Search Results (572)

The growing integration of plug-in electric vehicles (PEVs) into microgrids presents both challenges and opportunities, particularly through vehicle-to-grid (V2G) services. This paper proposes a dynamic horizon optimization (DHO) framework with adaptive pricing for real-time scheduling of PEVs in a renewable-powered microgrid. The system integrates solar and wind energy, V2G capabilities, and time-of-use (ToU) tariffs. The DHO strategy dynamically adjusts control horizons based on forecasted load, generation, and electricity prices, while considering battery health. A PEV-specific pricing scheme couples ToU tariffs with system marginal prices. Case studies on a microgrid with four heterogeneous EV charging stations show that the proposed method reduces peak load by 23.5%, lowers charging cost by 12.6%, and increases average final SoC by 12.5%. Additionally, it achieves a 6.2% reduction in carbon emissions and enables V2G revenue while considering battery longevity. Full article

Networked Microgrids (NMGs) have revolutionized the energy landscape by enhancing grid flexibility and decentralizing power generation, playing a pivotal role in the development of smart cities. Distributed Energy Resources (DERs) are a fundamental component of a typical NMG; hence, their trustworthiness is of utmost importance for the reliable and efficient operation of NMGs within smart city environments. However, the processing and analysis of unstructured data when performing trust assessments of these DERs is still not well explored. This research fills this gap by proposing a new trust framework that leverages the advanced capabilities of Neural Networks to assess the trustworthiness of DERs in NMGs. Furthermore, the proposed framework analyzes and converts the unstructured data from DERs into a structured format for generating trust scores for DERs. There are two primary components of this framework: (1) an SLM (Small Language Model)-based module for data analysis, (2) a neural network-based module for trust score calculation. These two components provide an end-to-end process for transforming an unstructured input into meaningful trust metrics. Several experiments were conducted to evaluate the performance of the proposed framework, and it turned out that the results produced by the proposed framework were highly precise, accurate and consistent. Furthermore, the proposed framework outperformed the existing frameworks in size and efficiency, making it a promising solution for trustworthy DER management in smart city microgrid ecosystems. Full article

Driven by carbon neutrality goals, electric–hydrogen coupled virtual power plants (EHCVPPs) integrate renewable hydrogen production with power system flexibility resources, emerging as a critical technology for large-scale renewable integration. As distributed energy resources (DERs) within EHCVPPs diversify, heterogeneous resources generate diversified market values. However, inadequate benefit allocation mechanisms risk reducing participation incentives, destabilizing cooperation, and impairing operational efficiency. To address this, benefit allocation must balance fairness and efficiency by incorporating DERs’ regulatory capabilities, risk tolerance, and revenue contributions. This study proposes a multi-stage benefit allocation framework incorporating risk–reward tradeoffs and an enhanced optimization model to ensure sustainable EHCVPP operations and scalability. The framework elucidates bidirectional risk–reward relationships between DERs and EHCVPPs. An individualized risk-adjusted allocation method and correction mechanism are introduced to address economic-centric inequities, while a hierarchical scheme reduces computational complexity from diverse DERs. The results demonstrate that the optimized scheme moderately reduces high-risk participants’ shares, increasing operator revenue by 0.69%, demand-side gains by 3.56%, and reducing generation-side losses by 1.32%. Environmental factors show measurable yet statistically insignificant impacts. The framework meets stakeholders’ satisfaction and minimizes deviation from reference allocations. Full article

Ensuring the resilience and efficiency of modern distribution networks is increasingly critical in the presence of distributed energy resources (DERs). This study presents a multi-objective optimization framework based on a Genetic Algorithm (GA) to improve voltage profiles, minimize active power losses, and enhance resilience in a radial distribution network. A simplified 6-bus radial test system with DERs at buses 2, 3, and 4 is considered as a proof-of-concept case study. The GA optimizes control variables, including DER setpoints and network reconfiguration, under operational and thermal constraints. The optimization employs a weighted objective function combining voltage profile improvement, loss minimization, and a resilience penalty term that accounts for bus voltage collapse and branch overloads during DER contingencies. Simulation results demonstrate that the GA significantly improves network performance: the minimum bus voltage rises from 0.92 pu to 0.97 pu, while the total real power losses decrease by 46% (from 55.3 kW to 29.7 kW). Moreover, in the event of a DER outage, the optimized configuration preserves 100% load delivery, compared to 89% in the base case. These findings confirm that GA is an effective and practical tool for enhancing distribution network operation and resilience under high DER penetration. Future work will extend the approach to larger IEEE benchmark systems and time-series scenarios. Full article

