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Why we must move beyond LCOE for Renewable Energy Design
Abstract
The inherent intermittency of wind and solar energy challenges the relevance of Levelized Cost of Energy (LCOE) for their future design which neglects the time-varying price of electricity. The Cost of Valued Energy (COVE) is an improved valuation metric that takes into account time-dependent electricity prices. In particular, it integrates short-term (e.g., hourly) wind and solar energy “generation devaluation”, whereby high wind and/or solar energy generation can lead to low, and even negative, energy prices for grids with high renewable penetration. These aspects are demonstrated and quantified using examples of two large grids with high renewable shares with three approaches to model hourly price: 1) residual demand, 2) wind and solar generation, and 3) statistical price-generation correlation. All three approaches indicate significant generation devaluation. The residual demand approach provides the most accurate price information while statistical correlations show that generation devaluation is most pronounced for the Variable Renewable Energy (VRE) that dominates market share (e.g., solar for California and wind for Germany). In some cases, the cost of valued energy relative to levelized cost can be 43% higher for solar (CAISO) and 129% higher for wind (ERCOT). This indicates that COVE is a much more relevant metric than LCOE in such markets. In particular, COVE is based on the annualized system costs relative to the annualized spot market revenue, and thus considers economic effects of costs vs. revenue as well as those of supply vs. demand. As such, COVE (instead of LCOE) is recommended to design and value next-generation renewable energy systems, including storage integration tradeoffs. However, more work is needed to develop generation devaluation models for projected grids and markets and to better classify grid characteristics as we head to a carbon-neutral energy future.
... Therefore, while a wind farm owner will have high electricity production on windy days, the production revenue can be practically zero. Loth et al. [3] projects that the revenue for open-market grids with high wind energy penetration can be halved due to this price volatility. ...
... Since spot prices change each hour and there are distinct yearly weather patterns, we use these time scales to calculate the Annual Energy Revenue (AER). We can calculate AER using time-series [3] or with a statistical approach [7]: to calculate the revenue. However, even though it is possible to dynamically simulate the spot price and wind speed at a wind farm location, these models are computationally expensive and have coarse spatial resolution. ...
... AEV has the same units (Wh) as AEP but includes the energy devaluation when production and market demand misalign. Like Loth et al. [3], we use the average annual price (v) to define the hourly normalised price (ṽ = v/v). If we combine this with Equation (2), we can write the AER as a function of the non-dimensional price: ...
This paper addresses the challenge of incorporating electricity prices into wind turbine design methods and shows how price volatility drives wind turbines towards larger rotors and lower specific power. Since wind speed and electricity prices fluctuate, current efforts to estimate a wind turbine’s revenue are based on time-series approaches. However, this paper presents a new way of accounting for price volatility based on wind distributions, which is computationally cheap and easily integrates with current wind turbine and farm design methods and tools. The new method demonstrates that a traditional wind turbine can lose more than 15% of its revenue in open energy markets like Denmark due to price volatility. Designing turbines with lower specific power can substantially increase revenue by producing more energy at low wind speeds with higher energy demand and electricity prices.
... In both cases, this results in a revenue penalty for wind's intermittency. This effect has been termed "generation deflation" or "generation devaluation" [11]. ...
... However, the individual data points (green and red) are widely spread from this black line indicating high volatility of the normalized price for a given hourly wind share. The high variation shown in Fig. 2 reflects worldwide trends, whereby increasing shares of wind and solar energy in grids has led to increasingly volatile hourly energy prices [11]. ...
... Normalized grid hourly spot price as a function of hourly grid wind share for: a) Germany in 2019 (green symbols) and b) CAISO in 2021 (red symbols), reproduced from [11]. ...
... This share is expected to grow even faster with rapidly falling costs and better designs. For some announced tenders in the North Sea, the levelized cost of electricity (LCoE) is already in the range of 50-60 EUR per megawatt hour (Wind & water works, 2022;Lensink and Pisca, 2019). The fall in the costs so far has been due to the reductions in the operations and maintenance (O&M) costs and the continuous upscaling of turbines (Lantz et al., 2012; International Renewable Energy Agency (IRENA), 2019; Veers et al., 2019). ...
... Since LCoE, as a metric, does not capture the varying electricity price per kilowatt-hour, the market value of wind goes unaccounted for, and this is why there is a need to look beyond LCoE (Loth et al., 2022). This has led to the expectation that such market-driven designs have larger rotors, to generate more electricity at high prices, during low-wind-speed periods. ...
Traditionally, wind turbine and wind farm designs have been optimized to minimize the cost of energy. Such a design would make sense when bidding in price-based auctions. However, in a future with a high share of renewables and zero subsidies, the wind farm developer is exposed to the volatility of market prices, where the price paid per kilowatt-hour of energy would not be constant anymore. The developer might then have to maximize the revenue earned by participating in different energy, capacity, or ancillary services markets. In such a scenario, a turbine designed for maximizing its market value could be more profitable for the developer compared to a turbine designed for minimizing the levelized cost of electricity (LCoE). This study is in line with this paradigm shift in the field of turbine and farm design. It is a continuation of a previous study conducted by the same authors , which explicitly focused on the drivers of turbine sizing with respect to LCoE. The goal of this study is to optimize the design for a new set of objective functions and analyze how various day-ahead market conditions and objectives drive turbine design. A simplified market model that can generate hourly day-ahead market prices is developed and coupled with a wind-farm-level multidisciplinary design analysis and optimization (MDAO) framework to evaluate key economic indicators of the wind farm. The results show how the optimum turbine design is driven by both the choice of the economic metric and the market scenario. However, an LCoE-optimized design is found to perform well with respect to profitability-based economic metrics like modified internal rate of return (MIRR) or profitability index (PI), indicating a limited need to redesign turbines for a specific day-ahead market scenario.
... Moreover, an extensive body of academic literature relies heavily on metrics such as the traditional (deterministic) DCF or the levelized cost of energy (LCOE) [5]. Despite this, this approach has significant gaps from a variety of perspectives [6], as well as ignoring important aspects of evaluating the economic attractiveness of energy projects, such as the time value of money and the unpredictable nature of renewable energy. ...
... In [6], it has been demonstrated that metrics such as the LCOE are insufficient for evaluating renewable energy sources. According to the authors, the LCOE ignores the time-dependent value of energy generation, overvalues variable renewable sources of energy, and does not adequately address the devaluation of renewable energy sources (RES) as they are generated. ...
The use of renewable energy sources has become strategic in the production of electricity worldwide due to global efforts to increase energy efficiency and achieve a net zero carbon footprint. Hybrid systems can maximize stability and reduce costs by combining multiple energy sources. A conventional metric, such as the levelized cost of energy (LCOE), that is appropriate for assessing the cost-effectiveness of an option may not be appropriate when evaluating the economic feasibility of hybrid systems. This study proposes a stochastic discounted cash flow model (DCF) to assess the economic viability of a hybrid renewable energy system (HRES) in Brazil. The objective is to determine the combinations that will provide the highest 50th percentile internal rate of return (IRR) and the lowest coefficient of variation (CV). Model variables include capital expenditures (CAPEX), operation and maintenance (O&M) costs, sectoral charges, taxes, and long-term energy production metrics. The results demonstrate that the synergies modeled contributed to the higher economic outcomes for the HRES obtained by combining both energy sources rather than opting for a stand-alone configuration. A wind-dominant combination of 60% wind was able to increase the 50th percentile of the IRR, while a solar-dominant combination of 65% solar minimized the CV.
... Electricity market prices are volatile and vary across years and regions. The power output of the wind farm directly influences these pricing patterns, especially as the share of wind power in total generation increases (Kölle et al., 2022;Seel et al., 2021;Loth et al., 2022;Swisher et al., 2022;Canet et al., 2023;Kainz et al., 2024). During periods of high wind generation, energy prices typically decrease. ...
We present the economic lifetime-aware formulation of wind farm control (WFC), a novel approach that incorporates the current state of damage of turbines – either provided by a digital twin or estimated from operational data – to compute control policies that optimize the long-term life management of a wind farm. The optimization is guided by an economic value function that balances power capture (taking into account variable spot market prices) with operation and maintenance (O&M) costs, including potential revenue losses due to turbine downtime. The optimization process is subject to constraints ensuring that the target lifetime of selected turbine components is met, considering varying inflow conditions. This results in optimal control setpoints for each turbine as functions of ambient conditions. After introducing the economic lifetime-aware WFC approach, the paper analyzes two case studies. The first is a synthetic scenario with a simplified set of environmental conditions, designed to demonstrate the behavior of the new control strategy and its impact on fatigue loads. The second case features the more realistic setup of a small wind farm, with wind climate data derived from a real meteorological mast. The benefits of the proposed methodology are highlighted by comparing it to a range of reference control strategies, which either ignore component damage or address fatigue loads in alternative simplified ways. When compared to greedy and power-maximizing WFC strategies, results show that only the lifetime-aware formulation ensures the achievement of the desired lifetime targets. Additionally, it also results in the best economic performance.
