Search Results (360)

Hydro-dominant distribution grids with high penetrations of distributed photovoltaic (PV) generation exhibit a clear operational asymmetry. PV output changes rapidly at the minute scale, whereas hydropower regulation is constrained by reservoir water balance, turbine ramping capability, and hydraulic safety limits. During high-inflow periods, mandatory hydropower generation further reduces the downward regulation margin and restricts midday PV accommodation. To address this issue, this paper develops an asymmetry-aware adaptive bi-level planning framework for photovoltaic hosting capacity (PVHC) assessment. A db4 discrete wavelet transform is used to decompose PV output into low-frequency energy trends and high-frequency fluctuation components. The upper layer performs hourly economic dispatch while maintaining reservoir water balance, and the lower layer conducts minute-level constrained tracking under ramping and vibration-zone avoidance constraints. A bisection-type capacity-search procedure is then used to identify the PVHC boundary by jointly checking curtailment, ramping, frequency proxy, voltage, line-loading, point-of-common-coupling exchange, and vibration-zone residence constraints. Case studies based on a 15 min PV dataset from a 30 MW station, hydropower operation records, and a modified 15-node feeder in Southwest China show that hydrological asymmetry materially affects PV accommodation. The obtained PVHC ranges from 53.17 MW under the most restrictive high-proxy condition to 65.33 MW under low-proxy operation. Compared with the no-coordination case, representative-month PVHC increases from 49.80 MW to 65.33 MW, while the simulated residence time within the predefined vibration-prone zone decreases from 447 min to 0 min. These results indicate that PVHC evaluation in hydro-dominant feeders should jointly consider electrical constraints, hydrological asymmetry, and hydraulic safety limits. Full article

To address the low-voltage violations that may occur in distribution networks with high penetration of distributed photovoltaic (PV) during sunset and evening peak periods under extreme high-temperature conditions, this paper establishes a source–grid–load electro-thermal coupling model that accounts for load thermal accumulation, transient conductor thermal inertia, temperature-dependent line impedance, and PV thermal derating. Based on a soft safety lower bound and a risk-preference utility function, the probability of voltage violation, violation depth, and expected violation duration are introduced to construct node-level and system-level comprehensive risk factors. The cumulant method combined with the Cornish–Fisher expansion is used to reconstruct the probability distribution of nodal voltages, enabling analytical risk calculation. Simulation results on the IEEE 33-bus system at 45 °C show that the proposed method can quantitatively reflect the temporal variations of nodal voltage distributions, physical violation depth, dimensionless severity utility, and expected violation duration, and identify weak nodes in the later part of the evening peak, providing a reference for risk early warning in distribution networks under extreme heat. Full article

With global demand for renewable energy increasing, the penetration of photovoltaic (PV) systems in power networks has risen significantly, introducing new challenges to microgrid stability. This study focuses on solar inverters using grid-forming (GFM) control, investigating their performance in black-start scenarios and in stabilizing microgrids with battery energy storage systems (BESSs). A MATLAB Simulink microgrid model integrating PV, BESS, and GFM inverters was developed to simulate scenarios including black start, load variation, grid synchronization, and power adjustment. Control techniques such as droop control, proportional–integral (PI) control, Clarke and Park transformations, and phase-locked loops (PLLs) were applied for precise regulation of voltage, frequency, and power. Results show that GFM inverters effectively stabilize voltage and frequency during load changes and PV grid connection, maintaining voltage between 0.96–1.003 p.u. and frequency within 59.87–60.07 Hz. The findings confirm the feasibility of GFM control for coordinated PV–BESS operation and support stable microgrid operation under high renewable penetration. Full article

Against the backdrop of the global energy transition towards clean, low-carbon sources and China’s “carbon peak, carbon neutrality” strategic goals, distributed photovoltaic (PV) power generation is being integrated into distribution networks at large scale and with a high penetration level. This trend profoundly changes the configuration and operational characteristics of traditional distribution networks, posing challenges in system planning, operation control, power quality, and economics. This paper innovatively treats the step-up transformers of multiple distributed PV stations as a “distributed generation collection network” that requires coordinated optimization and constructs an integer linear programming (ILP) model aimed at minimizing the total life-cycle cost. The model deeply integrates engineering practice, incorporates nonlinear construction, installation, operation, and maintenance costs related to cluster size, as well as power transmission costs proportional to distance, and it employs piecewise cost functions to accurately capture economies of scale. This research achieves a system-level coordination framework that moves beyond single-device optimization, reducing system costs for step-up transformer deployment in distributed PV stations under complex terrain conditions. Full article