The global energy transition, characterized by the proliferation of intermittent renewables and the evolution of electricity markets, has positioned virtual power plants (VPPs) as crucial aggregators of distributed energy resources. However, their participation in competitive markets is fraught with multifaceted uncertainties stemming from price volatility, renewable generation intermittency, and unpredictable prosumer behavior, which necessitate sophisticated, risk-averse bidding strategies to ensure financial viability. This review provides a comprehensive analysis of the state-of-the-art in risk-averse bidding for VPPs. It first establishes a resource-centric taxonomy, categorizing VPPs into four primary archetypes: DER-driven, demand response-oriented, electric vehicle-integrated, and multi-energy systems. The paper then delivers a comparative assessment of different optimization techniques—from stochastic programming with conditional value-at-risk and robust optimization to emerging paradigms such as distributionally robust optimization, game theory, and artificial intelligence. It critically evaluates their application contexts and effectiveness in mitigating specific risks across diverse market types. Finally, the review synthesizes these insights to identify persistent challenges—including computational bottlenecks, data privacy, and a lack of standardization—and outlines a forward-looking research agenda. This agenda emphasizes the development of hybrid AI–physical models, interoperability standards, multi-domain risk modeling, and collaborative VPP ecosystems to advance the field towards a resilient and decarbonized energy future. Full article

With the increasing penetration of distributed energy resources (DERs) in distribution networks, traditional passive distribution systems are evolving into active and flexible systems capable of participating in the transmission-level energy market. Integrating distribution networks into a transmission-centric market-clearing model introduces challenges, such as capturing internal operational constraints and reflecting the economic features of distribution systems. To this end, this paper proposes a market integration method for distribution networks based on a feasible region and an accompanying bidding strategic bidding method to enable their efficient participation in the transmission-level electricity market. With a two-stage adaptive robust optimization framework, the feasible region that preserves operational characteristics of the distribution system and ensures the satisfaction of operational constraints within the distribution system is first depicted. The feasible region appears as time-coupled box-shaped regions. On this basis, a strategic bidding method is proposed based on the nested segmentation of the feasible region, jointly considering power and reserve. With it, the bidding prices of energy and reserve can be prepared, and then, together with the feasible region, can be smoothly integrated into the transmission-level market model. Numerical case studies demonstrate the effectiveness of the proposed method. Full article

Climate change is accelerating the frequency and intensity of extreme weather events, posing a critical threat to the stability, efficiency, and resilience of modern renewable energy grids. In this study, we propose a modular, AI-integrated digital twin co-simulation framework that enables climate adaptive control of distributed energy resources (DERs) and storage assets in distribution networks. The framework leverages deep reinforcement learning (DDPG) agents trained within a high-fidelity co-simulation environment that couples physical grid dynamics, weather disturbances, and cyber-physical control loops using HELICS middleware. Through real-time coordination of photovoltaic systems, wind turbines, battery storage, and demand side flexibility, the trained agent autonomously learns to minimize power losses, voltage violations, and load shedding under stochastic climate perturbations. Simulation results on the IEEE 33-bus radial test system augmented with ERA5 climate reanalysis data demonstrate improvements in voltage regulation, energy efficiency, and resilience metrics. The framework also exhibits strong generalization across unseen weather scenarios and outperforms baseline rule based controls by reducing energy loss by 14.6% and improving recovery time by 19.5%. These findings position AI-integrated digital twins as a promising paradigm for future-proof, climate-resilient smart grids. Full article

The rapid growth of Distributed Energy Resources (DERs) exerts significant pressure on distribution network margins, requiring predictive and safe coordination. This paper presents a closed-loop framework combining a topology-aware Spatio-Temporal Transformer (STT) for multi-horizon forecasting, a cooperative multi-agent reinforcement learning (MARL) controller under Centralized Training and Decentralized Execution (CTDE), and a real-time safety layer that enforces feeder limits via sensitivity-based quadratic programming. Evaluations on three SimBench feeders, with OLTC/capacitor hybrid control and a stress protocol amplifying peak demand and mid-day PV generation, show that the method reduces tail violations by 31% and 56% at the 99th percentile voltage deviation, and lowers branch overload rates by 71% and 90% compared to baselines. It mitigates tail violations and discrete switching while ensuring real-time feasibility and cost efficiency, outperforming rule-based, optimization, MPC, and learning baselines. Stress maps reveal robustness envelopes and identify MV–LV bottlenecks; ablation studies show that diffusion-based priors and coordination contribute to performance gains. The paper also provides convergence analysis and a suboptimality decomposition, offering a practical pathway to scalable, safe, and interpretable DER coordination. Full article

