Future Electric Systems Research Articles

The value of airborne wind energy to the electricity system

AbstractAirborne wind energy (AWE) is a new power generation technology that harvests wind energy at high altitudes using tethered wings. The potentially higher energy yield, combined with expected lower costs compared to traditional wind turbines (WTs), motivates interest in further developing this technology. However, commercial systems are currently unavailable to provide more detailed information on costs and power generation. This study estimates the economic value of AWE in the future electricity system, and by that indicates which cost levels are required for AWE to be competitive. A specific focus is put on the relation between AWE systems (AWESs) and WTs. For this work, ERA‐5 wind data are used to compute the power generation of the wind power technologies, which is implemented in a cost‐minimizing electricity system model. By forcing a certain share of the annual electricity demand to be supplied by AWESs, the marginal system value (MSV) of AWE is investigated. The MSV is found to be affected by the AWE share, the wind resource, and the temporal distribution of the AWES's electricity generation. The MSV of AWE is location‐ and system‐dependent and ranges between 1.4 and 2.2 at a low share of AWE supply (0%–30%). At higher shares, the MSV drops. The power generation of WTs and AWESs are related, implying that the wind technologies present a similar power source and can be used interchangeably. Thus, the introduction of AWESs will have a low impact on the cost‐optimal wind power share in the electricity system, unless an AWES cost far below the system‐specific MSV is attained.

Impact of COVID-19 on Electricity Demand: Deriving Minimum States of System Health for Studies on Resilience

To assess the resilience of energy systems, i.e., the ability to recover after an unexpected shock, the system’s minimum state of service is a key input. Quantitative descriptions of such states are inherently elusive. The measures adopted by governments to contain COVID-19 have provided empirical data, which may serve as a proxy for such states of minimum service. Here, we systematize the impact of the adopted COVID-19 measures on the electricity demand. We classify the measures into three phases of increasing stringency, ranging from working from home to soft and full lockdowns, for four major electricity consuming countries of Europe. We use readily accessible data from the European Network of Transmission System Operators for Electricity as a basis. For each country and phase, we derive representative daily load profiles with hourly resolution obtained by k-medoids clustering. The analysis could unravel the influence of the different measures to the energy consumption and the differences among the four countries. It is observed that the daily peak load is considerably flattened and the total electricity consumption decreases by up to 30% under the circumstances brought about by the COVID-19 restrictions. These demand profiles are useful for the energy planning community, especially when designing future electricity systems with a focus on system resilience and a more digitalised society in terms of working from home.

The impacts of the electricity demand pattern on electricity system cost and the electricity supply mix: A comprehensive modeling analysis for Europe

Energy system models for long-term planning are widely used to explore the future electricity system. Typically, to represent the future electricity demand in these models, historical demand profiles are used directly or scaled up linearly. Although the volume change for the electricity demand is considered, the potential change of the demand pattern is ignored. Meanwhile, the future electricity demand pattern is highly uncertain due to various factors, including climate change, e-mobility, electric heating, and electric cooling. We use a techno-economic cost optimization model to investigate a stylized case and assess the effects on system cost and electricity supply mix of assuming different demand patterns for the models. Our results show that differences in diurnal demand patterns affect the system cost by less than 3%. Similarly, demand profiles with a flat seasonal variation or a winter peak result in only minor changes in system cost, as compared to the present demand profile. Demand profiles with a summer peak may display a system cost increase of up to 8%, whereas the electricity supply mix may differ by a factor of two. A more detailed case study is conducted for Europe and the results are consistent with the findings from the stylized case.

