Stochastic investigation for solid‐state transformer integration in distributed energy resources integrated active distribution network

A Solid-State Transformer as Enabling Technology for Microgrids for Integration into the Power Distribution System: An Overview

Now a days the usage of electrical power is increasing enormously and it is also becoming highly impossible in imagining the world without the electricity. So here is one of the techniques for maintaining the continuous supply i.e., by generating the electrical power in the distribution premises. Distribution Generation has been growing rapidly in power systems due to their capable solution for the issues like power system deregulation, to meet the demand of electric power and shortages in the electric power transmission capacities. Concept of Distributed generation (DG) plays an effective key role in the distribution networks. Among many of their merits, real power loss reduction, THD reduction voltage sag and voltage swell reduction and voltage profile improvement can be the salient merits of DG. Improper location of DG sources in to the electrical power system would lead to the increase energy or power loss, and also jeopardize the system operation. In this paper, determination of DG location is done by using Sensitivity Analysis and corresponding DG size is found by using Particle Swarm Optimization (PSO) to mitigate the real power losses in the distribution networks. Standard IEEE 15 bus test system is chosen to test the proposed PSO — Sensitivity Analysis approach. The results so obtained show the enhancement of voltage profile, reduction of real power loss using MATLAB.

Optimal sizing and scheduling of battery energy storage system with solar and wind DG under seasonal load variations considering uncertainties

This paper presents two enhanced variants of the Artificial Protozoa Optimizer (APO), namely the Adaptive Balanced Artificial Protozoa Optimizer (AB-APO) and the Fractional Calculus-Enhanced Artificial Protozoa Optimizer (FC-APO), for optimal multi-Distributed Energy Resources (DERs) planning in smart radial distribution networks. The proposed framework addresses the coordinated allocation of Electric Vehicle Charging Stations (EVCSs), photovoltaic (PV) units, and Battery Energy Storage Systems (BESS). The AB-APO introduces an adaptive balancing mechanism that dynamically regulates exploration and exploitation to improve convergence stability and robustness, while the FC-APO incorporates fractional-order dynamics to embed long-memory effects, enhancing numerical stability and search smoothness. The proposed optimizers are evaluated on the IEEE-33 and IEEE-69 bus systems under eight DERs penetration scenarios. Simulation results demonstrate significant reductions in real and reactive power losses, improved voltage profiles, and effective mitigation of EV-induced network stress. Real power loss reductions exceeding 54%, 38.53%, 53.78%, 38.20%, 61.68%, and 60.72% are achieved for the IEEE-33 system, while reductions of 64.32%, 63.51%, 64.33%, 63.51%, 67.31%, and 67.04% are obtained for the IEEE-69 system across Scenarios 3–8. Overall, the results highlight the effectiveness of adaptive balancing and fractional-order modeling in strengthening APO-based optimization and confirm the suitability of the AB-APO and FC-APO as efficient planning tools for future smart distribution networks.

To investigate the security issues of loop-closing operations in medium–low-voltage distribution networks under the influence of stochastic fluctuations from distributed generators (DGs) and loads, probabilistic power flow is introduced for analyzing loop-closing currents in active distribution networks. A novel method combining Latin Hypercube Sampling (LHS) and the Gram–Charlier (GC) series, termed the LHS-GC method, is proposed to calculate the probability distribution of loop-closing currents. By modeling DGs and loads as random variables, their cumulants are efficiently obtained through LHS. Based on a linearized formulation of loop-closing current equations, the cumulants of loop-closing currents are calculated, ultimately reconstructing the probability distribution function of loop-closing currents in active distribution networks. Subsequently, a security assessment framework for loop-closing operations is established using the probability distribution of loop-closing currents. This framework provides a quantitative evaluation from two dimensions: preliminary loop-closing success rate and the severity of current limit violations, offering data-driven decision support for loop-closing operations. Taking the IEEE 34-node distribution network as an example for feeder loop-closing current assessment, the proposed LHS-GC method achieves results with less than 4% deviation from simulation values in terms of cumulative probability distribution of loop-closing currents and safety assessment metrics. Under a sampling scale of 500 points, the computational time is 0.76 s, demonstrating its efficiency and reliability. These outcomes provide actionable references for decision-making support in loop-closing operations of active distribution networks.

