Generated Dataset related to Submission: Global Insights on the Potential of Community Systems for a Cost-Effective, Reliable and Sustainable Transition

This document describes the contents of the results folder, how to access them in Python, and information on processing typical day operation results for yearly analyses. Contents of the results folder of each climate cluster and how to access them The results of each climate cluster are saved in a folder named ‘Abc_X’, where ‘Abc’ refers to the Koppen Geiger climate sub-classification, and X identifies the cluster number in that climate sub-classification. The folder contains two types of ‘.mat’ files. The results are generated and saved using MATLAB R2022a programming language1, and the ‘.mat’ files are in MATLAB v7.2 file format. You can access .mat files in Python without a MATLAB license using Scipy libraries’ loadmat function, which is specifically designed to read MATLAB files. You can find more documentation at this link. Figure 1 shows a snippet of accessing .mat files in Python. Figure 1: Snippet on how to access ‘.mat’ files in a Python environment The two ‘.mat’ files are: 1. samples.mat 2. Soln_x.mat 1. samples.mat The file consists of the set of input parameter values used to generate the solutions Soln_x.mat. It comprises 1x65 struct called input_cz with the following fields: 1. name: Name of the input parameter 2. values: numerical array of size [nSamples, 1] where nSamples is the number of samples. Sample number x is used as input to the optimization model to result in Soln_x.mat. Table 1 provides the input parameter name and description. Table 1: Input parameter name as in the ‘samples.mat’ file, their description, and units S.No Name Description Units 1 Electricity.varOMTimeseries Timeseries of electricity retail prices CHF/MWh 2 Electricity.CO2Intensity Electricity grid carbon intensity kgCO2/MWh 3 Electricity.ForcedOutageRate Unavailability of electricity grid 1/h 4 Electricity.RepairTime Mean time to repair, electricity grid h 5 Gas.varOMTimeseries Timeseries of natural gas retail prices CHF/MWh 6 Gas.CO2Intensity Natural gas grid carbon intensity kgCO2/MWh 7 Gas.RepairTime Mean time to repair, natural gas grid h 8 PV.InvC Solar PV investment costs CHF/MW 9 PV.CO2Intensity Solar PV life-cycle emissions intensity kgCO2/MWh 10 PV.RepairTime Solar PV mean time to repair H 11 PV.fixOMC Solar PV fixed operations and maintenance costs [1/PV.InvC] 12 Wind.InvC Wind turbines investment costs CHF/MW 13 Wind.CO2Intensity Wind turbines lifecycle emissions intensity kgCO2/MWh 14 Wind.RepairTime Wind turbines mean time to repair h 15 Wind.fixOMC Wind turbines fixed operations and maintenance costs [1/Wind.InvC] 16 CHP.Eff1 Combined heat and power (CHP) electrical efficiency - 17 CHP.InvC CHP investment costs CHF/MW 18 CHP.CO2Intensity CHP life-cycle emissions intensity kgCO2/MWh 19 CHP.RepairTime CHP mean time to repair h 20 CHP.fixOMC CHP fixed operations and maintenance costs [1/CHP.InvC] 21 rHP.Eff1 Reversible heat pumps’ (rHP) heating - Coefficient of Performance (CoP) 22 rHP.Eff2 rHP cooling CoP - 23 rHP.InvC rHP investment costs CHF/MW 24 rHP.CO2Intensity rHP life-cycle emissions intensity kgCO2/MWh 25 rHP.RepairTime rHP mean time to repair h 26 rHP.fixOMC rHP fixed operations and maintenance costs [1/rHP.InvC] 27 GB.Eff1 Gas boiler (GB) efficiency - 28 GB.InvC GB investment costs CHF/MW 29 GB.CO2Intensity GB life-cycle emissions intensity kgCO2/MWh 30 GB.RepairTime GB mean time to repair h 31 GB.fixOMC GB fixed operations and maintenance costs [1/GB.InvC] 32 AC.Eff1 Absorption chiller (AC) efficiency - 33 AC.InvC AC investment costs CHF/MW 34 AC.CO2Intensity AC life-cycle emissions intensity kgCO2/MWh 35 AC.RepairTime AC mean time to repair h 36 AC.fixOMC AC fixed operations and maintenance costs [1/AC.InvC] 37 BS.Eff1 Battery storage (BS) round-trip efficiency - 38 BS.InvE BS investment costs CHF/MWh 39 BS.CO2Intensity BS life-cycle emissions intensity kgCO2/MWh 40 BS.RepairTime BS mean time to repair h 41 BS.fixOME BS fixed operations and maintenance costs [1/BS.InvE] 42 TS.Eff1 Thermal heat storage (TS) round-trip efficiency - 43 TS.InvE TS investment costs CHF/MWh 44 TS.CO2Intensity TS life-cycle emissions intensity kgCO2/MWh 45 TS.RepairTime TS mean time to repair h 46 TS.fixOME TS fixed operations and maintenance costs [1/TS.InvE] 47 CS.Eff1 Thermal cold storage (CS) round-trip efficiency - 48 CS.InvE CS investment costs CHF/MWh 49 CS.CO2Intensity CS life-cycle emissions intensity kgCO2/MWh 50 CS.RepairTime CS mean time to repair h 51 CS.fixOME CS fixed operations and maintenance costs [1/CS.InvE] 52 PV.Lifetime Solar PV lifetime y 53 Wind.Lifetime Wind turbines lifetime y 54 CHP.Lifetime