Intermittent Renewable Generation Research Articles

Renewable Energy and Equilibrium Hedging in Electricity Forward Markets

We study the impact of renewable energy on forward markets for electricity. Previous literature shows that forward prices are determined by time-varying demand and volatile spot prices. We introduce supply risk from renewable generation and find that stochastic renewable output mitigates income risk for generating firms, in particular when negative shocks to renewable output have large positive price impact. This risk off-setting effect leads to reduced hedging needs for generating companies and increases the forward premium. Using five years of high-frequency spot and futures market data, we confirm our model empirically. In sum, our findings suggest that intermittent renewable generation changes firm’s hedging incentives and has significant impact on forward prices for electricity. JEL Classification: G11, G13, Q42

Investments in transmission lines and storage units considering second-order stochastic dominance constraints

Increasing intermittent renewable generation to meet the climate goals entails a deep transformation of current power systems. The transmission system must adapt to ensure a rapid and flexible response to the changes in the energy flows. Flexibility can be provided by reinforcing the interconnection among all the market agents and/or by installing facilities with fast response to the changes. In this work, we study the investment problem of a central planner that seeks to expand the transmission network and install storage units considering decarbonizing measures. We propose a two-stage stochastic problem with uncertainty on the demand growth and including representative days to characterize the hourly demand and renewable power variability. To obtain better expansion strategies according to a limit on the carbon emissions, second-order stochastic dominance constraints are imposed. Numerical analyses based on the power system of the Canary island (Spain) are provided. The results show that to install storage units is key to efficiently integrate renewable generation. The investments in the transmission system are mostly in lower-voltage lines. The formulation with stochastic dominance constraints results in higher second-stage investments, allowing a better adaptation to the demand growth evolution.

Non-energy feedback: The unseen impacts of sensory, social, material and systemic feedback on household energy demand

Energy feedback is a central plank in demand management interventions aiming to reduce the quantity or shift the timing of domestic energy use – changes to patterns of consumption widely seen as necessary in energy systems powered by intermittent renewable generation. Despite significant resources and attention, however, the changes in consumption achieved to date through energy feedback have been relatively limited in both extent and duration. At the same time, the energy sector has largely overlooked the role of broader forms of everyday feedback that observably shape when and how energy-using practices are performed. Drawing on ethnographic research with 30 Australian households, this article argues that in order for the sector to both understand and better guide patterns of domestic energy use we need to decentre energy feedback, and instead refocus attention on the non-energy data that informs everyday practices. We bring this empirical material together with social science literature on energy policy, feedback and consumption to propose the concept of non-energy feedback. Specifically, we explore how sensory, social, material and systemic forms of feedback inform the way that people use and think about energy, and consider how conventional and non-energy forms of feedback are used in combination in the course of everyday life. We conclude by exploring both novel and potential feedback concepts that engage households on the basis of their existing interests and capacities, and consider how non-energy feedback can help to address the challenges posed by a decarbonising and decentralising grid in which demand-side participation is increasingly vital.

Interactive demand response and dynamic thermal line rating for minimizing the wind power spillage and carbon emissions

Spilling has already occurred as a result of rising the penetration of intermittent renewable generation, and it is anticipated that the level of renewable energy curtailment will continue to soar. This leads to an increase in operating costs, CO2 emissions, and not good utilization of renewable energy resources. A bi-level multi-objective optimization model is proposed in this paper to reduce wind power spillage (WPS) based on demand response (DR) and dynamic thermal line rating (DTLR). In the upper level, multiple objectives will be satisfied based on the optimal allocation and time of DR programs considering DTLR obtained in the lower level. The minimization of WPS, load shedding, power losses, and CO2 emissions are the objectives of this level. While the lower-level aims to maximize social welfare under different scenarios and overall system constraints. Under the uncertainty of the wind power and load demand, a collection of lower-level problems that represent the market clearing conditions is used to constrain the upper-level. The effectiveness of the proposed algorithm is examined on a modified two-area IEEE 24-bus test system. Results depict that the suggested bi-level model enables considerable reductions in the WPS by up to 32.7 %. Also, there is an enhancement in load shedding, power losses, and CO2 emissions by 28.93 %, 23.07 %, and 13.9 % respectively. Finally, the social welfare increased by up to 36.6 %.

