The electrification of residential demand through electric vehicles (EVs), heat pumps (HPs), photovoltaic (PV) systems, and battery energy storage systems (BESSs) creates new congestion challenges in low-voltage (LV) grids. This study evaluates a transparent, deterministic, and real-time-capable rule-based energy management system (EMS) for curative thermal congestion management within a §14a EnWG-oriented setting. The EMS is implemented in MATLAB/Simulink and tested on a representative four-feeder LV network supplying 56 households. Congestion is detected from maximum phase root-mean-square currents using conservative transformer and feeder thresholds. After a threshold is reached, the EMS first activates available BESS support and then applies simultaneous feeder-wide EV limitation, batched round-robin curtailment, or staged feeder-wide reduction toward 4.2 kW. In the uncontrolled case, the Feeder 3 and transformer overload areas are 62.84 Ah and 48.50 Ah, respectively. All controlled scenarios remove at least 98.70% of the feeder overload and eliminate the transformer overload within the reported numerical precision. The batched strategy requires 328.54 Ah of cumulative feeder-current reduction, compared with 977.34 Ah for simultaneous control and 816.00 Ah for staged control, and achieves the highest feeder-relief efficiency. It therefore provides a balanced trade-off between congestion relief and intervention intensity for the investigated deterministic case.
The growth of electric vehicles (EVs) and photovoltaic (PV) generation introduces new operational challenges for low voltage (LV) distribution networks, including transformer overloading, voltage deviations and bidirectional power flows. Mitigation measures often include the upgrading of conventional transformers and the re-conductoring of feeders. These methods become inefficient for uncertain load-growth paths. The technical effectiveness of coordinated distributed energy resources (DERs), such as rooftop PV systems, battery energy storage systems (BESS), and EV charging as non-wires alternatives (NWAs) to reduce the operational stress on local distribution transformers (LDTs) is assessed in this study. The representative Wellington suburban LV feeder is a 656-household feeder with 500-1000 kVA transformers. The feeder is modelled in DIgSILENT PowerFactory 2024 using the quasi-dynamic simulation language (QDSL) time-series at hourly resolution for one year. Four progressive scenarios are considered: (i) baseline operation without DERs, (ii) increasing PV penetration levels (5% in 2030, 10% in 2035, 28% in 2050), (iii) increasing number of EVs as load (5% in 2030, 10% in 2035, 28% in 2050) and (iv) integration of community-scale BESS for peak shaving. The results show that coordinated operation of DERs with BESSs can reduce transformer peak loading and improve voltage compared to standalone PV. In particular, the transformer-aware BESS dispatch offers a measurable peak shaving capacity and improves the local energy balancing, enabling a higher renewable penetration with less conventional infrastructure reinforcement. The practical role of DER coordination in improving LV network are more resilience and defers investment in the distribution systems as a scalable approach.
Unknown authors· Archives of Sustainable Ener...· 0 citations
The increasing penetration of photovoltaic systems, battery storage and electric vehicles in low-voltage distribution networks poses significant operational challenges, including voltage regulation, thermal overload, and energy curtailment. This paper proposes an uncertainty-aware two-stage coordination framework. The first stage employs a LinDist3Flow-based optimisation with Monte Carlo scenario generation to determine day-ahead charging and discharging schedules for batteries and electric vehicles while accounting for uncertainties in load demand, solar irradiance, temperature, and electric vehicle availability. The second stage uses an exact nonlinear alternating current optimal power flow formulation to optimise inverters’ active and reactive power set-points in near real-time operation. The framework is evaluated on a modified IEEE European low-voltage test feeder comprising 165 loads with 100% single-phase photovoltaic penetration. Four operational approaches are compared: local Volt-Watt/Volt-Var control, direct application of scheduled set-points without real-time correction, real-time optimal power flow with fixed time-of-use schedules, and the proposed two-stage framework. The results demonstrate that the proposed approach achieves the lowest total curtailment while maintaining voltage and thermal compliance, reducing the total curtailed energy by approximately 5% compared to the optimal power flow in real-time with fixed schedules. These findings highlight the importance of integrating uncertainty-aware scheduling with real-time coordination in future low-voltage networks with a high penetration of consumer energy resources.
Asaad Makhalfih, I. A. Ibrahim· IEEE Open Access Journal of...· 0 citations
This paper proposes a dual-layer coordinated framework that combines day-ahead battery energy storage system (BESS) scheduling with real-time Volt–VAr Control (VVC) for active distribution networks. The optimization minimizes distribution system technical losses while satisfying operational constraints related to voltage regulation, equipment loading, battery operation, voltage regulator (VR) tap commutation, and smart inverter (SI) operating limits defined by IEEE Std 1547-2018. The planning stage determines the optimal charging and discharging schedule of multiple BESS units over a 24-hour horizon, whereas the operational stage performs real-time VVC through the coordinated control of VRs, capacitor banks (CBs), and SI associated with distributed photovoltaic (DPV) and BESS units. The methodology was implemented in a Python–OpenDSS co-simulation environment and validated on a modified IEEE 34-bus feeder using real SCADA load measurements and solar irradiance data through daily and seasonal operating scenarios under both planning and actual operating conditions. Performance was also compared with conventional local VVC strategies. Results demonstrate that the proposed framework maintains voltages within prescribed limits, eliminates or substantially mitigates reverse power flow, reduces feeder peak demand, and significantly decreases network energy losses. Overall, the proposed strategy significantly enhances the operation of active distribution networks with high renewable energy penetration.
