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"ELECTRICITY SYSTEM"
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Exploring the Impacts of Carbon Pricing on Canada’s Electricity Sector
Canadian provinces are required to regulate their power sectors using carbon pricing systems that meet national minimum stringency standards, which are set by the federal government. A diverse set of systems has emerged as a result. However, there has been limited assessment of how different pricing mechanisms impact the evolution of Canada’s electricity system. To address this gap, we use an electricity system planning model called COPPER and a scenario-based approach to assess if, and to what extent, different policy regimes impact power sector greenhouse gas emissions and costs. Our results show that carbon pricing systems currently in place lead to significant carbon reductions over the long term, provided that free emissions allocations are reduced. However, the cost-optimal pathway for the power sector differs across provinces depending on the carbon pricing mechanism. Some provinces achieve least-cost emissions reductions by switching from high-carbon technologies to renewables, while others are better served by replacing high-carbon technologies with low-carbon fossil fuel alternatives. Further, provinces that implement cap-and-trade systems may affect the transitions of interconnected jurisdictions. Power sector climate policy design should reflect the heterogeneity of available assets, resources, and neighbouring approaches.
Journal Article
The Benefit of Collaboration in the North European Electricity System Transition—System and Sector Perspectives
This work investigates the connection between electrification of the industry, transport, and heat sector and the integration of wind and solar power in the electricity system. The impact of combining electrification of the steel industry, passenger vehicles, and residential heat supply with flexibility provision is evaluated from a systems and sector perspective. Deploying a parallel computing approach to the capacity expansion problem, the impact of flexibility provision throughout the north European electricity system transition is investigated. It is found that a strategic collaboration between the electricity system, an electrified steel industry, an electrified transport sector in the form of passenger electric vehicles (EVs) and residential heat supply can reduce total system cost by 8% in the north European electricity system compared to if no collaboration is achieved. The flexibility provision by new electricity consumers enables a faster transition from fossil fuels in the European electricity system and reduces thermal generation. From a sector perspective, strategic consumption of electricity for hydrogen production and EV charging and discharging to the grid reduces the number of hours with very high electricity prices resulting in a reduction in annual electricity prices by up to 20%.
Journal Article
Interval optimal scheduling of integrated electricity and district heating systems considering dynamic characteristics of heating network
Coordinated operation of integrated electricity and district heating system (IEDHS) has great potential to enhance the flexibility of the power system to cope with the wind power curtailment. This study proposes an interval optimal scheduling algorithm for IEDHSs, considering the dynamic characteristics of the heating network. The model of the district heating system with dynamic characteristics including transmission delay and heat losses, is established in detail, and the uncertainties of both wind power and electricity and heating loads are described with interval numbers. Then an interval optimal scheduling model of the IEDHS is formulated to minimise the IEDHS operation cost. The impacts of the transmission delay and heat losses of the heating network on the scheduling of the IEDHS are analysed. Case studies are performed on the PJM 5‐bus electricity system with a 6‐node district heating system and IEEE 39‐bus electricity system with a 12‐node district heating system to evaluate the effectiveness of the proposed model. The results demonstrate that the dynamic characteristics of the heating network can integrate more wind power and enlarge the width of the cost interval.
Journal Article
Tracking emissions in the US electricity system
Understanding electricity consumption and production patterns is a necessary first step toward reducing the health and climate impacts of associated emissions. In this work, the economic input–output model is adapted to track emissions flows through electric grids and quantify the pollution embodied in electricity production, exchanges, and, ultimately, consumption for the 66 continental US Balancing Authorities (BAs). The hourly and BA-level dataset we generate and release leverages multiple publicly available datasets for the year 2016. Our analysis demonstrates the importance of considering location and temporal effects as well as electricity exchanges in estimating emissions footprints. While increasing electricity exchanges makes the integration of renewable electricity easier, importing electricity may also run counter to climate-change goals, and citizens in regions exporting electricity from high-emission-generating sources bear a disproportionate air-pollution burden. For example, 40% of the carbon emissions related to electricity consumption in California’s main BA were produced in a different region. From 30 to 50% of the sulfur dioxide and nitrogen oxides released in some of the coal-heavy Rocky Mountain regions were related to electricity produced that was then exported. Whether for policymakers designing energy efficiency and renewable programs, regulators enforcing emissions standards, or large electricity consumers greening their supply, greater resolution is needed for electricsector emissions indices to evaluate progress against current and future goals.
