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Summary: This paper presents the development of a single‐phase voltage source inverter (VSI) of 3.5KW, applied to grid‐connected photovoltaic systems (GCPS). The proposed system has a boost interface converter connected to a full bridge inverter with an LCL filter. Each block that constitutes the proposed GCPS is described in detail, containing the specifications of the solar modules, the design of the power circuit components, the mathematical modeling, and the control design of the converters. The presentation of the Volt‐VAR Control (VVC) highlights the contribution of this work, where it is implemented with the intention of the inverter to supply/consume reactive power, in order to minimize voltage variations at the point of common coupling (PCC). To validate the inverter operation with VVC, three cases are presented, encompassing grid voltage and irradiance variations, and load steps. Through the PCC voltage profiles, currents, and power supplied/consumed, the means to demonstrate the effectiveness of the proposed control is shown. The results obtained through the PSIM simulations and verified by Typhoon Hardware in the Loop (HIL) model, showed the regulation of grid voltage levels for all established conditions. [ABSTRACT FROM AUTHOR]

The demand for energy and renewable energy utilization increases swiftly and accelerates technological development in the renewable field. Still, there is a lack of awareness and government policy that may boost capacity addition. In order to address the challenges presented by the unpredictable and intermittent character of renewable energy sources (RES), the utilization of RES in conjunction with their associated expenses has the potential to provide an affordable, environmentally friendly, and efficient alternative energy supply. This study centers on the techno-economic evaluation of hybrid renewable energy systems (HRESs) used for electricity production through Solid wastes. The present study explores the benefits of a grid-connected Hybrid Renewable Energy System over stand-alone HRES via Solid waste management due to technological and policy improvements. Furthermore, net metering and bill savings through self-consumption and sensitivity analysis for five different sell-back prices. The impact of sensitivity analysis on HRES economics is discussed in this paper. A commercially available software, HOMER@ pro, simulates the system for the optimal solution. The result shows that the stand-alone system's economy is high if there is an absence of sell-back from the consumer's end. Whereas, with the sell-back price introduction, the economy of the grid-interactive HRES is too low and trending to be minimal. Moreover, the study concludes that net metering with the sell-back price reduces the Net Present Value and Cost of Energy, i.e., sell-back price of Rs.2/kWh; the cost of energy production is Rs.1.43/kWh. Due to the sell from the consumer ends, the system generates yearly earnings, which is highest (Rs.133,920/year) in case of a sell-back price of Rs.2/kWh. Therefore, the installation of a Hybrid Renewable Energy System (HRES) tailored to the specific climatic circumstances would provide a durable and reliable and affordable energy solution that effectively addresses climate concerns, enhances energy stability, and promotes ecological accountability supporting SDG goals. [ABSTRACT FROM AUTHOR]

In this study, a new variant, the fast pelican optimizer (FPO), is proposed to improve the performance of the radial distribution network (RDEN). The proposed variant is characterized by creating a dynamic interaction between two phases, exploration and exploitation, during the search process. The modifications introduced within the standard algorithm allow the proposed new variant, namely FPO, to be fast and adaptive to efficiently solve various complex optimization problems. In the first stage, the proposed FPO is newly adapted and applied to solve the optimal locations of various types of distributed generation based renewable sources and multi shunt compensators, namely the DSTATCOM devices based FACTS technology, and in the second stage, the proposed FPO is applied to optimize the active power of DG units in coordination with the reactive power of multi DSTATCOM. Three objective functions, such as the total power losses, the total voltage deviation, and the margin stability, are optimized individually and in coordination to enhance the performances of the practical radial distribution network (RDEN) 33-bus. A deep comparative study in terms of solution quality and convergence accuracy based statistical analysis is elaborated to demonstrate the competitive aspect of the proposed FPO. [ABSTRACT FROM AUTHOR]

Most conventional power systems adopt radial distribution network wherein multiple loads are connected across the distribution transformer. As the number of loads increases, it results in poor voltage profile at the distant receiving end reducing power delivery. This issue worsens with the largescale influx of electric vehicles and power converter-fed loads, which draw constant power irrespective of supply voltage. Such loads exhibit negative incremental resistance behavior and also have a dynamic response which affects the network in a manner different from constant impedance loads. This paper compares the effects of constant power and constant impedance loads by modeling adjustable converter dynamics for constant power loads. It analyzes line currents, load voltages and power transmitted in a four-load radial test system with optional distributed sources. Results show poorer voltage profile and the effect of power converter dynamics in constant power loads compared to conventional loads. Adding distributed sources improves voltage profile considerably, and transmission losses are reduced. Steady state analysis is then extended to an IEEE 31-bus 23 kV distribution test system with similar results. Transmission losses are computed along different branches, and the influence of loads and sources are analyzed. The outcomes of the analysis can be used in arrival of loss allocation in a system where peer to peer energy sharing is envisaged. [ABSTRACT FROM AUTHOR]

The main goal of the power system is to congregate the power demand within the power grid while maintaining economical operation, system security and minimal environmental impact. Due to the increasing demand for electrical energy, many problems have arisen with the power systems. These problems include excessive load, uneven system performance, unsatisfactory voltage profile, and an increase in network power losses. To address these issues, more generation sources and improved transmission capacity are required. In order to meet increasing electricity demand, it is more efficient to integrate a sufficient number of smaller generation units. Utilities and consumers can get the significant benefit from installation of distributed generation (DG), which reduces power losses, progress voltage profile, increases power quality and reliability, delays system updates, supports local reactive power, standby generation and peak limiting. This article aims to enrich the performance of the entire network through the best possible placement and penetration of wind energy and solar photovoltaic (PV) dispersed generation. [ABSTRACT FROM AUTHOR]

