Optimal Distributed Generation Research Articles

Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm

AbstractAn islanded hybrid AC‐DC microgrid interconnects renewable energy sources, distributed generators, and energy storage, primarily for remote areas without grid access. Its reliability depends on variable renewable output and load demand, while an energy management system optimizes power scheduling and reduces costs. In the first phase of this paper, uncertainty parameters like day‐ahead power from renewable energy sources (RES) and load demand (LD) are forecasted using the long short‐term memory (LSTM) deep learning algorithm. The LSTM outperforms the artificial neural network (ANN) model in terms of mean square error (MSE) and prediction accuracy (R2) for both training and testing datasets. In the second phase, the forecasted RES power and LD are used for optimal distributed generator (DG) scheduling using the improved grey wolf optimization (IGWO) algorithm. The objective of energy management in an islanded hybrid microgrid (HMG) is to minimize daily operating costs by considering load demand and the bidding costs of energy sources and storage devices. Two operational scenarios are evaluated to minimize the operating costs and optimize battery life. The proposed method, validated with IEEE standard test systems, is compared against several metaheuristic techniques. Results demonstrate that the improved grey wolf optimization (IGWO) algorithm is more effective at reducing costs and provides faster optimal solutions.

Electric eel foraging optimization algorithm for distribution network reconfiguration with distributed generation for power system performance enhancement considerations different load models

Optimizing the placement of distributed generators (DGs) alongside network reconfiguration is crucial for enhancing the efficiency and reliability of modern distribution systems amidst growing energy demands. This paper introduces a novel Electric Eel Foraging Optimization (EEFO) algorithm for the optimal reconfiguration and placement of DGs in radial distribution networks (RDNs), considering different load models. EEFO utilizes an adaptive energy factor to balance exploration and exploitation across four search strategies, such as exploration, resting, hunting, and migrating. Additionally, its free-parameter feature eliminates the need for fixed settings, simplifying the optimization process. The effectiveness of EEFO is demonstrated through its application to the 33-node and practical 84-node RDNs under different DG power factor scenarios with and without network reconfiguration. The primary objective is to minimize active and reactive power losses while reducing voltage deviation. The results indicate that the scenario with DG allocation at optimal power factor and network reconfiguration achieves significant reductions in voltage deviation, active power loss, and reactive power loss, with improvements of up to 92.20 %, 94.03 %, and 92.33 % in the 33-node network and 55.95 %, 60.20 %, and 59.36 % in the 84-node network, respectively. Furthermore, the comparative analysis with the zebra optimizer, whale optimizer, and moth flame optimizer highlights EEFO's superior efficiency and effectiveness in optimal DG allocation and distribution network reconfiguration.

Integration of Distributed Generations and electric vehicles in the distribution system

Power quality challenges are a major problem these days because of the significant increase in load demand and the existence of various reasons for disruptions in the distribution system. A voltage sag or a decrease in the magnitude of the line voltage can be brought on by rapid fluctuations in load or distribution system issues. Enhancing the grid current profile, reducing grid voltage sag, and improving other performances can all be achieved through the combination of electric vehicles with distribution generation systems. In this paper, the integration of Distributed Generation with Electric Vehicles in the distribution system has been done to enhance system performances such as true power loss index, imaginary power loss index, voltage deviation index, short circuit current index, cost function, and environmental impact reduction index for 16 bus distribution system in constant impedance, constant current, and constant power load models, or ZIP load models through proper location, sizing, coordinated controlling, and types of Distributed Generation and Electric Vehicle pairs for both charging and discharging modes by adopting Hybrid Genetic Algorithm - Monte Carlo Simulation optimization methodologies. This study's primary goal is to determine the optimal distribution generation and electric vehicle pairing to improve the aforementioned parameters, which will assist researchers in their future planning. Researchers, industry professionals, scientists, and individuals working with Distributed Generation and Electric vehicles in the smart grid will find this statement to be of great use.

