Search Results (52)

This paper addresses the challenge of renewable energy curtailment, which stems from the inherent uncertainty and volatility of wind and photovoltaic (PV) generation, by developing a robust model predictive control (RMPC)-based scheduling strategy for an integrated wind–PV–hydrogen storage multi-energy flow system. By building a “wind–PV–hydrogen storage–fuel cell” collaborative system, the time and space complementarity of wind and PV is used to stabilize fluctuations, and the electrolyzer–hydrogen production–gas storage tank–fuel cell chain is used to absorb surplus power. A multi-time scale state-space model (SSM) including power balance equation, equipment constraints, and opportunity constraints is established. The RMPC scheduling framework is designed, taking the wind–PV joint probability scene generated by Copula and improved K-means and SSM state variables as inputs, and the improved genetic algorithm is used to solve the min–max robust optimization problem to achieve closed-loop control. Validation using real-world data from Xinjiang demonstrates a 57.83% reduction in grid power fluctuations under extreme conditions and a 58.41% decrease in renewable curtailment rates, markedly enhancing the local system’s capacity to utilize wind and solar energy. Full article

Hybridization of solar and wind energy sources is a promising solution to enhance the dispatch capability of renewables. The complementarity of wind and solar radiation, as well as the sharing of transmission lines and other infrastructures, can notably benefit the deployment of renewable power. Mapping of hybrid solar–wind potential can help identify new emplacements or existing power facilities where an extension with a hybrid system might work. This paper presents an analysis of a hybrid solar–wind potential by considering a reference power plant of 40 MW in the Iberian Peninsula and comparing the hybrid and non-hybrid energy generated. The generation of energy is estimated using SAM for a typical meteorological year, using PVGIS and ERA5 meteorological information as input. Modeling the hybrid plant in relation to individual PV and wind power plants minimizes the dependence on technical and economic input data, allowing for the expression of potential hybridization analysis in relative numbers through maps. Correlation coefficient and capacity factor maps are presented here at different time scales, showing the complementarity in most of the spatial domain. In addition, economic analysis in comparison with non-hybrid power plants shows a reduction of around 25–30% in the LCOE in many areas of interest. Finally, a sizing sensitivity analysis is also performed to select the most beneficial sharing between PV and wind. Full article

In response to the challenge of multi-objective optimal scheduling and efficient solution of hydropower stations under large-scale renewable energy integration, this study develops a multi-objective optimization model with the dual goals of maximizing total power generation and minimizing the variance of residual load. Four complementarity evaluation indicators are used to analyze the wind–solar complementarity characteristics. Building upon this foundation, Hyper-dominance Evolutionary Algorithm (HEA)—capable of efficiently solving high-dimensional problems—is introduced for the first time in the context of wind–solar–hydropower integrated scheduling. The case study results show that the HEA performs better than the benchmark algorithms, with the best mean Hypervolume and Inverted Generational Distance Plus across nine Walking Fish Group (WFG) series test functions. For the hydro-wind-solar scheduling problem, HEA obtains Pareto frontier solutions with both maximum power generation and minimal residual load variance, thus effectively solving the multi-objective scheduling problem of the hydropower system. This work provides a valuable reference for modeling and efficiently solving the multi-objective scheduling problem of hydropower in the context of emerging power systems. This work provides a valuable reference for the modeling and efficient solution of hydropower multi-objective scheduling problems in the context of emerging power systems. Full article

As a decentralized energy management paradigm, micro-energy grid (MEG) clusters enable synergistic operation of heterogeneous distributed energy assets, particularly through multi-energy vector coupling mechanisms that enhance distributed energy resource (DER) utilization efficiency in next-generation power networks. While individual MEGs demonstrate limited capability in responding to upper-grid demands using surplus energy after fulfilling local supply/demand balance, coordinated cluster operation significantly enhances system-wide flexibility. This paper proposes a two-stage robust optimization model that systematically addresses both the synergistic complementarity of multi-MEG systems and renewable energy uncertainty. First, the basic operation structure of MEG, including distributed generation, cogeneration units, and other devices, is established, and the operation mode of the MEG cluster responding to host utility grid flexibly is proposed. Then, aiming to reduce operation expenses, an optimal self-scheduling plan is generated by establishing a MEG scheduling optimization model; on this basis, the flexibility response capability of the MEG is measured. Finally, to tackle the uncertainty issue of wind and photovoltaic power generation, the two-stage robust theory is employed, and the scheduling optimization model of MEG cluster flexibility response to the host utility grid is constructed. A southern MEG cluster is chosen for simulation to test the model and method’s effectiveness. Results indicate that the MEG cluster’s flexible response mechanism can utilize individual MEGs’ excess power generation to meet the host utility grid’s dispatching needs, thereby significantly lowering the host utility grid’s dispatching costs. Full article

