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.
Active distribution networks with high penetration of PV, BESS, and EV charging face significant voltage regulation challenges and accelerated OLTC wear. This paper proposes a coordinated multi-layer voltage control framework operating across multiple time scales. It integrates fast local fuzzy Volt–Var and state-of-charge–aware P/Q control at the inverter/BESS level, intermediate coordinated capacitor switching, and a supervisory OLTC scheduler formulated as a model predictive control (MPC) problem. The resulting hybrid continuous–discrete closed-loop system is evaluated via time-domain co-simulation on a modified IEEE 13-bus feeder (IEEE 13-bus+SFVE) and a real 150-bus urban feeder in Manaus (RDRM), under realistic irradiance and load profiles. For the IEEE 13-bus+SFVE, the scheme reduced feeder-wide voltage violations from 15.22% to 4.83% and OLTC tap operations from 14 to 1 per day. For the RDRM feeder, violations dropped from 72.44% to 9.76%, with only one OLTC operation over 24 hours, both without PV curtailment. This integrative contribution demonstrates substantial improvements in volt-age quality and OLTC lifespan, highlighting a practical pathway for active distribution network management.
Weverson dos Santos Cirino, T. Soares, Israel Gondres Torné· Revista DCS· 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
The increasing photovoltaic (PV) penetration in distribution systems poses technical and economic challenges for both operation and planning. This paper proposes a short-term planning model formulated as a mixed-integer linear programming (MILP) problem that coordinates line reconductoring, allocation of fixed and switched capacitor banks (CBs), and installation of voltage regulators (VRs). The model incorporates PV inverters with Volt-VAR control (VVC), in accordance with the IEEE 1547-2018 standard, and represents the progressive evolution of load and distributed generation across three operational stages. The framework is validated on two distribution test systems with distinct characteristics: a 135-bus and the IEEE 123-bus feeders. The results show that, although both systems benefit from VVC, the effects on investment decisions are system-dependent. In the 135-bus feeder, the reactive support provided by smart inverters allows VRs to be eliminated once PV multiplying factor exceeds 1.6, while also reducing energy loss costs. In the IEEE 123-bus system, which exhibits a more pronounced voltage drop, the VR is installed regardless of VVC considerations; nonetheless, adopting PV-provided VVC allows deferral of investment in switched CBs, thereby reducing total planning costs. The proposed model, therefore, offers a flexible decision-support tool that adapts its reinforcement recommendations to each network.
J. V. G. de Araújo, João R. Muniz, W. Faria et al.· Journal of Control Automatio...· 0 citations
The increasing penetration of distributed energy resources and diverse load characteristics in interconnected multi-microgrid systems creates significant challenges for coordinated energy management and optimal resource planning. This study proposes a multi-objective optimization framework for the simultaneous sizing of photovoltaic (PV) systems and battery energy storage systems (BESSs), combined with coordinated energy management and bidirectional power exchange among residential, commercial, and industrial microgrids connected to the IEEE 33-bus distribution network. The framework incorporates 24 h load profiles, photovoltaic generation, time-of-use electricity pricing, and distribution network operational constraints. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is employed to simultaneously minimize the total daily cost, network power losses, and grid dependency while satisfying the voltage, feeder loading, and battery state-of-charge constraints. The economic objective combines the equivalent daily investment costs of PV and BESSs with daily operating costs using a capital recovery factor (CRF)-based formulation to ensure dimensional consistency. The best compromise solution is selected using the minimum normalized Euclidean distance to the ideal Pareto solution. Simulation results demonstrate that the proposed framework achieves a total daily cost of $13,412.52/day, total network power losses of 4179.1 kW, and grid dependency of 3262.28 kWh while maintaining all operational constraints within acceptable limits. Furthermore, coordinated PV–BESS operation improves voltage regulation, reduces feeder loading and network power losses, enhances renewable energy utilization, and decreases the reliance on the utility grid. The results demonstrate that the proposed framework provides an effective techno-economic approach for the coordinated planning and operation of interconnected multi-microgrid systems with high renewable energy penetration.
The large-scale integration of photovoltaic generation into distribution grids has introduced significant operational challenges, including voltage excursions, reverse power flows, and increased variability. Battery energy storage systems (BESSs) offer a versatile solution by providing coordinated active- and reactive-power support. However, their scheduling in active distribution networks is challenging because of the non-convex alternating-current (AC) power-flow equations, the nondifferentiability of battery-degradation modeling, and uncertainty in renewable generation and demand. This paper proposes a two-stage methodology for the day-ahead operation of BESSs in ADNs. In the first stage, parallel particle swarm optimization (PPSO) determines the hourly active- and reactive-power schedules of the BESS units. In the second stage, a matrix-based multi-period AC power flow based on successive approximations evaluates the schedules and verifies voltage, thermal, converter-capability, and state-of-charge (SoC) constraints. A rainflow-counting degradation model is incorporated into the objective function to account for cycling and calendar aging costs. The methodology is assessed through ablation analyses comparing active-power-only and coordinated P–Q dispatches, degradation-unaware and degradation-aware scheduling, and serial and parallel PSO implementations. It is validated on modified 33-, 69-, and 136-node systems under deterministic and uncertainty-based operating conditions, including 100 demand and PV-generation scenarios. PPSO is compared with parallel versions of the adaptive Jaya algorithm (AJAYA), genetic algorithm (GA), multi-verse optimizer (MVO), salp swarm algorithm (SSA), grey wolf optimizer (GWO), and vortex search algorithm (VSA), using operating-cost reduction, computational time, solution variability, feasibility indicators, BESS lifetime, and weekly cost analysis. Additionally, exact one-sided Wilcoxon signed-rank tests with Holm adjustment are used to assess the statistical significance of the economic differences between PPSO and the benchmark methods. Results show that PPSO provides the lowest or most competitive operating costs and the shortest computational time in the evaluated cases, while all network and storage constraints remain satisfied.
L. Grisales-Noreña, F. Machuca‐Martínez, O. D. Montoya· The Scientist· 0 citations
This paper presents an energy-management and state-of-charge (SoC) balancing scheme, denoted OEMSS, for a DC microgrid comprising photovoltaic generation, a fuel-cell source, two energy storage systems (ESSs), and six household loads. A demand-driven power-allocation layer first determines whether generation is sufficient, ESS support is required, or priority-based load scheduling must be activated. A supervisory balancing layer then allocates the fleet charging or discharging request by using a capacity-weighted average SoC and separate mode-dependent correction laws. The balancing command is dimensionally expressed as an energy-capacity deviation divided by the control interval and is projected onto the SoC and power limits. A Python simulation driven by recorded generation profiles is used to evaluate four seasonal operating conditions. In the tested equal-capacity case, the maximum inter-ESS SoC deviation is reduced from 18% to 4.8%, synchronization is reached within approximately 2 to 4 h, and simulated over-discharge events are avoided. The reported increase from 45% to approximately 70% is interpreted as a 25-percentage-point increase in the ESS storage contribution rate, rather than an increase in conversion efficiency. During shortage intervals, the retained priority demand is supplied, whereas satisfaction of the original uncurtailed demand is not claimed. A discrete-time Lyapunov analysis gives the nominal convergence condition 0<γb<2, and the online implementation has O(J+K+H) time complexity. The study provides simulation evidence for a simple coordinated allocation rule; hardware performance, battery-life extension, converter-level stability, and global optimality remain to be established.
M. Sadiq, Saher Javaid, Iacovos I. Ioannou et al.· Energies· 0 citations
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