Optimal DGs coordination strategy for managing unbalanced and islanded distribution networks

Abstract Distributed generators (DGs) have the potential to act as alternative power sources for balancing and restoring service in distribution system breakdowns. Nevertheless, DGs variety of characteristics and uncertain behavior pose difficulties in optimizing their operation while restoring the system. In this paper, a hierarchical structure is introduced to manage the unbalanced and islanded distribution networks with the optimal coordination strategy of DGs. The proposed approach encompasses multiple control tiers that manage DGs coordination process into three specific levels, each accountable for distinct functions involving voltage and frequency regulation, power distribution within individual islands, and overseeing power exchange between these units for minimizing imbalance and maximizing the load restoration. This research delivers a comprehensive solution for optimally coordinating DGs in unbalanced and isolated networks during restoration, thus elevating system reliability and resilience. The proposed model for distributed control restoration is validated using a modified IEEE 123-bus test setup.

Multi-stage framework for optimal incorporating of inverter based distributed generator into distribution networks

Recently, hydrogen-based distributed generators (DG) have gained significant attention for modern energy generation systems. These modem DGs are typically outfitted with power electronics converters, resulting in harmonic pollution. Furthermore, increasing the growth of modern nonlinear loads may result in exceeding the harmonic beyond the permitted level. This research proposes a framework for optimal incorporation of inverter-based distributed generation (a fuel cell connected to an AC distribution system) for minimizing power losses, enhancing the voltage profile, and limiting both total and individual harmonic distortion according to the IEEE-519 standard. In addition, for accounting system sustainability, the proposed framework considers load variation and the expected rise in demand. Therefore, the suggested framework comprises three stages, which include fundamental and harmonic power flow analysis. The first stage identifies the optimal size and location of the DG in relation to the base load operating condition. While, with the optimal DG of the first stage, the amount of harmonic pollution may violate the limits during a high level of nonlinear load penetration, as a result, the second stage resizes the DG, considering the connection bus of the first stage, to mitigate the harmonics and optimize the system at a higher level of nonlinear load penetration. Both the first and second stages are performed off-line, while the third stage optimizes the system operation during run time according to loading conditions, harmonic pollution, and the available DG capacity of the previous stages. DG’s harmonic spectrum is represented according to recently issued IEEE 1547-2018 for permissible DG’s current distortion limits. The suggested approach is applied and evaluated using an IEEE 33-bus distribution system for various combinations of linear and nonlinear loads. For run-time operation throughout the day, the presented framework reduces the energy losses from 5.281 to 2.452 MWh/day (about 53.57% energy savings). This saving is associated with voltage profile enhancement without violating the permissible standard levels of harmonics and other system constraints.

Enhancement of technical, economic & environmental benefits in multi-point PV & wind-based DG integrated radial distribution network using Aquila optimizer

Due to a larger R/X ratio, active power losses are higher in distribution networks (DNs) than in transmission networks. Due to the ever-increasing load demand, fossil fuel-based natural resources are becoming depleted, which is of great concern to network operators. Therefore, renewable energy sources (RES) are an effective solution to the growing energy issue and environmental pollution. The intermittent natures of PV and wind are characterized using probability density functions (PDFs). This study compares how well a radial distribution network performs when maximizing a constrained multi-objective function comprising voltage profile, economy, and emission benefit indices by optimal sizing and siting of distributed generators (DGs) using the Aquila optimizer (AO) algorithm. DGs based on photovoltaic / wind energy are injected at single, double, and triple points of standard IEEE 33, 69, 85, and 141 bus test systems. A statistical analysis of the results obtained by AO and some effective algorithms has been performed. The uniqueness of the work comes from the lack of prior literature reporting on simultaneous evaluations of technical, economic, and environmental consequences during optimal DG sizing and sitting. The maximum improvement ratios of the overall objective observed are 15.37, 15.69, 23.82, and 14.99 for the IEEE 33, 69, 85, and 141 bus DNs, respectively, concerning the base objective value. The active power losses corresponding to the best objective function of IEEE 33, 69, 85, and 141 bus DNs are reduced by 45.09 %, 43.24 %, 37.53 %, and 4.17 %, respectively.

