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.

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.

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.

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.

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.

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.

Configuration optimization model of multi-energy distributed generation system

To integrate different renewable energy resources effectively in a microgrid, a configuration optimization model of a multi-energy distributed generation（DG） system and its auxiliary equipment is proposed. The model mainly consists of two parts, the determination of initial configuration schemes according to user preference and the selection of the optimal scheme. The comprehensive evaluation index（CEI）, which is acquired through the analytic hierarchy process（AHP） weight calculation method, is adopted as the evaluation criterion to rank the initial schemes. The optimal scheme is obtained according to the ranking results. The proposed model takes the diversity of different equipment parameters and investment cost into consideration and can give relatively suitable and economical suggestions for system configuration.Additionally, unlike Homer Pro, the proposed model considers the complementation of different renewable energy resources, and thus the rationality of the multi-energy DG system is improved compared with the single evaluation criterion method which only considers the total cost.

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.

Enhancement of Distributed Generation by Using Custom Power Device

Wind energy （WE） has become immensely popular for distributed generation （DG）. This case presents the monitoring, modeling, control, and analysis of the two-level three-phase WE based DG system where the electric grid interfacing custom power device （CPD） is controlled to perform the smart exchanging of electric power as per the Indian grid code. WE is connected to DC link of CPD for the grid integration purpose. The CPD based distributed static compensator, i.e. the distributed static synchronous compensator （DSTATCOM）, is utilized for injecting the wind power to the point of common coupling （PCC） and also acts against the reactive power demand. The novel indirect current control scheme of DSTATCOM regulates the power import and export between the WE and the electric grid system. It also acts as a compensator and performs both the key features simultaneously. Hence, the penetration of additional generated WE power to the grid is increased by 20% to 25%. The burden of reactive power compensation from grid is reduced by DSTATCOM. The modeling and simulation are done in MATLAB. The results are validated and verified.

Advanced in power control for distributed generation system using cooperative algorithms

To develop efficient power control strategies for a distributed generation system in order to improve the overall system efficiency, we propose a cooperative algorithm to analyze and design the controller, in which elements of conventional mathematical optimization algorithms are combined with adaptive dynamic elements drawn from intelligent control theory. In our design, the sequential quadratic programming algorithm was first utilized to obtain an optimal solution for power distribution among multiple units. Fuzzy system was then developed to implement the optimal strategies on the basis of optimal solution. In addition, parameters of the fuzzy system were adapted via a genetic algorithm. Tbe simulation results illustrate that the methodology described is useful for a range of control system designs.

Optimal Placement and Sizing of Distributed Generation Using Metaheuristic Algorithm

Power loss and voltage uncertainty are the major issues prevalently faced in the design of distribution systems.But such issues can be resolved through effective usage of networking reconfiguration that has a combination of Distributed Generation(DG)units from distribution networks.In this point of view,optimal placement and sizing of DGs are effective ways to boost the performance of power systems.The optimum allocation of DGs resolves various problems namely,power loss,voltage profile improvement,enhanced reliability,system stability,and performance.Several research works have been conducted to address the distribution system problems in terms of power loss,energy loss,voltage profile,and voltage stability depending upon optimal DG distribution.With this motivation,the current study designs a Chaotic Artificial Flora Optimization based on Optimal Placement and Sizing of DGs(CAFO-OPSDG)to enhance the voltage profiles and mitigate the power loss.Besides,the CAFO algorithm is derived from the incorporation of chaos theory concept into conventional artificial flora optimization AFO algorithm with an aim to enhance the global optimization abilities.The fitness function of CAFO-OPSDG algorithm involves voltage regulation,power loss minimization,and penalty cost.To consider the actual power system scenario,the penalty factor acts as an important element not only to minimize the total power loss but to increase the voltage profiles as well.The experimental validation of the CAFO-OPSDG algorithm was conducted against IEEE 33 Bus system and IEEE 69 Bus system.The outcomes were examined under various test scenarios.The results of the experiment established that the presented CAFO-OPSDG model is effective in terms of reducing the power loss and voltage deviation and boost-up the voltage profile for the specified system.