The integration of electric vehicles (EVs) and fuel-cell electric vehicles (FCEVs) requires accessible and profitable facilities for fast charging. To promote fast-charging stations (FCSs), a systematic analysis that encompasses both planning and operation is required, including the incorporation of multi-energy resources and uncertainty. This paper presents an optimization framework that addresses a joint strategy for the configuration and operation of an EV/FCEV fast-charging station (FCS) integrated with distributed energy resources (DERs) and hydrogen systems. The framework incorporates uncertainties related to solar photovoltaic (PV) generation and demand for EVs/FCEVs. The proposed joint strategy comprises a four-phase decision-making framework. Phase 1 involves modeling EV/FECE demand, while Phase 2 focuses on determining an optimal long-term infrastructure configuration. Subsequently, in Phase 3, the operator optimizes daily power scheduling to maximize profit. A real-time uncertainty update is then executed in Phase 4 upon the realization of uncertainty. The proposed optimization framework, formulated as mixed-integer quadratic programming (MIQP), considers configuration investment, operational, maintenance, and penalty costs for excessive grid power usage. A heuristic algorithm is proposed to solve this problem. It yields good results with significantly less computational complexity. A case study shows that under the most adverse conditions, the proposed joint strategy increases the FCS owner’s profit by 3.32% compared with the deterministic benchmark. Full article

Active distribution systems (ADS) are increasingly strained by rising energy demand and the widespread deployment of distributed energy resources (DERs) and electric vehicle charging stations (EVCS), which intensify voltage deviations, power losses, and peak demand fluctuations. This study develops a coordinated optimization framework for Mobile Battery Energy Storage Systems (MBESS) and Dynamic Feeder Reconfiguration (DFR) to enhance network performance across technical, economic, and environmental dimensions. A Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to minimize six objectives the active and reactive power losses, voltage deviation index (VDI), voltage stability index (FVSI), operating cost, and CO₂ emissions while explicitly modeling the MBESS transportation constraints such as energy consumption and single-trip mobility within coupled IEEE 33-bus and 33-node transport networks, which provide realistic mobility modeling of energy storage operations. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to select compromise solutions from Pareto fronts. Simulation results across six scenarios show that the coordinated MBESS–DFR operation reduces power losses by 27.8–30.1%, improves the VDI by 40.5–43.2%, and enhances the FVSI by 2.3–2.4%, maintaining all bus voltages within 0.95–1.05 p.u. with minimal cost (0.26–0.27%) and emission variations (0.31–0.71%). The MBESS alone provided limited benefits (5–12%), confirming that coordination is essential for improving efficiency, voltage regulation, and overall system sustainability in renewable-rich distribution networks. Full article

The integration of distributed energy resources into distribution networks creates operational challenges, including voltage instability and power quality issues. While battery energy storage systems (BESSs) can address these challenges, research has focused primarily on transmission-level applications or single services. This paper bridges this gap through a comprehensive review of BESS technologies and control strategies for multi-service ancillary support in distribution networks. Real-world case studies demonstrate BESS effectiveness: Hydro-Québec’s $1.2$ MW system maintained voltage within 5% and responded to frequency events in under 10 ms; Germany’s hybrid 5 MW M5BAT project optimized multiple battery chemistries for different services; and South Africa’s Eskom deployment improved renewable hosting capacity by 15–70% using modular BESS units. The analysis reveals grid-forming inverters and hierarchical control architectures as critical enablers, with model predictive control optimizing performance and droop control ensuring robustness. However, challenges like battery degradation, regulatory barriers, and high costs persist. This paper identifies future research directions in degradation-aware dispatch, cyber-resilient control, and market-based valuation of BESS flexibility services. By combining theoretical analysis with empirical results from international deployments, this study provides utilities and policymakers with actionable insights for implementing BESS in modern distribution grids. Full article