... In power systems, the curtailment of solar/wind energy is often associated with the value of renewable electricity, which is strongly dependent on when the energy is produced because the selling price for electricity on the grid changes with time based on the merit-order curve and the supply of energy from renewables with zero operating costs 43 . The importance of temporal dependence for the value of renewables connected to the grid has led to metrics such as levelized avoided cost of energy 44 , cost of valued energy 45 , system levelized cost of energy 46,47 and market value 48 . However, similar analyses and metrics have not been applied to the temporal cost or value of renewable electricity powering a chemical plant and, therefore, electrification of the chemicals sector has been lacking continuity with the existing economic assessments concerning electrical grids. ...
Electrification of the chemical industry with renewable energy is critical for achieving net zero goals and the long-term storage of renewable energy in chemical bonds, particularly carbon-free molecules such as ammonia. Through an analysis of green ammonia production with solar and wind energy at more than 4,500 locations across Europe, this work demonstrates that maximizing cost efficiency is decoupled from maximizing energy utilization due to the intermittency of renewable energy. By devising the metric of levelized cost of utilization, the economic drive for energy curtailment is connected to the high cost to utilize portions of solar or wind energy profiles with unequal seasonal distribution. Combining solar and wind energy or ramping production decreases the cost of utilizing energy, thereby decreasing curtailment. A framework for evaluating the power-to-x economics within the context of electricity grids is illustrated using the value of utilizing energy, which indicates that electrified chemicals production is an attractive market for renewable energy at locations with high penetration on the grid.
... Further complicating the comparison of LCOE is the fact that capacity or plant factors are often based on highly dissimilar assumptions across studies or are excluded altogether. In systems with increasing shares of RE, the marginal energy value tends to decrease, also known as generation devaluation [65]. The declining correlation between electricity supply and demand leads to lower prices and, in the extreme, to increased curtailment, which lowers the capacity factor [27]. ...
Cost projections of renewable energy technologies are one of the main inputs for calculating energy transitions. Previous studies showed that these projections have been overestimated. In this study, we update the assessment of cost projections, comparing over 40 studies and 150 scenarios, between 2020 and 2050 of the main renewable energy technologies: utility-scale solar photovoltaics, rooftop solar photovoltaics, onshore and offshore wind, and Li-ion batteries. Generally, all studies reviewed expect a strong reduction in the levelised costs and capital expenditures, though with different reduction levels. While the revised cost projections have improved and are more aligned with historical trends, they are still too pessimistic. Most cost projections for 2050 are in the same ballpark as costs already observed today. Notably, the investment costs for utility-scale photovoltaics in the U.S. for 2050 are projected to be 30 % higher than current costs. We also observed a large disparity between cost projections, particularly for solar photovoltaics and offshore wind, where the most optimistic investment cost projections are up to four times lower than the most pessimistic. In the case of levelised costs, this dispersion can somewhat be explained by underlying issues such as arbitrary discount rate assumptions that fail to account for local costs of capital and risks. To sum up, global renewable energy technology costs are decreasing faster than what studies assume, highlighting an ongoing pessimism in cost projections.
... Furthermore, solar thermal and photovoltaic (PV) technologies can be utilized to capture sun energy and convert it into electrical power [1]. Renewable energy, particularly solar energy, has received more attention in recent years since it is clean and widely available [2]. However, the transformation and consumption of renewable energy frequently encounter temporal and spatial incompatibilities [3]. ...
... Key contributors to this delay include levelized cost of energy (LCOE) estimates remaining higher than other marine energy alternatives, like offshore wind, and the lack of a standard design. However, Loth et al. (2022) suggest that the use of LCoE is questionable ...
The untapped potential of wave energy offers another alternative to diversifying renewable energy sources and addressing climate change by reducing CO2 emissions. However, development costs to mature the technology remain significant hurdles to adoption at scale and the technology often must compete against other marine energy renewables such as offshore wind. Here, we conduct a real option valuation that includes the uncertain market price of wholesale electricity and managerial flexibility expressed in determining future optimal decisions. We demonstrate the probability that the project’s embedded compound real option value can turn a negative net present value wave energy project to a positive expected value. This change in investment decision uses decision tree analysis, where real options are developed as decision nodes, and models the uncertainty as a risk-neutral stochastic process using chance nodes. We also show how our results are analogous to a financial out-of-the-money call option. Our results highlight the distribution of outcomes and the benefit of a staged long-term investment in wave energy systems to better understand and manage project risk, recognizing that these probabilistic results are subject to the ongoing evolution of wholesale electricity prices and the stochastic process models used here to capture their future dynamics. Lastly, we show that the near-term optimal decision is to continue to fund ongoing development of a reference architecture to a higher technology readiness level to maintain the long-term option to deploy such a renewable energy system through private investment or private–public partnerships.
... The value of electricity is strongly influenced by the time at which it is made available. When assessing the cost of VRE, this issue was quickly realised and addressed by a broad body of literature [16,17,28,29,30]. With LCOS being equivalent to LCOE for storage technologies, DR assets can be considered to behave analogous to VRE generation in that they vary in their availability. ...
To make well-informed investment decisions, energy system stakeholders require reliable cost frameworks for demand response (DR) and storage technologies. While the levelised cost of storage (LCOS) permits comprehensive cost comparisons between different storage technologies, no generic cost measure for the comparison of different DR schemes exists. This paper introduces the levelised cost of demand response (LCODR) which is an analogous measure to the LCOS but crucially differs from it by considering consumer reward payments. Additionally, the value factor from cost estimations of variable renewable energy is adapted to account for the variable availability of DR. The LCODRs for four direct load control (DLC) schemes and twelve storage applications are estimated and contrasted against LCOS literature values for the most competitive storage technologies. The DLC schemes are vehicle-to-grid, smart charging, smart heat pumps, and heat pumps with thermal storage. The results show that only heat pumps with thermal storage consistently outcompete storage technologies with EV-based DR schemes being competitive for some applications. The results and the underlying methodology offer a tool for energy system stakeholders to assess the competitiveness of DR schemes even with limited user data.
... • Assessing performances of energy sector coupling and different energy storage systems in improving energy flexibility 15,16 . • Evaluating the levelized energy costs of distributed energy resources within their lifespan 17 . ...
Distributed energy resources (DERs) would play a crucial role in the transition towards decentralized and decarbonized energy systems. However, due to the limited availability of long-term, high-resolution datasets, there has been little research on the descriptive analysis of distributed energy systems throughout the lifespan of distributed power generators and beyond. To address this challenge, this study has collected and described a twenty-year dataset (from 2002 to 2021) consisting of hourly energy generation and consumption profiles from a campus distributed energy system, covering the entire lifespan of on-site generators. The dataset includes supply-side data such as gas consumption from combined heating and power (CHP) units (fuel cell, gas engine), absorption chiller, gas boiler, solar photovoltaic generation, CHPs output, and grid electricity import. Additionally, real-life electricity, hot water, space heating, and cooling energy load profiles from individual buildings within the campus were also collected. This long-term dataset can be utilized in various scenarios, providing researchers and policymakers with comprehensive insights into the energy efficiency of distributed energy systems.
... A good example of this overlap is energy and capacity, as is evidenced by the debate around energy-only markets (where capacity is incentivised by high energy prices during periods of scarcity) and capacity markets (where capacity is directly rewarded; Hogan, 2005). The focus is now shifting from minimising LCOE towards maximising the value of wind, which effectively captures all the services (Denny and O'Malley, 2007;Dykes et al., 2020;Loth et al., 2022). It no longer matters that the cheapest possible electrons are being put into the grid (as energy), but what matters is when and where those electrons are put into the grid (requiring the energy and capacity), adding additional value to the system. ...
