martavp · martavp · Aug 19, 2022 · Aug 19, 2022 · Aug 19, 2022 · Aug 19, 2022
diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
@@ -41,7 +41,8 @@ Heat demand
 Building heating in residential and services sectors is resolved regionally, both for individual buildings and district heating systems, which include different supply options [To do:link to next section]
 Annual heat demands per country are retrieved from `JRC-IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ and split into space and water heating. For space heating, the annual demands are converted to daily values based on the population-weighted Heating Degree Day (HDD) using the `atlite tool <https://github.com/PyPSA/atlite>`_, where space heat demand is proportional to the difference between the daily average ambient temperature (read from `ERA5 <https://doi.org/10.1002/qj.3803>`_) and a threshold temperature above which space heat demand is zero. A threshold temperature of 15 °C is assumed by default. The daily space heat demand is distributed to the hours of the day following heat demand profiles from `BDEW <https://github.com/oemof/demandlib>`_. These differ for weekdays and weekends/holidays and between residential and services demand.  
 
-•	Space heating
+*Space heating*
+
 The space heating demand can be exogenously reduced by retrofitting measures that improve the buildings’ thermal envelopes [Refer to PyPSA-Eur-Sec Config file, `line 212 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L212>`_.
 
 .. literalinclude:: ../config.default.yaml
@@ -53,12 +54,14 @@ Renovation of the thermal envelope reduces the space heating demand and is optim
 In a first step, costs per energy savings are estimated in `build_retro_cost.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/build_retro_cost.py>`_. They depend on the insulation condition of the building stock and costs for renovation of the building elements. In a second step, for those cost per energy savings two possible renovation strengths are determined: a moderate renovation with lower costs, a lower maximum possible space heat savings, and an ambitious renovation with associated higher costs and higher efficiency gains. They are added by step-wise linearisation in form of two additional generations in `prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_.
 Further information are given in the publication :
  `Mitigating heat demand peaks in buildings in a highly renewable European energy system, (2021)  <https://arxiv.org/abs/2012.01831>`_.
-•	Water heating
+
+*Water heating*
+
 Hot water demand is assumed to be constant throughout the year.
-•	Urban and rural heating
+*Urban and rural heating*
 For every country, heat demand is split between low and high population density areas. These country-level totals are then distributed to each region in proportion to their rural and urban populations respectively. Urban areas with dense heat demand can be supplied with large-scale district heating systems. The percent of urban heat demand that can be supplied by district heating networks as well as lump-sum losses in district heating systems is exogenously determined in the `Config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L153>`_. 
 
-•	Cooling demand
+*Cooling demand*
 Cooling is electrified and is included in the electricity demand. Cooling demand is assumed to remain at current levels.  An example of regional distribution of the total heat demand for network 181 regions is depicted below.
 
 .. image:: ../graphics/demand-map-heat.png
@@ -82,18 +85,18 @@ Heat supply
 
 Different supply options are available depending on whether demand is met centrally through district heating systems, or decentrally through appliances in individual buildings. 
 
-**Urban central heat:** 
+*Urban central heat*
 
 For large-scale district heating systems the following options are available: combined heat and power (CHP) plants consuming gas or biomass from waste and residues with and without carbon capture (CC), large- scale air-sourced heat pumps, gas and oil boilers, resistive heaters, and fuel cell CHPs. Additionally, waste heat from the `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_  and `Sabatier <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L240>`_ processes for the production of synthetic hydrocarbons can supply district heating systems. 
 
-**Residential and Urban decentral heat:**
+*Residential and Urban decentral heat*
 
 Supply options in individual buildings include gas and oil boilers, air- and ground-sourced heat pumps, resistive heaters, and solar thermal collectors.
 Ground-source heat pumps are only allowed in rural areas because of space constraints. Thus, only air- source heat pumps are allowed in urban areas. This is a conservative assumption, since there are many possible sources of low-temperature heat that could be tapped in cities (e.g. waste water, ground water, or natural bodies of water). Costs, lifetimes and efficiencies for these technologies are retrieved from the `Technology-data repository <https://github.com/PyPSA/technology-data>`_.
 
 Below are more detailed explanations for each heating supply component, all of which are modeled as `Links <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#link>`_. in PyPSA-Eue-Sec.
 
