Uncertain R&D, Backstop Technology and GHGs Stabilization

This paper analyses optimal investments in innovation when dealing with a stringent climate target and with the uncertain effectiveness of R&D. The innovation needed to achieve the deep cut in emissions is modelled by a backstop carbon-free technology whose cost depends on R&D investments. To better represent the process of technological progress, we assume that R&D effectiveness is uncertain. By means of a simple analytical model, we show how accounting for the uncertainty that characterizes technological advancement yields higher investments in innovation and lower policy costs. We then confirm the results via a numerical analysis performed with a stochastic version of WITCH, an energy-economy-climate model. The results stress the importance of a correct specification of the technological change process in economy-climate models.Climate Change, Information and Uncertainty, Environmental Policy, Optimal R&D Investments

Environmental and Technology Policies for Climate Mitigation

We assess different policies for reducing carbon dioxide emissions and promoting the innovation and diffusion of renewable energy. We evaluate the relative performance of policies according to incentives provided for emissions reduction, efficiency, and other outcomes. We also assess how the nature of technological progress through learning and R&D, and the degree of knowledge spillovers, affect the desirability of different policies. Due to knowledge spillovers, optimal policy involves a portfolio of different instruments targeted at emissions, learning, and R&D. Although the relative cost of individual policies in achieving reductions depends on parameter values and the emissions target, in a numerical application to the U.S. electricity sector, the ranking is roughly as follows: (1) emissions price, (2) emissions performance standard, (3) fossil power tax, (4) renewables share requirement, (5) renewables subsidy, and (6) R&D subsidy. Nonetheless, an optimal portfolio of policies achieves emissions reductions at significantly lower cost than any single policy.environment, technology, externality, policy, climate change, renewable energy

Optimal Energy Investment and R&D Strategies to Stabilise Greenhouse Gas Atmospheric Concentrations

The stabilisation of GHG atmospheric concentrations at levels expected to prevent dangerous climate change has become an important, global, long-term objective. It is therefore crucial to identify a cost-effective way to achieve this objective. In this paper we use WITCH, a hybrid climate-energy-economy model, to obtain a quantitative assessment of some cost-effective strategies that stabilise CO2 concentrations at 550 or 450 ppm. In particular, this paper analyses the energy investment and R&D policies that optimally achieve these two GHG stabilisation targets (i.e. the future optimal energy mix consistent with the stabilisation of GHG atmospheric concentrations at 550 and 450 ppm). Given that the model accounts for interdependencies and spillovers across 12 regions of the world, optimal strategies are the outcome of a dynamic game through which inefficiency costs induced by global strategic interactions can be assessed. Therefore, our results are somehow different from previous analyses of GHG stabilisation policies, where a central planner or a single global economy are usually assumed. In particular, the effects of free-riding incentives in reducing emissions and in investing in R&D are taken into account. Technical change being endogenous in WITCH, this paper also provides an assessment of the implications of technological evolution in the energy sector of different stabilisation scenarios. Finally, this paper quantifies the net costs of stabilising GHG concentrations at different levels, for different allocations of permits and for different technological scenarios. In each case, the optimal long-term investment strategies for all available energy technologies are determined. The case of an unknown backstop energy technology is also analysed.climate policy, energy R&D, investments, stabilisation costs

