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

Abstract

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.

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

“…In contrast, machine learning techniques rely on algorithms and data‐driven approaches like PCA, ANN, SVM, decision trees, and random forests to make predictions based on historical data (Bose, 2017; Nikkhah et al, 2019). Econometric models focus on economic factors impacting electricity prices like multi‐agent or game theoretic simulations, to analyze relationships between variables that influence electricity prices, such as weather, fuel prices, and demand (Ali & Choi, 2020; Nyangon & Byrne, 2023). Hybrid models combine multiple methods, such as statistical models and machine learning algorithms, for greater accuracy, while behavioral finance and market sentiment analysis emphasize human behavior's impact on pricing, to forecast price movements or develop counteractive measures.…”

“…As a premier independent system operator, CAISO plays a pivotal role in renewable energy development, particularly in solar PV and wind power. The paper delves into CAISO's resource adequacy planning, underlining how technological innovation, renewable energy integration, and regulatory policy decisions can impact generation mix changes (Byrne et al, 2015(Byrne et al, , 2022Nyangon & Byrne, 2023;Woo et al, 2016). This research seeks to enlighten policymakers and market operators about the value of unsupervised methods like PCA, especially in forecasting dayahead prices in an environment increasingly dominated by renewable energy sources.…”

Energy Transformation: Challenges and Opportunities — The Polish Case

“…Some will concentrate on single RE sources, like solar [Creutzig et al 2017;Lazard 2020], bioenergy [Reid et al 2019;Mandley et al 2021;Ambaye et al 2021] others point to the fact that the efficiency of RES (renewable energy sources) grows over the time and the prices of KWh generated drop, sometimes dramatically [Ringkjøb et al 2018;Lazard 2020;Infield, Freris 2020;Christophers 2022;Banks 2022]. Individual focus is given to specific solutions that can be effective in decarbonisation of the energy mix, like the simultaneous use of natural gas and RES within a wellbalanced energy mix, with strong potential to be used mainly in the United States and China [Pless et al 2016;Xu et al 2017;EIA 2021b;EIA 2021c] as well as the distributed or decentralised energy-production models, which could be used everywhere [Lund et al 2019;Burger et al 2019a;Burger et al 2019b;Nyangon, Byrne 2022;Banks 2022;Strezoski 2022]. My alternative scenarios both follow similar models.…”