Levels Of Renewable Generation Research Articles

El impacto de las energías renovables sobre el precio mayorista de la electricidad

Rationale Renewable electricity generation capacity is rising significantly in Spain. Given the merit order pricing system used in the wholesale electricity market, this could have profound implications for electricity price dynamics. Takeaways •The share of wind and solar energy in total electricity generation increased from 26% in 2019 to over 40% in 2024 H1. In the first half of this year, wholesale electricity prices were more than 40% lower than they would have been had wind and solar generation remained at 2019 levels. •The model estimated in this article suggests that under the renewable generation deployment scenarios envisaged in the update of the National Energy and Climate Plan, wholesale electricity prices could drop by an additional 50% by 2030. •In any event, the future behaviour of these prices is subject to considerable uncertainty, influenced by various supply-side factors and how demand responds to the new levels of renewable generation.

Analysis of the optimum allocation of BESS for contingency support

The development of energy storage technologies is considered a key issue to increase the flexibility of future power systems and to achieve high penetration levels of renewable generation. In recent years, battery-based storage has attracted great interest due to the cost reduction of lithium-ion batteries. In addition, the modularity of batteries makes possible to develop not only grid-scale energy storage facilities connected at transmission voltage levels but also smaller distributed energy storage systems connected at distribution voltage levels. However, it must be taken into account that the size and services provided by batteries depend on their location in the power system. In this paper, a methodology to determine the optimal location of battery based electrical energy storage systems for contingency support is presented. The methodology has been applied to a Spanish power system to evaluate the effect of batteries on contingency support when installed either in the transmission network or in the distribution network.

Mitigation of power system oscillations in weak grids with battery energy storage systems: A real-world case study

Sub-synchronous oscillations are becoming commonplace in weak areas of power systems with high levels of renewable generation, affecting their operation. Moreover, there is a lack of methods and techniques that facilitate the understanding of such phenomena and the analysis of mitigation alternatives from a practical-knowledge point of view. In this manuscript, the combination of static and dynamic techniques is utilized and consolidated to derive general conclusions when mitigating sub-synchronous oscillations by means of grid-forming battery energy storage systems (GFM BESSs). The grid impedance shaping effect of GFM BESSs, which allows the mitigation of undesired oscillations, is demonstrated through small-signal (static) and frequency scanning (dynamic) analyses when studying an actual and extremely weak grid, the Australian West Murray Zone. These findings are further validated when performing detailed real-time EMT digital simulations of the grid. Furthermore, additional dynamic simulations demonstrate the ability of GFM BESSs to provide system strength and increment the hosting capacity of renewables in weak areas. The methods and techniques presented in this work can be applied to any power system, further supporting existing industry practices.

Voltage Regulation Planning Based on Optimal Grid-Connected Renewable Energy Allocation Using Nature-Inspired Algorithms to Reduce Switching Cycles of On-Load Tap Changing Transformer

Due to the non-deterministic and probabilistic qualities of renewable energy resources (RER), integrating these resources has posed a challenge for energy companies and government policy planners. Hybrid power systems require frequent voltage regulation to address the changes in renewable generation and facilitate voltage management. This leads to an increment in the operating time of the tap changer in the power transformer, thus resulting in accelerated degradation of the on-load tap changer (OLTC) device. The primary focus of this paper is to optimally manage the penetration level of renewable generation while maintaining the minimum time for proper operation of voltage regulation devices. Accordingly, the major contribution of this paper is to address the switching cycle issues of the on-load tap-changing transformer. Initially, a framework is proposed to optimize the location and size of renewable power generation using a multi-objective cuckoo search optimization algorithm. The main goal here is to reduce power losses and tap changer operations while improving the system voltage profile under different levels of renewable energy penetration. The optimization problem takes into consideration the fluctuating nature of wind speed and solar irradiance for sunny and cloudy days over 24 h. The framework has been applied to the IEEE 57-bus and 118-bus systems with different load levels. The performance of the proposed approach has been benchmarked by comparing it with the genetic optimization algorithm to identify the higher potential buses for renewable generation allocation. The cuckoo search algorithm (CSA) shows outstanding results in reducing the number of switching cycles to about (37 – 43) % whereas the genetic algorithm (GA) only (17 – 18). In this sense, the number of changes in tap position when using the CSA is 1956 for sunny days and 1763 for cloudy days compared to GA 2558 for sunny days and 2547 for cloudy days. The voltage profiles for both algorithms are maintained in the range of (1.06 and 0.94) per unit. The results affirm the effectiveness of the proposed approach in determining the optimal placement and size of RER with voltage profile improvement and reduction in the switching cycles of OLTC.

