Advances in Applied Energy最新文献

The increasing frequency of major energy outages in recent years has significantly affected millions of people around the world, raising extensive concerns about enhancing infrastructure resilience to withstand and quickly recover from disasters. However, the post-disaster recovery of infrastructure functionality has been hindered by the lack of interdependency modeling of energy networks and priority identification of components, resulting in long-duration energy supply scarcity, wide-ranging service disruption, and huge social losses. Here, a skeleton-network based strategy for enhancing the resilience of integrated electricity-gas systems (IEGSs) is proposed, which can provide a clear representation of which network components should be protected and how to determine the component recovery priority considering interdependencies of power and gas systems. Using the modified energy systems in New England and Northwest China, the skeleton-network is uncovered to quickly recover more than 90% of system functionality using less than 44.3% of total resources, and consumer-affected time by energy outages decreases by more than 53%. The analysis also indicates that compared to conventional methods, the skeleton-network based strategy performs best in improving infrastructure resilience. These results elucidate the implications of skeleton-networks on quick recovery of infrastructure functionality and demonstrate resilience enhancement methods that are applicable to a wider class of coupled infrastructure networks in hazard-prone areas.

Modern societies use machine learning techniques to support complex decision-making processes (e.g., renewable energy and power demand forecasting in energy systems). Data fuels these techniques, so the quality of the data fed into them determines the accuracy of the results. While the amount of data is increasing with the adoption of internet-of-things, most of it is still private. Availability of data limits the application of machine learning. Scientists and industry pioneers are proposing a model that relies on the economics of data markets, where private data can be traded for a price. Cybersecurity analyses of such markets are lacking. In this context, our study makes two contributions. First, it designs a data market for electric vehicle charging stations, which aims to improve the accuracy of electric vehicle charging demand forecasts. Accurate demand forecasts are essential for sustainable operations of the electric vehicle - charging station - power grid ecosystem, which, in turn, facilitates the electrification and decarbonization of the transportation sector. On the other hand, erroneous demand forecasts caused by malicious cyberattacks impose operational challenges to the ecosystem. Thus, the second contribution of our study is to examine the feasibility of false data injection attacks on the data market for electric vehicle charging stations and to propose a defense mechanism against such attacks. We illustrate our results using data from electric vehicle charging stations in Manhattan, New York. We demonstrate that the data market improves forecasting accuracy of charging stations and reduces the effectiveness of false data injection attacks. The purpose of this work is not only to inform electric vehicle charging stations about the economic benefits of data markets, but to promote cyber awareness among data market pioneers and stakeholders.

Owing to the complexity of the sector, industrial activities are often represented with limited technological resolution in integrated energy system models. In this study, we enriched the technological description of industrial activities in the integrated energy system analysis optimisation (IESA-Opt) model, a peer-reviewed energy system optimisation model that can simultaneously provide optimal capacity planning for the hourly operation of all integrated sectors. We used this enriched model to analyse the industrial decarbonisation of the Netherlands for four key activities: high-value chemicals, hydrocarbons, ammonia, and steel production. The analyses performed comprised 1) exploring optimality in a reference scenario; 2) exploring the feasibility and implications of four extreme industrial cases with different technological archetypes, namely a bio-based industry, a hydrogen-based industry, a fully electrified industry, and retrofitting of current assets into carbon capture utilisation and storage; and 3) performing sensitivity analyses on key topics such as imported biomass, hydrogen, and natural gas prices, carbon storage potentials, technological learning, and the demand for olefins. The results of this study show that it is feasible for the energy system to have a fully bio-based, hydrogen-based, fully electrified, and retrofitted industry to achieve full decarbonisation while allowing for an optimal technological mix to yield at least a 10% cheaper transition. We also show that owing to the high predominance of the fuel component in the levelled cost of industrial products, substantial reductions in overnight investment costs of green technologies have a limited effect on their adoption. Finally, we reveal that based on the current (2022) energy prices, the energy transition is cost-effective, and fossil fuels can be fully displaced from industry and the national mix by 2050.

This paper proposes a novel bi-level optimization model to study the strategic retail pricing and demand bidding problems of an electricity retailer that considers the interactions between demand response and market clearing process. In order to accurately forecast the day-ahead demand bids submitted by the retailer, a novel deep learning framework based on convolutional neural networks and long short-term memory is proposed that can capture both local trends and long-term dependency of the forecasting data. In addition, uncertainties about the retailer’s served demand, rivals’ demand bids, and wind power generation are incorporated using the data-driven uncertainty set constructed from data. We further propose chance-constrained programming that introduces a set of chance constraints to represent the operational risk associated with the market uncertainties. To solve this problem, we first reformulate chance-constrained programming as a tractable second-order conic programming and then convert it into a single-level mathematical program with equilibrium constraints by using its Karush Kuhn Tucker conditions. The scope of the examined case studies is four-fold. First, they evaluate the benefits of the proposed forecasting framework in terms of higher accuracy and expected profit compared to the conventional forecasting methods. Second, they demonstrate how demand flexibility affects the retailer’s strategies and its business cases. Third, they highlight the added value of the proposed bi-level model capturing the market clearing process by comparing its outcomes against the state-of-the-art bi-level model with exogenous market prices. Finally, they analyze the retailer’s strategies and business cases at different confidence levels regarding the imposed chance constraints.

