Variable Renewable Power Research Articles

Positioning Nuclear Power in the Low-Carbon Electricity Transition

Addressing climate change requires de-carbonizing future energy supplies in an increasingly energy-dependent world. The IEA and the IPCC (2014) mention the following as low-carbon energy supply options: ‘renewable energy, nuclear power and fossil fuels with carbon capture and storage’. Positioning nuclear power in the decarbonization transition is a problematic issue and is overridden by ill-conceived axioms. Before probing these axioms, we provide an overview of five major, postwar energy-related legacies and some insight into who is engaged in nuclear activities. We check whether low-carbon nuclear power passes the full sustainability test and whether it is compatible with the unfettered deployment of variable renewable power sourced from the sun and from wind and water currents, which delivers two negative answers. We show that the best approach of the sustainable energy transition was Germany’s 2011 decision to phase out nuclear power for a fast development and full deployment of renewable power. This is the best approach for the sustainable energy transition. We offer five practical suggestions to strengthen and accelerate carbon- and nuclear-free transitions. They are related to institutional issues like the role of cost-benefit analysis and the mission of the International Atomic Energy Agency, to the costs of nuclear risks and catastrophes, and to the historical record of nuclear technology and business.

Methane Leaks from Natural Gas Systems Follow Extreme Distributions.

Future energy systems may rely on natural gas as a low-cost fuel to support variable renewable power. However, leaking natural gas causes climate damage because methane (CH4) has a high global warming potential. In this study, we use extreme-value theory to explore the distribution of natural gas leak sizes. By analyzing ∼15 000 measurements from 18 prior studies, we show that all available natural gas leakage data sets are statistically heavy-tailed, and that gas leaks are more extremely distributed than other natural and social phenomena. A unifying result is that the largest 5% of leaks typically contribute over 50% of the total leakage volume. While prior studies used log-normal model distributions, we show that log-normal functions poorly represent tail behavior. Our results suggest that published uncertainty ranges of CH4 emissions are too narrow, and that larger sample sizes are required in future studies to achieve targeted confidence intervals. Additionally, we find that cross-study aggregation of data sets to increase sample size is not recommended due to apparent deviation between sampled populations. Understanding the nature of leak distributions can improve emission estimates, better illustrate their uncertainty, allow prioritization of source categories, and improve sampling design. Also, these data can be used for more effective design of leak detection technologies.

Charging a renewable future: The impact of electric vehicle charging intelligence on energy storage requirements to meet renewable portfolio standards

Abstract Increased usage of renewable energy resources is key for energy system evolution to address environmental concerns. Capturing variable renewable power requires the use of energy storage to shift generation and load demand. The integration of plug-in electric vehicles, however, impacts the load demand profile and therefore the capacity of energy storage required to meet renewable utilization targets. This study examines how the intelligence of plug-in electric vehicle (PEV) integration impacts the required capacity of energy storage systems to meet renewable utilization targets for a large-scale energy system, using California as an example for meeting a 50% and 80% renewable portfolio standard (RPS) in 2030 and 2050. For an 80% RPS in 2050, immediate charging of PEVs requires the installation of an aggregate energy storage system with a power capacity of 60% of the installed renewable capacity and an energy capacity of 2.3% of annual renewable generation. With smart charging of PEVs, required power capacity drops to 16% and required energy capacity drops to 0.6%, and with vehicle-to-grid (V2G) charging, non-vehicle energy storage systems are no longer required. Overall, this study highlights the importance of intelligent PEV charging for minimizing the scale of infrastructure required to meet renewable utilization targets.

Improved flexibility with large-scale variable renewable power in cities through optimal demand side management and power-to-heat conversion

Solar and wind power are potential carbon-free energy solutions for urban areas, but they are also subject to large variability. At the same time, urban areas offer promising flexibility solutions for balancing variable renewable power. This paper presents models for optimal control of power-to-heat conversion to heating systems and shiftable loads in cities to incorporate large variable renewable power schemes. The power-to-heat systems comprise heat pumps, electric boilers, and thermal storage. The control strategies comprise optimal matching of load and production, and cost-optimal market participation with investment analysis. All analyses are based on hourly data. The models are applied to a case study in Helsinki, Finland. For a scheme providing ca. 50% of all electricity in the city through self-consumption of variable renewables, power-to-heat with thermal storage could absorb all the surplus production. A significant reduction in the net load magnitude was obtained with shiftable loads. Investments to both power-to-heat and load shifting with electric heating and commercial refrigeration have a positive net present value if the resources are controlled cost-optimally.

