Variable Renewable Energy Resources Research Articles

Pathway for a fully renewable power sector of Africa by 2050: Emphasising on flexible generation from biomass

The climate crisis coupled with energy poverty and injustice in Africa requires strategic measures to proffer sustainable solutions. A transition to a defossilised power sector is obtained for Africa by 2050, evolutionarily developed to fulfil the Paris Agreement. With the LUT Energy System Transition Model, the role of biomass in a least-cost power sector for Africa is investigated from 2020 to 2050 in five-year time steps. Techno-economic assumptions of employed technologies are applied. The study considers the current generation portfolio, lifespan of power plant technologies, and the current and future electricity demand to ascertain the best generation mix by 2050. Biomass potential of 334 TWh (17.5%) of the total African biomass potential of 1911 TWh is applied to the African power sector to develop two Best Policy Scenarios: a scenario without bioenergy (BPS-1) and a scenario with bioenergy (BPS-2). The results indicate a positive impact of bioenergy on the power sector: The total generation, total installed capacity, grid utilisation, levelised cost of electricity, and storage output decreased by 8.6%, 8.4%, 12.9%, 10.2%, 47.1% by 2050 respectively in BPS-2 relative to the BPS-1. In addition, flexible generation from biomass offers balancing to enhance variable renewable energy resources integration onto the power sector.

Fabrication, Modeling, and Testing of a Prototype Thermal Energy Storage Containment

Abstract Increasing penetration of variable renewable energy resources requires the deployment of energy storage at a range of durations. Long-duration energy storage (LDES) technologies will fulfill the need to firm variable renewable energy resource output year round; lithium-ion batteries are uneconomical at these durations. Thermal energy storage (TES) is one promising technology for LDES applications because of its siting flexibility and ease of scaling. Particle-based TES systems use low-cost solid particles that have higher temperature limits than the molten salts used in traditional concentrated solar power systems. A key component in particle-based TES systems is the containment silo for the high-temperature (>1100 ∘C) particles. This study combined experimental testing and computational modeling methods to design and characterize the performance of a particle containment silo for LDES applications. A laboratory-scale silo prototype was built and validated the congruent transient finite element analysis (FEA) model. The performance of a commercial-scale silo was then characterized using the validated model. The commercial-scale model predicted a storage efficiency above 95% after 5 days of storage with a design storage temperature of 1200 ∘C. Insulation material and concrete temperature limits were considered as well. The validation of the methodology means the FEA model can simulate a range of scenarios for future applications. This work supports the development of a promising LDES technology with implications for grid-scale electrical energy storage, but also for thermal energy storage for industrial process heating applications.

Temporal dynamics and extreme events in solar, wind, and wave energy complementarity: Insights from the Polish Exclusive Economic Zone

The Polish Exclusive Economic Zone (EEZ) in the Baltic Sea is an area of increasing strategic importance for Poland's pursuit of renewable energy, especially offshore wind. This research investigates the complementarity among solar, wind, and wave energy resources within the Polish EEZ to examine these energy sources' temporal dynamics, correlations, and extremes. The primary data source corresponds to a 31-year hourly time series dataset from the ERA5 reanalysis, whose reliability was evaluated through performance metrics. The results from complementarity metrics indicate varying levels of association among the three variable renewable energy resources (VRES) in the EEZ, spanning from weak similarity to weak complementarity. The findings of this research indicate that blackouts are most probable at offshore locations during winter and autumn for renewable power systems integrating wind and solar energy, with over 70 % of occurrences within these seasons. The investigation of extreme events highlights critical elements when evaluating VRES and their complementarity. This understanding aids in effectively planning and managing renewable energy systems, ensuring resilience and reliability under challenging weather conditions. Furthermore, while the complementarity may be consistent throughout the entire Polish EEZ, the feasibility and cost of implementing hybrid power systems can significantly vary between locations.

