Distributed Generation Resources Research Articles

Optimal Planning of User-side Scaled Distributed Generation Based on Stackelberg Game

BACKGROUND: User-side distributed generation represented by distributed photovoltaic and distributed wind turbine has shown an expansion trend of decentralized construction and disordered access, which is difficult to satisfy the demand for large-scale exploitation and sustainable development of distributed generation under the low-carbon transformation vision of the power system.
 OBJECTIVES: To address the interest conflict and operation security problems caused by scaled distributed generation accessing the distribution network, this paper proposes the optimal planning method of user-side scaled distributed generation based on the Stackelberg game.
 METHODS: Firstly, a cluster planning and operation mode of distributed generation is established. Then, a prediction method for planning behavior of user-side distributed generation is proposed in order to predict whether users will adopt the self-build mode or the leasing site mode for distributed generation. Finally, in order to reveal the game relationship between the distribution network operator and the users in the allocation of distributed generation resources, a bi-level planning model for scaled distributed generation is established based on the Stackelberg game.
 RESULTS: The simulation results show that the revenue of the distribution network operator under the gaming model increases by 10.15% and 16.88% compared to the models of all users self-build distributed generation and all users leasing distributed generation site, respectively, while at the same time, individual users also realize different degrees of revenue increase.
 CONCLUSION: The case analysis validates the effectiveness of the proposed method in guiding the rational and efficient planning of user-side distributed generation.

Review of Energy Management Systems in Microgrids

Microgrids usually employ distributed energy resources such as wind turbines, solar photovoltaic modules, etc. When multiple distributed generation resources with different features are used in microgrids, managing these resources becomes an important problem. The generated power of solar photovoltaic modules and wind turbines used in microgrids is constantly changing with solar irradiation and wind speed. Due to this impermanent and uncertain nature of renewable energy resources, generally, energy storage systems are employed in microgrid systems. To control the distributed energy resources and energy storage units and sustain the supply and demand balance within the microgrid and provide sustainable and reliable energy to the loads, energy management systems are used. Many methods are used to realize and optimize energy management in microgrids. This review article provides a comparative and critical analysis of the energy management systems used in microgrids. The energy management system can be tailored for different purposes, which are also discussed in detail. Additionally, various uncertainty measurement methods are summarized to manage the variability and intermittency of renewable energy sources and load demand. Finally, some thoughts about potential future directions and practical applications are given.

Optimum placement of distributed generation resources, capacitors and charging stations with a developed competitive algorithm

This study presents a novel approach for the optimal placement of distributed generation (DG) resources, electric vehicle (EV) charging stations, and shunt capacitors (SC) in power distribution systems. The primary objective is to improve power efficiency and voltage profiles while considering practical and nonlinear constraints. The proposed model combines competitive search optimization (CSO) with fuzzy and chaotic theory to develop an efficient and effective solution. The use of fuzzy theory in the model enables the identification of optimal locations for DG sources and SCs, leading to significant enhancements in power index, generation, power losses, and system voltage. Moreover, the proposed fuzzy method is employed to determine the best locations for EV charging stations, further optimizing the overall system performance. The theoretical analysis demonstrates substantial improvements in both accuracy and convergence speed, highlighting the robustness of the proposed approach. In addition, the utilization of chaos theory enhances the local search optimization process, making the proposed method more efficient in finding high-quality solutions. To validate the performance of the model, extensive simulations are conducted on a 69-bus distribution system and various test functions. The results consistently reveal the superiority of the proposed method compared to other conventional optimization techniques. The key contribution of this study lies in its development of a comprehensive and efficient approach for the optimal placement of DG, EV charging stations, and SCs in power distribution systems. The integration of CSO, fuzzy theory, and chaotic theory enables the simultaneous consideration of multiple objectives and constraints, resulting in enhanced power dissipation reduction and voltage profile improvement. The obtained results demonstrate the practical applicability and superiority of the proposed method, which can significantly benefit power system planners and operators in real-world scenarios.

