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1. Forecasting the Evolution of Hydropower Generation

   https://doi.org/10.1145/3394486.3403337  

   Zhou, Fan ; Li, Liang ; Zhang, Kunpeng ; Trajcevski, Goce ; Yao, Fuming ; Huang, Ying ; Zhong, Ting ; Wang, Jiahao ; Liu, Qiao ( July 2020 , The 26th {ACM} {SIGKDD} Conference on Knowledge Discovery and Data Mining, CA, August 2020)

   null (Ed.)

   Hydropower is the largest renewable energy source for electricity generation in the world, with numerous benefits in terms of: environment protection (near-zero air pollution and climate impact), cost-effectiveness (long-term use, without significant impacts of market fluctuation), and reliability (quickly respond to surge in demand). However, the effectiveness of hydropower plants is affected by multiple factors such as reservoir capacity, rainfall, temperature and fluctuating electricity demand, and particularly their complicated relationships, which make the prediction/recommendation of station operational output a difficult challenge. In this paper, we present DeepHydro, a novel stochastic method for modeling multivariate time series (e.g., water inflow/outflow and temperature) and forecasting power generation of hydropower stations. DeepHydro captures temporal dependencies in co-evolving time series with a new conditioned latent recurrent neural networks, which not only considers the hidden states of observations but also preserves the uncertainty of latent variables. We introduce a generative network parameterized on a continuous normalizing flow to approximate the complex posterior distribution of multivariate time series data, and further use neural ordinary differential equations to estimate the continuous-time dynamics of the latent variables constituting the observable data. This allows our model to deal with the discrete observations in the context of continuous dynamic systems, while being robust to the noise. We conduct extensive experiments on real-world datasets from a large power generation company consisting of cascade hydropower stations. The experimental results demonstrate that the proposed method can effectively predict the power production and significantly outperform the possible candidate baseline approaches. 

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2. Continuous-Time Marginal Pricing of Electricity

   https://doi.org/10.1109/TPWRS.2016.2597288  

   Parvania, Masood ; Khatami, Roohallah ( August 2016 , IEEE Transactions on Power Systems)

   The current practice of discrete-time electricity pricing starts to fall short in providing an accurate economic signal reflecting the continuous-time variations of load and generation schedule in power systems. This paper introduces the fundamental mathematical theory of continuous-time marginal electricity pricing. We first formulate the continuous-time unit commitment (UC) problem as a constrained variational problem, and subsequently define the continuous-time economic dispatch (ED) problem where the binary commitment variables are fixed to their optimal values. We then prove that the continuous-time marginal electricity price equals to the Lagrange multiplier of the variational power balance constraint in the continuous-time ED problem. The proposed continuous-time marginal price is not only dependent to the incremental generation cost rate, but also to the incremental ramping cost rate of the units, thus embedding the ramping costs in calculation of the marginal electricity price. The numerical results demonstrate that the continuous-time marginal price manifests the behavior of the constantly varying load and generation schedule in power systems. 

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3. Ambiguities and nonmonotonicities under prosumer power: Optimal distributed energy resource investment in a deregulated electricity industry

   https://doi.org/10.1007/s11750-022-00628-2  

   Siddiqui, Afzal S. ; Siddiqui, Sauleh A. ( April 2022 , TOP)

   Prosumers adopt distributed energy resources (DER) to cover part of their own consumption and to sell surplus energy. Although individual prosumers are too dispersed to exert operational market power, they may collectively hold a strategic advantage over conventional generation in selecting DER capacity via aggregators. We devise a bilevel model to examine DER capacity sizing by a collective prosumer as a Stackelberg leader in an electricity industry where conventional generation may exert market power in operations. At the upper level, the prosumer chooses DER capacity in anticipation of lower-level operations by conventional generation and DER output. We demonstrate that exertion of market power in operations by conventional generation and the marginal cost of conventional generation affect DER investment by the prosumer in a nonmonotonic manner. Intuitively, in an industry where conventional generation exerts market power in operations similar to a monopoly (MO), the prosumer invests in more DER capacity than under perfectly competitive operations (PC) to take advantage of a high market-clearing price. However, if the marginal cost of conventional generation is high enough, then this intuitive result is reversed as the prosumer adopts more DER capacity under PC than under MO. This is because the high marginal cost of conventional generation prevents the market-clearing price from decreasing, thereby allowing for higher prosumer revenues. Moreover, competition relieves the chokehold on consumption under MO, which further incentivises the prosumer to expand DER capacity to capture market share. We prove the existence of a critical threshold for the marginal cost of conventional generation that leads to this counterintuitive result. Finally, we propose a countervailing regulatory mechanism that yields welfare-enhancing DER investment even in deregulated electricity industries.

    

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4. Price-Aware Deep Learning for Electricity Markets

   Dvorkin, Vladimir ; Fioretto, Ferdinando ( February 2024 , NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning)

   While deep learning gradually penetrates operational planning, its inherent prediction errors may significantly affect electricity prices. This letter examines how prediction errors propagate into electricity prices, revealing notable pricing errors and their spatial disparity in congested power systems. To improve fairness, we propose to embed electricity market-clearing optimization as a deep learning layer. Differentiating through this layer allows for balancing between prediction and pricing errors, as oppose to minimizing prediction errors alone. This layer implicitly optimizes fairness and controls the spatial distribution of price errors across the system. We showcase the price-aware deep learning in the nexus of wind power forecasting and short-term electricity market clearing. 

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5. Inequality in air pollution mortality from power generation in India

   https://doi.org/10.1088/1748-9326/aca8bb  

   Sengupta, Shayak ; Thakrar, Sumil K. ; Singh, Kirat ; Tongia, Rahul ; Hill, Jason D. ; Azevedo, Ines M. L. ; Adams, Peter J. ( December 2022 , Environmental Research Letters)

   India’s coal-heavy electricity system is the world’s third largest and a major emitter of air pollution and greenhouse gas emissions. Consequently, it remains a focus of decarbonization and air pollution control policy. Considerable heterogeneity exists between states in India in terms of electricity demand, generation fuel mix, and emissions. However, no analysis has disentangled the expected, state-level spatial differences and interactions in air pollution mortality under current and future power sector policies in India. We use a reduced-complexity air quality model to evaluate annual PM_2.5mortalities associated with electricity production and consumption in each state in India. Furthermore, we test emissions control, carbon tax, and market integration policies to understand how changes in power sector operations affect ambient PM_2.5concentrations and associated mortality. We find poorer, coal-dependent states in eastern India disproportionately face the burden of PM_2.5mortality from electricity in India by importing deaths. Wealthier, high renewable energy states in western and southern India meanwhile face a lower burden by exporting deaths. This suggests that as these states have adopted more renewable generation, they have shifted their coal generation and associated PM_2.5mortality to eastern areas. We also find widespread sulfur emissions control decreases mortality by about 50%. Likewise, increasing carbon taxes in the short term reduces annual mortality by up to 9%. Market reform where generators between states pool to meet demand reduces annual mortality by up to 8%. As India looks to increase renewable energy, implement emissions control regulations, establish a carbon trading market, and move towards further power market integration, our results provide greater spatial detail for a federally structured Indian electricity system.

    

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