Search Results (602)

This research paper analyses the evolution of electricity price volatility in six European countries between 2015 and 2025, focusing on the relationship between the increasing penetration of renewable energy sources (RES) and short-term price fluctuations. Based on high-frequency data (at 15 min to hourly resolution) on electricity prices, solar and wind generation, and residual load, both year-on-year and structural changes in volatility are quantified. The results show a significant increase in volatility after 2021, with outliers appearing particularly during the 2022 energy crisis, most notably in countries with a high share of RES and limited system flexibility. The analysis identifies non-linear relationships between RES generation and the occurrence of negative prices, with country-specific threshold levels. Annual regression models show that the predictive power of these relationships is time-varying and influenced by externalities. The correlation matrices confirm regional differences in the impact of RES on price dynamics. The results support the design of rules for forecasting risk periods and point to the need for market mechanisms increasing flexibility, including accumulation, demand management, and cross-border integration. Full article

The increasing penetration of renewable energy sources presents significant challenges for power system stability and operation. Accurately assessing renewable energy absorption capacity is essential to ensuring grid reliability while maximizing renewable integration. This paper proposes a security-constrained sequential production simulation (SPS) framework, which incorporates grid voltage and frequency support constraints to provide a more realistic evaluation of renewable energy absorption capability. Additionally, hierarchical clustering (HC) based on dynamic time warping (DTW) and min-max linkage is employed for temporal aggregation (TA), significantly reducing computational complexity while preserving key system characteristics. A case study on the IEEE 39-bus system, integrating wind and photovoltaic generation alongside high-voltage direct current (HVDC) transmission, demonstrates the effectiveness of the proposed approach. The results show that the security-constrained SPS successfully prevents overvoltage and frequency deviations by bringing additional conventional units online. The study also highlights that increasing grid demand, both locally and through HVDC export, enhances renewable energy absorption, though adequate grid support remains crucial. Full article

With the increasing penetration of renewable energy, the scale of AC/DC hybrid transmission systems continues to grow, intensifying risks such as line overloads under N-1 contingencies, short-circuit current violations, and operational stability challenges arising from multi-DC coupling. This paper explores the complex coupling characteristics between AC/DC and multi-DC systems in hybrid configurations, proposing innovative evaluation indicators for coupling properties and a comprehensive assessment scheme for multi-DC coupling degrees. To enhance system stability, coordinated planning strategies are proposed for AC/DC hybrid transmission systems with multi-infeed High-voltage direct-current (HVDC) based on the AC/DC strong–weak balance principle. Specifically, planning schemes are developed for determining the locations, capacities, and converter configurations of newly added DC lines. Furthermore, to mitigate multi-DC simultaneous commutation failure risks, we propose an AC-to-DC conversion planning scheme and a strategy for adjusting the DC system technology route based on a through comprehensive multi-DC coupling strength assessment, yielding coordinated planning strategies applicable to the AC/DC hybrid transmission systems with multi-infeed HVDC. Finally, simulation studies on the IEEE two-area four-machine system validate the feasibility of the proposed hybrid transmission grid planning strategies. The results demonstrate its effectiveness in coordinating multi-DC coupling interactions, providing critical technical support for future hybrid grid development under scenarios with high renewable energy penetration. Full article

To facilitate power system decarbonization, optimizing clean energy integration has emerged as a critical pathway for establishing sustainable power infrastructure. This study addresses the multi-timescale operational challenges inherent in power networks with high renewable penetration, proposing a novel stochastic dynamic programming framework that synergizes intraday microgrid dispatch with a multi-phase carbon cost calculation mechanism. A probabilistic carbon flux quantification model is developed, incorporating source–load carbon flow tracing and nonconvex carbon pricing dynamics to enhance environmental–economic co-optimization constraints. The spatiotemporally coupled multi-microgrid (MMG) coordination paradigm is reformulated as a continuous state-action Markov game process governed by stochastic differential Stackelberg game principles. A communication mechanism-enabled multi-agent twin-delayed deep deterministic policy gradient (CMMA-TD3) algorithm is implemented to achieve Pareto-optimal solutions through cyber–physical collaboration. Results of the measurements in the MMG containing three microgrids show that the proposed approach reduces operation costs by 61.59% and carbon emissions by 27.95% compared to the least effective benchmark solution. Full article

