Search Results (618)

This study empirically isolates the marginal CO₂ abatement efficiency of wind and solar power within the Greek electricity system, utilizing hourly dispatch data from August 2012 to December 2018—a period characterizing the grid’s “pre-saturation” technical potential. By employing an econometric framework to capture ex-post displacement dynamics, we identify a statistically significant but highly heterogeneous abatement impact across renewable technologies. Our analysis reveals that wind power consistently achieves higher carbon savings per MWh than solar photovoltaics, primarily by driving deeper displacement of carbon-intensive thermal baseload. Conversely, solar generation exhibits a stronger propensity to displace zero-carbon hydroelectric output and net imports, thereby dampening its domestic abatement efficiency. Furthermore, we demonstrate that the marginal emissions avoided are non-linear, fluctuating significantly with system load, interconnection flows, and renewable penetration levels. These findings establish an “unconstrained efficiency” benchmark for the Greek grid, providing the necessary counterfactual to evaluate the diminishing returns and curtailment penalties characterizing the high-penetration era of renewables. Full article

In response to the reduced system inertia and increased frequency security risks in high-renewable power systems, as well as the limitations of single energy storage technologies, a coordinated optimal scheduling method for electrochemical energy storage (EES) and compressed air energy storage (CAES) considering the minimum inertia requirement (MIR) is proposed. The method constructs a coordination framework, leveraging the fast response of EES and the sustained support and equivalent inertia contribution of CAES. An MIR evaluation model considering RoCoF and frequency nadir constraints is established, and the inertia deficit is converted into fast reserve demand, forming an inertia–reserve coupling mechanism. To address nonlinear frequency constraints, an adaptive piecewise linearization method is adopted to transform the model into a mixed-integer linear programming problem. Case studies show that, compared with the benchmark hybrid energy storage scheduling strategy without inertia–reserve coordination, the proposed method reduces thermal generation cost by 4.5% and renewable curtailment by 74.8%. Moreover, the proposed APWL method improves computational efficiency by 47% compared with the conventional PWL method. Full article

The integration of high-penetration distributed photovoltaics (PV) into distribution networks triggers frequent voltage limit violations, fluctuations, and increased network losses. To address the limited communication infrastructure inherent in medium-voltage distribution networks, this paper employs PV inverters as fast-response voltage regulation devices and proposes a real-time distributed voltage control strategy specifically for such networks. Firstly, a distribution network communication topology and voltage regulation architecture based on adjacent asynchronous communication are established. A reactive power-voltage tracking regulation method at PV grid connection points is introduced, utilizing the division and equivalence of voltage regulation feeder segments. By partitioning the distribution network into feeder segments centered around individual PV units, rapid reactive power-voltage tracking regulation based on local and neighboring information is achieved. Secondly, a three-stage cascaded real-time distributed voltage control strategy integrating both reactive power regulation and active power curtailment is designed. Within each regulation stage of this strategy, a voltage estimation process is embedded, enabling dynamic evaluation of the regulation effectiveness and adaptive determination for transitioning between stages. Finally, the proposed strategy is applied to modified IEEE 33-node and IEEE 69-node test systems. Simulation results verify the effectiveness and superiority of the proposed method in improving voltage quality and reducing network losses. Full article

In this paper, a short-term active power curtailment (ST-APC) strategy for doubly-fed induction generator (DFIG) wind farms is proposed to enhance first-swing rotor angle stability under fault disturbances. While wind power is a clean renewable resource that is widely deployed, its large-scale integration heightens concerns about transient stability. After analyzing DFIG operating principles, this study advocates for using short-horizon active power control to mitigate the adverse stability impacts of wind farms. Using the Western System Coordinating Council (WSCC) three-machine nine-bus test system, the effectiveness of the ST-APC strategy across diverse operating conditions was verified. This study is based on the fundamental principle that reducing the output of wind turbines is required for first-swing stability after faults to increase the kinetic energy of synchronous machines. A closed-loop control strategy combining voltage drop, frequency change, and a timer is designed. The correlation laws between various control parameters such as control activation timing, duration, and modulation depth and first-swing stability are analyzed, providing references for parameter selection in engineering applications. The findings indicate that the proposed strategy is practical and adaptable, making it suitable for power systems with high wind power penetration. Full article