Electrochemical Devices to Power a Sustainable Energy Transition—An Overview of Green Hydrogen Contribution

This work discusses the current scenario and future growth of electrochemical energy devices, such as water electrolyzers and fuel cells. It is based on the pivotal role that hydrogen can play as an energy carrier to replace fossil fuels. Moreover, it is envisaged that the scaled-up and broader deployment of the technologies can hold the potential to address the challenges associated with intermittent renewable energy generation. From a sustainability perspective, this synergy between hydrogen and electricity from renewable sources is particularly attractive: electrolyzers convert the excess energy from renewables into green hydrogen, and fuel cells use this hydrogen to convert it back into electricity when it is needed. Although this transition endorses the ambitious goal to supply greener energy for all, it also entails increased demand for the materials that are essential for developing such cleaner energy technologies. Herein, several economic and environmental issues are highlighted, besides a critical overview regarding each technology. The aim is to raise awareness and provide the reader (a non-specialist in the field) with useful resources regarding the challenges that need to be overcome so that a green hydrogen energy transition and a better life can be fully achieved.

Research on Fast Frequency Response Control Strategy of Hydrogen Production Systems

With the large-scale integration of intermittent renewable energy generation presented by wind and photovoltaic power, the security and stability of power system operations have been challenged. Therefore, this article proposes a control strategy of a hydrogen production system based on renewable energy power generation to enable the fast frequency response of a grid. Firstly, based on the idea of virtual synchronous control, a fast frequency response control transformation strategy for the grid-connected interface of hydrogen production systems for renewable energy power generation is proposed to provide active power support when the grid frequency is disturbed. Secondly, based on the influence of VSG’s inertia and damping coefficient on the dynamic characteristics of the system, a VSG adaptive control model based on particle swarm optimization is designed. Finally, based on the Matlab/Simulink platform, a grid-connected simulation model of hydrogen production systems for renewable energy power generation is established. The results show that the interface-transformed electrolytic hydrogen production device can actively respond to the frequency disturbances of the power system and participate in primary frequency control, providing active support for the frequency stability of the power system under high-percentage renewable energy generation integration. Moreover, the system with parameter optimization has better fast frequency response control characteristics.

Method of Multi-Energy Complementary System Participating in Auxiliary Frequency Regulation of Power Systems

This research investigates a grid with two areas interconnected by a high-voltage direct-current (DC) link. One of the areas, called the sending-end region, has intermittent renewable generation and frequency stability issues. To address the lack of frequency-regulation (FR) resources in the sending-end region of the interconnected grid, the participation of hydroelectricity–photovoltaics and pumped storage complementary systems (HPPCSs) in auxiliary frequency-regulation (AFR) services is studied in the context of the construction of the electricity market. Firstly, the HPPCS participating in AFR services considering DC modulation is modeled by combining the operational characteristics of the actual power station. Taking the purchase cost of auxiliary service as the objective function, the optimum allocation of FR scheduling demand is achieved by the proposed method. The simulations confirm that the proposed method of HPPCS participation in the AFR service of the sending-end grid can effectively maintain the frequency stability of the regional interconnected grid while ensuring optimal economic efficiency. The proposed method provides the optimal scheduling solution for multiple energy resources participating in the AFR service of the grid.

A real time integrated modelling and control of modified pumped storage governor-PSS damping action for random renewable penetration

The modern power system network is continuously being penetrated by asynchronous intermittent renewable generations like solar and wind sources. Also, hydro plants can be an integral part for future power generation along with pumped storage units, but their governors need to be modified to provide adequate damping torque. So, to meet these challenges a coordinated strategic control of a new two degree of freedom pumped storage hydro governor with power system stabilizer has been proposed here to damp rotor angle oscillations in power system. The modified governor can provide good regulation along with tracking of error signal and power system stabilizer is modified to provide fast response along with reduced transient and steady state error. The proposed control action is optimally coordinated by a modified differential evolution with gray wolf optimizer algorithm. A two area four machine and 39 bus modified New England hydro dominated system have been taken in this work. Proposed controller's performance has been justified by different critical oscillatory conditions as random solar and wind penetrations with time and frequency analysis. These experimentations have been conducted in MATLAB Simulink and verified with real time simulation based in OPAL-RT 4510. For optimal coordination a multi objective function has been considered here including minimization of speed and real power deviation, undershoot and overshoot with settling time. This control action is simple, easy to implement, less costly and much efficient to damp oscillations and will enhance usability of existing hydro resources.