As the proportion of renewable energy continues to increase, the large-scale grid integration of photovoltaic (PV) generation presents new technical challenges for reactive power balance in power systems. This paper proposes a coordinated reactive power and voltage optimization method based on Filtered Multiband Decomposition (FMD). First, to address the stochastic fluctuations of PV power, an improved FMD-based prediction model is developed. The model employs an adaptive finite impulse response (FIR) filter to decompose signals and captures periodicity and uncertainty through kurtosis-based feature extraction. By utilizing adaptive function windows for multiband signal decomposition, combined with kernel principal component analysis (KPCA) for dimensionality reduction and a long short-term memory (LSTM) network for prediction, the model significantly enhances forecasting accuracy. Second, to tackle the challenges of integrating high-penetration distributed PV while maintaining reactive power balance, a multi-head attention-based velocity update strategy is introduced within a multi-objective particle swarm optimization (MOPSO) framework. This strategy quantifies the spatial distance and fitness differences of historical best solutions, constructing a dynamic weight allocation mechanism to adaptively adjust particle search direction and step size. Finally, the effectiveness of the proposed method is validated through an improved IEEE 33-bus test case. Full article

The global energy transition is accelerating, bringing new challenges to power systems. A high penetration of renewable energy increases grid volatility. Virtual power plants (VPPs) address this by dynamically responding to market signals. They integrate renewables, energy storage, and flexible loads. Additionally, they participate in multi-tier markets, including energy, ancillary services, and capacity trading. This study proposes a load factor-based VPP pre-dispatch model for optimal resource allocation. It incorporates the coupling effects of electricity–carbon markets. A Nash negotiation strategy is developed for multi-VPP cooperation. The model uses an accelerated adaptive alternating-direction multiplier method (AA-ADMM) for efficient demand response. The approach balances computational efficiency with privacy protection. Revenue is allocated fairly based on individual contributions. The study uses data from a VPP dispatch center in Shanxi Province. Shanxi has abundant wind and solar resources, necessitating advanced scheduling methods. Cooperative operation boosts profits for three VPPs by CNY 1101, 260, and 823, respectively. The alliance’s total profit rises by CNY 2184. Carbon emissions drop by 31.3% to 8.113 tons, with a CNY 926 gain over independent operation. Post-cooperation, VPP1 and VPP2 see slight emission increases, while VPP3 achieves major reductions. This leads to significant low-carbon benefits. This method proves effective in cutting costs and emissions. It also balances economic and environmental gains while ensuring fair profit distribution. Full article

This paper proposes a supercapacitor-battery hybrid energy storage scheme based on a series-parallel hybrid compensation structure and model predictive control to address the increasingly severe power quality issues in oilfield microgrids. By adopting the series-parallel hybrid structure, the voltage compensation depth can be properly improved. The model predictive control with a current inner loop is employed for current tracking, which enhances the response speed and control performance. Applying the proposed hybrid energy storage system in an oilfield DC microgrid, the fault-ride-through ability of renewable energy generators and the reliable power supply ability for oil pumping unit loads can be improved, the dynamic response characteristics of the system can be enhanced, and the service life of energy storage devices can be extended. This paper elaborates on the series-parallel compensation topology, operational principles, and control methodology of the supercapacitor-battery hybrid energy storage. A MATLAB/Simulink model of the oilfield DC microgrid employing the proposed scheme was established for verification. The results demonstrate that the proposed scheme can effectively isolate voltage sags/swells caused by upstream grid faults, maintaining DC bus voltage fluctuations within ±5%. It achieves peak shaving of oil pumping unit load demand, recovery of reverse power generation, stabilization of photovoltaic output, and reduction of power backflow. This study presents an advanced technical solution for enhancing power supply quality in high-penetration renewable energy microgrids with numerous sensitive and critical loads. Full article

With the growing integration of renewable sources on a large scale into modern power systems, the operation of distribution networks faces significant challenges under fluctuating renewable energy outputs. Therefore, achieving multi-objective optimization over multiple time periods, including minimizing energy losses and maximizing renewable energy utilization, has become a pressing issue. This paper proposes a Collaborative Intelligent Optimization Reconfiguration Strategy (CIORS) based on a dual-agent framework to achieve a global collaborative optimization of distribution networks in a multi-time period environment. CIORS addresses goal conflicts in multi-objective optimization by designing a collaborative reward mechanism. The discrete agent and continuous agent are responsible for optimizing the switch states within the distribution grid while coordinating the control of both active and reactive power flows through Soft Open Points (SOPs), respectively. To respond to the dynamic fluctuations of loads and renewable energy outputs, CIORS incorporates a dynamic weighting mechanism into the comprehensive reward function, allowing the flexible adjustment of the priority of each optimization objective. Furthermore, CIORS introduces a prioritized experience replay (PER) mechanism, which improves sample utilization efficiency and accelerates model convergence. Simulation results based on an actual distribution network in a specific area demonstrate that CIORS is effective under high-penetration clean energy scenarios. Full article

