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The never-ending issue of inadequate energy availability is constantly on the outermost layer. Consequently, an ongoing effort has been made to improve electric power plants and power system configurations. Photovoltaic Distributed Generators (PVDG) and compensators such as Distributed Static Var Compensator (DSVC) are the center of these recent advances. Due to its high complexity, these devices’ optimum locating and dimensions are a relatively new issue in the Electrical Distribution Grid (EDG). A modified version of Newton Raphson Based Optimizer (mNRBO) has been carried out to optimally allocate the PVDG and DSVC devices in tested IEEE 33 and 69 bus EDG. The mNRBO algorithm integrates four parameters to enhance NRBO’s performance by addressing its limitations in balancing exploration and exploitation. The article suggested novel Multi-Objective Functions (MOF), which have been considered to optimize concurrently the overall amount of active power loss (APL), voltage deviation (VD), relays operation time (TR ELAY ), as well as improve the coordination time interval (CTI) between primaries and backup relays set up in EDG. The proposed mNRBO algorithm surpasses its basic NRBO version, as long as another alternative algorithm, while providing very good results, such as minimizing the APL from 210.98 kW until 26.482 kW and 224.948 kW until 18.763 kW for the IEEE 33 and 69 bus respectively. Which proves the capability of the mNRBO algorithm of solving such power system challenges.

This study presents an advanced optimization approach, the self‐adaptive bonobo optimization technique (SABOT), designed specifically to facilitate the seamless integration of photovoltaic‐distributed generation (PV‐DG) in distribution networks. While retaining the foundational principles of the standard BOT, SABOT incorporates four distinct mating strategies: promiscuous, restrictive mating, consortship, and extra‐group mating. To enhance its capabilities, SABOT introduces advanced features such as a memory mechanism and a repulsion‐based learning technique for dynamic parameter adjustment across successive iterations. These enhancements significantly improve the algorithm’s exploration potential, enabling more effective identification of optimal solutions. The developed SABOT seeks to minimize the costs associated with carbon dioxide (CO 2 ) emissions from the power grid, operational expenses of PV units, and energy losses. To accurately model the variability of solar power generation, the beta probability density function (PDF) is employed, capturing the daily fluctuations in solar irradiation. The improved SABOT was rigorously evaluated on two test systems: a real‐world Ajinde Nigerian distribution network and the widely‐used IEEE 69‐bus system. The simulation results highlight SABOT’s superior performance, demonstrating substantial decreases in emissions and losses of energy, thereby underscoring its effectiveness as a robust optimization tool for sustainable energy solutions. The aggregate yearly costs of emissions and lost energy for the Ajinde system are significantly reduced by 31% using the suggested SABOT version in comparison to the original scenario. It also achieves a significant 35% decrease for the IEEE 69‐bus system. Additionally, the simulation results demonstrate the competitive performance of the proposed SABOT version in comparison to differential evolution (DE), particle swarm optimizer (PSO), the techniques, and the conventional BOT.

There is, at present, considerable interest in the storage and dispatchability of photovoltaic (PV) energy, together with the need to manage power flows in real-time. This paper presents a new system, PV-on time, which has been developed to supervise the operating mode of a Grid-Connected Utility-Scale PV Power Plant in order to ensure the reliability and continuity of its supply. This system presents an architecture of acquisition devices, including wireless sensors distributed around the plant, which measure the required information. It is also equipped with a high-precision protocol for synchronizing all data acquisition equipment, something that is necessary for correctly establishing relationships among events in the plant. Moreover, a system for monitoring and supervising all of the distributed devices, as well as for the real-time treatment of all the registered information, is presented. Performances were analyzed in a 400 kW transformation center belonging to a 6.1 MW Utility-Scale PV Power Plant. In addition to monitoring the performance of all of the PV plant’s components and detecting any failures or deviations in production, this system enables users to control the power quality of the signal injected and the influence of the installation on the distribution grid.

Integrating distributed generation (DG) into the main grid is a challenge for the safety and stability of the grid. The application of peer-to-peer (P2P) technology in microgrids with distributed generation is expected to facilitate increased self-consumption of distributed and renewable energy, and the rise of prosumers’ monetary benefits. A P2P energy trading model in microgrids with photovoltaic (PV) distributed generation and battery energy storage systems (BESSs) is proposed in this paper. We additionally designed a P2P electricity trading mechanism based on coalition game theory. A simulation framework of this model is presented which assumed a local community with 30 households under comprehensive constraints encompassing a customer load profile, PV system, BESSs, market signals including feed-in tariffs, and retail prices. Firstly, individual customers can post orders (purchasing orders or selling orders) and exchange information in a P2P energy trading market. Secondly, the microgrid operator can validate the orders based on how to achieve the minimum overall energy consumption in microgrids and set reasonable real-time purchasing and selling prices for P2P energy transactions. Thirdly, the orders can be automatically conducted and completed at the designed optimal price. This mechanism can be a practical solution motivating individual customers to participate in P2P electricity trading, assist with electricity cost reduction, benefit from electricity supply increases, and help the grid operators to make the most economically and socially friendly decisions.

Turkey is a developing country with rising energy demands. Energy access is one of the key parameters to sustain the development, since the country meets a considerable part of energy demands by imported fossil fuels. Distributed photovoltaic power generation (DPPG) is one of the sustainable solutions to increase renewable energy sources (RES) shares in primary energy demand. This paper investigates Turkey’s current policy system with its excellences and shortcomings in different stages of distributed photovoltaic power generation development in the country. By the assessment of the current situation, a strengths (S) and weaknesses (W) and external opportunities (O) and threats (T) analysis is also conducted to propose urgent strategies to contribute the development of DPPG sector in Turkey. In conclusion, future roles of the government and energy market are discussed to increase integration of DPPG in Turkish renewable energy market comprehensively.