The share of wind power in power systems is increasing dramatically, and this is happening in parallel with increased penetration of solar photovoltaics, storage, other inverter-based technologies, and electrification of other sectors. Recognising the fundamental objective of power systems, maintaining supply–demand balance reliably at the lowest cost, and integrating all these technologies are significant research challenges that are driving radical changes to planning and operations of power systems globally. In this changing environment, wind power can maximise its long-term value to the power system by balancing the needs it imposes on the power system with its contribution to addressing these needs with services. A needs and services paradigm is adopted here to highlight these research challenges, which should also be guided by a balanced approach, concentrating on its advantages over competitors. The research challenges within the wind technology itself are many and varied, with control and coordination internally being a focal point in parallel with a strong recommendation for a holistic approach targeted at where wind has an advantage over its competitors and in coordination with research into other technologies such as storage, power electronics, and power systems.
... The majority of the energy used in sectors, mainly in the making of raw materials, is obtained from fossil fuels [3]. From the sustainability and ecological impact points of view, use of energy production methods is essential compared to traditional fossil fuel energy sources [4]. Hence, using more viable sources such as biomass and municipal solid waste (MSW) in addition to traditional resources in energy production has recently become widespread and extensive [5,6]. ...
Industrial solid residual waste (ISRW) generated during and/or due to the making of energy, heat, and raw materials poses a major threat to a sustainable future due to its large production quantities and complex characteristics. Especially improper disposal of ISRW (e.g., coal ashes, municipal waste residue, and biomass ashes) not only threatens human health but can also cause environmental hazards such as water, soil, and air pollution, upsetting the global balance. Given the environmental impacts as well as increasingly stringent disposal regulations, lack of landfills, and economic constraints, more sustainable and naturally friendly management strategies are being adopted for ISRW. While numerous studies in the literature have considered various characteristics of ISRW, a complete appraisal of the entire practice, from making to disposal, is still lacking. This paper presents an overview of the making, features, and traditional and innovative managing tactics of ISRW within the context of a general legal framework. This paper provides a scientific review of the various production types, global production quantities, and characteristics of ISRW. Additionally, the orthodox management strategies of ISRWs are scrutinized from a sociological and ecological standpoint, and diverse techniques for more viable and secure management are elucidated. This review culminates in an examination of the global impact and advantages of ISRW management policies based on legislation and regulations. Consequently, this paper seeks to elucidate the extant practices and a few recent advancements pertaining to ISRWs. Additionally, it underscores the ecological, sociological, and economic issues engendered by ISRWs and proposes innovative applications and production technologies.
... And attracting such private investments should begin with alternative cost assessment methods like Systems LCOE [48] or Cost of Valued Energy (COVE) [49] that consider the full nuclear value chain which would make its pricing competitive with other power technologies. Debt instruments, tax incentives, and subsidies can further encourage private financing. ...
Since 1948, India's nuclear power journey has made remarkable strides, currently standing at 8.08 GW with plans to increase to 22.8 GW by 2031-32 and 100 GW by 2047. Our study forecasts power growth and resource needs for these targets, showing near-term goals are achievable, but long-term growth may face challenges. To achieve the 100 GW goal, we analyzed key factors and recommend specific actions: (i) Transparent land acquisition processes, public consultation, and fair compensation for new nuclear sites; (ii) Reclassifying the domestic nuclear industry from 'Red' to 'Orange' based on minimal emissions to reduce regulatory burdens and improve public perception; (iii) Securing international uranium agreements until India transitions to thorium-based reactors; (iv) Enhancing domestic nuclear supply chain to support diverse reactor technologies, requiring more certifications and skilled workers; (v) Adapting regulatory frameworks to new technologies and streamlining licensing; (vi) Encouraging private financing and participation in power production, supported by public subsidies and tax breaks; (vii) Addressing radiophobia through threshold-based radiation risk models to enable regulatory flexibility and increase public acceptance of nuclear energy. The findings from this study could provide a useful planning template for expanding nuclear power programs in other developing countries.
https://ssrn.com/abstract=4911840
... The impact of technological choices (for example, offshore support structures or powertrain type) on LCOE is well understood. Looking beyond LCOE, an increasing number of studies is now focusing on wind energy system optimization for economic value [1,2]. On the environmental side, various life cycle assessment (LCA) analyses have been conducted to quantify the environmental impact of different wind turbine and farm configurations [3][4][5]. ...
The ongoing energy transition towards fully sustainable energy systems requires designing wind farms looking beyond the sole levelized cost of energy, in order to concurrently ensure not only the economic profitability but also the environmental friendliness of future plants. Within this new approach to design, it becomes necessary to understand the effects that various possible technological choices have on both the economic and the environmental performance of wind farms. This study presents a framework designed to support these coupled economic-environmental assessments. The capabilities of the code are showcased by analysing the impact of different choices in terms of support structure type, specific power, tower height, powertrain type, and array and export voltage level for an exemplary offshore farm, chosen here as the IEA Wind 740-10-MW Reference Offshore Wind Plant with irregular layout. While the effects of many technological choices on the cost of energy are already well understood by industry, the present analysis shows that — at least in this specific case — climate change impacts are mainly driven by steel production, due to the massive amount of required material, but also, interestingly, by vessel activities. A low specific power, tall towers, and a high export cable voltage appear to offer the greatest potential for the concurrent improvement of the environmental and economic performance of the plant.
... The expression shown in Eq. (1) corresponds to the one commonly used when calculating the LCOE for annual periods. Other expressions for the LCOE include the influence of monetary inflation over time (Loth et al., 2022). This is the case when the aim is to evaluate the investment over the entire life cycle of a project, which, being relatively long, is subject to inflationary monetary dynamics (Johnston et al., 2020). ...
... The construction cost of pile foundations for offshore wind power and other marine projects accounts for about 20-30% of the total cost [3], much higher than the construction cost of similar wind power pile foundations onshore. Offshore wind power and other renewable energy industry has an important economic indicator: Levelized Cost of Energy (LCOE) [9,27,28], which is an important economic indicator to guide the offshore wind power industry for engineering construction. Many latest metrics are also based on this indicator [29]. ...
Foundation scour is the erosion of sediments around pile foundations by wave and current in offshore wind energy. This phenomenon destabilizes foundations and poses a threat to pile safety. Therefore, scour protection becomes a crucial challenge in offshore wind projects. This paper reviews and synthesizes recent publications and patented technologies related to scour protection. Considering the primary engineering concerns, the paper proposes design principles for effective scour protection schemes to standardize evaluation criteria. These principles prioritize efficacy, independence, and cost-efficiency, enabling the analysis of scour protection scheme applicability. In addition, this paper summarizes and describes common protection schemes in the literature. The effectiveness of their protection is analyzed and summarized, and their economic and performance independence is evaluated. This paper categorizes flow-altering scour protection schemes found in the literature. Based on a comprehensive understanding of the mechanisms and engineering requirements of scour protection, the paper proposes a focus on determining the erosion reduction rate curve (Ep−U/Uc curve) as a key criterion for evaluating the effectiveness of protection schemes under varying flow velocities and the erosion reduction rate of scour protection schemes under extreme conditions. The study highlights the necessity of establishing a comprehensive design evaluation methodology, which is crucial for addressing the significant challenges related to scour encountered in offshore wind power projects.
... Further complicating the analysis of the additional revenue from wind farm flow control is that pricing patterns are impacted by wind generation itself, and prices are more strongly tied to wind generation as wind power accounts for a larger portion of total generation within a region Swisher et al., 2022;Millstein et al., 2021;Prol et al., 2020;Brown and Sullivan, 2020;Loth et al., 2022). Energy prices typically decrease in a region during hours with substantial wind generation, so energy gain during these hours would provide little value. ...
Wind farm flow control represents a category of control strategies for achieving wind-plant-level objectives, such as increasing wind plant power production and/or reducing structural loads, by mitigating the impact of wake interactions between wind turbines. Wake steering is a wind farm flow control technology in which specific turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thus increasing overall wind plant power production. In addition to promising results from simulation studies, wake steering has been shown to successfully increase energy production through several recent field trials. However, to better understand the benefits of wind farm flow control strategies such as wake steering, the value of the additional energy to the electrical grid should be evaluated – for example, by considering the price of electricity when the additional energy is produced. In this study, we investigate the potential for wake steering to increase the value of wind plant energy production by combining model predictions of power gains using the FLOw Redirection and Induction in Steady State (FLORIS) engineering wind farm flow control tool with historical electricity price data for 15 existing US wind plants in four different electricity market regions. Specifically, for each wind plant, we use FLORIS to estimate power gains from wake steering for a time series of hourly wind speeds and wind directions spanning the years 2018–2020, obtained from the ERA5 reanalysis dataset. The modeled power gains are then correlated with hourly electricity prices for the nearest transmission node. Through this process we find that wake steering increases annual energy production (AEP) between 0.4 % and 1.7 %, depending on the wind plant, with average increases in potential annual revenue (i.e., annual revenue of production, ARP) 4 % higher than the AEP gains. For most wind plants, ARP gain was found to exceed AEP gain. But the ratio between ARP gain and AEP gain is greater for wind plants in regions with high wind penetration because electricity prices tend to be relatively higher during periods with below-rated wind plant power production, when wake losses occur and wake steering is active; for wind plants in the Southwest Power Pool – the region with the highest wind penetration analyzed (31 %) – the increase in ARP from wake steering is 11 % higher than the AEP gain. Consequently, we expect the value of wake steering, and other types of wind farm flow control, to increase as wind penetration continues to grow.