Different energy storage techniques: recent advancements, applications, limitations, and efficient utilization of sustainable energy

In order to fulfill consumer demand, energy storage may provide flexible electricity generation and delivery. By 2030, the amount of energy storage needed will quadruple what it is today, necessitating the use of very specialized equipment and systems. Energy storage is a technology that stores energy for use in power generation, heating, and cooling applications at a later time using various methods and storage mediums. Through the storage of excess energy and subsequent usage when needed, energy storage technologies can assist in maintaining a balance between generation and demand. Energy storage technologies are anticipated to play a significant role in electricity generation in future grids, working in conjunction with distributed generation resources. The use of renewable energy sources, including solar, wind, marine, geothermal, and biomass, is expanding quickly across the globe. The primary methods of storing energy include hydro, mechanical, electrochemical, and magnetic systems. Thermal energy storage, electric energy storage, pumped hydroelectric storage, biological energy storage, compressed air system, super electrical magnetic energy storage, and photonic energy conversion systems are the main topics of this study, which also examines various energy storage materials and their methodologies. In the present work, the concepts of various energy storage techniques and the computation of storage capacities are discussed. Energy storage materials are essential for the utilization of renewable energy sources and play a major part in the economical, clean, and adaptable usage of energy. As a result, a broad variety of materials are used in energy storage, and they have been the focus of intense research and development as well as industrialization. This review article discusses the recent developments in energy storage techniques such as thermal, mechanical, electrical, biological, and chemical energy storage in terms of their utilization. The focus of the study has an emphasis on the solar-energy storage system, which is future of the energy technology. It has been found that with the current storage technology, the efficiency of the various solar collectors was found to be increased by 37% compared with conventional solar thermal collectors. This work will guide the researchers in making their decisions while considering the qualities, benefits, restrictions, costs, and environmental factors. As a result, the findings of this review study may be very beneficial to many different energy sector stakeholders.

Active Disturbance Rejection Control for Distributed Energy Resources in Microgrids

Motivated by the significant efforts developed by researchers and engineers to improve the economic and technical performance of microgrids (MGs), this paper proposes an Active Disturbance Rejection Control (ADRC) for Distributed Energy Resources (DER) in microgrids. This approach is a nonlinear control that is based on a real-time compensation of different estimated disturbances. The DER operates along with the electrical grid to provide the load requirements. This load has a nonlinear and uncertain character, which presents a source of unmodeled dynamics and harmonic perturbations of the MG. The main objective of this paper is to ensure the stability and the continuity of service of the distributed generation resources by controlling the DC-AC converter. The ADRC as a robust control technique is characterized by its ability to compensate for the estimated total disturbances caused by the load variation and the external unmodeled perturbations to guarantee the high tracking performance of sinusoidal reference signals in the DER system. The ADRC technique is characterized by its nonlinear function, which provides a high robustness to the controlled system. However, in order to simplify the control structure by keeping its high reliability, this paper proposes to replace the nonlinear function with a simple error (termed linear ADRC), compares the impact of this modification on the system performances, and evaluates its operation in the presence of linear and nonlinear load variations. Simulation results are presented to demonstrate the efficiency of the proposed control approach for a three-phase DER.

Providing a New Multiobjective Two-Layer Approach for Developing Service Restoration of a Smart Distribution System by Islanding of Faulty Area

One of the essential capabilities of a smart distribution network is to improve network restoration performance using the postfault islanding method. Islanding of the faulty area can be done offline and online. Online islanding will decrease load shedding and operation cost. In this study, a novel two-step mathematical method for system restoration after the fault is presented. A new mathematical model for the optimal arrangement of the system for the faulty area in the first layer is proposed. In this layer, the main objective is to decrease the distribution system’s load shedding and operational costs. In this regard, after the fault event, the boundary of the islanded MGs is determined. Then, in the second layer, the problem of unit commitment in the smart distribution network is addressed. In addition to the load shedding, optimal planning of energy storage systems (ESSs) and nondispatchable distributed generation (DG) resource rescheduling are also determined in this layer. The important advantages of the proposed approach are low execution time and operational costs. A demand response (DR) program has also been used for optimal system restoration. Solving the problem using the multiobjective method with the epsilon-constraint method is another goal of the paper, which simultaneously minimizes the cost and the emissions of the smart distribution network. The proposed model has been tested on an IEEE 33-bus system. Better performance of the proposed model compared to the techniques in the literature has been proven.

