Sources In Distribution Systems Research Articles

Effect of multi-unit and multi-type DG installation using integrated optimization technique in distribution power system planning

With the rise in load demand, improving the voltage profile and reducing line loss is vital to ensure reliable power delivery to the customer. However, increasing power plant generation capacity is limited by environmental and economic factors, requiring a careful assessment of locally optimal options. Distributed Generation (DG) installation into the electricity grid is one of the reliable remedial actions to ensure a smooth power delivery. Installation of DG requires an optimization process to identify the appropriate placement and sizing. Inaccurate sizing and placement of the DG installation may result to over-compensation or under-compensation phenomena. This paper proposes a novel approach termed the Integrated Immune Moth Flame Evolutionary Programming technique (IIMFEP) for optimizing the installation of DG sources in distribution systems. It handles various scenarios, including multi-DG single-type and multi-DG multi-type installations, with the main objective of minimizing power loss in the system. This technique employs a hybrid approach that combines elements of immune algorithms, moth flame optimization, and evolutionary programming to achieve more accurate and efficient results. Two cases were considered in this study termed Case 1 and Case 2. Results in Case 1 discovered that the optimal sizing and placement of four Type III DGs exhibit the lowest power loss worth 2.74 kW (98.78 % reduction) for the 69-Bus RDS, while for the 118-Bus RDS the power loss is 319.89 kW (75.36 % reduction). In Case Study 2, the combination of DG Types I and III provided the highest power loss reduction. With one DG Type I and two DG Type III units installed, power loss was reduced by 97.15 % to 6.41 kW for the 69-Bus RDS and by 62.01 % to 493.21 kW for the 118-Bus RDS. The proposed IIMFEP managed to alleviate the setback experienced in the traditional EP, AIS and MFO which found to be stuck at local optimum. The IIMFEP method is compared to Moth Flame Optimization, Artificial Immune System and Evolutionary Programming and validated using the IEEE 69-Bus and 118-Bus Radial Distribution Systems, resulting in outstanding performance.

Optimal reconfiguration, renewable DGs, and energy storage units’ integration in distribution systems considering power generation uncertainty using hybrid GWO-SCA algorithms

ABSTRACT To optimize radial distribution systems, this study suggests the utilization of the Grey Wolf Optimizer (GWO), a hybrid metaheuristic optimization method, combined with the Sine Cosine method (SCA). The primary objective of this work is to enhance the distribution system by determining the most efficient network reconfiguration, sizing, and placement of various distributed energy sources in distribution system. The energy sources considered include capacitors, solar cells, wind turbines, biomass-based distributed generation units, and battery storage units. To achieve this goal, the proposed strategy incorporates the power loss sensitivity technique, which assists in identifying suitable candidate buses and accelerates the resolution process. Moreover, the model considers fluctuations in solar irradiance and wind speed using Weibull and Beta probability distribution functions, compensating for the intermittent nature of renewable energy sources and the variability in demand. To address power fluctuations, voltage surges, significant losses, and inadequate voltage stability challenges, battery energy storage, diesel generators, and dispatchable biomass DGs are employed to mitigate variability and enhance supply continuity. The proposed approach is evaluated and validated by comparing it to existing optimization strategies using IEEE 69-bus and 84-bus RDSs. The results demonstrate that the suggested technique achieves faster convergence to near-optimal solutions. The proposed methodology yields a significant reduction of up to 80% in power losses in the 69-bus system and a 35% reduction in the 84-bus system, signifying higher performance than existing methods.

Transactive control of community batteries for voltage regulation in distribution systems

With the proliferation of renewable energy sources in distribution systems, the grid operators are posed with the challenge of voltage regulation. Community batteries have significant capabilities in regulating network voltage but such potentials are rarely studied. The literature also lacks insights into how voltage regulation markets should co-exist with other grid service markets, such as battery energy arbitrage service and frequency control ancillary service (FCAS) markets. Therefore, this paper addresses these gaps by proposing a transactive platform for community batteries to trade voltage regulation services with the grid operator while providing other services. First, voltage sensitivity analysis is conducted to construct a voltage regulation demand curve based on the regulation needs of the system. Next, different operating conditions of battery inverters are investigated to model the cost of supplying voltage regulation. Various market-clearing rules are then established to determine the quantity and price of the voltage regulation service traded using the demand and supply curves. Technical analyses indicate that the proposed transactive platform is able to (1) price the voltage regulation service depending on the severity of voltage issues, (2) set a positive/negative regulation price when the system undergoes overvoltage/undervoltage conditions, (3) help battery owners gain considerable extra regulation revenue with proper incentivisation. This research provides guidance to incorporate voltage regulation service with the presence of other grid services and to implement voltage regulation markets to engage community batteries.

