Economic Power Dispatching from Distributed Generations: Review of Optimization Techniques

In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop control,predictive control,and fuzzy-based methods.These methods can deal with large-scale systems with many objectives and non-linear,non-convex optimization issues.The most popular approaches are the LP and QP models,while more difficult problems are handled using metaheuristic optimization techniques.In summary,in order to increase efficiency,reduce costs,and ensure a consistent supply of electricity,optimization techniques are essential tools used in economic power dispatching from DGs.

Optimal Location and Sizing ofMulti-Resource Distributed Generator Based onMulti-Objective Artificial Bee Colony Algorithm

Distribution generation(DG)technology based on a variety of renewable energy technologies has developed rapidly.A large number of multi-type DG are connected to the distribution network(DN),resulting in a decline in the stability of DN operation.It is urgent to find a method that can effectively connect multi-energy DG to DN.photovoltaic(PV),wind power generation(WPG),fuel cell(FC),and micro gas turbine(MGT)are considered in this paper.A multi-objective optimization model was established based on the life cycle cost(LCC)of DG,voltage quality,voltage fluctuation,system network loss,power deviation of the tie-line,DG pollution emission index,and meteorological index weight of DN.Multi-objective artificial bee colony algorithm(MOABC)was used to determine the optimal location and capacity of the four kinds of DG access DN,and compared with the other three heuristic algorithms.Simulation tests based on IEEE 33 test node and IEEE 69 test node show that in IEEE 33 test node,the total voltage deviation,voltage fluctuation,and system network loss of DN decreased by 49.67%,7.47%and 48.12%,respectively,compared with that without DG configuration.In the IEEE 69 test node,the total voltage deviation,voltage fluctuation and system network loss of DN in the MOABC configuration scheme decreased by 54.98%,35.93%and 75.17%,respectively,compared with that without DG configuration,indicating that MOABC can reasonably plan the capacity and location of DG.Achieve the maximum trade-off between DG economy and DN operation stability.

Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.

Coordinated voltage regulation strategy of OLTC and BESS considering switching delay

When large-scale distributed renewable energy power generation systems are connected to the power grid,the risk of grid voltage fluctuations and exceeding the limit increases greatly.Fortunately,the on-load tap changer(OLTC)can adjust the transformer winding tap to maintain the secondary side voltage within the normal range.However,the inevitable delay in switching transformer taps makes it difficult to respond quickly to voltage fluctuations.Moreover,switching the transformer taps frequently will decrease the service life of OLTC.In order to solve this critical issue,a cooperative voltage regulation strategy applied between the battery energy storage systems(BESSs)and OLTSs.is proposed By adjusting the charge and discharge power of BESSs,the OLTC can frequently switch the transformer taps to achieve rapid voltage regulation.The effectiveness of the proposed coordinated regulation strategy is verified in the IEEE 33 node distribution systems.The simulation results show that the proposed coordinated regulation strategy can stabilize the voltage of the distribution network within a normal range and reduce the frequency of tap switching,as such elongating the service life of the equipment.

Comprehensive Benefit Evaluation of SZ Distributed Photovoltaic Power Generation Project Based on AHP-Matter-Element Extension Model

With the introduction of the“dual carbon goals,”there has been a robust development of distributed photovoltaic power generation projects in the promotion of their construction.As part of this initiative,a comprehensive and systematic analysis has been conducted to study the overall benefits of photovoltaic power generation projects.The evaluation process encompasses economic,technical,environmental,and social aspects,providing corresponding analysis methods and data references.Furthermore,targeted countermeasures and suggestions are proposed,signifying the research’s importance for the construction and development of subsequent distributed photovoltaic power generation projects.

A Robust Asynchrophasor in PMU Using Second-Order Kalman Filter

Phasor Measurement Units(PMUs)provide Global Positioning System(GPS)time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system.Those synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition.A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view.However,such ongoing development and improvement to PMUs’principal work are essential to the network operators to enhance the grid quality and the operating expenses.This paper introduces a proposed method that led to lowcost and less complex techniques to optimize the performance of PMU using Second-Order Kalman Filter.It is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access point.The MATLAB model has been created to implement the proposed method in the presence of Gaussian and non-Gaussian.The results have shown the proposed method which is Second-Order Kalman Filter outperforms the existing model.The results were tested usingMean Square Error(MSE).The proposed Second-Order Kalman Filter method has been replaced with a synchronization unit into thePMUstructure to clarify the significance of the proposed new PMU.

