Techno-economic analysis of the impact of dynamic electricity prices on solar penetration in a smart grid environment with distributed energy storage

Sheha, Moataz N.; Mohammadi, Kasra; Powell, Kody M.

doi:10.1016/j.apenergy.2020.116168

Cited by 53 publications

(9 citation statements)

References 42 publications

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“…This confirms the findings from previous studies [58][59][60][61] that investment in solar PV is a viable option for the energy transition. On the contrary, some cases [62,63] found negative NPV for utility-scale and lower generation capacity solar PV. For instance, Assereto and Byrne [62] found that without policy support, investing in utility-scale solar PV might only be profitable under the best-case scenario, when technology costs are low.…”

Section: Discussionmentioning

confidence: 94%

“…For instance, Assereto and Byrne [62] found that without policy support, investing in utility-scale solar PV might only be profitable under the best-case scenario, when technology costs are low. Sheha et al [63] found that two out of ten studied were promising cases, one with a solar photovoltaic plant size of 200 MW and the other with 300 MW, while lower solar penetration had negative NPVs. Meanwhile, this research considers a large-scale deployment of solar PV at a 1 GW generating capacity.…”

Section: Discussionmentioning

confidence: 99%

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Will a Geopolitical Conflict Accelerate Energy Transition in Oil-Importing Countries? A Case Study of the Philippines from a Real Options Perspective

Agaton

2022

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Geopolitical conflicts, involving a major player in the world market, affect the prices of oil and commodities, particularly in oil-importing countries. Consequently, the unprecedented changes in oil prices impact investments in energy transition projects. This study aims to analyze the dynamics of investment decisions in energy transition under uncertainties in geopolitical risks. This research applies the Black-Scholes-based real options valuation to value the flexibility in postponing energy transition investments considering the repercussions of the Russia-Ukraine war. Applying the proposed model to the case of the Philippines, the valuation result with a net present value of USD 231 million for a 1 GW capacity shows that energy transition is a viable project when the cost savings from using fossil fuels to renewables are considered. On the other hand, real options valuation finds a higher flexibility value of USD 990 million, which implies a better decision to postpone the project. The uncertainty in geopolitical conflicts further increases this value causing a longer waiting period to implement the energy transition project. Sensitivity analysis shows that the results are robust to changes in the explanatory variables. Results provide policy implications on making an oil-importing country energy self-sufficient and robust from economic and geopolitical shocks.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Will a Geopolitical Conflict Accelerate Energy Transition in Oil-Importing Countries? A Case Study of the Philippines from a Real Options Perspective

Agaton

2022

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“…In this study, we compared the performance and applicability of two RL algorithms-ANN-PSO and PPO-by exploring their methods and applying them on stochastic steady-state economic optimization of a CSTR with FP-NLP as a benchmark algorithm [4][5][6]. We evaluate the RL algorithms' performance with their profitability and online computational times, and their applicability with their data requirements and training efficiencies.…”

Section: Discussionmentioning

confidence: 99%

“…Many chemical processes, like coal combustion and bioreactors, are complex, and thus deriving appropriate models and optimizing outputs is difficult [1][2][3]. Machine learning has shown success in optimizing complex systems such as scheduling electricity prices to manage demand and maximize power grid performance [4][5][6]. This motivates exploration of other machine learning techniques like reinforcement learning (RL) on model-free optimization [7].…”

Section: Motivationmentioning

confidence: 99%

Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process

2020

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One popular method for optimizing systems, referred to as ANN-PSO, uses an artificial neural network (ANN) to approximate the system and an optimization method like particle swarm optimization (PSO) to select inputs. However, with reinforcement learning developments, it is important to compare ANN-PSO to newer algorithms, like Proximal Policy Optimization (PPO). To investigate ANN-PSO’s and PPO’s performance and applicability, we compare their methodologies, apply them on steady-state economic optimization of a chemical process, and compare their results to a conventional first principles modeling with nonlinear programming (FP-NLP). Our results show that ANN-PSO and PPO achieve profits nearly as high as FP-NLP, but PPO achieves slightly higher profits compared to ANN-PSO. We also find PPO has the fastest computational times, 10 and 10,000 times faster than FP-NLP and ANN-PSO, respectively. However, PPO requires more training data than ANN-PSO to converge to an optimal policy. This case study suggests PPO has better performance as it achieves higher profits and faster online computational times. ANN-PSO shows better applicability with its capability to train on historical operational data and higher training efficiency.

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“…Although the United States is implementing solar, wind and battery systems into the grid with various optimization algorithms [1][2][3][4][5] and socio-economic as well as socio-technical objective functions and/or constraints [6][7][8][9][10][11][12][13][14][15], the pace of change is considered too slow and costly for the rapid integration needed to avoid the emerging global climate crisis and devastation from what writer Elizabeth Kolbert calls the "sixth extinction" [16]. Using frogs, who were the earliest representation of modern amphibians, as a timeline to predate mammals and birds, Kolbert argues that the high rate of extinction of frogs is a harbinger of an imminent extinction event, based on previous observed rates.…”

Section: Introductionmentioning

confidence: 99%

Sustaining 99.99% Reliability for US Electric Grid by using Renewable Energy Systems Complexity as a Benefit

Mechtenberg

Francois

Stratman

et al. 2021

Preprint

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Renewable energy system designers in status-quo and high-income countries (HICs) work to improve the environmental and societal impacts of energy systems by using increasingly complex technologies to predict the future. However, low-to-middle-income countries (LMICs) focus on innovative solutions by turning complexity into an asset. This paper presents the results from four optimization objectives, presented as scenarios with data from EIA utility regions, and with powerloads scaled using population data, solar radiance, wind speed, and wastewater treatment plant (WWTP) capabilities for biogas turbines. This allowed for the complexity of renewable energy systems to be used as a benefit instead of a deficit. For many US cities, an emerging LMIC-based optimization pathway can make possible energy system designs that increase reliability by including backup biogas generators. Here we show that the HIC-based approach is optimal for some cities; for others the status-quo pathway; for many, surprisingly, the LMIC approach is optimal.

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Techno-economic analysis of the impact of dynamic electricity prices on solar penetration in a smart grid environment with distributed energy storage

Cited by 53 publications

References 42 publications

Will a Geopolitical Conflict Accelerate Energy Transition in Oil-Importing Countries? A Case Study of the Philippines from a Real Options Perspective

Will a Geopolitical Conflict Accelerate Energy Transition in Oil-Importing Countries? A Case Study of the Philippines from a Real Options Perspective

Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process

Sustaining 99.99% Reliability for US Electric Grid by using Renewable Energy Systems Complexity as a Benefit

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