bus test system, with RDG penetration by considering the most critical scenario such as low power demand in DN and a peak power injection by RDG. For studying the impact of voltage rise and reverse power flow, a mathematical model of a DN integrating RDG is developed. Furthermore, a controller incorporating an advance control-algorithm is proposed to be installed at PCC between DN and RDG to regulate the voltage rise effects and to mitigate the reverse power flow when operating at a worst critical scenario of minimum load and maximum generation from RDG. The proposed control strategy also mitigates the voltage–current harmonic distortions, improves the power factor, and maintain the voltage stability at PCC. The simulations are carried out using MATLAB/Simulink software. Finally, recommendations are provided for the power producers to counteract the effects of voltage rise at PCC. The study has demonstrated that, voltage at PCC can be sustained with a high concentration of RDG during a worst-case scenario without a reverse power flow and voltage rise beyond grid code limits.

The Probabilistic Optimal Integration of Renewable Distributed Generators Considering the Time-Varying Load Based on an Artificial Gorilla Troops Optimizer

Renewable distributed generators (RDGs) are widely embedded in electrical distribution networks due to their economic, technological, and environmental benefits. However, the main problem with RDGs, photovoltaic generators, and wind turbines, in particular, is that their output powers are constantly changing due to variations in sun irradiation and wind speed, leading to power system uncertainty. Such uncertainties should be taken into account when selecting the optimal allocation of RDGs. The main innovation of this paper is a proposed efficient metaheuristic optimization technique for the sizing and placement of RDGs in radial distribution systems considering the uncertainties of the loading and RDG output power. A Monte Carlo simulation method, along with the backward reduction algorithm, is utilized to create a set of scenarios to model these uncertainties. To find the positions and ratings of the RDGs, the artificial gorilla troops optimizer (GTO), a new efficient strategy that minimizes the total cost, is used to optimize a multiobjective function, total emissions, and total voltage deviations, as well as the total voltage stability boosting. The proposed technique is tested on an IEEE 69-bus network and a real Egyptian distribution grid (East Delta Network (EDN) 30-bus network). The results indicate that the proposed GTO can optimally assign the positions and ratings of RDGs. Moreover, the integration of RDGs into an IEEE 69-bus system can reduce the expected costs, emissions, and voltage deviations by 28.3%, 52.34%, and 66.95%, respectively, and improve voltage stability by 5.6%; in the EDN 30-bus system, these values are enhanced by 25.97%, 51.1%, 67.25%, and 7.7%, respectively.

Enhancement of Large Renewable Distributed Generation Penetration Levels at the Point of Common Coupling

The occurrence of distortion and over voltage at the Point of Common Coupling (PCC) of Renewable Distributed Generation (RDG) limits its penetration levels to the power system and the RDG integration is expected to play a crucial role in power system transformation. For its penetrations to be sustained without disconnection from the system, there must be a solution to the voltage rise, distortion, unbalanced current and grid reactive power control strategy at PCC. It is an IEEE-1547 requirement that RDG integration to the power system should be regulated at PCC to avoid disconnection from the network due to power quality criteria. RDG integration must meet up with this specification to uphold power quality and avoid damage to the sensitive equipment connected at PCC. In this paper, voltage rise, unbalanced current, reactive power and distortion are being managed at PCC while Distribution Network (DN) accepts more RDG penetration levels without violation of the IEEE and South Africa grid code act. Active Power Filter and Full Bridge Multi-Level Converter (FBMC) are considered to safeguard power quality to the grid, they are modelled in MATLAB/SIMULINK and the results obtained shown that the proposed strategy can successfully regulate voltage rise, distortion, unbalanced current and continuously improve power quality with RDG integration at PCC. The proposed method's key innovation is the strategic generation and absorption of reactive power to curtain an overvoltage, reverse power flow, and distortion at the PCC, allowing more RDG penetration levels to the grid without disconnection while maintaining the standard requirement for power quality at the PCC. The simulation outcomes validate the superiority of the FBMC over the active power filter with respect of reactive power generation/absorption, dynamic response, and damping capability.