Allocation of Renewable Energy Resources in Distribution Systems While Considering the Uncertainty of Wind and Solar Resources via the Multi-Objective Salp Swarm Algorithm

Given the importance of renewable energy sources in distribution systems, this article addresses the problem of locating and determining the capacity of these sources, namely, wind turbines and solar panels. To solve this optimization problem, a new algorithm based on the behavior of salp is used. The objective functions include reducing losses, improving voltage profiles, and reducing the costs of renewable energy sources. In this method, the allocation of renewable resources is considered for different load models in distribution systems and different load levels using smart meters. Due to the fact that these objective functions are multi-objective, the fuzzy decision-making method is used to select the optimal solution from the set of Pareto solutions. The considered objective functions lead to loss reduction, voltage profile improvement, and RES cost reduction (A allocating RES resources optimally without resource limitations; B: allocating RES resources optimally with resource limitations). In addition, daily wind, solar radiation, and temperature data are taken into account. The proposed method is applied to the IEEE standard 33-bus system. The simulation results show the better performance of the multi-objective salp swarm algorithm (MSSA) at improving voltage profiles and reducing losses in distribution systems. Lastly, the optimal results of the MSSA algorithm are compared with the PSO and GA algorithms.

Under voltage load shedding and penetration of renewable energy sources in distribution systems: a review

ABSTRACT The growing energy demand and the emerging environmental concerns across the globe caused increasing penetration of renewable energy sources (RESs) in distribution systems. One of the major issues faced by modern distribution systems with high RESs penetration is voltage instability. Generally, voltage instability can lead to voltage collapse, which is one of the key drivers for blackout incidents around the world. Under voltage load shedding (UVLS) schemes are usually executed as the final option to prevent a large-scale blackout or voltage collapse. But ever-increasing integration of RESs in distribution systems creates an additional threat to the existing UVLS schemes. Therefore, new UVLS methods are necessary to provide the appropriate corrective scheme for distribution systems, particularly when connected with RESs. This paper reviews and updates the recent UVLS methods emphasizing on distribution network application. In addition, the contributions and limitations of the conventional, adaptive, and computational intelligence techniques of UVLS are highlighted. Moreover, this paper provides an overview of the voltage stability indices applied for UVLS methods. Also, at the end of this review article, the further research directions to improve the safety of modern distribution systems during emergencies using UVLS are drawn as the conclusion.

A novel fault detection and classification scheme for DC microgrid based on transient reactor voltage with localized back-up scheme

Integration of renewable energy sources in distribution system inflicts new challenges to the conventional protection scheme by substantially changing the amplitude and direction of fault current. In this paper, a novel protection scheme for DC microgrid based on the characteristics of reactor voltage during transient operation in both grid-connected and islanded mode of operation is proposed. The proposed protection scheme incorporates both primary as well as localized back-up protection scheme. In order to detect and classify faults, primary protection scheme utilizes the polarity of the transient reactor voltage at both ends of the cable under different operating conditions. As the primary protection uses the polarity of reactor voltage at both ends, thus the proposed scheme doesn’t require high speed communication and synchronous data. In case of communication failure, localized back-up protection scheme based on the change in reactor voltage at one end of the cable is used to identify the faulty section. The efficacy of the proposed scheme is verified against various internal and external faults along with system transients such as AC side faults, load variation, DG outage, noisy environment and generation uncertainties due to change in solar irradiance and wind speed. Finally, a scaled down hardware setup is used to validate the proposed protection scheme, which further illustrates the feasibility and reliability of the proposed protection scheme.