... While an increase of 8 EJ was observed in renewable energy between 2019 and 2021, the consumption of fossil fuels remained stable. Fossil fuels mostly dominate the worldwide energy supply [4]. Fossil fuels accounted for 82% of global energy use in 2020, 83% in 2019 and 85% in 2014. ...
... Nevertheless, the current approach can be supplemented with further inputs to reduce the uncertainties. Also, other value metrics, such as the cost of valued energy (COVE), could be integrated (Loth et al., 2022). A highly significant influence is the price of electricity, which is not constant over the entire lifetime. ...
Renewable energies have an entirely different cost structure than fossil-fuel-based electricity generation. This is mainly due to the operation at zero marginal cost, whereas for fossil fuel plants the fuel itself is a major driver of the entire cost of energy. For a wind turbine, most of the materials and resources are spent up front. Over its lifetime, this initial capital and material investment is converted into usable energy. Therefore, it is desirable to gain the maximum benefit from the utilized materials for each individual turbine over its entire operating lifetime. Material usage is closely linked to individual damage progression of various turbine components and their respective failure modes. In this work, we present a novel approach for an optimal long-term planning of the operation of wind energy systems over their entire lifetime. It is based on a process for setting up a mathematical optimization problem that optimally distributes the available damage budget of a given failure mode over the entire lifetime. The complete process ranges from an adaptation of real-time wind turbine control to the evaluation of long-term goals and requirements. During this process, relevant deterministic external conditions and real-time controller setpoints influence the damage progression with equal importance. Finally, the selection of optimal planning strategies is based on an economic evaluation. The method is applied to an example for demonstration. It shows the high potential of the approach for an effective damage reduction in different use cases. The focus of the example is to effectively reduce power of a turbine under conditions where high loads are induced from wake-induced turbulence of neighbouring turbines. Through the optimization approach, the damage budget can be saved or spent under conditions where it pays off most in the long term. This way, it is possible to gain more energy from a given system and thus to reduce cost and ecological impact by a better usage of materials.
... One of the reasons that wind turbines have grown in size is that larger wind turbines have led to lower LCOE (13). However, as wind market penetration has increased, other metrics (14) have been proposed to incorporate power grid balancing needs and running wind turbines only when the cost of electricity makes it worthwhile to do so; with these other metrics, storage becomes more beneficial because combined wind-storage systems make the energy more dispatchable. For instance, the cost of valued energy (COVE) differs from the LCOE in Equation 1 by weighting the energy produced with the normalized market value m ($/kWh) at the time of production: ...
Wind energy is recognized worldwide as cost-effective and environmentally friendly, and it is among the fastest-growing sources of electrical energy. To further decrease the cost of wind energy, wind turbines are being designed at ever-larger scales. To expand the deployment of wind energy, wind turbines are also being designed on floating platforms for placement in deep-water locations offshore. Both larger-scale and floating wind turbines pose challenges because of their greater structural loads and deflections. Complex, large-scale systems such as modern wind turbines increasingly require a control co-design approach, whereby the system design and control design are performed in a more integrated fashion. This article reviews recent developments in control co-design of wind turbines. We provide an overview of wind turbine design objectives and constraints, issues in the design of key wind turbine components, modeling of the wind turbine and environment, and controller coupling issues. Wind turbine control functions and the integration of control design in co-design are detailed with a focus on co-design compatible control approaches.
Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 7 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... In this scenario a value of levelised cost of energy (LCOE) value is desired. This cost metric has become a standard for assessing and comparing energy technologies and guiding design advancements [38]. Traditionally, LCOE is defined as the annualised costs relative to the annual energy production, represented by Eq. (13) [39]. ...
... Among them, the application of solar energy in building heating is conducive to reducing building energy consumptions and carbon emissions. However, the intermittency of solar energy and the mismatch between the demand and supply have seriously affected the performance of the solar energy in building heating [9]. ...
... The Cost of Valued Energy relies on system costs in relation to spot market revenue on an annual basis and thus takes into account not only the economic impact of supply vs. demand but also of cost vs. revenue [222]. Although spot markets could be irrelevant in decentralized off-grid energy systems, this highlights once again the need for novel metrics to compare energy systems. ...
Recent global events emphasize the importance of a reliable energy supply. One way to increase energy supply security is through decentralized off-grid renewable energy systems, for which a growing number of case studies are researched. This review gives a global overview of the levelized cost of electricity (LCOE) for these autonomous energy systems, which range from 0.03 2021 /kWh worldwide. The average LCOEs for 100% renewable energy systems have decreased by 9% annually between 2016 and 2021 from 0.54 2021 /kWh, most likely due to cost reductions in renewable energy and storage technologies. This review identifies and discusses seven key reasons why LCOEs are frequently overestimated or underestimated in research, and how this can be prevented in the future. This overview can be employed to verify findings on off-grid systems, to assess where these systems might be deployed and how costs evolve.
... For future research, we recommend calculating and comparing Value Adjusted LCOE (VLCOE) when LCOE is a priority in choosing the preferred power generator. Cost of Valued Energy (COVE) considers time-dependent electricity prices that is recommended to design and value nextgeneration renewable energy systems, including storage integration trade-offs (Loth et al., 2022) is another valuation metric that can be applied in the future. ...
The potential output of photovoltaic (PV) panels is influenced by several factors, including the direction of solar radiation from the sun toward the panel’s surface. The maximum output of the panels is obtained when the panels are vertical to the sun's rays. In this study, a techno-economic analysis is conducted to examine whether an automatic one-axis sun tracker system is an economically feasible option for installing a large-scale PV park in the Nicosia district in the central part of Cyprus. The performance of a one-axis sun tracker with an installed capacity of 781 kWp is compared to a PV system with a fixed flat structure having the same capacity and larger capacity at 1034 kWp. Output generated by the three PV system options is simulated by three alternative simulation software (SolarGIS, PVSyst, and PVGIS). Financial analysis is performed utilizing simulated PV power output, accounting for electricity feed-in tariff and overall cost of the project. The cash-flow model is run for several scenarios defined by different leverage ratios, including no leverage. Considering the technical parameters of a PV system and solar panel characteristics, such as the degradation effect on solar panel efficiency and solar radiation, we estimate the solar tracking system produces about 20%–30% more energy compared to a fixed structure. We find both technologies are economically viable options, however, a one-axis tracker system performs better financially. LCOE in all scenarios is below the highest acceptable level for solar PV projects in Cyprus which is 103 EUR per MWh. LCOE for a solar tracker PV is 39 EUR per MWh with a 30% leverage ratio and up to 79 EUR per MWh with 85% leverage. LCOE for a sun-tracker is ~20% lower than LCOE for a PV with a fixed axis of comparable size. Despite higher investment costs, the solar tracking PV system performs with a 12% higher equity internal rate of return, and a 9% shorter loan payback period compared to the same installed power of a fixed structure. The Financial analysis is complemented by quantified benefits due to avoided carbon emissions. Accounting for carbon benefits makes a sun-tracker PV system economically a better option over the fixed tracker PV system, resulting in 228,000 EUR more benefits. Overall, the present value of net benefits of a solar-tracker PV amounts to 1.39 mil. EUR and due to high irradiation in Cyprus, the carbon footprint of PV power output represents only 6% of the footprint of generating electricity in thermal power plants. When these benefits are accounted for the sum of NPV and social benefits will turn out to be higher for a one-axis tracker compared to the total social benefits of a fixed tracker of the same size.
... This research assesses the levelized cost of electricity (LCOE) incurred by constructing and operating a PV system and compared it with the price of purchasing energy from the electrical distribution network. An evaluation of LCOE, considering several implications in renewable energy design, such as wind and PV systems, can be accessed in [25]. The research in [26] discusses the improvements in LCOE and grid parity considering the useful lifetime of the PV technologies. ...