Impacts of electricity grid tariffs on flexible use of electricity to heat generation

District heating plants having both electric boilers and alternative fuel options could hold a key in providing flexibility needed for cost efficient integration of variable renewable power. The electricity grid tariff is an important component of the electricity costs of electric boilers, and these tariffs may promote or hamper flexible use of power-to-heat. In this paper, a mixed integer cost minimization model that schedules the operation of different boilers in a district heating plant is developed and applied to analyze the impact of different tariff structures on flexibility provided by electric boilers. The results confirm that the structure of electric grid tariffs significantly influences the flexibility provided and the annual shares of electric boiler use, caused by differences in grid tariff structures, vary from 2% to 17%. Novel tariff structures with time-varying elements increase utilization electricity in low price periods and improve the profitability of power-to-heat as a flexibility solution. The study clearly demonstrates that system effects should be considered when grid tariffs for flexible electric boilers are designed and that novel tariff designs should be more widely adapted.

Modeling flexibility and optimal use of existing power plants with large-scale variable renewable power schemes

Increasing the use of variable renewable electricity (VRE) such as solar and wind power will impose major challenges to existing energy systems. The increased variability needs to be considered, but also how to optimally operate the remaining power system. In this paper, we present a fast and easy-to-use optimization model to find cost-optimal ways to manage the energy system with large-scale VRE. This is done by minimizing the running costs and maximizing revenues of the energy production. The model accounts for the energy system dynamics. It can handle both electric and thermal loads at different scales (building, city) and allows incorporating different flexibility strategies. The model is applied to analyzing a very large wind power scheme for Helsinki city (Finland), coupled to the existing CHP-dominated district heating schemes through power-to-heat conversion for more optimal system integration leading to major CO2 emission reductions. The model is well suited to studying integration of renewable energy, to design better policies for introducing VRE and to find solutions to increase energy system flexibility, among others.

Backup flexibility classes in emerging large-scale renewable electricity systems

High shares of intermittent renewable power generation in a European electricity system will require flexible backup power generation on the dominant diurnal, synoptic, and seasonal weather timescales. The same three timescales are already covered by today’s dispatchable electricity generation facilities, which are able to follow the typical load variations on the intra-day, intra-week, and seasonal timescales. This work aims to quantify the changing demand for those three backup flexibility classes in emerging large-scale electricity systems, as they transform from low to high shares of variable renewable power generation. A weather-driven modelling is used, which aggregates eight years of wind and solar power generation data as well as load data over Germany and Europe, and splits the backup system required to cover the residual load into three flexibility classes distinguished by their respective maximum rates of change of power output. This modelling shows that the slowly flexible backup system is dominant at low renewable shares, but its optimized capacity decreases and drops close to zero once the average renewable power generation exceeds 50% of the mean load. The medium flexible backup capacities increase for modest renewable shares, peak at around a 40% renewable share, and then continuously decrease to almost zero once the average renewable power generation becomes larger than 100% of the mean load. The dispatch capacity of the highly flexible backup system becomes dominant for renewable shares beyond 50%, and reach their maximum around a 70% renewable share. For renewable shares above 70% the highly flexible backup capacity in Germany remains at its maximum, whereas it decreases again for Europe. This indicates that for highly renewable large-scale electricity systems the total required backup capacity can only be reduced if countries share their excess generation and backup power.

A new thermostat for real-time price demand response: Cost, comfort and energy impacts of discrete-time control without deadband

Thermostatically controlled electrical loads can provide valuable energy storage and are prime candidates for fast acting demand response (DR) that can be used to mitigate highly variable renewable power generation and limited availability of ramping resources. When conventional thermostats are retrofitted for real-time price DR control, significant control errors can arise, particularly in the form of dispatch control drift. This paper identifies the underlying causes and presents a new residential thermostat design that enables accurate aggregate load control. The new design gives rise to linear time-invariant models of aggregate load control and demand response, which facilitate the design of highly accurate load-based regulation services for electricity interconnections. Detailed simulation and performance studies coupling a residential house and feeder models are presented to show how consumer comfort and cost savings are achieved and how energy use is impacted for cities in three different climatic zones. During peak times, the new thermostat imparts the entire residential load an energy demand elasticity of about 10–25%. Larger demand elasticities could be achieved by extending the control strategy to other residential thermostatic loads. The proposed thermostat design can operate in the real-time distribution capacity auction system and can provide all the benefits associated with transactive systems, and in particular facilitate increased integration of renewable resources.

