Improving costs and scale reflect looming opportunities
Solar energy has the potential to play a central role in the future global energy system because of the scale of the solar resource, its ...predictability, and its ubiquitous nature. Global installed solar photovoltaic (PV) capacity exceeded 500 GW at the end of 2018, and an estimated additional 500 GW of PV capacity is projected to be installed by 2022–2023, bringing us into the era of TW-scale PV. Given the speed of change in the PV industry, both in terms of continued dramatic cost decreases and manufacturing-scale increases, the growth toward TW-scale PV has caught many observers, including many of us (
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), by surprise. Two years ago, we focused on the challenges of achieving 3 to 10 TW of PV by 2030. Here, we envision a future with ∼10 TW of PV by 2030 and 30 to 70 TW by 2050, providing a majority of global energy. PV would be not just a key contributor to electricity generation but also a central contributor to all segments of the global energy system. We discuss ramifications and challenges for complementary technologies (e.g., energy storage, power to gas/liquid fuels/chemicals, grid integration, and multiple sector electrification) and summarize what is needed in research in PV performance, reliability, manufacturing, and recycling.
Load forecasting is an essential task in the operation management of a power system. Electric power companies utilize short-term load forecasting (STLF) technology to make reasonable power generation ...plans. A forecasting model with low prediction errors helps reduce operating costs and risks for the operators. In recent years, machine learning has become one of the most popular technologies for load forecasting. In this paper, a two-stage STLF model based on long short-term memory (LSTM) and multilayer perceptron (MLP), which improves the forecasting accuracy over the entire time horizon, is proposed. In the first stage, a sequence-to-sequence (seq2seq) architecture, which can handle a multi-sequence of input to extract more features of historical data than that of single sequence, is used to make multistep predictions. In the second stage, the MLP is used for residual modification by perceiving other information that the LSTM cannot. To construct the model, we collected the electrical load, calendar, and meteorological records of Kanto region in Japan for four years. Unlike other LSTM-based hybrid architectures, the proposed model uses two independent neural networks instead of making the neural network deeper by concatenating a series of LSTM cells and convolutional neural networks (CNNs). Therefore, the proposed model is easy to be trained and more interpretable. The seq2seq module performs well in the first few hours of the predictions. The MLP inherits the advantage of the seq2seq module and improves the results by feeding artificially selected features both from historical data and information of the target day. Compared to the LSTM-AM model and single MLP model, the mean absolute percentage error (MAPE) of the proposed model decreases from 2.82% and 2.65% to 2%, respectively. The results demonstrate that the MLP helps improve the prediction accuracy of seq2seq module and the proposed model achieves better performance than other popular models. In addition, this paper also reveals the reason why the MLP achieves the improvement.
The increasing reliance on photovoltaic (PV) generation as a cornerstone of carbon neutrality has led to transformative changes in the energy structure, further impacting electricity market trading ...mechanisms and price volatility. The electric power system reform also promoted wholesale trading in the Japan Electric Power Exchange (JEPX) spot market. This study explores an effective JEPX spot market price forecasting model that enables PV power suppliers to make informed production decisions and ensure revenue optimization. We found that understanding the net demand (total demand minus PV generation) is crucial for accurate price forecasting, as it allows for a more precise reflection of the gradual evolution of the solar-dominated energy structure of the dynamic electricity demand. We also conducted parameter tests and a comparative analysis of different training loops and periods in the basic form using an artificial neural network (ANN) and support vector regression (SVR) algorithms. The results indicated that the narrow ANN and SVR models with a linear kernel function and training in the continuous loop method performed better in spot market price forecasting than other model settings. Our proposed approach can provide essential insights into future price trends, facilitating informed sustainable energy planning and resource allocation for power generation to guarantee the benefits of achieving solar promotion and net-zero transition.