High penetration of distributed photovoltaic (PV) generation has intensified voltage violations and stochastic voltage fluctuations in distribution networks, while existing voltage–var control methods still have limitations in terms of communication dependence, scalability, and edge deployment. To address these issues, this paper proposes a stability-constrained voltage–var self-optimizing control method for distribution networks based on the Bandit-Guided Online Self-Tuning Group Relative Policy Optimization (BOST-GRPO) algorithm. First, based on the LinDistFlow linearized power-flow model, a communication-free, decentralized, and locally observable reinforcement learning control environment is constructed, enabling each node to independently generate reactive power regulation commands using only local voltage measurements. Second, a contraction-mapping-based stability constraint is embedded into the policy output layer, theoretically guaranteeing the local exponential convergence of nodal voltage deviations around the equilibrium point and reducing the risk of voltage instability caused by overly aggressive policy actions. Meanwhile, device capacity constraints are incorporated into the policy output through a tanh-based action mapping, ensuring the physical feasibility of control commands. On this basis, BOST-GRPO realizes the online self-tuning of key hyperparameters within a single training process through a Bandit-guided mechanism, thereby avoiding the repeated training overhead caused by traditional offline hyperparameter tuning. Simulation results on the IEEE 33-bus system show that the proposed method outperforms benchmark reinforcement learning algorithms in final test cost, voltage deviation suppression, steady-state error, and regulation speed. Further tests under sensitivity matrix mismatch, different initial voltage disturbance intensities, and the extended IEEE 69-bus system demonstrate that the proposed method achieves good robustness and scalability. Full article

To address fault-induced boundary variations and conflicting commands among heterogeneous controllers in microgrid clusters with high distributed generation penetration, this paper proposes a multilayer multiagent control strategy based on cross-coordinated multiagent control and conflict coordination. The method uses a hierarchical distributed hybrid architecture. Local grid-forming (GFM) energy storage and photovoltaic (PV) converters provide autonomous voltage source support, microgrid coordination controllers generate distributed candidate commands, and the system-level coordination controller performs event-triggered arbitration. Unlike consensus-based cooperative control with fixed exchanged variables, the proposed method enables overlapping supervisory authority, weighted command fusion, explicit conflict classification, and feasible command projection under resource, state-of-charge (SOC), ramping, and load priority constraints. Direction, capacity, and objective conflicts are resolved through system-level arbitration, which converts multiple candidate commands into a single executable command. Comparative simulations show that the proposed method reduces frequency and voltage deviations, shortens power recovery time, improves SOC balancing among energy storage units, and enhances constrained hydropower coordination compared with conventional droop control and one-to-one hierarchical control. These results verify its effectiveness in improving dynamic stability and coordinated support capability in microgrid clusters. Full article

The growing penetration of electric vehicles (EVs) and distributed photovoltaic (PV) generation is increasing operational uncertainty in distribution networks and intensifying long-standing challenges such as higher power losses, rising peak demand, and voltage instability. To address these issues, this paper proposes a multi-objective optimization framework for the strategic placement of smart meters equipped with demand response (DR) capability in radial distribution systems. Unlike conventional placement approaches that mainly focus on monitoring or reducing non-technical losses, the proposed method integrates active load control into the planning stage and explicitly considers the stochastic behavior of loads, PV generation, and electric vehicle charging stations (EVCSs). The problem is formulated with four objectives: minimizing total power losses, substation peak demand, voltage deviation penalty, and installation cost. A scenario-based stochastic model is employed to represent operational variability across the network. The resulting nonlinear mixed discrete optimization problem is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), an evolutionary multi-objective optimization technique that generates a set of Pareto-optimal solutions representing trade-offs among conflicting objectives. Smart meters are allowed to curtail a portion of controllable demand during critical loading conditions, which helps reduce feeder loading and improve voltage profiles. The proposed approach is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. Simulation results demonstrate significant reductions in power losses and peak demand, with the IEEE 33-bus system achieving up to a 26.2% reduction in power losses and 52.5% reduction in substation peak demand compared with existing metaheuristic approaches. The results also indicate improved voltage stability and effective performance in the IEEE 69-bus system, confirming the importance of topology-aware DR-enabled planning. Overall, the findings show that embedding demand response capability within smart meter allocation can significantly enhance the resilience and operational efficiency of modern distribution networks with high EV and PV penetration. Full article

