Search Results (152)

The increasing penetration of renewable energy sources (RESs), particularly solar photovoltaic (PV) sources, has introduced significant uncertainty into power system operations, challenging traditional scheduling models and threatening system reliability. This study proposes a Fuzzy Unit Commitment Model (FUCM) designed to address uncertainty in load demand, solar PV generation, and spinning reserve requirements by applying fuzzy linear programming techniques. The FUCM reformulates uncertain constraints using triangular membership functions and integrates them into a mixed-integer linear programming (MILP) framework. The model’s effectiveness is demonstrated through two case studies: a 30-generator test system and a national-scale power system in Thailand comprising 171 generators across five service zones. Simulation results indicate that the FUCM consistently produces stable scheduling solutions that fall within deterministic upper and lower bounds. The model improves reliability metrics, including reduced loss-of-load probability and minimized load deficiency, while maintaining acceptable computational performance. These results suggest that the proposed approach offers a practical and scalable method for unit commitment planning under uncertainty. By enhancing both operational stability and economic efficiency, the FUCM contributes to the sustainable management of RES-integrated power systems. Full article

With the increasing penetration of renewables, the importance of electrical energy storage (EES) for power supply stabilization is growing. The intermittency of renewable energy sources remains the main issue limiting their rapid integration; however, the development of high-capacity batteries capable of storing large quantities of energy offers a way to address this challenge. This article presents and describes dedicated algorithms for controlling the EES system to enable the provision of individual system services. Five services are planned for implementation: RES power stabilization; voltage regulation using active and reactive power; reactive power compensation; power stabilization of unstable loads; and power reduction on demand. The aim of this paper is to develop new, dedicated energy storage control algorithms for delivering these specific services. Additionally, the voltage regulation algorithm includes two operating modes: short-term regulation (voltage fluctuation stabilization) and long-term regulation (triggered by an operator signal). Full article

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

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

The increasing penetration of renewable energy sources (RESs) has reduced the inertia and reserve levels of microgrids, posing challenges to frequency security during power imbalances. To address these challenges, this paper proposes a multi-objective distributionally robust frequency-constrained economic dispatch (DRFC-ED) model. First, the model aims to jointly optimize generation dispatch, reserve deployment, and the virtual inertia and damping constants of inverter-based resources to achieve a comprehensive optimization of both economic efficiency and frequency response performance. Then, the model further considers the distinctions between inertia and damping in the frequency response for more effective parameter deployment. Furthermore, the model leverages deep neural networks (DNNs) to convexify non-convex frequency constraints and employs a distributionally robust chance-constrained approach with Wasserstein distance-based ambiguity sets to handle RES uncertainty. Additionally, a method of directly obtaining the compromise optimal solution is used to transform the multi-objective problem into a single-objective one. Finally, the model is formulated as a mixed-integer linear programming problem and validated through case studies, demonstrating (1) an 8.03% reduction in the frequency integral time absolute error (ITAE) with only a 2.1% increase in economic cost compared to single-objective approaches, while (2) maintaining maximum frequency deviation (MFD) < 0.5 Hz during disturbances. Full article

Future decarbonization targets demand a higher penetration of renewable energy (RE) sources into the system. However, challenges such as an uneven spatial and temporal distribution of various RE sources’ potential for green electricity (GE) generation demand alternative ways to store and later utilize the generated energy. In addition to that, sustainable development goals (SDGs) highlight the need for the responsible use of resources with increased recycling and a reduction in corresponding waste generation while ensuring access to affordable, reliable, sustainable, and modern energy for all. In this paper, an attempt is made to address both the issues of biodegradable waste (BW) processing and sustainable hydrogen (SH) production through it. Thermochemical conversion technology (TCC) and, within that, especially ‘thermocatalytic reforming’ (TCR^®) technology have been explored as options to provide viable solutions. An added advantage of decentralized hydrogen production can be envisioned here that can also contribute to regional energy security to some degree. To analyze the concept, the Bavarian region in Germany, along with open-source data for bio-waste from two main sources, namely domestic household and sewage sludge (SS), were considered. Based on that, the corresponding regional hydrogen demand coverage potential was analyzed. Full article

In Europe and elsewhere in the world, current ambitious decarbonization targets push towards a gradual decommissioning of all fossil-fuel-based dispatchable electrical generation and, at the same time, foster a gradual increase in the penetration of Renewable Energy Sources (RES). Moreover, considerations tied to decarbonization as well as to the security of supply, following recent geo-political events, call for a gradual replacement of gas appliances with electricity-based ones. As RES generation is characterized by a variable generation pattern and as the electric carrier is characterized by scarce intrinsic flexibility, and since storage capabilities through electrochemical batteries, as well as demand-side flexibility contributions, remain rather limited, it is quite natural to think of other energy carriers as possible service providers for the electricity system. Gas and heat networks and, in the future, hydrogen networks could provide storage services for the electricity system. This could allow increasing the amount of RES penetration to be managed safely by the electric system without incurring blackouts and avoiding non-economically motivated grid reinforcements to prevent the curtailment of RES generation peaks. What is explained above calls for a new approach, both in electricity network dispatch simulations and in grid-planning studies, which extends the simulation domain to other carriers (i.e., gas, heat, hydrogen) so that a global optimal solution is found. This simulation branch, called multi-energy or multi-carrier, has been gaining momentum in recent years. The present paper aims at describing the most important approaches to static ME modeling by comparing the pros and cons of all of them with a holistic approach. The style of this paper is that of a tutorial aimed at providing some guidance and a few bibliographic references to those who are interested in approaching this theme in the next years. Full article

