Variable Renewable Generation Research Articles

A stochastic techno-economic comparison of generation-integrated long duration flywheel, lithium-ion battery, and lead-acid battery energy storage technologies for isolated microgrid applications

Different energy storage technologies can be potentially integrated into microgrids to support variable renewable energy generators. Long-duration flywheel energy storage is considered a new contender in the energy storage market. This energy storage technology has been previously evaluated in a techno-economic study, but it did not consider uncertainties in the model input data. In this work, stochastic techno-economic comparison is performed using microgrid modeling and Monte-Carlo methods to compare long-duration flywheels, lithium-ion batteries, and lead-acid batteries for isolated microgrid and industrial facility. Results generally show a relatively high probability for long-duration flywheels to yield a lower leveized cost of storage (LCOS) and levelized cost of electricity (LCOE) compared to lithium-ion batteries in 2020. Higher probability can be attained when the overnight component cost of long-duration flywheels is reduced or when overnight diesel prices are high. However, the chances of long-duration flywheels yielding lower LCOS and LCOE rapidly decreases as time progresses. Long-duration flywheel manufacturers and promoters must find ways to accelerate price reduction by 2.5× from baseline to secure a better chance of yielding a lower LCOS in 2050.

Scenario Partitioning Methods for Two-Stage Stochastic Generation Expansion Under Multi-Scale Uncertainty

Generation Expansion Planning (GEP) can inform regulation, electricity market design, and regional system planning by identifying adaptive investment strategies. Relevant uncertainties include hourly variability in load and renewable generation and decadal-scale uncertainty in technology, markets, and regulation. A multi-stage and multi-scale stochastic GEP model that represents these uncertainties at sufficient resolution becomes intractable. We present an approach for representing this multi-scale uncertainty, and compare it to existing methods, applied to a two-stage stochastic GEP model with a cumulative carbon emission target. For long-term uncertainty, we compare partitioning methods, which reduce the number of decision variables but retain all scenarios, to representative scenario methods, which retain only a subset of the original scenarios. For short-term uncertainty, we compare methods that select representative weeks based on distance metrics in the parameter space to methods that use the covariance of outcomes across feasible decisions to select weeks. We find that scenario reduction methods struggle to find the appropriate investment levels for variable renewable generation and consequently produce more costly plans than scenario partitioning methods. While simple approximating methods perform well with larger models, covariance-based approximations have the best performance overall.

Collective effects and synchronization of demand in real-time demand response

Future energy systems will be dominated by variable renewable power generation and interconnected sectors, leading to rapidly growing complexity. Flexible elements are required to balance the variability of renewable power sources, including backup generators and storage devices, but also flexible consumers. Demand response (DR) aims to adapt the demand to the variable generation, in particular by shifting the load in time. In this article, we provide a detailed statistic analysis of the collective operation of many DR units. We establish and simulate a model for load shifting in response to real-time electricity pricing using local storage systems. We show that DR drives load shifting as desired but also induces strong collective effects that may threaten system stability. The load of individual households synchronizes, leading to extreme demand peaks. We provide a detailed statistical analysis of the grid load and quantify both the likelihood and extent of extreme demand peaks.

Modeling the Energy Cooperation between Regionally Interconnected Aggregators using Bilateral Agreements

Traditional centralized power dispatching systems have inherent limitations in dealing with the increasing penetration of variable renewable energy generation in distribution networks. This study presents a flexible energy exchange mechanism for regionally interconnected aggregators in regards to VRE penetration. Direct energy exchange between neighboring aggregators is proposed as a promising method of maximizing social welfare (i.e. maximization of end-users benefits and minimization of energy costs). The main idea of the proposed mechanism is to develop a parallel energy exchange cooperation between physically connected aggregators ensuring operational efficiency of the power systems at a local level. Given that neighboring aggregators may have differences in their energy generation and usage patterns these differences can be utilized to bridge the mismatch of each other’s supply shortage. An operational scenario of regionally interconnected aggregators is presented and investigated. The initial simulation results have illustrated the effectiveness and reliability of the proposed model and method.

