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Wave energy is an important renewable energy source. Previous studies of wave energy conversion (WEC) have focused on the maximum power take-off (PTO) techniques of a single machine. However, there is a lack of research on the energy and power quality of wave farm systems. Owing to the pulsating nature of ocean waves and popular PTO devices, the generated electrical power suffers from severe fluctuations. Existing solutions require extra energy storage and overrated power converters for wave power integration. In this study, we developed a master-slave wave farm system with rotor inertia energy storage; this system delivers self-smoothed power output to the grid and reduces the number of converters. Two control methods based on the moving average filter (MAF) and energy filter (EF) are proposed to smooth the output power of wave farms. RTDS simulations show that the proposed systems and control methods facilitate simple and smooth grid integration of wave energy. Keywords: Wave farm, Energy storage, Power smoothing, Power quality, Energy quality

{"references": ["U.S. Energy Information Administration. \"How much energy is\nconsumed in residential and commercial buildings in the United States?\"\nAvailable at: http://www.eia.gov/tools/faqs/faq.cfm?id=86&t=1", "S. Darby, \"The effectiveness of feedback on energy consumption.\"\nEnvironmental Change Institute, University of Oxford, 2006. Available\nat: http://www.globalwarmingisreal.com/energyconsump-feedback.pdf.\nVisited: September 2015", "J. S. John, \"Putting energy disaggregation tech to the test,\" November,\n2013. Greentech Media. Available at:\nhttp://www.greentechmedia.com/articles/read/putting-energydisaggregation-tech-to-the-test.\nVisited: September 2015", "A. Zoha, A. Gluhak, M. A. Imran, S. Rajasegarar, \"Non-intrusive load\nmonitoring approaches for disaggregated energy sensing: a survey,\"\nSensors, vol. 12, no. 12, pp. 16838-16866, December 2012.", "G. W. Hart, \"Nonintrusive appliance load monitoring,\" in Proc. of the\nIEEE, vol. 80, pp. 1870-1891, December 1992.", "M. Baranski, J. Voss, \"Non-intrusive appliance load monitoring based\non Optical Sensor,\" IEEE Bologna PowerTech Conference, Bologna,\nItaly, June 2003. Available at:\nhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1304732", "L. Farinaccio, R. Zmeureanu, \"Using a pattern recognition approach to\ndisaggregate the total electricity consumption in a house into the major\nen-uses,\" Elsevier, Energy and Buildings, vol. 30, no. 3, pp. 245-259,\nAugust 1999.", "J. M. Abreu, F. C. Pereira, P. Ferr\u00e3o, \"Using pattern recognition to\nidentify habitual behavior in residential electricity consumption,\"\nElsevier, Energy and Buildings, vol. 49, pp. 479-487, June 2012.", "C. Beckel, L. Sadamori, S. Santini, \"Automatic socio-economic\nclassification of households using electricity consumption data,\" in\nProc. of the 4th international conference on future energy systems, New\nYork, 2013, pp. 75-86.\n[10] H. Zhao, F. Magoul\u00e8s, \"A review on the prediction of building energy\nconsumption,\" Elsevier, Renewable and Sustainable Energy Reviews,\nvol. 16, no. 6, pp. 3586-3592, August 2012.\n[11] G. K. F. Tso, K. K. W. Yau, \"Predicting electricity energy consumption:\nA comparison of regression analysis, decision tree and neural networks,\"\nElsevier, Energy, vol. 32, no. 9, pp. 1761-1768, September 2007.\n[12] F. Farzan, S. A. Vaghefi, K. Mahani, M. A. Jafari, J. Gong, \"Operational\nplanning for multi-building portfolio in an uncertain energy market,\"\nElsevier, Energy and Buildings, vol. 103, pp. 271-283, September 2015."]} Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.