Application of Many-Objective Arithmetic Optimization Algorithm and TOPSIS for Optimal Planning of DGS in Distribution Systems

The traditional planning of distribution networks is changing because of the accelerated expansion of distributed generation (DG) technologies in various capacities and forms. However, the improper integration of DGs in current distribution networks can give rise to several technical difficulties despite the advantages provided by distributed generation technologies. This paper presents the optimal DG planning in the distribution system using a Pareto-based many-objective arithmetic optimization algorithm (MOAOA) for optimal DG planning problems in the distribution system. This work focuses on improving four technical metrics related to distribution systems: mitigation of electrical energy not served (EENS), total voltage deviation (TVD) minimization, voltage stability index (VSI) maximization, and energy loss mitigation. Two scenarios are considered: the first scenario primarily focuses on optimal planning of DGs supporting active power only (e.g. Micro-Turbines DGs), and the second scenario focuses on optimal planning of DGs supporting both active and reactive power support (e.g. BIOMASS DGs). The optimal Pareto fronts between the competing objectives are generated using the Pareto-based MOAOA algorithm. The TOPSIS (a technique for order performance by similarity to ideal solution) multi-criteria decision-making technique is utilized for selecting the best trade-off solution from the optimal Pareto front. The posited method is examined on two standard IEEE-69 bus distribution systems. The efficacy of the MOAOA is compared with the outcomes of MOPSO, MOGWO and NSGA-II.

A Review of Distributed Generation Optimization with Shunt Capacitors in Reconfigured Distribution Networks

A Review of Distributed Generation Optimization with Shunt Capacitors in Reconfigured Distribution Networks

Particle Swarm Optimization (PSO) And Evolutionary Programming (EP) Technique for Optimal Placement and Sizing DG for Minimizing Loss in IEEE-30 Bus System

In recent years, most growing nations worldwide have experienced an unavoidably rising demand for electricity, which may result in a dropping of the voltage profile and a reduction in system stability, both of which have the potential to overload the power generation system significantly. One of the ways to reduce loss system network in this study is by distributed generation (DG) allocation, which the DG optimal location and sizing are the two most significant considerations for integration of DG. Improper placement and DG sizing in the power system not only result in increased total power losses but also affect the electrical system’s capability to be function properly. This paper provides a comparative analysis of two meta- heuristic optimization techniques, that is, Particle Swarm Optimization (PSO) and Evolutionary Programming (EP), to examine the optimal placement and sizing of distributed-generation (DG) for photovoltaic systems with the aim of minimizing the losses in interconnected distributed generation, IEEE-30 bus system. The objective function of this study is minimization the power loss with considered some constraints which are voltage limits and DG sizing limits are being covers in this study. The optimization issue has been solved by working with MATLAB/m-files software. The outcomes derived from the simulation results in this study demonstrated that PSO performance of two units of DG is better compared to EP algorithm. The voltage profile was improved drastically, and the power loss was reduced as lowest as 17.4486MW in the first case, 17.4368MW in the second case, and 17.4179MW in the third case.

Enhancing Distribution Network Efficiency with Andean Condor Algorithm-Driven Optimal Placement of Distributed Generation and Network Reconfiguration

This research introduces an innovative methodology for optimizing placement of Distributed Generations (DGs) within distribution networks, with primary focus on reducing power losses and enhancing energy efficiency. The surge in electric power demand has necessitated integration of DGs into distribution networks, bolstering system security. However, this integration brings about structural changes and alters system parameters, such as power fluctuations, voltage profiles and fault current levels. To address these complexities, this research contributes comprehensive approach that integrates Andean Condor Algorithm (ACA) and Network Reconfiguration (NR) to optimize DG placement. The proposed methodology aims to minimize both active and reactive power losses in distribution networks. MATLAB simulations using the 69 and 33 bus distribution networks are used to thoroughly validate the system’s efficacy. The suggested ACA achieves an active power loss of 120 KW and a reactive power loss of 28.66 KVAr for 33 bus system. In the same way, 69 bus system manages to achieve a 16 KVAr reactive power loss and a 92.3 KW active power loss. The results show that suggested methodology is both effective and better than popular CPSO method, which makes it valuable addition to the field of distributed generation optimization in power distribution networks.