Variable Speed AC-excited Pumped Storage Units (VSACPSUs) demonstrate advantages in flexibility, high efficiency, and fast response, and they play a crucial regulatory role in power systems with increasing renewable energy penetration. Typically connected to weak grids, conventional low-voltage ride-through (LVRT) control methods for these units suffer from single control objectives, poor adaptability, and neglect of electromechanical coupling characteristics. To address these limitations, this paper proposes a multi-objective optimization strategy considering electromechanical coupling under a grid voltage fault. Firstly, a positive/negative-sequence mathematical model of doubly-fed machines is established. Based on stator winding power expressions, the operational characteristics under a grid fault are analyzed, including stator current imbalance as well as oscillation mechanisms of active power, reactive power, and electromagnetic torque. Considering the differences in rotor current references under different control objectives, a unified rotor current reference expression is constructed by introducing a time-varying weighting factor according to expression characteristics and electromechanical coupling properties. The weighting factor can be dynamically adjusted based on operating conditions and grid requirements using turbine input power, grid current unbalance, and voltage dip depth as key indicators to achieve adaptive control optimization. Finally, a multi-objective optimization model incorporating coupling characteristics and operational requirements is developed. Compared with conventional methods, the proposed strategy demonstrates enhanced adaptability and significantly improved low-voltage ride-through performance. Simulation results verify its effectiveness. Full article

High penetration of green energy sources presents substantial challenges to grid stability and resilience, primarily due to inherent voltage and frequency variability, which worsens during critical events. This study proposes an integrated framework for stability and resilience enhancement in renewable-dense power grids by designing optimized universal droop controllers (UDCs) tailored for grid-forming operations under high-impact contingencies. The UDC incorporates fault localization functionality via grid-forming inverters embedded with phasor measuring capabilities (phase voltage magnitude and angle) to facilitate real-time fault detection and response, thus augmenting operational reliability. Leveraging integrated solution environments, the developed framework employs numerical optimization routines for resource allocation, load prioritization, economic dispatch of distributed energy resources (DERs), and adaptive network reconfiguration under constrained conditions and during critical events that may necessitate decentralized network configurations in the wake of main grid failures. Validation conducted on the IEEE 123-node distribution network indicates that the optimized UDC framework achieves superior voltage and frequency regulation compared to conventional droop-based methods, ensuring optimal resource distribution and sustained load support. Full article

In the context of the clean and low-carbon transformation of power systems, addressing the challenge of day-ahead electricity market price prediction issues triggered by the strong stochastic volatility of power supply output due to high-penetration renewable energy integration, as well as problems such as limited dataset scales and short market cycles in test sets associated with existing electricity price prediction methods, this paper introduced an innovative prediction approach based on a multi-modal feature fusion and BiGRUSA-ResSE-KAN deep learning model. In the data preprocessing stage, maximum–minimum normalization techniques are employed to process raw electricity price data and exogenous variable data; the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods are utilized for multi-modal decomposition of electricity price data to construct a multi-scale electricity price component matrix; and a sliding window mechanism is applied to segment time-series data, forming a three-dimensional input structure for the model. In the feature extraction and prediction stage, the BiGRUSA-ResSE-KAN multi-branch integrated network leverages the synergistic effects of gated recurrent units combined with residual structures and attention mechanisms to achieve deep feature fusion of multi-source heterogeneous data and model complex nonlinear relationships, while further exploring complex coupling patterns in electricity price fluctuations through the knowledge-adaptive network (KAN) module, ultimately outputting 24 h day-ahead electricity price predictions. Finally, verification experiments conducted using test sets spanning two years from five major electricity markets demonstrate that the introduced method effectively enhances the accuracy of day-ahead electricity price prediction, exhibits good applicability across different national electricity markets, and provides robust support for electricity market decision making. Full article

Probabilistic modeling of net load forecast errors is an important approach for reserve decision-making in power systems with a high penetration of renewable energy. However, existing probabilistic modeling methods face issues such as insufficient estimation accuracy in the small probability interval of the tails or increased complexity in probability decision-making problems. A probabilistic reserve decision-making method based on cumulative probability approximation is proposed. By using key points on the cumulative probability distribution curve of net load forecast error samples, this method enhances the fitting accuracy of the normal distribution model in the small probability interval of the tail, resulting in an optimal reserve outcome with the desired comprehensive expected profit. Using relevant renewable energy output and load data from actual transmission networks in Guangdong Province, China, the proposed method demonstrates good practical value. Full article