Controlled Islanding Resilience With High Penetration of Renewable Energy Resources

Controlled islanding is a last-resort recovery scheme for preventing cascading failures. Although effective, high levels of uncertainty from renewable energy resources challenge the promise of controlled islanding to prevent additional failures. Such uncertainties can lead to subsequent failures in individual islands, render the overall recovery approach ineffective, and reduce the grid resilience. This paper develops a stochastic cascading failure model to assess the resilience of islands against cascading failures under high penetration of renewables. The stochastic model incorporates physical system constraints and system data to predict the failures. Hence, the most probable cascades in islands with high uncertainties are efficiently identified, which enables evaluating the islands' resilience to cascades. The developed model significantly improves the efficiency and accuracy of the resilience study in the presence of uncertainties. Case studies indicate that the increased penetration of intermittent renewables can improve or degrade the system resilience depending on the level of renewable generation. The case studies also reveal that an islanding strategy to minimize the imbalance of load and generation can lead to less resilient islands. These findings substantially enhance the understanding of controlled islanding under high generation uncertainties and improve resilience-oriented grid planning.

Influence of Increasing Renewable Power Penetration on the Long-Term Iberian Electricity Market Prices

In recent years, there has been a significant increase in investment in renewable energy sources, leading to the decarbonization of the electricity sector. Accordingly, a key concern is the influence of this process on future electricity market prices, which are expected to decrease with the increasing generation of renewable power. This is important for both current and future investors, as it can affect profitability. To address these concerns, a long-term analysis is proposed here to examine the influence of the future electricity mix on Iberian electricity prices in 2030. In this study, we employed artificial intelligence forecasting models that incorporated the main electricity price-driven components of MIBEL, providing accurate predictions for the real operation of the market. These can be extrapolated into the future to predict electricity prices in a scenario with high renewable power penetration. The results, obtained considering a framework featuring an increase in the penetration of renewables into MIBEL of up to 80% in 2030, showed that electricity prices are expected to decrease by around 50% in 2030 when compared to 2019, and there will be a new pattern of electricity prices throughout the year due to the uneven distribution of renewable electricity. The study’s findings are relevant for ongoing research on the unique challenges of energy markets with high levels of renewable generation.

Grid Reserve and Flexibility Planning Tool (GRAF-Plan) for Assessing Resource Balancing Capability Under High Renewable Penetration

High penetration of intermittent generation increases uncertainty and variability in balancing reserve needs. New tools are needed to help the balancing authority system operator plan for intraday and intra-hour balance between generation and load. The Grid Reserve and Flexibility Planning tool (GRAF-Plan) helps plan for adequate balancing reserves for future years or seasons for expected wind and solar generation. It also assesses the flexibility of the scheduled generation fleet to meet such requirements. The estimations are based on utilities’ operational practices (e.g., forecasting and time frame of reserve deployment), and it incorporates detailed data from renewable generation and load. Application of the tool in estimating reserve requirements in Central America under different levels of renewable generation (high and low) and for the Western Electricity Coordinating Council (WECC) 2030 Anchor Data Set scenario is discussed.

Grid forming inverter and its applications to support system strength–A case study

With the increasing penetration level of renewable generation, a shortage of system strength becomes a concern for the stable operation of the power system. Most commonly, Inverter Based Resources (IBR) plants are operated with grid following inverters (GFLI). However, a grid forming inverter (GFMI), which work as a voltage source and does not require direct reference and system strength from the grid, is now receiving increased attention. Here, Hardware-in-the-loop (HIL) testing of a GFMI and its capability to actively damp sub-synchronous oscillations and to provide synthetic inertia has been discussed. The improvement to system strength and transient stability have been presented using modelling of the transmission network in the Queensland (Australia). The study shows that with proper tuning and coordination with other generation in the area, the GFMI BESS will not only provide system strength to the grid, but also solve other stability issues.