Hourly accounting of carbon emissions from electricity consumption

Carbon accounting is important for quantifying the sources of greenhouse gas (GHG) emissions that are driving climate change, and is increasingly being used to guide policy, investment, business, and regulatory decisions. The current practice for accounting emissions from consumed electricity, guided by standards like the GHG protocol, uses annual-average grid emission factors, although previous studies have shown that grid carbon intensity varies across seasons and hours of the day. Previous case studies have shown that annual-average carbon accounting can bias emission inventories, but none have shown that this bias is substantial or widespread. This study addresses this gap by calculating emission inventories for thousands of residential, commercial, industrial, and agricultural facilities across the US, and explores the magnitude and direction of this bias compared to hourly accounting of emissions. Our results show that annual-average accounting can over- or under-estimate carbon inventories as much as 35% in certain settings but result in effectively no bias in others. Bias will be greater in regions with high variation in carbon intensity, and for end-users with high variation in their electricity consumption across hours and seasons. As variation in carbon intensity continues to grow with growing shares of variable and intermittent renewable generation, these biases will only continue to worsen in the future. In most cases, using monthly-average emission factors does not substantially reduce bias compared to annual averages. Thus, the authors recommend that hourly accounting be adopted as the best practice for emissions inventories of consumed electricity.

An assessment of electric vehicles and vehicle to grid operations for residential microgrids

Electric Vehicles (EVs) are a rapidly growing technology which can lower greenhouse-gas emissions in the transport and energy sectors. The EV batteries can discharge the stored energy back to grid, also known as Vehicle-to-Grid (V2G) which can support the integration of variable distributed renewable generation. Previous research identified financial barriers to the implementation of V2G, however, recent advancements in battery technology present new opportunities to make V2G technology viable. Using the current and predicted EV technology trends, this paper evaluates the annual operation and benefits of EVs and V2G in a microgrid environment and demonstrates different modes of operation. Guided by the gaps identified in the literature, one of the main contributions of this research is to uncover the impact of EV charging scenarios on the V2G operations. Furthermore, the research reveals the interactions between V2G and variable renewable generation coupled with utility scale battery over yearlong simulations to assess seasonal characteristics of V2G operations, which was mostly unexplored to date. Simulation results indicate that the operation of V2G in an optimised microgrid environment improves the economic operation of the system and reduces the levelized cost of electricity by up to 5.7%. Additionally, V2G provides more benefit to grids with higher solar generation proportion The results suggest that the latest advancements in EV technology have improved the economic viability of V2G as well as its potential to improve grid efficiency through providing additional storage capacity and peak demand management.

Characterisation and mitigation of renewable droughts in the Australian National Electricity Market

In a decarbonising world, the electricity generation mix in Australia’s National Electricity Market (NEM) is likely to be heavily dependent on wind and solar. Designing an electricity system dominated by variable renewable energy generation requires careful examination of periods of low renewable output to ensure storage or other back up generation is sufficient to avoid loss of load. This study uses 15 years of climate and electricity demand data to examine the frequency and nature of the occurrence of low renewable periods. It examines strategies for their mitigation so that unserved energy standards are not breached. We have found that the winter period, May to August, is the time where the NEM is at greatest risk of loss of load. This winter period is when the demand in southern Australian states is higher, solar generation is lower and a series of low wind periods can drain storage. It has been demonstrated that any proposed generation mix reliant on renewable energy generation should be stress tested across a low wind winter, like the complex winter of 2010, not just a single isolated low wind period. Storage was found to be ideal to provide energy for a few hours overnight, but firm dispatchable thermal generation is likely to be a lower cost option than long term storage for extended low wind periods. Diversifying generation with the addition of offshore wind may reduce the need for storage, although the need for floating wind turbines may make this alternative too expensive to add any value in the Australian context.

Impact of model parametrization and formulation on the explorative power of electricity network congestion management models - Insights from a grid model comparison experiment

Integrating increasing shares of weather-dependent renewable energies into energy systems while maintaining high levels of security of supply constitutes a challenge for system operators. The system integration of large shares of variable renewable energy generation requires an effective operation of electricity grids and efficient coordination among grid operators. Hence, the detailed modelling of grid operation has increasingly become important in recent years.Methods for modelling the operation of (extra) high-voltage grids are undergoing persistent enhancements in academia and industry. Existing approaches vary in data granularity and computational methods. Moreover, assumptions on technical details in grid models vary. The results are thus subject to differences in input data and modelling methods.The paper at hand aims to identify the most relevant differences that exist amongst grid simulation models and methods for studying congestion management in a European context. Differences are studied based on a comparison of grid simulation models from eight German energy modelling institutions. The effects of model parameterization and formulation on congestion management results are investigated based on three different case studies focusing on outage simulation, line constraint relaxation and the modelling of cross-border measures.Results indicate that data parametrization can have large impacts on model results with respect to congestion management volumes and the geographic distribution of the respective measures. The analysis also highlights the need to thoroughly calibrate key model parameters. The research findings will assist the grid modelling community and power system planners in simulating congestion management and increase the validity and explorative power of grid simulation models.