This study aims to advance the development of the active distribution network (ADN) by optimizing resource allocation across different stages to enhance overall system performance and economic benefits. First, an ADN optimization model is constructed based on a two-stage robust optimization approach. The first stage focuses on determining optimal decision variables within the uncertainty set, while the second stage adjusts control variables based on the initial stage decisions. This model effectively addresses source-load uncertainties while preserving the flexibility and adaptability of decision-making solutions. Additionally, this study explores uncertainty models that incorporate correlation factors. The IEEE33-node model is employed to validate the effectiveness and superiority of the proposed optimization strategy through numerical simulations. Simulation results demonstrate that Model 3 comprehensively accounts for photovoltaic and wind turbine generator planning by optimizing their capacity configurations, leading to a 23% increase in distributed generation (DG) penetration. During high-load periods (e.g., 13:00 and 16:00), DG output reaches 47% and 50% of the demand load, underscoring the critical role of DG in supporting the power grid during peak hours. Overall, the proposed two-stage optimization strategy considers source-load uncertainties, significantly reducing economic costs, enhancing DG output, and improving overall system performance. In scenarios with correlated uncertainties, the optimized results exhibit greater accuracy and reliability, providing robust support for the planning and operation of practical distribution networks. [ABSTRACT FROM AUTHOR]

Incorporating distributed generation (DG) technology into modern power systems heralds a multitude of technological, economic, and environmental advantages. These encompass enhanced reliability, reduction in power losses, heightened efficiency, low initial investment requirements, abundant availability, and a minimized environmental footprint. However, the rapid detection and disconnection of DG units during islanding events are paramount to circumvent safety hazards and potential equipment damage. Although passive techniques are predominantly utilized for islanding protection due to their minimal systemic interference, their susceptibility to substantial nondetection zones (NDZ) instigates a transition toward more innovative techniques. Addressing this crucial need, the study employs a novel amalgamation of signal processing methodologies and a suite of intelligent classifiers to augment the detection of islanding events in power systems. Concealed features from a variety of signals are extracted by the methodology and utilized as robust inputs to the intelligent classifiers. This empowers these classifiers to make a reliable distinction between islanding events and other types of disturbances. The breadth of the study is expanded to evaluate a range of sophisticated models including gradient boosting, decision tree–based models, and multilayer perceptrons (MLPs). These models are thoroughly tested on a radial distribution system integrated with two DG units and subjected to rigorous simulations and comparative analysis using the DIgSILENT Power Factory software. The findings underscore the efficacy of the proposed method, showcasing a significant improvement over conventional techniques in terms of efficiency and resilience. Above all, the methodology exhibits an exceptional ability to discriminate between islanding events and other system disturbances. Specifically, the islanding detection method demonstrated a detection time of less than one cycle (20 ms), ensuring rapid response. Moreover, the proposed algorithm demonstrated the capability to detect all ranges of power mismatch, including zero‐power mismatch, thereby eliminating the NDZ. These results illuminate the benefits of integrating signal processing methodologies with intelligent classifiers, marking a significant stride forward in the realm of islanding event detection. [ABSTRACT FROM AUTHOR]

As power generation systems with increasingly higher capacities are interconnected with distribution networks, a pressing need arises for a thorough analysis of their integration and the subsequent impacts on medium-voltage lines. This study conducts a comprehensive evaluation, encompassing both steady-state and transient behaviours, leading to a holistic assessment of a real-world biogas generation system integrated into a medium-voltage network. Although the methodology does not introduce revolutionary concepts, its detailed application on a real feeder under various operating conditions adds practical value to the existing body of knowledge. The methodology explores various aspects, including voltage profiles, load capacity, power losses, short-circuit currents, and protection coordination in steady-state conditions. Additionally, a transient analysis is performed to examine the system's response under fault conditions. This systematic approach provides a deep understanding of the system's behaviour across diverse operational scenarios, enriching the field with practical insights. The key contributions of this study include identifying the effects of distributed generation systems (DGSs) on short-circuit currents, protection coordination, and defining voltage levels that briefly exceed the CBEMA quality curve. The benefits of incorporating a generation system into a distribution network are discussed from various technical perspectives. In a peak demand scenario, with a 1.72 MW generation capacity, the phase current experiences a notable reduction of 35.78%. Concurrently, the minimum peak demand voltage increases from 12.62 to 12.83 kV compared to a nominal voltage of 12.7 kV. Furthermore, the contribution of the generation system to the short-circuit current remains minimal, staying below 4% even under the most adverse conditions. However, our findings reveal that voltage levels exceed the upper limit of the CBEMA quality curve briefly during a single-phase fault with generation, which could potentially damage electronic equipment connected to the grid. Nonetheless, the likelihood of encountering a single-phase grounding fault with zero resistance remains low. [ABSTRACT FROM AUTHOR]

To address the issues arising from the integration of a high proportion of distributed generation (DG) into the distribution network, which has led to the transition from traditional single-source to multi-source distribution systems, resulting in increased complexity of the distribution network topology and difficulties in fault localization, this paper proposes a fault localization method based on graph convolutional networks (GCNs) for distribution networks with a high proportion of distributed generation. By abstracting busbars and lines into graph structure nodes and edges, GCN captures spatial coupling relationships between nodes, using key electrical quantities such as node voltage magnitude, current magnitude, power, and phase angle as input features to construct a fault localization model. A multi-type fault dataset is generated using the Matpower toolbox, and model training is evaluated using K-fold cross-validation. The training process is optimized through early stopping mechanisms and learning rate scheduling. Simulations are conducted based on the IEEE 33-node distribution network benchmark, with photovoltaic generation, wind generation, and energy storage systems connected at specific nodes, validating the model's fault localization capability under various fault types (single-phase ground fault, phase-to-phase short circuit, and line open circuit). Experimental results demonstrate that the proposed model can effectively locate fault nodes in complex distribution networks with high DG integration, achieving an accuracy of 98.5% and an AUC value of 0.9997. It still shows strong robustness in noisy environments and is significantly higher than convolutional neural networks and other methods in terms of model localization accuracy, training time, F1 score, AUC value, and single fault detection inference time, which has good potential for practical application. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Accurate and timely fault diagnosis is of great significance for the stable operation of a distribution network. Traditional artificial intelligence-based localization methods rely heavily on large-scale labeled datasets, making them prone to being affected by distributed generators. To address this issue, this study proposes a fault location method for distribution systems using a cost-sensitive graph attention network model. In this approach, the physical structure of the distribution network is considered a crucial constraint for model training, thereby enhancing its information perception capabilities. Specifically, the electrical nodes and lines of the distribution network are mapped to the vertices and edges within the graph attention network, with the attention weights determined by the correlation of adjacent node fault characteristics, thus improving the sensitivity to grounding faults. Additionally, a cost-sensitive matrix is used to balance class distribution, enhancing the robustness and generalization ability of the model. Fault localization experiments were conducted on the IEEE-33 bus distribution system to validate the effectiveness of the proposed fault localization method. Factors such as data disturbance, varying fault grounding resistances, and distributed power supply access were employed to assess the model's anti-interference performance. The experimental results demonstrate that the fault location method exhibits high positioning accuracy and excellent robustness. [ABSTRACT FROM AUTHOR]