R. R. Biazzi, D. Bernardon, Maurício Sperandio· IEEE Access· 0 citations
With the advancement of net-zero targets and the large-scale deployment of distributed photovoltaic (PV) systems, battery energy storage systems (BESS), and electric vehicles (EVs) in residential sectors, home energy management systems (HEMS) have become central to improving end-use energy efficiency. However, reliance on synthetic data, simplified and homogeneous load modeling, and lack of coordination between dynamic pricing mechanisms and multi-device scheduling, make them practically inefficient. Furthermore, most studies address only unilateral user-side optimization while neglecting the distribution network operational constraints and omitting rigorous anti-arbitrage mechanisms to preclude speculative user behavior. To address these gaps, this paper proposes a data-driven coordinated scheduling framework for residential PV-BESS and multi-device systems formulated within bi-level game-theoretic architecture. The upper-level employs particle swarm optimization (PSO) to determine dynamic additional price signals for peak shaving and distribution network security, with explicit constraints on distribution transformer capacity, node voltage deviation, and load ramp rate adapted to three-user scenarios. The lower-level formulates a mixed-integer quadratic programming (MIQP) model to achieve multi-objective optimization of user electricity cost, thermal comfort, device usage preference, battery cycle degradation, and end-of-cycle energy balance. Simulation results for representative summer and winter days indicate that the proposed framework reduces user-side electricity cost by around 30%, elevates PV self-consumption rate to over 70%, and achieves about 20% peak load reduction with around 15% peak-valley difference narrowing on the grid side. All distribution network security constraints and anti-arbitrage rules are strictly satisfied. The framework effectively reconciles the objectives of both residential users and the distribution grid.
Yitong Wu, Shitikantha Dash, D. Srinivasan· Sustainability· 0 citations
With the growing demand for electricity, the penetration of Renewable Distributed Generators (RDGs), alongside the transition from Internal Combustion Engine Vehicles (ICEVs) to Electric Vehicles (EVs), has become a pressing challenge for the stable and efficient operation of distribution networks. This research focuses on a critical task of determining the optimal integration of RDGs, including solar photovoltaic systems, wind turbines, biomass units, and EV charging stations, into an Unbalanced Radial Distribution System (URDS). This work proposes an optimization approach aiming to minimise the total costs (TCs), active power losses (APLs), voltage unbalance factor (VUF), and voltage deviation (VD) of the network under consideration simultaneously. The integration of RDGs is carried out using a metaheuristic technique, which accounts for the intermittent nature of renewable energy sources, the stochastic behaviour of EVs, and the variability of load demands over 24 h a day. Fuzzy decision-making is applied to select an optimal trade-off solution from the Pareto front. The effectiveness of the developed approach is assessed comprehensively on a Pakistani 60-bus URDS as a primary study, while the IEEE-123 bus system is employed as a validation case to demonstrate the applicability and scalability of the proposed methodology. Among the five analysed case studies, the simulation results indicate that coordinated integration of RDGs and EVCSs into the system yields significant benefits, including a decreased reliance on conventional centralised generation, with a reduction of 56.29% in costs, 46.61% in losses, 7.17% in voltage unbalance, and 27.13% in voltage deviation as compared to the base case.
Maaz Ahmad, M. I. Mohmand, Aamir Nawaz et al.· World Electric Vehicle Journ...· 0 citations
Growing deployment of distributed renewable energy resources is essential to realizing sustainability and carbon neutrality in contemporary power networks. Nevertheless, the increasing penetration of these resources in power distribution networks can create operational uncertainties due to solar intermittency, stochastic load variations, and voltage instability. Thus, efficient coordination of these dispersed resources needs smart energy management schedules that have the capability to adapt dynamically under varying operational situations. This work proposes a PV–battery integrated 82-bus Owerri urban distribution network model using MATLAB/Simulink. To address the real-time uncertainties in solar generation and load demand, an AI– based energy management framework is proposed. This is done through a framework that integrates RL with PSO to dynamically cooperate with photovoltaic generation, battery energy storage, and grid power. BFS methodology is adopted for the computation of power flow applied to the distribution grid under stochastic operating conditions for the evaluation of voltage stability and power loss performance. Numerical results showed that the proposed AI-based energy management system is superior to traditional manual control strategies. It was equally observed that the RL-based controller enhanced the voltage magnitude from 0.86 to 0.96 p.u. while the value of real power loss declined by 42.9% (210kW-120kW). The findings validated that the proposed adaptive AI-based energy management can significantly increase the incorporation of PV systems, voltage stability, and operational resilience for forthcoming smart network distribution systems, in line with standards stipulated by IEEE, NERC, and NEMSA.
Okwe Gerald, Mustapha Abdullahi, Okafor Izuchukwu et al.· International journal of rec...· 0 citations
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