Control of Distributed Generation Using Non-Sinusoidal Pulse Width Modulation

The islanded mode is one of the connection modes of the grid distributed generation resources.In this study,a distributed generation resource is connected to linear and nonlinear loads via a three-phase inverter where a control method needing no current sensors or compensator elements is applied to the distribute generation system in the islanded mode.This control method has two main loops in each phase.The first loop controls the voltage control loops that adjust the three-phase point of common coupling,the amplitude of the non-sinusoidal reference waveform and the near-state pulse width modulation(NSPWM)method.The next loop compensates the harmonic compensator loop that calculates the voltage harmonics of the point of common coupling in each phase,and injects them to compensate the non-sinusoidal reference waveforms of each phase.The simulation results in MATLAB/SIMULINK show that this method can generate balanced threephase sinusoidal voltage with an acceptable total harmonic distortion(THD)at the joint connection point.

Analysis and Power Quality Improvement in Hybrid Distributed Generation System with Utilization of Unified Power Quality Conditioner

This paper presents a comprehensive study that includes the sizing and power flow by series and parallel inverters in a distributed generation system(DGs)that integrates the system of hybrid wind photovoltaic with a unified power quality conditioner(UPQC).In addition to supplying active power to the utility grid,the system of hybrid wind photovoltaic functions as a UPQC,compensating reactive power and suppressing the harmonic load currents.Additionally,the load is supplied with harmonic-free,balanced and regulated output voltages.Since PVWind-UPQC is established on a dual compensation scheme,the series inverter works like a sinusoidal current source,while the parallel inverter works like a sinusoidal voltage source.Consequently,a smooth alteration from interconnected operating modes to island operating modes and vice versa can be achieved without load voltage transients.Since PV-Wind-UPQC inverters handle the energy generated through the hybrid wind photovoltaic system and the energy demanded through the load,the converters should be sized cautiously.A detailed study of the flow of power via the PV-Wind-UPQC is imperative to gain a complete understanding of the system operation and the proper design of the converters.Thus,curves that allow the sizing of the power converters according to the power flow via the converters are presented and discussed.Simulation results are presented to assess both steady state and dynamic performances of the grid connected hybrid system of PV-Wind-UPQC.This investigation is verified by simulating and analyzing the results with Matlab/Simulink.

Identification of Type of a Fault in Distribution System Using Shallow Neural Network with Distributed Generation

A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.

Availability Capacity Evaluation and Reliability Assessment of Integrated Systems Using Metaheuristic Algorithm

Contemporarily,the development of distributed generations(DGs)technologies is fetching more,and their deployment in power systems is becom-ing broad and diverse.Consequently,several glitches are found in the recent studies due to the inappropriate/inadequate penetrations.This work aims to improve the reliable operation of the power system employing reliability indices using a metaheuristic-based algorithm before and after DGs penetration with feeder system.The assessment procedure is carried out using MATLAB software and Mod-ified Salp Swarm Algorithm(MSSA)that helps assess the Reliability indices of the proposed integrated IEEE RTS79 system for seven different configurations.This algorithm modifies two control parameters of the actual SSA algorithm and offers a perfect balance between the exploration and exploitation.Further,the effectiveness of the proposed schemes is assessed using various reliability indices.Also,the available capacity of the extended system is computed for the best configuration of the considered system.The results confirm the level of reli-able operation of the extended DGs along with the standard RTS system.Speci-fically,the overall reliability of the system displays superior performance when the tie lines 1 and 2 of the DG connected with buses 9 and 10,respectively.The reliability indices of this case namely SAIFI,SAIDI,CAIDI,ASAI,AUSI,EUE,and AEUE shows enhancement about 12.5%,4.32%,7.28%,1.09%,4.53%,12.00%,and 0.19%,respectively.Also,a probability of available capacity at the low voltage bus side is accomplished a good scale about 212.07 times/year.