Price spikes, temporary price caps, and welfare effects of regulatory interventions on wholesale electricity markets

There is growing empirical evidence of increasing price volatility and price spikes in electricity markets as a result of variable renewable energy generation, extreme weather events, and other factors. While price spikes are needed to cover the fixed costs of power plants, they may also signal market flaws and anti-competitive behavior. Regulatory authorities have imposed market price caps to protect consumers and prevent abusive supplier behavior. In addition, some regulators have imposed temporary price caps during or after severe events; however, these caps may be driven by political motives rather than economic logic in weak institutional settings. This paper evaluates the welfare effect of the temporary price cap implemented in 2017 on the Turkish electricity market. By using matching and panel data methods, we show that the temporary price cap reduced the total welfare but did not affect market clearing price and projected supply. Our detailed analysis shows that this decision was driven by non-economic motives, and identifies a number of fundamental problems in the Turkish market that limit the effective functioning of the market.

Impacts of power sector model features on optimal capacity expansion: A comparative study

The transition towards decarbonized energy systems requires the expansion of renewable and flexibility technologies in power sectors. Many powerful tools exist to find optimal capacity expansion. In a stylized comparison of six models, we evaluate the capacity expansion results of basic power sector technologies. The technologies under investigation include base- and peak load power plants, electricity storage, and transmission. We define four highly simplified and harmonized test cases that focus on the expansion of only one or two specific technologies to isolate their effects on model results. We find that deviating assumptions on limited availability factors of technologies cause technology-specific deviations between optimal capacity expansion in models in almost all test cases. Fixed energy-to-power ratios of storage can entirely change optimal expansion outcomes, especially shifting the ratio between short- and long-duration storage. Fixed initial and final-period storage levels can affect the seasonal use of long-duration storage. Models with a pre-ordered dispatch structure substantially deviate from linear optimization models, as missing foresight and limited flexibility can lead to higher capacity investments. A simplified net transfer capacity approach underestimates the need for grid infrastructure compared to a more detailed direct current load flow approach. We further find deviations in model results of optimal storage and transmission capacity expansion between regions, and link them to variable renewable energy generation and demand characteristics. We expect that the general effects identified in our stylized setting also hold in more detailed model applications, although they may be less visible there.

Long-Term Hydrogen Storage—A Case Study Exploring Pathways and Investments

Future low-carbon systems with very high shares of variable renewable generation require complex models to optimise investments and operations, which must capture high degrees of sector coupling, contain high levels of operational and temporal detail, and when considering seasonal storage, be able to optimise both investments and operations over long durations. Standard energy system models often do not adequately address all these issues, which are of great importance when considering investments in emerging energy carriers such as Hydrogen. An advanced energy system model of the Irish power system is built in SpineOpt, which considers a number of future scenarios and explores different pathways to the wide-scale adoption of Hydrogen as a low-carbon energy carrier. The model contains a high degree of both temporal and operational detail, sector coupling, via Hydrogen, is captured and the optimisation of both investments in and operation of large-scale underground Hydrogen storage is demonstrated. The results highlight the importance of model detail and demonstrate how over-investment in renewables occur when the flexibility needs of the system are not adequately captured. The case study shows that in 2030, investments in Hydrogen technologies are limited to scenarios with high fuel and carbon costs, high levels of Hydrogen demand (in this case driven by heating demand facilitated by large Hydrogen networks) or when a breakthrough in electrolyser capital costs and efficiencies occurs. However high levels of investments in Hydrogen technologies occur by 2040 across all considered scenarios. As with the 2030 results, the highest level of investments occur when demand for Hydrogen is high, albeit at a significantly higher level than 2030 with increases in investments of large-scale electrolysers of 538%. Hydrogen fuelled compressed air energy storage emerges as a strong investment candidate across all scenarios, facilitating cost effective power-to-Hydrogen-to-power conversions.

Challenges and Mitigation Measures in Power Systems with High Share of Renewables—The Australian Experience

Australia is one of the leading countries in energy transition, and its largest power system is intended to securely operate with up to 75% of variable renewable generation by 2025. High-inertia synchronous condensers, battery energy storage systems, and grid-forming converters are some of the technologies supporting this transformation while facilitating the secure operation of the grid. Synchronous condensers have enabled 2500 MW of solar and wind generation in the state of South Australia, reaching minimum operational demands of ≈100 MW. Grid-scale battery energy storage systems have demonstrated not only market benefits by cutting costs to consumers but also essential grid services during contingencies. Fast frequency response, synthetic inertia, and high fault currents are some of the grid-supporting capabilities provided by new developments that strengthen the grid while facilitating the integration of new renewable energy hubs. This manuscript provides a comprehensive overview, based on the Australian experience, of how power systems are overcoming expected challenges while continuing to integrate secure, low cost, and clean energy.