State-Space Modeling and Small-Signal Stability Analysis of an Independent Microgrid with Multiple Distributed Generation Resources

State-Space Modeling and Small-Signal Stability Analysis of an Independent Microgrid with Multiple Distributed Generation Resources

Optimizing Distributed Generation Resources for Power Grid Quality Improvement Using Hybrid Optimization Technique

Distributed generation (DG) integration is a critical requirement in modern distribution networks to enhance power grid quality. An essential aspect of this endeavor involves strategically placing and sizing various DG resources optimally. The primary objective of this study is to determine an efficient approach for allocating Photovoltaic (PV) and Wind Turbine resources to minimize overall power losses within the grid. To achieve this goal, we introduce a novel hybrid optimization technique that combines Particle Swarm Optimization (PSO) with Genetic Algorithms (GAs). Furthermore, we explore the influence of factors such as variations in PV daily consumption, load profile fluctuations, penetration levels, and changing climatic conditions on the optimization process across two comprehensive case studies presented in this research. Our modeling results reveal that the hybrid PSO-GA approach outperforms traditional PSO in terms of integration speed, power loss reduction, and enhancements to grid quality, including voltage and frequency profiles. Additionally, this study highlights the dynamic impact of load curve fluctuations and climate changes on the optimal location and capacity of DG resources, leading to a substantial reduction in grid power losses.

Fault Detection in a Single-Bus DC Microgrid Connected to EV/PV Systems and Hybrid Energy Storage Using the DMD-IF Method

Variations in fault currents, short times to clear the fault, and a lack of a natural current zero-crossing point are the most important challenges that DC microgrid protection faces. This challenge becomes more complicated with the presence of electric vehicles and energy storage systems due to their uncertainties. For this reason, in this paper, a new method for fault detection in DC microgrids with the presence of electric vehicles and energy storage systems is proposed. The new proposed method uses the combination of dynamic mode decomposition and instantaneous frequency for fault detection. In this method, first, a reference signal is made using the voltage and current signal sampled from the DC microgrid using the dynamic mode decomposition method. Next, in order to detect the fault, the instantaneous frequency value of the reference signal is calculated by the Hilbert transform. The simultaneous use of voltage and current signals reduces the transient effects of the control system on the proposed protection method. In order to measure voltage and current signals, only one intelligent electronic device unit is used in this paper. The proposed new method has been tested on a single-bus DC microgrid with the presence of electric vehicles and energy storage systems in MATLAB 2019b software. The results show that this method can detect all types of faults in DC microgrids, electric vehicles, and photovoltaics. Also, this method is immune to the uncertainties of the generation of distributed generation resources and the existence of noise distortions in the measured signals.

Optimal placement of time-varying distributed generators by using crow search and black widow - Hybrid optimization

This paper focuses on the optimal reorganization of radial distribution system (RDS) and the superlative location of time-varying power generation distributed generators, considering time-varying loads. The electric generators, utilizing non-conventional energy sources, are directly connected to the distribution grids with lower ratings compared to conventional source-driven generators. In this study, a combination of optimization techniques, such as the black widow and crow search techniques, is employed to enhance performance in terms of optimal reorganization and superlative location of distribution generation. The reorganization problem is examined and analyzed by considering various scenarios in a standard 69-bus grid, as well as in larger-scale 119-bus and 135-bus systems, with and without the presence of distributed generation resources. The analysis of the proposed optimization techniques with the obtained results is well discussed in this paper.