ABSTRACTThe optimal design and installation of hybrid photovoltaic (PV), wind turbine (WT) distributed generation (DG), and battery energy storage system (BESS) in radial distribution network (RDN) using dynamic arithmetic optimization algorithm (DAOA) is the main purpose of this work. The DAOA algorithm is the improved version of arithmetic optimization algorithm (AOA) which is widely used in the field of Electrical Engineering to solve the different optimization tasks. The simultaneous minimization of active power loss and voltage deviation is taken as the objectives to improve the efficiency of the distribution network using hybrid PV/WT/BESS system. To establish the efficacy of the suggested DAOA method, it is examined on two very well‐known test systems (33‐bus and 69‐bus). The uncertainty of power generation from PV‐ and WT‐based DG due to solar irradiance and wind speed variation also is taken into consideration. The cost analysis of the hybrid PV/WT/BESS system as well as active power loss cost are also assessed in this study. The result comparison shows that the DAOA algorithm gives supreme result in case of active power losses, voltage deviation, and benefits in active power lost cost. This algorithm has good speed of response and solution quality when hybrid PV/WT/BESS system allocation problem of the distribution network with simultaneous power loss reduction and voltage deviation minimization is executed in RDN. The placement of hybrid PV/WT/BESS system in 33‐bus reduces the active power loss by 44.27%, 41.47%, 46.23% using DAOA and 44.26%, 39.87%, 45.18% using AOA for normal loading, 15% increased loading, and 10% decreased loading, respectively. Similar results are also observed for 69‐bus test system. Moreover, if the performance of the DAOA, AOA, immune clone selection algorithm (ICSA), clone selection algorithm (CSA), manta ray foraging optimization (MRFO), and particle swarm optimization (PSO) for PV, WT, BESS‐ based 33‐bus system are observed, it can be noticed that DAOA gives annual savings of 10$, 496$, 505$, 669$, and 554$ as compared to AOA, ICSA, CSA, MRFO, and PSO, respectively. Similarly for PV, WT, BESS‐based 69‐bus system, it is observed that DAOA gives annual savings of 29$, 3147$, 3154$, 3342$, and 3158$ as compared to AOA, ICSA, CSA, MRFO, and PSO, respectively. In case of solution quality, the DAOA method grants its superiority over several optimization methods found in the literature.

Dispatching active distribution networks (ADNs) is an energy-intensive application that, if implemented via battery-energy storage systems (BESSs), can require a large capacity of these assets in order to fully balance the uncertainties caused by the stochastic demand and generation. The insufficient capacity of the BESSs often leads to their state-of-charge (SOC) saturation; this results in unreliable dispatch tracking. In this work, we propose and experimentally validate a real-time control scheme that achieves a highly-reliable dispatching of ADNs and ensures that the BESSs’ SOC is not saturated during the daily operation. Our proposed scheme uses a two-layer model predictive control (MPC). The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">upper-layer</i> MPC, running every 5 minutes, optimizes the BESSs’ SOC trajectories while minimizing the tracking error, considering the prosumption forecast of the whole day. Then, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lower layer</i> MPC, running every 30 seconds, takes the BESSs’ SOC trajectories as constraints while achieving a high-resolution tracking of the dispatch plan over the current 5-minute time horizon. Both layers account for the grid constraints by using the augmented relaxed optimal power-flow (AR-OPF) model; an exact convex relaxation of the original AC-OPF and used in this paper (for the first time in the literature) to solve a real-time constrained control problem for ADNs. Our proposed framework was experimentally validated using a 1.5 MVA/2.5 MWh BESS connected to an actual 24-node medium-voltage (MV) ADN that, in Aigle, Switzerland, hosted an uncontrollable 3.2 MWp distributed photovoltaic generation, 3.4 MVA hydro-power generations, and a 2.8 MW base demand.

Conventional voltage regulation strategies that use reactive or active power injection are ineffective in active distribution networks (ADNs) used as conventional power systems. This paper proposes a new way to operate solid-state transformers (SSTs) to improve voltage quality in active distribution networks (ADNs). The proposed control method uses the unused capacity of SSTs to regulate voltage in ADNs without adding any new hardware or overloading the SST. Since SSTs can participate in voltage regulation via reactive and active power control with the proposed method, they can be operated as “enhanced smart inverters” rather than smart inverters that provide voltage regulation service by reactive power regulation. In this paper, the fundamentals of the proposed method are explained and its effectiveness is confirmed with simulation results obtained from an IEEE 13 node test feeder modeled in MATLAB/SIMULINK.

&lt;span&gt;This paper proposes a new optimal operation of Microgrids (MGs) in a distribution system with wind energy generators (WEGs), solar photovoltaic (PV) energy systems, battery energy storage (BES) systems, electric vehicles (EVs) and demand response (DR). To reduce the fluctuations of wind, solar PV powers and load demands, the BES systems and DR are utilized in the proposed hybrid system. The detailed modeling of WEGs, solar PV units, load demands, BES systems and EVs has been presented in this paper. The objective considered here is the minimization of total operating cost of microgrid, and it is formulated by considering the cost of power exchange between the main power grid and microgrid, cost of wind and solar PV energy systems, cost of BES systems, EVs and the cost due to the DR in the system. Simulations are performed on a test microgrid, and they are implemented using GAMS software. Various case studies are performed with and without considering the proposed hybrid system.&lt;/span&gt;