Uncertainty-Aware Capacity Allocation in Flow-Based Market Coupling

The effective allocation of cross-border trading capacities is one of the central challenges for the implementation of a pan-European internal energy market. In contrast to traditional power flow-ignorant methods like net transfer capacities (NTC), flow-based market coupling (FBMC) has been shown to increase price convergence between market areas, while improving congestion management effectiveness. However, explicitly analysing FBMC for a future power system with a very high share of intermittent renewable generation is often overlooked in the current literature. This paper provides a comprehensive summary on the technical specification of the FBMC process and FBMC modeling methods. It discusses implications of policy considerations and explicitly discusses the impact of high-shares of intermittent generation on FBMC performance. Further, we propose probabilistic security margins compatible with the current FBMC implementation to better account for renewable uncertainty in FBMC modeling. We conduct numerical experiments on the IEEE 118 bus test system to showcase the proposed model formulations and our data and implementation is published open source.

Innovative Power Smoothing Technique for Enhancing Renewable Integration in Insular Power Systems Using Electric Vehicle Charging Stations

The reliance on imported fuels for electricity generation and internal transportation in insular electrical systems has historically posed a significant challenge due to their geographic isolation. The vulnerability of insular ecosystems to pollution has driven the need to transition toward renewable energy sources. Despite their inherent variability, wind and solar energy have gained acceptance. Integrating these renewable technologies into insular grids presents technical challenges that impact the quality of the power supply, particularly with the increasing presence of electric vehicles. Nevertheless, the batteries of these vehicles provide an opportunity to enhance network performance. This article introduces an innovative power smoothing technique that utilizes electric vehicle batteries to optimize self-consumption and reduce power fluctuations. The proposed method is an enhanced version of the ramp-rate energy smoothing method, incorporating adaptability through real-time control of the ramp-rate using fuzzy logic. It employs an aggregated model of lithium-ion batteries with a bidirectional power electronic converter. Experimental validation is carried out in the Micro-Grid Laboratory of the University of Cuenca, Ecuador. Experimental results demonstrate a significant 14% reduction in energy generation variability, resulting in a more stable electrical supply profile. Additionally, there is a marginal improvement in energy delivery, with an additional injection of 0.23 kWh compared to scenarios without the participation of electric vehicle batteries in power smoothing tasks. These findings support the effectiveness of the proposed approach in optimizing the integration of intermittent renewable generators and electric vehicle charging in insular energy systems.

Physics-Informed Multi-Agent deep reinforcement learning enabled distributed voltage control for active distribution network using PV inverters

With the recent rapid uptake of photovoltaic (PV) resources, the overvoltage condition during low load periods or intermittent renewable generation is one of the most critical challenges for electricity distribution networks. Traditional model-based local or centralized control methods that rely on proper system parameters are difficult to mitigate rapid changes in power systems. Utilizing model-free/data-driven multiagent deep reinforcement learning (MADRL) has been recognized as an effective solution to active voltage control. However, existing MADRL-based control approaches trained solely on data are agnostic to the underlying real-world physics principles. Therefore, this paper aims to incorporate physical knowledge of regional distribution networks into MADRL’s decision-making. The main contributions of this paper are summarized as follows. First, a novel physics-informed MADRL -based distributed voltage control method is proposed, which is still under a centralized training and distributed execution framework and only requires local measurements. Second, graph neural networks are employed to help MADRL agents learn graph knowledge (node features and topological information). Further, transformer is introduced to extract discriminative representations and ensure agents’ cooperative control. Third, we adopt a physics-guided architecture of neural networks in the actor network to stabilize the training process and improve sample efficiency. Finally, simulation results based upon modified IEEE 33-bus and 141-bus networks validate the proposed method's effectiveness, robustness and computation efficiency.

Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution

During the last years, European intraday power markets have gained importance for balancing forecast errors due to the rising volumes of intermittent renewable generation. However, compared to day-ahead markets, the drivers for the intraday price process are still sparsely researched. In this paper, we propose a modelling strategy for the location, shape and scale parameters of the return distribution in intraday markets, based on fundamental variables. We consider wind and solar forecasts and their intraday updates, outages, price information and a novel measure for the shape of the merit-order, derived from spot auction curves as explanatory variables. We validate our modelling by simulating price paths and compare the probabilistic forecasting performance of our model to benchmark models in a forecasting study for the German market. The approach yields significant improvements in the forecasting performance, especially in the tails of the distribution. At the same time, we are able to derive the contribution of the driving variables. We find that, apart from the first lag of the price changes, none of our fundamental variables have explanatory power for the expected value of the intraday returns. This implies weak-form market efficiency as renewable forecast changes and outage information seems to be priced in by the market. We find that the volatility is driven by the merit-order regime, the time to delivery and the closure of cross-border order books. The tail of the distribution is mainly influenced by past price differences and trading activity. Our approach is directly transferable to other continuous intraday markets in Europe.