Smart Sag Detection and Reactive Current Injection Control for a PV Microgrid under Voltage Faults

Low-power photovoltaic distributed generators are increasing rapidly in number by the grids of the modern era, but they also bring up the concern of grid stability. To maintain grid stability, it is essential for the network operators to update the grid codes at regular intervals due to the case of highly penetrated grid-connected photovoltaic systems (GCPVs). Integration of large-scale electrical grids with renewable energy sources, one being photovoltaic systems, faces the challenge of riding through low-voltage (LVRT) phases. As compared to the previous grid codes for power generation, recent advances require distributed generation resources to provide for such capabilities under grid faults. This work contributes to the ongoing investigation of this specified and destabilizing fault condition. Various simulations of a PV microgrid system are carried out with the ability to ride through low-voltage faults, with the help of a DC-chopper circuit to absorb DC-link over-voltage, and the current is also maintained within acceptable limits according to the required standards. The fundamental contribution of this research is a proposed neural network (NN) control framework. This framework effectively detects voltage sags, comprehends their characteristics, and provides support to the system by injecting reactive current in accordance with the demands imposed by designated grid codes. This NN control model has been systematically developed utilizing data gathered from a vast array of testing simulations conducted under different and dynamic conditions. The proposed strategy, on the other hand, is compared with the RMS fault detection method in combination with the conventional LVRT algorithm. The NN control approach showed better results as compared to the conventional methods in terms of accuracy and robustness, especially when confronted with difficult sag situations.

The importance of reconfiguration of the distribution network to achieve minimization of energy losses using the dragonfly algorithm

In this article, a method for reconfiguring the distribution network with the presence of production sources to minimize energy losses is presented. Also, besides the reconfiguration issue, the Super distribution tap transformers and the power factor of resources in the network will have their optimal values based on the amount of energy consumption as factors affecting the number of losses. According to the stated parameters, the optimization problem in this article is a non-linear problem with a large search space, for which the dragonfly optimization algorithm was used. To make the results realistic, all types of loads, including commercial, residential and office, have been considered in 24 h and two seasons of the year, cold and hot. The program was prepared with the help of MATLAB software on a sample network of 16 buses, and its applications and results have been evaluated. The obtained results show that determining the optimal reconfiguration is very effective in reducing network losses because, with this method, the amount of losses and unsupplied energy of network subscribers has been reduced significantly. Also, by coordinating the reconfiguration with tap transformers and the power factor of distributed generation resources, the loss reduction rate can be made more effective. The results obtained are compared with the genetic algorithm and the results show the superiority of the dragonfly optimization method.

A new hybrid CBSA-GA optimization method and MRMI-LSTM forecasting algorithm for PV-ESS planning in distribution networks

Renewable energy sources have gained significant attention as a priority option for cost reduction and emissions reduction from fossil fuels. However, their utilization introduces uncertainty and decreases reliability in distribution networks. To effectively harness these resources and mitigate their uncertainties, this paper proposes a model for determining the optimal size and location of photovoltaic (PV) systems and energy storage systems (ESS) in electrical energy distribution networks. The objective is to minimize network power losses, voltage deviations, customer interruption cost, PV system installation cost, and ESS installation cost. To achieve this, the study employs a hybrid forecasting model based on MRMI feature selection and LSTM deep neural network to forecast daily solar power and electrical load for 12 different days across various months of the year, considering the desired electrical energy distribution system. Subsequently, these predicted values serve as 12 scenarios to determine the optimal location and size of PV systems and ESSs. The optimization problem is tackled using a novel hybrid optimization method known as the Coot Bird Search Algorithm-Genetic Algorithm (CBSA-GA). The efficiency of the proposed algorithm is evaluated by comparing its results with GA, PSO, and CBSA algorithms. Among the optimization methods discussed in this paper, CBSA-GA excels in reducing the objective function by accurately determining the capacities of PV resources and ESS. Compared to scenarios where distributed generation (DG) is not integrated into the distribution network, the implementation of CBSA-GA algorithm results in significant improvements in the objective function for Cases 1 to 3. The reductions achieved in the objective function are 9.10 %, 11.88 %, and 24.39 % for Cases 1, 2, and 3, respectively. These findings highlight the effectiveness of CBSA-GA in optimizing the integration of DG resources, specifically in terms of reducing the objective function and improving system performance.

