Community ResilienceOriented Optimal MicroGrid Capacity Expansion Planning: The Case of Totarabank EcoVillage, New Zealand
Abstract
:1. Introduction
2. TestCase System: Totarabank, New Zealand
2.1. Candidate Technologies
2.1.1. PV Modules
2.1.2. Wind Turbines
2.1.3. Battery Arrays
2.1.4. Hybrid Inverter
2.1.5. Utility Grid
2.1.6. Electric Vehicle Charging Station
2.2. Key Assumptions
 The MG capacity expansion planning was carried out from a macro (centralised) perspective. Accordingly, this study does not focus on how to optimally assign the equipment capacity to each lot.
 The costs associated with the replacement and operation and maintenance (O&M) of the existing installed solar inverters were not reflected in the model. The reason lies in the fact that these assets are privately owned, while the new capacity additions were assumed to be shared by the community. That is, accounting for the replacement and O&M costs of the currently privately held solar inverters will require more sophisticated market designs (such as peertopeer markets) for the intracommunity electricity exchanges to establish a fair playing field—which are currently cleared under a pure flatrate tariff structure. Note that the rationale behind making this assumption stems from the difference in the service life of PV panels and solar inverters.
 The energy stored in the stationary battery bank is not allowed to be used for electric vehicle (EV) charging purposes—allowed only for critical loads to address the resilience of the community and to improve the service life of the stationary battery bank.
 A cooperative energy scheduling strategy (for example the one proposed in [35]), to be materialised in the implementation phase, was assumed to be able to coordinate the flexible charging of EVs, such that the daily periods of time the EV charging infrastructure sits unused is minimised.
 Vehicletogrid (V2G) services [36], as well as the effect of load growth due to the ecovillage’s population growth, were not taken into consideration. Rather, the system expansion is planned to meet the expectation of growing loads from the existing number of inhabitants.
 The product models were chosen, based on the authors’ judgement of both efficiency and costeffectiveness, from the options available in the Australia and New Zealand renewable energy markets, while costs are always cited in New Zealand currency.
2.3. Data
3. Methodology
3.1. Modelling Approach
3.2. Characterisation of Energy Resilience
3.3. Model Assumptions and Design Standards
4. Results and Discussion
4.1. Feasibility and Optimal Capacity Configuration
4.2. The Cost of Energy Resilience
 The values of total annual energy imports and exports indicate that the MG’s net purchased electricity from the grid was approximately a monotonically decreasing function of the grid unreliability. That is, as the failure frequency of the grid and/or its mean repair time increased, the total power sold back to (purchased from) the grid increased (decreases) or remained constant. The underlying reason responsible for this model behaviour is the increase in the excess nondispatchable power generation capacity of the MG during normal, gridconnected operations, as the grid reliability decreases. However, the increase in revenues generated from trading with the grid, as the MG’s resilience to grid outages improved, only partially offset the additional costs incurred. This emphasises the necessity to include a minimum acceptable limit for the target community’s energy resilience—to be derived from specificallydeveloped surveys to estimate the value of lost load—in the resilienceoriented MG capacity expansion planning processes to improve the accuracy of results.
 Nonsurprisingly, increasing the unreliability level of the grid through dedicated parameters, increased the TNPC of the MG capacity expansion, which, in turn, increased the LCOE associated with the costminimal system.
 The failure frequency of the grid and its mean repair time show almost the same degree of negative effect on the system’s lifecycle cost when normalised to the same scale (e.g., in the range of (0 to 1)). Additionally, there seems to exist a front of solutions with respect to the grid reliability parameters, beyond which the wholelife cost of the system grows exponentially. For example, in the middle case scenario, where the grid’s mean repair time and failure frequency were respectively considered to be 84 h and 10 per year, the system’s TNPC was increased only by about 48%. However, a further 10% increase of either of the above two parameters raised the MG’s TNPC by a further 26%. This observation can be rationalised by the change in the MG architecture when the grid unreliability level reaches a critical point, which is discussed in the next subsection.
4.2.1. Optimal MG System Type
4.2.2. Indicative Resilient System Optimisation Analysis
4.3. Capital Budgeting Metrics
4.3.1. Return on Investment
4.3.2. Internal Rate of Return
4.3.3. Discounted Payback Period
4.3.4. Resulting Cash Flow Metrics
5. Conclusions
