Target-oriented robust optimization of a microgrid system investment model

doi:10.1007/s11708-018-0563-1

Front. Energy

2018, Vol. 12

Issue (3) : 440-455 https://doi.org/10.1007/s11708-018-0563-1

RESEARCH ARTICLE

Target-oriented robust optimization of a microgrid system investment model

Lanz UY, Patric UY, Jhoenson SIY, Anthony Shun Fung CHIU, Charlle SY(

)

Department of Industrial Engineering, De La Salle University, Manila 1004, the Philippines

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Abstract

An emerging alternative solution to address energy shortage is the construction of a microgrid system. This paper develops a mixed-integer linear programming microgrid investment model considering multi-period and multi-objective investment setups. It further investigates the effects of uncertain demand by using a target-oriented robust optimization (TORO) approach. The model was validated and analyzed by subjecting it in different scenarios. As a result, it is seen that there are four factors that affect the decision of the model: cost, budget, carbon emissions, and useful life. Since the objective of the model is to maximize the net present value (NPV) of the system, the model would choose to prioritize the least cost among the different distribution energy resources (DER). The effects of load uncertainty was observed through the use of Monte Carlo simulation. As a result, the deterministic model shows a solution that might be too optimistic and might not be achievable in real life situations. Through the application of TORO, a profile of solutions is generated to serve as a guide to the investors in their decisions considering uncertain demand. The results show that pessimistic investors would have lower NPV targets since they would invest more in storage facilities, incurring more electricity stock out costs. On the contrary, an optimistic investor would tend to be aggressive in buying electricity generating equipment to meet most of the demand, however risking more storage stock out costs.

Keywords microgrid renewable resources robust optimization target-oriented robust optimization

Corresponding Author(s): Charlle SY

Just Accepted Date: 16 April 2018 Online First Date: 04 June 2018 Issue Date: 05 September 2018

Cite this article:

Lanz UY,Patric UY,Jhoenson SIY, et al. Target-oriented robust optimization of a microgrid system investment model[J]. Front. Energy, 2018, 12(3): 440-455.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-018-0563-1
https://academic.hep.com.cn/fie/EN/Y2018/V12/I3/440

Fig.1 Microgrid system

Item	Symbol	Description
Indices	i	Discrete generation technologies, where i = {diesel generator ‘dg’ }
	l	Continuous generation technologies, where l = {photovoltaic ‘pv’, wind ‘w’, battery ‘batt’}
	m	Month index with respect to the present year 0, where m = {0, 1, 2, 3, …, N}
Parameters	$l m$	Customer load (electricity) in kilowatts (kW) for end use month m
	me	Market generation carbon emission conversion rate converting kWh to kg-carbon (kg-carbon/kWh)
	de	Diesel generation carbon emission conversion rate converting kWh to kg-carbon (kg-carbon/kWh)
	$c c l, m$	Capacity constraint of continuous generation technology l in month m (kW)
	$c d i, m$	Capacity constraint of discrete generation technology i in month m (kW)
	fc	Fraction of electricity charged into battery that is not lost in energy transfer
	fe	Fraction of electricity discharged from battery that is not lost in energy transfer
	fd	Fraction of electricity in battery that is lost in one time period
	uc	Useful life of continuous generation technology l
	ud	Useful life of discrete generation technology i
	$i m$	Fraction of maximum solar insolation incident upon location during month m
	$e s m$	Efficiency of the solar panel previously purchased in month m
	$w m$	Efficiency of the wind turbine in converting wind energy to electricity during month m
	$e w m$	Efficiency of the wind generators previously purchased in month m
	$e b m$	Efficiency of the battery previously purchased in month m
	$d m$	Number of days the batteries can supply the community in case of emergency during month m
	$e g m$	Efficiency of the generator previously purchased in month m
	$p i$	Nameplate power rating of discrete generation technology i (kW)
	$c t m$	Maximum amount of allowable carbon emission generated by the system during month m
	$d b m$	Total amount of budget per year y allotted for investment (Php) during month m
	$s m$	Number of days in a month

Tab.1 Model nomenclature – indices and parameters

Item	Symbol	Description
Decision variables	$c l, m$	Number of kW of continuous generation technology l installed in month m (kW)
	$b i, m$	Number of units of discrete generation technology i installed in month m (Units)
	g_m	Amount of electricity purchased from the grid during month m (kWh)
	z	Binary variable on deciding whether the electricity from PV would go to the battery or to the consumer
System variables	$b c l, m$	Number of continuous generation technology l that break down in month m (kW)
	$b d l, m$	Number of discrete generation technology i that break down in month m (kW)
	$n c l, m$	Net amount of continuous generation technology l existing in month m (kW)
	$n d l, m$	Net amount of discrete generation technology i existing in month m (kW)
	$c m m$	Total number of carbon emitted in excess to the carbon target in month m
	$j i, m$	Generated electricity of discrete generation technology i installed in month m sold to customer (kWh)
	$k m$	Total electricity supply from wind power (kWh)
	$k c m$	Electricity from wind power that is supplied to the customer during month m (kWh)
	$o m$	Electricity supply from photovoltaic power during month m (kWh)
	$o c m$	Electricity supply from photovoltaic power that is supplied to the customer during month m (kWh)
	$o b m$	Electricity supply from photovoltaic power that is send to the battery during month m (kWh)
	$f m$	Total electricity stored in batteries during month m (kW)
	$f o m$	Total net electricity supply from batteries during month m (kW)
	$h m$	Total electricity supply during month m (kWh)
	$u m$	Amount of electricity sold to the grid (kWh)
	$k s m$	Electricity from wind power that is sold to the grid during month m (kWh)
	$o s m$	Electricity supply from photovoltaic power that is sold to the grid during month m (kWh)

Tab.2 Model nomenclature – decision variables and system variables

Fig.2 Continuous and discrete generation technology

Fig.3 Electricity source

Scenario	o	k	j_dg	g	u
$c l pv = 1$	33080945	0	0	0	2515599000
$c l wind = 1$	32	20890100	0	0	2956656520
$c l diesel = 1$	7050	1211217	60000	0	1,040,181
g = 1	7050	1107699	52470	6024	935364

Tab.3 Extreme cost scenarios

Fig.4 Comparison of emission contribution and carbon target

Fig.5 Continuous generating technology

Fig.6 Flowchart of deterministic Monte Carlo analysis

Fig.7 Deterministic Monte Carlo result

Fig.8 Flowchart of TORO base run

Fig.9 TORO result

Fig.10 Summary of cost

Fig.11 Total DER purchases for each alpha

Fig.12 TORO Monte Carlo analysis

Fig.A1 Complete set of parameter values used

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