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

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

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

Outlier removal and the relation with reporting errors and quality of psychological research

The removal of outliers to acquire a significant result is a questionable research practice that appears to be commonly used in psychology. In this study, we investigated whether the removal of outliers in psychology papers is related to weaker evidence (against the null hypothesis of no effect), a higher prevalence of reporting errors, and smaller sample sizes in these papers compared to papers in the same journals that did not report the exclusion of outliers from the analyses.We retrieved a total of 2667 statistical results of null hypothesis significance tests from 153 articles in main psychology journals, and compared results from articles in which outliers were removed (N = 92) with results from articles that reported no exclusion of outliers (N = 61). We preregistered our hypotheses and methods and analyzed the data at the level of articles. Results show no significant difference between the two types of articles in median p value, sample sizes, or prevalence of all reporting errors, large reporting errors, and reporting errors that concerned the statistical significance. However, we did find a discrepancy between the reported degrees of freedom of t tests and the reported sample size in 41% of articles that did not report removal of any data values. This suggests common failure to report data exclusions (or missingness) in psychological articles.We failed to find that the removal of outliers from the analysis in psychological articles was related to weaker evidence (against the null hypothesis of no effect), sample size, or the prevalence of errors. However, our control sample might be contaminated due to nondisclosure of excluded values in articles that did not report exclusion of outliers. Results therefore highlight the importance of more transparent reporting of statistical analyses

Ion spectroscopy in methane activation

Contains fulltext : 253677.pdf (Publisher’s version ) (Open Access)19 mei 202

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

Domestic energy management methodology for optimizing efficiency in Smart Grids

Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of domestic technologies have been developed to improve this efficiency. These technologies on their own already improve the efficiency, but more can be gained by a combined management. Multiple optimization objectives can be used to improve the efficiency, from peak shaving and Virtual Power Plant (VPP) to adapting to fluctuating generation of wind turbines. In this paper a generic management methology is proposed applicable for most domestic technologies, scenarios and optimization objectives. Both local scale optimization objectives (a single house) and global scale optimization objectives (multiple houses) can be used. Simulations of different scenarios show that both local and global objectives can be reached

Management and Control of Domestic Smart Grid Technology

Emerging new technologies like distributed generation, distributed storage, and demand-side load management will change the way we consume and produce energy. These techniques enable the possibility to reduce the greenhouse effect and improve grid stability by optimizing energy streams. By smartly applying future energy production, consumption, and storage techniques, a more energy-efficient electricity supply chain can be achieved. In this paper a three-step control methodology is proposed to manage the cooperation between these technologies, focused on domestic energy streams. In this approach, (global) objectives like peak shaving or forming a virtual power plant can be achieved without harming the comfort of residents. As shown in this work, using good predictions, in advance planning and real-time control of domestic appliances, a better matching of demand and supply can be achieved.\ud \u