 Over the 25year project lifecycle (planning horizon), the optimally expanded MG system can gain resilience against two outages per year, each up to four days in length, at relatively small discounted cost increases of 16% (equating to NZ$5892). This lends support to the idea that at current costs of renewable energy technologies, it is financially feasible for a communitylevel site to achieve a sufficient degree of survivability against sustained grid outages.
 The optimal architecture (component type combination), namely gridconnected existing PV/added PV/added WT/added BESS MG system, determined for the case with a 100% reliable grid, seems to be highly robust against a wide range of grid unreliability values. Much of the reason for this lies in the fact that solar resource tends to complement wind resource at the site. However, the evidence from this study shows that in high wind, low solar regions it is not costoptimal to add PV capacity when the degree of grid unreliability passes certain limits. This can mainly be attributed to the lower capacity factor of the PV plant.
 With an IRR of about 55%, the initial investment required for the capacity expansion of the site’s energy infrastructure to meet the projected energy consumption growth (driven primarily by the decarbonisation of the transport), can be recouped in less than five years, while additionally ensuring backup power supply to critical loads for two outages per year, each up to four days in length. This, together with the capital affordability of the project, suggests that it can be financed completely by the local community. Moreover, given the demonstrated evidence of the costefficiency of such programmes, it is expected to be attractive to many thirdparty investors.
 All the estimates were based on defining energy resilience in terms of sustained grid outages. That is, the study has not accounted for the impact of extreme weather episodes on the operability of the considered site’s renewable energy generation assets.
 The proposed method does not account for the planned extended power outages related to grid capacity additions or equipment maintenance and repair.
 The study did not include any inputs from the site on the value the community places on the unserved energy during nongridconnected operations. It is not implausible that the average annual loads that are deemed critical by endconsumers be different from the adjusted threshold criterion for load partitioning in this study.
 While the integration of unidirectional EV charging infrastructure is shown to be both technically feasible and economically viable, no attempt is made in this study to investigate the role that bidirectional charging (powered by V2G technology) can play in improving the profitability of the project.
Future Work
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Nomenclature
Acronyms  
BESS  Battery energy storage system 
DPP  Discounted payback period 
EV  Electric vehicle 
EVSE  Electric vehicle supply equipment 
HOMER  Hybrid Optimization of Multiple Energy Resources 
IRR  Internal rate of return 
LCOE  Levelised cost of energy 
MG  Microgrid 
O&M  Operation and maintenance 
PV  Photovoltaic 
ROI  Return on investment 
TNPC  Total net present cost 
V2G  Vehicletogrid 
WT  Wind turbine 
Indices  
$i$  Index of year 
Scalars  
$\Delta t$  Length of timestep $t$ in hours 
${\eta}_{PV}$  PV module’s efficiency 
$\gamma $  Wind shear exponent 
$dr$  Real discount rate 
$DOD$  Depth of discharge 
${E}_{B,min}^{\text{}},{E}_{B,max}^{\text{}}$  Minimum/maximum usable capacity of each battery module 
${f}_{PV}$  PV module’s derating factor 
${G}_{T,STC}$  Solar irradiance at standard test conditions 
${h}_{hub}$  Hub height of the wind turbine 
${h}_{ref}$  Reference height of wind speed records 
${k}_{p}$  PV module’s temperature coefficient 
$NOCT$  Nominal operating cell temperature 
${P}_{PV,r}$  PV module’s rated power 
$PL$  Project lifetime 
${T}_{STC}$  Cell temperature at standard test conditions 
Parameters  
${P}_{L}^{t}$  Total load power demand on the microgrid in time $t$ 
${P}_{PV}^{t}$  Power output from the PV system in time $t$ 
${P}_{WT}^{t}$  Power output from the wind turbine system in time $t$ 
${\pi}_{ex}^{t}$  Wholesale electricity price in time $t$ 
$FiT$  Feedintariff 
${T}_{PV}^{t}$  PV module’s temperature in time $t$ 
${G}_{T}^{t}$  Global horizontal irradiance in time $t$ 
${T}_{a}^{t}$  Ambient temperature in time $t$ 
${V}_{hub}^{t}$  Hubheight wind speed in time $t$ 
Variables  
$cos{t}_{g}^{t}$  Cost of power exchange with the main grid 
$CC\left(i\right),C{C}_{ref}\left(i\right)$  Capital cost of the suggested microgrid/reference system in year $i$ 
${E}_{B}^{t}$  Energy content of the battery bank in time $t$ 
$NCI\left(i\right),NC{I}_{ref}\left(i\right)$  Net cash inflow sequence of the proposed microgrid/baseline case in year $i$ 
${P}_{B}^{t}$  Charging/discharging power of the battery bank in time $t$ 
${P}_{g}^{t}$  Imported/exported power from/to the national grid in time $t$ 
Functions  
$PV$  Present value function 
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Ref.  Technologies Considered in the Candidate Pool  Optimisation Approach  Key Contribution(s)  Key Insight(s) from Case Study Analyses 