The rapid electrification of urban freight transport requires new optimization approaches that jointly consider logistics operations and energy system constraints. The problem is formulated as a mixed-integer linear programming (MILP) model that captures the interdependencies between vehicle operations, battery constraints, charging infrastructure availability and the temporal variability of photovoltaic energy. A multi-objective structure is adopted to minimize total energy costs and CO₂ emissions while maximizing the utilization of locally generated renewable energy. The model is evaluated using scenario-based simulations under three solar integration levels (0%, 30% and 60%). The results demonstrate that integrating solar energy into routing and charging decisions significantly reduces grid dependency, lowers emissions and improves overall system efficiency. Three types of charging stations are considered in the study (S1, S2, and S3), differing in photovoltaic (PV) energy penetration levels, ranging from conventional grid-based charging (S1) to high renewable integration stations (S3). The quantitative analysis reveals a clear resource and emission structure across the simulated scenarios. Incorporating charging stops grid-wide increases the total distance from theoretical routes to real tracks with stops to overcome the 120 kW battery limit. However, the integration of solar energy significantly alters the system’s environmental costs: total CO₂ emissions drop non-linearly by 33.4%, decreasing from 364.64 kg in the ‘Low Sun’ scenario to 243 kg in the ‘High Sun’ scenario. Furthermore, the localized impact shows that utilizing pure grid energy (S1) results in 405 kg of CO₂, while maximizing solar integration up to 60% (S3) reduces emissions to 162 kg. The sensitivity analysis showed how varying the share of solar energy at the two main stations (S2 and S3) affects the total CO₂ emissions, while maintaining the same routes. Three scenarios were examined: low (10% and 30%), base (30% and 60%) and high (50% and 90%) solar energy shares. As the share of solar energy in the system increases, a clear effect of emission reduction and energy cost optimization is observed. Full article

High renewable penetration introduces stochastic variability in distribution-network operation, requiring probabilistic AC power-flow tools that remain accurate in the tails while avoiding the computational burden of large Monte Carlo simulation. This paper presents a fully reproducible non-intrusive polynomial chaos expansion (PCE) framework for uncertainty propagation through nonlinear Newton–Raphson AC power flow. The method uses sparse-grid quadrature to train PCE surrogates from deterministic power-flow evaluations and is benchmarked against high-fidelity Monte Carlo simulations. In the validation, the IEEE 33-bus feeder is evaluated using up to 50,000 Monte Carlo samples, 95% bootstrap confidence intervals, PCE orders 2–5, correlated uncertainty scenarios, realistic thermal-loading recalibration, reactive-power sensitivity of renewable injections, multi-feeder testing on IEEE 33-bus, CIGRE MV, CIGRE LV, and IEEE 118-bus networks, and a 365-snapshot full-year daily screening. For the base IEEE 33-bus case, third-order PCE required only 494 deterministic power-flow evaluations and reproduced the 50,000-sample Monte Carlo benchmark with relative mean errors of 0.014% for minimum voltage, 0.119% for active losses, and 0.113% for substation import. The corresponding wall-clock speed-up was 13.29×, while reducing deterministic evaluations by approximately 101×. Correlated load–PV uncertainty increased the upper tail of substation import from 6.06 MW to 6.30 MW, and realistic thermal recalibration revealed line-loading p99 values above 100% for the 60% target case, demonstrating the operational value of physically meaningful ampacity settings. The proposed workflow provides an open, scalable, and tail-aware basis for uncertainty-informed distribution-network planning under renewable variability. Full article