Performance gaps between energy system planning and operation: a study exploring the impacts of model fidelity and dispatch strategy

Effective integration of renewables is essential in the energy transition, which necessitates efficient management of intermittent renewable energy generation, system cost minimization and continuous balance between supply and demand. Due to the problem’s multifaceted nature, tools with a simplified representation of energy system operation are often used to ensure computational tractability, causing performance gaps between planning and operation stages. This work quantifies the gaps focusing on the impacts of model fidelity and dispatch strategy, such as the representation of physical constraints at different levels and varying forecast horizons. More specifically, a real-world case study with three energy hubs is considered. A Pareto front is first obtained considering the trade-offs between cost and carbon footprint using the Ehub tool, a state-of-the-art energy system planning tool. Following that, the cost-optimal and the emission-optimal designs are selected for evaluating the performance gaps, using the dispatch strategies obtained from the Ehub tool as the baseline. Results show that detailed considerations of physical constraints influence grid dependencies and fuel consumption but there are no significant impacts on resultant total costs. The cost increase due to detailed physical constraints is higher for the cost-optimal system than for the emission-optimal system. Moreover, limiting the forecast horizon to 24 hours has significant impacts on the emission-optimal system with an increase in total system cost by 20.3 %. In contrast, there is only a marginal increase of 0.8 % for the cost-optimal system.

Advanced electrode enabled by lignin-derived carbon for high-performance vanadium redox flow battery

Vanadium redox flow batteries (VRFBs) are promising energy storage systems with the potential to bridge the gap between intermittent renewable electricity generation and continuous supply of reliable electricity. The electrodes found in VRFB cells affect their energy efficiency (EE) and power density. It is important to fabricate electrodes with intriguing properties to enable VRFBs to have high performance. Herein, the abundant and cost-effective lignin is employed as the precursor to produce amorphous carbon particles after undergoing thermal decomposition treatment. The carbon particles cover the surface of carbon felt (CF). The resulting CF modified by lignin-derived carbon particles (Lignin-CF) with increased active sites and improved hydrophilicity displays superior electrochemical activity towards the VO2+/VO2+ pair than both the pristine CF and the heated bare CF. Remarkably, the VRFB consisting of Lignin-CF which acts as the positive electrode shows high performance in terms of the average EE (83.3 %) and average voltage efficiency (VE) (85.0 %) over 1000 cycles (long cycling life) for more than 16 days at 100 mA cm−2, and high power density of 1053.2 mW cm−2. It is noted that the EE and VE are comparable to the highest reported value of CF modified by carbon-based materials, aside having evidently longer cycling life. This study provides a feasible strategy for fabricating an affordable electrode for high-performance VRFBs.

Copula based model for representation of hybrid power plants in non-sequential Monte Carlo reliability evaluation

This article aims to propose models that allow the representation of the correlation between intermittent renewable generation in reliability evaluation based on non-sequential Monte Carlo Simulation (MCS). The models make use of the theory of Copulas, specifically the theory of Archimedean Copulas. To represent hybrid power plants with different types of energy sources (wind and solar, for example), it is used the theory of Hierarchical Archimedean Copulas. The models are validated by calculating generation reliability indices by three different approaches that are then compared: sequential MCS (that uses the chronological simulation), classical non-sequential MCS (that assumes independency between random variables) and the non-sequential MCS incorporating the Copula based models. The results show that the use of Copulas is able to represent accurately the correlation and complementarity between the sources, without the computational burden of the chronological simulation.

The benefits of low-carbon energy efficiency technology adoption for data centres

Economies are struggling to achieve a low-carbon transition while also fostering increased digitalisation. EU-level targets for renewable generation, emissions reduction and energy efficiency present a unique challenge for Ireland which features substantial intermittent renewable generation, limited district heating and data centres forecast to consume 37% of national electricity demand by 2028. This study quantifies the key private and public benefits associated with large-scale adoption of energy efficiency technology for data centre cooling. It considers technology that provides cooling to data centres, surplus hot water supply and electricity storage. A unique plant-level model informs a national forecast of data centre energy savings and estimates of hot water supply for use in district heating. National public benefits of adoption are quantified using a power systems model (ENGINE) of the Irish transmission system, subject to 2030 climate policy constraints. Results show that technology adoption could lower sectoral energy use by 26% and supply 12.40 TWh of hot water for a 4th generation district heating network. Technology adoption could reduce renewable electricity generation requirements by 6.92% and lower system-wide emissions by 3%. Results highlight the potential for technology adoption to deliver multiple benefits to private and public stakeholders.

