Decentralized Generation and Storage Technologies in Future Energy Systems of Swiss Communities

摘要

OverviewPower systems worldwide have traditionally been structured according to a centralized generation and distributionscheme. However, the development and uptake of decentralized generation and storage technologies (DGSTs) israpidly evolving and this motivates the need for a reassessment of the role of DGSTs in the future energy systemsplanning of our cities.Energy system and policy planners face decision-making under uncertainty, including future technologydevelopment, resource availability, and cost uncertainties. However, scenario analysis using a long-term energysystems model can aid in the decision-making and planning process. In this study, a model is developed to examinethe role of DGSTs in the future energy systems of Swiss rural and urban agglomerations.Using a cost optimization approach, the model determines the energy capacity investment and operation required tosatisfy electricity and heat demand across major agglomeration sectors until 2050. A number of scenarios aredeveloped to analyse various uncertainties within the system. Scenarios introduce different technologies, carbonmitigation policies, and energy carrier and technology price sensitivities, and cost-optimal solutions are compared toa baseline scenario.Both a rural and urban community are modelled, represented by different energy systems and scales. The modelscan serve as valuable inputs for local policymakers.MethodsThe cost optimization model for this case study is developed using the MARKAL/TIMES framework (IEA-ETSAP,2011). TIMES is a bottom-up, energy systems, cost optimization modelling framework maintained by theInternational Energy Agency. It enables the development of perfect-foresight models which provide details onoptimal capacity allocation and dispatch patterns for given scenarios. The developed model captures the entireenergy system and conversion chain of the agglomeration, including residential, services, industrial, and agriculturalsectors. End-use energy demand includes building space heat, domestic hot water, process heat, and electricity.TIMES has been used to develop several national and international scale models (Goldstein & Tosato, 2008;Ramachandran & Turton, 2013); however, in this study, a lower-level model is developed in which individualdecentralized technologies are modelled on an aggregated building level in a community. Time slices are alsointroduced with a relatively high time resolution, with average days represented on an hourly scale for each season.Baseline scenarios reflect a centralized generation scheme, while alternative technology scenarios introducedecentralized and storage technology options. Decentralized heat and electricity generation technologies includesmall hydro (rural case only), gas micro-CHP (urban case only), photovoltaics, solar thermal heaters, and boilers,while storage options include a hydro reservoir (rural case), batteries, and heat storages. The impacts of carbonmitigation policies on capacity planning are also evaluated, including different carbon tax and feed-in tariffscenarios. Technology and energy carrier price sensitivities are measured as well.ResultsDGSTs enter into the cost optimal solution in both the rural and urban community studies. The results presentedbelow focus on the rural community study as an example.Small hydro and photovoltaics enable the rural community to become largely self-sufficient with over 80%reductions in national grid electricity usage by 2050 compared to the baseline scenario in one case. Storage isessential for full utilisation of the local hydro resource, however. To illustrate, Figure 1 below compares theelectricity supply to the community in a decentralized technology scenario with and without storage options.The deployment of storage results in cost savings through transmission network upgrade deferrals, and prompts a30% increase in photovoltaic installations as well. The overall decrease in electricity demand observed in Figure 1 isdue to the replacement of electric heaters in 2010 with higher efficiency heat pumps over time.The introduction of DGSTs results in a significant reduction in total discounted system costs for the ruralcommunity. The system cost reduction is approximately 8.5% compared to the baseline scenario when decentralizedtechnologies are introduced without storage options. With the introduction of storage, the system cost reduction is14%.Investment decisions in small hydro are robust against hydro power technology cost variations, while heatingtechnology investment decisions are found to be sensitive to oil and grid electricity prices.DGSTs are found to play an important role in the future energy system of the urban case study as well. PV and gasmicro-CHP technologies, in particular, enable a 60% reduction in national grid electricity imports by 2050 comparedto the baseline scenario. Investment decisions in these technologies are not highly sensitive to cost variations.Carbon pricing policies are found to be effective in mitigating local fossil fuel emissions in both the rural and urbancase studies.ConclusionsDGSTs play a significant role in the cost optimal, future energy systems of the communities considered. Smallhydro with storage (in the rural case), gas micro-CHP (in the urban case), and photovoltaics (in both) play adominant role in optimal capacity planning, even under uncertain cost conditions. The deployment of DGSTs resultsin increased self-sufficiency for the communities and enables electricity network deferrals. It must be borne in mind,however, that these results are specific to the communities considered; results will differ depending on site-specificconditions, including the existing energy system, local resource availability, and technology access.Further cases are being developed in order to identify the broader conditions under which DGST uptake isfavourable in Switzerland.