OPTIMAL ENVIRONMENTAL POLICY DESIGN IN THE PRESENCE OF UNCERTAINTY AND TECHNOLOGY SPILLOVERS

The stylized model presented in this paper extends the approach developed by Fischer and Newell (2008) by analysing the optimal policy design in a context with more than one externality while taking explicitly into account uncertainty surrounding future emission damage costs. In the presence of massive uncertainties and technology spillovers, well-designed sup-port mechanisms for renewables are found to play a major role, notably as a means for compensating for technology spillovers, yet also for reducing the investors’ risks. How-ever, the design of these support mechanisms needs to be target-aimed and well-focused. Besides uncertainty on the state of the world concerning actual marginal emission damage, we consider the technological progress through R&D as well as learning-by-doing. A portfolio of three policy instruments is then needed to cope with the existing externalities and optimal instrument choice is shown to be dependent on risk aversion of society as a whole as well as of entrepreneurs. To illustrate the role of uncertainty for the practical choice of policy instruments, an em-pirical application is considered. The application is calibrated to recent global data from IEA and thus allows identifying the main drivers for the optimal policy mix. In addition to assumptions on technology costs and uncertainty of emission damage cost, the impor-tance of technology spillover clearly plays a key role. Yet under some plausible parame-ter settings, direct subsidies to production are found to be of lower importance than very substantial R&D supports.Externality, technology, learning, uncertainty, climate change, spillover, renewable energy, policy

ENTICE: Endogenous Technological Change in the DICE Model of Global Warming

Despite growing empirical evidence of the link between environmental policy and innovation, most economic models of environmental policy treat technology as exogenous. For long-term problems such as climate change, this omission can be significant. In this paper, I modify the DICE model of climate change (Nordhaus 1994, Nordhaus and Boyer 2000) to allow for induced innovation in the energy sector. Ignoring induced technological change overstates the welfare costs of an optimal carbon tax policy by 8.3 percent. However, cost-savings, rather than increased environmental benefits, appear to drive the welfare gains, as the effect of induced innovation on emissions and mean global temperature is small. Sensitivity analysis shows that potential crowding out of other R&D and market failures in the R&D sector are the most important limiting factors to the potential of induced innovation. Differences in these key assumptions explain much of the variation in the findings of other similar models.

The WITCH Model. Structure, Baseline, Solutions

WITCH – World Induced Technical Change Hybrid – is a regionally disaggregated hard-link hybrid global model with a neoclassical optimal growth structure (top-down) and a detailed energy input component (bottom-up). The model endogenously accounts for technological change, both through learning curves that affect the prices of new vintages of capital and through R&D investments. The model features the main economic and environmental policies in each world region as the outcome of a dynamic game. WITCH belongs to the class of Integrated Assessment Models as it possesses a climate module that feeds climate changes back into the economy. Although the model’s main features are discussed elsewhere (Bosetti et al., 2006), here we provide a more thorough discussion of the model’s structure and baseline projections, to describe the model in greater detail. We report detailed information on the evolution of energy demand, technology and CO2 emissions. We also explain the procedure used to calibrate the model parameters. This report is therefore meant to provide effective support to those who intending to use the WITCH model or interpret its results.Climate Policy, Hybrid Modelling, Integrated Assessment, Technological Change

Dynamic heterogeneous R&D with cross-technologies interactions

In many countries, inducing large-scale technological changes has become an important policy objective, as in the context of climate policy or energy transitions. Such large-scale changes require the development of strongly interlinked technologies. But current economic models have little flexibility for describing such linkages. We present a model of induced technological change that covers a fairly large set of cross-technology interactions and that can describe a wide variety of long-run developments. Using this model, we analyse and compare the development induced by optimal fifrm behaviour and the socially optimal dynamics. We show that the structure of cross-technology interactions is highly important. It shapes the dynamics of technological change in the decentralised and the socially optimal solution, including the prospects of continued productivity growth. It determines whether the decentralised and the socially optimal solution have similar or qualitatively difffferent dynamics. Finally, it is highly important for the question whether simple r&d policies can induce effifficient technological change

Finding common ground when experts disagree: robust portfolio decision analysis

We address the problem of decision making under “deep uncertainty,” introducing an approach we call Robust Portfolio Decision Analysis. We introduce the idea of Belief Dominance as a prescriptive operationalization of a concept that has appeared in the literature under a number of names. We use this concept to derive a set of non-dominated portfolios; and then identify robust individual alternatives from the non-dominated portfolios. The Belief Dominance concept allows us to synthesize multiple conflicting sources of information by uncovering the range of alternatives that are intelligent responses to the range of beliefs. This goes beyond solutions that are optimal for any specific set of beliefs to uncover defensible solutions that may not otherwise be revealed. We illustrate our approach using a problem in the climate change and energy policy context: choosing among clean energy technology R&D portfolios. We demonstrate how the Belief Dominance concept can uncover portfolios that would otherwise remain hidden and identify robust individual investments