Economic evaluation of battery storage systems bidding on day-ahead and automatic frequency restoration reserves markets

In future electricity systems, not only electricity generation but also frequency stabilization must be provided by low-carbon technologies. Battery systems are a promising solution to fill this gap. However, uncertainties regarding their revenue potential may hinder investments. Therefore, we apply the agent-based electricity market model AMIRIS to simulate a day-ahead market and an automatic frequency restoration reserves market. Demonstrating the model setup, we chose a scenario with high shares of renewable energies. First, we back-test our model with historic market data from Germany in 2019. The simulation results’ mean day-ahead prices of 39.20EUR/MWh are close to the historic ones of 38.70EUR/MWh. Second, we model both markets in a scenario for 2030. The simulated day-ahead market prices are higher on average than observed today, although, we find around 550 h/yr in which the load is fully covered by renewable energies. The variance in simulated prices is slightly higher compared to historic values. Bids on the reserve capacity market are derived from opportunity costs of not participating in the day-ahead market. This results in prices of up to 45EUR/MW for positive reserve while the prices for negative reserve are 0EUR/MW. Finally, we evaluate revenue potentials of battery storages. Compared to 2019, we see an improved economic potential and increased importance of the day-ahead market. High power battery storages perform best whereas improvements in round-trip efficiency only marginally improve revenues. Although demonstrated for Germany, the presented modular approach can be adapted to international markets enabling comprehensive battery storage assessments.

Scheduling of energy storage.

The increasing reliance on renewable energy generation means that storage may well play a much greater role in the balancing of future electricity systems. We show how heterogeneous stores, differing in capacity and rate constraints, may be optimally, or nearly optimally, scheduled to assist in such balancing, with the aim of minimizing the total imbalance (unserved energy) over any given period of time. It further turns out that in many cases the optimal policies are such that the optimal decision at each point in time is independent of the future evolution of the supply-demand balance in the system, so that these policies remain optimal in a stochastic environment. This article is part of the theme issue 'The mathematics of energy systems'.

Smart grid evolution and mobile communications—Scenarios on the Finnish power grid

This paper focuses on the evolution of the Finnish power grid until 2035 and the role of mobile communications networks in this evolution. It outlines alternative futures (i.e. scenarios) and identifies the role of mobile communications networks in these scenarios. The paper uses an established scenario planning process and a group of experts to determine three scenarios for the evolution of services offered to customers in a future electricity system and four scenarios describing the evolution of grid management. Finally, the role of mobile communications (cellular networks) is analyzed in each of the scenarios. Results show that mobile networks can serve as a key enabler in those scenarios where smart grid evolution would be the most transformative.

CO2 quota attribution effects on the European electricity system comprised of self-centred actors

Abstract We propose to rethink collaboration between the European countries to robustly materialise the electricity system of the near future. The European countries all agree that carbon dioxide ( CO 2 ) emissions need to be decreased. The dividing question is who must contribute by how much. Three aspects of collaboration are investigated and set into relation to one another. First, the individual countries can be attributed a CO 2 emission quota in different ways. Second, they can collaborate by relaxing their need for autonomy, e.g. becoming less self-sufficient by placing generation capacity in countries with better prerequisites. Third, collaboration can also be strengthened by extending the cross-border transmission grid. We investigate a near future European electricity system where the primary source of emission-neutral electricity is coming from different variable renewable energy sources, but which also includes current and planned nuclear, coal, lignite and gas-fuelled power plants. We show that different CO 2 emission quota attributions have an immense effect on the required local CO 2 prices. Furthermore, we investigate how this influences the technology mix in the individual countries. We conclude that it is significantly easier for certain countries to decarbonise their electricity production than for others. The difficulty in the specific country depends strongly on how emission allowances are allocated in Europe. Deep collaboration between the countries leads both to a lowered total system cost and, perhaps even more importantly, to CO 2 emissions and required CO 2 prices that are much more equal between the European partners.

CO2 Intensities and Primary Energy Factors in the Future European Electricity System

The European Union strives for sharp reductions in both CO2 emissions as well as primary energy use. Electricity consuming technologies are becoming increasingly important in this context, due to the ongoing electrification of transport and heating services. To correctly evaluate these technologies, conversion factors are needed—namely CO2 intensities and primary energy factors (PEFs). However, this evaluation is hindered by the unavailability of a high-quality database of conversion factor values. Ideally, such a database has a broad geographical scope, a high temporal resolution and considers cross-country exchanges of electricity as well as future evolutions in the electricity mix. In this paper, a state-of-the-art unit commitment economic dispatch model of the European electricity system is developed and a flow-tracing technique is innovatively applied to future scenarios (2025–2040)—to generate such a database and make it publicly available. Important dynamics are revealed, including an overall decrease in conversion factor values as well as considerable temporal variability at both the seasonal and hourly level. Furthermore, the importance of taking into account imports and carefully considering the calculation methodology for PEFs are both confirmed. Future estimates of the CO2 emissions and primary energy use associated with individual electrical loads can be meaningfully improved by taking into account these dynamics.