-•	Large Combined Heat and Power plants are included in the model if it is specified in the `config file. <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L235>`_.
+Large Combined Heat and Power plants are included in the model if it is specified in the `config file. <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L235>`_.
 
 CHPs are based on back pressure plants operating with a fixed ratio of electricity to heat output. The efficiencies of each are given on the back pressure line, where the back pressure coefficient cb is the electricity output divided by the heat output. (For a more complete explanation of the operation of CHPs refer to the study by Dahl et al. : `Cost sensitivity of optimal sector-coupled district heating production systems <https://arxiv.org/pdf/1804.07557.pdf>`_.
 
@@ -103,10 +106,12 @@ The methane CHP is modeled on the Danish Energy Agency (DEA) “Gas turbine simp
 
 NB: The old PyPSA-Eur-Sec-30 model assumed an extraction plant (like the DEA coal CHP) for gas which has flexible production of heat and electricity within the feasibility diagram of Figure 4 in the study by `Brown et al. <https://arxiv.org/abs/1801.05290>`_ We have switched to the DEA back pressure plants since these are more common for smaller plants for biomass, and because the extraction plants were on the back pressure line for 99.5% of the time anyway. The plants were all changed to back pressure in PyPSA-Eur-Sec v0.4.0.
 
--  Micro-CHP 
+*Micro-CHP*
+
 Pypsa-eur-sec allows individual buildings to make use of `micro gas CHPs <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L236>`_ that are assumed to be installed at the distribution grid level.
 
-•	Heat pumps
+*Heat pumps*
+
 The coefficient of performance (COP) of air- and ground-sourced heat pumps depends on the ambient or soil temperature respectively. Hence, the COP is a time-varying parameter[refer to `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L206>`_ file). Generally, the COP will be lower during winter when temperatures are low. Because the ambient temperature is more volatile than the soil temperature, the COP of ground-sourced heat pumps is less variable. Moreover, the COP depends on the difference between the source and sink temperatures:
 
 $$ &Delta; T = T_(sink) − T_(source) $$ 
@@ -119,28 +124,37 @@ for ground-sourced heat pumps (GSHP), we use the function:
 
 $$  COP(&Delta; T) = 8.77 + 0.150&Delta; T + 0.000734&Delta; T^2 $$     
 
-•	Resistive heaters (can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ option)
+*Resistive heaters*
+
+Can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ option
 Resistive heaters produce heat with a fixed conversion efficiency (refer to `Technology-data repository <https://github.com/PyPSA/technology-data>`_ ). 
 
-•	Gas, oil, and biomass boilers (can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ , `oil boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L233>`_ , and `biomass boiler <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L234>`_ option)
+*Gas, oil, and biomass boilers* 
+
+Can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ , `oil boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L233>`_ , and `biomass boiler <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L234>`_ option.
 Similar to resistive heaters, boilers have a fixed efficiency and produce heat using gas ,oil or biomass.
 
-•	Solar thermal collectors (can be activated in the Config file from the `solar_thermal <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L237>`_ option)
+*Solar thermal collectors* 
+
+Can be activated in the Config file from the `solar_thermal <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L237>`_ option.
 Solar thermal profiles are built based on weather data and also have the `options <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L134>`_ for setting the sky model and the orientation of the panel in the Config file, which are then used by the atlite tool to calculate the solar resource time series.
 
-•	Waste heat from Fuel Cells, Methanation and Fischer-Tropsch plants
+*Waste heat from Fuel Cells, Methanation and Fischer-Tropsch plants*
+
 Waste heat from `fuel cells <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256>`_ in addition to processes like `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_ , methanation, and Direct Air Capture (DAC) is dumped into  district heating networks. 
 
-**Existing heating capacities and decommissioning**
+*Existing heating capacities and decommissioning*
 
 For the myopic transition paths, capacities already existing for technologies supplying heat are retrieved from `“Mapping and analyses of the current and future (2020 - 2030)” <https://ec.europa.eu/energy/en/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment>`_ . For the sake of simplicity, coal, oil and gas boiler capacities are assimilated to gas boilers. Besides that, existing capacities for heat resistors, air-sourced and ground-sourced heat pumps are included in the model. For heating capacities, 25% of existing capacities in 2015 are assumed to be decommissioned in every 5-year time step after 2020.
 
-**Thermal Energy Storage** (Activated in Config from the `tes <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L228>`_ option)
+*Thermal Energy Storage* 
+
+Activated in Config from the `tes <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L228>`_ option.
 