In this paper, we present modeling and analysis of day-ahead spatio-temporal energy markets in which each competitive player or aggregator aims at making the highest profit by managing a complex ...mixture of different energy resources, such as conventional generators, storage batteries, and uncertain renewable resources. First, we develop an energy market model in terms of an adjustable robust convex program. This market modeling is novel in the sense that the prosumption cost function of each aggregator, which evaluates the cost or benefit to realize an amount of spatio-temporal energy prosumption, is a multi-variable function resulting from a “parameterized” max-min program, in which the variable of the prosumption cost function is involved as a continuous parameter and the variable of dispatchable resources is involved as an adjustable variable for energy balance. This formulation enables to reasonably evaluate a reward for intertemporal dispatchability enhancement and a penalty for renewable energy uncertainty in a unified way. In addition, it enables to enforce a market regulation in which every aggregator is responsible for absorbing his/her renewable energy uncertainty by managing his/her own dispatchable energy resources. Second, in view of social economy as well as personal economy, we conduct a numerical analysis on the premise of several photovoltaic penetration levels. In this numerical analysis, using a bulk power system model of the north east area in Japan, we demonstrate that renewable generators do not always have priority of energy supply higher than conventional generators due to their uncertainty and limited dispatchability, meaning that the merit order of conventional and renewable generators can reverse. Furthermore, we analyze long-term evolution of competitive energy markets demonstrating that there can be found a social equilibrium of battery penetration levels, at which maximum personal profit with respect to battery system enhancement is attained.
•We present modeling and analysis of day-ahead spatio-temporal energy markets.•Each player aims at making the highest profit by managing a complex mixture of different energy resources.•The energy market model is developed in terms of an adjustable robust convex program.•We evaluate a reward for intertemporal dispatchability enhancement and a penalty for renewable energy uncertainty.•Long-term evolution of competitive energy markets is analyzed to find a social equilibrium of battery penetration levels.
Although anatomical anterior cruciate ligament reconstruction (ACLR) can provide satisfactory outcomes, little is known about how this procedure impacts patellar height. Since harvesting ...bone-patellar tendon-bone (BTB) autografts is a potential risk factor for decreased patellar height, we examined changes in patellar height after anatomical ACLR with BTB autograft with a focus on the size of the harvested graft.
Subjects were 84 patients (49 males, 35 females; mean age, 23 years) who underwent primary anatomical ACLR with central third BTB autograft. Preoperative to postoperative Caton-Deschamps index (CDI) ratio was calculated using lateral knee radiographs before and 6 months after surgery. The length and cross-sectional area (CSA) of the graft were measured intraoperatively, and the CSA of the contralateral patellar tendon was measured by ultrasound 6 months postoperatively. The difference in graft CSA relative to the contralateral tendon CSA, expressed as a percentage (gCSA:ctCSA percentage), was also calculated.
Patellar height decreased slightly after surgery (preoperative CDI: 0.856 ± 0.113; postoperative CDI: 0.841 ± 0.113), with a mean difference between preoperative and postoperative CDIs of -0.015 (range: -0.293 to 0.101). Although the CDI of male subjects significantly decreased after surgery (preoperative: 0.852 ± 0.117; postoperative: 0.827 ± 0.115), no significant changes were noted in female subjects (preoperative: 0.862 ± 0.108; postoperative: 0.861 ± 0.108). Graft length and CSA did not significantly impact the CDI ratio (r = -0.138 and r = -0.038, respectively). Moreover, no significant relationship was observed between the gCSA:ctCSA percentage and CDI ratio (r = 0.118).
Although patellar height slightly, but significantly, decreased at 6 months after anatomical ACLR with BTB autograft, it was not affected by the length and CSA of harvested grafts. The decrease in postoperative patellar height was observed only in male subjects, suggesting the potential importance of sex differences in soft tissue healing during the postoperative period.