This study aims to provide an efficient framework for the coordinated integration of AC and DC chargers, intermittent solar Photovoltaic (PV) Distributed Generation (DG) units, and a Distribution Static Compensator (DSTATCOM) across residential, commercial, and industrial zones of a Radial Distribution Network (RDN) considering the benefits of various stakeholders: Electric Vehicle (EV) charging station owners, EV owners, and distribution network operators. The model uses a multi-zone planning method and healthy-bus strategy to allocate Electric Vehicle Charging Stations (EVCSs), Photovoltaic Distributed Generation (PVDG) units, and DSTATCOMs. The proposed framework optimally determines the numbers of EVCSs, PVDG units, and DSTATCOMs using Harris Hawk Optimization, considering the maximization of techno-economic benefits while satisfying all the security constraints. Further, to showcase the benefits from the perspective of EV owners, an EV waiting-time evaluation is performed. The simulation results show that integrating EVCSs (with both AC and DC chargers) with solar PVDG units and DSTATCOMs in the existing RDN improves the voltage profile, reduces power losses, and enhances cost-effectiveness compared to the system with only EVCSs. Furthermore, the zonal division ensures that charging infrastructure is distributed across the network increasing accessibility to the EV users. It is also observed that combining AC and DC chargers across the network provides overall benefits in terms of voltage profile, line loss, and waiting time as compared to a system with only AC or DC chargers. The proposed framework improves EV owners’ access and reduces waiting time, while supporting distribution network operators through enhanced grid stability and efficient integration of EV loads, PV generation, and DSTATCOM.

One of the industries that use fossil fuels most frequently worldwide is transportation. Therefore, electrifying the transportation system, such as the creation of plug-in electric vehicles (PEV), has become essential to reducing the effects of carbon dioxide emissions and using less traditional energy supplies that are not ecologically friendly. PEV deployment must be flawless, necessitating a well-developed charging infrastructure. The best location for fast charging stations (FCSs) is a crucial issue. As a result, this article offers a practical method for choosing the best site for FCSs using the east delta network (EDN). When transportation is made electric, the infrastructure of the electrical distribution network may also need to be changed. Therefore, when adopting FCSs, three factors need to be considered: actual power loss, reactive power loss, and investment cost. The energy demand from the electrical grid is also increased by including FCSs in the power distribution network. To lessen the impact of FCSs on the system, this research report suggests integrating photovoltaic distributed generation (PVDG) at certain places in the distribution network. Consequently, the system becomes dependable and self-sustaining. After deploying the FCSs and PVDGs, the distribution system’s dependability is also examined. Six case studies (CS) have also been suggested for deploying FCSs with or without DG integration. As a result, the CS-6’s active power loss decreased from 1015.38 to 830.58 kW.

In addition to active power generation, photovoltaic inverters can be used to provide ancillary services to grids, including reactive power compensation. This paper proposes a metaheuristic approach based on particle swarm optimization for the allocation and sizing of photovoltaic inverters that perform the complementary functions of static synchronous compensator (PV-STATCOM) units. The objective of the aforementioned approach is to reduce the initial investment cost in the acquisition of PV-STATCOM units. The proposed methodology considers both the daily load curve and generation and is applied to a 33-bus test system. The methodology is validated based on an exhaustive search algorithm and tested over 1000 consecutive simulations for the same problem; consequently, the methodology produces low standard deviations and errors, indicating its robustness. The methodology demonstrates an improved grid voltage profile throughout the day when applied to the 33-bus test system. Furthermore, the photovoltaic inverter efficiently performs its main function of active power generation. As a major contribution, the proposed methodology may assist investors in determining the allocation and sizing of PV-STATCOM units to perform the ancillary service of reactive power compensation in grids

Distributed photovoltaics (DPVs) are widely distributed and the output is random, which brings challenges to the safe operation of the distribution network, so the construction of photovoltaic aggregations can effectively participate in the flexible regulation of the power system. At present, the extraction of DPV clustering features is not sufficient, only considering the output characteristics of PVs. Certain PVs under some nodes may have a more pronounced regulation effect, but they may be ignored in the clustering process. To address the above problems, this paper proposes a DPV aggregation approach considering the distribution network topology. It combines the voltage sensitivity and the power curve and regards them as clustering features to form the DPV aggregation with the highest average voltage sensitivity participating in voltage regulation. The simulation on the IEEE 33-node system verifies that the proposed aggregation approach can select DPV aggregation more suitable for voltage regulation, and make full use of the aggregation to realize the optimal voltage regulation effect.

The integration of large-scale distributed photovoltaic (PV) units into a microgrid poses critical challenges to transient stability. Developing an effective model of distributed PV generation systems is essential for stability analysis. However, detailed modeling of individual PV units leads to prohibitive computational costs. To address this issue, this paper proposes an equivalent model for distributed PV generation systems in a microgrid. By thoroughly analyzing the PV units’ responses during the low voltage ride-through (LVRT) process, the dominant active power responses are identified, and two segmentation thresholds for clustering are analytically derived. To improve engineering applicability of the proposed clustering method, one voltage-dip-dependent segmentation threshold is approximated. Moreover, for PV units exhibiting post-fault active power ramp recovery, an additional clustering based on average pre-fault steady-state active power is introduced to better represent the dynamic behaviors of actual distributed PV generation systems. On this basis, a four-machine equivalent model is proposed, which captures key dynamic characteristics while ensuring both computational efficiency and modeling accuracy. Extensive simulations under various operating conditions and fault scenarios verify the effectiveness of the proposed equivalent model in reproducing transient behavior of distributed PV generation systems in a microgrid.