Low-emission hydrogen generation systems require large amounts of energy from renewable energy sources. This article characterizes the production of low-emission hydrogen, emphasizing its scale and the necessity for its continuity. For hydrogen production defined in this way, it is possible to select the appropriate renewable energy sources. The research part of the article presents a case study of the continuous production of large amounts of hydrogen. Daily production capacities correspond to the demand for the production of industrial chemicals and artificial fertilizers or for fueling a fleet of hydrogen buses. The production was placed in the Lublin region in Poland, where there is a large demand for low-emission hydrogen and where there are favorable conditions for the production of energy from a photovoltaic–wind mix. Statistical and probabilistic analyses were performed related to the generation of power by a photovoltaic system with a peak power of 3.45 MWp and a wind turbine with an identical maximum power. The conducted research confirmed the complementarity and substitutability relationship between one source and another within the energy mix. Then, unsupervised clustering was applied using the k-Means algorithm to divide the state space generated in the power mix. The clustering results were used to perform an operational analysis of the low-emission hydrogen generation system from a renewable energy sources mix. In the analyzed month of April, 25% of the energy generated in the photovoltaic–wind mix came from the photovoltaic system. The low-emission hydrogen generation process was in states (clusters), ensuring that the operation of the electrolyzer with nominal power amounted to 57% of the total operating time in that month. In May, the share of photovoltaics in the generated power was 45%. The low-emission hydrogen generation process was in states, ensuring that the operation of the electrolyzer with nominal power amounted to 43% of the total time in that month. In the remaining states of the hydrogen generation process, the power must be drawn from the energy storage system. The cluster analysis also showed the functioning of the operating states of the power generation process from the mix, which ensures the charging of the energy storage. The conducted research and analyses can be employed in planning and implementing effective climate and energy transformations in large companies using low-emission hydrogen. Full article

The integration of non-conventional renewable energy sources (NRES) into electricity systems introduces variability and intermittency, challenging power systems traditionally designed for stable and predictable generation. These challenges require policymakers to develop strategies aimed at maintaining reliability, affordability, and sustainability while increasing the share of NRES. One promising solution is leveraging the complementary nature of NRES to mitigate variability. However, the translation of this complementarity into effective policy and incentive structures remains underexplored in existing research. This study addresses this gap by employing system dynamics modeling to analyze the effects of incentivizing complementarity between NRES and electricity system availability. In contrast to traditional methods, which assess complementarity between two or more generation sources, this study evaluates how individual sources complement the system’s availability. The resulting complementarity values are used to guide the design of incentives for new NRES investments. The model is applied to a case study of the Colombian electricity market. The findings suggest that incentivizing complementarity can enhance grid stability, reduce dependence on thermal generation, and lower overall system costs. Future research should refine these metrics to better account for minimum availability and focus on short-term variations to further optimize system flexibility and resilience. Full article

Large-scale penetration of renewable energy generation brings various challenges to the power system in terms of safety, reliability, economy and flexibility. The development of large-scale, high-security energy-storage technology can effectively address these challenges and improve the capabilities of power systems in power-supply guarantee and flexible adjustment. This paper proposes a novel distributed risk-averse optimization scheduling model of a hybrid wind–solar–storage system based on the adjustability of the storage system and the complementarity of renewable energy generation. The correlation of wind power and photovoltaic generation is quantified based on a Copula function. A risk-averse operation optimization model is proposed using conditional value at risk to quantify the uncertainty of renewable energy generation. A linear formulation of conditional value at risk under typical scenarios is developed by Gibbs sampling the joint distribution and Fuzzy C-Means clustering algorithm. A distributed solution algorithm based on an alternating-direction method of multipliers is developed to derive the optimal scheduling of hybrid wind–solar–storage system in a distributed manner. Numerical case studies based on IEEE 34-bus distribution network verify the effectiveness of the proposed model in reducing the uncertainty impact of renewable energy generation on an upstream grid (the overall amount of renewable energy generation sent back to the upstream grid has decreased about 80.6%) and ensuring the operational security of hybrid wind–solar–storage system (overall voltage deviation within 5.6%). Full article