The aim of this research is to evaluate the performance of the distribution network by connecting wind distributed generation (DG) and determining the optimal location and size using the particle swarm optimization (PSO) technique, once the wind DG is connected at the optimal location, the output of wind turbines is not constant but varies with changes in wind speed. Wind turbines are designed to generate the energy from the wind. As the output of the wind turbines changes, it influences the power flow and voltage levels in the distribution network. The injection of power from the wind turbines can cause variations in voltages within the distribution network. Additionally, the changing power flow may contribute to power losses in the distribution system. In this paper, the voltages and active power losses are evaluated with the change in wind speed for the IEEE 15 Bus system by conducting load flow analysis in MATLAB. The results reveal optimized solutions that contribute to reduced power losses, increased renewable energy generation, and improved voltage profiles. This research underscores the potential of PSO-based optimization in conforming more efficient and sustainable distribution networks. [ABSTRACT FROM AUTHOR]

The results of experimental and modeling studies of the distributed generation sources (DG) influence on the occurrence in the Interconnected Power System (IPS) of Ukraine asynchronous modes (AM) caused by accidents in its main electrical network are presented. According to simulation scenarios it was assumed that the DG power was introduced into electrical distribution networks instead of the power units of thermal power plants of the IPS of Ukraine which were destroyed as a result of missile attacks by the Russians. In the absence of information about DGs, certain generalizations and assumptions are made regarding the possibility of DG groups modeling of in the form of turbine generators with different inertia constants. Various scenarios of the accidents occurrence and the functioning of relay protection and anti-emergency automation devices were considered. Conducted studies results show that the DG introduction into distribution electrical networks does not contribute to the AM occurrence in the main electrical network of the IPS of Ukraine. References 11, figures 10, tables 3. [ABSTRACT FROM AUTHOR]

The transformation of power system networks is slowly taking shape, the advent of interruptive technological platforms dealing with digitalization and real‐time trading of power has gained attention based on incorporation of more renewables into the grid. The stochastic nature of renewables pauses security of supply challenges and other related stability concerns, and for this reason efficient methods are investigated in this review to build an understanding of microgrid energy management system (MG‐EMS) and distribution‐based energy management strategies aimed at transforming the conventional grid network into smart grid network. In essence, propagating a technological shift to microgrids which have proven to be ideal distribution networks for residential and commercial loads, have become indispensable in handling distributed energy resources (DER), such as solar, PV, wind, battery energy storage systems (BESS) and small‐scale microgrids, for example in case of excess supply, energy storage system (ESS) has been formulated as a solution to curb excess supply and can offer ancillary services to the grid network. Within the perspective of electricity generation and distribution, microgrid control methodologies, distribution network (DN) management approaches and incumbent optimization strategies used to coordinate and manage grid‐level uncertainties are investigated. In addition, this study proposes distributionally robust optimization (DRO), to manage and mitigate risks associated to shortage or oversupply of power from RESs. [ABSTRACT FROM AUTHOR]

To protect power lines, distance relays are used which can rely on a modified distance resistance. But usually the operating range of these relays changes as the network conditions change (network topology, load value, output value, etc.) and leads to false trips, by using methods that can process information and recognize patterns. For example, the use of micro and intelligent processor algorithms can use the new relays with high precision and thus provide adequate protection. In this study, a distance relay was modeled using a neural network, and it was observed that the neural relay had higher accuracy than the conventional relay. In addition to detecting the fault and its location, type and phase of the fault, threestage simultaneous protection can be performed. As a result, the number of linear relays can be reduced by using relays based on neural technologies. An MLP (multilayer perceptron) neural network is used to model a sequence of distances. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on the dynamic modeling of distribution power systems with a focus on the integration of renewable energy sources (RESs) for stability analysis. Our research delves into the static and dynamic behavior of distribution systems, emphasizing the need for enhanced load modeling to mitigate planning and operational uncertainties. Using MATLAB/Simulink®, we simulate four distinct study cases characterized by varying load types and levels of distributed generation (DG), particularly solar PV, under both balanced and unbalanced conditions. Our findings highlight the critical role of DG in influencing voltage stability, revealing that deviations in voltage and current during grid imbalances remain within acceptable limits. The study underscores the importance of DG-based inverters in maintaining grid stability through reactive power support and sets the stage for future research on microgrid simulations and battery storage integration to further enhance system stability and performance. [ABSTRACT FROM AUTHOR]

The continuous increase in energy demand strains distribution networks, resulting in heightened power losses and a decline in overall performance. This negatively impacts distribution companies' profits and increases consumer electricity costs. Optimal distributed generation (DG) allocation in distribution networks can mitigate these issues by enhancing power supply capabilities and improving network performance. However, achieving optimal DG allocation is a complex optimization problem that requires advanced mathematical techniques. Nature-inspired (NI) swarm intelligence (SI)-based optimization techniques offer potential solutions by emulating the natural collective behaviors of animals. This paper reviews the application of NI-SI algorithms for optimal DG allocation, specifically focusing on reducing power losses as a key objective function. The review analyzes a significant body of literature demonstrating the effectiveness of NI-SI techniques in addressing power loss challenges in distribution networks. Additionally, future research directions are provided to guide further exploration in this field. [ABSTRACT FROM AUTHOR]

