Energy Disaggregation for Real-Time Building Flexibility Detection

Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we propose the use of energy disaggregation techniques to perform this task. Firstly, we investigate the use of existing classification methods to perform energy disaggregation. A comparison is performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors, Support Vector Machine and AdaBoost. Secondly, we propose the use of Restricted Boltzmann Machine to automatically perform feature extraction. The extracted features are then used as inputs to the four classifiers and consequently shown to improve their accuracy. The efficiency of our approach is demonstrated on a real database consisting of detailed appliance-level measurements with high temporal resolution, which has been used for energy disaggregation in previous studies, namely the REDD. The results show robustness and good generalization capabilities to newly presented buildings with at least 96% accuracy.Comment: To appear in IEEE PES General Meeting, 2016, Boston, US

De Reus van Schimmert: from water tower to data center

The water tower of Schimmert was built in 1926 to cover the needs of water of Schimmert and the surrounding areas as well. This imposing 38 meters high tower dwarfs any nearby buildings, providing a 360° view of the surrounding area and deserves its pseudonym de Reus van Schimmert (the Giant of Schimmert). In the attempt to find a sustainable business model for the iconic building the concept of installing a data center in its core is investigated. The waste heat from the servers will be transferred to the reservoir on the top and from there used to power a district heating system in Schimmert

De Reus van Schimmert: from water tower to data center

The water tower of Schimmert was built in 1926 to cover the needs of water of Schimmert and the surrounding areas as well. This imposing 38 meters high tower dwarfs any nearby buildings, providing a 360° view of the surrounding area and deserves its pseudonym de Reus van Schimmert (the Giant of Schimmert). In the attempt to find a sustainable business model for the iconic building the concept of installing a data center in its core is investigated. The waste heat from the servers will be transferred to the reservoir on the top and from there used to power a district heating system in Schimmert

Medium voltage DC power systems on ships: An offline parameter estimation for tuning the controllers' linearizing function

Future shipboard power systems using Medium Voltage Direct (MVDC) technology will be based on a widespread use of power converters for interfacing generating systems and loads with the main DC bus. Such a heavy exploitation makes the voltage control challenging in the presence of tightly controlled converters. By modeling the latter as constant power loads (CPLs), one possibility to ensure the bus voltage stability is offered by the linearizing via state feedback technique, whose aim is to regulate the generating DC-DC power converters to compensate for the destabilizing effect of the CPLs. Although this method has been shown to be effective when system parameters are perfectly known, only a partial linearization can be ensured in case of parameter mismatch, thus, jeopardizing the system stability. In order to improve the linearization, therefore, guaranteeing the voltage stability, an estimation method is proposed in this paper. To this aim, offline tests are performed to provide the input data for the estimation of model parameters. Such estimated values are subsequently used for correctly tuning the linearizing function of the DC-DC converters. Simulation results for bus voltage transients show that in this way converters become sources of stabilizing power

Light robust co-optimization of energy and reserves in the day-ahead electricity market

To accommodate the stochasticity of variable renewable energy sources (VRES) while efficiently dispatching generation resources and procuring adequate reserves, previous research proposed co-optimizing energy and reserves in the day-ahead (DA) using various uncertainty-based mechanisms. However, the co-optimized markets based on these mechanisms exhibit implementation limitations related to their high computational burden, complex customized solution algorithms, and over-conservative solutions. To address these shortcomings, this paper proposes a practical light robust optimization (LR) approach for the DA co-optimization of energy and reserves. The method results in a linear market clearing mechanism that easily enables the control of the robustness level of the solution through a tunable conservativeness parameter. In addition, the paper explores three different formulations for specifying the system reserve requirements considering, namely, fixed reserve requirements (LRF1), variable reserve requirements based on system uncertainty (LRF2), and a combined approach (LRF3). The formulations integrate the uncertainty from VRES in the market setting using a new bid format called uncertainty bid. The three formulations are then compared using a case study. The numerical results show the effects of the variation of the conservativeness parameter and the reserve requirements on the total socio-economic welfare (SEW), dispatched energy quantities, anticipated activation costs, and procured reserves. Moreover, the analyses showcase that sizing reserves based on system uncertainty (in LRF2) results in a 27%–61% decrease in reserve procurement costs when compared with LRF1, while the combined approach (in LRF3) results in a better performance than LRF2 in terms of reserve activation costs, with costs 61%–263% lower than in LRF2

optimizing the operation of energy storage using a non linear lithium ion battery degradation model

Abstract Given their technological and market maturity, lithium-ion batteries are increasingly being considered and used in grid applications to provide a host of services such as frequency regulation, peak shaving, etc. Charging and discharging these batteries causes degradation in their performance. Lack of data on degradation processes combined with requirement of fast computation have led to over-simplified models of battery degradation. In this work, the recent experimental evidence that demonstrates that degradation in lithium-ion batteries is non-linearly dependent on the operating conditions is incorporated. Experimental aging data of a commercial battery have been used to develop a scheduling model applicable to the time constraints of a market model. A decomposition technique that enables the developed model to give near-optimal results for longer time horizons is also proposed

Demand Forecasting at Low Aggregation Levels using Factored Conditional Restricted Boltzmann Machine.

The electrical demand forecasting problem can be regarded as a non-linear time series prediction problem depending on many complex factors since it is required at various aggregation levels and at high resolution. To solve this challenging problem, various time series and machine learning approaches has been proposed in the literature. As an evolution of neural network-based prediction methods, deep learning techniques are expected to increase the prediction accuracy by being stochastic and allowing bi-directional connections between neurons. In this paper, we investigate a newly developed deep learning model for time series prediction, namely Factored Conditional Restricted Boltzmann Machine (FCRBM), and extend it for demand forecasting. The assessment is made on the EcoGrid EU dataset, consisting of aggregated electric power consumption, price and meteorological data collected from 1900 customers. The households are equipped with local generation and smart appliances capable of responding to real-time pricing signals. The results show that for the energy prediction problem solved here, FCRBM outperforms the benchmark machine learning approach, i.e. Support Vector Machine