Smart Energy Frameworks for Smart Cities: The Need for Polycentrism

Nyangon, Joseph

doi:10.1007/978-3-030-15145-4_4-1

Cited by 6 publications

(7 citation statements)

References 86 publications

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“…In conclusion, our results suggest that decision makers and policy authorities should carefully evaluate the potential complementarity and distributional values of flexible distributed resources when considering how and when to deploy them in the grid for greater societal benefits best‐practices‐through to successful electricity market transformations. In addition to increasing grid‐connected DERs and flexible gas‐fired systems, investment in PV‐battery‐electric grid‐based energy systems for optimum grid reliability and availability can help lower electricity costs, when variables such as varying electricity tariff are taken into consideration (Brandstätt et al, 2017; Nyangon, 2021). In respect of economic performance, this requires developing grid configurations that improve system resiliency and reliability through safe islanding operation and adaptive relay setting in case of an electrical outage or blackout (Cossent, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, while none of the PJM markets have invested in large‐scale energy storage technologies for power production during the study period (except pumped hydro technology), investment in low‐cost battery‐storage technologies will likely grow significantly in the future as the cost of distributed solar and energy battery storage declines in the PJM territory and nationally. Following Bowen and Lacombe (2017), we added a dummy variable to the model specification to control for potential growth in energy storage and smart grid technologies (Nyangon, 2021) that influence solar PV development.…”

Section: Estimation Approachmentioning

confidence: 99%

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Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability

Nyangon

Byrne

2022

WIREs Energy & Environment

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Expansion of distributed solar photovoltaic (PV) and natural gas-fired generation capacity in the United States has put a renewed spotlight on methods and tools for power system planning and grid modernization. This article investigates the impact of increasing natural gas-fired electricity generation assets on installed distributed solar PV systems in the Pennsylvania-New Jersey-Maryland (PJM) Interconnection in the United States over the period 2008-2018. We developed an empirical dynamic panel data model using the systemgeneralized method of moments (system-GMM) estimation approach. The model accounts for the impact of past and current technical, market and policy changes over time, forecasting errors, and business cycles by controlling for PJM jurisdictions-level effects and year fixed effects. Using an instrumental variable to control for endogeneity, we concluded that natural gas does not crowd out renewables like solar PV in the PJM capacity market; however, we also found considerable heterogeneity. Such heterogeneity was displayed in the relationship between solar PV systems and electricity prices. More interestingly, we found no evidence suggesting any relationship between distributed solar PV development and nuclear, coal, hydro, or electricity consumption. In addition, considering policy effects of state renewable portfolio standards, net energy metering, differences in the PJM market structure, and other demand and cost-related factors proved important in assessing their impacts on solar PV generation capacity, including energy storage as a non-wire alternative policy technique.

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Section: Discussionmentioning

confidence: 99%

Section: Estimation Approachmentioning

confidence: 99%

Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability

Nyangon

Byrne

2022

WIREs Energy & Environment

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“…In a highly electrified economy with high shares of variable solar and wind electricity systems, reducing systemic mismatches between the generation and energy demand assets in an efficient manner requires investments in smart energy management systems. Energy management systems consist of two main categories: (a) supply-side devices from the electric utility-side used to manage the fluctuation of the load demand such as substations, and (b) the demand-side management devices used to manage energy consumption and meet the available power from the generation side [25][26][27][28]. Substations encompass transformers, switchgear, and protection, control and automation systems, and connect parts of the electric grid that operate at different voltage levels and managing these multidirectional power flows while ensuring reliability and security is critical as the share of decentralized and renewable energy increases.…”

Section: Leveraging Digitalization and Business Model Innovations Formentioning

confidence: 99%

“…In the case of a 1.5°C temperature limit or even lower, this budget would be drastically contracted. It is for this reason that national, state and local governments must prioritize low-carbon transformations; for instance, (i) ramping up renewable energy over the next two decades, (2) switching from oil to less carbonintensive gas [5,15,25,42,48,55,[75][76][77], and (3) keeping large global deposits of coal, oil, and gas reserves "in the ground" ( Table 2) [11,13,79]. This call has led to the "keep fossil fuels in the ground" initiative, "fossil fuel divestment" campaign, and "unburnable carbon" resistance movement, as a way to compel companies which are active in hydrocarbons or with high coal, oil, and gas reserves in their portfolios to reinvest elsewhere [17,63,[79][80][81][82][83][84].…”