To address the problem that the occurrence of large-scale blackouts in the future may cause the lack of power supply in the area and lead to the loss of power to the important places in it and a significant decrease in the living standard of the residents, this paper researches the reliability of the distribution network with electric vehicle virtual energy storage (EVVES). This paper proposes an aggregation model of EVVES applied to distribution networks, establishes the relevant models of EVVES in a virtual energy storage system (VESS) based on it, and proposes a distribution network reliability evaluation method with EVVES. Firstly, an aggregation model of EVVES is proposed to ensure that islands and important loads in the distribution network do not lose power. Secondly, the relevant models in EVVES based on the aggregation model of EVVES are studied. Finally, a distribution network reliability evaluation method with EVVES is proposed based on the traditional reliability evaluation. The improved IEEE-RBTS Bus6 is adopted as an example for reliability analysis, and the simulation results prove the validity of the model established in the paper and demonstrate that the reliability of the distribution network can be significantly improved with EVVES. [ABSTRACT FROM AUTHOR]

In response to the numerous adverse effects caused by the integration of a large number of distributed generators into the distribution network, this paper proposes a two- layer planning model that comprehensively considers the siting and sizing of distributed generators (DGs) and distribution network reconfiguration. The upper layer minimizes the annual comprehensive cost of DG investments to determine the siting and sizing of distributed generators, while the lower layer minimizes active power network losses for distribution net- work reconfiguration. The Black Widow algorithm is introduced to solve the model. Firstly, the two-dimensional chaotic mapping is used for population initialization to enhance population diversity. Secondly, adaptive reproductive and sibling cannibalism rates are introduced to reduce the convergence time of the algorithm. Lastly, the mutation operator is applied to increase the probability of population mutation, enabling the algorithm to escape local optima and search for global optima. Simulation results verify that the proposed two-layer planning model significantly reduces network losses and im-proves voltage quality. The improved Black Widow algorithm demonstrates superior performance in terms of con-vergence speed and precision. [ABSTRACT FROM AUTHOR]

As global energy demand continues to rise, integrating renewable energy sources (RES) into power systems has become increasingly important. However, the intermittent nature of RES, such as solar and wind, presents challenges for maintaining a stable energy supply. To address this issue, energy storage systems are essential. One promising technology is micro-compressed air energy storage (micro-CAES), which stores excess energy as compressed air and releases it when needed to balance supply and demand. This study investigates the integration of micro-CAES with RES in a 19-home microgrid in northern Portugal. The research aims to evaluate the effectiveness of a microgrid configuration that includes 100 kW of solar PV, 70 kW of wind power, and a 50 kWh micro-CAES system. Using real-world data on electricity consumption and local renewable potential, a simulation is conducted to assess the performance of this system. The findings reveal that this configuration can supply up to 68.8% of the annual energy demand, significantly reducing reliance on the external grid and enhancing the system's resilience. These results highlight the potential of micro-CAES to improve the efficiency and sustainability of small-scale renewable energy systems, demonstrating its value as a key component in future energy solutions. [ABSTRACT FROM AUTHOR]

A powerful and efficient program for restoring the electrical distribution system by effectively utilizing the maximum capabilities within the system, including soft open points, distributed generation resources, and network configuration changes, can significantly reduce both the quantity and duration of lost loads caused by permanent faults. In this research study, we address the issue of distribution network restoration with a focus on soft open points (SOPs) and intentional islanding using distributed generations. This problem is formulated as a constrained optimization problem in order to determine the optimal distribution network configuration, quality of intentional islanding through DGs, control function of SOPs, and amount of load shedding. The objective is to minimize lost load while minimizing switching operations and preferably minimal islanding. Given that this problem involves multiple complexities such as combinatorial nature, mixed-integer variables, non-linearity, non-convexity along with numerous variables and constraints; we employ an evolutionary method known as simulated annealing (SA) algorithm to solve it without any simplifications or assumptions about convexity. To enhance efficiency during implementation of SA algorithm, Kruskal’s algorithm is utilized for generating radial solutions which restricts search space to feasible solutions resulting in quicker attainment of high-quality optimal solutions. Finally, the outcomes of implementing the suggested approach on the 69-bus IEEE distribution system are presented and examined. It is demonstrated that by leveraging the potential of soft open points in load transfer control and network voltage regulation, along with utilizing intentional islanding capability provided by distributed generation resources, the restoration capability of the distribution system can be greatly enhanced. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We measure the cost of technical inefficiency for local electricity distribution firms in Sweden using Stochastic Frontier Analysis, and explore how small-scale generation, the number of electric vehicles and the introduction of dynamic pricing schemes affects the transient inefficiency and efficiency scores. Our results show little to no effect of these environmental variables on the cost of technical inefficiency of electricity distribution grids in Sweden. [ABSTRACT FROM AUTHOR]

Abstract This study aims to advance the development of the active distribution network (ADN) by optimizing resource allocation across different stages to enhance overall system performance and economic benefits. First, an ADN optimization model is constructed based on a two-stage robust optimization approach. The first stage focuses on determining optimal decision variables within the uncertainty set, while the second stage adjusts control variables based on the initial stage decisions. This model effectively addresses source-load uncertainties while preserving the flexibility and adaptability of decision-making solutions. Additionally, this study explores uncertainty models that incorporate correlation factors. The IEEE33-node model is employed to validate the effectiveness and superiority of the proposed optimization strategy through numerical simulations. Simulation results demonstrate that Model 3 comprehensively accounts for photovoltaic and wind turbine generator planning by optimizing their capacity configurations, leading to a 23% increase in distributed generation (DG) penetration. During high-load periods (e.g., 13:00 and 16:00), DG output reaches 47% and 50% of the demand load, underscoring the critical role of DG in supporting the power grid during peak hours. Overall, the proposed two-stage optimization strategy considers source-load uncertainties, significantly reducing economic costs, enhancing DG output, and improving overall system performance. In scenarios with correlated uncertainties, the optimized results exhibit greater accuracy and reliability, providing robust support for the planning and operation of practical distribution networks.

