Price signals from distribution networks (DNs) guide energy communities (ECs) in adjusting their energy usage, enabling effective coordination for reliable power system operation. However, this coordinated operation faces significant challenges due to the limited availability of ECs' internal information (i.e., only the aggregated energy usage of ECs is available to DNs), and the high computational burden of accounting for uncertainties and the associated risks through numerous scenarios. To address these challenges, we propose a quantum learning and estimation approach to enhance coordinated operation between DNs and ECs. Specifically, by leveraging advanced quantum properties such as quantum superposition and entanglement, we develop a hybrid quantum temporal convolutional network-long short-term memory (Q-TCN-LSTM) model to establish an end-to-end mapping between ECs' responses and the price incentives from DNs. Moreover, we develop a quantum estimation method based on quantum amplitude estimation (QAE) and two phase-rotation circuits to significantly accelerate the optimization process under numerous uncertainty scenarios. Numerical experiments demonstrate that, compared to classical neural networks, the proposed Q-TCN-LSTM model improves the mapping accuracy by 69.2\% while reducing the model size by 99.75\%. Compared to classical Monte Carlo simulation, QAE achieves comparable accuracy with a substantial reduction in computational resources. In addition, the estimated computation time for quantum learning and estimation on ideal quantum devices is over 90\% shorter than that of traditional methods.
Quentin Raillard-Cazanove, Thibaut Knibiehly, Robin Girard
The decarbonisation of the energy system is crucial for achieving climate goals and is inherently linked to the decarbonisation of industry. Despite this, few studies explore the simultaneous impacts of decarbonising both sectors. This paper aims to examine how industrial decarbonisation in Europe affects the energy system and vice versa. To address this, an industry model incorporating key heavy industry sectors across six European countries is combined with an energy system model for electricity and hydrogen covering fifteen European regions, refered to as the EU-15, divided into eleven zones. The study evaluates various policy scenarios under different conditions.The results demonstrate that industrial decarbonisation leads to a significant increase in electricity and hydrogen demand. This additional demand for electricity is largely met through renewable energy sources, while hydrogen supply is predominantly addressed by blue hydrogen production when fossil fuels are authorized and the system lacks renewable energy. This increased demand results in higher prices with considerable regional disparities. Furthermore, the findings reveal that, regardless of the scenario, the electricity mix in the EU-15 remains predominantly renewable, exceeding 85%.A reduction in carbon taxes lowers the prices of electricity and hydrogen, but does not increase consumption, as the lower carbon tax makes the continued use of fossil fuels more attractive to industry. In scenarios that enforce a phase-out of fossil fuels, electricity prices rise, leading to a greater reliance on imports of low-carbon hydrogen and methanol. Results also suggest that domestic hydrogen production benefits from synergies between electrolytic hydrogen and blue hydrogen, helping to maintain competitive prices.
During very early age of neutron stars, the core cools down faster compared to the crust creating a large thermal gradient in the interior of the star. During $10-100$ years, a cooling wave propagates from the core to the crust causing the interior of the star to thermalize. During this duration thermal properties of the core material is of great importance to understand the dynamics of the interior of the star. The heat capacity and thermal conductivity of the core depends on the behaviour of matter inside the core. We investigate these two properties in case of magnetars. Due to presence of large magnetic field, the proton superconductivity is quenched partially inside the magnetars depending upon the comparative values of upper critical field and the strength of the magnetic field present. This produces non-uniformity in the behaviour of matter throughout the star. Moreover, such non-uniformity arises from the variation of nature of the pairing and values of the pairing gap energy. We find that the heat capacity is substantially reduced due to the presence of superfluidity. On the other hand, the thermal conductivity of neutron is enhanced due to proton superconductivity and gets reduced due to neutron superfluidity. Hence, the variation of the thermal properties due to superfluidity in presence of magnetic field is different at different radius inside the star. However, in all the cases the %minimum maximum variation is of the order one. This affects the thermal relaxation time of the star and eventually its the thermal evolution.