Climate change impact on the cost of decarbonisation in a hydro-based power system

Understanding the impact of climate change on renewable energy resources is increasingly vital as our energy systems transition towards higher levels of renewable generation. This paper explores power system transition under climate change impacts on hydro resources, as well as the impact of climate change on the cost of decarbonisation. An integrated energy systems assessment tool is used to investigate the impact of altered seasonal availability factors on the optimal energy investment pathways. The cost of decarbonisation is assessed under climate change impact on the hydro resources, as well as the impact of discount rate assumptions on the total cost of decarbonisation. Results show that in the case of New Zealand, more hydro will be available in winter due to climate change, but less in summer, which is compensated for with increased solar capacity. Decarbonisation in New Zealand sees a major transformation in the transport sector, supported by a relatively moderate increase in overall demand in the electricity sector. While climate change impact on the hydro resource may reduce the total cost of decarbonisation in New Zealand, this cannot be generalised, as these impacts vary across countries and regions.

Distributionally Robust Co-optimization of Transmission Network Expansion Planning and Penetration Level of Renewable Generation

Transmission network expansion can significantly improve the penetration level of renewable generation. However, existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable generation. This paper proposes a distributionally robust optimization model to minimize the cost of transmission network expansion under uncertainty and maximize the penetration level of renewable generation. The proposed model includes distributionally robust joint chance constraints, which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set. The proposed formulation yields a two-stage robust optimization model with variable bounds of the uncertain sets, which is hard to solve. By applying the affine decision rule, second-order conic reformulation, and duality, we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers. Case studies are carried on the Garver 6-bus and IEEE 118-bus systems to illustrate the validity of the proposed method.

Market design options for electricity markets with high variable renewable generation

Variable renewable electricity generation presents challenges for traditional power markets. The island of Ireland has high levels of renewable generation by international standards, with even higher levels envisaged, and so must address these challenges. Market design to accommodate a growing share of variable renewable generation is informed and constrained by EU policy, and progress to date has been mixed. A qualitative review finds that market redesign is advised to address price cannibalisation, reveal consumer preferences for security and protect vulnerable households. Options for market design are presented, and recommendations for short and medium-term policy action are made.

Development of a Framework for Activation of Aggregator Led Flexibility

This paper presents a novel framework architecture for an online, real-time flexibility assessment and activation platform targeted at unlocking the flexibility potential of commercial buildings and smaller industrial sites, thereby enabling greater levels of renewable grid integration. Renewable integration targets in Europe of up to 40% of power generation from renewable sources by 2030 and over 90% by 2050 aim to decarbonize the electrical grid and increase electrification of transport, industry, and buildings. As renewable integration targets increase, participation in flexibility programs will be required from a much greater range of buildings and sites to balance grids hosting high levels of renewable generation. In this paper, an online implementation of a standardized flexibility assessment methodology, previously developed for offline contract negotiations between stakeholders, is modified to automate the assessment. The automated assessment is then linked to an aggregator-based multi-building or site optimization stage, enabling increased participation of multiple buildings and sites. To implement the assessment, models for individual flexible systems were reviewed, selected, and adapted, including physics-based, data-driven, and grey-box models. A review of optimization for flexibility found mixed-integer linear programming to be the optimal approach for the selection of flexible systems for demand response events.

Machine learning-based frequency security early warning considering uncertainty of renewable generation

Loss of massive generation caused by HVDC blocking or tripping large power plants is a severe threat to receiving-end grids' frequency security, especially those with a high penetration level of renewable generation. Frequency security early warning is necessary to send warning messages in time. With the warning messages, appropriate measures can be taken in advance to minimize possible losses. In this paper, a machine learning-based frequency security early warning method considering the uncertainty of renewable generation is proposed. It includes three core parts: future scenario generation, assessment model establishment, and early warning indicator establishment. In the future scenario generation part, Markov Chain Monte Carlo (MCMC) is combined with Generative Adversarial Networks(GAN) for generating numerous future scenarios reflecting possible future operation modes of the system, considering the uncertainty of renewable generation and loads. In the assessment model establishment part, the assessment model with clustering based on metric learning is applied to establish the machine learning-based frequency security assessment model. The model is continuously retrained with Domain Adaptation Metric Learning (DAML) and a transitive closure-based constraint propagation clustering approach to improve assessment accuracy. Future frequency security risk indicators are established in the early warning indicator establishment part based on future scenarios' assessment results. According to the risk indicators, future frequency security is classified into different early warning levels. The future frequency security can be expressed clearly and intuitively with the early warning levels. A simplified provincial power system of China is adopted as an example to verify the validity of the proposed early warning method.