Small-Scale Hybrid Photovoltaic-Biomass Systems Feasibility Analysis for Higher Education Buildings

[EN] Applications of renewable electricity in cities are mostly limited to photovoltaics, and they need other renewable sources, batteries, and the grid to guarantee reliability. This paper proposes a hybrid system, combining biomass and photovoltaics, to supply electricity to educational buildings. This system is reliable and provides at least 50% of electricity based on renewable sources. Buildings with small (70%) implies high electricity costs.This work was supported in part by the European Commission through project "Holistic And Scalable Solution For Research, Innovation And Education In Energy Tran project" (Agreement number: 837854). This work was supported in part by the European Commission through GROW GREEN project (Agreement number: 730283 - GROW GREEN-H2020-SCC-2016-2017/H2020-SCC-NBS-2stage-2016. http://growgreenproject.eu/). This work was completed in the framework of the activities of the Renewable Area research group of the IUIIE (Instituto Universitario de Investigación en Ingeniería Energética) in regional, national, and international projects. The authors deeply thank the Universitat Politècnica de València, IMPIVA-Generalitat Valenciana, the Spanish Ministry of Science and Technology, and the European Commission for the funded projects coming from this organization.Alfonso-Solar, D.; Vargas-Salgado Carlos; Sánchez-Diaz, C.; Hurtado-Perez, E. (2020). Small-Scale Hybrid Photovoltaic-Biomass Systems Feasibility Analysis for Higher Education Buildings. Sustainability. 12(21):1-14. https://doi.org/10.3390/su12219300S1141221Pérez-Navarro, A., Alfonso, D., Ariza, H. E., Cárcel, J., Correcher, A., Escrivá-Escrivá, G., … Vargas, C. (2016). Experimental verification of hybrid renewable systems as feasible energy sources. Renewable Energy, 86, 384-391. doi:10.1016/j.renene.2015.08.030Prasad, M., & Munch, S. (2012). State-level renewable electricity policies and reductions in carbon emissions. Energy Policy, 45, 237-242. doi:10.1016/j.enpol.2012.02.024Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., & Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 24, 38-50. doi:10.1016/j.esr.2019.01.006Bracco, S. (2020). A Study for the Optimal Exploitation of Solar, Wind and Hydro Resources and Electrical Storage Systems in the Bormida Valley in the North of Italy. Energies, 13(20), 5291. doi:10.3390/en13205291Directorate-General for Energy, EU Commission. Clean Energy for All Europeanshttps://ec.europa.eu/energy/topics/energy-strategy/clean-energy-all-europeans_enURLÓhAiseadha, C., Quinn, G., Connolly, R., Connolly, M., & Soon, W. (2020). Energy and Climate Policy—An Evaluation of Global Climate Change Expenditure 2011–2018. Energies, 13(18), 4839. doi:10.3390/en13184839Hart, E. K., & Jacobson, M. Z. (2011). A Monte Carlo approach to generator portfolio planning and carbon emissions assessments of systems with large penetrations of variable renewables. Renewable Energy, 36(8), 2278-2286. doi:10.1016/j.renene.2011.01.015Acevedo-Arenas, C. Y., Correcher, A., Sánchez-Díaz, C., Ariza, E., Alfonso-Solar, D., Vargas-Salgado, C., & Petit-Suárez, J. F. (2019). MPC for optimal dispatch of an AC-linked hybrid PV/wind/biomass/H2 system incorporating demand response. Energy Conversion and Management, 186, 241-257. doi:10.1016/j.enconman.2019.02.044Bajpai, P., & Dash, V. (2012). Hybrid renewable energy systems for power generation in stand-alone applications: A review. Renewable and Sustainable Energy Reviews, 16(5), 