CHP lifetime y 55 rHP.Lifetime rHP lifetime y 56 GB.Lifetime GB lifetime y 57 AC.Lifetime AC lifetime y 58 BS.Lifetime BS lifetime y 59 TS.Lifetime TS lifetime y 60 BS.Eff2 BS self-discharge 1/h 61 TS.Eff2 TS self-discharge 1/h 62 CS.Lifetime CS lifetime y 63 CS.Eff2 CS self-discharge 1/h 64 discountRate Discount rate - 65 fitRatio Feed-in tariff ratio [1/(electricity retail price)] 2. Soln_x.mat The file stores the optimization results. Loading the solution file “Soln_x.mat” outputs a results structure (1x1 struct). Below, we show the structure architecture with its variable names in black, description in blue, value units in red: results date Optimization date, [dd-MMM-YYYY] costTotal: Annual system costs, CHF costInstallation: Total installation costs, CHF costOperation: Annual operation costs, CHF costeventOp: Total operation costs in all failure events, CHF emissionsTotal: Annual system emissions, kgCO2 eventemissionsTotal: Total system emissions in all failure events, kgCO2 expEnergyNotSupplied: Expected energy not served, MWh carriers: [1xnCarr] struct, nCarr: number of carriers name Carrier name cost Net import costs of carrier, CHF emissions Net emissions from importing carrier, kgCO2 Import* Hourly resolved time-series of carrier imports, MWh export* Hourly resolved time-series of carrier exports, MWh demand* Hourly resolved time-series of carrier demand, MWh price* Hourly resolved time-series of import costs, CHF/MWh technologies [1xnTech] struct, nTech: number of technologies name Technology name type Technology type: renewables/conventional/storage cost Total investment costs on technology, CHF emissions Total life-cycle emissions from installing technology, kgCO2 size Installed capacity of technology, MW or MWh (storage) input* Hourly resolved time-series of input to technology, MWh utilization Annual utilization of technology: % of maximum energy output for conventional technologies, load cycles/annum for storage output* Hourly resolved time-series of output from technology, MWh energy Hourly-resolved energy level of storage technology, MWh eventTree probability Probability of event occurrence technologyState State of each technology (in technologies struct) in the event : 0: failed, 1: normal carrierState State of each carrier grid (in carriers struct) in the event: 0: failed, 1: normal DNS Demand not supplied in the event of all demand carriers (in carriers struct), MWh EENS Expected energy not supplied in the event of all demand carriers (in carriers struct), MWh tstart Start time of the failure event trepair Repair duration of the failure event, h costTotal Total system cost in the event, CHF costInstallation Total installation costs in the event, CHF costOperation Total costs of operations in the event, CHF emissionsTotal Total system emissions in the event, kgCO2 carriers [1xnCarr] struct, containing list of carriers and their operations during the event technologies [1xnTech] struct, containing list of technologies and their operations during the event diagnostic yalmipversion YALMIP version yalmiptime YALMIP time, s solvertime Time to solve the problem, s info Info comment from Gurobi solver problem Problem identifier from Gurobi, 0: no problem typDays k Number of typical days considered TiWeights Weight of each typical day tOrig Days of the year from the original time-series considered as typical days tStart Start time of each typical day tEnd End time of each typical day iTypDay [1x365] matrix that contains the maps each day of the year to its typical day *: simplified operations (considering ten typical days). Sequence time series using the method described below to conduct operational analyses. Post-processing of time-series operational results Optimizations are carried out for ten typical days of the year, based on the M1 method described by Gabrielli et al2. The computational simplification is considered to make computations time tractable without losing much accuracy. The continuity in storage operations is maintained in this method, thereby keeping good accuracy in results. Therefore, the hourly-resolved results of system operations (marked with * in results) should be sequenced using the functions ‘sequenceTypDays.m’ in MATLAB or ‘sequenceTypDays_py.ipynb’ in Jupyter notebook for analyses. Each field and the results.typDays struct must be input to the function to generate an hourly-resolved timeseries profile. References 1. MATLAB (R2022a). Natick, Massachusetts: The MathWorks Inc. https://ch.mathworks.com/products/new_products/release2022a.html 2. Gabrielli P, Gazzani M, Martelli E, Mazzotti M. Optimal design of multi-energy systems with seasonal storage. Applied Energy. 2018;219(June 2017):408-424. doi:10.1016/j.apenergy.2017.07.142