This work is a continuation of a previous effort (Panaitescu 2019) to study the cooling of relativistic electrons through radiation (synchrotron and self-Compton) emission and adiabatic losses, with application to the spectra and light-curves of the synchrotron Gamma-Ray Burst produced by such cooling electrons. Here, we derive the low-energy slope b_LE of GRB pulse-integrated spectrum and quantify the implications of the measured distribution of b_LE. If the magnetic field lives longer than it takes the cooling GRB electrons to radiate below 1-10 keV, then radiative cooling processes of power P(gamma) ~ gamma^n with n geq 2, i.e. synchrotron and inverse-Compton (iC) through Thomson scatterings, lead to a soft low-energy spectral slope b_LE leq -1/2 of the GRB pulse-integrated spectrum F_eps ~ eps^{b_LE} below the peak-energy E_p, irrespective of the duration of electron injection t_i. IC-cooling dominated by scatterings at the Thomson--Klein-Nishina transition of synchrotron photons below E_p has an index n = 2/3 -> 1 and yield harder integrated spectra with b_LE in [0,1/6], while adiabatic electron-cooling leads to a soft slope b_LE = -3/4. Radiative processes that produce soft integrated spectra can accommodate the harder slopes measured by CGRO/BATSE and Fermi/GBM only if the magnetic field life-time t_B is shorter than the time during which the typical GRB electrons cool to radiate below 1-10 keV, which is less than (at most) ten radiative cooling timescales t_rad of the typical GRB electron. In this case, there is a one-to-one correspondence between t_B and b_LE. To account for low-energy slopes b_LE > -3/4, adiabatic electron-cooling requires a similar restriction on t_B. In this case, the diversity of slopes arises mostly from how the electron-injection rate varies with time and not from the magnetic field timescale.
Muhammad Akbar Husnoo, Adnan Anwar, Nasser Hosseinzadeh
et al.
As Smart Meters are collecting and transmitting household energy consumption data to Retail Energy Providers (REP), the main challenge is to ensure the effective use of fine-grained consumer data while ensuring data privacy. In this manuscript, we tackle this challenge for energy load consumption forecasting in regards to REPs which is essential to energy demand management, load switching and infrastructure development. Specifically, we note that existing energy load forecasting is centralized, which are not scalable and most importantly, vulnerable to data privacy threats. Besides, REPs are individual market participants and liable to ensure the privacy of their own customers. To address this issue, we propose a novel horizontal privacy-preserving federated learning framework for REPs energy load forecasting, namely FedREP. We consider a federated learning system consisting of a control centre and multiple retailers by enabling multiple REPs to build a common, robust machine learning model without sharing data, thus addressing critical issues such as data privacy, data security and scalability. For forecasting, we use a state-of-the-art Long Short-Term Memory (LSTM) neural network due to its ability to learn long term sequences of observations and promises of higher accuracy with time-series data while solving the vanishing gradient problem. Finally, we conduct extensive data-driven experiments using a real energy consumption dataset. Experimental results demonstrate that our proposed federated learning framework can achieve sufficient performance in terms of MSE ranging between 0.3 to 0.4 and is relatively similar to that of a centralized approach while preserving privacy and improving scalability.
In peer-to-peer (P2P) energy trading, a secured infrastructure is required to manage trade and record monetary transactions. A central server/authority can be used for this. But there is a risk of central authority influencing the energy price. So blockchain technology is being preferred as a secured infrastructure in P2P trading. Blockchain provides a distributed repository along with smart contracts for trade management. This reduces the influence of central authority in trading. However, these blockchain-based systems still rely on a central authority to pair/match sellers with consumers for trading energy. The central authority can interfere with the matching process to profit a selected set of users. Further, a centralized authority also charges for its services, thereby increasing the cost of energy. We propose two distributed mechanisms to match sellers with consumers. The first mechanism doesn't allow for price negotiations between sellers and consumers, whereas the second does. We also calculate the time complexity and the stability of the matching process for both mechanisms. Using simulation, we compare the influence of centralized control and energy prices between the proposed and the existing mechanisms. The overall work strives to promote the free market and reduce energy prices.
Mehdi Hatamian, Bivas Panigrahi, Chinmaya Kumar Dehury
Renewable Energies (RE) have gained more attention in recent years since they offer clean and sustainable energy. One of the major sustainable development goals (SDG-7) set by the United Nations (UN) is to achieve affordable and clean energy for everyone. Among the world's all renewable resources, solar energy is considered as the most abundant and can certainly fulfill the target of SDGs. Solar energy is converted into electrical energy through Photovoltaic (PV) panels with no greenhouse gas emissions. However, power generated by PV panels is highly dependent on solar radiation received at a particular location over a given time period. Therefore, it is challenging to forecast the amount of PV output power. Predicting the output power of PV systems is essential since several public or private institutes generate such green energy, and need to maintain the balance between demand and supply. This research aims to forecast PV system output power based on weather and derived features using different machine learning models. The objective is to obtain the best-fitting model to precisely predict output power by inspecting the data. Moreover, different performance metrics are used to compare and evaluate the accuracy under different machine learning models such as random forest, XGBoost, KNN, etc.