This paper develops a probabilistic Available Transfer Capability (ATC) framework that quantifies export headroom for renewables across transmission-distribution interfaces under time-varying uncertainty. Static transmission reliability margins can unnecessarily curtail exports. A dynamic transmission reliability margin (TRM) is embedded within ATC using rolling window statistics and adaptive confidence factor scheduling to release capacity in calm periods and tighten margins during volatile transitions. Uncertainty is modeled as net nodal power imbalance variability from load and renewable deviations, together with stochastic thermal limit fluctuations. Correlated multivariate scenarios are generated via Latin Hypercube Sampling with Iman-Conover correlation preservation and propagated through full AC power flow analysis. Validation on the IEEE 39-bus system and New Zealand’s HVDC inter-island corridor recovers 93.31 MW of usable transfer capacity on the IEEE system relative to the pooled Monte Carlo P95 constant-margin baseline, with 78.11 MW attributable to rolling window volatility tracking and 15.20 MW to adaptive confidence factor scheduling, and 59.51 MW (+7.6%) on the New Zealand corridor relative to the corresponding pooled Monte Carlo P95 baseline, with the gain arising primarily from rolling window volatility tracking. Relative to a 95% one-sided reliability target, achieved coverage is 93.9% for IEEE and 91.8% for New Zealand, translating into increased export headroom and reduced curtailment. Full article

This work uses battery-coupled power electronic converter systems and distributed backstepping controllers to improve the reliability of electrical distribution networks. The motivation is to prevent blackouts such as the 28 April 2025 outage in Spain, Portugal, and the south of France. It is now accepted that a rapid rise in solar power injections caused AC overvoltage above grid code limits, triggering photovoltaic (PV) park disconnections as overvoltage self-protection. This case study considers near-Zero-Energy Buildings (nZEBs) connected to the Madeira Island isolated microgrid, where PV power installation is increasing excessively. The main university facility will be upgraded as an nZEB, using roughly 3000 m² of unshaded rooftops plus coverable parking areas to install PV panels. Optimizing the profits/energy cost ratio, a PV power system of around 560 kW can be planned, and the Battery Storage System (BSS) energy capacity can be estimated. The BSS is connected to the university nZEB via backstepping-controlled multilevel converters to manage PV and BSS, enabling the building to contribute to voltage and frequency regulation. Distributed multilevel converters inject renewable energy into the medium-voltage network, regulating active and reactive power to prevent overvoltages shutting down the PV inverters. This removes sustained overvoltage and maximizes PV penetration while augmenting AC grid reliability and resilience. When there is excess solar power and reactive power is insufficient to reduce voltage, controllers slightly curtail PV active power to eliminate overvoltage, maintaining operation with minimal revenue loss while preventing long interruptions, thereby improving grid reliability and power quality. Full article

The increasing penetration of rooftop photovoltaic (RTPV) systems in low-voltage (LV) distribution networks introduces challenges such as voltage rises, reverse power flow, and reduced hosting capacity, thereby necessitating effective active power regulation (APR) in module-level micro-inverters. This paper proposes a dual-layer control framework for a 250 watt-peak (Wp) three-switch rooftop PV micro-inverter, integrating quantum-behaved particle swarm optimization with reinforcement learning (QPSO-RL) for accurate maximum power point tracking (MPPT) and a linear quadratic regulator (LQR) for reserve-aware APR. The QPSO-RL algorithm improves available-power estimation under varying irradiance, temperature, and partial-shading conditions, while the LQR-based controller ensures fast, well-damped, and grid-compliant power regulation. The proposed framework was developed and validated using MATLAB/Simulink 2024 for simulation studies and LabVIEW with NI myRIO 2022 for real-time hardware implementation. Both simulation and experimental results confirm that the proposed method achieves 99.5% MPPT accuracy, convergence within 20 ms, grid-injected current total harmonic distortion (THD) below 3%, and a near-unity power factor. In addition, the reserve-based regulation strategy improves feeder compliance and reduces converter stress, thereby supporting reliable rooftop PV integration. These results demonstrate that the proposed QPSO-RL + LQR framework offers a practical and intelligent solution for high-performance, grid-supportive rooftop PV micro-inverter applications. Full article

To address the problem of renewable energy curtailment and the need for operational economic optimization in integrated energy systems with high penetration of wind and solar power, a coordinated optimization method integrating thermoelectric decoupling, ammonia-blended combustion technology, and energy storage capacity leasing is proposed. First, a chaotic-improved Latin Hypercube Sampling (C-LHS) method, combined with an improved K-means clustering algorithm, is employed to generate representative wind–solar–load scenarios. This approach improves the efficiency of uncertainty scenario generation while reducing computational burden and maintaining solution accuracy. Secondly, by coordinating the operation of thermal energy storage and electric boilers, the “heat-led power generation” constraint is relaxed, and, in combination with ammonia-blended combustion in combined heat and power (CHP) units, the system’s flexibility and renewable energy accommodation capability are enhanced. Finally, with the objective of minimizing total operating cost, a day-ahead scheduling model incorporating electrical energy storage (EES) leasing optimization is established. For EES, under a shared energy storage market mechanism, the golden section search (GSS) algorithm is employed to optimize the day-ahead leasing capacity. The simulation results demonstrate that the proposed method improves renewable energy accommodation while maintaining economic performance, and effectively reduces the overall operating cost of the system. These findings confirm the effectiveness of the proposed strategy in enhancing both system flexibility and economic performance. Full article

