Integration Of Variable Renewable Energy Research Articles

Hydropower Enhancing the Future of Variable Renewable Energy Integration: A Regional Analysis of Capacity Availability in Brazil

As the share of variable renewables in the power system generation mix increases, meeting capacity requirements becomes challenging. In this context, hydropower reservoirs can play a vital role in integrating renewable energy due to their storage potential, contributing to meeting power supply criteria. However, given that reservoirs serve multiple purposes, various constraints can limit their capacity potential. This article introduces an analytical methodology that is designed to evaluate the maximum available power of hydro plants in critical scenarios. By applying concepts related to hydropower production calculations for the peak power demand and metrics evaluating the compliance with supply criteria, this study distinguishes itself from region-specific investigations. It conducts a generalized analysis of power availability across all regions of Brazil, with a focus on identifying the reasons for the most significant power losses and their specific locations. The results of this analysis demonstrate the feasibility of enhancing the available power of reservoirs, effectively addressing demand fluctuations, and sustainably improving energy security. This is particularly crucial in countries that are heavily reliant on renewables, including hydropower, for a huge portion of their electricity. The findings underscore the feasibility of increasing the penetration of variable renewable generation by optimizing the operation of existing hydropower plants. This optimization not only enhances energy security but also contributes to a more resilient and sustainable future, benefiting policy makers, energy planners, and stakeholders in the field of hydropower with reservoirs.

Systems for Energy Modeling in the Transport Sector

The study of models and systems for increasing energy efficiency in the transport sector and development up to 2050 is of strategic importance for the Bulgarian economy. The role and impact of different electricity supply models are examined in detail, in particular the role of energy storage systems and their evolution in the transport sector. Models and scenarios are explored to investigate the impact of the deployment of energy stor­age systems as well as the contribution in the integration of variable renewable energy in the electricity system. Approaches are used to model hourly transport-energy costs based on five scenarios that represent possible solutions to provide the necessary energy supply in the sec­tor in the long run.

A hydrogen-fuelled compressed air energy storage system for flexibility reinforcement and variable renewable energy integration in grids with high generation curtailment

Globally, the increasing share of renewables, prominently driven by intermittent sources such as solar and wind power, poses significant challenges to the reliability of current electrical infrastructures, leading to the adoption of extreme measures such as generation curtailment to preserve grid security. Within this framework, it is essential to develop energy storage systems that contribute to reinforce the flexibility and security of power grids while simultaneously reducing the share of generation curtailment. Therefore, this study investigates the performance of an integrated photovoltaic-hydrogen fuelled-compressed air energy storage system, whose configuration is specifically conceived to enable the connection of additional intermittent sources in already saturated grids. The yearly and seasonal performance of the integrated energy storage system, specifically designed to supply flexibility services, are evaluated for a scenario represented by a real grid with high-variable renewables penetration and frequent dispatchability issues. Results show that the integrated system, with performance-optimized components and a new energy management strategy, minimizes photovoltaic energy curtailment, otherwise around 50%, to as low as 4% per year, achieving system efficiencies of up to 62%, and reinforces the grid by supplying inertial power for up to 20% of nighttime hours. In conclusion, the integrated plant, operating with zero emissions, on-site hydrogen production, and optimized for non-dispatchable photovoltaic energy utilization, proves to be effective in integrating new variable renewable sources and reinforcing saturated grids, particularly during spring and summer.

General structures of control area cooperation for variable renewable energy integration in electric power systems

To integrate large-scale variable renewable energy resources (RESs) in modern power grids, the coordinating control area (CA) operation is the most cost-effective method. This article reviews the technical aspects of CA cooperation. Firstly, a brief overview of the active balancing control within each CA is discussed. Secondly, three general control structures for CA cooperation are innovatively proposed, the corresponding implementation details are analyzed, and some representative technologies are also provided in the systematic analysis. Then, some future research directions such as large-scale power sharing by DC, active power control of RES bases, and the new structure for distributed energy resources in local power grids are prospected. Finally, the changes in power systems brought about by their evolution and importance for further promoting cooperation between CAs are summarized.