[11]  Solar photovoltaic (PV), wind turbine, fuel cell, diesel generator, heat storage, and battery  Mixedinteger linear programming solved using the General Algebraic Modeling System (GAMS) 


[12]  Solar PV, wind turbine, diesel generator, and battery  Scenariobased stochastic optimisation 


[13]  Wind turbine, bidirectional electric vehicle charging infrastructure  Semidynamic, semistatic programming 


[14]  Solar PV, wind turbine, diesel generator, battery, and utility line  Mixedinteger nonlinear programming solved using metaheuristic optimisation algorithms 


[15]  Solar PV and wind turbine  Multiagent systems 


Ref.  Technologies/Resources/Systems Considered in the Candidate Pool  Optimisation Approach  Key Contribution(s)  Key Insight(s) from Case Study Analyses 

[16]  Unspecified distributed generation  Mixedinteger linear programming solved using GAMS 


[17]  Hydro, wind, solar, geothermal, nuclear, coal, natural gas, and oil  Multiobjective optimisation of the nationallevel, energy infrastructure capacity expansion problem 


[18]  Electric power system and natural gas system  Twostage robust optimisation 


[19]  Solar PV, battery, and combined heat and power  HOMER software 


[20]  Unspecified distributed generation technologies  Twostage robust optimisation 


[21]  Solar PV, wind turbine, diesel generator, and battery  Twostage robust optimisation 


[22]  Battery and diesel generator  Mixedinteger linear programming solved using GAMS 


Specification  Component  

PV Modules ^{1}  Wind Turbines ^{2}  Battery Arrays  Converter  
Manufacturer part number  TSM285 PD05, Trina Solar  X2000L  RESU 3.3, LG Chem  SPMC240AU, Selectronic 
Rated capacity  285 W  2 kW  3.3 kWh  3 kW 
Capital cost  $237/unit  $3967/unit  $3645/unit  $4600/unit 
$832/kW  $1984/kW  $1105/kWh  $1533/kW  
Replacement cost ^{3}  $237/unit  $3229/unit  $3645/unit  $4600/unit 
O&M cost ^{3}  $0.7/unit/year  $26.4/unit/year  $7.3/unit/year  $3.9/unit/year 
Useful life  25 years  20 years ^{4}  15 years  15 years 
Efficiency  17.4%  N/A ^{5}  95% ^{6}  96% 
Source  [37,38]  [26]  [39,40,41]  [42,43] 
Scalar  Value  Source  Scalar  Value  Source 

${f}_{PV}$  88%  [51]  ${h}_{hub}$  7 m  (this paper) 
${k}_{p}$  −0.41%/°C  [37]  ${h}_{ref}$  50 m  [52] 
$NOCT$  44  [37]  $FiT$  $0.08/kWh  [53] 
${G}_{T,STC}$  1 kW/m^{2}  [54]  $\Delta t$  1 h  [55] 
${T}_{STC}$  25 °C  [54]  ${E}_{B,max}^{\text{}}$^{1}  2.9 kWh  [40] 
$\gamma $  0.15  [56]  ${E}_{B,min}^{\text{}}$^{2}  0.29 kWh  [40] 
Parameter  Value  Source 

Nominal discount rate  4.5%  [63] 
Expected inflation rate  1.9%  [64] 
Project lifetime  25 years  (this paper) 
Minimum autarky ratio ^{1}  80%  (this paper) 
Maximum annual capacity shortage in meeting critical loads  0%  (this paper) 
Load growth rate  1.1% per annum  [65] 
PV ^{1,2} [kW]  WT ^{1} [kW]  Battery ^{1} [kWh]  Inverter ^{1} [kW]  EVSE ^{1,3,4} [kW]  TNPC ^{5} [$]  LCOE [$/kWh] 

0.855  4  6.6  3  14.72  35,891  0.094 
PV (kW)  WT (kW)  Battery (kWh)  Inverter (kW)  EVSE (kW)  TNPC ($)  LCOE ($/kWh) 

0.855  4  9.9  3  14.72  41,783  0.109 
Metric  Return on Investment (%)  Internal Rate of Return (%)  Discounted Payback Period (Years) 

Value  47.63  54.51  4.74 
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Mohseni, S.; Brent, A.C.; Burmester, D. Community ResilienceOriented Optimal MicroGrid Capacity Expansion Planning: The Case of Totarabank EcoVillage, New Zealand. Energies 2020, 13, 3970. https://doi.org/10.3390/en13153970
Mohseni S, Brent AC, Burmester D. Community ResilienceOriented Optimal MicroGrid Capacity Expansion Planning: The Case of Totarabank EcoVillage, New Zealand. Energies. 2020; 13(15):3970. https://doi.org/10.3390/en13153970
Chicago/Turabian StyleMohseni, Soheil, Alan C. Brent, and Daniel Burmester. 2020. "Community ResilienceOriented Optimal MicroGrid Capacity Expansion Planning: The Case of Totarabank EcoVillage, New Zealand" Energies 13, no. 15: 3970. https://doi.org/10.3390/en13153970