Journal Article
Fuzzy Approach for Managing Renewable Energy Flows for DC-Microgrid with Composite PV-WT Generators and Energy Storage System
Recently, the implementation of software/hardware systems based on advanced artificial intelligence techniques for continuous monitoring of the electrical parameters of intelligent networks aimed at managing and controlling energy consumption has been of great interest. The contribution of this paper, starting from a recently studied DC-MG, fits into this context by proposing an intuitionistic fuzzy Takagi–Sugeno approach optimized for the energy management of isolated direct current microgrid systems consisting of a photovoltaic and a wind source. Furthermore, a lead-acid battery guarantees the stability of the DC bus while a hydrogen cell ensures the reliability of the system by avoiding blackout conditions and increasing interaction with the loads. The fuzzy rule bank, initially built using the expert’s knowledge, is optimized with the aforementioned procedure, maximizing external energy and minimizing consumption. The complete scheme, modeled using MatLab/Simulink, highlighted performance comparable to fuzzy Takagi–Sugeno systems optimized using a hybrid approach based on particle swarm optimization (to structure the antecedents of the rules) and minimum batch squares (to optimize the output).
Journal Article
Multivariate Empirical Mode Decomposition and Recurrence Quantification for the Multiscale, Spatiotemporal Analysis of Electricity Demand—A Case Study of Japan
In the new energy systems’ modeling paradigm with high temporal and spatial resolutions, the complexity of renewable resources and demand dynamics is a major obstacle for the scenario analysis of future energy systems and the design of sustainable solutions. Most advanced models are indeed currently restricted by past temporal energy demand data, improper for the analysis of future systems and often insufficient in terms of quantity or spatial resolution. A deeper understanding on energy demand dynamics is thus necessary to improve energy system models and expand their possibilities. The present study introduces noise-assisted multivariate empirical mode decomposition and recurrence quantification analysis for the study of this problematic variable with a case study of Japan’s electricity demand data per region. These tools are adapted to nonlinear, complex systems’ data and are already applied in a wide range of scientific fields including climate studies. The decomposition of electricity demand as well as the detection of irregularities in its dynamics allow to identify relations with temperature variations, demand sector shares, life style and local culture at different temporal scales.
Journal Article
Reliability evaluation of integrated electricity–gas system utilizing network equivalent and integrated optimal power flow techniques
The wide utilization of gas-fired generation and the rapid development of power-to-gas technologies have led to the intensified integration of electricity and gas systems. The random failures of components in either electricity or gas system may have a considerable impact on the reliabilities of both systems. Therefore, it is necessary to evaluate the reliabilities of electricity and gas systems considering their integration. In this paper, a novel reliability evaluation method for integrated electricity–gas systems (IEGSs) is proposed. First, reliability network equivalents are utilized to represent reliability models of gas-fired generating units, gas sources (GSs), power-to-gas facilities, and other conventional generating units in IEGS. A contingency management schema is then developed considering the coupling between electricity and gas systems based on an optimal power flow technique. Finally, the time-sequential Monte Carlo simulation approach is used to model the chronological characteristics of the corresponding reliability network equivalents. The proposed method is capable to evaluate customers’ reliabilities in IEGS, which is illustrated on an integrated IEEE Reliability Test System and Belgium gas transmission system.
Journal Article
Hourly accounting of carbon emissions from electricity consumption
Carbon accounting is important for quantifying the sources of greenhouse gas (GHG) emissions that are driving climate change, and is increasingly being used to guide policy, investment, business, and regulatory decisions. The current practice for accounting emissions from consumed electricity, guided by standards like the GHG protocol, uses annual-average grid emission factors, although previous studies have shown that grid carbon intensity varies across seasons and hours of the day. Previous case studies have shown that annual-average carbon accounting can bias emission inventories, but none have shown that this bias is substantial or widespread. This study addresses this gap by calculating emission inventories for thousands of residential, commercial, industrial, and agricultural facilities across the US, and explores the magnitude and direction of this bias compared to hourly accounting of emissions. Our results show that annual-average accounting can over- or under-estimate carbon inventories as much as 35% in certain settings but result in effectively no bias in others. Bias will be greater in regions with high variation in carbon intensity, and for end-users with high variation in their electricity consumption across hours and seasons. As variation in carbon intensity continues to grow with growing shares of variable and intermittent renewable generation, these biases will only continue to worsen in the future. In most cases, using monthly-average emission factors does not substantially reduce bias compared to annual averages. Thus, the authors recommend that hourly accounting be adopted as the best practice for emissions inventories of consumed electricity.
Journal Article
Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids
Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability.
Journal Article