At present, a worldwide paradigm shift has become apparent, with more and more consumers consuming the energy generated by renewable energy sources (RES) systems, such as wind or photovoltaic (PV) energy, sometimes benefiting from appropriate incentives by individual governments. Consequently, it is necessary to carry out technical–economic assessments to understand the evolution of the viability of RES investments. Within the framework of an intelligent network control environment, the smart grid (SG) concept is associated with this model, and is an important tool in the management of energy distribution networks. This article aims to make a further contribution to this issue by analyzing the economic feasibility of investing in residential consumers, considering different RES configurations. Scenarios covered in this study include: “inject all on the low voltage network/consume all on the low voltage network”, self-consumption, net-metering, and storage systems. The economic study results in this article show that self-consumption with and without the injection of excess electricity into the grid is quite attractive. The bi-hourly tariff was found to be more profitable than other tariffs. Variable tariffs (bi or tri-hourly) are more profitable than fixed tariffs. It is also concluded that investment in storage systems is not yet an economically viable solution due to the high price of energy storage.
The integration of non-conventional renewable energy sources (NCRES) plays a critical role in achieving sustainable and decentralized power systems. However, accurately assessing the economic feasibility of NCRES projects requires methodologies that account for policy-driven incentives and financing mechanisms. To support the shift towards NCRES, evaluating their financial viability while considering public policies and funding options is important. This study presents an improved version of the Levelized Cost of Electricity (LCOE) that includes government incentives such as tax credits, accelerated depreciation, and green bonds. We apply a flexible investment model that helps to find the most cost-effective financing strategies for different renewable technologies. To do this, we use three optimization techniques to identify solutions that lower electricity generation costs: Teaching Learning, Harmony Search, and the Shuffled Frog Leaping Algorithm. The model is tested in a case study in Colombia covering battery storage, large- and small-scale solar power, and wind energy. Results show that combining smart financing with policy support can significantly lower electricity costs, especially for technologies with high upfront investments. We also explore how changes in interest rates affect the results. This framework can help policymakers and investors design more affordable and financially sound renewable energy projects.
Wind power accounted for 8% of global electricity generation in 2023 and is one of the cheapest forms of low-carbon electricity. Although fully commercial, many challenges remain in achieving the required scale-up, relating to integrating wind farms into wider technical, economic, social, and natural systems. We review the main challenges, outline existing solutions, and propose future research needed to overcome existing problems. Although the techno-economic challenges of grid and market integration are seen as significant obstacles to scaling up wind power, the field is replete with solutions. In many countries, planning and permitting are immediate barriers to wind-power deployment; although solutions are emerging in the EU and several countries, the effectiveness and long-term acceptance of fast-track permissions and go-to areas remains to be seen. Environmental impacts on wildlife and recycling challenges are rising issues for which tested and scalable solutions are often still lacking, pointing to large remaining research requirements.
In this paper, the enthalpy-porosity method is used to develop a three-dimensional numerical model for systematically investigating the melting dynamics enhancement of double-tube latent heat storage units. The study explores the combined effects of the melting temperature using three paraffin waxes (i.e., RT-50, RT-60, and RT-65) and three fin structures (i.e., longitudinal, single cross, and double cross) on the melting dynamics. Then, it explores the effect of varying the charging temperature (i.e., 75, 80, 85, and 90 °C) on the optimal fin structure unit and best wax. The final part investigates the impact of storage orientations on the melting dynamics for the optimal charging temperature. The results show that the double-cross fin has the highest melting rates for the three waxes. Using RT-55 without fins, the melting times of RT-60 and RT-65 increase by 12.6 % and 32.1 %, respectively. Furthermore, the longitudinal fin, single-cross fin, and double-cross fin cases reduce the melting times of RT-55 by (62.6 %, 71.1 %, and 75.9 %), RT-60 by (63.9 %, 71.7 %, and 76.5 %%), and RT-65 by (63.8 %, 72.0 %, and 76.9 %), respectively. The double cross fin using RT-55 reduces the cyclic charging and energy storage capacities by 17.3 % and 9.7 %, increasing the charging rate, storage rate, and thermal effectiveness by 2.4, 2.8, and 1.6 times, respectively, relative to the basic storage scenario. The best charge temperature reduces the melting time by 32.8 % and improves the charging capacity, thermal energy stored, charging rate, storage rate, and thermal effectiveness by 11.6 %, 8.2 %, 66.2 %, 62.2 %, and 4.0 %, respectively. The horizontal orientation outperforms the vertical one in terms of the charging capacity, charging rate, and effectiveness by 116.2 %, 115.5 %, and 131.4 %, respectively.
The falling levelised cost of electricity (LCOE) of renewables is often used to argue that electricity costs in Germany will decline. However, the LCOE is not a reliable basis for estimating future electricity costs because gaps between demand and supply of insufficient renewable production will have to be covered by complementary technologies such as battery storage or gas-fired power plants and, in the future, hydrogen-fueled power plants. The investment costs of these plants and their operation must be included in the calculation of the costs of meeting demand. The “Levelised Cost of Load Coverage” (LCOLC) calculated in this way does not indicate that electricity costs will fall significantly in the coming decade.
A global transition to sustainable energy systems is underway, evident in the increasing proportion of renewables like solar and wind, which accounted for 12 % of global power generation in 2022. The shift to a low-carbon economy will likely require a substantial increase in energy storage in the near future. In this context, concentrating solar power (CSP) is viewed as a promising renewable energy source in the coming decades. However, high generation costs compared to other renewable technologies remain a key barrier inhibiting wider deployment of CSP. Compared to solar PV and onshore wind alternatives, CSP cannot currently compete on the levelized cost of electricity (LCoE). This review provides a comprehensive overview of the vital economic factors and considerations for large-scale CSP expansion. The current state of the market reveals about 8 GW of installed global CSP capacity in 2023, with rapid growth occurring in China, Chile, South Africa and the Middle East. Key economic parameters discussed in this study include capital costs, capacity factors, operating expenses and LCoE. Installation costs for CSP declined by 50 % over the past decade, falling to the current range of 20–60 per kW is now considered economical. Capacity factors increased from 30 % to more than 50 % (depending on location) through larger storage capacities and higher operating temperatures. Operations and maintenance costs now range from 0.31 per kWh in 2010 to $0.10 per kWh in 2022. Ongoing innovations in materials, components integrated systems and optimization can further reduce capital expenditures, enhance performance and decrease LCoE. However, appropriate incentives and financing mechanisms remain vital to support continued CSP technology maturation and cost reductions. With its inherent dispatchability and storage capabilities, CSP can become a cost-competitive renewable energy source, but design optimizations and accurate economic appraisals are imperative for CSP to achieve its vast sustainability potential.
Traditionally, wind turbine and wind farm designs have been optimized to minimize the cost of energy. Such a design would make sense when bidding in price-based auctions. However, in a future with a high share of renewables and zero subsidies, the wind farm developer is exposed to the volatility of market prices, where the price paid per kWh of energy would not be constant anymore. The developer might then have to maximize the revenue earned by participating in different energy, capacity, or ancillary services markets. In such a scenario, a turbine designed for maximizing its market value could be more profitable for the developer compared to a turbine designed for minimizing the Levelized Cost of Electricity (LCoE). This study is in line with this paradigm shift in the field of turbine and farm design. It is a continuation of a previous study conducted by the same authors (Mehta et al., 2023), which explicitly focused on the drivers for turbine sizing w.r.t. LCoE. The goal of this study is to optimize the design for a new set of objective functions and analyze how various day-ahead market conditions and objectives drive turbine design. A simplified market model that can generate hourly day-ahead market prices is developed and coupled with a wind farm-level Multi-disciplinary Design Analysis and Optimization (MDAO) framework to evaluate key economic indicators of the wind farm. The results show how the optimum turbine design is driven by both the choice of the economic metric and the market scenario. However, an LCoE-optimized design is found to perform well w.r.t. profitability-based economic metrics like MIRR/PI, indicating a limited need to redesign turbines for a specific day-ahead market scenario.
The share of wind power in power systems is increasing dramatically and this is happening in parallel with increased penetration of solar photovoltaics, storage, other inverter-based technologies, and electrification of other sectors. Integrating all these technologies in a cost-effective manner while maintaining (or improving) power system reliability is challenging and is driving radical changes to planning and operations paradigms. Wind power can maximise its long-term value to the power system by balancing the needs it imposes on the power system with its contribution to addressing these needs with services. Research in wind power should be guided by this balanced approach and by concentrating on its advantages over competitors. The research challenges within the wind technology itself are many and varied with control and coordination internally being a focal point in parallel with a strong need for coordination with research in other technologies such as storage, power electronics and power systems that together are fundamental and potentially profound. This is all driven by the unchanging nature of the fundamental objective of power systems – maintaining supply demand balance reliably at least cost.