Long-term multi-objective optimal scheduling for large cascaded hydro-wind-photovoltaic complementary systems considering short-term peak-shaving demands

The effective synergy among hydropower, wind power, and photovoltaic enhances variable renewable energy (VRE) utilization. However, existing long-term hydropower operating rules that consider runoff uncertainty neglect escalating peak-shaving demands. This paper optimizes the long-term decision-making of hydropower by supplementing the demand to buffer short-term VRE uncertainty. Latin hypercube sampling and K-means clustering reduction are utilized to generate coupling scenario sets to quantify long-term runoff and short-term VRE uncertainties. Then, a double-layer multi-objective optimization model is developed, the outer layer maximizes the long-term expected energy production of the whole system, and the inner layer minimizes hourly residual load fluctuation of power grids under daily electricity boundaries. The daily electricity is dependent on the load profile and monthly generation. Moreover, A power-electricity coupling approach is proposed to bridge long- and short-term operations and reduce the computational burden. An optimal set of pareto solutions is finally obtained with different constraint boundaries, where a compromise solution is selected by a fuzzy decision method. The model has been implemented on the Lancang River hydro-wind-photovoltaic complementary system in Southwest China. The results indicate that: (1) the model can transfer hydropower electricity from flood season to dry season, facilitate VRE integration, increase overall benefits, and improve short-term hydropower regulation capacity, especially in wet years; (2) the obtained Pareto solution set has significant advantages. Under multi-year average conditions, energy production increases by 11 million kWh, hydropower curtailment decreases by 751 million kWh, and residual load fluctuations reduce by 2200 MW, and (3) increasing transmission capacity is more conducive to improving system efficiency in wet years than in dry years, and is helpful to enhance hydropower flexibility.

Enhancing Generation Expansion Planning With Integration of Variable Renewable Energy and Full-Year Hourly Multiple Load Levels Balance Constraints

Enhancing Generation Expansion Planning With Integration of Variable Renewable Energy and Full-Year Hourly Multiple Load Levels Balance Constraints

Estimating the impacts of a new power system on electricity prices under dual carbon targets

The construction of a new power system dominated by renewables is crucial for achieving China's goals of carbon emission peaking and carbon neutrality. While numerous studies have investigated grid parity for large-scale renewables, some have overlooked the fact that the integration of variable renewable energy requires coordination with regulating resources. This coordination introduces additional costs, leading to subsequent increases in electricity prices. Our study comprehensively considers the total system costs and proposes a dynamic recursive linear optimization model. This model aims to simulate both short-term and long-term impacts of constructing a new power system on electricity prices in China. It evaluates the short-term effects of the Russia-Ukraine conflict and the COVID-19 pandemic on energy prices and demand, as well as the long-term effects of renewable energy penetration on ancillary service costs and technological progress on system costs. The results show that China's electricity price is projected to rise by 10.2% in 2030 for carbon peaking. By 2060, with an 83.3% renewable energy penetration for carbon neutrality, the electricity price will reach 0.859 CNY/kWh, representing a 27.2% increase. Ancillary service costs will experience the highest growth rate, accounting for 15.5% of system costs by 2060. Our study also proposes optimal technological pathways for the new power system and provides valuable data to support the development of effective electricity price mechanisms.

Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

This review paper provides a summary of methods in which artificial intelligence (AI) techniques have been applied in the management of variable renewable energy (VRE) systems, and an outlook to future directions of research in the field. The VRE types included are namely solar, wind and marine varieties. AI techniques, and particularly machine learning (ML), have gained traction as a result of data explosion, and offer a method for integration of multimodal data for more accurate forecasting in energy applications. The VRE management aspects in which AI techniques have been applied include optimized power generation forecasting and integration of VRE into power grids, including the aspects of demand forecasting, energy storage, system optimization, performance monitoring, and cost management. Future directions of research in the applications of AI for VRE management are proposed and discussed, including the issue of data availability, types and quality, in addition to explainable artificial intelligence (XAI), quantum artificial intelligence (QAI), coupling AI with the emerging digital twins technology, and natural language processing.