Optimal Operation of Electric Vehicles and Distributed Generation Resources in Smart Grid Considering Load Management

Technology advancement and the global tendency to use renewable energy in distributed generation units in the distribution network have been proposed as sources of energy supply.Despite the complexity of their protection,as well as the operation of distributed generation resources in the distribution network,factors such as improving reliability,increasing production capacity of the distribution network,stabilizing the voltage of the distribution network,reducing peak clipping losses,as well as economic and environmental considerations,have expanded the influence of distributed generation(DG)resources in the distribution network.The location of DG sources and their capacity are the key factors in the effectiveness of distributed generation in the voltage stability of distribution systems.Nowadays,along with the scattered production sources of electric vehicles with the ability to connect to the network,due to having an energy storage system,they are known as valuable resources that can provide various services to the power system.These vehicles can empower the grid or be used as a storage supply source when parked and connected to the grid.This paper introduces and studies a two-stage planning framework for the concurrent management of many electric vehicles and distributed generation resources with private ownership.In the first stage,the aim is to increase the profit of electric vehicles and distributed generation sources;finally,the purpose is to reduce operating costs.The proposed scheduling framework is tested on a distribution network connected to bus 5 of the RBTS sample network.Besides distributed generation sources and electric vehicles,we integrate time-consistent load management into the system.Due to distributed generation sources such as photovoltaic systems and wind turbines and the studied design in the modeling,we use the Taguchi TOAT algorithm to generate and reduce the scenario to ensure the uncertainty in renewable energy.MATLAB software is used to solve the problem and select the optimal answer.

Optimal design of coordination control strategy for distributed generation system

This paper presents a novel design procedure for optimizing the power distribution strategy in distributed generation system. A coordinating controller, responsible to distribute the total load power request among multiple DG units, is suggested based on the conception of hierarchical control structure in the dynamic system. The optimal control problem was formulated as a nonlinear optimization problem subject to set of constraints. The resulting problem was solved using the Kuhn-Tucker method. Computer simulation results demonstrate that the proposed method can provide better efficiency in terms of reducing total costs compared to existing methods. In addition, the proposed optimal load distribution strategy can be easily implemented in real-time thanks to the simplicity of closed-form solutions.

Optimal Intelligent Reconfiguration of Distribution Network in the Presence of Distributed Generation and Storage System

In the present paper,the distribution feeder reconfiguration in the presence of distributed generation resources(DGR)and energy storage systems(ESS)is solved in the dynamic form.Since studies on the reconfiguration problem have ignored the grid security and reliability,the non-distributed energy index along with the energy loss and voltage stability indices has been assumed as the objective functions of the given problem.To achieve the mentioned benefits,there are several practical plans in the distribution network.One of these applications is the network rearrangement plan,which is the simplest and least expensive way to add equipment to the network.Besides,by adding the DGRs to the distribution grid,the radial mode of the grid and the one-sided passage of power are eliminated,and the ordinary and simple grid is replaced with a complex grid.In this paper,an improved particle clustering algorithm is used to solve the distribution network rearrangement problem with the presence of distributed generation sources.The PQ model and the PV model are both considered,and for this purpose,a model based on the compensation technique is used to model the PV busbars.The proposed developed model has particularly improved the local and global search of this algorithm.The reconfiguration problem is discussed and investigated considering different scenarios in a standard 33-bus grid as a well-known power system in different scenarios in the presence and absence of the DGRs.Then,the obtained results are compared.

Solution of Inverse Problem on Distributed Generation Using Complex-Valued Network Inversion

Solutions of inverse problems are required in various fields of science and engineering. The concept of network inversion has been studied as a neural-network-based solution to inverse problems. In general, inverse problems are not limited to a real-valued area. Recently, complex-valued neural networks have been actively studied in the field of neural networks. As an extension of network inversion to complex numbers, a complex-valued network inversion has been proposed. Moreover, inverse problems for estimating the parameters of distributed generation systems such as distributed energy plants or smart grids from observed electric circuit data have been studied in the field of natural energy. These emphasize the need to handle complex numbers in an alternating current （AC） circuit. In this paper, the authors propose an application of the complex-valued network inversion to the inverse estimation of a distributed generation. Further, the authors confirm the effectiveness of the complex-valued network inversion on the basis of simulation results.