Standalone hybrid PV/MHP/BES system sizing with complementarity adjustment

ABSTRACT The paper proposes a method to size a standalone hybrid PV/MHP/BES (photovoltaic/micro-hydro/battery energy storage) system. The proposed method is simple and easy to use. The size and location decoupled proposed approach can effectively size cost-effective systems. The method exploits variable renewable energy (VRE) resources adjustable complementarity proposing a CMACVRE (VRE Adjusted Complementarity Critical Month) term to size the hybrid system. By reducing capital investment and enhancing load sharing between VRE resources, the method inherently sizes cost-effective systems that minimise SnD gaps and operational expenses. The results show that the proposed method cannot only size a Homer Pro comparable system but can optimise a Homer Pro sized system further. Additionally, that the system sized by Homer Pro employing the proposed approach with adjustable complementarity delivers better results than the system sized by Homer Pro without it. The results show that the sized hybrid system meets load demand even considering ± 10% uncertainty. A North American site produces 4,069 kWh vs. 3,686 kWh of the energy required yearly; the Southeast Asian site produces 656 kWh vs. 607 kWh of the required; and the European site produces 12,982 kWh vs. 12548 kWh of the required, each considering −10% uncertainty.

Modeling electrical particle thermal energy storage systems for long-duration, grid-electricity storage applications

Particle-based thermal energy storage technologies could facilitate increased penetration of variable renewable energy resources by decreasing the costs of providing dispatchable, carbon-free generation resources. However, the technology is not yet commercially available, and its performance has not been thoroughly characterized. This work creates a system modeling platform for particle-based thermal energy storage systems that can characterize performance and is transferable to emerging forms and applications of particle-based thermal energy storage. A library of key component models developed for particle-based thermal energy storage is described and benchmarked against high-fidelity models or with experimental results. A notional 135 MWe power plant employing particle thermal energy storage for grid-scale, electricity storage applications is conceived and simulated. The results show these systems can achieve greater than 50% annual round-trip efficiencies in a variety of durations, locations, dispatch schedules, and key design parameters. A 1.5% decrease in round-trip efficiency was observed when increasing storage duration from 10 to 100 h. Dispatch schedules can impact round-trip efficiency by greater than 0.5%. In regards to component design and performance, the insulation thickness of the silos and the heat exchanger approach temperature impacted performance the most. Particle-based thermal energy storage has the potential to assist in the decarbonization of not only electricity but industry and building sectors as well. This work enables the analysis of the performance of systems in these sectors and serves as a detailed technical model for design optimization in the future.

A critical review of challenges and opportunities for the design and operation of offshore structures supporting renewable hydrogen production, storage, and transport

Abstract. The climate emergency has prompted rapid and intensive research into sustainable, reliable, and affordable energy alternatives. Offshore wind has developed and exceeded all expectations over the last 2 decades and is now a central pillar of the UK and other international strategies to decarbonise energy systems. As the dependence on variable renewable energy resources increases, so does the importance of the necessity to develop energy storage and nonelectric energy vectors to ensure a resilient whole-energy system, also enabling difficult-to-decarbonise applications, e.g. heavy industry, heat, and certain areas of transport. Offshore wind and marine renewables have enormous potential that can never be completely utilised by the electricity system, and so green hydrogen has become a topic of increasing interest. Although numerous offshore and marine technologies are possible, the most appropriate combinations of power generation, materials and supporting structures, electrolysers, and support infrastructure and equipment depend on a wide range of factors, including the potential to maximise the use of local resources. This paper presents a critical review of contemporary offshore engineering tools and methodologies developed over many years for upstream oil and gas (O&G), maritime, and more recently offshore wind and renewable energy applications and examines how these along with recent developments in modelling and digitalisation might provide a platform to optimise green hydrogen offshore infrastructure. The key drivers and characteristics of future offshore green hydrogen systems are considered, and a SWOT (strength, weakness, opportunity, and threat) analysis is provided to aid the discussion of the challenges and opportunities for the offshore green hydrogen production sector.