[Introduction] Distributed generation (DG) has a significant impact on the operational characteristics of the load side of the power grid. However, current research focuses mainly on analyzing and modeling the characteristics of a single type of distributed generation, and there is limited research on general models that can uniformly describe DGs with certain similarities. [Method] This article constructed an inverter-interfaced distributed generator (IIDG) model and employed a novel optimization algorithm. Firstly, based on the analysis of the characteristics and common features of IIDG, a comprehensive load model containing IIDG was constructed; According to the unified model, analytical calculations were performed to derive the state differential equation of the IIDG system and the output equation of the system model. To calculate the state and response of the system at each moment through continuous iteration and switch function updates, a formula for calculating the initial value of the system's state was provided. Combined with the sample data, the unified model identification process was given. Finally, the white shark optimizer (WSO) algorithm was employed to identify the parameters of the model; considering the voltage drop disturbance, the system model was sampled and analyzed. [Result] The simulation results show that the proposed unified model can better reflect the IIDG characteristics in the case of different levels of voltage drop. [Conclusion] The modeling error and parameter identification results show that the proposed unified model has good self-describing ability and parameter stability.

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.

One of the directions of development of the electric power industry is decentralization, aimed at improving the reliability of energy supply, reducing losses during transmission of electric energy and ensuring energy independence of consumers. It is possible to simulate decentralized power systems, including distributed generation facilities, by implementation of multi-agent systems that allow solving design and control problems taking into account the needs of each participant in the process of production, transmission, distribution and consumption of electricity. The development of distributed generation using a multi-agent approach requires the creation of models for assessing the technical and economic efficiency of decisions made by each agent, both at the strategic and tactical levels. The strategic decisions of agents related to distributed generation include, among other things, the creation of power facilities and power plants based on renewable energy sources. An important factor for making such decisions is the estimation of the capacity factor. However, currently there are no models for its estimation with high reliability. The present paper proposes a new algorithm for estimating the capacity factor for the entire territory of a certain administrative unit and a model for its forecasting based on climatic and geographical parameters. The study was conducted on a data sample of 221 generation facilities (solar and wind power plants) in four oblasts (regions) of the Russian Federation. It has been determined that the capacity factor can be forecasted with a mean error within 4 % for photovoltaic power plants and 9 % for wind power plants. Therefore, it is possible to use the developed algorithm and model both in decision support systems when choosing the location of this types of power plants, and in systems that model the development of power systems using a multi-agent approach.

The continuous increase in energy demand strains distribution networks, resulting in heightened power losses and a decline in overall performance. This negatively impacts distribution companies' profits and increases consumer electricity costs. Optimal distributed generation (DG) allocation in distribution networks can mitigate these issues by enhancing power supply capabilities and improving network performance. However, achieving optimal DG allocation is a complex optimization problem that requires advanced mathematical techniques. Nature-inspired (NI) swarm intelligence (SI)-based optimization techniques offer potential solutions by emulating the natural collective behaviors of animals. This paper reviews the application of NI-SI algorithms for optimal DG allocation, specifically focusing on reducing power losses as a key objective function. The review analyzes a significant body of literature demonstrating the effectiveness of NI-SI techniques in addressing power loss challenges in distribution networks. Additionally, future research directions are provided to guide further exploration in this field.

The paper highlights that integrating distributed power generation facilities into power supply systems for rural consumer with voltage up to 1000 volts leads to the challenges in managing the confguration of the associated electrical grids. To manage the configuration of power supply systems for rural consumers with voltage up to 1000 volts, it is proposed to use multicontact switching systems. However, there are currently no established methods or criteria for determining optimal locations for multi-contact switching systems in power supply systems for rural consumers with voltage up to 1000 volts that include distributed generation facilities. (Research purpose) The study aims to develop a method for determining the optimal placement of multicontact switching systems in rural electrical grids with voltage up to 1000 volts that include distributed generation facilities. (Materials and methods) An analysis is conducted to evaluate the implementation of configuration management devices in the examined power supply systems. The study explores the impact of installing multi-contact switching systems and identifies the main types of damage caused by power supply interruptions for agricultural consumers. Additionally, a method was developed to determine the optimal placement of multi-contact switching systems. (Results and discussion) The paper demonstrates the practical application of the developed methodology through a case study on the reconstruction of a rural electrical grid with distributed generation facilities. Among the proposed options, the optimal location for placing multi-contact switching systems was selected. (Conclusions) The installation of multi-contact switching systems helps mitigate damage from power interruptions for rural consumers. The choice of optimal locations for multi-contact switching systems is influenced by various factors, any of which can be decisive, depending on the specific circumstances. The developed methodology enables a comprehensive evaluation of all relevant factors, allowing for the most appropriate choice to maximize the overall efficiency of the power supply system. Using the proposed methodology to determine the optimal location for multi-contact switching systems results in a 55 percents damage reduction in the rural electrical grid caused by power outages.

The rapid development of electric vehicles (EVs) and unmanned aerial vehicles (UAVs) provides new ways for personnel search and material distribution during emergency periods. This paper proposes an EV-UAV joint rescue system, in which the UAVs use the EVs as charging and maintenance base stations to provide various services for the objects to be rescued, and the EVs can use distributed generations to obtain diversified electricity supply, which improves the adaptability and endurance level of the system in emergencies. The coordinated scheduling model of the EV-UAV system is established in the mixed-integer linear programming (MILP) formulation, which considers factors including electricity consumption, electricity replenishment, loading capacity, distribution route, and distribution time window of the EVs and the UAVs. Case studies verify the validity of the model proposed, compare the EV-UAV and ground vehicle (GV)-UAV rescue systems, and illustrate the technical characteristics and application potential of the EV-UAV system in emergency assistance.