Multi-objective optimal distributed generators integration using firefly algorithm with Fuzzy decision making

Multi-objective optimal distributed generators integration using firefly algorithm with Fuzzy decision making

Distributed Generation and Demand Response Coordination for Optimal Operation of Microgrid Using Modified Elephant Herd Optimization

The optimal operation and coordination of distributed generation (DG) sources and demand response (DR) programs help to minimize the operating cost of microgrids along with other mutual benefits. The model is developed, and the multiobjective problem subjected to the set of constraints is solved by using the modified elephant herd optimization (MEHO) technique in this work. The expected output of the model is the optimal generation scheduling plan of both the dispatchable and non-dispatchable DG and optimized cost for the isolated microgrid operation. The other objectives related to the environmental aspects and reliability are also considered for optimization. DG operation is coordinated with the day ahead DR programs to minimize the costs while satisfying the operation limits of the DG sources and consumer demand. The novelty of the work lies in finding the optimal solution considering the multiobjective for optimal DG operation and analyzing the impact of different types of DR programs on microgrid operation by the MEHO technique. The effectiveness of the proposed model and validity of the algorithm is further tested on a 14 bus microgrid benchmark system. Through simulation and optimization results it is observed that the proposed algorithm has less computational time and better accuracy.

Effects of integration of distributed generation on reliability in distribution system

The reliability of power supply to consumers is the main factor that is to be considered while designing, planning and operation of a distribution network. This problem can be solved by incorporating distributed generation (DG) at the consumer side itself. In order to enhance service availability, power quality, reliability, and loss reduction, DG is positioned optimally at the consumer end within the system. The optimal DG location and size is to be calculated in such a way that the total loss in the system should be minimal and cost savings should be maximal. In this study, for the investigation of effects of DG on reliability of distribution system, a practical 11 KV feeder with 41-bus, was employed. The selected practical feeder is modelled using the power world simulator (PWS) software and reliability of the system is verified by calculating various reliability indices such as system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), customer average interruption duration index (CAIDI), average system utility index (ASUI) and average service availability index (ASAI). After applying proposed loss sensitivity factor (LSF) method, the active and reactive power loss reduction are 41.30 % and 38.64 % respectively, least bus voltage is enhanced to 0.9405 pu from 0.8978 pu and the reliability indices are also improved.

Using the MCDM Method Distributed Generation (DG) System

Distributed Generation (DG) system These two major categories of DG optimization methodologies are different from the components of the examined studies. Distributed generation (DG) power systems are the most popular technique for extending the power network to rural areas and, more recently, as a sustainable electrification technique The consequences of seasonal load variation and distributed hybrid system architecture without load shedding generation (TG) are explored in light of the dwindling availability of traditional fossil fuels, the fluctuating cost of fuel, and the decrease of environmental pollutants owing to increased demand. Numerous DGs connected to integrated power quality system conditioners. Today, a lot of distributed generation (DG) technology for renewable energy is interface-based. In grid-connected converters, these harmonic functions are taken into account by sensing control, enhancing converter versatility when local controllers use assessment techniques for harmonic distribution system adjustment. As a result, systems ought to implement common current-regulated and voltage-regulated DG harmonics correction functions. A wind solar hybrid system produces electricity by combining the two renewable energy sources, wind and sunlight. The system is made to produce electricity utilizing both modest wind generators and solar panels. The task of supplying the engine with fuel falls on the fuel system, which consists of a fuel tank, pump, filter, and injectors or a carburetor. Each part of the car needs to be faultless in order for it to function and be as dependable as anticipated. A photovoltaic (PV) system combines one or more solar panels with an inverter and other electrical and mechanical components to generate power from the sun. There are many different sizes available for PV systems, ranging from small rooftop or portable devices to massive utility-scale power plants. In isolated (cold or more temperate) places with no other electrical supply, PV offers a suitable energy source. Photovoltaic systems, for instance, can be used to power: water pipes, communications repeater stations, and more. The components of a typical system include a building sewer, a septic tank, a standard trench, a shallow trench, a chamber trench, a deep wall trench, and an absorbent bed for seepage pits. EDAS approach is proposed for their role category. The top advantage of EDAS compared to other methods for classification is that it has high accuracy performance and less mathematical calculations. In EDAS, each evaluation of substitutions appreciates size and a form standard solution introduces a durable EDAS technique for finding providers depending on the location of character substitution. Strong waste for disposal in site determination suggested a purely intuitive fuzzy model based on EDAS. In this study, EDAS was integrated into analyzer boundaries for RE development Application of EDAS technique in MCDM. First, a basic definition of projects and a distance method are briefly suggested. Next, the augmented EDAS approach is traditional under the real context inspired by the EDAS method. Results: The final result is done by using the EDAS method. Fuel system is highest Value and PV system is lowest value. resulting in Fuel system ranked first, there Fuel system has low rank.