2926-2939. doi:10.1016/j.rser.2012.02.009Bernal-Agustín, J. L., & Dufo-López, R. (2009). Simulation and optimization of stand-alone hybrid renewable energy systems. Renewable and Sustainable Energy Reviews, 13(8), 2111-2118. doi:10.1016/j.rser.2009.01.010Karakoulidis, K., Mavridis, K., Bandekas, D. V., Adoniadis, P., Potolias, C., & Vordos, N. (2011). Techno-economic analysis of a stand-alone hybrid photovoltaic-diesel–battery-fuel cell power system. Renewable Energy, 36(8), 2238-2244. doi:10.1016/j.renene.2010.12.003Kusakana, K. (2015). Optimal scheduled power flow for distributed photovoltaic/wind/diesel generators with battery storage system. IET Renewable Power Generation, 9(8), 916-924. doi:10.1049/iet-rpg.2015.0027Koutroulis, E., Kolokotsa, D., Potirakis, A., & Kalaitzakis, K. (2006). Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms. Solar Energy, 80(9), 1072-1088. doi:10.1016/j.solener.2005.11.002Ipsakis, D., Voutetakis, S., Seferlis, P., Stergiopoulos, F., & Elmasides, C. (2009). Power management strategies for a stand-alone power system using renewable energy sources and hydrogen storage. International Journal of Hydrogen Energy, 34(16), 7081-7095. doi:10.1016/j.ijhydene.2008.06.051Mata, É., Sasic Kalagasidis, A., & Johnsson, F. (2014). Building-stock aggregation through archetype buildings: France, Germany, Spain and the UK. Building and Environment, 81, 270-282. doi:10.1016/j.buildenv.2014.06.013HOMER Energyhttps://www.homerenergy.com/Oladigbolu, J. O., Ramli, M. A. M., & Al-Turki, Y. A. (2020). Optimal Design of a Hybrid PV Solar/Micro-Hydro/Diesel/Battery Energy System for a Remote Rural Village under Tropical Climate Conditions. Electronics, 9(9), 1491. doi:10.3390/electronics9091491Hurtado, E., Peñalvo-López, E., Pérez-Navarro, Á., Vargas, C., & Alfonso, D. (2015). Optimization of a hybrid renewable system for high feasibility application in non-connected zones. Applied Energy, 155, 308-314. doi:10.1016/j.apenergy.2015.05.097Kebede, A. A., Berecibar, M., Coosemans, T., Messagie, M., Jemal, T., Behabtu, H. A., & Van Mierlo, J. (2020). A Techno-Economic Optimization and Performance Assessment of a 10 kWP Photovoltaic Grid-Connected System. Sustainability, 12(18), 7648. doi:10.3390/su12187648Hafez, O., & Bhattacharya, K. (2012). Optimal planning and design of a renewable energy based supply system for microgrids. Renewable Energy, 45, 7-15. doi:10.1016/j.renene.2012.01.087European Pellet Report. European Pellet Quality Certification (PELLCERT) project. PellCert. Published on April 2012https://ec.europa.eu/energy/intelligent/projects/sites/iee-projects/files/projects/documents/pellcert_european_pellet_report.pdf/Alfonso, D., Perpiñá, C., Pérez-Navarro, A., Peñalvo, E., Vargas, C., & Cárdenas, R. (2009). Methodology for optimization of distributed biomass resources evaluation, management and final energy use. Biomass and Bioenergy, 33(8), 1070-1079. doi:10.1016/j.biombioe.2009.04.002Perpiñá, C., Alfonso, D., Pérez-Navarro, A., Peñalvo, E., Vargas, C., & Cárdenas, R. (2009). Methodology based on Geographic Information Systems for biomass logistics and transport optimisation. Renewable Energy, 34(3), 555-565. doi:10.1016/j.renene.2008.05.047Technology Roadmap: Delivering Sustainable Bioenergyhttps://www.ieabioenergy.com/publications/technology-roadmap-delivering-sustainable-bioenergy/HOMER Pro 3.14 User Manualhttps://www.homerenergy.com/products/pro/docs/latest/index.htmlLao, C., & Chungpaibulpatana, S. (2017). Techno-economic analysis of hybrid system for rural electrification in Cambodia. Energy Procedia, 138, 524-529. doi:10.1016/j.egypro.2017.10.23

