Capacity Expansion Model Research Articles

Adequacy of time-series reduction for renewable energy systems

To reduce computational complexity, macro-energy system models commonly implement reduced time-series data. For renewable energy systems dependent on seasonal storage and characterized by intermittent renewables, like wind and solar, adequacy of time-series reduction is in question. Using a capacity expansion model, we evaluate different methods for creating and implementing reduced time-series regarding loss of load and system costs. Results show that adequacy greatly depends on the length of the reduced time-series and how it is implemented into the model. Implementation as a chronological sequence with re-scaled time-steps prevents loss of load best but imposes a positive bias on seasonal storage resulting in an overestimation of system costs. Compared to chronological sequences, grouped periods require more time so solve for the same number of time-steps, because the approach requires additional variables and constraints. Overall, results suggest further efforts to improve time-series reduction and other methods for reducing computational complexity.

Techno-economic analysis of long-duration energy storage and flexible power generation technologies to support high-variable renewable energy grids

Techno-economic analysis of long-duration energy storage and flexible power generation technologies to support high-variable renewable energy grids

What is different about different net-zero carbon electricity systems?

In deeply decarbonized electricity systems with significant shares of variable renewable energy, the additional availability of at least one firm electricity generating technology can overcome reliability challenges and substantially reduce electricity costs. Firm resources can operate at any time of the year and for as long as needed to maintain electricity system reliability. Low- and zero-carbon firm technologies include flexible resources with high variable and low capital costs, such as biogas or hydrogen combustion, capital-intensive resources with low or zero variable cost, including nuclear and geothermal, as well as intermediate resources such as natural gas plants with carbon capture and sequestration (CCS). This paper explains the distinct roles of nuclear, CCS, and combustion of zero-carbon fuels in decarbonized electricity systems as examples of each class of firm resources. We analyze and compare results from three long-term electricity system capacity expansion models for California and the U.S. Western Interconnection, demonstrating robustness of our conclusions to different model assumptions and domains. Individually, each firm technology delivers substantial cost reductions relative to portfolios restricted to wind, solar, and energy storage alone. Additionally, because each technology occupies a distinctive functional niche in the electricity system, having all of these technologies available optimizes the utilization rate of each resource and reduces system costs by up to 10% relative to cases with just one class of firm resource. The analysis highlights the benefits of an expansive range of technology options to meet emissions reductions goals for the power sector while maintaining operational reliability and affordability.

A Stochastic Rolling Horizon-Based Approach for Power Generation Expansion Planning

Variable renewable energy sources introduce significant amounts of short-term uncertainty that should be considered when making investment decisions. In this work, we present a method for representing stochastic power system operation in day-ahead and real-time electricity markets within a capacity expansion model. We use Benders’ cuts and a stochastic rolling-horizon dispatch to represent operational costs in the capacity expansion problem (CEP) and investigate different formulations for the cuts. We test the model on a two-bus case study with wind power, energy storage, and a constrained transmission line. The case study shows that cuts created from the day-ahead problem gives the lowest expected total cost for the stochastic CEP. The stochastic CEP results in 3% lower expected total cost compared to the deterministic CEP capacities evaluated under uncertain operation. The number of required stochastic iterations is efficiently reduced by introducing a deterministic lower bound, while extending the horizon of the operational problem by persistence forecasting leads to reduced operational costs.

Assessing effects of renewable deployment on emissions in the Saudi power sector until 2040 using integer optimization

We quantify the impacts of renewable deployment on carbon emissions and natural gas supply in the Saudi power sector. A capacity expansion model, which simultaneously considers generation and transmission builds covering a planning horizon up to 2040, was created. The simulated scenarios, which entailed retiring liquid fuels from the Saudi power sector and accounted for different gas prices, consider the following candidate build technologies: nuclear, gas, solar photovoltaics, wind, concentrated solar power with storage, and battery storage. Renewables can reduce carbon emissions by 66 million tons to 114 million tons (25–41 %) by 2040 depending on the gas price. The abatement costs were estimated to range between 20 $/ton and 50 $/ton of carbon dioxide. Within Saudi Arabia, renewable deployment can also defer national gas supply expansion plans but not investments in expanding domestic gas transport capacities. Finally, under certain conditions when deploying significant renewable capacity, better transmission interconnection between regions manages renewable intermittency more cost-effectively than storage deployment.