Energy Loss Analysis of Distributed Rooftop Photovoltaics in Industrial Parks

The analysis of the loss of distributed photovoltaic power generation systems involves the interests of energy users,energy-saving service companies,and power grid companies,so it has always been the focus of the industry and society in some manner or another.However,the related analysis for an actual case that considers different cooperative corporations’benefits is lacking in the presently available literature.This paper takes the distributed rooftop photovoltaic power generation project in an industrial park as the object,studies the analysis and calculation methods of line loss and transformer loss,analyzes the change of transformer loss under different temperatures and different load rates,and compares the data and trend of electricity consumption and power generation in industrial parks before and after the photovoltaic operation.This paper explores and practices the analysis method of the operating loss of distributed photovoltaic power generation and provides an essential reference for the benefit analysis and investment cost estimation of distributed photovoltaic power generation systems in industrial parks.The analyzed results reveal that the change loss is stable after the photovoltaic is connected,and there is no additional transformer loss.And before and after the photovoltaic system installation,there was no significant change in the total monthly data difference between the total meter and the sub-meter.

Research on Coordinated Development and Optimization of Distribution Networks at All Levels in Distributed Power Energy Engineering

The uncertainty of distributed generation energy has dramatically challenged the coordinated development of distribution networks at all levels.This paper focuses on the multi-time-scale regulation model of distributed generation energy under normal conditions.The simulation results of the example verify the self-optimization characteristics and the effectiveness of real-time dispatching of the distribution network control technology at all levels under multiple time scales.

Maximum Power Tracking of Photovoltaic Modules Based on Fuzzy Logic Employing Four-Phase Intervaled Boost Converter

Currently, there are significant investments in the study of distributed generation, including solar energy by the photovoltaic conversion method. Basically, a cell directly converts solar energy to electricity. For this, static converters are required. However, relevant issues arise in this process: point of maximum efficiency of module generation, strategy of control of the flow of energy to the network. The aim of this work is to monitor the main variables of a photovoltaic system, specifically the voltage and current module and their derivates. The goal is to implement the maximum power tracking technique using Fuzzy logic. In addition, the energy provided by the cell will be employed in an inverter stage that can operate as an active filter, voltage regulator, or generator of reactive and active power. The feasibility of using Fuzzy logic will also be studied. The first stage of this work involves parameterization and simulation of photovoltaic modules. The initial study examines the compatibility of a commercial module and its catalog data with the results of simulation. The simulated I-V characteristics show almost identical results to the catalog data. In sequence, a boost or lift DC-DC converter is employed to emulate variable load for maximum power transfer.

Optimal Placement and Sizing of Distributed Generations for Power Losses Minimization Using PSO-Based Deep Learning Techniques

The integration of distributed generations (DGs) into distribution systems (DSs) is increasingly becoming a solution for compensating for isolated local energy systems (ILESs). Additionally, distributed generations are used for self-consumption with excess energy injected into centralized grids (CGs). However, the improper sizing of renewable energy systems (RESs) exposes the entire system to power losses. This work presents an optimization of a system consisting of distributed generations. Firstly, PSO algorithms evaluate the size of the entire system on the IEEE bus 14 test standard. Secondly, the size of the system is allocated using improved Particles Swarm Optimization (IPSO). The convergence speed of the objective function enables a conjecture to be made about the robustness of the proposed system. The power and voltage profile on the IEEE 14-bus standard displays a decrease in power losses and an appropriate response to energy demands (EDs), validating the proposed method.

Evaluating the reliability of distributed photovoltaic energy system and storage against household blackout

Distributed energy resources have been proven to be an effective and promising solution to enhance power system resilience and improve household-level reliability.In this paper,we propose a method to evaluate the reliability value of a photovoltaic(PV)energy system with a battery storage system(BSS)by considering the probability of grid outages causing household blackouts.Considering this reliability value,which is the economic profit and capital cost of PV+BSS,a simple formula is derived to calculate the optimal planning strategy.This strategy can provide household-level customers with a simple and straightforward expression for invested PV+BSS capacity.Case studies on 600 households located in eight zones of the US for the period of 2006 to 2015 demonstrate that adding the reliability value to economic profit allows households to invest in a larger PV+BSS and avoid loss of load caused by blackouts.Owing to the differences in blackout hours,households from the 8 zones express distinct willingness to install PV+BSS.The greater the probability of blackout,the greater revenue that household can get from the PV+BSS.The simulation example shows that the planning strategy obtained by proposed model has good economy in the actual operation and able to reduce the economic risk of power failure of the household users.This model can provide household with an easy and straightforward investment strategy of PV+BSS capacity.