Efficient voltage control of low voltage distribution networks using integrated optimized energy management of networked residential multi-energy microgrids

The optimal energy management system (EMS) of individual and networked residential microgrids and multi-energy microgrids (MEMGs) has received a great deal of attention. Several solutions have been studied to mitigate voltage limit violations due to the increasing penetration level of distributed generation resources (DGRs) and other issues. However, a research gap exists in developing a voltage control-oriented EMS for networked MEMGs (NMEMGs) to overcome the under/over voltage concerns. This paper aims to respond to this research gap by proposing a new voltage control-oriented EMS instead of conventional solutions, e.g., on-load tap changers (OLTCs) and limiting/curtailing output power of DGRs. The simultaneous management of the electrical and thermal supply/demand sides empowers the proposed method from the viewpoint of cost reduction and satisfaction of voltage constraints. Another contribution of this research is using the concept of sensitivity matrix information to calculate/control the voltage of NMEMGs, which is adequately fast and accurate. The voltage control of NMEMGs based on the flexibility of controllable residential thermal/electrical loads and appliances is one of the major advantages of this study. Moreover, the emission costs are considered in the proposed framework. The comparative test results illustrate that significant voltage constraint violations appear if the voltage limits are not considered. On the other hand, obtained results show that less than a 12% increase in the operation cost of some MGs/MEMGs has appeared due to additional constraints of the introduced voltage control-oriented EMS, while all voltage constraints are satisfied.

Optimal energy modeling and planning in the power system via a hybrid firefly and cuckoo algorithm in the presence of renewable energy sources and electric vehicles

The integration of distributed generation resources, including electric vehicles (EVs), has become increasingly important in supplying grid loads. EVs have the potential to act as a distributed generation source and help reduce the electrical company's expenses. However, their extensive use in distribution networks may cause some difficulties for the electricity grids, such as economic and scientific functioning problems and the potential for other applications. In this study, we propose a model to control coordinated and uncoordinated charging systems of grid-connected electric cars using two wind turbines and one solar power plant as distributed generation sources. The model divides EVs into four classes based on their grid shares and the number of accidental vehicles per class, utilizing the normal distribution function. The suggested model is solved using a hybrid firefly and cuckoo method. The goal function of the model is a combination of yearly energy loss cost and the operational cost of distributed generating units. The simulation, conducted on a 33-bus IEEE grid, demonstrates that the proposed model is highly effective and efficient. The results indicate that random EV charging leads to significantly higher expenses compared to the coordinated charging approach. Additionally, the peak demand reduces when EV charging is coordinated.

A reliable fault detection algorithm for distribution network with DG resources

The natural disaster though may have less probability, but their impact can be extreme. For instance, windstorms can result in not only single instantaneous impact, but on multiple locations in the power grid. In such situations, detection of faults in active distribution networks (ADNs), having integration of different types of distributed generation (DG) resources, further escalates the challenging task for existing conventional techniques. The reliability of existing detection schemes is further compromised in case of high impedance faults (HIFs). This paper presents a unified approach for reliable detection of both low-impedance and high-impedance faults, while maintaining the discrimination against non-fault events. In the proposed detection algorithm, the fault detection index is calculated using a newer version of empirical mode decomposition (EMD), i.e., improved complete ensemble EMD with adaptive noise (ICEEMDAN). The performance is demonstrated for detection of multiple faults occurrence at different locations in the ADN. It is shown that with selection of uniform threshold value for all the relays in the network, only the dedicated primary and back-up relays operate reliably at the time of fault conditions. The scheme exhibits accurate, fast and reliable operation as compared to available works in the literature.