Impact Assessment and Mitigation Techniques for High Penetration Levels of Renewable Energy Sources in Distribution Networks: Voltage-control Perspective

The integration of renewable distributed generation (RDG) into distribution networks is promising and increasing nowadays. However, high penetration levels of distributed generation (DG) are often limited as they may have an adverse effect on the operation of distribution networks. One of the operation challenges is the interaction between DG and voltage-control equipment, e. g., an under-load tap changer (ULTC), which is basically designed to compensate for voltage changes caused by slow load variations. The integration of variable DGs leads to rapid voltage fluctuations, which can negatively affect the tap operation of ULTC. This paper investigates the impact of high penetration levels of RDG on the tap operation of ULTC in distribution networks through simulations. Various mitigation techniques that can alleviate this impact are also examined. Among these techniques, constant power-factor mode is regarded as the best trade-off between the simplicity and effectiveness of minimizing the number of tap operations. Simulations are performed on a Canadian benchmark rural distribution feeder using OpenDSS software.

Coyote multi-objective optimization algorithm for optimal location and sizing of renewable distributed generators

Research on the integration of renewable distributed generators (RDGs) in radial distribution systems (RDS) is increased to satisfy the growing load demand, reducing power losses, enhancing voltage profile, and voltage stability index (VSI) of distribution network. This paper presents the application of a new algorithm called ‘coyote optimization algorithm (COA)’ to obtain the optimal location and size of RDGs in RDS at different power factors. The objectives are minimization of power losses, enhancement of voltage stability index, and reduction total operation cost. A detailed performance analysis is implemented on IEEE 33 bus and IEEE 69 bus to demonstrate the effectiveness of the proposed algorithm. The results are found to be in a very good agreement.

Distribution System Restoration With Renewable Resources for Reliability Improvement Under System Uncertainties

Integration of renewable distributed generation (DG) units into distribution networks is gaining widespread popularity. However, uncertainties in generation availability associated with renewable DG units pose a major challenge. These uncertainties should be properly addressed to ensure acceptable system performance and improve customer-side reliability. In this article, the reliability assessment of distribution systems embedded with renewable DG sources has been carried out giving emphasis to system uncertainties and optimal restoration strategies. The uncertainties associated with the power output from renewable resources, time-varying load demand, stochastic prediction errors, and random fault events have been accounted for in the restoration optimization formulation for reliability evaluation. In this article, a parameter-free particle swarm optimization (PSO) technique is applied to address the complexity involved in the formulation. Moreover, a problem-specific encoding scheme is also proposed in conjunction with PSO to ensure optimality.

Optimal Sizing of Photovoltaic Generation in Radial Distribution Systems Using Lagrange Multipliers

The integration of renewable distributed generation into distribution systems has been studied comprehensively, due to the potential benefits, such as the reduction of energy losses and mitigation of the environmental impacts resulting from power generation. The problem of minimizing energy losses in distribution systems and the methods used for optimal integration of the renewable distributed generation have been the subject of recent studies. The present study proposes an analytical method which addresses the problem of sizing the nominal power of photovoltaic generation, connected to the nodes of a radial distribution feeder. The goal of this method is to minimize the total energy losses during the daily insolation period, with an optimization constraint consisting in the energy flow in the slack bus, conditioned to the energetic independence of the feeder. The sizing is achieved from the photovoltaic generation capacity and load factors, calculated in time intervals defined in the typical production curve of a photovoltaic unit connected to the distribution system. The analytical method has its foundations on Lagrange multipliers and relies on the Gauss-Jacobi method to make the resulting equation system solution feasible. This optimization method was evaluated on the IEEE 37-bus test system, from which the scenarios of generation integration were considered. The obtained results display the optimal sizing as well as the energy losses related to additional power and the location of the photovoltaic generation in distributed generation integration scenarios.