An exclusively renewable energy economy is imperative for sustained industrial expansion. Despite notable progress, renewable energy sources fulfilled only 12.6% of global energy demand in 2022. This can be largely...
Published forecasts underestimate renewable energy capacity growth and potential cost reductions, creating uncertainty around investment decisions and slowing progress. Scenario-based projections diverge widely, driven by variations in modelling techniques and underlying assumptions, with policy-based models typically being overly conservative. With historical generation capacity and cost data readily available, this research demonstrates that data-driven approaches can be leveraged to improve long-term capacity and cost forecasts of solar, wind, and battery storage technologies. Unlike exponential growth models prevailing over shorter time scales, logistic curves requiring asymptotic limits, or machine learning algorithms dependent on extensive datasets, this analysis demonstrates that temporal quadratic regressions are a better starting point to represent capacity growth trends over two to three decades. When coupled with published learning rates, trend-based capacity forecasts provided tighter and lower capital and levelized cost of energy outlooks than most reviewed scenarios, with photovoltaics global average levelized cost of energy reducing from 0.057/kWh to below USD 0.03/kWh by 2030 and below USD 0.02/kWh by 2040. Greater transparency on manufacturing ecosystems is proposed so that more advanced analytical techniques can be utilized. This analysis indicates that without direct interventions to accelerate the growth in wind power generation, global renewable energy technology deployment will fall short of the generation capacities required to meet climate change objectives.
The current context of the climate emergency highlights the need for the decarbonization of the energy sector by replacing current fossil fuels with renewable energy sources. In this regard, concentrating solar power (CSP) technology represents a commercially proven alternative. However, these types of plants are associated with high production costs and difficulties in controlling production during temporary variations in solar resource availability. In order to minimize these drawbacks, this study proposes the hybridization of CSP technology with direct biomass combustion, with the particularity of an innovative process scheme that does not correspond to traditional series or parallel configurations. This paper focuses on the techno-economic evaluation of this novel configuration in a small-scale power plant. To achieve this, both solar resource and biomass production, which are dependent on the selected location, were analyzed. Additionally, the plant was characterized from both technical and economic perspectives. The obtained results allowed for the characterization of the Levelized Cost of Energy (LCOE) based on various parameters such as the size of the solar field and biomass boilers, as well as limitations on biomass consumption.
As markets continue to evolve, hybrid power plants (HPPs) are attracting the interest of plant operators and industry players alike. With their ability to provide flexible dispatchability, HPPs are poised to become a competitive solution in future energy markets, particularly in grid ancillary markets (such as frequency control, reactive power control and black-start). Moreover, resource complementarities present in some sites have the potential to significantly improve production predictability of collocated HPPs. To assess the profitability of HPPs in European markets, a hybrid sizing algorithm is applied to three locations across Europe with distinct resource distribution characteristics. The sizing tool implemented includes several novel features that are not typically incorporated into existing sizing software currently available on the market. This includes turbine selection, a simplified physical design of the wind power plant, a surrogate wake model, and a simplified solar panel degradation model. NPV-over-CAPEX optimal plant designs, tend to favor hybridization, by either collocating wind and solar power with storage, or only solar power with storage. On the hand, minimizing the levelized cost of energy generally results in single-technology power plants. The results show significant potential for hybrid power plants in European markets. However, to truly demonstrate their economic competitiveness, the focus should shift from LCOE to other optimization metrics that consider revenue from different electricity markets.
Phase change heat storage is the backbone of energy storage technology, but its storage time is affected by the low thermal conductivity of phase change materials. Therefore, the melting performance of a triplex-tube latent heat thermal energy storage unit (T-LHTESU) in a phase change heat storage system is studied in this paper, and the rotation mechanism is applied to the unit. Firstly, a numerical model of the T-LHTESU considering the rotation mechanism is constructed, and the validity of the rotation unit is verified by comparison with experimental data. In this unit, N-eicosane is used as a phase change material for heat exchange. The effects of different rotational speeds on the liquid phase distribution, temperature distribution, flow velocity distribution, total energy storage, and energy storage efficiency of the T-LHTESU are studied. The results show that the melting time of this unit at 0.1 and 1 rpm is 46.98 and 69.35% lower than that of the stationary model, respectively. The total amount of stored heat is decreased by 0.67 and 2.17%, and the heat storage efficiency is increased by 87.34% and 219.19%, respectively. This indicates that the addition of the rotation mechanism greatly increases the heat storage efficiency of the T-LHTESU and reduces its total melting time, while the reduction of the total energy stored in the melting cycle is small. Then it is proved that rotation improves the single heat transfer mechanism of the stationary model and eliminates the thermal deposition caused by natural convection by studying the internal temperature/velocity response of the stationary model and the speed of 0.1 rpm. The related geometric structure of the model is optimized by response surface optimization design based on 0.1 rpm rotation speed. The influence of each variable on the target response is obtained, and compared with the original model, the melting time of the optimized model is reduced by 12.24%. Finally, based on the geometric optimization design, the influence of element physical factors (temperature and material of fin/tube wall) on the related melting properties is studied. This study is helpful to promote the effective use of rotation mechanism in phase change heat storage systems and has a certain guiding role in the structural design.
Wind farm flow control represents a category of control strategies for increasing wind plant power production and/or reducing structural loads by mitigating the impact of wake interactions between wind turbines. Wake steering is a wind farm flow control technology in which specific turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thus increasing overall wind plant power production. In addition to promising results from simulation studies, wake steering has been shown to successfully increase energy production through several recent field trials. However, to better understand the benefits of wind farm flow control strategies such as wake steering, the value of the additional energy to the electrical grid should be evaluated—for example, by considering the price of electricity when the additional energy is produced. In this study, we investigate the potential for wake steering to increase the value of wind plant energy production by combining model predictions of power gains using the FLOw Redirection and Induction in Steady State (FLORIS) engineering wind farm control tool with historical electricity price data for 15 existing U.S. wind plants in four different electricity market regions. Specifically, for each wind plant, we use FLORIS to estimate power gains from wake steering for a time series of hourly wind speeds and wind directions spanning the years 2018–2020, obtained from the ERA5 reanalysis data set. The modeled power gains are then correlated with hourly electricity prices for the nearest transmission node. Through this process we find that wake steering increases annual energy production (AEP) between 0.5 % and 2 %, depending on the wind plant, with average increases in potential annual revenue (i.e., annual value production (AVP)) 10 % higher than the AEP gains. For all wind plants, AVP gain was found to exceed AEP gain. But the ratio between AVP gain and AEP gain is greater for wind plants in regions with high wind penetration because electricity prices tend to be relatively higher during periods with below-rated wind plant power production, when wake losses occur and wake steering is active; for wind plants in the Southwest Power Pool—the region with the highest wind penetration analyzed (31 %)—the increase in AVP from wake steering is 21 % higher than the AEP gain. Consequently, we expect the value of wake steering, and other types of wind farm flow control, to increase as wind penetration continues to grow.
The Chinese government has set long-term carbon neutrality and renewable energy (RE) development goals for the power sector. Despite a precipitous decline in the costs of RE technologies, the external costs of renewable intermittency and the massive investments in new RE capacities would increase electricity costs. Here, we develop a power system expansion model to comprehensively evaluate changes in the electricity supply costs over a 30-year transition to carbon neutrality. RE supply curves, operating security constraints, and the characteristics of various generation units are modelled in detail to assess the cost variations accurately. According to our results, approximately 5.8 TW of wind and solar photovoltaic capacity would be required to achieve carbon neutrality in the power system by 2050. The electricity supply costs would increase by 9.6 CNY¢/kWh. The major cost shift would result from the substantial investments in RE capacities, flexible generation resources, and network expansion.
Policies in the US increasingly stipulate the use of variable renewable energy sources, which must be able to meet electricity demand reliably and affordably despite variability. The value of grid services provided by additional marginal capacity and storage in existing grids is likely very different than their value in a 100% variable renewable electricity system under such policies. Consequently, the role of concentrated solar power (CSP) and thermal energy storage (TES) relative to photovoltaics (PV) and batteries has not been clearly evaluated or established for such highly reliable, 100% renewable systems. Electricity generation by CSP is currently more costly than by PV, but TES is much less costly than chemical battery storage. Herein, we analyze the role of CSP and TES compared to PV and batteries in an idealized least-cost solar/wind/storage electricity system using a macro-scale energy model with real-world historical demand and hourly weather data across the contiguous United States. We find that CSP does not compete directly with PV. Instead, TES competes with short-duration storage from batteries, with the coupled CSP+TES system providing reliability in the absence of other grid flexibility mechanisms. Without TES, little CSP generation is built in this system because CSP and PV have similar generation profiles, but PV is currently cheaper on a dollar-per-kWh basis than CSP. However, CSP with TES can provide grid flexibility in the modeled least-cost system under some circumstances due to the low cost of TES compared to batteries. Cost-sensitivity analysis shows that penetration of CSP with TES is primarily limited by high CSP generation costs. These results provide a framework for researchers and decision-makers to assess the role of CSP with TES in future electricity systems.