Investing in Distributed Generation Technologies at Polish University Campuses during the Energy Transition Era

The functioning of universities during the energy transition can be quite a challenge for them. On one hand, it is necessary to pursue a sensible policy of sustainable development based on the growth of their own renewable energy sources and electricity storage facilities. The aim of such measures is to increase self-consumption and ensure partial energy self-reliance while reducing carbon dioxide emissions into the atmosphere. On the other hand, the current geopolitical situation has indicated significant problems in the energy sectors of European Union countries. From the point of view of decision-makers at universities, the main concern should be ensuring the continuity of the operation of such a facility, including ensuring the energy security of the sites under management. Thus, it is necessary to merge these two areas and consider the development of an energy management strategy on university campuses oriented towards the development of distributed generation resources. For this purpose, one of the methods of multi-criteria decision aiding the ELECTRE I was used. As a result of the analyses, an energy management strategy was established for the main campus of the Warsaw University of Technology, which simultaneously ensures energy security and sustainability efforts.

Presenting a new scheme for developing a distribution system restoration using online islanding method in the presence of dispatchable and non‐dispatchable DGRs and energy storages

AbstractThis paper proposes a two‐layer method for restoring service. Firstly, a new strategy is used to obtain binary variables relating to closed switches and tie switches. In addition, after the fault occurs it is also possible to obtain optimal islands online. Secondly, a non‐linear model is solved to optimally determine the islands created in the first step, in which distributed generation resources (DGRs), energy storages (ESs) and load shedding (LSH) are reprogrammed. When LSH is performed, the sensitive loads must experience the least amount of outage possible. Therefore, the loads are categorized into residential, industrial, and sensitive categories. In order to demonstrate the validity and effectiveness of the proposed method, a modified IEEE 33‐bus system is simulated. In simulations, the proposed strategy has been compared with one of the important existing methods. Based on these results, the proposed method can reduce the cost function by modifying the critical load less than other methods. In this method, the critical LSH is 220 kW, which is 105 kW less than the compared method. By reducing the amount of load, 46,000 dollars have been saved in system expenses. The results confirm that the proposed strategy is considerably superior to the existing method.

Optimal Allocation and Capacity Determination of Hybrid Distributed Generation Resources to Provide Load, Optimal Use of Renewable Resources, Reduce Net Present Cost, and Improve Network Voltage Stability

Optimal Allocation and Capacity Determination of Hybrid Distributed Generation Resources to Provide Load, Optimal Use of Renewable Resources, Reduce Net Present Cost, and Improve Network Voltage Stability

Co-optimization of integrated energy systems in the presence of renewable energy, electric vehicles, power-to-gas systems and energy storage systems with demand-side management

AbstractWith the widespread penetration of renewable energy sources and energy storage systems, the problem of energy management has received increasing attention. One of the systems that network owners consider today is the power-to-gas (P2G) system. This system causes surplus electricity generated from renewable energy resources or batteries in the network to be converted into gas and sold to the gas network. Two reasons for the existence of gas distributed generation resources and P2G systems cause the two power and gas networks to interact. Energy management and profit making considering these two networks, as a co-optimization of integrated energy systems, is a topic that has been discussed in this study to achieve the best optimal answer. Since the production of renewable energy resources and the purchase price of energy are uncertain, a scenario-based method has been chosen for modelling. Demand-side management is also one of the important problems in optimal operation of the electricity network, which can have a significant impact on reducing peak load and increasing profits. In this paper, a mixed-integer quadratic programming model for co-optimization of electric distribution and gas networks in the presence of distributed generation resources, P2G systems, storage facilities, electric vehicles and demand-side management is presented. The 33-bus distribution network is intended to analyse the proposed model. The results of different scenarios show the efficiency of the proposed model. Several key points are deduced from the obtained results: (i) demand-side management is able to reduce the peak load of the network, (ii) the presence of renewable resources and batteries can cause the network to convert excess electricity into gas and sell it to the gas network in the market and (iii) distributed generation can reduce the purchase of energy from the upstream network and cause a 36% reduction in the cost function.