Residential and commercial sectors - PROMETHEUS
The residential sector in PROMETHEUS includes households, services and agricultural sectors. In the residential sector, energy is consumed as input in processes that provide services to the households, such as space heating, water heating, cooking, cooling, specific electricity uses, lighting and other needs. The model distinguishes between residential sector’s demand for specific electric uses (e.g. electric appliances for non-heating purposes, air-conditioning, lighting, electronic equipment etc.) and useful energy demand for space and water heating. Demand for non-substitutable electricity is driven by growth in economic activity and disposable income of households and residential electricity price, while useful energy demand for heating purposes is related to income growth and the evolution of fuel prices.
Residential and tertiary consumers decide about the level of energy consumption taking into account their need for heating, which is further related to changes in income and fuel prices. Different iso-elastic demand equations are estimated for each type of residential sector’s demand and for each region. As the pattern of energy consumption is not usually controlled directly by the consumer, but is determined by the installed technology and is largely embodied in the characteristics of the durable equipment, responses to price shifts and environmental policies usually involve long lags. Changes in consumption patterns for developing regions are also modelled through a gradual convergence procedure to developed countries’ consumption patterns.
The competition between technologies to cover energy demand for space and water heating is modelled using the substitution specification, based on the notion of "gap" (as described in previous sections) and the competition of technologies to satisfy new energy requirements, based on their total production costs (including capital, operation and maintenance, carbon and fuel costs) and in other non-market factors, like technology maturity, mimetism, TRL levels, information, uncertainty etc. The model differentiates between “cold” and “warm” regions based on their climatic conditions, as in the latter (India, Emerging economies, the Middle East and North Africa and the Rest of the World region) energy demand for space heating is relatively insignificant, i.e. energy demand for water heating dominates. The evolution of useful energy demand is also assumed to depend on income levels, costs for energy services, consumer behaviour and regional climatic characteristics.
Energy demand for heating purposes is covered by natural gas, oil, coal, electric resistances, fuel cells (using hydrogen or natural gas as a fuel) and heat-pumps. Substitution between fuels and technologies is triggered by their total production cost, which includes capital, fixed O&M, variable O&M and fuel cost, their transformation efficiency, the scrapping rates of their equipment and their relative “technology maturity” factors. Technological trends, infrastructure and social network effects are assumed to influence technologies’ maturities, especially for fuel cells and heat-pumps, are incorporated in the decision mechanism, in order to represent in a realistic way the consumption patterns, the evolution of technology and fuel mix and the rigidities involved in the decision mechanism.
Energy performance of buildings largely depends on the characteristics of the dwelling (thermal integrity) and the technology of the equipment which uses energy. Individual energy consumers can spend money to improve energy efficiency and select solutions with upfront costs and utilisation performance leading to reasonable pay-back periods (e.g. deep retrofitting strategies in buildings). Energy efficiency progress implies high upfront cost but saves on variable and energy purchase costs during the lifetime of the energy equipment.
Energy meets fundamental needs of households. In developed economies (like North America, OECD Western Pacific and the EU) income elasticity is expected to be less than one, while in developing regions income elasticity can exceed one. Econometrics are used to estimate such elasticity value in all PROMETHEUS regions.
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|Institution||E3Modelling (E3M), Greece, https://e3modelling.com/modelling-tools.|
|Solution concept||Partial equilibrium (price elastic demand)|
|Anticipation||Energy system simulation.Foresight is included only is some sub-modules (i.e. electricity generation)|