Help: THERMOS Supply Model Description

A day type is a kind of archetypal day. In principle the supply model could have 365 different days in a year, or 3650 for ten years, and so on.

Instead it is more reasonable and easier for the model if we go in the other direction and characterise the year in terms of a few types of day. For example in the default inputs the supply model considers the following day types (days / year in parentheses):

• Normal Weekday (197/365)
• Normal Weekend (78/365)
• Winter Weekday (62/365)
• Winter Weekend (27/365)
• Peak Day (1/365)

Each day type is then divided into a number of equal intervals

Within a given day type there are a certain number of intervals, which all have equal length. The standard inputs have 24 intervals in each day type, so each interval represents 1 hour from the day of that type.

This means that the optimiser can make different decisions about what happens down to that hour, but not to the half-hour or below.

Plant and storage definitions each have capital cost terms, broken down as a fixed cost, cost per kW capacity, and cost per kWh.

The capital cost for plant is

\[ \text{fixed cost} + [ \text{cost per kW} \times \max_{t \in T} \text{output}_t ] + [ \text{cost per kWh} \times \sum_{t \in T} \text{output}_t \times \text{weight}_t ] \]

Where \(\text{weight}_t\) is the number of kWh per year that interval \(t\) would contribute at an output of 1 kW; for example, an interval representing half an hour, in a day that occurs 50 days per year, would have a weight of 25. \(T\) is the set of all the intervals modelled in the profile.

The capital cost for storage is similar, except that the cost per kWh is in terms of kWh storage capacity rather than kWh output:

\[ \text{fixed cost} + [ \text{cost per kW} \times \max_{t \in T} \text{flow}_t ] + [ \text{cost per kWh} \times \max_{t \in T} \text{charge}_t ] \]

Where \(\text{flow}_t\) is the charging rate during interval \(t\) (so the maximum of this is the capacity of the connection between the store and the plant), and \(\text{charge}_t\) is the number of kWh of heat that are in the store at time \(t\) (so the maximum of this is the capacity of store required).

Both plant and storage have a given lifetime - if the plant or store must be replaced during the accounting period of the optimisation, the capital costs are paid again each time the lifetime is reached.

Operating costs are only calculated for the plant, and follow the same formula as the plant capital cost formula; the resulting cost is incurred every year the plant operates.

Fuel costs are incurred by plant when it operates; for each plant, in every time interval \(t\) there is a fuel price per kWh entered by the user. Each plant also has a heat production efficiency. The annual fuel cost for the plant is then given by

\[ \sum_{t\in T} \frac{\text{output}_t}{\text{efficiency}} \times \text{price}_t \times \text{weight}_t \]

Emissions costs are similar, except with an additional time-varying emissions factor for the fuel:

\[ \sum_{t\in T, e \in E} \frac{\text{output}_t}{\text{efficiency}} \times \text{factor}_{t,e} \times \text{weight}_t \times \text{unit cost}_e \]

where \(e\) is an emissions type (CO₂, NO_X, …) from the set of modelled emissions types \(E\)

Some plant is marked as CHP, and has a power efficiency as well as a heat efficiency. For these types of plant, an additional revenue (or potentially cost) is incurred when the plant runs, as it produces power to sell to the grid.

The value for this is analogous to the fuel cost, but with a different price and efficiency.

An important consideration for both CHP and electrically powered plant is substation headroom. In THERMOS, substation capacity is modelled as a hard constraint on how much power the substation can deliver or accept in any time interval.

Each substation also has a user-input timeseries of pre-existing demand, which determines the headroom (spare capacity); in every modelled interval the sum of pre-existing demand and new demand net of any CHP generation must not exceed the substation's capacity.

Curtailment gives the optimiser the possibility to undersupply heat in a given interval. It is included mostly so that the problem cannot become infeasible. Each kWh of heat un-supplied due to curtailment incurs a very high cost, so the optimiser should only choose to undersupply if the maximum possible plant capacity is insufficient, or if the cost of producing heat is exceedingly large.

The decisions to be made are then

• \(Purchase_p\), whether to buy plant \(p\) (binary)
  • \(Cap_p\), the capacity of that plant to buy
• \(Purchase_s\), whether to buy storage \(s\) (binary)
  • \(CapF_s\), the flow capacity to buy for \(s\)
  • \(CapS_s\), the storage capacity to buy for \(s\)
• \(Output_{p, t}\), the heat output from plant \(p\) in interval \(t\)
• \(FlowIn_{s, t}\) and \(FlowOut_{s, t}\) the heat flow in and out of store \(s\) in time \(t\)
• \(Curtailment_{t}\), the curtailment in time \(t\).

The objective is to minimize

\[ TotalCost = PlantCost + StoreCost + FuelCost + CurtailmentCost - GridRevenue \]

Where

\[ PlantCost = \sum_p Purchase_p \times CFix_p + Cap_p \times CCap_p + (\sum_t w_t \times COut_p \times Output_{p, t}) \]

and

\[ StoreCost = \sum_s Purchase_s \times CFix_s + CapF_s \times CCapF_s + CapS_s \times CCapS_s \]

and

\[ FuelCost = \sum_{t, p} w_t \times Price_{t, p} \times Output_{p, t} / eH_{p} \]

and

\[ CurtailmentCost = \sum_t w_t \times Curtailment_t \times C \]

where \(C\) is a big number

and

\[ GridRevenue = \sum_{t, p} w_t \times Grid_{t} \times eP_{p} \times Output_{p, t} / eH_{p} \]

summing over CHP plants only

To make the optimiser pay for what it uses, the objective is solved subject to:

Satisfied demand

\(\forall t : \sum_p Output_{p, t} + \sum_s (e_s \times FlowOut_{s, t} - FlowIn_{s, t}) + Curtailment_t \geq h_t\)

Ensures that in each time slice, at least as much heat is produced as is needed. Storage cycle efficiency is applied here, through the multiplication with \(e_s\).

This can be made an equality, if overproduction of heat to sell power is not allowed.

Indicators

\(\forall p : Cap_p \leq Purchase_p \times MaxCap_p\) and \(\forall s : CapF_s \leq Purchase_s \times MaxCapF_s\)

These constraints ensure that the fixed costs are paid if any storage or plant capacity is used.

Plant capacity sufficient

\(\forall p, t: Output_{p, t} \leq Cap_p\)

Store flow sufficient

\(\forall s, t: FlowIn_{s, t} \leq CapF_s\), and \(\forall s, t: FlowOut_{s, t} \times e_s \leq CapF_s\)

Store size sufficient

\(\forall s, t: Charge_{s, t} \leq CapC_s\)

Store charge balance

\(\forall s, t: Charge_{s, t} = Charge_{s, t-1} + H_t \times (FlowIn_{s, t-1} - FlowOut{s, t-1})\)

Where \(H_t\) is the number of hours time-slice \(t\) represents in the day, and \(t-1\) wraps around within the day at the start (so the charge state at the end of the day comes back to the start of the day).

Because of this, charge within the store cannot be passed between representative day types, or stored for more than a single day.

Substation headroom

\(\forall t, n : \sum_{p | Sub_p = n} -R_n \times Cap_n \leq L_{n, t} - eP_p \times Output_n/eH_p \leq Cap_n\)

Where \(eP_p = -1\) if the plant is not a CHP but is electrically powered.