Redesigning capacity market to include flexibility via ramp constraints in high-renewable penetrated system

Capacity markets can co-exist alongside the energy and ancillary markets to ensure the medium-term and long-term supply adequacy by remunerating the generation resources for their availability. With the substantially increasing deployment of renewable generation, the system needs more flexibility to quickly mitigate the variability and uncertainty caused by the renewable generation. However, the present capacity market model does not differentiate the flexibility of generating units, so it might not give appropriate pricing signals to more flexible units that are usually more expensive. This is particularly important considering the increasing penetration levels of renewable generation on the electric grid, which increases the need for flexible units. This paper proposes a novel capacity market model considering flexibility requirement (FR) under the high penetration levels of renewable generation. Therefore, the proposed model can give the market-clearing price (MCP) for not only peak load generation capacity but also flexibility requirement. Thus, flexible units will tend to receive more economic incentives than non-flexible units. In the case studies, the profitability of different generation resources under the proposed capacity market model is analyzed and compared to the present capacity market model. The results show that the proposed method can maintain system reliability with regard to both peak load and flexibility requirement efficiently.

Innovative system services for facilitating the integration of high levels of renewable generation in Ireland and Northern Ireland

Variable renewable generation technologies such as wind and solar photovoltaic have introduced new challenges in power system management. Grid operators have traditionally used ancillary services such as reserve and reactive power to help to maintain a secure and reliable power system. In addition, conventional synchronous generators have inherent characteristics such as inertial response, which help the power system to recover after an event. In Ireland and Northern Ireland, a weakly-interconnected island system with a high penetration of wind, new ancillary or ‘system’ services have been devised to address the technical scarcities arising from the displacement of synchronous generation. Twelve system services addressing the frequency and voltage control needs have been procured to date, allowing the power system to be operated with up to 65% instantaneous non-synchronous renewable power on the system. This study outlines the implementation of the innovative system services framework. It includes an overview of the services procured and examples of the changed behaviour of existing units in response to financial incentives. It also examines how new technologies have responded to the signals provided, questions how aspects of the arrangements may be further improved and details the challenges of integrating the use of these innovative services into operational policy.

Experimental assessment and model validation of power quality parameters for vehicle-to-grid systems

Vehicle-to-grid (V2G) and dynamic charging have been pointed out as potential assets to provide grid ancillary services and enable higher levels of renewable generation. However, such strategies may require that EV chargers operate below their nominal power, which can raise power quality (PQ) concerns. This work aims to propose a PQ characterization procedure for V2G systems, as well as to assess their PQ performance. Experimental tests were carried out for a 10-kW V2G charger connected to a Nissan Leaf, operating at different relative power levels in charging and discharging modes. Power factor (PF), harmonic distortion, and voltage unbalance were assessed as PQ metrics. Results show that although the V2G system complies with the limits established in international standards when operating at nominal power, the PQ metrics degrade for lower power values; discharging mode perform worse in terms of electricity pollution. For phase 3, lowering the relative power from 85% to 10% increased total harmonic distortion in current (THDi) from 3.1% to 19% and from 4.6% to 33% when in charge and discharge mode, respectively. Additionally, mathematical models used to describe the PQ behavior of grid connected PV systems proved to accurately describe the behavior of the system under analysis.

