The reliance of Gulf States on fossil fuels has led to domestic challenges as well. Unsustainable energy and water use in the region, driven in part by energy subsidies, has contributed to environmental degradation: from increasing groundwater salinity [3] to urban air pollution [4]. Moreover, anthropogenic climate change will lead to severe environmental and social consequences in the Middle East [5]. It is in the interest of all countries in the region to mitigate the effects of these changes through long-term planning by deploying technologies and policies that can lead to sustainable resource use.
Optimization tools were first applied to economic planning and later extended to energy systems modeling [6]. Until now, there has been limited use of such tools in the Gulf States. Almansoori and Betancourt-Torcat modeled the electricity system in the UAE, using a stochastic approach to determine the effects of uncertain natural gas prices [7]. Established energy system models have also been used to study energy policies for Kuwait (using TIMES-VEDA) [8] and the UAE (using MARKAL) [9]. The Saudi case was modeled using a mixed-complementarity model that integrated the energy system into the wider economy [10].
Qatar''s energy economy is unique in that it is tailored towards the export of processed hydrocarbons. The country''s domestic resource consumption uses only a small fraction of the energy infrastructure. Our work is the first optimization approach applied to the Qatari energy system as a whole: across the largest sectors of the economy, and covering major energy products, from natural gas and hydrocarbon fuels to electricity and desalinated water. Our aims are two-fold: first, to develop an open-source tool that can be used for national-level planning and policymaking, and second to use this tool to generate key technology and policy insights that can aid the transition of Qatari energy infrastructure in the long term.
Qatar has a unique energy system. The country''s infrastructure is geared towards producing and exporting large volumes of natural gas, either directly (in a gaseous or liquefied state), or conversion to liquid fuels (gas-to-liquids) and petrochemicals. Domestic demand for electricity, water (mostly produced by thermal desalination), and liquid fuels, plays only a small part in the national energy economy, and these resources are subsidized by the state. Large investments in infrastructure, across all sectors, are funded wholly or partially by the government. All large-scale industries are either state-owned or closely regulated by the government. Hence, we assumed that there is only one actor, the state, whose economic objective is to be maximized.
We developed a tailor-made optimization model, called the Qatar Energy System Modelling and Analysis Tool (QESMAT), to accurately capture the peculiarities of the Qatari energy system. The Arabic word ''kismet'', also used in English, means ''fate'' or ''destiny''. Our optimization model can be used to plan for Qatar''s kismet. The following sub-sections describe various parts of our research methodology.
Historical population data and forecast
We developed an energy service demand forecast to 2050, which generated demands for residential and commercial energy consumption (for cooling, water, and electricity), along with service demands for passenger and freight transportation.
For sectors that were based on demographics, such as residential and commercial infrastructure, per-capita service demands were used to project energy needs based on currently available technologies and changing population. We used two values of per-capita service demands (a higher and lower value) to determine ''high'' and ''low'' domestic demand scenarios for the uncertainty analysis. In the residential sector, we divided the population into small and large households (aligned with the census data), and determined the energy service needs of populations living in each type.
Residential electricity and water consumption is linked to the populations that reside in ''households''—the state utility considers the demand from labor accommodation within the ''commercial'' sector, and we use the same approach to maintain consistency.
Electricity requirements for household populations were calculated using per-capita cooling, lighting, and appliance needs, adjusted by annual factors for efficiency improvements, increased energy needs, and increased cooling need due to climate change. These parameters, for high- and low-demand scenarios, are listed in Table 2. All of these parameters are estimated by us, so that the resulting forecast, when extrapolated to the past, provides upper and lower bounds for the historical data (from IEA, Kahramaa, and ministry reports) (see Fig. 2 for an example of this approach).
Historical water demand (residential use) and forecast
The annual residential electricity demand is then calculated using Eq. (1)
where 2010 was considered a "base year" for the parameters.