RETRACTED: Microgrid operation relying on economic problems considering renewable sources, storage system, and demand-side management using developed gray wolf optimization algorithm

Density of renewable energy sources in distribution systems has developed a novel structure, called microgrid (MG), which consists of small-scale power grids with controllable and uncontrollable loads. MGs are a combination of different distributed generation resources (DGRs) which act as a controllable system at the distribution voltage level and supply power or heat to a group of local loads. Due to the high fluctuations of available power at the distribution voltage level, MGs may fail to supply major consumers. Therefore, several MGs are used by dividing consumers into smaller units; each unit is supplied by one MG. In this study, renewable sources such as wind turbine (WT), photovoltaic (PV) cell, and hydrogen storage system are considered as MGs. A novel energy management strategy is proposed using hydrogen storage system and considering uncertainties of renewable sources in the MG. This strategy aims to minimize the operating costs of batteries and hydrogen storage systems as well as unsupplied and surplus energy costs considering load supply constraints. Various resource constraints are employed in the proposed strategy. Moreover, demand response (DR) program is applied for MG optimal operation. The proposed model is implemented on a system using MATLAB software and meta-heuristic gray wolf optimization (GWO) algorithm. The results indicate applying hydrogen storage system and demand-side management (DSM) program could reduce the final cost of MGs and the proposed method has high efficiency in solving complex problems.

A Novel Analytical Approach for Optimal Integration of Renewable Energy Sources in Distribution Systems

The present research article focuses on an analytically based method for the optimal allocation and sizing of a renewable energy source (RES) capable of injecting both active and reactive powers in the distribution network. The placement of distributed generation (DG) in the distribution network reduces the magnitude of branch current in between the reference bus and the bus where DG is to be installed. Due to this, system power loss decreases significantly. The proposed method considers different levels of load in addition to peak load demand. The goal of the developed method is to minimize system losses by optimal DG allocations. In the proposed method, the optimum size of the DG is obtained on the basis of maximum loss saving criterion. For the execution of proposed method, only a base case load flow solution is required. The developed method has been tested on IEEE 69-bus and 33-bus radial distribution networks. On the basis of obtained results, it has been realized that the developed method is more capable of diminishing system energy losses.

A mutated salp swarm algorithm for optimum allocation of active and reactive power sources in radial distribution systems

The purpose of this paper is to get shunt capacitors (SCs) and distributed generations (DGs) such as photovoltaic (PV) allocated to compensate the reactive power of the distribution systems and enhance the system performance. Finding the optimal sizing and siting of mentioned devices needs a powerful optimization tool. Hence, this paper presents an efficient, simple, and accurate optimization algorithm based on modifying the standard version of salp swarm algorithm (SSA) named mutated salp swarm algorithm (MSSA). In other words, a mutation mechanism is applied on the standard version of SSA to escape from the local minima and enhancing the diversity in population. To validate the efficiency of proposed optimization algorithm, it is firstly applied on optimizing some well-known benchmark functions. Then, it is handled to cope with allocating SCs as the reactive source and PVs as the active power source in the distribution systems. The proposed method is accomplished on the IEEE 33 and 69-nod distribution systems and the obtained results are compared with some published papers. The results illustrate that MSSA has better performance for planning the distribution systems compared with existing methods.

Fuzzy multi-objective placement of renewable energy sources in distribution system with objective of loss reduction and reliability improvement using a novel hybrid method

One of methods for loss reduction and reliability improvement of radial distribution system is using of renewable energy generation. In this paper, a new optimal placement and sizing of renewable energy sources based on photovoltaic panels (PVs) and wind turbines (WTs) in the distribution network is presented with the objective of loss reduction and reliability improvement based on energy not-supplied (ENS). A multi-objective evolutionary algorithm based on fuzzy decision-making method, called the Multi-Objective Hybrid Teaching–Learning Based Optimization-Grey Wolf Optimizer (MOHTLBOGWO) is proposed to solve the optimization problem. The proposed hybrid method has a high convergence speed and not trapped at all in local optimal. The proposed method is implemented in the form of single-objective and multi-objective on 33 and 69 bus IEEE radial distribution networks. The simulation results clear that the multi-objective optimization is a more precise approach to network utilization taking into account all objective indices than the single objective method. The results show that the proposed method has better convergence speed and less convergence tolerance in achieving to best solution in comparison with TLBO and GWO methods in loss reduction, reliability improvement and increasing the net saving and also in comparison with last studies. Moreover, the results show that dispersion of the size and location of distributed renewable generation leads to a further reduction in losses and a better improvement of the reliability criterion.