The article the assessment of the China competitiveness is carried out taking into account geopolitical and economic factors. In terms of geopolitics, attention is paid to such initiatives as “Pivot to the East”, “One Belt One Road” and the initiative of countering the spread of COVID-19. In the context of economic indicators, an integral assessment of China's competitiveness as an investor is conducted on the basis of such indicators as the level of GDP, the global competitiveness index, the indicator of investment activity of China in the world and in Russia, as well as the indicator of investment activity in the energy sector of Russia, the number of Chinese investment energy projects and their current number. Based on the results of the analysis, it is concluded that, China has an exceptional competitive advantage from the point of view of geopolitics and macroeconomics. However, within the energy sector, China has been losing ground due to a decrease in investment activity.
Battery-based energy storage has emerged as an enabling technology for a variety of grid energy optimizations, such as peak shaving and cost arbitrage. A key component of battery-driven peak shaving optimizations is peak forecasting, which predicts the hours of the day that see the greatest demand. While there has been significant prior work on load forecasting, we argue that the problem of predicting periods where the demand peaks for individual consumers or micro-grids is more challenging than forecasting load at a grid scale. We propose a new model for peak forecasting, based on deep learning, that predicts the k hours of each day with the highest and lowest demand. We evaluate our approach using a two year trace from a real micro-grid of 156 buildings and show that it outperforms the state of the art load forecasting techniques adapted for peak predictions by 11-32%. When used for battery-based peak shaving, our model yields annual savings of $496,320 for a 4 MWhr battery for this micro-grid.
The Peng-Robinson equation of state (PR-EoS) has become one of the most extensively applied equations of state in chemical engineering and petroleum industry due to its excellent accuracy in predicting the thermodynamic properties of a wide variety of materials, especially hydrocarbons. Although great efforts have been made to construct efficient numerical methods for the diffuse interface models with PR-EoS, there is still not a linear numerical scheme that can be proved to preserve the original energy dissipation law. In order to pursue such a numerical scheme, we propose a novel energy factorization (EF) approach, which first factorizes an energy function into a product of several factors and then treats the factors using their properties to obtain the semi-implicit linear schemes. We apply the EF approach to deal with the Helmholtz free energy density determined by PR-EoS, and then propose a linear semi-implicit numerical scheme that inherits the original energy dissipation law. Moreover, the proposed scheme is proved to satisfy the maximum principle in both the time semi-discrete form and the cell-centered finite difference fully discrete form under certain conditions. Numerical results are presented to demonstrate the stability and efficiency of the proposed scheme.
A. R. de Queiroz, D. Mulcahy, A. Sankarasubramanian
et al.
Seasonal climate variations affect electricity demand, which in turn affects month-to-month electricity planning and operations. Electricity system planning at the monthly timescale can be improved by adapting climate forecasts to estimate electricity demand and utilizing energy models to estimate monthly electricity generation and associated operational costs. The objective of this paper is to develop and test a computationally efficient model that can support seasonal planning while preserving key aspects of system operation over hourly and daily timeframes. To do so, an energy system optimization model is repurposed for seasonal planning using features drawn from a unit commitment model. Different scenarios utilizing a well-known test system are used to evaluate the errors associated with both the repurposed energy system model and an imperfect load forecast. The results show that the energy system optimization model using an imperfect load forecast produces differences in monthly cost and generation levels that are less than 2% compared with a unit commitment model using a perfect load forecast. The enhanced energy system optimization model can be solved approximately 100 times faster than the unit commitment model, making it a suitable tool for future work aimed at evaluating seasonal electricity generation and demand under uncertainty.
Alejandro Guarnizo, Juan P. Beltrán Almeida, César A. Valenzuela-Toledo
We consider a model based on $p-$form kinetic Lagrangians in the context of dark energy. The Lagrangian of the model is built with kinetic terms of the field strength for each $p$-form coupled to a scalar field $φ$ through a kinetic function. We assume that this scalar field is responsible for the present accelerated expansion of the Universe. Since we are interested in cosmological applications, we specialize the analysis to a 4-dimensional case, using an anisotropic space-time. By studying the dynamical equations, we investigate the evolution of the dark energy density parameter, the effective equation of state and the shear induced by the anisotropic configuration.