The increasing global adoption of variable renewable energy (VRE) sources has transformed the use of forecasting, scenario planning, and other techniques for managing their inherent generation uncertainty and interdependencies. What were once desirable enhancements are now fundamental requirements. This is more prominent in Brazil, given the large hydro capacity that has been installed. Given the need to understand the interdependencies within variable renewable energy systems, copula-based techniques are receiving increasing consideration. The objective is to explore and model the correlation and complementarity, based on the copula approach, evaluating the potential of this methodology considering a case test composed of hydro, wind, and solar assets. The proposed framework simulated joint scenarios for monthly natural energy (streamflows transformed into energy), wind speed and solar radiation, applied to a small case test, considering historical data from the Brazilian energy system. The results demonstrate that simulated scenarios are validated by their ability to replicate key statistical attributes of the historical record, as well as the interplay and complementarity among hydrology, wind speed, and solar radiation. Full article

Wind and solar energy sources, while sustainable, are inherently variable in their power generation, posing challenges to grid stability due to their non-dispatchable nature. To address this issue, this study explores the synergistic optimization of wind and solar photovoltaic resources to mitigate power output variability, reducing the strain on local grids and lessening the reliance on balancing power in high-penetration renewable energy systems. This critical role of providing stability can be effectively fulfilled by run-of-river hydropower plants, which can complement fluctuations without compromising their standard operational capabilities. In this research, we employ a straightforward energy balance model to analyze the feasibility of a 100 MW virtual hybrid power plant, focusing on the northern region of Portugal as a case study. Leveraging actual consumption and conceptual production data, our investigation identifies a specific run-of-river plant that aligns with the proposed strategy, demonstrating the practical applicability of this approach. Full article

Due to the radial network structures, small cross-sectional lines, and light loads characteristic of existing AC distribution networks in mountainous areas, the development of active distribution networks (ADNs) in these regions has revealed significant issues with integrating distributed generation (DGs) and consuming renewable energy. Focusing on this issue, this paper proposes a wide-range thyristor-controlled series compensation (TCSC)-based ADN and presents a deep reinforcement learning (DRL)-based optimal operation strategy. This strategy takes into account the complementarity of hydropower, photovoltaic (PV) systems, and energy storage systems (ESSs) to enhance the capacity for consuming renewable energy. In the proposed ADN, a wide-range TCSC connects the sub-networks where PV and hydropower systems are located, with ESSs configured for each renewable energy generation. The designed wide-range TCSC allows for power reversal and improves power delivery efficiency, providing conditions for the optimization operation. The optimal operation issue is formulated as a Markov decision process (MDP) with continuous action space and solved using the twin delayed deep deterministic policy gradient (TD3) algorithm. The optimal objective is to maximize the consumption of renewable energy sources (RESs) and minimize line losses by coordinating the charging/discharging of ESSs with the operation mode of the TCSC. The simulation results demonstrate the effectiveness of the proposed method. Full article

A hydro–solar hybrid system is an important solution for expanding renewable generation capacity under the percepts of the energy transition. This type of association allows for the coordinated dispatch of solar and hydropower plants, resulting in operational benefits in terms of energy generation and reservoir management, that is, the better use of available water and energy resources. As in this case, the operation of the hydropower plant is associated with the cascade in which it operates, when it is hybridized (for example, by associating with a solar power plant), in addition to local changes, there are impacts on the operating conditions of the other hydropower plants in the same cascade. From such a perspective, this study presents an energy system management model for hybrid power plants composed of hydro and solar sources, aiming to optimize the joint operation and measure the operational consequences at the local level and in the cascade. The results from a case study of a hydro–solar power plant hybridization in the Tietê River (Brazil) revealed increased energy production and improvement in the operating conditions of the cascade’s reservoirs, while the grid capacity was found to be an important constraint that limits the capture of synergies resulting from the generation sources complementarity and thus on the benefits to the cascade. Full article