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 (levelized cost of electricity) 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.

The February 2021 Texas winter power outage has led to hundreds of deaths and billions of dollars in economic losses, largely due to the generation failure and record-breaking electric demand. In this paper, we study the scaling-up of demand flexibility as a means to avoid load shedding during such an extreme weather event. The three mechanisms considered are interruptible load, residential load rationing, and incentive-based demand response. By simulating on a synthetic but realistic large-scale Texas grid model along with demand flexibility modeling and electricity outage data, we identify portfolios of mixing mechanisms that can completely avoid outages, where individual mechanisms may fail due to decaying marginal effects. We also reveal that interruptible load and residential load rationing are complementary, while incentive-based demand response exhibits counterintuitive nonlinear effects on the efficacy of other mechanisms. The quantitative results can provide instructive insights for developing demand response programs against extreme weather conditions.

Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal operation of building energy systems in the past two decades. However, MPC field tests are still uncommon, especially for small- and medium-sized commercial buildings and for buildings integrated with onsite renewables. This paper describes the implementation and the long-term performance evaluation of an MPC controller in a small commercial building equipped with behind-the-meter photovoltaics and electrochemical batteries. MPC controls space conditioning, commercial refrigeration, and the battery system. We tested two types of demand flexibility applications in the field: electricity bill minimization under time-of-use tariffs and responses to grid flexibility events. Results show that the proposed controller achieves 12% of annual electricity cost savings and 34% peak demand reduction against the baseline, while respecting thermal comfort and food safety. The field tests also demonstrate the ability of the MPC controller to provide a multitude of grid services including real-time pricing, demand limiting, load shedding, load shifting, and load tracking, using the same optimization framework.

The North Sea Offshore Grid concept has been envisioned as a promising alternative to: 1) ease the integration of offshore wind and onshore energy systems, and 2) increase the cross-border capacity between the North Sea region countries at low cost. In this paper we explore the techno-economic benefits of the North Sea Offshore Grid using two case studies: a power-based offshore grid, where only investments in power assets are allowed (i.e. offshore wind, HVDC/HVAC interconnectors); and a power-and-hydrogen offshore grid, where investments in offshore hydrogen assets are also permitted (i.e. offshore electrolysers, new hydrogen pipelines and retrofitted natural gas pipelines). In this paper we present a novel methodology, in which extensive offshore spatial data is analysed to define meaningful regions via data clustering. These regions are incorporated to the Integrated Energy System Analysis for the North Sea region (IESA-NS) model. In this optimization model, the scenarios are run without any specific technology ban and under open optimization. The scenario results show that the deployment of an offshore grid provides relevant cost savings, ranging from 1% to 4.1% of relative cost decrease (2.3 bn € to 8.7 bn €) in the power-based, and ranging from 2.8% to 7% of relative cost decrease (6 bn € to 14.9 bn €) in the power-and-hydrogen based. In the most extreme scenario an offshore grid permits to integrate 283 GW of HVDC connected offshore wind and 196 GW of HVDC meshed interconnectors. Even in the most conservative scenario the offshore grid integrates 59 GW of HVDC connected offshore wind capacity and 92 GW of HVDC meshed interconnectors. When allowed, the deployment of offshore electrolysis is considerable, ranging from 61 GW to 96 GW, with capacity factors of around 30%.

Energy efficiency (EE) has long been recognized as a source of value to the electricity grid. Especially with increasing penetration of variable renewable generation, demand response (DR) can also provide system value and support the evolving needs of the grid. Yet there has been little study to date of interactions between EE and DR that may complicate their grid impacts. In this study we perform bottom-up modelling of the interactive effects between EE and DR in buildings for three representative regions of the United States electricity grid. Leveraging new simulation tools that enable detailed modelling of the building stock, we synthesize system-level demand profiles for several scenarios representing different portfolios of EE measures. In each scenario, we couple the underlying building models with a database of DR-enabling technologies to estimate building-level DR capabilities and compute a system-level supply curve for DR. We assess the resulting EE and DR interactive effects based on an existing conceptual framework. The results show a complex relationship between EE and DR, with interactive effects whose size and direction can vary widely depending on the grid system, type of DR, and the framework level being considered. Most often, the overall effect is competition between EE and DR, but significant complementarity can also occur, especially when the EE portfolio includes controls measures. Our results suggest that EE and DR programs developed without considering interactive effects may erode the benefits of both resources, whereas a more integrated approach may yield increased benefits.

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.