The annual cooling demand is given by Eq. (2)
Seasonal/diurnal variation in these demands is captured in a separate parameter array called ''demfrac'', which splits this annual demand into six slices, i.e., for two seasons (summer and winter) and three times of day (morning, evening and night), as seen in Table 7.
The Qatari national utility company estimated a per-capita water consumption (at supply) of 224 m3 per year in 2017 [18]. We selected a lower and upper bound of 200 and 300 m3 per person per year, multiplied by the total household population, to forecast future demand. Historical data were plotted against an extrapolation of this future demand to validate our approach, as shown in Fig. 2.
The IEA database aggregates the energy use of labor accommodation, private businesses, and public buildings into a single category called "commercial use". We have retained this definition for the current study. The commercial sector''s energy use is dependent on total population. Thus, electricity and water consumption was calculated using the same approach as in the residential sector (1), but with different per-capita parameters, estimated by us, as shown in Table 3.
Passenger and freight demands were estimated by us as service demands, and the total annual service demand is obtained by multiplying the parameters in Table 4 with the total population in any given year.
As Qatar has transformed into an international aviation hub, with most passengers only transiting through Doha''s Hamad International Airport, the total population of Qatar cannot be used to infer aviation fuel requirements. Thus, we had to follow another approach.
In his doctoral thesis on the Qatari food–energy–water nexus, Al-Ansari studied the feasibility of domestic agriculture, and concluded that Qatar can meet its food security target of producing 40% of its food demand by using 160 million m3 of water and 1300 GWh of electricity annually [21]. We used these estimates for agricultural water and electricity demands.
The total fixed demands are enumerated in the tables below, for both high- (Table 5) and low- (Table 6) demand scenarios. The electricity, water, and cooling demands for residential and commercial sectors are aggregated. The agricultural electricity demand is added to the residential and commercial demands, but agricultural water demand is kept separate as this can also be met by treated sewage effluent (TSE) production.
Our projections for electricity and road fuels demand, when extrapolated to the past, provide upper and lower bounds for the IEA data on electricity and fuel consumption. This validates our forecasting approach.
Temporal granularity in QESMAT (showing a deterministic model without uncertainty-incorporating scenarios)
Linear programming models are commonly used in energy system optimization. The most popular energy systems modeling tool, TIMES, also uses a linear programming approach. This is because linear programs are generally guaranteed to reach global optimality within a reasonable solution time when using the simplex algorithm (implemented as CPLEX) [23]. When looking at energy systems on a country scale, we can model each technology as a black box—with any non-linearities in the operation of individual technologies approximated linearly—this assumption is valid at large scales.
For the same reason, our model assumes a linear trend between technology capacity and its capital cost, i.e., it does not capture economies of scale. Since all of our technology costs are derived from large-scale estimates, and the optimal solution deploys key technologies in significant capacities, the effect of this approximation is limited. This is, again, similar to the approach used in the TIMES model [23]. Capturing economies of scale would require us to adopt a mixed integer approach instead of a linear program—this would allow the model to deploy technologies only in fixed increments. However, the resulting model would be intractable, due to its size, when implementing stochastic uncertainty. We believe that our approach balances practicality with big-picture accuracy.
The objective function is the sum of all future expenses and revenues, with an annualized discount rate of 1%. Stern argues against using a high discount rate for long-term planning, especially climate change mitigation [24]. A low discount rate means that long-term decisions, which will affect the lives of future citizens of the country, are not discounted by policymakers who are otherwise more concerned about the immediate future. We observe this in our own model—increasing the discount rate reduces the decarbonization of the energy system, as the model penalizes the high up-front capital costs of deploying clean technologies. A small discount rate, such as 1%, represents a balance between capturing the time value of money and still valuing future generations.
Note that due to the low discount rate, the objective function must not be used as an absolute indicator of economic performance, but rather as a comparative metric between various scenarios, to provide quantitative backing for specific energy policies. The model is deterministic and assumes perfect foresight, but uncertainties are captured using four scenarios after 2030, as explained below. QESMAT has been successfully benchmarked against an open-source energy systems model called OSeMOSYS [25].
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