This paper applies the Chameleon Swarm Algorithm (CSA) and Snow Geese Algorithm (SGA) for optimizing the placement of electric vehicle charge stations (EVCSs), renewable energy sources (RESs), and shunt capacitors (SCs). The actual power ranges of the EVCSs of the Vinfast company in Vietnam are used to check the stabilization of the IEEE 85-node distribution power grid by considering four penetration levels of EVCSs, namely 25%, 50%, 75%, and 100%. All penetration levels of EVCSs violate the operating load voltage limits, and the grid cannot work for all the penetration levels. Different scenarios are performed to find the minimum RES penetration level and the most possible SC penetration level to satisfy the operating voltage limits. The use of only SCs cannot satisfy the voltage limits even for the 25% EVCS penetration level. The placement of RESs provides the capability to maintain voltage within the allowed range for 25% and 50% EVCS penetration but not for 75% and 100%. Using both RESs and SCs, the operating voltage limits are satisfied by using RESs with 1385 kW (about 30.44% of loads and EVCSs) and SCs with 2640 kVAr for the 75% EVCS penetration level and using RESs with 2010 kW (about 38.58% of loads and EVCSs) and SCs with 2640 kVAr (100% of loads) for the 100% EVCS penetration level. The study indicates that the installation of EVCSs should be calculated for stable operation of the distribution power grid, and the combination of both RESs and SCs can satisfy the maximum penetration level of EVCSs in the distribution power grids. Full article

This research paper examines how to assess potential locations for wind turbines and photovoltaic modules by combining Geographic Information Systems (GIS) with multi-criteria decision-making (MCDM). These potential locations depend on the current legislation, where many areas are buffer zones due to limitations. The study area is Karpathos, which faces energy and water scarcity. The need to increase the penetration rate of renewable energy sources (RES) by 2030 can help this island to fulfill both its energy and water needs through RES. To apply the weighted linear combination technique, this approach considers all eligibility criteria according to the legislation. After classifying them into four zones, the MCDM results in a suitability map that displays the spatial distribution of the final score, ranging from sites that are not appropriate to areas that are highly suitable. In the photovoltaic module scenario, the buffer zone corresponds to 61% of the island, while in the wind turbine scenario, this number increases to 85%, highlighting the difficulty of finding suitable sites. A sensitivity analysis is performed to determine the impact of the criteria on the suitability of a site for both scenarios. Full article

The increasing penetration of renewable energy sources (RESs) into medium-voltage (MV) and low-voltage (LV) power systems presents significant challenges in ensuring power grid stability and energy sustainability. Advanced power conversion technologies are essential to mitigate voltage and frequency fluctuations while meeting stringent power quality standards. RES-based generation systems typically employ multistage power electronics to achieve: (i) maximum power point tracking; (ii) galvanic isolation and voltage transformation; (iii) high-quality power injection into the power grid. In this context, this paper provides a comprehensive review of up-to-date isolated DC–DC converter topologies tailored for the integration of RES. As a contribution to support this topic, recent advancements in solid-state transformers (SSTs) are explored, with particular emphasis on the adoption of wide bandgap (WBG) semiconductors technologies, such as silicon carbide (SiC) and gallium nitride (GaN). These devices have revolutionized modern power systems by enabling operation at a higher switching frequency, enhanced efficiency, and increased power density. By consolidating state-of-the-art advancements and identifying technical challenges, this review offers insights into the suitability of power converter topologies in light of future trends, serving as a valuable resource for optimizing grid-connected RES-based sustainable power systems. Full article

The growing penetration of renewable energy sources (RES) has exacerbated operational flexibility deficiencies in modern power systems under time-varying conditions. To address the limitations of existing flexibility management approaches, which often exhibit excessive conservatism or risk exposure in managing supply–demand uncertainties, this study introduces a data-driven distributionally robust optimization (DRO) framework for power system scheduling. The methodology comprises three key phases: First, a meteorologically aware uncertainty characterization model is developed using Copula theory, explicitly capturing spatiotemporal correlations in wind and PV power outputs. System flexibility requirements are quantified through integrated scenario-interval analysis, augmented by flexibility adjustment factors (FAFs) that mathematically describe heterogeneous resource participation in multi-scale flexibility provision. These innovations facilitate the formulation of physics-informed flexibility equilibrium constraints. Second, a two-stage DRO model is established, incorporating demand-side resources such as electric vehicle fleets as flexibility providers. The optimization objective aims to minimize total operational costs, encompassing resource activation expenses and flexibility deficit penalties. To strike a balance between robustness and reduced conservatism, polyhedral ambiguity sets bounded by generalized moment constraints are employed, leveraging Wasserstein metric-based probability density regularization to diminish the probabilities of extreme scenarios. Third, the bilevel optimization structure is transformed into a solvable mixed-integer programming problem using a zero-sum game equivalence. This problem is subsequently solved using an enhanced column-and-constraint generation (C&CG) algorithm with adaptive cut generation. Finally, simulation results demonstrate that the proposed model positively impacts the flexibility margin and economy of the power system, compared to traditional uncertainty models. Full article