Section: Investments Stranding Risk Factors and Unburnable Fossil Fmentioning

confidence: 99%

“…Urban energy planning: With ML and AI applications, available building 1 energy use data can be extrapolated to predict energy use at the city level. Furthermore, ML is uniquely capable of supporting improvements in "smart energy frameworks for smart cities" [25], including building codes, informing policymakers about utilizing urban rooftops for solar PV electricity generation [55,108], retrofitting strategies using automated performance control [117], public-private partnerships to improve low-and moderate-income (LMI) stipulations and equitable electricity access [15,64].…”

Section: Nuclear Fission and Fusion: Applicationmentioning

confidence: 99%

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Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

Nyangon¹

2021

Sustainable Energy Investment - Technical, Market and Policy Innovations to Address Risk

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The Paris Agreement on climate change requires nations to keep the global temperature within the 2°C carbon budget. Achieving this temperature target means stranding more than 80% of all proven fossil energy reserves as well as resulting in investments in such resources becoming stranded assets. At the implementation level, governments are experiencing technical, economic, and legal challenges in transitioning their economies to meet the 2°C temperature commitment through the nationally determined contributions (NDCs), let alone striving for the 1.5°C carbon budget, which translates into greenhouse gas emissions (GHG) gap. This chapter focuses on tackling the risks of stranded electricity assets using machine learning and artificial intelligence technologies. Stranded assets are not new in the energy sector; the physical impacts of climate change and the transition to a low-carbon economy have generally rendered redundant or obsolete electricity generation and storage assets. Low-carbon electricity systems, which come in variable and controllable forms, are essential to mitigating climate change. These systems present distinct opportunities for machine learning and artificial intelligence-powered techniques. This chapter considers the background to these issues. It discusses the asset stranding discourse and its implications to the energy sector and related infrastructure. The chapter concludes by outlining an interdisciplinary research agenda for mitigating the risks of stranded assets in electricity investments.

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Principal component analysis of day‐ahead electricity price forecasting in CAISO and its implications for highly integrated renewable energy markets

Nyangon,

Akintunde

2023

WIREs Energy & Environment

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Electricity price forecasting is crucial for grid management, renewable energy integration, power system planning, and price volatility management. However, poor accuracy due to complex generation mix data and heteroskedasticity poses a challenge for utilities and grid operators. This paper evaluates advanced analytics methods that utilize principal component analysis (PCA) to improve forecasting accuracy amidst heteroskedastic noise. Drawing on the experience of the California Independent System Operator (CAISO), a leading producer of renewable electricity, the study analyzes hourly electricity prices and demand data from 2016 to 2021 to assess the impact of day‐ahead forecasting on California's evolving generation mix. To enhance data quality, traditional outlier analysis using the interquartile range (IQR) method is first applied, followed by a novel supervised PCA technique called robust PCA (RPCA) for more effective outlier detection and elimination. The combined approach significantly improves data symmetry and reduces skewness. Multiple linear regression models are then constructed to forecast electricity prices using both raw and transformed features obtained through PCA. Results demonstrate that the model utilizing transformed features, after outlier removal using the traditional method and SAS Sparse Matrix method, achieves the highest forecasting performance. Notably, the SAS Sparse Matrix outlier removal method, implemented via proc RPCA, greatly contributes to improved model accuracy. This study highlights that PCA methods enhance electricity price forecasting accuracy, facilitating the integration of renewables like solar and wind, thereby aiding grid management and promoting renewable growth in day‐ahead markets.This article is categorized under: Energy and Power Systems > Energy Management Energy and Power Systems > Distributed Generation Emerging Technologies > Digitalization

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Smart Energy Frameworks for Smart Cities: The Need for Polycentrism

Cited by 6 publications

References 86 publications

Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability

Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

Principal component analysis of day‐ahead electricity price forecasting in CAISO and its implications for highly integrated renewable energy markets

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