The role of hydropower reservoirs in deep decarbonization policy

This paper analyzes the role of hydropower reservoirs in the deep decarbonization of power systems. Extending previous work, this study models the impact of hydro reservoirs on optimal planning decisions. It further provides a more holistic assessment of the role and economic value of hydro reservoirs through the use of a detailed capacity expansion and dispatch model. Our model is parameterized based on the power system of New England, U.S. and the hydro-based system of Quebec, Canada. We find that expanding transmission access to hydro reservoirs reduces the need for fossil-fuel power plants that may otherwise be deployed to balance renewable intermittency. Our results show that hydro access can accelerate decarbonization by decreasing optimal gas plant capacity and generation. At levels of very deep decarbonization, reservoir hydro reduces the need for Carbon Capture and Storage. Our modeling shows how hydro reservoirs accomplish this by serving as both a short- and long-term energy storage resource. We further show that, by reducing the need for more expensive balancing technologies and by enabling a more efficient utilization of variable renewables, hydro access lowers the cost of decarbonization, and that this benefit grows non-linearly with the decarbonization target.

The strong effect of network resolution on electricity system models with high shares of wind and solar

Energy system modellers typically choose a low spatial resolution for their models based on administrative boundaries such as countries, which eases data collection and reduces computation times. However, a low spatial resolution can lead to sub-optimal investment decisions for wind and solar generation. Ignoring power grid bottlenecks within regions tends to underestimate system costs, while combining locations with different wind and solar capacity factors in the same resource class tends to overestimate costs. We investigate these two competing effects in a capacity expansion model for Europe’s power system with a high share of renewables, taking advantage of newly-available high-resolution datasets as well as computational advances. We vary the number of nodes, interpolating between a 37-node model based on country and synchronous zone boundaries, and a 1024-node model based on the location of electricity substations. If we focus on the effect of renewable resource resolution and ignore network restrictions, we find that a higher resolution allows the optimal solution to concentrate wind and solar capacity at sites with better capacity factors and thus reduces system costs by up to 10% compared to a low resolution model. This results in a big swing from offshore to onshore wind investment. However, if we introduce grid bottlenecks by raising the network resolution, costs increase by up to 23% as generation has to be sourced more locally at sites with worse capacity factors. These effects are most pronounced in scenarios where grid expansion is limited, for example, by low local acceptance. We show that allowing grid expansion mitigates some of the effects of the low grid resolution, and lowers overall costs by around 16%.

How structural differences influence cross-model consistency: An electric sector case study

Multiple models are often employed to examine a range of possible outcomes for one or more scenarios, yielding insights into causal relationships and their uncertainties. Electric sector capacity expansion scenarios are commonly explored in such efforts due to the electric sector's overall economic importance, but multi-model results typically span a broad solution space despite efforts to harmonize input assumptions, making decision implications difficult to discern. This study investigates the relationship between efforts to harmonize input assumptions across multiple models and the resulting consistency of model outputs under different electric sector scenarios. We compare cross-model consistency between two electric sector capacity expansion models (GCAM-USA and ReEDS) for six electric sector scenarios with alternate assumptions about fossil fuel prices, technology innovation, and economy-wide transitions under four harmonization configurations that vary model representations of electricity demand, fuel prices, renewable resources, and capacity retirements. These comparisons reveal that cross-model consistency can vary across scenarios under a given harmonization configuration, suggesting that harmonization efforts must often be scenario-specific to achieve comparable cross-model consistency. Harmonization does not always improve consistency due to complex interactions between input assumptions, harmonized features, and unharmonized features. However, thorough harmonization can reduce uncertainty bounds and reveal insights into what drives cross-model consistency between two or more models. This work provides researchers and decision makers with a framework to carefully consider tradeoffs between effort and impact of harmonization efforts within multi-model analysis activities.

Deep decarbonization in Northeastern North America: The value of electricity market integration and hydropower

In several countries, electricity systems are under strong decarbonization pressure. In particular, the Canadian provinces of Quebec and Ontario as well as the states of the northeastern United States have committed to cut their greenhouse emissions by more than 70% (with respect to emission levels of 1990). Increased collaboration and integration between jurisdictions could decrease such decarbonization costs, especially when important hydropower resources are available.Using a capacity expansion and dispatch model of the Northeastern North American electricity sector, we analyze the impact of emission reduction targets, load levels and availability of power technologies in a range of scenarios, in order to assess the benefits of regional cooperation. Our results show that for deep decarbonization, the electricity system costs can be significantly reduced through integration, especially by adding more interconnection capacity. These costs savings and benefits are however not evenly allocated between jurisdictions, creating potentially difficult collaboration incentives.