Hybrid power generation systems are an effective solution for matching energy production with electrical load demand. In this study, we examine the viability of a grid-connected hybrid PV/Wind system for meeting the electricity demand of the Lafarge cement factory in Al-Tafilah, Jordan, using HOMER Pro software. The results indicate that the optimal configuration consists of a $6.1 \mathrm{M}\mathrm{W}$ wind turbine and a $22.8 \mathrm{M}\mathrm{W}$ PV array, producing $71.94 \mathrm{G}\mathrm{W}\mathrm{h}$ annually, with wind and PV contributing $31.3\%$ and $68.7\%,$ respectively. The system achieves a 100% renewable fraction while maintaining a high level of reliability, with unmet load and capacity shortage limited to $0.057\%$ and $0.1\%,$ respectively. The economic evaluation reveals a levelized cost of energy (LCOE) of $0.13 \mathrm{U}\mathrm{S}\mathrm{D}/\mathrm{k}\mathrm{W}\mathrm{h}$ and a net present cost (NPC) of $\mathrm{U}\mathrm{S}\mathrm{D} 25.827 \mathrm{m}\mathrm{i}\mathrm{l}\mathrm{l}\mathrm{i}\mathrm{o}\mathrm{n}$, representing a $27.8\%$ reduction in LCOE compared to the national grid tariff. In this study, we present a novel large-scale PV/Wind system for the cement industry in Jordan, based on real data, with enhanced techno-economic performance. The innovation of this research lies in the development and optimization of a large-scale grid-connected hybrid PV/Wind system for the cement industry in Jordan, utilizing actual industrial load data and site-specific renewable energy resources. Unlike previous PV-dominated studies, the proposed system integrates a significant contribution of wind energy to improve system reliability and renewable energy penetration, reduce dependency on the national grid, and improve the overall techno-economic performance under actual industrial operating conditions. Full article

This paper presents a comprehensive review of voltage stability challenges in South Africa’s constrained power grid, particularly in the context of rising hybrid renewable energy integration. With the growing deployment of inverter-based resources (IBRs) like solar PV, wind, and battery energy storage systems (BESS), especially under programmes through the Independent Power Procurement Office, voltage stability has emerged as a key concern, particularly in weak grid areas like the Northern Cape Province. We highlight how weak grids characterized by low short-circuit capacity, long transmission lines, and limited reactive power support are more susceptible to voltage instability, especially with high penetration of non-synchronous generation. Using a modified IEEE 14-bus system with hybrid generation, the study simulates a weak grid scenario. Findings point to significant reactive power losses and capacitive over-voltages in long and lightly loaded lines, mirroring some of the weak-grid-transmission challenges experiences in an area of the South African power grid. The study underscores the importance of dynamic load modelling (e.g., ZIP and exponential models) and inverter behaviour in stability analysis. It concludes that hybrid systems, when optimally designed and integrated with storage, can help support grid stability. However, proactive planning, advanced modelling, and compliance with evolving grid codes remain essential for securing reliable renewable integration. Full article

As declared in the European Green Deal, the decarbonization of the EU energy system is essential for achieving Europe’s climate neutrality targets, demanding a substantial expansion of renewable energy sources and the rapid phase-out of coal and gas. It is therefore essential that newly installed PV products within the EU are designed to avoid creating additional environmental burdens due to environmental impacts during production and at the end of life (EOL) of photovoltaic (PV) modules. This study presents a life cycle assessment (LCA) of sustainable/green PV module designs in terms of recyclability using advanced high-quality recycling technologies. It compares two product systems both based on mono c-Si PV technology and the glass–glass (G–G) module design: 1. Passivated Emitter and Rear Contact (PERC) and 2. Tunnel Oxide Passivated Contact (TOPCon) cell technologies, which are assessed under production scenarios in China and Germany, and two recycling scenarios (hypothetical high-recovery recycling and partial recycling) using inventory data from eco-invent and literature sources. The results across most impact categories show that the PERC and TOPCon module designs produced in Germany with high-recovery recycling as the end-of-life strategy exhibit lower impacts than those produced in China with partial recycling as the end-of-life strategy under the adopted assumptions such as electricity mix and end-of-life modelling choices for module-only impacts (excluding BOS components). The climate change results show that TOPCon cell design under high-recovery recycling yields 10.4% lower emissions than the PERC cell design under partial recycling in Germany and 9.7% lower in China. However, both module designs emit 26.6% and 27.2% less GHG emissions when produced in Germany compared to production in China, respectively, which is line with earlier studies. With the exception of human toxicity, both PERC and TOPCon cell technologies perform better in this study than previously reported in reviewed LCA studies, reflecting the use of more recent state-of-the-art industry data concerning manufacturing requirements. The sensitivity analysis carried out on the design changes and electricity grid mix available shows that any improvements in the design process and increases in renewable energy penetration into the grid corresponds to a proportional reduction in environmental impacts across all impact categories. Full article