 Thermal energy can be stored in large water pits associated with district heating systems and individual thermal energy storage (TES), i.e., small water tanks. Water tanks are modeled as `stores <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#store,  which are connected to heat demand buses through water charger/discharger links>`_.
 A thermal energy density of 46.8 kWhth/m3 is assumed, corresponding to a temperature difference of 40 K. The decay of thermal energy in the stores: 1-exp(-1/24τ) is assumed to have a time constant  of  t=180 days for central TES and  t=3 days for individual TES, both modifiable through `tes_tau <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L229>`_ in Config file. Charging and discharging efficiencies are 90% due to pipe losses.
 
-**Retrofitting of the thermal envelope of buildings**
+*Retrofitting of the thermal envelope of buildings*
 
 Co-optimising building renovation is only enabled if in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L222>`_ file. To reduce the computational burden,
 default setting is set as false
@@ -330,30 +344,40 @@ Inside each country the industrial demand is then distributed using the `Hotmaps
 .. image:: ../graphics/hotmaps.png
 
 
-Industry supply
-================
+*Iron and Steel*
 
-Process switching (e.g. from blast furnaces to direct reduction and electric arc furnaces for steel) is defined exogenously.
+*Chemicals Industry*
 
-Fuel switching for process heat is mostly also done exogenously.
+The chemicals industry includes a wide range of diverse industries, including the production of basic organic compounds (olefins, alcohols, aromatics), basic inorganic compounds (ammonia, chlorine), polymers (plastics), and end-user products (cosmetics, pharmaceutics).
+
+The chemicals industry includes a wide range of diverse industries, including the production of basic organic compounds (olefins, alcohols, aromatics), basic inorganic compounds (ammonia, chlorine), polymers (plastics), and end-user products (cosmetics, pharmaceutics).
+
+The chemicals industry consumes large amounts of fossil-fuel based feedstocks (see `Levi et. al <https://pubs.acs.org/doi/10.1021/acs.est.7b04573>`_), which can also be produced from renewables as outlined for hydrogen (LINK TO HYDROGEN SUPPLY), for methane (LINK TO METHANE SUPPLY), and for oil-based products (LINK TO OIL-BASED PRODUCTS SUPPLY). The ratio between synthetic and fossil-based fuels used in the industry is an endogenous result of the opti- misation.
+
+The basic chemicals consumption data from the `JRC IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ database comprises high- value chemicals (ethylene, propylene and BTX), chlorine, methanol and ammonia. However, it is necessary to separate out these chemicals because their current and future production routes are different.
+
+Statistics for the production of ammonia, which is commonly used as a fertilizer, are taken from the `USGS <https://www.usgs.gov/media/files/nitrogen-2017-xlsx>`_ for every country. Ammonia can be made from hydrogen and nitrogen using the Haber-Bosch process.
+
+$$
+N_2 + 3H_2 → 2NH_3
+$$
 
-Solid biomass is used for up to 500 Celsius, mostly in paper and pulp and food and beverages.
 
-Higher temperatures are met with methane.
+The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( $6.5 MWh_{H_2}$ / t $_{NH_3}$ ) and electricity ( $1.17 MWh_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (LINK TO HYDROGEN SUPPLY)
 
 Transportation
 =========================
 Annual energy demands for land transport, aviation and shipping for every country are retrieved from `JRC-IDEES data set <http://data.europa.eu/89h/jrc-10110-10001>`_. Below, the details of how each of these categories are treated is explained.
 
-Land transport
------------------
+*Land transport*
+
+
+*Aviation*
 
-Aviation
------------------
 The `demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modeled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin [link to oil product].
 
-Shipping
------------------
+*Shipping*
+
 Shipping energy demand is covered by a combination of oil and hydrogen. The amount of oil products that are converted into hydrogen follow an `exogenously defined path <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L198>`_. To estimate the `hydrogen demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2089>`_, the average fuel efficiency of the fleet is used in combination with the efficiency of the fuel cell defined in the technology data. The average fuel efficiency is set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L196>`_.
 
 The consumed hydrogen comes from the general hydrogen bus where it can be produced by SMR, SMR+CC or electrolysers [link to hydrogen]. The fraction that is not converted into hydrogen use oil products, i.e. is connected to the general oil bus.