Impact of high solar and wind penetrations and different reliability targets on dynamic operating reserves in electricity generation expansion planning

Wind and solar are increasingly cost-competitive as well as environmentally less harmful alternatives to the fossil-fuel generation that dominates most electricity industries. However, their highly variable and somewhat unpredictable output still requires high levels of dispatchable plants to ensure demand can be met at times of low renewables availability. While this capacity overhead has associated costs, it does offer potentially useful outcomes for dynamic operating reserves. We present a method for assessing these potential outcomes in electricity industry planning. We use an evolutionary programming-based capacity expansion model, NEMO, that solves least-cost generation mixes through full operational dispatch of candidate solutions, using high-temporal resolution demand and wind and solar profiles, over a year or more. We apply our method through a case study of the Java-Bali grid, considering future scenarios both with and without variable renewables, and under different carbon pricing scenarios, reliability targets, and minimum operating reserves requirements. Our study suggests that not only might high renewable penetrations reduce industry costs and emissions, their inclusion provides significantly higher operating reserves over most of the year, hence the ability to cover unexpected plant failures and other disruptions. Lower reliability targets reduce this capacity overhang but still see improved operating reserves.

Feasibility study of China's electric power sector transition to zero emissions by 2050

The study explores the potential transition of China's electric power sector to zero emissions by 2050. Using a capacity expansion model (CEPRO) with 31 regions, hourly time resolution, and 39 years of historical reanalysis weather data (MERRA-2), we simulate the expansion and operation of the power sector, considering solar and wind energy as the primary source of generation. The results suggest that zero-emissions power generation can be achieved with mostly wind and solar energy and alternative sets of balancing technologies. We show that a high-renewables power system can operate without significant storage and provide a high level of reliability. Besides storage, the balancing can be achieved by a partially flexible load, expansion of power grid, and backup capacity. The four balancing options and the level of reliability of the system are substitutes for one another, making at least storage or backup generation optional. Most importantly, a high-renewables power system is competitive, with the supply-side levelized system cost of electricity as low as 0.2–0.3 CNY/kWh (about 3–4 US cents per kWh) even without consideration of further reductions in the cost of renewables. • High spatial and temporal resolution modeling of China's electric power sector in 2050 with optimization of generating capacity structure, long-distance grid, energy storage, regional and temporal allocation of demand • 64 scenarios demonstrate feasibility of high-VER generation with high reliability of supply • With current costs of renewables, system-wide cost of supply might be lower than current thermal generation

Offshore Wind Replaces Coal and Reduces Transmission, Enabling China to Meet the 1.5°C Climate Imperative

Author(s): Li, Bo; Ma, Ziming; He, Gang; Hidalgo-Gonzalez, Patricia; Fedorova, Natalie; Chen, Minyou; Kammen, Daniel M

The Role of Carbon Capture and Storage in Decarbonizing California’s Electricity Grid

Affordable net-zero carbon electricity systems are critical in achieving a net-zero carbon economy, and California, who has long been a leader in climate policy, established a goal to reach a 100% clean grid by 2045. A capacity expansion and dispatch model is used to assess large-scale electricity power systems for California designed to meet policy standards while minimizing total system costs. The results of the analysis show that despite its comparatively high capital cost, including gas with carbon capture and storage (Gas-CCS) in the mix of electricity generation resources greatly reduces the cost of decarbonization for California as early as 2030 and in 2045. Without Gas-CCS, enormous capacities of solar and battery resources are needed in the future to reliably balance the load of the system throughout the entire year, which results in a highly overbuilt and expensive electricity system. Gas-CCS is able to generate electricity during the night-time and throughout times of the year when solar is not readily available, resulting in cost-effective decarbonization of the grid. In addition or as an alternative to Gas-CCS, we show that bioenergy with carbon capture and storage (BECCS) and direct air capture with sequestration (DAC), may also have important roles in play in decarbonizing California’s electricity sector. The scale of CCS development needed in California to cost-effectively meet its 2045 goals is technically feasible and California also has suitable storage sites for CO2. Implementation of CCS in California can be a robust strategy and effective tool for decarbonizing its electricity system and beyond.