Risk-averse energy management of hydro/thermal/pumped storage complementarily operating with wind/solar: Balancing risk, cost and carbon emission

Large-scale grid integration of variable renewable energy is crucial for achieving decarbonized development. However, this integration requires frequent regulation of flexible power sources for complementary operation, which can lead to wear-and-tear and fatigue damage to key components. This poses potential risks to flexible power sources. Existing studies have primarily focused on limiting unit startups, while have neglected the risk of frequent power regulation. Thus, this work proposes a risk-averse short-term scheduling method for a Wind-Solar-Cascade hydro-Thermal-Pumped storage hybrid energy system to balance frequent regulation risk, cost, and carbon emission: (1) a risk-averse short-term scheduling model is proposed, considering multilayer constraints; (2) a multi-objective hybrid African vulture optimization algorithm is proposed to effectively solve the scheduling problem including continuous and discrete variables. A case study in the Songhua River basin, China shows that: (1) compared with traditional models, the proposed model reduces the risk by 31.4% and enhances the comprehensive performance in balancing the three objectives by 22.4%; (2) the proposed algorithm performs robustness and search capability advantages, with improvements of 33.01% and 21.44% respectively, in solving the problem of challenging constraints and mixed decision variables. Overall, this work contributes to enhancing the management of large hybrid energy systems.

Comparison of electrical load forecast methods validated on the hourly consumption profiles of 10 heat pumps located in Switzerland

The integration of variable renewable energy in the electrical grid necessitates the exploitation of load flexibility. The high contribution of space heating to the residential sector, thereby, puts the balancing of heat pumps in focus. The paper compares three different approaches to predict the heat demand of 10 Swiss residential buildings on 9 winter days. We compare two different simulation based grey-box models and an elementary statistical model approach. Unexpectedly, the statistical approach almost always performs better than the others, although the anticipated advantages of the simulation-based approaches could be seen in several cases. The comparison between the simulation-based methods shows that the one requiring more detailed, higher data quality clearly outperformed the simplified version. The work led to the conclusion that in the used setup too little system information was available to fully exploit the potential of a simulation-based approach at one hour resolution.

An electricity-carbon joint market optimization model for coal-fired power system under China's “dual-carbon” target

In the process of low-carbon transformation of power system in China, providing enough flexible service from coal-fired power units (CFPUs) is one of the most crucial answers to reduce or even avoid the operation risk which is caused by large-scale expansion of variable renewable energy (VRE). To spur CFPUs release flexibility, this paper suggests a two-stage optimal model, taking into account various peaking compensation mechanisms (VPCM) in a day-ahead power and carbon emissions trade (CET) joint market. Pre-clearing is the first stage, which maximizes social welfare, and formal clearing is the second stage, which maximizes the profit of the coal-fired power system. In contrast to the existing fixed peaking compensation mechanism (FPCM), the variable and fixed costs associated with providing peaking regulation are calculated by the VPCM. In addition, the work considers the CET market as an external carbon emissions constraint and internal constraint and no constraint are set to study the effectiveness of the current CET market. The result indicates that the VPCM can adapt more successfully to the cost of CFPUs when providing peaking regulation and increase the profit of the coal-fired power system. Then, “VPCM + external carbon constraint” enables larger profits for coal-fired power systems, whereas “FPCM + internal carbon constraint” stimulates VRE integration and reduces carbon emissions. Finally, the current CET market mechanism is insufficient to support coal-fired power plants in reducing carbon emissions as VRE rises.

Frequency support by wave farms in low inertia power systems

New ancillary services and additional requirements for the grid integration of variable renewable energy (VRE) are being defined worldwide, in response to the technical challenges caused by increasing levels of VRE utilization in electric power grids. Currently, the use of wave energy is still limited to a few applications or demonstrations, where the level of penetration of wave power into the grid is not significant. To anticipate the future requirements for wave power integration, and the possibilities for provision of services, this paper considers the lessons being learned through the challenges caused by high penetration levels of other VRE sources into the grid, particularly wind power. On this basis, this paper presents an overview of grid support services that wave power plants can be expected to provide in power systems dominated by converter-interfaced generation, i.e. low inertia systems. Specifically, the focus is on services that support the active power balance in the power system. Then, the current capabilities and future perspectives for the provision of frequency support by wave farms are discussed.