Optimal Generation Scheduling with Dynamic Profiles for the Sustainable Development of Electricity Grids

[EN] The integration of renewable generation in electricity networks is one of the most widespread strategies to improve sustainability and to deal with the energy supply problem. Typically, the reinforcement of the generation fleet of an existing network requires the assessment and minimization of the installation and operating costs of all the energy resources in the network. Such analyses are usually conducted using peak demand and generation data. This paper proposes a method to optimize the location and size of different types of generation resources in a network, taking into account the typical evolution of demand and generation. The importance of considering this evolution is analyzed and the methodology is applied to two standard networks, namely the Institute of Electrical and Electronics Engineers (IEEE) 30-bus and the IEEE 118-bus. The proposed algorithm is based on the use of particle swarm optimization (PSO). In addition, the use of an initialization process based on the cross entropy (CE) method to accelerate convergence in problems of high computational cost is explored. The results of the case studies highlight the importance of considering dynamic demand and generation profiles to reach an effective integration of renewable resources (RRs) towards a sustainable development of electric systems.The stay of the corresponding author that made this research possible was funded by a grant "Jose Castillejo" number CAS18/00291 of the Spanish Ministerio de Educacion, Cultura y Deporte.Roldán-Blay, C.; Miranda, V.; Carvalho, L.; Roldán-Porta, C. (2019). Optimal Generation Scheduling with Dynamic Profiles for the Sustainable Development of Electricity Grids. Sustainability. 11(24):1-26. https://doi.org/10.3390/su11247111S1261124Höök, M., & Tang, X. (2013). Depletion of fossil fuels and anthropogenic climate change—A review. Energy Policy, 52, 797-809. doi:10.1016/j.enpol.2012.10.046Van de Ven, D. J., & Fouquet, R. (2017). Historical energy price shocks and their changing effects on the economy. Energy Economics, 62, 204-216. doi:10.1016/j.eneco.2016.12.009Osório, G., Shafie-khah, M., Lujano-Rojas, J., & Catalão, J. (2018). Scheduling Model for Renewable Energy Sources Integration in an Insular Power System. Energies, 11(1), 144. doi:10.3390/en11010144Lasseter, R. H. (2011). Smart Distribution: Coupled Microgrids. Proceedings of the IEEE, 99(6), 1074-1082. doi:10.1109/jproc.2011.2114630Moriarty, P., & Honnery, D. (2016). Can renewable energy power the future? Energy Policy, 93, 3-7. doi:10.1016/j.enpol.2016.02.051Ghosh, S., Ghoshal, S. P., & Ghosh, S. (2010). Optimal sizing and placement of distributed generation in a network system. International Journal of Electrical Power & Energy Systems, 32(8), 849-856. doi:10.1016/j.ijepes.2010.01.029Gomez-Gonzalez, M., López, A., & Jurado, F. (2012). Optimization of distributed generation systems using a new discrete PSO and OPF. Electric Power Systems Research, 84(1), 174-180. doi:10.1016/j.epsr.2011.11.016Li, Y., Li, Y., Li, G., Zhao, D., & Chen, C. (2018). Two-stage multi-objective OPF for AC/DC grids with VSC-HVDC: Incorporating decisions analysis into optimization process. Energy, 147, 286-296. doi:10.1016/j.energy.2018.01.036Yassine, A. A., Mostafa, O., & Browning, T. R. (2017). Scheduling multiple, resource-constrained, iterative, product development projects with genetic algorithms. Computers & Industrial Engineering, 107, 39-56. doi:10.1016/j.cie.2017.03.001Dias, B. H., Oliveira, L. W., Gomes, F. V., Silva, I. C., & Oliveira, E. J. (2012). Hybrid heuristic optimization approach for optimal Distributed Generation placement and sizing. 