The rapid growth of solar photovoltaic (PV) capacity is increasingly reshaping the operation of electricity systems, particularly in countries where renewable energy already represents a large share of generation. In Albania, where electricity production is strongly dominated by hydropower, increasing solar penetration is expected to affect short-term system behaviour, especially in terms of variability, surplus generation, and ramping dynamics. This study investigates PV integration at the system level using hourly electricity demand data for 2024 together with PV generation profiles scaled to different capacity scenarios. PV scenarios representing installed capacities of 150, 300, and 450 MWp, based on real PV deployment data, are analysed under varying levels of hydropower dominance. The analysis combines net-load modeling, ramping assessment, and a simplified flexibility-oriented mitigation approach to evaluate operational impacts under different hydropower conditions. The results indicate that increasing PV capacity significantly modifies the net-load profile. During summer periods, high solar generation substantially reduces midday net load, creating pronounced net-load valleys, whereas winter conditions remain more strongly influenced by electricity demand. As PV penetration increases, ramping intensity also increases. For example, extreme ramp values (Q99) rise from 80.87 MW/h at 300 MWp to 111.45 MW/h at 450 MWp, while the share of hours with ramp events exceeding 100 MW/h increases from 0.05% to 2.55%. The results of a conceptual flexibility approach that limits ramps to 60 MW/h show that extreme ramp events can be effectively mitigated, while moderate variability is largely unaffected. In summary, the results show that increasing solar PV penetration shifts the main operational challenge in Albania from energy balancing toward flexibility and variability management. The findings are particularly relevant for long-term system planning in hydropower-dominated systems and highlight the growing importance of flexibility measures and surplus management under high PV penetration. Full article

Addressing the challenge of power supply stability caused by the intermittent nature of photovoltaic power generation in off-grid microgrids, this study uses a commercial park in Wuhan as a case study and optimizes the capacity configuration of a photovoltaic–storage–hydrogen fuel cell hybrid microgrid system based on HOMER Pro software. First, a topology of the off-grid microgrid is constructed, comprising photovoltaic (PV), lithium-ion batteries, hydrogen fuel cells, and a diesel generator as backup. The power output characteristics, efficiency curves, and life-cycle cost models of each component are accurately established. On this basis, two typical operation strategies, namely Load Following (LF) and Cycle Charging (CC), are proposed and compared. The influence of different strategies on the optimal capacity configuration and operational economics is systematically analyzed, and the Cycle Charging strategy is identified as the optimal operation strategy for this scenario. Subsequently, a multi-scenario capacity optimization design is further conducted based on the optimal operation strategy. The minimization of net present cost (NPC) is taken as the primary objective, while multiple evaluation indicators such as renewable fraction (RF), levelized cost of electricity (LCOE), energy storage cycle life degradation, and system redundancy rate are comprehensively considered. The results show that, while ensuring 100% power supply reliability, the proposed model reduces the net present cost (NPC) by approximately 14.4% compared with the conventional PV-storage scheme. The renewable fraction (RF) reaches 95.8%, while the reliance on lithium-ion battery capacity is significantly reduced (battery capacity configuration decreased by 24.3%). This effectively extends the energy storage lifespan and enhances the overall economic and environmental benefits. The results provide a theoretical basis and technical reference for the planning and design of off-grid microgrids with high penetration of renewable energy. Full article

The increasing penetration of inverter-based renewable energy sources has significantly reduced system inertia, leading to faster frequency deviations in low-inertia power systems. This paper proposes an asynchronous distributed model predictive control (AD-MPC) strategy to coordinate flywheel energy storage systems (FESSs) and battery energy storage systems (BESSs) for enhanced frequency stability in low-inertia power grids. A modified IEEE 39-bus system integrating a 3 MW wind energy conversion system (WECS), a 2 MW PV solar unit, and an electric vehicle (EV) load emulator unit was simulated to evaluate the system performance of the controller under a 30% increase in load disturbance. The results show that the coordinated FESS–BESS operation using the proposed AD-MPC controller achieves faster frequency recovery and reduces frequency deviation by 4% compared to single storage configurations. The proposed approach demonstrates that the high-speed FESS can provide a rapid inertial response, while the BESS delivers primary frequency support, offering a promising solution for maintaining dynamic stability in future renewable-dominated power systems. Full article