Optimal siting and sizing of renewable sources in distribution system planning based on life cycle cost and considering uncertainties

The renewable sources have made an impact on economic benefits and the electrical quality of the distribution system. Therefore, a multi-scenario optimization model is proposed, targeting to optimize the investment of renewable sources considering uncertainties based on the minimum life cycle cost. The mathematic model allows selecting the siting, sizing and type of renewable sources in distribution system. The objective function is minimizing life-cycle cost of the project during the planning period, including the investment and operation cost of renewable sources, the cost of purchasing energy from the grid, the emission taxes and residual value of the equipment at the end of the planning period. The nonlinear power flow model alternating current is utilized to concurrently balance both active and reactive power at each state as well as the constraints for the limit capacity of feeders. Connectable substation and the constraints in selecting the renewable sources are also represented and thus improving the accuracy of the calculated results of the distribution system. The uncertainty parameters of renewable sources (photovoltaic and wind turbine), electricity price and load were modeled by the probability density functions and are considered in the optimization model. The clustering technique was utilized to divide each parameter into states then all states of parameters are integrated by the combined model. The general algebraic modeling system (GAMS) was applied to solve the optimization problem with the test system and demonstrated the advantages of the proposed model.

Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems

Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems

Optimal Location of Distributed Generation Sources and Capacitance of Distribution Network to Reduce Losses, Improve Voltage Profile, and Minimizing the Costs Using Genetic and Harmonic Search Algorithm

Reducing losses and improving the voltage profile have been the main objectives of electrical power system designers. One of the suggested solutions for achieving these goals is the use of parallel capacitors and distributed generation sources in distribution systems. A location that is optimized for DG installation may not be the best place to minimize losses in improving the system voltage profile. In this paper, determining the optimal location of the dispersed generation unit and the capacitive bank with the goal of optimizing a target function, including losses, improving the voltage profile, and the cost of investment in capacitors and dispersed production. In this paper, IEEE standard 33 buses is considered for simulation, and the results are obtained by using genetic and harmonic search algorithm indicate that DG optimization and capacitor with a target function in which the loss reduction and improvement of the voltage profile is considered to reduce costs, reduce losses, and improve the voltage profile, which are remarkable improvements.

Energy and Reserve under Distributed Energy Resources Management—Day-Ahead, Hour-Ahead and Real-Time

The increasing penetration of distributed energy resources based on renewable energy sources in distribution systems leads to a more complex management of power systems. Consequently, ancillary services become even more important to maintain the system security and reliability. This paper proposes and evaluates a generic model for day-ahead, intraday (hour-ahead) and real-time scheduling, considering the joint optimization of energy and reserve in the scope of the virtual power player concept. The model aims to minimize the operation costs in the point of view of one aggregator agent taking into account the balance of the distribution system. For each scheduling stage, previous scheduling results and updated forecasts are considered. An illustrative test case of a distribution network with 33 buses, considering a large penetration of distribution energy resources allows demonstrating the benefits of the proposed model.

Smart utilization of renewable energy sources in a microgrid system integrated with plug-in hybrid electric vehicles

Mass roll-out of plug-in hybrid electric vehicles (PHEVs) and significant penetration of renewable energy sources in distribution system play a major role in delivering low carbon environment. However, placing and utilizing these units randomly result in overloading, increased power loss, and reduced voltage profile. This paper responds to these technical challenges by using a strategic placement method for locating the distributed generation (DG) and the charging station (CS) of PHEVs in a multi-zone distribution system. For simultaneously scheduling of these units in each zone, the smart energy management framework is proposed in this paper. Apart from usual energy management constraints, this paper also incorporates the real-time constraints involving the capacity of PHEV batteries, the mobility pattern, and the power level of the charging infrastructure. The simulation studies are carried out for each hour of a day. To cope with this time constraint execution, particle swarm optimization algorithm-based approach is used. The proposed framework is tested in IEEE 33 and IEEE 69 bus radial distribution system. The obtained results imply that the presented energy management framework provides maximum profits for the vehicle owner, and meanwhile it fulfills preferences of the user in each zone simultaneously.

