Measurement of the Energy Asymmetry in Top Quark Pair plus Jet Production with the CMS Experiment

The measurement of the energy asymmetry in top quark pair production in association with one additional high-$p_{\text{T}}$ jet is presented, using 137.1 fb$^{-1}$ of data recorded with the CMS detector at the CERN LHC in proton-proton collisions at a center-of-mass energy of 13 TeV. The presented measurement focuses on the semileptonic decay process of the top quark pair and requires a boosted event topology of the $\text{t}\bar{\text{t}}j$ system. Different types of clustered final-state particles are considered for the reconstruction, having the minimal common requirement of one charged electron or muon, missing transverse momentum due to the corresponding neutrino, and one hard jet in the central region of the detector. The primary focus is to reconstruct events in the boosted regime using top tagged fat jets. If this is not possible, an attempt is taken to reconstruct events in the resolved regime with slim jets under the employment of boosted decision trees. In order to unfold the results, a reconstruction of simulated signal process events on particle level is performed either in the boosted regime, the resolved regime, or using parton information of the event. The unfolding is performed with a maximum likelihood fit, splitting the signal process into different subcategories according to the event kinematic properties on particle level. Both signal and background processes are obtained fully from simulation and the systematic uncertainties are considered as nuisance parameters in the fit. This analysis is the first measurement of the energy asymmetry and yields an observed value of \begin{align*} A_{E, \text{unf.}}^{\text{opt}} = -3.0\,\% \;^{+4.0\,\%}_{-5.5\,\%} \,(\text{stat + syst}) \end{align*} in a fiducial phase space. This result is in agreement with the corresponding SM expectation of $A_{E}^{\text{opt}} = -1.59\,\% \pm 1.00\,\%\,\text{(stat)} \pm 0.37\,\%\,\text{(syst)}$

Algorithms for balancing demand-side load and micro-generation in Islanded Operation

Micro-generators are devices installed in houses pro-\ud ducing electricity at kilowatt level. These appliances can\ud increase energy efficiency significantly, especially when\ud their runtime is optimized. During power outages micro-\ud generators can supply critical systems and decrease dis-\ud comfort.\ud In this paper a model of the domestic electricity infras-\ud tructure of a house is derived and first versions of algo-\ud rithms for load/generation balancing during a power cut\ud are developed. In this context a microCHP device, produc-\ud ing heat and electricity at the same time with a high effi-\ud ciency, is used as micro-generator.\ud The model and the algorithms are incorporated in a sim-\ud ulator, which is used to study the effect of the algorithms for\ud load/generation balancing. The results show that with some\ud extra hardware all appliances in a house can be supplied,\ud however not always at the preferred time.\u

Using heat demand prediction to optimise Virtual Power Plant production capacity

In the coming decade a strong trend towards distributed electricity generation (microgeneration) is expected. Micro-generators are small appliances that generate electricity (and heat) at the kilowatt level, which allows them to be installed in households. By combining a group of micro-generators, a Virtual Power Plant can be formed. The electricity market/network requires a VPP control system to be fast, scalable and reliable. It should be able to adjust the production quickly, handle in the order of millions of micro-generators and it should ensure the required production is really produced by the fleet of microgenerators. When using micro Combined Heat and Power microgenerators, the electricity production is determined by heat demand. In this paper we propose a VPP control system design using learning systems to maximise the economical benefits of the microCHP appliances. Furthermore, ways to test our design are\ud described

Islanded house operation using a micro CHP

The µCHP is expected as the successor of\ud the conventional high-efficiency boiler producing next to\ud heat also electricity with a comparable overall efficiency.\ud A µCHP appliance saves money and reduces greenhouse\ud gas emission.\ud An additional functionality of the µCHP is using the\ud appliance as a backupgenerator in case of a power outage.\ud The µCHPcould supply the essential loads, the heating and\ud reduce the discomfort up to a certain level. This requires\ud modifications on the µCHP appliance itself as well as on\ud the domestic electricity infrastructure. Furthermore some\ud extra hardware and a control algorithm for load balancing\ud are necessary.\ud Our load balancing algorithm is supposed to start and\ud stop the µCHP and switch off loads if necessary. The first\ud simulation results show that most of the electricity usage\ud is under the maximum generation line, but to reduce the\ud discomfort an electricity buffer is required.\u

Quality Measures of Parameter Tuning for Aggregated Multi-Objective Temporal Planning

Parameter tuning is recognized today as a crucial ingredient when tackling an optimization problem. Several meta-optimization methods have been proposed to find the best parameter set for a given optimization algorithm and (set of) problem instances. When the objective of the optimization is some scalar quality of the solution given by the target algorithm, this quality is also used as the basis for the quality of parameter sets. But in the case of multi-objective optimization by aggregation, the set of solutions is given by several single-objective runs with different weights on the objectives, and it turns out that the hypervolume of the final population of each single-objective run might be a better indicator of the global performance of the aggregation method than the best fitness in its population. This paper discusses this issue on a case study in multi-objective temporal planning using the evolutionary planner DaE-YAHSP and the meta-optimizer ParamILS. The results clearly show how ParamILS makes a difference between both approaches, and demonstrate that indeed, in this context, using the hypervolume indicator as ParamILS target is the best choice. Other issues pertaining to parameter tuning in the proposed context are also discussed.Comment: arXiv admin note: substantial text overlap with arXiv:1305.116

Steering the Smart Grid

Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of technologies and optimization methodologies were developed to increase the efficiency, maintain the grid stability and support large scale introduction of renewable sources. In previous work, we showed the effectiveness of our three-step methodology to reach these objectives, consisting of 1) offline prediction, 2) offline planning and 3) online scheduling in combination with MPC. In this paper we analyse the best structure for distributing the steering signals in the third step. Simulations show that pricing signals work as good as on/off signals, but pricing signals are more general. Individual pricing signals per house perform better with small prediction errors while one global steering signal for a group of houses performs better when the prediction errors are larger. The best hierarchical structure is to use consumption patterns on all levels except the lowest level and deduct the pricing signals in the lowest node of the tree

Improved Heat Demand Prediction of Individual Households

One of the options to increase the energy efficiency of current electricity network is the use of a Virtual Power Plant. By using multiple small (micro)generators distributed over the country, electricity can be produced more efficiently since these small generators are more efficient and located where the energy is needed. In this paper we focus on micro Combined Heat and Power generators. For such generators, the production capacity is determined and limited by the heat demand. To keep the global electricity network stable, information about the production capacity of the heat-driven generators is required in advance. In this paper we present methods to perform heat demand prediction of individual households based on neural network techniques. Using different input sets and a so called sliding window, the quality of the predictions can be improved significantly. Simulations show that these improvements have a positive impact on controlling the distributed microgenerators