To address the frequency regulation requirements of hybrid energy storage (HES) in renewable-dominated power grids, this paper proposes an asymmetric droop control strategy based on an improved backpropagation (BP) neural network. First, a simulation model of HES (comprising supercapacitors for the power support and batteries for the energy balance) participating in primary frequency regulation is established, with a stepwise frequency regulation dead zone designed to optimize multi-device coordination. Second, an enhanced Sigmoid activation function (with controllable parameters a, b, m, and n) is introduced to dynamically adjust the power regulation coefficients of energy storage units, achieving co-optimization of frequency stability and State of Charge (SOC). Simulation results demonstrate that under a step load disturbance (0.05 p.u.), the proposed strategy reduces the maximum frequency deviation by 79.47% compared to scenarios without energy storage (from 1.7587 × 10⁻³ to 0.0555 × 10⁻³) and outperforms fixed-droop strategies by 44.33%. During 6-min continuous random disturbances, the root mean square (RMS) of system frequency deviations decreases by 4.91% compared to conventional methods, while SOC fluctuations of supercapacitors and batteries are reduced by 49.28% and 45.49%, respectively. The parameterized asymmetric regulation mechanism significantly extends the lifespan of energy storage devices, offering a novel solution for real-time frequency control in high-renewable penetration grids. Full article

The rapid development of prosumer renewable energy sources (RESs) observed in Poland in recent years causes problems in distribution networks such as current amplitude and voltage asymmetry increases, power and energy loss increases, and reverse power flows, and related are voltage control problems, deterioration of energy quality, etc. Moreover, in the case of planned repair/maintenance works in the network and the need to supply energy consumers in an islanded MV grid, the problem of the correct operation of such a subsystem appears. This occurs when the power production by the prosumers’ energy sources at a given moment exceed the power consumption. In such a case, reverse power flows occur in MV/LV transformers, i.e., from the LV network to the MV network. This causes reverse power flow to the diesel generator, leading to its shutdown and, in extreme cases, to damage. The solution to this problem is to use a mobile system equipped with energy storage in addition to a diesel generator and an LV/MV transformer. An additional problem in the case of using a mobile system (diesel generator) to power an MV island is the islanded MV network grounding. Grid islanding changes the earth fault current and electric shock voltages. In general, MV networks in Poland operate as compensated, i.e., grounding transformers are used, the star point of which is grounded by a compensation choke. Unfortunately, in the case of powering an MV island from a mobile system, there is no real possibility of grounding the star point of the LV/MV transformer used there. This article proposes an algorithm of a diesel generator with an energy storage selection, including electric shock protection requirements verification, for the use in suppling energy via an islanded MV network. Full article

To modernize electrical power systems on isolated islands, countries around the world have increased their interest in combining green energy with conventional power plants. Wind energy (WE) is the most adopted renewable energy source due to its technical readiness, competitive cost, and environmentally friendly characteristics. Despite this, a high penetration of WE in conventional power systems could affect their stability. Moreover, these isolated island power systems face frequency deviation issues when operating in hybrid generation mode. Generally, under contingency or transient conditions for hybrid isolated wind–diesel power systems (WDPSs), it is only the diesel generator that provides inertial support in frequency regulation (FR) because wind turbines are unable to provide inertia themselves. Frequency deviations can exceed the pre-defined grid code limits during severe windy conditions because the diesel generator’s inertial support is not always sufficient. To overcome this issue, we propose a control strategy named emulation inertial and proportional (EI&P) control for Variable-Speed Wind Turbines (VSWTs). VSWTs can also contribute to FR by releasing synthetic inertia during uncertainties. In addition, to enhance the effectiveness and smoothness of the blade pitch angle control of WTs, a pitch compensation (PC) control loop is proposed in this paper. The aim of this study was to provide optimal primary frequency regulations to hybrid wind–diesel power systems (WDPSs). Therefore, the hybrid WDPS on San Cristobal Island was considered in this study. To achieve such goals, we used the above-mentioned proposed controls (EI&P and PC) and optimally tuned them using the Student-Psychology-Based Algorithm (SPBA). The effectiveness of this algorithm is in its ability to provide the best optimum controller gain combinations of the proposed control loops. As a result, the FD in the WDPS on San Cristobal Island was reduced by 1.05 Hz, and other quality indices, such as the integral absolute error (IAE), integral square error (ISE), and controller quality index (Z), were improved by 159.65, 16.75, and 83.80%, respectively. Moreover, the proposed PC control, which was further simplified using exhaustive searches, resulted in a reduction in blade pitch angle control complexity. To validate the results, the proposed approach was tested under different sets of perturbations (sudden loss of wind generator and gradual increase in wind speed and their random behavior). Furthermore, hybrid systems were tested simultaneously under different real-world scenarios, like various sets of load or power imbalances, wind variations, and their combinations. The Simulink results showed a significant improvement in FR support by minimizing frequency deviations during transients. Full article