Multi‐objective multi‐period optimal site and size of distributed generation along with network reconfiguration

AbstractWhile extensive research has focused on enhancing distribution networks through either maximizing Distributed Generation (DG) integration or network reconfiguration at specific times, there is a need for further investigation into concurrently optimal network reconfiguration and DG allocation. To reduce the cost of energy delivered, the cost of energy loss, and voltage deviation, this study gives a dynamic multi‐objective network reconfiguration together with siting and sizing of dispatchable and non‐dispatchable DGs. The widely used IEEE 33‐bus and a large‐scale 118‐bus radial test system are employed while considering the time sequence fluctuations in sunlight irradiation and load. To address the pointed‐out challenge of multiperiod optimal DG allocation and reconfiguration while simultaneously decreasing the cost of energy supplied, the cost of energy lost, and the voltage deviation, a novel Multi‐objective Bidirectional co‐evolutionary algorithm (BiCo) is implemented. For better exploration and exploitation, the proposed algorithm integrating the constraint domination principle evolves the population from the feasible and infeasible search space with the help of a novel angle‐based density section. Simulation results demonstrate that the proposed approach outperforms previously published Multi‐objective Evolutionary Algorithms (MOEAs) by discovering a vast collection of uniformly spaced non‐dominated solutions in a single simulation run. Further, a fuzzy set theory is applied to find the best compromise solution among obtained final non‐dominated solutions. The results establish that the Pareto solutions significantly improved the system's voltage profile, with savings of over 22% compared to the baseline case and an exceptional improvement of over 80% in voltage deviation and power loss.

Techno-economic evaluation of meshed distribution network planning with load growth and expansion with multiple assets across multiple planning horizons

The smart grid paradigm has ushered in an era where modern distribution systems are expected to be both robust and interconnected in topology. This paper presents a techno-economic-based sustainable planning (TESP) strategy, which can be used as a planning framework for linked distribution systems, seeking to discover a realistic solution among competing criteria of diverse genres. In this comparative analysis-based study, three voltage stability assessment indices—VSA_A, VSA_B, and VSA_W—and a loss minimization condition (LMC)-based framework are used in the initial stage to achieve optimal distributed generation (DG)-based asset optimization for siting, followed by sizing. The respective techniques are evaluated across two variants of multiple load growth horizons spread across 10 years. The suggested TESP technique is tested on two variants of a mesh-configured microgrid (MCMG) with varied load growth scenarios. One variant considers a 65-bus MG with a fixed load growth of 2.7% across two load growth horizons. The other variant considers a 75-bus MG with varied load growth across four load growth horizons, encapsulating an expansion-based planning perspective. The numerical results of the suggested TESP approach in a comparative study demonstrate its effectiveness, and it can be used by researchers and planning engineers as a planning framework for interconnected distribution tools across multiple planning horizons. The proposed study would contribute to enhancing the robustness and interconnectivity of smart grid distribution systems. This dual focus could lead to more cost-effective and reliable power distribution systems.