In this article, the robust scheduling of the distribution network is presented considering electric vehicles, distributed generation, and energy storage, in which the energy management of the mentioned elements is considered, and also only one scenario is needed. The proposed deterministic problem is an optimization problem whose objective function is equal to minimizing energy cost. Also, the limitations of the problem are equal to the power flow equations of the network, the limitations of the technical indicators of the network such as the voltage of the buses and the passing power of the lines, the operation equations of electric vehicles, energy storages, and distributed generation. It is worth mentioning that the mentioned problem is non-linear. In the following, to achieve the global optimal point with a high solution speed, the linear model of the mentioned problem is presented with a very low calculation error. In this research, the uncertainty parameters of the problem are equal to active and reactive loads, energy prices, parameters of electric vehicles, and renewable productions. Finally, to simplify the decision-making of the distribution network operator, a robust model of the mentioned problem was presented. Finally, the proposed problem is applied to the IEEE standard 33-bus radial distribution network using GAMS optimization software, and then the capabilities of the proposed design are evaluated.

In order to improve the orderly control and flexible adjustment capabilities of large-scale distributed power sources connected to the distribution network, it is currently an effective method to reasonably divide the distributed power sources into clusters and use the cluster as the basic unit for regulation. To this end, a distributed power cluster partitioning method that considers the adjustable capacity and response speed of flexible resources is proposed. Firstly, the modularity index and the active power balance index are proposed to characterize the structural and power balance capability of clusters. Secondly, based on the analysis of the cluster's flexible response capability, and in view of the problem that it is difficult to quantify the impact of the flexible resource response capacity and response speed on the adjustment capability, it is proposed to take the cumulative area of deviation between the response curve of cluster output and the net load curve as the indicator of the flexible response capability of clusters, and the influence of the allocation of energy storage capacity on the delineation of clusters is also taken into account. Then, a cluster division objective function that comprehensively considers the modularity index, active power balance index, cluster flexible response capability index and energy storage unit allocation index is proposed, and this function is solved using an improved adaptive genetic algorithm. Finally, the proposed method is verified in the improved IEEE 33-node system and IEEE 69-node system. The results show that the cluster partitioning method based on the proposed comprehensive performance index can improve the cluster flexible response capability by more than 20% on the basis of ensuring the structural strength of the cluster, which verifies the effectiveness and superiority of the cluster partitioning method proposed in this paper.

The integration of electricity grids with technologies for small-scale distributed generation based on natural gas consumption is one of the most important innovative directions for improving the reliability and energy efficiency of power systems. Synchronous operation of large energy consumers and distributed generation systems is a new element of the electricity system. This element is an industrial microgrid, which has a unique ability to control the operating modes of the electrical energy system compared to conventional industrial electricity consumers. The aim of this study is to develop a theory and methodology for the use of microgrids in industrial areas under the market-based conditions of electricity and natural gas purchase. An analysis of trends in the use of small-scale distributed generation shows that the operation of distributed generation based on renewable energy sources is not economically feasible in all countries. In some countries, such as Russia, for example, the use of distributed energy sources powered by natural gas is the most important. The study of pricing principles for the purchase of energy in the wholesale and retail electricity markets and for the purchase of natural gas from regional suppliers and on commodity exchanges shows that managing the volatility of energy demand schedules can have a significant impact on the prices of electricity and natural gas used by industrial companies and large consumers of energy resources. As part of a unified power system, industrial enterprises and small distributed generation systems simultaneously consume electricity from the Unified Energy System, generate electricity through a distributed generation system into the internal network of the enterprise and external network the power system, and consume natural gas through the enterprise consumption system and the distributed generation system. Synchronised operation of an industrial enterprise and a small system of distributed generation system in the unified control system of a microgrid enables the enterprise to reduce the combined costs of purchasing energy resources and increase the efficiency and reliability of power supply to equipment. The article presents a system of factors influencing the demand of microgrids for electricity and natural gas consumption and develops a model for the integrated control of industrial microgrids under their integration with the technology for managing the demand of industrial enterprises for electricity and natural gas consumption. The control algorithm developed for a microgrid takes into account the cross effects of changes in its energy and gas demand, energy market price factors, internal constraints for industrial load management, external external of the Unified Energy System and the Unified Gas Supply System, and the ability to meet the energy demand of external consumers of the microgrid. The paper is of scientific and practical importance and can be used in the process of developing, implementing and managing microgrids at industrial enterprises in Russia and around the world.

Abstract The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

Abstract The paper proposes a new stochastic multiobjective technoeconomic model for integrating photovoltaic (PV) and wind energy resources in electricity price (EP)‐driven distribution systems. The primary goal of this paper is to determine the optimal location and capacity for renewable energy‐based distributed generation, specifically PV and wind resources, while considering weather and system uncertainties. These uncertainties include stochastic variations in PV illumination intensity, wind speed, EP, and load fluctuations. To address these uncertainties, the paper employs scenario modeling techniques named as Latin hypercube sampling with Cholesky decomposition. This technique generates multiple correlated scenarios that represent uncertain variables. Subsequently, a scenario reduction technique is applied to identify the scenario with the highest probability. Later, a mathematical model is developed to minimize an objective function that encompasses various factors like system losses, node voltage deviations, the cost of purchasing power from the grid; and simultaneously maximize the total annual energy savings. The objective is to find optimal solutions that strike a balance between different objectives. To obtain an efficient optimum solution, this paper employs an effective meta‐heuristic technique named as JAYA algorithm. The results obtained by the JAYA algorithm are juxtaposed with those obtained using particle swarm optimization and genetic algorithm techniques. The proposed method is evaluated using Institute of Electrical and Electronics Engineers (IEEE) 33‐node and IEEE 69‐node test feeders to validate its feasibility and effectiveness. However, the effectiveness of the proposed method is not limited to any size of test systems.

Modern electricity networks are facing significant challenges in terms of power quality due to the increasing integration of power electronics. Even though low order harmonic control has largely been achieved, the emergence of supraharmonics is becoming a new cause for concern. This topic has gained interest in the past decade since power quality issues have become important due to the proliferation of highly sensitive electrical and electronic equipment. This special case of harmonics is mainly due to the power electronic converters utilized in industrial as well as residential applications, including electromobility, motor drive systems, and photovoltaic installations. While the adverse effects of supraharmonics have been pointed out in numerous studies and intensive research is underway on the crucial subject of supraharmonic measurements, the parameters affecting their levels have not received adequate attention. We attempted to shed more light on this important issue in the specific case of a small grid-connected PV system. In particular, the supraharmonic emission levels of the system were investigated through experimental measurements, and useful conclusions on the impact of specific environmental factors were derived.