An ensemble-based approach for short-term load forecasting for buildings with high proportion of renewable energy sources

The increasing integration of renewable energy sources into large buildings and structures has emphasized the importance of effective control systems to optimize resource use, grid stability, and reliability. In this context, load forecasting is critical for predicting future energy demand and managing the intermittency and variability of renewable energy resources to ensure a stable performance. This study proposes an ensemble-based short-term load forecasting (STLF) method tailored for buildings. It combines parallel and series approaches for a 24-hour forecasting horizon. The parallel approach uses similar historical days to capture the general load trend, holidays, and user behavior, while the series component employs a deep learning-based neural network to address short-term changes. The final ensemble-based forecast is the culmination of these two methods. Evaluated using real load profile data sets with 15-minute resolution, the proposed method demonstrates more than 23% and 24% improvements in MAE and RMSE, respectively, compared to other methods. These results demonstrated the superior performance of the proposed method in terms of both accuracy and robustness, making it an effective solution for short-term load forecasting in buildings heavily reliant on renewable energy.

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.

Demand response programs: Comparing price signals and direct load control

Canada's electric power system is responsible for approximately 9 % of national emissions, 53 % of which occur in the province of Alberta. Integrating new variable renewable energy resources is a key part of the supply-side decarbonization pathway, while end-use electrification unlocks further opportunities on the demand side. The inherent variability of variable renewable energy output necessitates network flexibility. Supply-side flexibility solutions require significant investment, but demand-side management programs have potential to deliver network flexibility at a lower cost. An integrated framework consisting of demand and supply models investigates the efficacy of demand response programs for improving network flexibility in Alberta's power system, as measured by multiple operational metrics. The framework is applied to two demand response programs: real-time pricing and direct load control. These programs are assessed for two generation capacity mixes derived from an expansion planning model. Results indicate that substantial network flexibility benefits are achievable in a zero-emission power system via both programs resulting in avoided wind curtailment by 7.7 %. Improvement in operational costs, up to 1.4 % of the base output, is also observed in all scenarios accompanied by savings in household power bill expenditure by up to 15 %. These improvements, however, are only achieved fully when sufficient flexible load is provided.

Application of data‐driven methods in power systems analysis and control

AbstractThe increasing integration of variable renewable energy resources through power electronics has brought about substantial changes in the structure and dynamics of modern power systems. In response to these transformations, there has been a surge in the development of tools and algorithms leveraging real‐time computational power to enhance system operation and stability. Data‐driven methods have emerged as practical approaches for extracting reliable representations from non‐linear system data, enabling the identification of dynamics and system parameters essential for analysing stability and ensuring reliable operation. This study provides a comprehensive review of recent contributions in the literature concerning the application of data‐driven identification, analysis, and control methods in various aspects of power system operation. Specifically, the focus is on frequency support, power oscillation detection, and damping, which play crucial roles in maintaining grid stability. By discussing the challenges posed by parametric uncertainties, load and source variability, and reduced system inertia, this review sheds light on the opportunities for future research endeavours.

Novel heat transfer fluid for maximum recovery of heat of compression: A steady state analysis on improving the performance of liquid air energy storage

BackgroundEnergy storage is a key technology required to manage the limitation of the variability of renewable energy resources. In this study, a concept of energy storage based on Liquid Air Energy Storage (LAES) is presented, with proposed designs to improve the performance based on the heat transfer fluid. The heat generated from the compression of ambient air during the charging stage is usually stored in the heat storage tank and later used for power recovery in the discharging process. The challenge with the LAES is their low round-trip efficiency. Studies have proposed different system configurations and materials for improving the efficiency of the LAES technology, by incorporating multigeneration systems (like organic Rankine cycle, refrigeration system etc.), packed beds for the charging and discharging process, and fluids for cold storage. MethodThis study presents a first-of-its-kind nanofluid analysis for heat recovery in the charging process of ambient air. The effect of hybridization and volume fraction of carbon nanotubes was analysed, in comparison with other oils (Therminol-VP1, Therminol-66, and Dowtherm Q). ResultThe results show that nanofluid@0.005 requires 3331 m3 of storage tank, which caused the least investment cost of the high-temperature storage tank, with $ 2.318 Million. The levelized cost of storage for the LAES systems utilizing nanofluid@0.005, nanofluid@0.001, nanofluid@0.0005, Therminol-VP1, Dowtherm-Q, and Therminol-66 are 208.4 $/MWh, 209.1 $/MWh, 209.7 $/MWh, 211.1 $/MWh, 211.7 $/MWh, and 210.2 $/MWh respectively.