The global shift towards renewable energy sources (RESs) presents significant challenges to power grid stability, particularly in grids with a high penetration of inverter-based resources (IBRs). The shift to RESs is critical to improve planetary health; however, grids must remain reliable and affordable throughout the transition to ensure economies can thrive and critical infrastructure remains secure. Towards that goal, this review introduces the issues of declining system strength and inertia in such grids, illustrated by case studies of curtailment measures employed by system operators in the deregulated electricity markets of Australia, Ireland, and Texas. In these high-IBR markets, curtailment has become essential to maintain system security. This paper presents the current mitigation strategies used by system operators and discusses their limitations. In addition, the paper presents a comprehensive review and analysis of current research on system strength and inertia estimation techniques, grid modelling approaches, and advanced inverter control, with a particular focus on virtual inertia. Future research directions and recommendations are outlined based on the identified gaps. These recommendations are intended to minimise system operator intervention and RES curtailment while maintaining reliable and affordable grid operation. The insights presented in this paper provide a framework to guide system operators, researchers, and policymakers toward enhancing grid stability while targeting 100% RES. Full article

As the penetration of renewable energy sources (RESs) in power systems continues to increase, their volatility and unpredictability have exacerbated the burden of frequency regulation (FR) on conventional generator units (CGUs). Therefore, to reduce frequency deviations caused by comprehensive disturbances and improve system frequency stability, this paper proposes an integrated strategy for hybrid energy storage systems (HESSs) to participate in primary frequency regulation (PFR) of the regional power grid. Once the power grid frequency exceeds the deadband (DB) of the HESS, the high-frequency signs of the power grid frequency are managed by the battery energy storage system (BESS) through a division strategy, while the remaining parts are allocated to pumped hydroelectric energy storage (PHES). By incorporating positive and negative virtual inertia control and adaptive droop control, the BESS effectively maintains its state of charge (SOC), reduces the steady-state frequency deviation of the system, and provides rapid frequency support. When the system frequency lies within the DB of the HESS, an SOC self-recovery strategy restores the BESS SOC to an ideal range, further enhancing its long-term frequency regulation (FR) capability. Finally, a regional power grid FR model is established in the RT-1000 real-time simulation system. Simulation validation is conducted under three scenarios: step disturbances, short-term continuous disturbances, and long-term RES disturbances. The results show that the proposed integrated strategy for HESS participation in PFR not only significantly improves system frequency stability but also enhances the FR capability of the BESS. Full article

A new type of power system with a high proportion of renewable energy sources (RES) penetration has become a global development trend. Meanwhile, the marketization reforms of the electricity market pose challenges to traditional energy. A multi-energy model including a wind turbine (WT), photovoltaic (PV) energy, energy storage (ES), and a thermal power system is proposed in this paper, participating in a joint market mechanism for energy and frequency regulation ancillary services. Unlike existing joint markets, this paper considers the market coupling clearing of various energy sources and the uncertainty of RES generation. Specially, a mechanism for the participation of storage and thermal power units in the frequency regulation ancillary service market is designed. Finally, a practical 118-node case study is provided to validate the impact of renewable generation uncertainty on the participation of multi-energy coupled systems in joint and single energy markets. Compared to the single electricity energy market, the simulation results show that the model can reduce the impact of RES uncertainty on ES generation and increase the cleared electricity quantity of thermal power units by 16%. Moreover, the model also increases the market revenue of thermal power units and storage by 30% and 44%, respectively. Full article

The 2050 global ambition for a carbon-neutral society is increasing the penetration of the most competitive variable renewable technologies, onshore wind and solar PV. These technologies are known for their near-zero marginal costs but highly variable time-dependent generation. Power systems with major penetrations of variable generation need high balancing flexibility to guarantee their stability by maintaining the equilibrium between demand and supply. This work presents the open-access Multi-agent Trading of Renewable Energy Sources (RES.Trade) system, which includes different market designs of the imbalance settlement and the secondary and tertiary reserves. A new imbalance settlement is also proposed in this work. The main features of RES.Trade are demonstrated using two case studies and projected 2030 scenarios: the first analysed four imbalance settlement mechanisms in Portugal, achieving a 43% reduction in penalties using the new method; the second case study assesses the impact of five procurement mechanisms of secondary power reserves in the Spanish power system, resulting in a cost reduction by 34% in the case of dynamic reserves. Full article