Integration of renewable distributed generation with storage and demand side load management in rural islanded microgrid

The integration between distributed generations, storage, and load for islanded microgrids located in rural areas of developing countries offer serious challenges. The rural microgrid, catering the energy needs of rural communities, has no access to modern information and communication amenities like internet, communication architectures, and backup power. The lack of information and communication technologies (ICT) and backup power sources in rural microgrid lead to uncontrolled load consumption and inefficient storage dispatch resulting in system-wide outages. This paper proposes a novel approach to overcome challenges such as affordability and accessibility. A proactive load controlling and prioritized smart load curtailment is proposed in real time using local system parameters and human activities. The objective is to reduce blackouts and ensure maximum load supply without using conventional backup sources, computational forecasting, scheduling tools, and sophisticated communication architectures. The fuzzy logic controller is proposed to proactively decide for a percentage of load to be curtailed. Human Activities Tracking System (HATS) for smart load curtailment is used for balanced and efficient integration between the distributed generation, storage, and load. Four system parameters, storage drain time, state of charge (SoC), running load, and solar irradiations, are used as input to the fuzzy controller. HATS controller is used to smartly prioritize the load curtailment based on human occupancy in different buildings. Human activities are monitored using pyroelectric sensors installed at the entrance of different buildings. The performance of the system has been assessed while comparing the proposed methodology, the SoC controlling method (SCM). While using the proposed methodology, the blackout was drastically reduced to 96 to 100%. State of charge depletion rate was considerably less than conventional methods.

Reliability assessment of distribution system with the integration of renewable distributed generation

Recent studies have shown that renewable energy resources will contribute substantially to future energy generation owing to the rapid depletion of fossil fuels. Wind and solar energy resources are major sources of renewable energy that have the ability to reduce the energy crisis and the greenhouse gases emitted by the conventional power plants. Reliability assessment is one of the key indicators to measure the impact of the renewable distributed generation (DG) units in the distribution networks and to minimize the cost that is associated with power outage. This paper presents a comprehensive reliability assessment of the distribution system that satisfies the consumer load requirements with the penetration of wind turbine generator (WTG), electric storage system (ESS) and photovoltaic (PV). A Markov model is proposed to access the stochastic characteristics of the major components of the renewable DG resources as well as their influence on the reliability of a conventional distribution system. The results obtained from the case studies have demonstrated the effectiveness of using WTG, ESS and PV to enhance the reliability of the conventional distribution system.

Reactive power coordinated optimisation method with renewable distributed generation based on improved harmony search

The reactive power coordinated optimisation problem for distribution network (DN) with the integration of renewable distributed generation (DG) is investigated in this study. By actively participating in reactive power control together with an under-load tap changer and shunt capacitors, DG can operate more effectively in the DN. Non-dispatchable DG is considered in the coordinated optimisation method. The objective of the proposed reactive power coordinated optimisation method is to minimise active power loss and to decrease the number of switching device operations while maintaining the grid voltage within the allowable range. The optimisation problem is to solve a variable combination including integers and continuous variables, which will formulate a mixed integer non-linear programming (MINLP) problem. The control variable includes operations of traditional reactive power control devices and the reactive power adjustments of DGs. To solve the MINLP problem, a heuristic searching method, an improved harmony search algorithm is proposed with adaptive parameters process. Numerical tests on a standard 10-kV distribution system and a real distribution system demonstrate the applicability of the proposed reactive power coordinated optimisation method.

Integration of renewable distributed generators into the distribution system: a review

Recent advances in renewable energy technologies and changes in the electric utility infrastructures have increased the interest of the power utilities in utilisation of distributed generation (DG) resources to generate electricity. The recent trends in the development and utilisation of DG resources for power generation application are subject to the deregulation of the electric power sector and technical constraints to extend distribution and transmission networks to some areas. The electric power system planners, regulators and the policy makers have derived many benefits from integration of DG units into the distribution networks. These benefits depend on the characteristics of DG units such as photovoltaic (PV), wind system and reciprocating engines, characteristics of the loads, local renewable resources and network configuration. This study comprehensively reviews various research works on the technical, environmental and economic benefits of renewable DG integration such as line-loss reduction, reliability improvement, economic benefits and environmental pollution optimisation. These benefits can be optimised if all the renewable DG units are optimally sized, located and configured. This study also reviews the current status of renewable DG technologies based on different characteristics and the operational issues of integration of renewable DG into the electric power systems.