Firm Photovoltaic Power Generation: Overview and Economic Outlook

Grid‐connected photovoltaic electricity production steadily grows at the margin of conventional power generation, but its management becomes more complex. To overcome this challenge, a transformation of variable renewable energy (VRE) resources into firm power generation is proposed. Drawing on insights from the International Energy Agency Photovoltaic Power System Task 16 case studies, it becomes evident that achieving nearly 100% VRE power grids that reliably meet demand year‐round can be economically viable through optimal VRE transformation. This transformation involves various traditional methods, e.g., storage, VRE blending, geographical dispersion, and load flexibility. However, overbuilding VRE capacity and controlled curtailment, acting as implicit energy storage, are now seen as essential prerequisites for this transformation. Nevertheless, aligning this vision with the current market rules poses a dilemma as it doesn't necessarily align with VRE producers’ interests. This predicament calls for a reconsideration of VRE market regulations. Current designs based on marginal energy production signals do not suffice. Instead, it is advocated for market rules grounded in the capacity of firmly enabled VREs rather than their energy output. Ultimately, the economic model should harmonize with the variability of VRE resources, rather than forcing VRE resources to adapt to existing market structures.

Evaluation of Various Flexibility Resources in Power Systems

Variable Renewable Energy Resources (VRES), especially wind and solar power, are known for their intermittent, uncertain, and low-energy-density nature. The increasing adoption of these stochastic sources presents irregularity in the net load in the power system network; therefore, it poses a challenge to the reliable operation of power systems. Consequently, there's an increasing need for power system flexibility to cope with VRES-related challenges. Flexibility planning will therefore be a crucial aspect for power system management, particularly as the penetration of VRES continues to rise. To reach this objective, the diversification of flexibility options emerges as a promising solution. Various strategies are prominent in the literature for enhancing power system flexibility to adapt to VRES variability. These include the utilization of flexible generators, adjusting load profiles through demand-side management, integrating energy storage systems and electric vehicle batteries, developing grid infrastructure, using surplus energy for various daily applications (e.g., heating), and the implementing of curtailment practices. Demand-side management and energy storage, for example, offer valuable flexibility by allowing consumers to adjust their consumption patterns to electricity supply and demand fluctuations. Additionally, flexible generation technologies like gas turbines and combined heat and power systems provide rapid responses, aiding grid balance during high VRES output variability periods. Overall, this paper provides an overview of power system flexibility, exploring the various flexibility resources available to VRES-related challenges. Finally, this paper emphasizes the importance of continued innovation in developing new flexibility solutions to meet the growing demand for sustainable and reliable power systems.

Hybrid lithium-ion battery and hydrogen energy storage systems for a wind-supplied microgrid

Microgrids with high shares of variable renewable energy resources, such as wind, experience intermittent and variable electricity generation that causes supply–demand mismatches over multiple timescales. Lithium-ion batteries (LIBs) and hydrogen (H2) are promising technologies for short- and long-duration energy storage, respectively. A hybrid LIB-H2 energy storage system could thus offer a more cost-effective and reliable solution to balancing demand in renewable microgrids. Recent literature has modeled these hybrid storage systems; however, it remains unknown how anticipated, but uncertain, cost reductions and performance improvements will impact overall system cost and composition in the long term. Here, we developed a mixed integer linear programming (MILP) model for sizing the components (wind turbine, electrolyser, fuel cell, hydrogen storage, and lithium-ion battery) of a 100% wind-supplied microgrid in Canada. Compared to using just LIB or H2 alone for energy storage, the hybrid storage system was found to provide significant cost reductions. A sensitivity analysis showed that components of the H2 subsystem meaningfully impact the total microgrid cost, while the impact of the LIB subsystem is dominated by its energy storage capacity costs. Regarding efficiency, decreased electrolyzer efficiency causes the greatest increase in total system cost, whereas increased fuel cell efficiency has the greatest potential to reduce total system cost. As technologies evolve, the H2 subsystem assumes a greater role (i.e., it is larger and receives/supplies more energy over more hours) compared to the LIB subsystem, but LIB continues to provide frequent intra-day balancing in the microgrid.