Economic Dispatch Considering Hourly Capacity Allocation with a Variable Renewable and Hydro-Based Generation Portfolio

The objective of this paper is to assess an economic dispatch considering a power system portfolio, which includes predominant amount of hydro power and increasing quantities of intermittent renewables in relation to the total electric capacity. With growing importance of intermittent wind and solar generation taking part into power systems worldwide, there is need for greater chronological resolution to estimate the flexibility of the power system to offer firm capacity. In this way, a linear optimization model operating hourly is developed to calculate the minimum power system cost, while stablishing the capacity allocation to meet the projected load throughout one-year simulation, as an estimation of how the hourly economic dispatch impacts the scheduling of generators belonging to a power system with this portfolio composition. A central focus is how to operate the available hydro capacity to back up intermittent renewables, evaluating the physical hydro operating constraints, monthly energy balance and maximum power availability. A case study was simulated based on the Brazil’s power system configuration, showing that existing hydro capacity provide hourly flexibility to back-up intermittent renewables, potentially saving 1.2 Billion R$, about 3.6% of total system cost referred to 2019. It is worthwhile to realize that the developed methodology can be employed to other power systems with similar capacity portfolio structure for the purpose of calculating its optimum allocation for a specified region and target year.

Scenarios for future power system development in Finland

This paper demonstrates how various part-solutions can be combined in different scenarios for a more climate-neutral electric energy system. The case study is the Finnish electric energy system. Four scenarios are discussed. A base scenario for 2030 consists of already agreed future investments in new energy production facilities by 2022 supplemented by additional consumption and a moderate increase in renewable energy generation. Alongside the base scenario 2030, two scenarios with more ambitious renewable energy targets and flexibility issues are examined. The fourth scenario is for year 2050 analyzing the operational properties of the system with high share of variable renewable generation, flexible loads, and high-capacity energy storages. These scenarios help to assess how, for example, investing in wind or solar production, heat pumps on a large scale or the battery storage of electric vehicles influences the other components of the system and the implications for emissions.

Power-based modelling of renewable variability in dispatch models with clustered time periods

The planning of future power systems with high shares of renewable generation requires modelling large and complex systems over long time periods, resulting in models which are computationally heavy to solve. For this reason methods that can be used to decrease the size of power system dispatch models are needed. A common method in large scale planning models is to decrease the model size by increasing the size of the time steps. However, using larger time steps makes the representation of variability of renewable generation and load less accurate, which can affect the results from the model. In this paper, we investigate the possibility to use a power-based version of an economic dispatch model to decrease the model time resolution while getting results which are close to the original high-resolution model. We implement both power-based and the conventional, energy-based, versions of a dispatch model with different time resolutions, and show that the power-based model has better agreement with the high-resolution model, especially as the model time step increases. For example, using the power-based model gives more accurate results for wind power curtailment in a high-renewable scenario.

On wholesale electricity prices and market values in a carbon-neutral energy system

Climate and energy policy targets of the European Commission aim to make Europe the first climate-neutral continent by 2050. For low-carbon and net-neutral energy systems primarily based on variable renewable power generation, issues related to the market integration, cannibalisation of revenues, and cost recovery of wind and solar photovoltaics have become major concerns. The traditional discussion of the merit-order effect expects wholesale power prices in a system with 100% renewable energy sources to alternate between very high and very low values. Unlike previous work, we present a structured and technology-specific analysis of the cross-sectoral demand bidding effect for the price formation in low-carbon power markets. Starting from a stylised market arrangement and by successively augmenting it with all relevant technologies, we construct and quantify the cross-sectoral demand bidding effects in future European power markets with the cross-sectoral market modelling framework SCOPE SD. As the main contribution, we explain and substantiate the market clearing effects of new market participants in detail. With this, we focus on hybrid heat supply systems consisting of combined heat and power plants, fuel boilers, thermal storage, and electrical backup units and derive the opportunity costs of these systems. Furthermore, we show the effects of cross-border integration for a large-scale European net-neutral energy scenario. Finally, the detailed information on market clearing effects allows us to evaluate the resulting revenues of all major generation and consumption technology categories on future electricity markets.

