Nicolas Apfel, Holger Breinlich, Nick Green
et al.
Gravity equations are often used to evaluate counterfactual trade policy scenarios, such as the effect of regional trade agreements on trade flows. In this paper, we argue that the suitability of gravity equations for this purpose crucially depends on their out-of-sample predictive power. We propose a methodology that compares different versions of the gravity equation, both among themselves and with machine learning-based forecast methods such as random forests and neural networks. We find that the 3-way gravity model is difficult to beat in terms of out-of-sample average predictive performance, especially if a flexible specification is used. This result further justifies its place as the predominant tool for applied trade policy analysis. However, when the goal is to predict individual bilateral trade flows, the 3-way model can be outperformed by an ensemble machine learning method.
Dinanath Prasad, Narendra Kumar, Rakhi Sharma
et al.
An adaptive control approach for a three-phase grid-interfaced solar photovoltaic system based on the new Neuro-Fuzzy Inference System with Rain Optimization Algorithm (ANROA) methodology is proposed and discussed in this manuscript. This method incorporates an Adaptive Neuro-fuzzy Inference System (ANFIS) with a Rain Optimization Algorithm (ROA). The ANFIS controller has excellent maximum tracking capability because it includes features of both neural and fuzzy techniques. The ROA technique is in charge of controlling the voltage source converter switching. Avoiding power quality problems including voltage fluctuations, harmonics, and flickers as well as unbalanced loads and reactive power usage is the major goal. Besides, the proposed method performs at zero voltage regulation and unity power factor modes. The suggested control approach has been modeled and simulated, and its performance has been assessed using existing alternative methods. A statistical analysis of proposed and existing techniques has been also presented and discussed. The results of the simulations demonstrate that, when compared to alternative approaches, the suggested strategy may properly and effectively identify the best global solutions. Furthermore, the system's robustness has been studied by using MATLAB/SIMULINK environment and experimentally by Field Programmable Gate Arrays Controller (FPGA)-based Hardware-in-Loop (HLL).
The building sector is one of the largest contributors to global energy consumption. Improving its energy efficiency is essential for reducing operational costs and greenhouse gas emissions. Energy management systems (EMS) play a key role in monitoring and controlling building appliances efficiently and reliably. With the increasing integration of renewable energy, intelligent EMS solutions have received growing attention. Reinforcement learning (RL) has recently been explored for this purpose and shows strong potential. However, most RL-based EMS methods require a large number of training steps to learn effective control policies, especially when adapting to unseen buildings, which limits their practical deployment. This paper introduces MetaEMS, a meta-reinforcement learning framework for EMS. MetaEMS improves learning efficiency by transferring knowledge from previously solved tasks to new ones through group-level and building-level adaptation, enabling fast adaptation and effective control across diverse building environments. Experimental results demonstrate that MetaEMS adapts more rapidly to unseen buildings and consistently outperforms baseline methods across various scenarios.
Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 81 data-driven KPIs from 91 recent publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, such as designing utility programs, standardizing energy flexibility quantification and verification processes; and (4) curating datasets with proper description for energy flexibility assessments.
Markus Millinger, Lina Reichenberg, Fredrik Hedenus
et al.
Abatement options for the hard-to-electrify parts of the transport sector are needed to achieve ambitious emissions targets. Biofuels based on biomass, electrofuels based on renewable hydrogen and a carbon source, as well as fossil fuels compensated by carbon dioxide removal (CDR) are the main options. Currently, biofuels are the only renewable fuels available at scale and are stimulated by blending mandates. Here, we estimate the system cost of enforcing such mandates in addition to an overall emissions cap for all energy sectors. We model overnight scenarios for 2040 and 2060 with the sector-coupled European energy system model PyPSA-Eur-Sec, with a high temporal resolution. The following cost drivers are identified: (i) high biomass costs due to scarcity, (ii) opportunity costs for competing usages of biomass for industry heat and combined heat and power (CHP) with carbon capture, and (iii) lower scalability and generally higher cost for biofuels compared to electrofuels and fossil fuels combined with CDR. With a -80% emissions reduction target in 2040, variable renewables, partial electrification of heat, industry and transport and biomass use for CHP and industrial heat are important for achieving the target at minimal cost. Abatement of remaining liquid fossil fuel use increases system cost, with a 50% biofuel mandate increasing costs by 128-229 billion EUR, or 39-82% of the liquid fuel cost. With a negative -105% emissions target in 2060, fuel abatement options are necessary, and electrofuels or the use of CDR to offset fossil fuel emissions are more competitive than biofuels. Biomass is preferred in CHP and industry heat, combined with carbon capture to serve negative emissions or electrofuel production, thereby utilising biogenic carbon several times. Sensitivity analyses reveal significant uncertainties but consistently support that higher biofuel mandates lead to higher costs.
High quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Unfortunately, actionable information about energy systems is often of limited availability, incomplete, or only accessible for a substantial fee or through a non-disclosure agreement. Recently, remotely sensed data (e.g., satellite imagery, aerial photography) have emerged as a potentially rich source of energy systems information. However, the use of these data is frequently challenged by its sheer volume and complexity, precluding manual analysis. Recent breakthroughs in machine learning have enabled automated and rapid extraction of useful information from remotely sensed data, facilitating large-scale acquisition of critical energy system variables. Here we present a systematic review of the literature on this emerging topic, providing an in-depth survey and review of papers published within the past two decades. We first taxonomize the existing literature into ten major areas, spanning the energy value chain. Within each research area, we distill and critically discuss major features that are relevant to energy researchers, including, for example, key challenges regarding the accessibility and reliability of the methods. We then synthesize our findings to identify limitations and trends in the literature as a whole, and discuss opportunities for innovation. These include the opportunity to extend the methods beyond electricity to broader energy systems and wider geographic areas; and the ability to expand the use of these methods in research and decision making as satellite data become cheaper and easier to access. We also find that there are persistent challenges: limited standardization and rigor of performance assessments; limited sharing of code, which would improve replicability; and a limited consideration of the ethics and privacy of data.
Felix Bünning, Benjamin Huber, Adrian Schalbetter
et al.
Because physics-based building models are difficult to obtain as each building is individual, there is an increasing interest in generating models suitable for building MPC directly from measurement data. Machine learning methods have been widely applied to this problem and validated mostly in simulation; there are, however, few studies on a direct comparison of different models or validation in real buildings to be found in the literature. Methods that are indeed validated in application often lead to computationally complex non-convex optimization problems. Here we compare physics-informed Autoregressive-Moving-Average with Exogenous Inputs (ARMAX) models to Machine Learning models based on Random Forests and Input Convex Neural Networks and the resulting convex MPC schemes in experiments on a practical building application with the goal of minimizing energy consumption while maintaining occupant comfort, and in a numerical case study. We demonstrate that Predictive Control in general leads to savings between 26% and 49% of heating and cooling energy, compared to the building's baseline hysteresis controller. Moreover, we show that all model types lead to satisfactory control performance in terms of constraint satisfaction and energy reduction. However, we also see that the physics-informed ARMAX models have a lower computational burden, and a superior sample efficiency compared to the Machine Learning based models. Moreover, even if abundant training data is available, the ARMAX models have a significantly lower prediction error than the Machine Learning models, which indicates that the encoded physics-based prior of the former cannot independently be found by the latter.
The development of renewable energy generation empowers microgrids to generate electricity to supply itself and to trade the surplus on energy markets. To minimize the overall cost, a microgrid must determine how to schedule its energy resources and electrical loads and how to trade with others. The control decisions are influenced by various factors, such as energy storage, renewable energy yield, electrical load, and competition from other microgrids. Making the optimal control decision is challenging, due to the complexity of the interconnected microgrids, the uncertainty of renewable energy generation and consumption, and the interplay among microgrids. The previous works mainly adopted the modeling-based approaches for deriving the control decision, yet they relied on the precise information of future system dynamics, which can be hard to obtain in a complex environment. This work provides a new perspective of obtaining the optimal control policy for distributed energy trading and scheduling by directly interacting with the environment, and proposes a multiagent deep reinforcement learning approach for learning the optimal control policy. Each microgrid is modeled as an agent, and different agents learn collaboratively for maximizing their rewards. The agent of each microgrid can make the local scheduling decision without knowing others' information, which can well maintain the autonomy of each microgrid. We evaluate the performances of our proposed method using real-world datasets. The experimental results show that our method can significantly reduce the cost of the microgrids compared with the baseline methods.
We use an open, hourly-resolved, networked model of the European energy system to investigate the storage requirements under decreasing CO$_2$ emissions targets and several sector-coupling scenarios. For the power system, significant storage capacities only emerge for CO$_2$ reductions higher than 80% of 1990 level in that sector. For 95% CO$_2$ reductions, the optimal system includes electric batteries and hydrogen storage energy capacities equivalent to 1.4 and 19.4 times the average hourly electricity demand. Coupling heating and transport sectors enables deeper global CO$_2$ reductions before the required storage capacities become significant, which highlights the importance of sector coupling strategies in the transition to low carbon energy systems. A binary selection of storage technologies is consistently found, i.e., electric batteries act as short-term storage to counterbalance solar photovoltaic generation while hydrogen storage smooths wind fluctuations. Flexibility from the electric vehicle batteries provided by coupling the transport sector avoid the need for additional stationary batteries and reduce the usage of pumped hydro storage. Coupling the heating sector brings to the system large capacities of thermal energy storage to compensate for the significant seasonal variation in heating demand.
The vast majority of the pulsars detected by the Fermi Large Area Telescope (LAT) display spectra with exponential cutoffs falling in a narrow range around a few GeV. Early spectral modelling predicted spectral cutoff energies of up to 100 GeV. More modern studies estimated spectral cutoff energies in the 1-20 GeV range. It was therefore not expected that pulsars would be visible in the very-high-energy (VHE; >100 GeV) regime. The VERITAS detection (confirmed by MAGIC) of pulsed emission from the Crab pulsar up to 400 GeV (and now possibly up to 1 TeV) therefore raised important questions about our understanding of the electrodynamics and local environment of pulsars. H.E.S.S. has now detected pulsed emission from the Vela pulsar in the 20-120 GeV range, making this the second pulsar detected by a ground-based Cherenkov telescope. We will review the latest developments in VHE pulsar science, including an overview of recent observations and refinements to radiation models and magnetic field structures. This will assist us in interpreting the VHE emission detected from the Crab and Vela pulsars, and predicting the level of VHE emission expected from other pulsars, which will be very important for the upcoming CTA.