Comparing concentrating solar and nuclear power as baseload providers using the example of South Africa

Despite the increasing importance of variable renewable power generation, baseload, that is stable and predictable power generators, remain the backbone of many countries’ power systems. We here compare CSP (concentrating solar power) and nuclear power as baseload electricity providers for the case of South Africa, which is adding significant new generation capacity, has an abundant solar resource, but also one existing and additional planned nuclear power plants. Both of these technologies are considered baseload-capable with sufficient available fuel (sunlight or fissible material) to provide large amounts of nearly emissions-free electricity. We find that under a range of technological learning assumptions, CSP compares favorably against nuclear on costs in the period to 2030, and has lower investment and environmental risks. The results suggest that while nuclear power may be an important low-emissions power technology in regions with little sun, in the case of South Africa, CSP could be capable of providing a stable baseload supply at lower cost than nuclear power, and may have other non-cost benefits.

Recent results on the integration of variable renewable electric power into the US grid

ABSTRACT

Potential and Limitations for Industrial Demand Side Management

The growing share of variable renewable power such as wind and solar photovoltaic (PV) emphasize the importance of demand side management. In a couple of years consumers have to adapt their consumption more to available generated power in order to maintain grid stability and to reduce their electricity costs. In this study the potential and limitations for demand side management in about 15 different companies in Sweden has been investigated. A brief description of some of the company's production process is given, with focus on electrical consumption. Suggestions on different demand side management possibilities are presented as well as limitations. The suggestions are given for different time horizons, from load shifting between hours, to weeks and years. Potential savings achieved with demand side management have also been evaluated based on historical electricity prices from different countries.

Assessment of Japan's Optimal Power Generation Mix Considering Massive Deployment of Variable Renewable Power Generation

SummaryThis paper analyzes Japan's optimal power generation mix considering massive deployment of solar photovoltaic (PV) systems and wind power generation. The extensive introduction of PV systems and wind power system is expected to play an important role in addressing energy security and climate change concerns in Japan. Considering this expected large‐scale deployment of PV systems in the electric power system, it is necessary to investigate the optimal power generation mix which is technologically capable of controlling and accommodating the intermittent output‐power fluctuations inherent in PV and wind energy systems. Against these backgrounds, we develop an optimal power generation mix model, explicitly analyzing the impact of output fluctuation in variable renewables in the detailed resolution of time intervals like 10 minutes for 365 consecutive days, with the role of stationary battery technology incorporated. Simulation results reveal that considerable deployment of those variable renewables does not necessarily require a scale of battery capacity similar to that of the variable renewable capacity, due to quick load‐following treatment by thermal power plants, pumped‐storage hydropower, and battery technology over renewable output fluctuation.

Effects of variable renewable power on a country-scale electricity system: High penetration of hydro power plants and wind farms in electricity generation

The present article analyses the effects caused by variable power. The analysis concerns a country-scale electricity system with a relatively high penetration of seasonally variable hydro power plants and wind farms in the total electricity generation in 2030. For this purpose, the Latvian electricity system was chosen as an appropriate case study, as around half of its electricity is already generated from hydro power and numerous wind farm installations are planned for 2030. Results indicate that in such systems high renewable power variations occur between seasons causing a high probability of power deficit in the winter and power surplus in the spring. Based on the results, the wind farms' influence on the power deficit and surplus occurrences are discussed in detail. Wind farm generation decreases the probability of the electricity system being in power deficit, but increases the probability of the system being in power surplus. In the latter situation, the maximum value of power surplus increases since it is enhanced by the wind farm generation. Probability equations to express these changes are provided.

Assessment of Japan's Optimal Power Generation Mix Considering Massive Deployment of Variable Renewable Power Generation

Summary This paper analyzes Japan's optimal power generation mix considering massive deployment of solar photovoltaic (PV) systems and wind power generation. The extensive introduction of PV systems and wind power system is expected to play an important role in addressing energy security and climate change concerns in Japan. Considering this expected large-scale deployment of PV systems in the electric power system, it is necessary to investigate the optimal power generation mix which is technologically capable of controlling and accommodating the intermittent output-power fluctuations inherent in PV and wind energy systems. Against these backgrounds, we develop an optimal power generation mix model, explicitly analyzing the impact of output fluctuation in variable renewables in the detailed resolution of time intervals like 10 minutes for 365 consecutive days, with the role of stationary battery technology incorporated. Simulation results reveal that considerable deployment of those variable renewables does not necessarily require a scale of battery capacity similar to that of the variable renewable capacity, due to quick load-following treatment by thermal power plants, pumped-storage hydropower, and battery technology over renewable output fluctuation.