The integrated energy system at the park level, renowned for its diverse energy complementarity and environmentally friendly attributes, serves as a crucial platform for incorporating novel energy consumption methods. Nevertheless, distributed energy generation, characterized by randomness, fluctuations, and intermittency, is significantly influenced by the surrounding environment. Within the park, the output of multiple devices frequently diverges significantly from the actual demand, potentially resulting in energy waste phenomena, such as the curtailment of wind and solar power. To tackle the dual challenges of balancing energy supply and demand while reducing carbon emissions in the industrial park, this paper introduces a low-carbon integrated energy system that incorporates distributed renewable and clean energy sources. Mathematical models are formulated for the source–grid–load–storage components of this low-carbon integrated energy system. Furthermore, various operational scenarios for the park-level integrated energy system are analyzed. The ultimate goal is to devise an economically viable, low-carbon, and efficient operational strategy for the integrated energy system, aiming to satisfy the diverse objectives of various stakeholders. Full article

A considerable penetration of rooftop PV generation and increasing demand for heating loads will enlarge the peak-to-valley difference, imposing a great challenge to the reliable operation of distribution systems under cold climates. The objective of this paper is to establish a distributionally robust demand response (DR) model for building energy systems for suppressing peak-to-valley load ratios by exploiting cooperative complementarity and flexible transformation characteris-tics of various household appliances. The thermodynamic effect of buildings is modeled for harvesting intermittent renewable energy sources (RESs) on the building roof in the form of thermal energy storages to reduce RES curtailments and eliminate thermal comfort violations in cold weather. Furthermore, the Wasserstein metric is adopted to develop the ambiguity set of the uncertainty probability distributions (PDs) of RESs, and thus, only historical data of RES output is needed rather than prior knowledge about the actual PDs. Finally, a computationally tractable mixed-integer linear programming reformulation is derived for the original distributionally robust optimization (DRO) model. The proposed DRO-based DR strategy was performed on multiple buildings over a 24 h scheduling horizon, and comparative studies have validated the effectiveness of the proposed strategy for building energy systems in reducing the peak/valley ratio and decreasing operation costs. Full article

The issue of energy security in the new development paradigm featuring dual circulation has been paid attention to by all sectors, but at present, there are few results from relevant quantitative analyses. With a focus on China’s actual energy trade, this research examines the core elements of energy security in international and Chinese cycles. In this context, the “gravity model” and “Allen substitution elasticity” are optimized and expanded. An integrated assessment methodology is developed as a result of this effort. This methodology consists of an international-cycle trade gravity model and a Chinese-cycle price elasticity model. Additionally, it empirically analyzes the effects of China’s renewable energy substitution from the perspective of the “dual cycles” of energy security, and illustrates the current state of China’s energy security through the analysis of energy substitution relationship data. The results show that Chinese renewable energy does have a buffer effect on imported coal in terms of trade efficiency, scale, and behavior, as well as performance, and the energy trade price has a direct guiding significance for this buffer function, but the enhancement function of economy-driven efficiency is indirect. Furthermore, as far as the absolute price elasticity and net price elasticity are concerned, although Chinese wind power generation is a substitute for imported coal, its price elasticity also confirms that Chinese wind power generation is not a “normal commodity”. Moreover, at present, Chinese photovoltaic power generation shows the attribute of a “normal commodity”, but it has a certain degree of complementarity with imported coal, although this complementarity will weaken in the near future with the trend of changing to substitution. Full article

We evaluate the temporal complementarity in daily averages between wind and solar power potential in Chile using Spearman’s correlation coefficient. We used hourly wind speed and solar radiation data for 176 geographic points from 2004 to 2016. The results allow us to identify four zones: Zone A1 on the coast and in the valleys in the north of Chile between latitudes 18° S and 36° S, with moderate positive correlation; Zone A2 in the north Andes between latitudes 25° S and 33° S, with weak negative correlation; Zone B in the center-south part of the country between latitudes 36° S and 51° S with moderate negative correlation; and Zone C in the south, between latitudes 51° S and 55° S with null or weak positive correlation. On the one hand, the interannual analysis shows that Zone A1 keeps uniform correlation values with negative asymmetry, i.e., higher correlation values. On the other hand, there is positive asymmetry in most of the years in Zone A2, i.e., lower (or negative) values of correlation. Zone B shows an interannual oscillation of the median correlation, while Zone C shows a larger dispersion in the interannual results. Significance analysis shows that 163 out of the 176 points are statistically significant, while Zones A1, A2, and B have significant correlations, with Zone C being marginally significant. The results obtained are relevant information for further studies on the location of hybrid generation facilities. We expect our methodology to be instrumental in Chile’s energetic transition to a 100% renewable generation matrix. Full article