Large-scale integration of distributed energy resources (DERs) into distribution networks causes topological-operational situations with multidirectional power flows. One of the main components of distribution networks is the power transformer, which does not have the capabilities for real-time control of distribution network parameters with DERs. The use of solid-state transformers (SSTs) for connecting medium-voltage (MV) and low-voltage (LV) distribution networks of both alternating and direct current has great potential for constructing new distribution networks and enhancing the existing ones. Electricity losses in distribution networks can be reduced through the establishment of MV and LV DC networks. In hybrid AC-DC distribution networks, the SSTs can be especially effective, ensuring compensation for voltage dips, fluctuations, and interruptions; regulation of voltage, current, frequency, and power factor in LV networks; and reduction in the levels of harmonic current and voltage due to the presence of power electronic converters (PECs) and capacitors in the DC link. To control the operating parameters of hybrid distribution networks with solid-state transformers, it is crucial to develop and implement advanced control systems (CSs). The purpose of this review is a comprehensive analysis of the features of the creation of CSs SSTs when they are used in hybrid distribution networks with DERs to identify the most effective principles and methods for managing SSTs of different designs, which will accelerate the development and implementation of CSs. This review focuses on the design principles and control strategies for SSTs, guided by their architecture and intended functionality. The architecture of the solid-state transformer control system is presented with a detailed description of the main stages of control. In addition, the features of the SST CS operating under various topologies and operating conditions of distribution networks are examined. Full article

The rapid proliferation of residential electric vehicles (EVs), rooftop photovoltaics (PVs), and behind-the-meter batteries is transforming energy communities while introducing new operational stresses to local distribution grids. Short-duration transformer overloads, often overlooked in conventional hourly or optimization-based planning models, can accelerate asset aging before voltage limits are reached. This study introduces a second-by-second, multi-agent-based simulation (MABS) framework that couples empirically calibrated Distributed Energy Resource (DER) adoption trajectories with real-time-price (RTP)–driven household charging decisions. Using a real 160-household feeder in Denmark (2024–2025), five progressively integrated DER scenarios are evaluated, ranging from EV-only adoption to fully synchronized EV–PV–battery coupling. Results reveal that uncoordinated EV charging under RTP shifts demand to early-morning hours, causing the first transformer overload within four months. PV deployment alone offers limited relief, while adding batteries delays overload onset by 55 days. Only fully coordinated EV–PV–battery adoption postponed the first overload by three months and reduced total overload hours in 2025 by 39%. The core novelty of this work lies in combining empirically grounded adoption behavior, second-level temporal fidelity, and agent-based grid dynamics to expose transient overload mechanisms invisible to coarser models. The framework provides a diagnostic and planning tool for distribution system operators to evaluate tariff designs, bundled incentives, and coordinated DER deployment strategies that enhance transformer longevity and grid resilience in future energy communities. Full article

The increasing penetration of distributed energy resources (DERs) in unbalanced distribution networks presents significant challenges for optimal operation, particularly concerning power loss minimization and voltage regulation. This paper proposes a comprehensive Optimal Power Flow (OPF) model that coordinates various assets, including on-load tap changers (OLTCs), reactive power compensators, and controllable electric vehicles (EVs). To solve this complex and non-convex optimization problem, we developed the Shrinking Net Algorithm (SNA), a novel metaheuristic with mathematically proven convergence. The proposed framework was validated using the standard IEEE 123-bus test system. The results demonstrate significant operational improvements: total active power loss was reduced by 32.1%, from 96.103 kW to 65.208 kW. Furthermore, all node voltage violations were eliminated, with the minimum system voltage improving from 0.937 p.u. to a compliant 0.973 p.u. The findings confirm that the proposed SNA is an effective and robust tool for this application, highlighting the substantial economic and technical benefits of coordinated asset control for modern distribution system operators. Full article

As power systems decentralize, Virtual Power Plants (VPPs) offer a promising approach to coordinate distributed energy resources (DERs) and enhance grid flexibility. However, real-world validation of VPP performance in Indonesia remains limited, especially regarding internationally aligned test standards. This study presents the design and experimental validation of a cluster-based VPP framework integrated with a centralized VPP Management System (VMS). Each cluster integrates solar photovoltaic (PV) system, battery energy storage system (BESS), and controllable load. A Local Control Unit (LCU) manages cluster operations, while the VMS coordinates power export–import dispatch, cluster-level aggregation, and grid compliance. The framework proposes a scalable VPP architecture and presents the first comprehensive experimental verification of key VPP performance indicators, including response time, adjustment rate, and accuracy, in the Indonesian context. Testing was conducted in alignment with the IEC TS 63189-1:2023 international standard. Results suggest real time responsiveness and indicate that, even at smaller scales, VPPs may contribute effectively to voltage control while exhibiting minimal influence on system frequency in interconnected grids. These findings confirm the capability of the proposed VPP framework to provide reliable real time control, ancillary services, and aggregated energy management. Its cluster-based architecture supports scalability for broader deployment in complex grid environments. Full article