The value of electricity generated from wind and solar sources declines as supply increases. This decline in value has varied over time and across regions, indicating that strategies to mitigate value decline will need to be carefully targeted. To help guide development of these strategies, we empirically determine wind and solar value at ∼2,100 plants within United States wholesale markets by using local prices and plant-specific generation profiles. We determine how each plant loses (or gains) value because of its output profile, transmission congestion, and curtailment. In regions where wind or solar account for roughly 20% of electricity generation, its value is 30% to 40% below the regional average value of a flat output profile at all plants. Solar value reductions are most sensitive to output profile and wind value reductions are sensitive to both profile and congestion, region dependent. Curtailment was not a major source of value reduction.
This paper provides a new framework for the calculation of levelized cost of stored energy. The framework is based on the relations for photovoltaics amended by new parameters. Main outcomes are the high importance of the C rate and the less dominant role of the roundtrip efficiency. The framework allows for comparisons between different storage technologies. The newly developed framework model is applied to derive the LCOE for a PV and storage combined power plant. The derived model enables quick comparison of combined PV and storage power plants with other forms of energy generation, for example diesel generation. This could prove helpful in the current discussion about diesel substitution in off-grid applications. In general, the combined levelized cost of energy lies between the LCOE of PV and LCOE of storage.
Economic evaluations of alternative electric generating technologies typically rely on comparisons between their expected "levelized cost" per MWh supplied. I demonstrate that this metric is inappropriate for comparing intermittent generating technologies like wind and solar with dispatchable generating technologies like nuclear, gas combined cycle, and coal. It overvalues intermittent generating technologies compared to dispatchable base load generating technologies. It also likely overvalues wind generating technologies compared to solar generating technologies. Integrating differences in production profiles, the associated variations in wholesale market prices of electricity, and life-cycle costs associated with different generating technologies is necessary to provide meaningful comparisons between them.
The global growth of wind energy markets offers opportunities to reduce greenhouse gas emissions. However, wind variability and intermittency (across multiple timescales) indicate that these energy resources must be carefully integrated into the power system to avoid mismatches with grid demand and associated grid reliability issues. At the same time, community concerns regarding the local installation of renewable energy and energy storage systems have already delayed or even halted the proposed projects. We propose a broadly defined, co-design approach that considers wind energy from a full social, technical, economic, and political viewpoint. Such a co-design can address the coupled inter-related challenges of cost, technology readiness, system integration, and societal considerations of acceptance, adoption, and equity. Such a successful design depends on the understanding of the needs of relevant communities, the regional grid infrastructure and its demand variability, local and global grid decarbonization targets, available land and resources for system siting, policy and political constraints for energy development, and the projected regional and global impact of these systems on the environment, jobs, and communities.
To analyze the role of nuclear power in an integrated energy system, we used the IESA-Opt-N cost minimization model focusing on four key themes: system-wide impacts of nuclear power, uncertain technological costs, flexible generation, and cross-border electricity trade. We demonstrate that the LCOE alone should not be used to demonstrate the economic feasibility of a power generation technology. For instance, under the default techno-economic assumptions, particularly the 5% discount rate and exogenous electricity trade potentials, it is cost-optimal for the Netherlands to invest in 9.6 GWe nuclear capacity by 2050. However, its LCOE is 34 €/MWh higher than offshore wind. Moreover, we found that nuclear power investments can reduce demand for variable renewable energy sources in the short term and higher energy independence (i.e., lower imports of natural gas, biomass, and electricity) in the long term. Furthermore, investing in nuclear power can reduce the mitigation costs of the Dutch energy system by 1.6% and 6.2% in 2040 and 2050, and 25% lower national CO2 prices by 2050. However, this cost reduction is not significant given the odds of higher nuclear financing costs and longer construction times. In addition, with 3% interest rate value (e.g., EU taxonomy support), even high cost nuclear (10 B€/GW) can be cost-effective in the Netherlands. In conclusion, under the specific assumptions of this study, nuclear power can play a complementary role (in parallel to the wind and solar power) in supporting the Dutch energy transition from the sole techno-economic point of view.
Given the depletion of shallow water wind energy resources and maturity of Floating Offshore Wind Turbine (FOWT), offshore wind technology is shifting from bottom fixed to floating. As a consequence, the research has concentrated on the points such as energy production and cost, which affect the commercialization of the wind farm. Researchers have studied the individual factors affecting these points, and used oversimplified methods. In order to provide a systematic and high quality analysis, we conducted a comprehensive study of wind farm based on unsteady Reynolds Average Navier-Stokes simulations two FOWTs and detailed decomposition of cost components. Specifically, under different scenarios, we compare the two FOWT performances, such as the power output, torque and six degrees of freedom motion response. The influences of the relative rotating direction and relative positions are analyzed. More importantly, we extensively discuss the impact of the interaction on the FOWT levelized cost of energy under various scenarios. It is found that tandem layout with a distance of 9.25 is the practical optimal parameter choice. This study is expected to provide guidances and insights for offshore wind researchers and governmental decision makers in future wind farm plannings.
This work is part of an ongoing study, creatively named the “LowWind Project” (Madsen et al., 2020), whose goal is to investigate at what price point a hypothetical 3.4 MW 100 W/m2 low wind (LW) turbine with a hub height of 127.5 m, a rotor diameter of 208 m, and a cut-out wind speed of 13 m/s becomes competitive in Northern and Central Europe’s energy system, as well as what impact the introduction of this technology has on the system. Similarly, the impact system flexibility has on LW investment is also analysed by limiting future transmission investment. Furthermore, this paper also analyses the amount of revenue this LW technology could generate compared to conventional turbines to further investigate the business case for this technology. The main finding here is that this LW technology begins to see investment at a 45% price increase over a conventional onshore wind turbine with an equal hub height (127.5 m) and a smaller rotor diameter (142 m vs 208 m). The addition of LW technology also leads to a reduction in transmission investment, and similarly, reductions in transmission capacity lead to further investment in LW technology. Lastly, it is shown that in the future Northern and Central European energy system, in wind dominated areas such as Denmark, this LW technology could generate revenues that are more than double that of conventional turbines (per MW), making the case that this technology could be a worthy endeavor.
In 2020, average wholesale electricity prices in the United States fell to $21/MWh, their lowest level since the beginning of the 21st century. Low natural gas prices and the proliferation of low marginal cost resources like wind and solar had already established a trend toward lower wholesale prices, and this trend was exacerbated by declining electricity demand due to the Covid-19 pandemic in 2020. Negative real-time hourly wholesale prices occurred in about 4% of all hours and wholesale market nodes across the United States, but these were not distributed evenly. Regional clusters emerged, for example, in the Permian Basin in western Texas, and in Kansas and western Oklahoma in the Southwest Power Pool (SPP), negative prices accounted for more than 25% of all hours. Negative electricity prices result either from local congestion of the transmission system leading supply to exceed demand locally or due to system-wide oversupply. Looking at the latter condition in SPP, we find that all major generator types contribute to this excess supply, because of limited ramping flexibility or self-scheduled out-of-market unit commitments. Additional monetary production incentives such as renewable energy credits or tax credits also enable negative bids; indeed, negative prices predominantly occur when demand levels are low and wind production levels are high. Frequent negative prices can inform the value of additional renewable energy investments at specific locations, the need for transmission and storage development, and opportunities load growth or adaptation.