Active Distribution Grid Management Based on Robust AC Optimal Power Flow

Further integration of distributed renewable energy sources in distribution systems requires a paradigm change in grid management by the distribution system operators (DSOs). DSOs are currently moving to an operational planning approach based on activating flexibility from distributed energy resources in day/hour-ahead stages. This paper follows the DSO trends by proposing a methodology for active grid management by which robust optimization is applied to accommodate spatial-temporal uncertainty. The proposed method entails the use of a multi-period AC-OPF, ensuring a reliable solution for the DSO. Wind and PV uncertainty is modeled based on spatial-temporal trajectories, while a convex hull technique to define uncertainty sets for the model is used. A case study based on real generation data allows illustration and discussion of the properties of the model. An important conclusion is that the method allows the DSO to increase system reliability in the real-time operation. However, the computational effort grows with increases in system robustness.

Optimal placement and sizing of wind / solar based DG sources in distribution system

Proper placement and sizing of Distributed Generation (DG) in distribution system can obtain maximum potential benefits. This paper proposes quantum particle swarm algorithm (QPSO) based wind turbine generation unit (WTGU) and photovoltaic (PV) array placement and sizing approach for real power loss reduction and voltage stability improvement of distribution system. Performance modeling of wind and solar generation system are described and classified into PQ\\PQ (V)\\PI type models in power flow. Considering the WTGU and PV based DGs in distribution system is geographical restrictive, the optimal area and DG capacity limits of each bus in the setting area need to be set before optimization, the area optimization method is proposed . The method has been tested on IEEE 33-bus radial distribution systems to demonstrate the performance and effectiveness of the proposed method.

An Improved of Multiple Harmonic Sources Identification in Distribution System with Inverter Loads by Using Spectrogram

This paper introduces an improved of multiple harmonic sources identification that been produced by inverter loads in power system using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is a very applicable method to represent signals in time-frequency representation (TFR) and the main advantages of spectrogram are the accuracy, speed of the algorithm and use low memory size such that it can be computed rapidly. The identification of multiple harmonic sources is based on the significant relationship of spectral impedances which are the fundamental impedance (Z1) and harmonic impedance (Zh) that extracted from TFR. To verify the accuracy of the proposed method, MATLAB simulations carried out several unique cases with different harmonic producing loads on IEEE 4-bus test feeder cases. It is proven that the proposed method is superior with 100% correct identification of multiple harmonic sources. It is envisioned that the method is very accurate, fast and cost efficient to localize harmonic sources in distribution system.

Power Management Strategy for Hybrid Microgrid System under Islanded Condition using Adaptive Droop Control

Background/Objectives: PV array systems are thought of to be a higher possibility among all non-conventional energy sources in distribution system with the features of increasing potency, high responsibility and low maintenance. This paper proposes the concept of energy management for grid connected hybrid system under islanding condition. Methods/ Statistical Analysis: Power management Control strategy used in this paper enables photovoltaic/battery unit as a primary supply that employs an adaptive droop control to share the load with alternative sources such as charging the battery. Additionally, this control technique is intended to switch the PV in operation to match the load demand autonomously whenever the available PV power is beyond the load and also the battery is totally charged. In addition with islanding microgrid conditions, such as voltage and frequency regulation and also power deficit circumstances the battery also provide operational functions with a separate storage unit. Findings: In this paper PV/battery unit is controlled using adaptive droop control to work as voltage supply, in contrast to the control strategy used in photovoltaic system from the literature wherever the Solar units are controlled to work as current controlled sources. Generally, in this system first the hybrid unit delivers power to load and stores in battery if there is any excess power generation. And consequently, in the second case, it will track and supply the most PV power to the microgrid provided that there’s any deficit in load at microgrid. Applications/Improvements: The proposed control strategy is very useful for integration of hybrid power systems with the gird and to meet the upcoming future of the customers.

A dynamic approach to identification of multiple harmonic sources in power distribution systems

This paper provides a novel algorithm for identifying harmonic sources in power distribution systems. This algorithm is developed based on an observer design to carry out harmonic estimation for a combination of suspicious nodes. The estimation error is analyzed to determine the existence of harmonic sources in the specified node combinations. This approach is used to determine the source of both single and multiple harmonic sources in distribution systems with time varying load parameters. For systems with a large number of suspicious nodes, the system may be divided into sub-systems and the algorithm is applied to each sub-system to identify the harmonic sources present. Simulations are carried out on a benchmark IEEE distribution test feeder for both single and multiple harmonic sources and a number of scenarios are simulated to verify the accuracy and robustness of the proposed approach. The results show that the node combinations which represent the harmonic sources yield an estimation error which approaches zero asymptotically.