Climate change is one of the preeminent policy issues of our day, and the social cost of carbon (SCC) is one of the foremost tools for determining the socially optimal policy response. The SCC is estimated using Integrated Assessment Models (IAMs), of which Nordhaus’ DICE is the oldest and one of the best respected. These numerical models capture the various steps in the climate and economic processes that translate a marginal unit of CO2 emissions into economic damage. While accuracy at each of these steps is necessary to precisely estimate the SCC, correct calibrating the climate damage function, which translates a temperature change into a percentage change in GDP, is critical. Calibration of the damage function determines which climate damages are included and excluded from the cost of carbon. Traditionally, Nordhaus calibrated the DICE damage function using a global damage estimate calculated by aggregating a series of region-sector specific damage estimates (Nordhaus and Boyer, 2000; Nordhaus, 2008). However, in DICE-2013, Nordhaus moved to calibrating the DICE damage function using a meta-analysis at the global scale (Nordhaus and Sztorc, 2013). This paper critiques this meta-analysis approach as it is currently applied and re-estimates the DICE-2013 damage function using up-to-date meta-analysis techniques to more accurately reflect climate damages and the uncertainty underlying them. This paper finds that DICE-2013 damage function significantly under-estimates climate damages by a factor of two to three. This is a working paper.

Photovoltaic (PV) technologies constitute a leading renewable energy source with a worldwide installed capacity of 135 GW in 2013 that may increase to nearly 4700 GW in 2050. To achieve this production level while minimizing environmental impacts, decision makers must rely at national level on relevant technological, economic and planning aspects which are highly geographically dependent. The access to performance data is a critical issue in the decision-making process and determines the successful development of efficient PV systems. For this reason, a new interactive tool is proposed here to provide the users with easy-to-use data and maps for the solar irradiation and screening level environmental results of representative PV technologies. The calculation procedures account for the geographic location and the PV system layout (installation, orientation and inclination angles). The tool has a worldwide coverage with a multi-criteria scope, both in terms of the numerous technological scenarios and of the wide range of environmental indicators. Moreover, the user is given the possibility to compare the PV environmental performance to the corresponding country electricity mix environmental footprint. 32nd European Photovoltaic Solar Energy Conference and Exhibition; 2869-2873

In the Meyer-Burger labs, pilot production of its proprietary 6''-Heterojunction (HJT) cells has been conducted on full-scale production tools (Roth & Rau Helia PECVD & Helia PVD). Overall, close to 10.000 cells have been manufactured with efficiencies up to 21,1%. From theses HJT cells, 60-cell modules have been produced. Temperature coefficient measurements of both cells and modules have been conducted by independent institutes, resulting in outstanding -0,20%/K and -0,22%/K, respectively. The modules have been deployed on an outdoor test field in Mid-European climate conditions for over 1 year. The results demonstrate the durability of the HJT modules and comparison with standard crystalline modules show an increase of energy yield of up to 7% on sunny days already in spring time. Under hot climate conditions in southern regions this increase is supposed to be even higher, making HJT modules extremely suitable for southern climates and low LCOE´s. 28th European Photovoltaic Solar Energy Conference and Exhibition; 1887-1889

Utility regulators frequently attempt to use tariff structures to pursue both distributional and efficiency goals. Efficiency necessitates setting prices as close to marginal costs as possible while still allowing the firm to cover its costs. The common distributional goal is to protect low-income customers from high prices. Perhaps nowhere is the conflict between these goals greater than in the use of increasing-block residential utility pricing, in which the marginal price to the customer increases as the customer’s usage rises. Since the 2000-01 California electricity crisis, the state has adopted some of the most steeply increasing-block tariffs in electric utility history, but the distributional and efficiency effects have not been analyzed in detail. Using a novel approach for matching customer bill data with census data on area income distributions, I derive estimates of the income redistribution effected by the increasing-block tariffs used by California regulated electric utilities. I find that the rate structure does redistribute income to lower-income groups, but that the effect is fairly modest, particularly compared to a means-tested program also in use. While the distributional impact of these tariffs do not seem to be large, the efficiency costs may not be great either. Examining the distribution of customer demand quantities, I find preliminary evidence that customers do not respond to the increasing marginal prices they face.