Energy Management and Economic Operation Optimization of Microgrid under Uncertainty

Microgrid provides an effective means to promote renewable energy utilization via deploying multiple distributed generations (DGs) with energy storage systems (ESSs), loads, control devices and protect devices, which can operate in either islanded mode or grid-connected mode. In order to coordinate the output of different DGs and realize the potential of renewable energy, energy management and economic dispatch of microgrid is needed. Both distributed energy resources (DERs) and user loads in microgrid have uncertainty characteristics; so the randomness of the wind speed and solar radiation intensity are modeled by interval mathematics and the interval output of the wind turbine and photovoltaic (PV) generation system are obtained. Then, a microgrid economic optimization model based on interval optimization method is proposed. Next, combined with the time-of-use characteristic, issue of the power exchange with the external grid has been considered. Finally, Considering the effect of ESS, this chapter discusses the impacts of uncertainty of renewable energy power and load power on optimization results, as well as the effects of the degree of load uncertainty or load fluctuation on scheduling results. The results verify the robustness and effectiveness of the proposed method in dealing with uncertainty optimization problem of microgrid

Fixed and mobile energy storage coordination optimization method for enhancing photovoltaic integration capacity considering voltage offset

Mobile energy storage has the characteristics of strong flexibility, wide application, etc., with fixed energy storage can effectively deal with the future large-scale photovoltaic as well as electric vehicles and other fluctuating load access to the grid resulting in the imbalance of supply and demand. To this end, this paper proposes a coordinated two-layer optimization strategy for fixed and mobile energy storage that takes into account voltage offsets, in the context of improving the demand for local PV consumption. Among them, the upper layer optimization model takes into account the minimum operating cost of fixed and mobile energy storage, and the lower layer optimization model minimizes the voltage offset through the 24-h optimal scheduling of fixed and mobile energy storage in order to improve the in-situ PV consumption capacity. In addition, considering the multidimensional nonlinear characteristics of the model, the interaction force of particles in the Universe is introduced, and the hybrid particle swarm-gravitational search algorithm (PSO-GSA) is proposed to solve the model, which is a combination of the individual optimization of the particle swarm algorithm and the local search capability of the gravitational search algorithm, which improves the algorithm’s optimization accuracy. Finally, the feasibility and effectiveness of the proposed model and method are verified by simulation analysis with IEEE 33 nodes

TimeGPT in Load Forecasting: A Large Time Series Model Perspective

Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. Inspired by the outstanding performance of large language models (LLMs) in computer vision and natural language processing, this paper aims to discuss the potential of large time series models in load forecasting with scarce historical data. Specifically, the large time series model is constructed as a time series generative pre-trained transformer (TimeGPT), which is trained on massive and diverse time series datasets consisting of 100 billion data points (e.g., finance, transportation, banking, web traffic, weather, energy, healthcare, etc.). Then, the scarce historical load data is used to fine-tune the TimeGPT, which helps it to adapt to the data distribution and characteristics associated with load forecasting. Simulation results show that TimeGPT outperforms the benchmarks (e.g., popular machine learning models and statistical models) for load forecasting on several real datasets with scarce training samples, particularly for short look-ahead times. However, it cannot be guaranteed that TimeGPT is always superior to benchmarks for load forecasting with scarce data, since the performance of TimeGPT may be affected by the distribution differences between the load data and the training data. In practical applications, we can divide the historical data into a training set and a validation set, and then use the validation set loss to decide whether TimeGPT is the best choice for a specific dataset.Comment: 10 page

Design, Performance and Calibration of the CMS Forward Calorimeter Wedges

We report on the test beam results and calibration methods using charged particles of the CMS Forward Calorimeter (HF). The HF calorimeter covers a large pseudorapidity region (3\l |\eta| \le 5), and is essential for large number of physics channels with missing transverse energy. It is also expected to play a prominent role in the measurement of forward tagging jets in weak boson fusion channels. The HF calorimeter is based on steel absorber with embedded fused-silica-core optical fibers where Cherenkov radiation forms the basis of signal generation. Thus, the detector is essentially sensitive only to the electromagnetic shower core and is highly non-compensating (e/h \approx 5). This feature is also manifest in narrow and relatively short showers compared to similar calorimeters based on ionization. The choice of fused-silica optical fibers as active material is dictated by its exceptional radiation hardness. The electromagnetic energy resolution is dominated by photoelectron statistics and can be expressed in the customary form as a/\sqrt{E} + b. The stochastic term a is 198% and the constant term b is 9%. The hadronic energy resolution is largely determined by the fluctuations in the neutral pion production in showers, and when it is expressed as in the electromagnetic case, a = 280% and b = 11%

Design, Performance, and Calibration of the CMS Hadron-Outer Calorimeter

The CMS hadron calorimeter is a sampling calorimeter with brass absorber and plastic scintillator tiles with wavelength shifting fibres for carrying the light to the readout device. The barrel hadron calorimeter is complemented with an outer calorimeter to ensure high energy shower containment in the calorimeter. Fabrication, testing and calibration of the outer hadron calorimeter are carried out keeping in mind its importance in the energy measurement of jets in view of linearity and resolution. It will provide a net improvement in missing \et measurements at LHC energies. The outer hadron calorimeter will also be used for the muon trigger in coincidence with other muon chambers in CMS

Calibration of the CMS hadron calorimeters using proton-proton collision data at root s=13 TeV

Methods are presented for calibrating the hadron calorimeter system of theCMSetector at the LHC. The hadron calorimeters of the CMS experiment are sampling calorimeters of brass and scintillator, and are in the form of one central detector and two endcaps. These calorimeters cover pseudorapidities vertical bar eta vertical bar ee data. The energy scale of the outer calorimeters has been determined with test beam data and is confirmed through data with high transverse momentum jets. In this paper, we present the details of the calibration methods and accuracy.Peer reviewe

Interval Energy Flow Analysis in Integrated Electrical and Natural-Gas Systems Considering Uncertainties

As integrated electrical and natural-gas systems (IENGS) are popularized, the uncertainties brought by variation of electrical load, power generation, and gas load should not be ignored. The aim of this paper is to analyze the impact of those uncertain variables on the steady-state operation of the whole systems. In this paper, an interval energy flow model considering uncertainties was built based on the steady-state energy flow. Then, the Krawczyk–Moore interval iterative method was used to solve the proposed model. To obtain precise results of the interval model, interval addition and subtraction operations were performed by affine mathematics. The case study demonstrated the effectiveness of the proposed approach compared with Monte Carlo simulation. Impacts of uncertainties brought by the variation of electrical load, power generation, and gas load were analyzed, and the convergence of energy flow under different uncertainty levels of electrical load was studied. The results led to the conclusion that each kind of uncertainties would have an impact on the whole system. The proposed method could provide good insights into the operating of IENGS with those uncertainties