Wind and Solar Resource Droughts in California Highlight the Benefits of Long-Term Storage and Integration with the Western Interconnect.

As reliance on wind and solar power for electricity generation increases, so does the importance of understanding how variability in these resources affects the feasible, cost-effective ways of supplying energy services. We use hourly weather data over multiple decades and historical electricity demand data to analyze the gaps between wind and solar supply and electricity demand for California (CA) and the Western Interconnect (WECC). We quantify the occurrence of resource droughts when the daily power from each resource was less than half of the 39-year daily mean for that day of the year. Averaged over 39 years, CA experienced 6.6 days of solar and 48 days of wind drought per year, compared to 0.41 and 19 for WECC. Using a macro-scale electricity model, we evaluate the potential for both long-term storage and more geographically diverse generation resources to minimize system costs. For wind-solar-battery electricity systems, meeting California demand with WECC generation resources reduces the cost by 9% compared to constraining resources entirely to California. Adding long-duration storage lowers system costs by 21% when treating California as an island. This data-driven analysis quantifies rare weather-related events and provides an understanding that can be used to inform stakeholders in future electricity systems.

Energy-Efficient On-Platform Target Classification for Electric Air Transportation Systems

Due to the predicted rise of Unmanned Aircraft Systems (UAS) in commercial, civil, and military operations, there is a desire to make UASs more energy efficient so they can proliferate with ease of deployment and maximal life per charge. To address current limitations, a three-tiered approach is investigated to mitigate Unmanned Aerial Vehicle (UAV) hover time, reduce network datalink transmission to a ground station, and provide a real-time framework for Sense-and-Avoidance (SAA) target classification. An energy-efficient UAS architecture framework is presented, and a corresponding SAA prototype is developed using commercial hardware to validate the proposed architecture using an experimental methodology. The proposed architecture utilizes classical computer vision methods within the Detection Subsystem coupled with deeply learned Convolutional Neural Networks (CNN) within the Classification Subsystem. Real-time operations of three frames per second are realized enabling UAV hover time and associated energy consumption during SAA processing to be effectively eliminated. Additional energy improvements are not addressed in the scope of this work. Inference accuracy is improved by 19% over baseline COTS models and current non-adaptive, single-stage SAA architectures. Overall, by pushing SAA processing to the edge of the sensors, network offload transmissions and reductions in processing time and energy consumption are feasible and realistic in future battery-powered electric air transportation systems.

Molehills into mountains: Transitional pressures from household PV-battery adoption under flat retail and feed-in tariffs

With Australia's significant existing household PV capacity, decreasing battery costs may lead to widespread household PV-battery adoption. As the sizing of these systems are heavily influenced by retail tariffs, this paper analyses the effect of flat retail tariffs on households free to invest in PV battery systems. Using Perth, Australia for context, an open-source model is used to simulate household PV battery investments over a 20-year period. We find that flat usage and feed-in tariffs lead to distinct residual demand patterns as households' transition from PV-only to PV-battery systems. Qualitatively analysing these patterns from the bottom-up, we identify transitional tipping points that may challenge future electricity system management, market participation and energy policies. The continued use of flat tariffs incentivises PV-battery households to maximise self-consumption, which reduces annual grid-imports, increases annual grid-exports, and shifts residual demand towards winter. Diurnal and seasonal demand patterns continue to change as PV-battery households eventually become net-generators. Unmanaged, these bottom-up changes may complicate energy decarbonisation efforts within centralised electricity markets and suggest that policymakers should prepare for PV-battery households to play a more active role in the energy system.