Deep Learning for Variable Renewable Energy: A Systematic Review

In recent years, both fields, AI and VRE, have received increasing attention in scientific research. Thus, this article’s purpose is to investigate the potential of DL-based applications on VRE and as such provide an introduction to and structured overview of the field. First, we conduct a systematic literature review of the application of Artificial Intelligence (AI), especially Deep Learning (DL), on the integration of Variable Renewable Energy (VRE). Subsequently, we provide a comprehensive overview of specific DL-based solution approaches and evaluate their applicability, including a survey of the most applied and best suited DL architectures. We identify ten DL-based approaches to support the integration of VRE in modern power systems. We find (I) solar PV and wind power generation forecasting, (II) system scheduling and grid management, and (III) intelligent condition monitoring as three high potential application areas.

VRE Integrating in PIAT grid with aFRR using PSS, MPPT, and PSO-based Techniques: A Case Study Kabertene

The Fluctuations in demand and weather conditions have a significant impact on the frequency and the voltage of Algeria's isolated PIAT power grid. To maintain stability and reliable power supply, it is crucial to keep these quantities close to their expected levels. An automatic (FRR) is employed to regulate real-time frequency deviations caused by integrating variable renewable energy (VRE), specifically wind and solar power in the Kabertene region. In order to mitigate wind power fluctuations, a power system stabilizer is implemented, which helps dampen oscillations. The use of Maximum Power Point Tracking (MPPT) techniques optimizes the extraction of power from solar panels under varying conditions. For efficient scheduling and dispatch of VRE generation, particle swarm optimization (PSO)-based algorithms are used. These algorithms ensure optimal utilization of renewable energy sources by considering their intermittent nature. This study proves the effectiveness of these techniques in enhancing grid stability, reducing frequency deviations, and improving VRE integration. Valuable insights are provided on their practical implementation, playing a crucial role in transitioning to a cleaner and more sustainable energy system.

Pumped-hydro storage plants influencing the optimal hourly dispatch in hydro-thermal systems with massive integration of variable renewable energy

Pumped-hydro storage plants influencing the optimal hourly dispatch in hydro-thermal systems with massive integration of variable renewable energy

Flexible supply meets flexible demand: prosumer impact on strategic hydro operations

Ambitious climate packages promote the integration of variable renewable energy (VRE) and electrification of the economy. For the power sector, such a transformation means the emergence of so-called prosumers, i.e., agents that both consume and produce electricity. Due to their inflexible VRE output and flexible demand, prosumers will potentially add endogenous net sales with seasonal patterns to the power system. With its vast hydro reservoirs and ample transmission capacity, the Nordic region is seemingly well positioned to cope with such intermittent VRE output. However, the increased requirement for flexibility may be leveraged by incumbent producers to manipulate prices. Via a Nash-Cournot model with a representation of the Nordic region’s spatio-temporal features and reservoir volumes, we examine how hydro producers’ ability to manipulate electricity prices through temporal arbitrage is affected by (i) VRE-enabled prosumers and (ii) the latter plus a high CO_2 price. We find that hydro reservoirs could exploit prosumers’ patterns of net sales to conduct temporal arbitrage more effectively, viz., by targeting periods in which prosumers are net buyers (net sellers) to withhold (to “dump”) water. Meanwhile, a higher CO_2 price would further enhance hydro reservoirs’ market power because flexible price-taking thermal plants would be unable to ramp up production in order to counter such producers’ strategy to target VRE’s intermittency. Hence, in spite of a flexible demand side to complement additional intermittent VRE output, strategic hydro producers may still exacerbate price manipulation in a future power sector via more tailored exercise of market power.

Decarbonizing the power system by co-planning coal-fired power plant transformation and energy storage