There is a clear European Strategy to transition by 2050 from a fossil fuel-based economy to a completely new system based on renewable energy resources, with electricity as the main energy carrier. Positive Energy Districts (PEDs) are urban areas that produce at least as much energy as their yearly consumption. To meet this objective, they must incorporate distributed generation based on renewable systems within their boundaries. This article considers the fluctuations in electricity prices and local renewable availability and develops a PED model with a centralised energy storage system focused on electricity self-sufficiency and self-consumption. We present a fuzzy logic-based energy management system which optimises the state of charge of the energy storage solution considering local electricity production and loads along with the contracted electric tariff. The methodology is tested in a PED comprising 360 households in Bilbao (a city in the north of Spain), setting various scenarios, including changes in the size of the electric storage, long-term climate change effects, and extreme changes in the price of energy carriers. The study revealed that the assessed PED could reach up to 75.6% self-sufficiency and 76.8% self-consumption, with climate change expected to improve these values. On economic aspects, the return on investment of the proposal ranges from 6 up to 12 years depending on the configuration choice. Also, the case that boosts the economic viability is tight to non-business as usual (BaU), whichever event spiked up the prices or climate change conditions shortens the economic variables. The average bill is around 12.89 EUR/month per house for scenario BaU; meanwhile, a catastrophic event increases the bill by as much as 76.7%. On the other hand, climate crisis events impact energy generation, strengthening this and, as a consequence, slightly reducing the bill by up to 11.47 EUR/month.

Because of the low voltage level and high current in the distribution system, as well as the high ohmic resistance of the conductors, the majority of power system losses are attributed to the distribution system. As a result, it is necessary to address the issue of reducing distribution system losses. To reduce losses in distribution systems, a wide variety of methods and algorithms have been proposed and are still being developed. The allocation of capacitors in a distribution system is one of the earliest and most effective methods for reducing system losses. With the growth of distributed generations and the diversity of their productive power, the use of capacitors in distribution systems has decreased slightly. Distributed generation resource allocation has replaced this method. Conversely, the reconfiguration of distribution systems has proven to be one of the easiest and cheapest methods for reducing distribution system losses in the past half‐century. Distribution system reconfiguration has been the subject of many studies, each with different objectives. Many studies have also reconfigured the distribution system on a daily and hourly basis. A novel method for dynamic distribution network reconfiguration is presented in this study, which utilizes capacitors and distributed generation resources simultaneously. This method divides 24 h into several periods instead of considering real‐time and hourly data. By reconfiguring and allocating capacitors and distributed generation resources during these periods, the voltage profile is improved and losses are reduced. An IEEE 33 bus network is used to test the simulations. A genetic algorithm was used to reduce losses and improve the voltage profile using MATLAB software. Based on the simulation results, this model provides better results than previous approaches. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic generation has been raised as a dependable electricity substitute for implementing a decarbonised energy transition. The dynamics of PV systems with battery storage have led to new electricity end-user classifications that select their electricity systems and resources wisely, i.e. Prosumers and Prosumagers. This work presents a technical and economic assessment to analyse the effect of decision-making independent end-users and electricity management strategies through a novel time-discrete deterministic simulation model. The end-user's choice of battery storage technology and their approach to electricity management emerge as pivotal factors influencing their strategic decisions. Adopting a prosumer strategy, such as storing and selling surplus self-production, reduces the Levelized Cost of Energy (LCOE), benefiting the end-user economically. Notably, the most effective battery storage technology may only sometimes be the one that is most readily available in the market. These findings enhance our understanding of novel end-user types and underscore the practical implications of their evolutionary shift towards independence. [ABSTRACT FROM AUTHOR]

Voltage breakdown in sub-transmission networks resulting from an increase in load demand can be addressed by incorporating distributed generation (DG) units such as hydro, wind, photovoltaic, and geothermal in some buses. This approach can lower power losses and the price of the primary generated energy while increasing network dependability and efficiency. Feasibility of dispersed generation integration & its effect on sub-transmission system operation were investigated using Ben Walied 66 kV sub-transmission network in Libya as a state. This study presents modified particle swarm optimization (MOPSO) technique to select the best option with dispersed generation units under various operating conditions. The Ben Walied 66 kV sub-transmission network has been subject of effects research on both normal operation and load growth scenarios. The goal of this study was to find best way to adjust penetration level of the three DGs in response to changes in the network loading. Three DG units have been refitted to study the effects on test networks for sub-transmission, which consist of 30 and 51 buses. A comparison analysis shows that while the ideal locations of DG units remain constant with load expansion, the optimal solution for the penetration level percentage of three DG units was boosted by new optimal sizes. Research has been done on how the power factor of distributed generators (DGs) affects the practical performance of 66 kV networks in steady-state conditions. The integration of DG units in regulating these losses and voltage variations is demonstrated by the results, which indicate that the ideal solution for DG unit’s power factor was at a value of 0.87 lagging. [ABSTRACT FROM AUTHOR]

In order to mitigate the impact of DG (distributed generation) integration into distribution networks on grid safety and economic operation, it is necessary to optimize the planning of DG. Considering the uncertainties and correlations of wind and photovoltaic outputs, a bi-level planning model was established. The upper-level planning model was formulated with the objective function of minimizing the comprehensive costs, which included investment costs of DG, operation and maintenance expenses, active distribution network electricity purchase costs, micro gas turbine fuel costs, pollution control costs, and network loss costs. The lowerlevel operational model aimed to minimize the annual comprehensive operational costs for each scenario, subjected to constraints such as power balance, node voltage, branch capacity, and DG penetration rate. To address the issues of slow convergence speed and local optima in the whale optimization algorithm, three improvements were introduced: tent mapping, the incorporation of inertia weight, and a nonlinear convergence factor. The proposed improvements were validated through simulation case studies. The results demonstrate that the improved method enhances the performance of the whale optimization algorithm, thereby providing a more effective solution to the proposed model. [ABSTRACT FROM AUTHOR]