An autopilot for energy models – Automatic generation of renewable supply curves, hourly capacity factors and hourly synthetic electricity demand for arbitrary world regions

Energy system models are increasingly being used to explore scenarios with large shares of variable renewables. This requires input data of high spatial and temporal resolution and places a considerable preprocessing burden on the modeling team. Here we present a new code set with an open source license for automatic generation of input data for large-scale energy system models for arbitrary regions of the world, including sub-national regions, along with an associated generic capacity expansion model of the electricity system. We use ECMWF ERA5 global reanalysis data along with other public geospatial datasets to generate detailed supply curves and hourly capacity factors for solar photovoltaic power, concentrated solar power, onshore and offshore wind power, and existing and future hydropower. Further, we use a machine learning approach to generate synthetic hourly electricity demand series that describe current demand, which we extend to future years using regional SSP scenarios. Finally, our code set automatically generates costs and losses for HVDC interconnections between neighboring regions. The usefulness of our approach is demonstrated by several different case studies based on input data generated by our code. We show that our model runs of a future European electricity system with high share of renewables are in line with results from more detailed models, despite our use of global datasets and synthetic demand.

MENA compared to Europe: The influence of land use, nuclear power, and transmission expansion on renewable electricity system costs

Most studies that examine CO2-neutral, or near CO2-neutral, power systems by using energy system models investigate Europe or the United States, while similar studies for other regions are rare. In this paper, we focus on the Middle East and North Africa (MENA), where weather conditions, especially for solar, differ substantially from those in Europe. We use a green-field linear capacity expansion model with over-night investment to assess the effect on the system cost of (i) limiting/expanding the amount of land available for wind and solar farms, (ii) allowing for nuclear power and (iii) disallowing for international transmission. The assessment is done under three different cost regimes for solar PV and battery storage.First, we find that the amount of available land for wind and solar farms can have a significant impact on the system cost, with a cost increase of 0–50% as a result of reduced available land. In MENA, the impact on system cost from land availability is contingent on the PV and battery cost regime, while in Europe it is not. Second, allowing for nuclear power has a minor effect in MENA, while it may decrease the system cost in Europe by up to 20%. In Europe, the effect on system cost from allowing for nuclear power is highly dependent on the PV and battery cost regime. Third, disallowing for international transmission increases the system cost by up to 25% in both Europe and MENA, and the cost increase depends on the cost regime for PV and batteries.The impacts on system cost from these three controversial and policy-relevant factors in a decarbonized power system thus play out differently, depending on (i) the region and (ii) uncertain future investment costs for solar PV and storage. We conclude that a renewable power system in MENA is likely to be less costly than one in Europe, irrespective of future uncertainties regarding investment cost for PV and batteries, and policies surrounding nuclear power, transmission, and land available for wind- and solar farms. In MENA, the system cost varies between 42 and 96 $/MWh. In Europe, the system cost varies between 51 and 102 $/MWh.

The Economics of the Site C Hydroelectric Project in British Columbia

The Site C hydroelectric project is an 1100-Megawatt hydroelectric facility under development on the Peace River in British Columbia. Construction costs for the project are currently estimated to be $10.7 billion. The project has been criticized for its high cost and the impacts it will have on the river system, including the flooding of land to establish the reservoir. Project proponents argue that Site C will provide low-carbon electricity that will help BC and Canada reach their greenhouse gas emission reduction goals. In this paper, we assess the cost-effectiveness of the Site C project using a detailed linear programming capacity expansion and dispatch model of the British Columbia and Alberta electricity systems. Our model includes hourly electricity demand data for sixteen sub-regions within BC and Alberta, hourly wind resource data for 681 grid cells in the two provinces, hourly solar irradiation data from 112 weather stations to characterize the solar resource, and a detailed hydroelectric model that accounts for the chaining of hydroelectric facilities and reservoirs within the Peace River, Columbia River, Kootenay River and Bridge River watersheds. We assess the value of the Site C project by calculating the optimal electricity system for supplying BC and Alberta electricity demand in 2030 both with and without the presence of the Site C project. Our model simulations reveal that under current electricity sector policies, the Site C project is uneconomic. We find that the value created by Site C only exceeds its costs in scenarios where BC and Alberta coordinate electricity markets, build additional transmission capacity, and work to reduce greenhouse gas emissions significantly below baseline levels.

Multi-input, Multi-output Hybrid Energy Systems

Summary Jurisdictions and industries are setting ambitious goals to decarbonize energy systems. Low-cost wind, solar, and natural gas and the resultant dynamic electric grid require energy technologies to adapt in order to meet key attributes for modern energy systems: resilience, reliability, security, affordability, flexibility, and sustainability. When considering energy sources independently and competitively, value-added synergies among energy technologies may be overlooked for meeting demanding, multidimensional requirements. This paper presents novel concepts for tightly coupled hybrid energy systems that leverage capabilities of diverse energy generators, including renewable, nuclear, and fossil with carbon capture, to provide power, heat, mobility, and other energy services. The paper also presents a framework for engineering-based modeling and analysis for complex optimization of energy generation, transmission, services, processes and products, and market interactions. New modeling capabilities are needed to adequately represent multi-input, multi-output tightly coupled hybrid energy systems that utilize multiple feedstocks to create multiple products and services in novel and synergistic ways through increased coordination of energy systems and tightly coupled hybrid system configurations.