2012 IEEE Power and Energy Society General Meeting. doi:10.1109/pesgm.2012.6345653Prakash, D. B., & Lakshminarayana, C. (2016). Multiple DG Placements in Distribution System for Power Loss Reduction Using PSO Algorithm. Procedia Technology, 25, 785-792. doi:10.1016/j.protcy.2016.08.173Hung, D. Q., Mithulananthan, N., & Bansal, R. C. (2013). Analytical strategies for renewable distributed generation integration considering energy loss minimization. Applied Energy, 105, 75-85. doi:10.1016/j.apenergy.2012.12.023Syahputra, R., Robandi, I., & Ashari, M. (2015). Reconfiguration of Distribution Network with Distributed Energy Resources Integration Using PSO Algorithm. TELKOMNIKA (Telecommunication Computing Electronics and Control), 13(3), 759. doi:10.12928/telkomnika.v13i3.1790Ueckerdt, F., Brecha, R., & Luderer, G. (2015). Analyzing major challenges of wind and solar variability in power systems. Renewable Energy, 81, 1-10. doi:10.1016/j.renene.2015.03.002Kansal, S., Kumar, V., & Tyagi, B. (2013). Optimal placement of different type of DG sources in distribution networks. International Journal of Electrical Power & Energy Systems, 53, 752-760. doi:10.1016/j.ijepes.2013.05.040De Magalhaes Carvalho, L., Leite da Silva, A. M., & Miranda, V. (2018). Security-Constrained Optimal Power Flow via Cross-Entropy Method. IEEE Transactions on Power Systems, 33(6), 6621-6629. doi:10.1109/tpwrs.2018.2847766Zimmerman, R. D., Murillo-Sanchez, C. E., & Thomas, R. J. (2011). MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education. IEEE Transactions on Power Systems, 26(1), 12-19. doi:10.1109/tpwrs.2010.2051168Matpower 7.0 User’s Manual; PSERC, USAhttps://matpower.org/docs/manual.pdfWang, H., Murillo-Sanchez, C. E., Zimmerman, R. D., & Thomas, R. J. (2007). On Computational Issues of Market-Based Optimal Power Flow. IEEE Transactions on Power Systems, 22(3), 1185-1193. doi:10.1109/tpwrs.2007.901301Abdi, H., Beigvand, S. D., & Scala, M. L. (2017). A review of optimal power flow studies applied to smart grids and microgrids. Renewable and Sustainable Energy Reviews, 71, 742-766. doi:10.1016/j.rser.2016.12.102Red Eléctrica de Españahttp://www.ree.esOperador del Mercado Ibérico-Polo Español S.Ahttp://www.omie.esAlsac, O., & Stott, B. (1974). Optimal Load Flow with Steady-State Security. IEEE Transactions on Power Apparatus and Systems, PAS-93(3), 745-751. doi:10.1109/tpas.1974.293972The IEEE 30-Bus Test Systemhttps://labs.ece.uw.edu/pstca/pf30/pg_tca30bus.htmOpen Energy Information—Transparent Cost Databasehttps://openei.org/apps/TCDB/Real Decreto 1955/2000, de 1 de Diciembre, Por el Que se Regulan las Actividades de Transporte, Distribución, Comercialización, Suministro y Procedimientos de Autorización de Instalaciones de Energía Eléctrica, (in Spanish)https://www.boe.es/boe/dias/2000/12/27/pdfs/A45988-46040.pdfThe IEEE 118-Bus Test Systemhttps://labs.ece.uw.edu/pstca/pf118/pg_tca118bus.ht