There are approximately 200,000 households living in detached houses and gers (yurts) with small coal stoves that burn raw coal in Ulaanbaatar city. A proper heating system and improvement of the ...energy efficiency of residential dwellings are vitally important for Ulaanbaatar city to reduce air pollution as well as for the operation of the current central energy system. This study shows the experimental results for two gers with two different heating systems and different thermal insulation, for investigating the merits of each. The technical feasibility of the system consisting of an electric thermal storage (ETS) heater with a daytime charging schedule and areal photovoltaic (PV) system was also examined by using a simulation with software developed in MATLAB (R2020a, MathWorks, USA). As a result of the experiment, the indoor comfort level and energy efficiency of the ger with added insulation and an ETS heater with nighttime charging were shown to be enhanced compared with those of the reference ger. The ger with added insulation and the ETS heater consumed 3169 kWh for electric appliances and 5989 kWh for the heating season. The simulation showed that the PV self-consumption rate is 76% for the Ger 2 with the ETS heater because of the daytime charging schedule of the ETS heater. The PV system supplied 31% of the total energy consumed, with the remaining 69% from the main grid.
Large-scale penetration of photovoltaic (PV) power generators and storage batteries is expected in recently constructed power systems. For the realization of smart energy management, we need to make ...an appropriate day-ahead schedule of power generation and battery charge cycles based on the prediction of demand and PV power generation, which inevitably involves nontrivial prediction errors. With this background, a novel framework is proposed to maintain the balance among the total amounts of power generation, demand, and battery charging power with explicit consideration of the prediction uncertainty, assuming that consumer storage batteries are not directly controllable by a supplier. The proposed framework consists of the following three steps: 1) the day-ahead scheduling of the total amount of generation power and battery charging power; 2) the day-ahead scheduling of utility energy consumption requests to individual consumers, which aim to regulate battery charging cycles on the consumer side; and 3) the incentive-based management of the entire power system on the day of interest. In this paper, we especially focus on the day-ahead scheduling problems in steps 1) and 2), and show that they can be analyzed in a manner originating from spatiotemporal aggregation. Finally, we demonstrate the validity of the proposed framework through numerical verification of the power system management.
With increasing installation of residential photovoltaic (PV) systems, distributed battery energy storage systems can be utilized for supply-demand balancing. Aggregators perform day-ahead scheduling ...for efficient energy management in some optimized method as ways. For optimization, however, criterions of values could change with the time or situation. In this study, we propose a method to investigate and compare various kinds of day-ahead charge/discharge schedulings based on different evaluation indices. First, we show that feasible schedulings are expressed as superpositions of reference schedulings obtained by simple strategies. Then, the method is applied to an aggregator with consumers who each has rooftop PVs and battery energy storage systems. A fairness index is defined as a correlation coefficient between battery usage and grid usage. Simulation results show a trade-off relationship between the total amount of charge/discharge of the batteries and the dispersion of power flows, and a relationship between the fairness index and the strategy of day-ahead scheduling.
To stably operate the power grid under a mass introduction of photovoltaic systems, a method to estimate the areal photovoltaic power in real time is necessary. The estimation method proposed in this ...paper achieves high accuracy by separately estimating the high- and low-frequency components of photovoltaic power. The high-frequency component is estimated using power flow at the distribution substation, whereas the low-frequency component is estimated using solar irradiance, ambient temperature, power flow, and if available 30-min integrated values of the electric load measured by smart meters. To evaluate the proposed method, simulations using measured data were performed. These were performed under four scenarios depending on the acceptance of a 15-min delay and the availability of smart meter data. In the best-case scenario, where 15-min delays to estimate the areal photovoltaic power is accepted and smart meter data are available, the estimation was performed with yearly root-mean-square error of approximately 2.2% of the total system capacity. Even in the worst-case scenario, where 15-min delays were unaccepted and smart meters not installed, the estimation was performed with yearly root-mean-square error of approximately 3.7% of the system capacity.
Short circuit current (Isc) depends on the effective irradiance incident upon a PV module. Effective irradiance is highly correlated with broadband irradiance, but can vary slightly as the spectral ...content of the incident light changes. We explore using a few spectral wavelengths with broadband irradiance to predict Isc for ten modules of varying technologies (silicon, CIGS, CdTe). The goal is to identify a few spectral wavelengths that could be easily (and economically) measured to improve PV performance modeling.
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