The time-varying mismatch between electricity supply and demand is a growing challenge for the electricity market. This difference will be exacerbated with the fast-growing renewable energy penetration to the grid, due to its inherent volatility. Energy storage systems can offer a solution for this demand-generation imbalance, while generating economic benefits through the arbitrage in terms of electricity prices difference. In the present study, a method to estimate the potential revenues of typical energy storage systems is developed. The revenue is considered as the income from the energy storage plant with various roundtrip efficiencies. Thus, an optimal methodology was developed to determine the largest revenue through the charging and discharging operations based on the price profile. It is then applied to the California market in the United States to investigate the potential economic performance of three primary energy storage technologies: Lithium-Ion Batteries, Compressed Air Energy Storage and Pumped Hydro Storage. In particular, the maximum daily revenue from arbitrage is calculated considering various strategies of charging and discharging times as well as the technology’s roundtrip efficiency. The estimated capacity cost of energy storage for different loan periods is also estimated to determine the breakeven cost of the different energy storage technologies for an arbitrage application scenario. Pumped hydro storage (PHS) is found to be the most cost-effective but is not a good candidate for increased capacity in many countries due to concerns associated with developing new dams. Compressed Air Energy Storage (CAES), was found to be the second most cost-effective but still requires much more technology development before it is ready for widespread usage. Lithium battery is well-developed but is currently much too costly (by a factor of four) for a large scale energy storage application. The proposed method can be applied as these and other technologies and their associated costs evolve.
Offshore wind power projects are increasingly attractive in many regions even though capacity is impacted by intermittency as it is with other renewable power sources. We examine balancing the intermittency with an Offshore Compressed Air Energy Storage (OCAES) system that combines near-isothermal compression and expansion processes via water spray injection with air storage in saline aquifers. Spray injection maintains the air at nearly constant temperatures to improve round-trip efficiency, and saline aquifers are abundant in near-shore environments at suitable depths. This techno-economic analysis estimates the efficiency, cost, and value of OCAES, and demonstrates it in the context of the Atlantic coast of the United States, for a wind lease near Virginia. The round-trip efficiency of the OCAES system is projected using a thermal fluid process model that accounts for machinery performance as well as geophysical subsurface characteristics and gradients. Cost estimates are based on combining axial gas turbine technology with water spray injection retrofits and drilling experience from the oil and gas industry. Value to the electric grid is quantified with a price-taker dispatch model that optimizes the value of delivered electricity. The study analyzed power capacities from 10 to 390 MW, and our results show that for the geophysical conditions considered, a 200 MW OCAES system is expected to have a round-trip efficiency of 77% and a capital cost of 0.031/kWh, without storage, to 0.22/kWh, 81% less than with 10-hour lithium-ion battery technology.
Wind energy has rapidly increased and is expected to continue to do so over the next few decades. This will exacerbate the issue whereby its intermittent energy production does not generally coincide with energy demand. This can be addressed by integrating cost-effective energy storage with wind farms. The present study develops a concept that leverages the capacity of underground reservoirs of abandoned oil or gas wells to avoid the costs of expensive storage vessels and employs isothermal processes for the compressed air energy storage to improve round-trip efficiency. By levelizing the production using compressed air energy storage, the electrical generator size (and associated) cost may be reduced while maintaining the same average power production. These generator cost savings are projected to offset the cost of the storage system, so increased dispachtability is obtained with little to no added cost. This allows the predicted Cost of Valued Energy to be lowered by more than 10% for a typical wind farm, used as a case study. In addition, the simulated dispatchability ratio can reach 86.7%, which is far better than the 55.7% of a wind farm without storage. Importantly, the siting of wind farms near abandoned wells and mines also has the potential for significant new infrastructure investment and jobs in areas that might be economically-depressed. However, experimental verification with a pilot facility combined with ramifications of operational costs and geological factors are needed to demonstrate and quantify the benefits of this concept.
We investigate six different lithium-ion battery modeling approaches to highlight the importance of accurately representing batteries in decision tools. Advanced mixed-integer-linear battery models account for efficiencies as a function of the discharge power, power-limits as a function of the state-of-charge, along with degradation, which are usually not accounted for in power systems models. The revenue potential from offshore wind paired with battery systems is then examined using the more advanced representation where degradation is the sum of the capacity fades resulting from calendar- and cycle-aging. The impacts of variability of offshore wind output along with energy- and capacity-market prices are evaluated using publicly available data from 2010 to 2013 using NYISO as a test case. For 2013, results highlight that without accurate battery representations, models can overestimate battery revenues by up to 35%, resulting primarily from degradation-tied costs. Advanced dispatch algorithms that account for calendar- and cycle-aging of the battery can help operate the battery more efficiently. Locating the battery onshore yields higher revenues and with wider useable SOC windows, it is possible to monetize higher arbitrage opportunities, which can compensate for any additional degradation-tied costs. The added value of a MWh of energy storage varies from 4.5 per MWh of wind energy, which leads to a breakeven cost range of $50–115 per kWh for the battery systems. As such, energy- and capacity-market revenues were found to be insufficient in recovering the investment costs of current battery systems for the applications considered in this analysis.
The design of renewable energy systems such as wind turbines or solar panels conventionally employs Levelized Cost of Energy (LCOE), but this metric fails to account for the time-varying value of energy. This is true both for a single turbine or an entire wind farm. To remedy this, two novel, relatively simple metrics are developed herein to value energy based on the time of generation and the grid demand: Levelized Avoided Cost of Energy simplified (LACEs) and Cost of Valued Energy (COVE). These two metrics can be obtained with: 1) a linear price-demand relationship, 2) an estimate of hourly demand, and 3) an estimate of predicted hourly generation data. The results show that value trends for both wind and solar energy were reasonably predicted with these simplified models for the PJM region (a mid-Atlantic region in the USA) with less than 6% error on average, despite significant stochastic variations in actual price and demand throughout the year. A case study with wind turbine machine design showed that increasing Capacity Factor can significantly reduce COVE and thus increase Return on Investment. As such, COVE and LACEs can be valuable tools (compared to LCOE) when designing and optimizing renewable energy systems.
The potential for wind power in the United States and globally is vast. The U.S. wind resource alone could supply more than 7.5 times the nation’s total electricity generation in the year 2016. The nation has already begun to harness this potential. In 2016, new investments in U.S. wind power capacity were estimated at 23/megawatt-hour and below, a reduction of 50% or more from current cost levels. Under this scenario, wind energy deployments in the United States could increase to more than 200 gigawatts by 2030 and 500 gigawatts by 2050, supplying respectively 20% and 47% of U.S. electricity with wind. Relative to a business-as-usual scenario, this investment in technology research and innovation could support as much as $150 billion in cumulative electric sector cost savings from 2017 to 2050.
A review of capacity markets in the United States in the context of increasing levels of variable renewable energy finds substantial differences with respect to incentives for operational performance, methods to calculate qualifying capacity for variable renewable energy and energy storage, and demand curves for capacity. The review also reveals large differences in historical capacity market clearing prices. The authors conclude that electricity market design must continue to evolve to achieve cost-effective policies for resource adequacy.
Solar power is increasingly economical, but its value to the grid decreases as its penetration grows, and existing technologies may not remain competitive. We propose a mid-century cost target of US$0.25 per W and encourage the industry to invest in new technologies and deployment models to meet it.
The integration of wind and solar generators into power systems causes “integration costs” – for grids, balancing services, more flexible operation of thermal plants, and reduced utilization of the capital stock embodied in infrastructure, among other things. This paper proposes a framework to analyze and quantify these costs. We propose a definition of integration costs based on the marginal economic value of electricity, or market value – as such a definition can be more easily used in economic cost-benefit assessment than previous approaches. We suggest decomposing integration costs intro three components, according to the principal characteristics of wind and solar power: temporal variability, uncertainty, and location-constraints. Quantitative estimates of these components are extracted from a review of 100 + published studies. At high penetration rates, say a wind market share of 30–40%, integration costs are found to be 25–35 €/MWh, i.e. up to 50% of generation costs. While these estimates are system-specific and subject to significant uncertainty, integration costs are certainly too large to be ignored in high-penetration assessments (but might be ignored at low penetration). The largest single factor is reduced utilization of capital embodied in thermal plants, a cost component that has not been accounted for in most previous integration studies.
This paper provides a comprehensive discussion of the market value of variable renewable energy (VRE). The inherent variability of wind speeds and solar radiation affects the price that VRE generators receive on the market (market value). During windy and sunny times the additional electricity supply reduces the prices. Because the drop is larger with more installed capacity, the market value of VRE falls with higher penetration rate. This study aims to develop a better understanding on how the market value with penetration, and how policies and prices affect the market value. Quantitative evidence is derived from a review of published studies, regression analysis of market data, and the calibrated model of the European electricity market EMMA. We find the value of wind power to fall from 110% of the average power price to 50–80% as wind penetration increases from zero to 30% of total electricity consumption. For solar power, similarly low value levels are reached already at 15% penetration. Hence, competitive large-scale renewable deployment will be more difficult to accomplish than as many anticipate.
The lurking threat to solar power's growth
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