Control and Optimisation of Power Grids Using Smart Meter Data: A Review.

This paper provides a comprehensive review of the applications of smart meters in the control and optimisation of power grids to support a smooth energy transition towards the renewable energy future. The smart grids become more complicated due to the presence of small-scale low inertia generators and the implementation of electric vehicles (EVs), which are mainly based on intermittent and variable renewable energy resources. Optimal and reliable operation of this environment using conventional model-based approaches is very difficult. Advancements in measurement and communication technologies have brought the opportunity of collecting temporal or real-time data from prosumers through Advanced Metering Infrastructure (AMI). Smart metering brings the potential of applying data-driven algorithms for different power system operations and planning services, such as infrastructure sizing and upgrade and generation forecasting. It can also be used for demand-side management, especially in the presence of new technologies such as EVs, 5G/6G networks and cloud computing. These algorithms face privacy-preserving and cybersecurity challenges that need to be well addressed. This article surveys the state-of-the-art of each of these topics, reviewing applications, challenges and opportunities of using smart meters to address them. It also stipulates the challenges that smart grids present to smart meters and the benefits that smart meters can bring to smart grids. Furthermore, the paper is concluded with some expected future directions and potential research questions for smart meters, smart grids and their interplay.

Experimental and numerical investigation of proppant embedment and conductivity reduction within a fracture in the Caney Shale, Southern Oklahoma, USA

The current worldwide energy supply is insufficient to meet the rising demand. As a result, the energy prices are expected to keep soaring despite the recent increases in a variety of renewable energy resources. Although not renewable, shale oil and gas — “unconventional” hydrocarbon resources are relatively clean forms of energy resources, which still hold a vast share of the energy market. For many oil and gas companies, meeting profitable production goals from shale reservoirs is sometimes challenging, due to the loss of fracture conductivity and premature declines in the production. In this paper we investigate the stress-dependent changes in the hydraulic conductivity of proppant-filled fractures and mechanical fracture–proppant interactions in Caney Shale, a calcareous, organic-rich mudrock, through laboratory experiments and numerical modeling. American Petroleum Institute (API) fracture conductivity tests were conducted using 2% KCl on five locations within the Caney Shale that consisted of selecting three brittle (reservoir) zones and two ductile zones. Confining pressures ranged from 1,000 psi to 12,000 psi at 210 °F. Conductivity, permeability as well as embedment were measured during the test. Also, an additional, laboratory in-situ visualization test was conducted to examine the detailed proppant-shale matrix interaction under elevated stress (3,920 psi effective stress) and temperature (252 °F), with a synthetic reservoir fluid. Our experimental results have confirmed that improved fracture conductivity is attributed to proppant size, and that the increase in porosity of the proppant pack, closure pressure changes and the reduction in fracture conductivity are a function of many factors such as fracture closure stress.

Wide area monitoring system operations in modern power grids: A median regression function-based state estimation approach towards cyber attacks

Modern power grid is a generation mix of conventional generation facilities and variable renewable energy resources (VRES). The complexity of such a power grid with generation mix has routed the utilization of infrastructures involving phasor measurement units (PMUs). This is to have access to real-time grid information. However, the traffic of digital information and communication is potentially vulnerable to data-injection and cyber attacks. To address this issue, a median regression function (MRF)-based state estimation is presented in this paper. The algorithm was stationed at each monitoring node using interacting multiple model (IMM)-based fusion architecture. An exogenous variable-driven representation of the state is considered for the system. A mapping function-based initial regression analysis is made to depict the margins of state estimate in the presence of data-injection. A median regression function is built on top of it while generating and evaluating the residuals. The tests were conducted on a revisited New England 39-Bus system with large scale photovoltaic (PV) power plant. The system was affected with multiple system disturbances and severe data-injection attacks. The results show the effectiveness of the proposed MRF method against the mainstream and regression methods. The proposed scheme can accurately estimate the states and evaluate the contaminated measurements while improving the situation awareness of wide area monitoring systems (WAMS) operations in modern power grids