The optimization of energy systems within a multi-microgrid framework, enriched by shared Battery Energy Storage Systems (BESS), has emerged as a compelling avenue for enhancing the efficiency of distributed energy networks. In response to the increasing integration of BESS in modern energy systems, this study investigates the implications of incorporating BESS within connected residential-commercial Microgrids (MGs). Unlike previous studies that primarily focused on cost and reliability, this research fills a significant gap in the literature by investigating the optimization of load demands patterns. Specifically, we explore the impact of shared BESS on load demand patterns in commercial-residential MGs. The research introduces two innovative critical load metrics, peak-to-average ratio (PAR) and demand profile smoothness (DPS), to assess the influence of BESS on demand profiles. In addition, the study explores the integration of a Dynamic Thermal Rating (DTR) system, compared to traditional fixed thermal rating systems, to further optimize the performance and efficiency of connected residential-commercial MGs enriched by shared BESS. Three distinct case studies, each comprising a commercial MG (shopping mall, hotel, and office building) paired with a residential MG, were considered. Utilizing a Firefly Algorithm (FA) for optimization, the study determines the optimized BESS capacity for minimum total cost. The results highlight that the implementation of shared BESS, especially in collaboration between commercial and residential MGs, significantly reduces imported energy from the main grid, enhancing MG flexibility and resilience. While the economic benefits of shared BESS may not be substantial (up to 3.25 % cost reduction), the study underscores its contribution to more balanced and smoother load demand curves, with improvements in PAR (up to 15.63 %) and DPS (up to26.05 %). Moreover, considering DTR for transmission lines, instead of fixed thermal rating, improves the PAR and DPS up to 28.52 % and 41.06 % respectively. The findings of this study highlight the importance of considering load demand patterns in the design and operation of MGs and underscore the multifaceted benefits of shared BESS beyond economic considerations.

An investigation for battery energy storage system installation with renewable energy resources in distribution system by considering residential, commercial and industrial load models

In order to meet the high permeability and the access of large scale distributed generation, at present and in the future, active distribution network becomes the trend of smart grid research. This paper introduces the influence of distribution network voltage distribution and stability of distributed energy access based on active distribution network; The reactive power and voltage optimization control method of active power distribution network is summarized from the aspects of optimal voltage regulation measure, voltage regulation measure of power network equipment, reactive power optimal dispatch of power system, and so on. Analysis of the current status of the research, and summarized the existing active distribution network with a distributed power pickup distribution network reactive power and voltage control of the existence of some problems and the insufficiency still some problems that must be solved in the study of reactive power and voltage optimization control of active distribution network are pointed out.

In the active distribution networks (ADNs), the emerging trend of high penetration of sustainable energy sources such as wind turbines, photovoltaics, and the forthcoming integration of energy storage devices and additional dynamic loads like plug‐in‐electric vehicles (PEVs) influences to change the nature of electric distribution networks. Consequently, it deteriorates power quality and reliability in terms of unacceptable voltage rise‐drop, harmonic distortion, voltage unbalance, and excessive power loss. In this context of transition into the active distribution network, the probabilistic optimal power flow (P‐OPF) in the ADN is considered an important tool that helps the distribution grid operators for defining the optimal settings of the system's control variables. However, the optimal solution of P‐OPF becomes highly complex while considering the multiple variables, time‐varying characteristics and operational uncertainties associated with sustainable resources, consumer behaviour and electricity market prices.This review aims at promoting the research and discussion of P‐OPF studies in the context of ADNs characteristics and challenges. First, we perform a data‐driven and visualized scientometric analysis of 1988 quality documents retrieved from the Scopus during the last ten years using VOSviewer software. Second, an in‐depth investigation of the recent and emerging techniques for solving the P‐OPF problem is presented. Third, a set of recommendations and highlights of future challenges for P‐OPF in ADNs are offered. The review findings showed high interest among the scientific community in sustainable energy source integration, given the recent rise in publications and citations. Also, there is still a lack of innovative optimization techniques that can adequately handle the current issues associated with P‐OPF and objectives with fair computational efficiency and accuracy. Finally, this paper offers a guide for P‐OPF researchers to navigate the recent studies considering the sustainable sources, spot the new research frontiers, and identify the most impactful countries and publishers.

Dynamic active and reactive power compensation in distribution networks with batteries: A day-ahead economic dispatch approach

With the increasing number of distributed power sources connected to the active distribution network, it brings greater uncertainty to the operation of the distribution network, which is intertwined with the uncertainty of the load, and brings challenges to the power flow calculation of the distribution network. To solve this problem, a probabilistic power flow calculation algorithm for active distribution network is proposed. The power flow equation of the active distribution system is linearized by Taylor series. In order to improve the efficiency of power flow calculation, the semi-invariant method and Gram-Charlier expansion are used to deal with random variables in the power flow model. The proposed method is verified on the IEEE-33 system, and the results show that the proposed method can significantly reduce the calculation time while ensuring the calculation accuracy.