In the scenario where renewable energy sources (RESs) are connected to the power system (PS) through a flexible high-voltage direct current (HVDC) transmission system, their output becomes highly intermittent and volatile due to meteorological factors like wind direction and speed. This variability poses significant challenges to the real-time power balance and control of the PS. To address the uncertainties in system operation and the challenges of RES consumption, this paper proposes an artificial intelligence (AI) algorithm-driven collaborative optimization strategy for virtual power plants (VPPs) considering RESs transmitted by flexible HVDC. Firstly, a self-attention mechanism and multiple gated structures are integrated into a long short-term memory (LSTM) deep learning model. This enhancement improves the model’s ability to capture multi-timescale characteristics of RESs, increasing forecasting accuracy and robustness. Based on these forecasts, a total cost optimization model for VPP operation is developed, which includes high penalty costs for wind and solar curtailment. By embedding economic constraints that prioritize RESs usage, the model can reduce waste caused by traditional cost-driven scheduling. Additionally, to solve the high-dimensional nonlinear optimization problem in VPP scheduling, an improved population-based incremental learning (PBIL) algorithm is introduced. It incorporates an elite retention strategy and an adaptive mutation operator to boost global search efficiency and convergence speed. Simulations based on an VPP incorporating typical offshore wind and solar RESs transmitted via flexible HVDC demonstrate that the improved LSTM reduces MAPE by 7.14% for wind and 4.27% for PV compared to classical LSTM, and the proposed method achieves the lowest curtailment rates (wind 10.74%, PV 10.23%) and total cost (43,752 RMB), outperforming GA, PSO, and GW by 10–18% in cost reduction. Simulation results show that the proposed strategy enhances RESs consumption while maintaining system economy under flexible HVDC transmission. This work offers theoretical and practical insights for optimizing PS with high RES penetration and supports the low-carbon transition of new-type PS. Full article

Korea’s rising shares of variable renewable energy (VRE) and inflexible baseload increases the need for fast-responding and cost-effective flexibility. Most studies on power-to-heat (P2H) emphasize district-heating (DH) economics or load shifting, leaving the system-level impacts of its reserve provision capability unclear. We develop a mixed-integer linear programming model for reserve-constrained unit commitment (RCUC) that co-optimizes the power and DH systems. In addition, the model incorporates a P2H system capable of providing multiple reserve services. Reserve requirements are divided into static and dynamic terms, with the dynamic term represented as a piecewise-linear approximation of short-term VRE variability derived from weather-based generation profiles and evaluated at the scheduled VRE output. Using a 2030 winter week for Korea, we compare five cases: no EB; EB as load only; and EB contributing only to the secondary/regulation reserve requirement, only to the primary reserve requirement, or both. Under the KRW 1000/kWh curtailment-penalty case, EB as load reduces system operating cost compared to the baseline, and enabling reserve provision yields additional cost savings, with the largest benefit observed when primary reserve is provided. EB operation also shifts dispatch from coal and gas toward nuclear, VRE, and pumped storage, while reducing renewable curtailment. Overall, enabling P2H to contribute to reserve procurement, particularly in the primary reserve, delivers substantially greater value than representing P2H solely as a controllable load for energy shifting. Full article

Typhoon-induced extreme weather poses a severe threat to power systems with high offshore wind penetration. Source-side wind turbine tripping and grid-side transmission line failures are likely to occur simultaneously, which may trigger cascading outages and large-scale load shedding. A multi-level source-grid-load-storage preventive resilience dispatch strategy is proposed. A typhoon spatiotemporal evolution model is first established based on the Batts gradient wind model. Failure probability models for offshore wind turbines and overhead transmission lines are developed while considering strong wind and lightning strike effects. The most probable and severe fault scenario is identified using an entropy-based quantification method. A two-stage robust preventive dispatch model is subsequently formulated. In the day-ahead stage, unit commitment, multi-type reserve allocation, and pumped storage scheduling are optimized at a 1 h resolution. In the real-time stage, combined wind-storage systems are coordinated at a 10 min resolution to accommodate rapid wind power ramps caused by high-wind shutdown events. The model is reformulated through Lagrangian duality and solved by the Benders decomposition algorithm. Case studies on a modified IEEE-RTS 24-bus system with three offshore wind farms demonstrate that the proposed strategy reduces wind curtailment by 66.3%, load shedding by 74.6%, and total cost by 14.8% compared with the case without energy storage. The combined operation cost of storage resources accounts for only 3.1% of the total cost, confirming its favorable cost-effectiveness for resilience enhancement. The proposed strategy contributes to the sustainable integration of offshore wind energy by ensuring a reliable power supply during extreme weather events. Full article