The role of modified diesel generation within isolated power systems

Traditional engine load limits restrict a diesel engine's range and its ability to load share. Accordingly, to achieve high renewable penetrations, diesel generation is conventionally turned off, positioning diesel in competition with variable renewable generation. While this approach maximises renewable penetration (environmental gain), it is uneconomic, requiring costly and complex enabling technologies such as battery storage. For many isolated communities the commercial, technical and social barriers associated with storage integration prove insurmountable. A stalemate results, with owners unable to transition to renewable generation due to inflexible diesel technologies. In this respect, it is easy to view diesel generation as an obstacle to renewable integration. Thankfully, modified diesel technologies can offer improved flexibility without the cost or complexity of energy storage. This paper details the development and testing of both low load and variable speed technologies, each able to improve the performance of diesel generation. Two isolated power system case studies are modelled, with Cape Barren island increasing renewable penetrations to between 41.3% to 58.0% compare to conventional penetrations of 7.7% to 10.1%. For Lady Elliot island the modelled range is 26.7% to 28.5% in comparison to a base of 5.3%. The results serve to recommend modified diesel application as a precursor to battery storage integration.

Oscillatory spreading and inertia in power grids.

The increase in variable renewable generators (VRGs) in power systems has altered the dynamics from a historical experience. VRGs introduce new sources of power oscillations, and the stabilizing response provided by synchronous generators (SGs, e.g., natural gas, coal, etc.), which help avoid some power fluctuations, will lessen as VRGs replace SGs. These changes have led to the need for new methods and metrics to quickly assess the likely oscillatory behavior for a particular network without performing computationally expensive simulations. This work studies the impact of a critical dynamical parameter-the inertia value-on the rest of a power system's oscillatory response to representative VRG perturbations. We use a known localization metric in a novel way to quantify the number of nodes responding to a perturbation and the magnitude of those responses. This metric allows us to relate the spread and severity of a system's power oscillations with inertia. We find that as inertia increases, the system response to node perturbations transitions from localized (only a few close nodes respond) to delocalized (many nodes across the network respond). We introduce a heuristic computed from the network Laplacian to relate this oscillatory transition to the network structure. We show that our heuristic accurately describes the spread of oscillations for a realistic power-system test case. Using a heuristic to determine the likely oscillatory behavior of a system given a set of parameters has wide applicability in power systems, and it could decrease the computational workload of planning and operation.

Optimal energy transition with variable and intermittent renewable electricity generation

We propose one of the first dynamic models of the optimal transition from fossil fuels to renewables in electricity generation that takes into account the variability and intermittency of renewable energy sources as well as storage. We take as an example solar energy, which is variable (no sun at night) and intermittent (few or no sun at day when there are clouds). We show that when the clouds phenomenon is not too severe, intermittency can be safely ignored and the planner just needs to take into account the deterministic variability of the renewable source. In this case, the optimal transition consists in using fossil fuels at day and night and complement them by solar electricity at day while investing to build up solar capacity; then abandoning fossils at day and keeping them for night electricity generation only; then, when solar capacity is large enough, starting to store electricity; and finally abandoning totally fossils when the carbon budget is exhausted. However, if the cloud problem is severe, intermittency matters a lot and precaution requires to start storage earlier, before fossils have been abandoned at day. We show that renewable electricity generation and storage are complement: absent storage devices, the long run solar capacity is smaller, and so is electricity consumption. We finally provide a quantitative illustration for the case of the Spanish energy transition. We show that in Spain intermittency can be safely ignored. We compute the carbon value corresponding to a 2 ∘C carbon budget, the dates at which storage starts, and the path of investment in solar capacity.

An Energy Management System With Short-Term Fluctuation Reserves and Battery Degradation for Isolated Microgrids

Due to the low-inertia and significant renewable generation variability in isolated microgrids, short time-scale fluctuations in the order of seconds can have a large impact on a microgrid’s frequency regulation performance. In this context, the present paper presents a mathematical model for an Energy Management System (EMS) that takes into account the operational impact of the short-term fluctuations stemming from renewable generation rapid changes, and the role that renewable curtailment and batteries, including their degradation, can play to counter-balance these variations. Computational experiments on the real Kasabonika Lake First Nation microgrid and CIGRE benchmark test system show the operational benefits of the proposed EMS, highlighting the need to properly model short-term fluctuations and battery degradation in EMS for isolated microgrids with significant renewable integration.