(Invited) Investigation of Dynamic Processes within Zinc Aqueous Electrolyte Batteries: In-Situ and Operando Methods

Emerging new applications such as large-scale storage coupled with intermittent renewable energy generation such as wind and solar demand expanded battery function. However, in beyond lithium batteries full understanding of many processes, including complex phase transitions of electroactive materials, the kinetics of ion transport, and electrode-electrolyte interfacial reactions, remain elusive. Of particular note are systems that contain aqueous electrolytes where multiple charge carriers need to be considered including the possible participation of proton or hydronium ions.This presentation will explore operando methods and their relevance to zinc anode batteries. Diffraction remains a valuable technique for the characterization of active material evolution as a function of charge and discharge. Further, the use of spectroscopic methods such as x-ray absorption spectroscopy that are not dependent on long range order to obtain useful information are valuable for systems involving poorly crystalline or amorphous materials. Thus, the use of x-ray diffraction, x-ray absorption spectroscopy, and x-ray microfluorescence mapping in relationship to aqueous electrolyte zinc anode systems will be featured.

A fully robust home energy management model considering real time price and on-board vehicle batteries

On-board batteries from electric vehicles can be used in domestic installations as an auxiliary source of energy through their vehicle-to-home features. To properly manage this kind of assets, Home Energy Management systems are expected to be essential tools in future smart grids, enabling a more efficient use of energy in residential applications. However, these tools normally perform under severe uncertain environments caused by intermittent renewable generation, uncertain prices, uncertain demand as well as uncertain behaviour of electric vehicles. In this regard, further research should be conducted in order to develop advanced robust energy management tools that account for the effect of uncertainties. So far, most of the existing literature focused on simple stochastic approaches, that may pose various issues, while other more sophisticated uncertainty models have not been sufficiently studied. In this regard, this paper proposes a fully robust Home Energy Management model, which accounts for all the inherent uncertainties that may arise in domestic installations. In contrast to previous researches, robust optimization is applied to all the uncertainties, resulting in a tractable, but robust yet paradigm model which allows to adapt the degree of robustness. A case study is conducted on a benchmark prosumer paradigm to validate the new proposal as well as highlight the benefits of flexible appliances. Results reveal the usefulness of the new tool, which allows to handle very unfavourable uncertain profiles, especially in case of the electric vehicles, for which a very low initial state-of-charge and early departure time can be assumed, leading to more conservative results that suppose incrementing the electricity bill by 38 %.

Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-Based Guidance

Modern power systems will have to face difficult challenges in the years to come: frequent blackouts in urban areas caused by high peaks of electricity demand, grid instability exacerbated by the intermittency of renewable generation, and climate change on a global scale amplified by increasing carbon emissions. While current practices are growingly inadequate, the pathway of artificial intelligence (AI)-based methods to widespread adoption is hindered by missing aspects of trustworthiness. The CityLearn Challenge is an exemplary opportunity for researchers from multi-disciplinary fields to investigate the potential of AI to tackle these pressing issues within the energy domain, collectively modeled as a reinforcement learning (RL) task. Multiple real-world challenges faced by contemporary RL techniques are embodied in the problem formulation. In this paper, we present a novel method using the solution function of optimization as policies to compute the actions for sequential decision-making, while notably adapting the parameters of the optimization model from online observations. Algorithmically, this is achieved by an evolutionary algorithm under a novel trajectory-based guidance scheme. Formally, the global convergence property is established. Our agent ranked first in the latest 2021 CityLearn Challenge, being able to achieve superior performance in almost all metrics while maintaining some key aspects of interpretability.

Forward engineering of local load control for primary frequency and voltage regulation

Recently, more frequency and voltage stability issues have been caused by the uncertain load demand and intermittent renewable energy generation in smart grids. This has put significant pressure on turbine governor action. One potential solution is load participation in primary control. In this study, we investigate a general class of local load control (LLC) to provide grid frequency and voltage support while minimizing the pre-defined disutility to utilities and clients. Our approach relies only on local frequency and voltage measurements. We prove that the network dynamics under optimality conditions solve the dual problem of the disutility function adaptively, providing a principled way to guide forward engineering in local load control design. Simulation-based comparative study of choosing different types of disutility functions is conducted on response performance concerning frequency/voltage nadir and steady-state error. Moreover, the effectiveness and rationality of the proposed approach are verified in MATLAB/SIMULINK under various scenarios, including load variations, renewable power output fluctuations, and different penetrations of renewable energy.