Wind–solar coupled hydrogen production DC microgrids have significant potential for improving renewable energy utilization and reducing the cost of hydrogen production. However, the randomness of wind–solar power causes frequent electrolyzer start–stop operations, accelerating lifetime degradation, while a single energy storage system cannot simultaneously suppress power fluctuations and regulate energy. Therefore, this study proposes a two-stage day-ahead energy scheduling optimization framework. A DBSCAN–K-means hybrid clustering method generates representative wind–solar power scenarios. A supercapacitor-based strategy mitigates high-frequency power fluctuations using empirical mode decomposition. Furthermore, a dual-scenario-driven electrolyzer scheduling strategy adapted to different wind–solar output conditions is developed, where power allocation is determined by battery state-of-charge and electrolyzer operating states, enabling stepwise power compensation and dynamic operating-state optimization. Case studies comparing wind–solar-only supply, a conventional strategy, and the proposed strategy demonstrate that the proposed strategy balances hydrogen production and economic objectives, and reduces annual electrolyzer start–stop cycles by 73%, thereby prolonging electrolyzer lifetime. Furthermore, the proposed framework enhances renewable energy utilization, reduces curtailment, and lowers lifecycle costs, thereby contributing to the development of sustainable hydrogen production systems. Full article

High-precision wind power prediction improves grid stability and reduces curtailment losses. Existing methods face three limitations: static graphs cannot capture dynamic spatial correlations under weather changes, time series models miss multi-scale temporal features, and frequency-domain analyses lack physical constraints. We propose: (1) a dynamic distance correlation weighted graph that adaptively combines geographic and power correlations for weather–terrain coupling; (2) a spatio-temporal-frequency fusion framework integrating graph networks, bidirectional GRUs, and a patchwise sparse time–frequency module; (3) a turbine power curve-constrained frequency mixer for physical consistency. On the SDWPF dataset, our model achieves MAE reductions of 37.47–43.32% and RMSE reductions of 37.93–42.70% versus baselines, outperforming state-of-the-art methods. The approach demonstrates superior performance in complex spatio-temporal scenarios. Full article

Great Britain’s substantial growth in wind and solar power has created surplus renewable energy. However, intermittency and variable demand cause demonstrable curtailment. This study analyses wind and solar curtailment trends across Great Britain using derived wind and modelled solar data to explore the potential for green hydrogen production. An economic analysis establishes the Levelised Cost of Hydrogen (LCOH), considering potential revenue streams and future market evolution. Key findings indicate that based on our macroscopic proxy data, combining wind and solar sources potentially enhances energy capture by up to 2.26 times in specific regions compared to single-source generation. Furthermore, scenario-dependent projections indicate LCOH reduction trajectory reaching £3/kg by 2060 under optimal, zero-marginal cost conditions, together with rendering hydrogen from curtailed energy competitive with current market averages. This research offers a holistic understanding of the economic viability of repurposing curtailed energy, contributing to a sustainable energy transition. Full article

The transition toward new power systems requires enhanced operational flexibility, within which circulating fluidized bed (CFB) units exhibit considerable peak regulation potential. This study develops an economic optimization framework to evaluate the benefits of flexibility retrofits for CFB units operating in coordination with wind power. A representative integrated system consisting of two 300 MW CFB units and a 300 MW wind farm is analyzed to compare ramping capability enhancement, minimum load reduction, and their combined implementation. The results indicate that the combined retrofit delivers the highest overall economic benefit at the system level. Even under a highly fluctuating wind power scenario, the combined retrofit achieves complete wind power accommodation, demonstrating the effectiveness of coordinated flexibility expansion. The minimum load reduction retrofit and the ramping capability retrofit reduce wind curtailment by 81% and 13%, respectively. Moreover, the economic benefit achieved by the combined retrofit exceeds the aggregate benefit of the two independent measures by about 6%, indicating a synergistic interaction between ramping flexibility and minimum load reduction retrofit. For the studied system, minimum load reduction retrofit contributes substantially greater economic gains than ramping capability enhancement when applied individually. Sensitivity analysis further highlights the influence of coal prices and feed-in tariff structures on retrofit profitability. Compared with existing studies focusing primarily on conventional pulverized coal units, this work establishes a quantitative framework tailored to CFB-specific flexibility retrofits, providing practical support for power systems with high renewable penetration. Full article