The integration of variable renewable energy (VRE) and the gradual phase-out or functional transformation to coal-fired power plants (CFPP) are two essential transition pathways in achieving the carbon neutrality in China. However, the withdrawal of a large amount of CFPPs may lead to the reduction of inertia and the deficiency of flexibility in power systems, as a flexible alternative, energy storage is playing an increasingly important role in leveling fluctuant VRE output. Therefore, this paper proposes a co-planning approach to the CFPP transformation and battery energy storage system (BESS) accompanying with VRE integration. Two options are considered in the transformation path of CFPPs: retirement or reconstruction. Carnot battery, a novel thermal storage reconstruction scheme, is proposed for CFPP reconstruction, which is then comprehensively compared with BESS in terms of technical, economy and CO2 emission reduction. To ensure the frequency stability in the power system low-carbon transition, the frequency security constraints considering the fast frequency response provided by BESS is embedded in the co-planning model. In addition, the uncertainty of VRE is considered through a robust optimization method. To efficiently solve the problem, the nested column-and-constraint generation (C&CG) algorithm is employed. The case studies are implemented on the IEEE test system and the Shanxi Power System in China. The results show that the co-planning of CB reconstruction to CFPPs and BESS can promote carbon emission reduction and VRE generation accommodation, ensure operation security of decarbonized power systems. Some generation transition paths for decarbonizing the Chinese Power System are suggested in the end.

Temporally detailed modeling and analysis of global net zero energy systems focusing on variable renewable energy

This study newly develops a recursive-dynamic global energy model with an hourly temporal resolution for electricity and hydrogen balances, aiming to assess the role of variable renewable energy (VRE) in a carbon-neutral world. This model, formulated as a large-scale linear programming model (with 500 million each of variables and constraints), calculates the energy supply for 100 regions by 2050. The detailed temporal resolution enables the model to incorporate the variable output of VRE and system integration options, such as batteries, water electrolysis, curtailment, and the flexible charging of battery electric vehicles. Optimization results suggest that combing various technical options suitable for local energy situations is critical to reducing global CO2 emissions cost-effectively. Not only VRE but also CCS-equipped gas-fired and biomass-fired power plants largely contribute to decarbonizing power supply. The share of VRE in global power generation in 2050 is estimated to be 57% in a cost-effective case. The results also imply economic challenges for an energy system based on 100% renewable energy. For example, the average mitigation cost in 2050 is 69USD/tCO2 in the cost-effective case, while it increases to 139USD/tCO2 in the 100% renewable case. The robustness of this argument is tested by sensitivity analyses.

Evaluation of flexibility provided by cascading hydroelectric assets for variable renewable energy integration

The integration of a significant VRE capacity into Canada's electricity system is a crucial step toward decarbonization. Cascading hydro assets have been found to bolster the generation fleet with the flexibility necessary to mitigate VRE uncertainty and variability. The integration of British Columbia's cascading hydro-dominated system, known for its high flexibility, with Alberta's considerable wind and solar potential can create a hybrid hydro-VRE system with sufficient flexibility for aggressive VRE integration in Alberta. A flexibility metric is introduced into a unit commitment model to quantitatively assess network flexibility while accounting for pertinent operational and technical constraints to examine this proposition. This study also analyzes changes to the effectiveness of cascading hydro assets for network flexibility and VRE integration under distinct climate change scenarios. Results indicate that the integration of the British Columbian and Albertan networks improves the flexibility index approximately two-fold, allowing a 13% increase in VRE penetration in Alberta. However, the study also highlights the significant impact climate change may have on cascading hydro operation and associated network flexibility, which could potentially challenge the capacity of these assets to provide the flexibility required for VRE integration.

Flexible electric vehicle charging and its role in variable renewable energy integration

Uptake of electric vehicles is accelerating as governments around the world aim to decarbonize transportation. However, swift and widespread electric vehicle (EV) adoption will require some degree of controlled charging to mitigate the adverse impacts of electric vehicle adoption. Simulating the interaction between transportation and power requires new modelling tools with operational detail and spatial-temporal granularity. This analysis evaluates the potential benefits of utility-controlled charging (UCC) with the objective of reducing variable renewable energy (VRE) curtailment in decarbonized power systems using a framework that links travel and power system models using an intermediate charging model. Results show that the addition of VRE generation infrastructure shows the most impact on electricity system operating emissions and costs, but EV charging plays a significant role as well. Within EV charging strategies, UCC charging decreases emissions by 7% compared to uncontrolled charging. UCC is proven to be most effective in the summer due to higher electric vehicle fuel economy. Finally, the type of VRE generation infrastructure on the grid may have implications for siting of EV charging infrastructure due to the typical temporal peaks of wind and solar energy. These findings demonstrate how the use of distinct but linked travel and power sector models can be deployed to reduce multi-sector emissions and costs.