A Review on DC-DC Converters with Photovoltaic System in DC Micro Grid

Photovoltaic system is the low-cost source of electrical power in high solar energy regions. The benefits of PV system are like nonpolluting and minimum maintenance. Solar energy changes as per irradiance and temperature and also one factor which reduces the power output is the partial shading in the cells. Hence f o r th, various algo rith ms a r e p u t fo rth to obta in t h e maximum power f r o m t h e PV arrangement and dc-dc converters intend to regulate the supply. The concept of micro grid is emerging as an excellent solution for inter connecting renewable energy sources and loads. DC micro grid is a necessity in today’s world. There is wide increase in usage of DC systems in commercial, residential and industrial systems. DC micro grids are dominant in reliability, control and efficiency. Direct current architectures will be used in demand in the future electrical distribution systems. This paper reviews on all above concepts to be used in DC micro grid for future DC applications.

Toward a Secure and Resilient All-Renewable Energy Grid for Smart Cities

The concept of smart cities is driven by the need to enhance citizens’ quality of life. It is estimated that 70% of the world population will live in urban areas by 2050. The electric grid is the energy backbone of smart city deployments. An electric energy system immune to adverse events, both cyber and physical risks, and able to support the integration of renewable sources will drive a transformational development approach for future smart cities. This article describes how the future electric energy system with 100% electricity supply from renewable energy sources requires the “birth of security and resiliency” incorporated with its ecosystem.

Implications of renewable resource dynamics for energy system planning: The case of geothermal and hydropower in Kenya

Kenya's current electricity production is mainly relying on hydro and geothermal resources. Both of these resources are subject to complex dynamics that affect their sustainable utilization. Neither the current power expansion plan for Kenya, nor the existing electricity supply models address such effects. The research question in this paper is: What are the implications of hydro and geothermal resource dynamics for short- and long-term (sustainable) electricity system planning in Kenya? To answer this question a bottom-up system dynamics model representing the most prevalent technologies of Kenya's future electricity system, including the dynamics of geothermal and hydro resource utilization, is developed. The stock-flow system dynamics modelling allows to capture the resources characteristics and the link to electricity production. Due to the stock-like nature of geothermal resource, excessive utilization can lead to production capacity losses. In the case of hydro resources, climate change can reduce their availability significantly. A total of eight scenarios varying in demand size and consideration of resource dynamics are simulated and compared. The results indicate that when geothermal and hydro resource dynamics are considered, higher installed capacity will be required in the long-term to compensate production losses, which in turn leads to a higher electricity generation cost.

Tubular laminated composite structural battery

This paper introduces a polymer-matrix composite-based structural lithium-ion battery system with tubular morphology. Its shape and closed cross-section, fabricated during the autoclave process, allow for high electrochemical performance by controlling the electrode distance and battery environment. To prevent the degradation of the organic liquid electrolyte within this continuous resin polymer system, extra components such as thermoplastic hot melt film, a double glass load-bearing separator, aluminum film, and polydimethylsiloxane were implanted. The electrochemical properties of this manufactured multi-functional structural battery design, named the tubular laminated composite battery (TLCB), were tested with a galvanic cell testing machine. The initial specific capacity of TLCB was found to be approximately 120–130 mAh/g with good cycling performance and steady coulombic efficiency. Furthermore, mechanical load resistance and correlation with electrochemical performance were also assessed under the four-point bending load condition. These results show that the TLCB has the potential to operate as a subsidiary energy storage for future electric vehicle systems with a certain level of structural specific capacity and load-bearing properties.

Electricity Price Distributions in Future Renewables-Dominant Power Grids and Policy Implications