PSO‐based optimal placement of electric vehicle charging stations in a distribution network in smart grid environment incorporating backward forward sweep method

AbstractThe transition from conventional fossil‐fuel vehicles to electric vehicles (EVs) is critical for mitigating environmental pollution. The placement of electric vehicle charging stations (EVCS) significantly impacts the utility operator and electrical network. Inappropriately placed EVCS lead to challenges such as increased load, unbalanced generation, power losses, and reduced voltage stability. Incorporating distributed generation (DG) helps mitigate these issues by maximizing EV usage. This study focuses on optimizing EVCS and DG placement in radial distribution networks. The methodology employs a backward and forward sweep method for load flow analysis and utilizes the particle swarm optimization (PSO) algorithm to determine optimal EVCS and DG locations and sizes. This approach, validated on the IEEE‐33 bus system, outperforms existing methods. Results indicate a 2.5 times greater power loss reduction compared to simulated annealing (SA), 1.6 times better than artificial bee colony, and parity with genetic algorithm (GA). Overall, the PSO algorithm demonstrates superior optimization effectiveness and computational efficiency, showcasing 1–2.5 times better performance than other methodologies. Employing this approach yields significantly improved results, making it a promising technique for optimizing EVCS and DG placement in distribution networks.

Optimal inverter-based distributed generation in ULP Way Halim considering harmonic distortion

Integration of distributed generation (DG) based on the use of new renewable energy is considered to be able to increase the capability of the electric power distribution system. However, the use of inverter-based DG is not optimal, it can worsen the condition of the system, especially in terms of the spread of harmonic distortion which can damage the equipment. This is due to the inverter-based DG technology, apart from supplying electrical energy, DG also injects harmonic currents from existing semiconductor components. This research discusses optimization placement of inverter-based DG using the multi objective particle swarm optimization (MOPSO) method which was tested on the Unit Layanan Pelaksana (ULP) Way Halim 88-bus radial distribution system based on MALTAB 2020b to increase the efficiency of the electrical system by reducing losses and %THDv. The inverter-based DG placed on 24 bus points with a capacity of 690 kW can reduce losses by up to 12.74 kW or 14.96% and all %THDv values for each bus are below 5%.

Profile Optimisation in Real Distribution Networks (Case Study Long MV Feeder)

Abstract In the dynamic field of power systems, integrating distributed generation (DG) sources like solar photovoltaic (PV) plants is crucial for enhancing reliability and fostering sustainability. However, this integration poses voltage profile management challenges in electrical grids. This study investigates voltage profile optimization in Bosnia and Herzegovina’s Gračanica network, focusing on a medium voltage feeder with 68 buses. Using DIgSILENT PowerFactory software, six scenarios with different configurations of solar plants are analyzed for their impact on voltage profiles and power losses. Results show that while DG integration offers benefits, incorrect sizing or placement can increase power losses. Optimal DG benefits are linked to specific sizes and locations. This research emphasizes the need for balancing PV generation with load demands and provides insights for optimal PV plant size and output to minimize negative impacts. These findings aid energy planners and policymakers in implementing distributed solar PV in medium voltage networks.

Distributed Generation Allocation in Distribution Network for Energy Losses Reduction

Abstract This paper presents a method for distributed generation (DG) allocation in medium voltage (MV) distribution system based on energy loss minimization. The main objective of the research is to design, implement and test a DG allocation (siting and sizing) method and to investigate how optimal DG allocation influence the operational parameters of the system from the Distribution System Operator (DSO) perspective. The problem is formulated as a single objective optimization problem solved by using both genetic algorithm and particle swarm optimization techniques. Model of a realistic Electric Power Distribution System (EPDS) and IEEE 37-bus EPDS are used as test systems. The results confirm that proposed algorithms can be used for practical DG allocation. The research presented contributes to the field as it provides a DG allocation method for energy loss reduction performed on a EPDS which can be applied in realistic planning and regulatory situations using open-source software.