Long‐Term Optimal Operation of the Cascade Hydro‐Wind‐Photovoltaic Hybrid System considering Transmission Section Constraints

With the sharply increased development of variable renewable energy resources (VRERs) in recent years, the hydro‐wind‐photovoltaic (PV) hybrid system (HWPHS) has the prospective to enhance the grid integration of VRERs. Nevertheless, the intense variation associated with wind and PV generation causes uncertainties in the long‐term operation of the HWPHS. To overcome this drawback, this paper develops a novel method to derive adaptive operating rules for a cascade HWPHS. First, a scenario‐generating method coupling Kernel density estimation with the copula function is proposed to characterize the wind and PV forecast errors. Second, based on the power generation scenarios, an optimal scheduling model for the cascade HWPHS considering transmission section constraints is proposed to simulate the hydro‐wind‐PV complementary operation; finally, the long‐term operating rules for the cascade HWPHS are extracted by grey relational analysis and BP neural network. As a case study, the HWPHS of the Wu River basin in China is chosen. Results demonstrate that the proposed model can effectively utilize the flexibility of cascade hydropower stations, improve transmission section utilization efficiency, and promote clean energy absorption.

Optimization of Renewable Energy in Indonesian Energy Mix 2025

Access to energy is considered one of basic need for human welfare. Energy is also needed by industry to produce gadgets and provide services. In transportation, energy is needed to move from one point to another point. Due to the increment number of human beings and their changing lifestyle, energy demand is ever increased. Currently, the most commonly used primary energy sources are oil and coal; however, these fossil fuels are non-renewable, unsustainable, and depleted (Sorrell, 2012). Many oil fields have already reached their peak production (Krumdieck, 2010). This decrease poses challenges to oil-dependent economies around the world, including Indonesia. Fossil fuels are also notoriously source of green house gas emission. Indonesia has pledged to reduce its emissions by 29% by 2030 (PLN, 2016). To reach this target, Government Regulation No. 79 of 2014 mandated 23% of the national energy consumed to be supplied by renewable energy (RE) by 2025. Indonesia has a variety of RE resources (RES), including biomass, hydropower, geothermal, municipal solid waste (MSW), ocean, and solar. As Indonesia is an agricultural-based economy, expansive farms provide waste biomass, especially from the production of palm oil and rice. They produce straw, rice husks, leaves, trunks, and other waste materials (Weldekidan, et al., 2020). Located at the intersection of the Ring of Fire and the Alpide belt, Indonesia is estimated to have the greatest geothermal potential in the world. Moreover, as two-thirds of the Indonesian territory are covered by water, hydropower and ocean energy can be used (Farizal and Asri, 2018). However, as most RES require more capital investment than fossil fuel and are not as technologically mature as their fossil fuel counterparts, selecting which RES to develop is important. In energy planning, especially when planning RES to substitute fossil fuel, an energy source is selected not only due to its potential availability (abundant resource) but also its dependability, reliability, cost as well as its accessibility. Keywords: Renewable energy, energy mix, optimization, mixed integer non linear programming.

Online updating of a Markovian solar forecast representation

Risk management is an essential task in power systems operations, and temporal variability of renewable energy resources requires new techniques to capture the effects of such variability on the system. We utilize use a Markovian representation of probabilistic solar power forecasts to enhance the temporal variability assessment of balancing area-wide solar power production. Using this representation, we determine the optimal amount of historical forecast and observational data to use when updating the Markov transition matrix – which captures the past variability of the observations with respect to the forecasts – for use in stochastic optimization problems. We propose two novel multivariate scoring metrics based upon the observation vector’s band depth and discuss the performance with respect to these two metrics, as well as the variogram-based score, for multiple transition matrices. The optimal amount of data to use for these online transition matrices depends upon the season and likely upon the prevailing weather regime. • Transition matrices updated by ranking recent solar observations among forecasts. • Introduced two novel transition matrix scoring metrics based on band depth. • Fewer transitions in the spring and summer months improve transition matrices. • Compared changes in reserves and Area Control Error with respect to a fixed matrix. • Using an online matrix improved the Area Control Error often substantially.