Future electricity systems with tight constraints on carbon emissions will rely much more on wind and solar generation, with zero marginal cost, than today. We use capacity expansion modelling of Texas in 2050 to illustrate wholesale price distributions in future energy-only, carbon-constrained grids without price caps under a range of technology/system assumptions. Tightening carbon emissions constraints dramatically increases the frequency of very low prices. The frequency of high prices also increases, and all resources earn the bulk of their energy market revenues in relatively few hours. The presence of demand response, long-duration energy storage, dispatchable low-carbon generation, or a robust market for hydrogen for non-electricity use (and for energy storage) weakens but does not undo these results. Financial instruments to hedge price volatility will consequently be more costly and it is likely that we will need to redesign capacity remuneration mechanisms to provide adequate incentives for optimal investment in VRE generation and, particularly, storage. In order to encourage economy-wide electrification, the marginal retail price of electricity should be low whenever the wholesale price is low. With automated control of demand via demand response contracts, the risks of price volatility faced by retail customers can be mitigated without sacrificing efficiency. To encourage economy-wide electrification, the marginal retail price of electricity should be low when the wholesale spot price is low. We discuss ways of reducing consumers’ risk in this world while providing adequate investment incentives.

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.

Distributed machine learning for energy trading in electric distribution system of the future

Machine Learning (ML) has seen a great potential to solve many power system problems along with its transition into Smart Grid. Specifically, electric distribution systems have witnessed a rapid integration of distributed energy resources (DERs), including photovoltaic (PV) panels, electric vehicles (EV), and smart appliances, etc. Electricity consumers, equipped with such DERs and advanced metering/sensing/computing devices, are becoming self-interested prosumers who can behave more actively for their electric energy consumption. In this paper, the potential of distributed ML in solving the energy trading problem among prosumers of a future electric distribution system - building DC grid cell, is explored, while considering the limited computation, communication, and data privacy issues of the edge entities. A fully distributed energy trading framework based on ML is proposed to optimize the load and price prediction accuracy and energy trading efficiency. Computation resource allocation, communication schemes, ML task scheduling, as well as user sensitive data preserving issues in the distributed ML framework are addressed with consideration of all the economic and physical constraints of the electric distribution systems.

Evaluating cross-sectoral impacts of climate change and adaptations on the energy-water nexus: a framework and California case study

Electricity and water systems are inextricably linked through water demands for energy generation, and through energy demands for using, moving, and treating water and wastewater. Climate change may stress these interdependencies, together referred to as the energy-water nexus, by reducing water availability for hydropower generation and by increasing irrigation and electricity demand for groundwater pumping, among other feedbacks. Further, many climate adaptation measures to augment water supplies—such as water recycling and desalination—are energy-intensive. However, water and electricity system climate vulnerabilities and adaptations are often studied in isolation, without considering how multiple interactive risks may compound. This paper reviews the fragmented literature and develops a generalized framework for understanding these implications of climate change on the energy-water nexus. We apply this framework in a case study to quantify end-century direct climate impacts on California’s water and electricity resources and estimate the magnitude of the indirect cross-sectoral feedback of electricity demand from various water adaptation strategies. Our results show that increased space cooling demand and decreased hydropower generation are the most significant direct climate change impacts on California’s electricity sector by end-century. In California’s water sector, climate change impacts directly on surface water availability exceed demand changes, but have considerable uncertainty, both in direction and magnitude. Additionally, we find that the energy demands of water sector climate adaptations could significantly affect California’s future electricity system needs. If the worst-case water shortage occurs under climate change, water-conserving adaptation measures can provide large energy savings co-benefits, but other energy-intensive water adaptations may double the direct impacts of climate change on the state’s electricity resource requirement. These results highlight the value of coordinated adaptation planning between the energy and water sectors to achieve mutually beneficial solutions for climate resilience.

High Quality of Service in Future Electrical Energy Systems: A New Time-Domain Approach

In this paper we study dynamical distortion problems in future electrical energy systems with high renewable penetration. We introduce a new time-domain modeling of electrical energy systems comprising inverter-controlled distributed energy resources (DERs). This modeling is first used to quantify the relations between distortions and real/reactive power dynamics. Next, to ensure acceptable Quality of Service (QoS), a novel nonlinear distributed inverter control is introduced. Sufficient conditions are established for the guaranteed performance of the proposed control. These conditions further support the practical implementation of the derived controller. The effectiveness of this enhanced control is illustrated using simulations for the case of avoiding system instability during sudden grid reconfigurations. Simulations also show that distortions can be suppressed in systems with parallel-connected solar photovoltaics (PVs).