The efficient coordination of the distance and directional overcurrent relays (DOCRs) is jeopardized by the massive deployment of renewable distributed generation (DG) into electrical power networks. The two key aspects influencing the relay coordination are the bidirectional flow of current and increased short-circuit currents due to DG integration. This study investigates the consequences of variable DG penetration on the distance and dual-setting DOCRs (DS-DOCRs) coordination. As a result, common optimal relay settings have been obtained that can be utilized for variable DG penetration levels in modified IEEE-14 bus test system. For the distance relays (DRs), a fixed zone 1 and variable zone 2 operation have been considered. The proposed combined protection scheme is tested on a modified IEEE-14 bus test system using DS-DOCRs and DRs. The relay coordination problem is formulated as a constrained non-linear optimization problem and solved by genetic algorithm (GA) and gray wolf optimization (GWO). In this paper, DS-DOCRs with user-defined relay characteristics have demonstrated superior performance in line protection compared to conventional DOCRs with normal inverse (NI) characteristics. In addition, a comprehensive comparative analysis of optimization algorithms has been performed between GA and GWO algorithms. [ABSTRACT FROM AUTHOR]

This article presents the evaluation of the performance of the distance relay (ANSI function 21) when integrating Distributed Energy Resources (DERs) in a Local Distribution System (LDS). The aim is to understand the impacts of and the necessary modifications required in the operation of distance relays, considering different levels of DER aggregation, and identifying any threshold levels before issues arise. To achieve this, first, a comprehensive review was carried out to analyze the impacts generated in the protection systems. Second, by using the DigSilent Power Factory software, the implementation of the distance relay using a IEEE 13 Node Test Feeder was validated. The aggregation of the three fundamental types of DG, synchronous machines, solar panels, and wind turbines, was evaluated. The threshold at which distributed generation power injection begins to compromise distance protection performance was identified. This study compares the outcomes of using mho and quadrilateral protection schemes. [ABSTRACT FROM AUTHOR]

The penetration of renewable energy resources for distribution networks can significantly impact the security of power supply systems. To improve the penetration efficiency, a hierarchical optimization planning for a distributed generation (DG) network using the improved sand cat swarm optimization (ISCSO) is proposed to determine the optimal location and capacity of DG into the distribution network. Firstly, the sensor is used to collect data, and the ISCSO algorithm is used to construct a complex nonlinear DG planning problem, thus reducing the impact of DG uncertainty. Second, a DG hierarchical planning model is established to select the optimal location and capacity of DG access to the distribution network. Finally, in the classical test system, the effectiveness of the proposed model is verified by setting up multiple cases. The test results confirm that the total annual cost and power loss of the DG system can be reduced by 13.1 and 40.5%, respectively. [ABSTRACT FROM AUTHOR]

The affordability and accessibility of electrical data related to microgrids and active distribution networks (ADN) have significantly improved due to smart digital relays with advanced communication capabilities. These relays can communicate with each other and central protection systems. Due to data accessibility a machine learning (ML) based module is proposed. This module predicts both fault type and location within an IEEE 34 bus active distribution system featuring distributed generations. To design this module, current measurements are retrieved from five specified bus locations and further Fast Fourier Transform is applied for feature extraction. The feature creation method is much simpler without any complexity. These features are further used for ML module's training. Practical ADN operating conditions are considered when curating the training dataset. Weighted K-nearest neighbors are used for fault type prediction and linear regression is applied for fault location estimation and they are proven to be superior to other ML approaches. The obtained results show high accuracy and reliability in predictions, making these modules suitable for ADN protection. They also exhibit robustness against input measurement noise, ensuring consistent protection in various operating scenarios. The ADN is simulated in SIMULINK 2020a, and ML modeling is done using MATLAB 2020a. [ABSTRACT FROM AUTHOR]

In this study, organic Rankine cycle (ORC) combined with geothermal energy applied for distributed generation (DG) was developed to resolve the problem in Indonesia, especially for remote areas. High investment cost for building traditional power plant in remote areas is a big problem. To increase the utilization of renewable energies, geothermal energy is a great heat source and able to be chosen for resolving energy shortage in Indonesia, in which the useful range of heat source temperature for ORC is between 110 and 180 °C on predictions in this work. The potential energy calculated in Mataloko-02, East Nusa Tenggara, Indonesia, for geothermal was 11,795.57 kW. The predictions of net power generation, net thermal efficiency and exergy were carried out with various heat source temperatures with different working fluids, which were R245fa, R1233zde, R1224ydz, Novec649, R1336mzzz, R1234zez, R141b and Butane. The result showed that R141b has a good performance with the highest net thermal efficiency and exergy. The highest net power generation achieved by using Butane. According to environmental consideration, R141b had high values of Global Warming Potential (GWP) and Ozone Depletion Potential (ODP) compared with other working fluids selected. Based on predictions, R1233zde and R1224ydz presented as potential working fluids to replace the R141b with the best performance in ORC system. In addition, there is an idea proposed to combined ORC and DG to resolve the problem facing of sustainability electricity development and decrease the high investment cost on grid power supply and power losses for the remote areas. [ABSTRACT FROM AUTHOR]

Especially in the energy supply of multi-family houses, a wide variety of stakeholders are involved, from owners, to users, to energy service providers and society. They usually have different requirements and understandings of optimality, but ultimately have to make joint decisions and thus sensible compromises. In Germany in particular, there are a large number of multi-family houses and, at the same time, many government restrictions and subsidies in terms of energy supply. This makes it difficult to make clear recommendations for the choice of an energy supply concept that takes all stakeholder interests into account. We first identify the relevant stakeholders and define their objectives. In order to relate these with one another, we present a methodology based on energy system simulation and TOPSIS to make energy concepts objectively evaluable. A generic multi-family house with 40 residential units is examined, combining different energy technologies and insulation standards. There is no energy concept that satisfies all stakeholders equally and it is difficult to build coalitions between them. The best results are achieved by air-source heat pumps in combination with photovoltaic. [ABSTRACT FROM AUTHOR]