Distributed Generation Islanding Effect on Distribution Networks and End User Loads Using the Master-Slave Islanding Method

This study aims to address the feasibility of planned islanding operation and to investigate the effect of unplanned islanding using the master-slave islanding method for controlling the distributed generation units during grid-connected and islanding operation. Neplan desktop power simulation tool was used for the modelling and simulation of a realistic MV network with four different distributed generation technologies (diesel, gas, hydro and wind) along with their excitation and governor control systems, while an exponential model was used to represent the loads in the network. The dynamic and steady state behavior of the four distributed generation technologies were investigated during grid-connected operation and two transition modes to the islanding situation, planned and unplanned. The obtained results that validated through various case studies have shown that a suitable planned islanding transition could provide support to critical loads at the event of electricity utility outages.

Allocation of Hybrid Distributed Generations and Energy Management in Radial Electrical Systems

This paper presents a method for optimal sizing of a Micro grid connected to a hybrid source to ensure the continuity and quality of energy in a locality with a stochastically changing population. The hybrid system is composed of a solar photovoltaic system, a wind turbine, and an energy storage system. The reliability of the system is evaluated based on the voltage level regulation on IEEE 33-bus and IEEE 69-bus standards. Power factor correction is performed, despite some reliability and robustness constraints. This work focuses on energy management in a hybrid system considering climatic disturbances on the one hand, and on the other hand, this work evaluates the energy quality and the cost of energy. A combination of genetic algorithms of particle swarm optimization (CGAPSO) shows high convergence speed, which illustrates the robustness of the proposed system. The study of this system shows its feasibility and compliance with standards. The results obtained show a significant reduction in the total cost of production of this proposed system.

A Low-Cost Efficient Hardware-in-the-Loop Testbed for Distributed Generation Penetration Analysis

In this paper a real-time testbed using hardware-in-the-loop for the analysis of the effects of DGs （distributed generators） on microgrids is presented. The distribution network is implemented in SIMULINK using the IEEE 15-node distribution feeder connected to two DGs feeding the grid using two smart inverters. The inverters＇ active and reactive power control is performed by TI C2000-based controllers and the hardware connections to the system are done through dSPACE interface module. The system is designed such that it can easily be modified to change the location of the DGs and/or to change the number of DGs connected to the grid. Several case study results are presented and compared against simulations to verify the effectiveness and accuracy of the system, model, and the employed power control schemes.

Optimal Configuration for Distributed Generations in Micro-grid System Considering Diesel as the Main Control Source

This paper proposes an optimal configuration of the distributed hybrid renewable generations based on the stand-alone micro-grid system, considering the diesel as the main control source. Due to the natural sources and load of user changes randomly and the non-tinearity of the power output by renewable generations, an intelligent optimization method based on the improvement of the genetic algorithm and the control strategy are discussed. The instance analysis is compared with the optimization result of the hybrid system based on HOMER （hybrid optimization of multiple energy resources） and GA （genetic algorithm） method on Matlab software. The simulation result of the optimal configuration showed the new hybrid renewable system and would improve the power supply situation which decreased the cost of energy greatly compared with the conventional form of power supply system which was operated only by diesel. The conclusion of the comparing result between HOMER and GA method shows the advantages of the strategy for the diesel as main control sources.

Network Reconfiguration for Load Balancing in Distribution System with Distributed Generation and Capacitor Placement

This paper presents an efficient algorithm for optimization of radial distribution systems by a network reconfiguration to balance feeder loads and eliminate overload conditions. The system load-balancing index is used to determine the loading conditions of the system and maximum system loading capacity. The index value has to be minimum in the optimal network reconfiguration of load balancing. The tabu search algorithm is employed to search for the optimal network reconfiguration. The basic idea behind the search is a move from a current solution to its neighborhood by effectively utilizing a memory to provide an efficient search for optimality. It presents low computational effort and is able to find good quality configurations. Simulation results for a radial 69-bus system. The study results show that the optimal on/off patterns of the switches can be identified to give the best network reconfiguration involving balancing of feeder loads while respecting all the constraints.