Optimal mix of solar and wind distributed generations considering performance improvement of electrical distribution network

Renewable energy sources are gaining more and more interest because they are nonpolluting and sustainable. Recently, a notable number of renewable distributed generations (DGs) having intermittent generation patterns are being interconnected with the distribution network to meet growing load demand and nullify environmental threats. Appropriate integration of renewable DGs in distribution networks is crucial to guarantee the qualitative network operational benefits. In this paper a simple but efficient approach has been proposed for optimal placement and sizing of solar and wind DGs in distribution territory by considering electrical network power loss minimization, voltage stability and network security improvement. The stochastic nature of solar irradiance and wind speed are accounted using suitable probabilistic models. Weighted aggregation particle swarm optimization technique is employed to optimize the objective functions considering bus voltage limit, line loading capacity, discrete size limit and penetration constraints of DGs. Strategic weight selection technique has been adopted to assess the well trade-off solution by persuasion of multiple objectives regarding the performance of distribution network. The proposed method has been applied to a typical Indian rural distribution network, and the satisfactory results are obtained.

Self-adaptable hierarchical clustering analysis and differential evolution for optimal integration of renewable distributed generation

In a previous paper, we have introduced a simulation and optimization framework for the integration of renewable generators into an electrical distribution network. The framework searches for the optimal size and location of the distributed renewable generation units (DG). Uncertainties in renewable resources availability, components failure and repair events, loads and grid power supply are incorporated. A Monte Carlo simulation–optimal power flow (MCS–OPF) computational model is used to generate scenarios of the uncertain variables and evaluate the network electric performance with respect to the expected value of the global cost (ECG). The framework is quite general and complete, but at the expenses of large computational times for the analysis of real systems. In this respect, the work of the present paper addresses the issue and introduces a purposely tailored, original technique for reducing the computational efforts of the analysis. The originality of the proposed approach lies in the development of a new search engine for performing the minimization of the ECG, which embeds hierarchical clustering analysis (HCA) within a differential evolution (DE) search scheme to identify groups of similar individuals in the DE population and, then, ECG is calculated for selected representative individuals of the groups only, thus reducing the number of objective function evaluations. For exemplification, the framework is applied to a distribution network derived from the IEEE 13 nodes test feeder. The results show that the newly proposed hierarchical clustering differential evolution (HCDE) MCS–OPF framework is effective in finding optimal DG-integrated network configurations with reduced computational efforts.

Optimal wind turbine sizing to minimize energy loss

The integration of renewable distributed generation (DG) in power systems has been increasing day by day. One of the most promising DG technologies is wind turbine among the renewable sources. Therefore, the optimization of DG whose the output power is varying with time is very crucial for the future power systems. However, it is difficult to establish a suitable objective function by taking into account of time varying characteristics. In this paper, a methodology based on weighting factors is proposed in order to minimize energy loss by finding the optimal sizes of wind turbines. The optimization is carried out by using the genetic algorithm with utilizing power flow analysis. The contribution of this paper is to allow considering the time varying characteristics of both load and wind-generation profile in a pairwise manner without violating the harmony of correspondence between load and generation profile. In addition, the proposed methodology is merged with the fuzzy-c means clustering to reduce execution time and allow long term planning due to the fact that the computational burden of the genetic algorithm is substantially high. The proposed methodology is applied to the IEEE-30 bus test system for 4days and annual energy loss minimization scenarios. The results show that energy loss can be reduced significantly by using the proposed methodology.

An approach for the integration of renewable distributed generation in hybrid DC/AC microgrids

This paper presents an investigation of a hybrid DC/AC integration paradigm to establish microgrids (MGs) by using a conventional three-phase local power delivery system. This approach adds an additional DC power line to the local power distribution system in order to collect energy generated by distributed domestic renewable sources. The local renewable distributed generation (DG) works in conjunction with the conventional grid utility to reduce the power draw from the grid. Researchers designed an energy conversion station to mix energy from the local DGs with energy from the grid utility. This approach, therefore, uses a continuous energy mixing strategy for DC integration of local generation and grid energy to supply energy to MG consumers via the conventional three-phase power distribution system. Thus, local distributed renewable generators do not have to contend with AC integration problems, such as AC stability and line synchronization. This approach can facilitate the transformation of conventional local power distribution systems into reliable MGs in an affordable way for stakeholders and it is a step towards construction of future smart grids.