Assessing transport emissions reduction while increasing electric vehicles and renewable generation levels

Electric Vehicles (EVs) appear as an environmental solution for transport sector since they emit zero emissions while driving. Nonetheless, the carbon intensity (CI) of the energy sources involved in the electricity generation system could seriously compromise this solution. Hence, this study proposes a methodology to verify the sustainability of the sector by the introduction of EVs. By means of the “Well-to-Wheel” tool, it compares emissions generated by two fleets: one based on internal combustion engine vehicles (ICEVs) and another one that also contemplates different EVs penetration levels. This methodology develops an iterative process on the contribution of renewable sources to the electricity generation system until a certain level of emissions reduction is achieved. The needed evolution of the CI for the electricity system is therefore deduced. The methodology has been applied to Spain by the mid-term future, given these country policies for both a high penetration of EVs and a progressive introduction of renewable sources in its electricity system. Results indicate that the current Spanish electricity mix allows for a reduction in CO2 emissions by the introduction of EVs, but a 100% renewable system will be needed for reductions up to 74 million tons per year. This research is a first-ever study to relate the forecasted Spanish environmental policies, in terms of urban transport and configuration of the power system, with a sustainable introduction of EVs in the urban fleet. Hence, this paper would be very helpful for policy makers on evaluation of the requirements for a transport fleet electrification.

Learning to control in power systems: Design and analysis guidelines for concrete safety problems

Rapid progress in machine learning and artificial intelligence (AI) has brought renewed attention to its applicability in power systems for modern forms of control that help integrate higher levels of renewable generation and address increasing levels of uncertainty and variability. In this paper we discuss these new applications and shine light on the most relevant new safety risks and considerations that emerge when relying on learning for control purposes in electric grid operations. We build on recent taxonomical work in AI safety and focus on four concrete safety problems. We draw on two case studies, one in frequency regulation and one in distribution system control, to exemplify these problems and show mitigating measures. We then provide general guidelines and literature to help people working on integrating learning capabilities for control purposes to make safety risks a central tenet of design.

Impacts of Fault Ride Through Behavior of Wind Farms on a Low Inertia System

In view of the increasing levels of renewable generation across the world, the operational dynamics of power systems are changing. One of the dimensions of this transformational change is the nature of fault ride through behavior of renewable technologies such as wind and the associated differences compared to conventional generation. This paper investigates the impacts of fault ride through behavior of wind farms on the system stability in detail, using full dynamic model of a low inertia island power system. Recorded PMU measurements are used to establish the variations in wind farm behavior during and after a fault. A detailed sensitivity analysis on the active and reactive power injection behavior of wind farms is carried out to establish the component of fault ride through behavior that is most critical to system stability, using the full model of the Ireland and Northern Ireland Power system (All island power system). Detailed investigation of the mechanics of a fault in a low inertia, wind generation dominated system is carried out to reveal potential technical scarcities. Finally, mitigations for identified scarcities arising from non-ideal fault ride through behavior of wind farms are presented in terms of Transmission System Operator led novel system services.

Quantifying the benefits of electric vehicles on the future electricity grid in the midwestern United States

Rising numbers of electric vehicles (EVs) along with increasing renewable electricity will create challenges for electricity grids, potentially leading to significant increases in peak load, overgeneration of renewables during low load periods, and increased ramping requirements. However, controlled EV charging could largely mitigate these issues, transforming EVs from a grid liability into a valuable asset. In this study, we model the evolution of the Midcontinent Independent System Operator (MISO) grid, located in the midwestern United States (U.S.) with large numbers of forecasted EVs and renewable generation by the late 2030s, over the period 2019–2039. Uncontrolled charging leads to peak load increases of up to 10% or 8 GW, and exacerbated ramping requirements. In contrast, controlled charging significantly ameliorates these challenges, with unidirectional (“V1G”) charging largely avoiding peak load increases, filling valley loads and reducing ramp rates. Bidirectional (“V2G”) charging provides much larger load flexibility to dramatically reduce peak loads and ramp rates. This study investigates EV load growth in the midwestern U.S. and the impact that different types of controlled charging can have on mitigating the grid challenges presented both by large numbers of EVs and increasing levels of renewable generation. Moreover, above ~3 million EVs, V2G charging can exhibit multi-day optimization behavior and very high load flexibility, suggesting the possibility of new load planning tools extending over multiple days. Such capabilities would provide significant aid in optimally scheduling resources with a larger percentage of intermittent renewables.