Model of decentralized cross-chain energy trading for power systems

With the development of the energy Internet, more distributed generators are connected to the power grid, resulting in numerous heterogeneous energy networks. However, different energy networks cannot perform efficient energy trading in the centralized management mode, this deeply affecting the complementary ability of heterogeneous energy, resulting in the islanded energy phenomenon. In this model, the same energy on the chain is traded within the chain, and the heterogeneous energy on different chains is traded across chains. To trade energy between heterogeneous energy networks more efficiently, the blockchain-based cross-chain model is proposed based on the existing infrastructure. Heterogeneous energy nodes are assigned to different energy sub-chains and cross-chain energy transactions are performed through a relay-chain, which utilizes the improved Boneh–Lynn–Shacham signature scheme consensus algorithm based on the proof-of-stake and practical Byzantine fault tolerance. The experimental simulations on energy trading efficiency, throughput, and security, show its superiority over existing systems. Further, the simulation results provide a reference for the application of cross-chain technology in energy interconnection.

Design of Smart Home System Based on ZigBee Technology and R&D for Application

In this paper, a smart home system based on ZigBee technology is designed. The system includes home network, home server and mobile terminal. The program is highly scalable and cost-effective. This paper developed the home server-side application based on MFC technology and the mobile terminal application. The mobile client can remotely control home devices and query the running state, electric energy information and historical data of home devices. At the same time, the home server-side application can store electric energy information and electricity consumption of home devices. Combined with household distributed photovoltaic generation system, the system can be applied to home energy management system. Through running tests and application, the results show that the system has realized basic functions of smart home and achieved the desired design goals.

Coordinated Cyber-Physical equipment planning for distributed generation based on chance constrained

With development of distributed generation(DG),configuration of optimization equipment is crucial for absorbing excess electricity and stabilizing fluctuations.This study proposes a two-layer configuration strategy coordinates active cyber control and the physical energy storage(ES)system.First,an upper economic model is developed.Based on chance-constrained programming,an operation model accounts for inherent uncertainty are then developed.Under constraint of voltage risk level,a lower operation model is developed.Finally,a solution based on differential evolution is provided.An IEEE 33 bus system simulation was used to validate efficacy of model.The effects of risk level,equipment price,and chance-constrained probability were analyzed,providing a foundation for power consumption and expansion of cyber-physical systems.

Evaluation of intermittent-distributed-generation hosting capability of a distribution system with integrated energy-storage systems

The penetration rate of distributed generation is gradually increasing in the distribution system concerned.This is creating new problems and challenges in the planning and operation of the system.The intermittency and variability of power outputs from numerous distributed renewable generators could significantly jeopardize the secure operation of the distribution system.Therefore,it is necessary to assess the hosting capability for intermittent distributed generation by a distribution system considering operational constraints.This is the subject of this study.An assessment model considering the uncertainty of generation outputs from distributed generators is presented for this purpose.It involves different types of regulation or control functions using on-load tap-changers(OLTCs),reactive power compensation devices,energy storage systems,and the reactive power support of the distributed generators employed.A robust optimization model is then attained It is solved by Bertsimas robust counterpart through GUROBI solver.Finally,the feasibility and efficiency of the proposed method are demonstrated by a modified IEEE 33-bus distribution system.In addition,the effects of the aforementioned regulation or control functions on the enhancement of the hosting capability for intermittent distributed generation are examined.

Hybrid Renewable Energy Resources Management for Optimal Energy Operation in Nano-Grid

Renewable energy resources are deemed a potential energy production source due to their cost efficiency and harmless reaction to the environment,unlike non-renewable energy resources.However,they often fail to meet energy requirements in unfavorable weather conditions.The concept of Hybrid renewable energy resources addresses this issue by integrating both renewable and non-renewable energy resources to meet the required energy load.In this paper,an intelligent cost optimization algorithm is proposed to maximize the use of renewable energy resources and minimum utilization of non-renewable energy resources to meet the energy requirement for a nanogrid infrastructure.An actual data set comprising information about the load and demand of utility grids is used to evaluate the performance of the proposed nanogrid energy management system.The objective function is formulated to manage the nanogrid operation and implemented using a variant of Particle Swarm Optimization(PSO)named recurrent PSO(rPSO).Firstly,rPSO algorithm minimizes the installation cost for nanogrid.Thereafter,the proposed NEMS ensures cost efficiency for the post-installation period by providing a daily operational plan and optimizing renewable resources.State-of-the-art optimization models,including Genetic Algorithm(GA),bat and different Mathematical Programming Language(AMPL)solvers,are used to evaluate the model.The study’s outcomes suggest that the proposed work significantly reduces the use of diesel generators and fosters the use of renewable energy resources and beneficiates the eco-friendly environment.