The Potential to Improve the Value of U.S. Geothermal Electricity Generation Through Flexible Operations

Abstract Geothermal power plants have typically been operated as baseload plants. The recent expansion of wind and solar power generation creates a potential opportunity to increase the value of geothermal generation through flexible operations. In recent years, California’s wholesale electricity markets have exhibited frequent, short-term periods of negative pricing, indicating that additional flexibility would be valued in the market. Here, we examine local nodal hourly price records at all geothermal plants located in the western United States. We describe how the frequency and temporal characteristics of negative pricing episodes have changed over recent years. Based on these price series, we calculate the value of multiple strategies of flexible operations. Additionally, we use the estimates of future prices, developed through a capacity-expansion model and a dispatch model, to explore how the value of such flexible operations might change along with further penetration of variable renewable power sources. Based on the historical pricing records, we find that simple curtailment of operations during negative pricing episodes could increase the average energy value by 1–2 $/MWh and that allowing for increased production during limited high-priced hours, in addition to curtailment during negative priced hours, could potentially double the increase in value (up to 4 $/MWh). The forward-looking simulations indicate reduced values of flexibility from geothermal power despite higher penetrations of variable renewable energy. This result highlights the possibility that increasing flexibility options throughout the system may counteract the influence of increased variable renewable energy deployment.

The prospective impacts of 2019 state energy policies on the U.S. electricity system

Over the past several years state energy policies have been evolving rapidly, and are more frequently including higher targets, including 100% clean energy targets. This study assesses the aggregate impacts of state clean energy standards and emissions policies on national electricity generation, power sector carbon dioxide (CO2) emissions, and electricity prices and system costs. To do so, we apply the Regional Energy Deployment System (ReEDS) model, which is a detailed electric sector capacity expansion model, to evaluate scenarios with and without state policies and using a range of renewable energy technology cost projections. Across the scenarios analyzed, we find that the state policies drive 1.9%–10.7% of total nationwide clean energy and reduce cumulative power sector CO2 emissions by 2.6%–5.4% over the 2020–2050 study period. This incremental generation is predominantly, but not exclusively, from renewable energy technologies. In most cases, the state policies result in increases to electricity prices and electricity system costs, and policy costs are sensitive to the future cost of clean energy technologies. Across all scenarios, the levelized cost of incremental policy-driven clean energy generation is estimated to be $17–38/MWh and the average cost of CO2 abatement from the state-level policies is estimated to be $29–74/metric ton.

Considerations for maintaining resource adequacy of electricity systems with high penetrations of PV and storage

Solar photovoltaic (PV) and energy storage deployment has grown rapidly in recent years and is expected to continue as the costs of these technologies continues to decline. As PV penetration grows, peak net load hours—where net load is load minus variable renewable energy generation—become increasingly important for understanding resource adequacy of power systems. In this work, we evaluate scenarios of a future U.S. power system with high penetrations of PV. We do so by using a national-scale capacity expansion model to consider 15 scenarios with national annual PV generation penetration levels of 16%–47%, combined wind and PV penetrations are 40%–64%, and storage capacity ranging from 5% to 47% of peak demand. The modeled scenarios incorporate 7-years of coincident weather data and load profiles (2007–2013) for the contiguous United States. In doing so, we evaluate when peak net load hours occur and how their occurrence is impacted by PV penetration, region, and weather. We find that with increasing PV penetration, peak net load hours move from afternoon periods to evenings or from midmorning to early morning, and that the frequency of winter peak net load hours increases. We also simulate hourly system dispatch in 3 of the 15 scenarios using a commercially available unit commitment and dispatch model—PLEXOS-- to evaluate with much greater resolution how the systems behave under specific weather conditions observed from 2007 to 2013. The hourly results show substantial shifts in how peak net load hours are served as PV penetration increases, and that how the system serves those top peak net load hours varies considerably across different regions of the country. Finally, we employ a simple method for estimating the impacts of snow cover in the high PV penetration scenarios and find that for the moderate and highest levels of PV we examine, snow cover has the potential to result in capacity and energy shortfalls.