微电网系统投资模型的目标导向鲁棒优化

doi:10.1007/s11708-018-0563-1

Frontiers in Energy

2018, Vol. 12

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

本期目录

微电网系统投资模型的目标导向鲁棒优化

UY 兰兹, UY 查尔茜, SIY 乔森, 周纯峰安东尼, SY 查勒(

)

菲律宾马尼拉德拉萨大学工业工程系（1004）

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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摘要:

解决能源短缺的一个新兴的替代解决方案是建设微电网系统。本文提出了一种多周期多目标投资组合的混合整数线性规划微电网投资模型。采用目标导向鲁棒优化法（TORO）进一步研究了不确定性需求的影响。通过在不同场景下使用模型来对其进行验证和分析。因此，可以看出影响模型决策的因素有四个:成本、预算、碳排放和使用寿命。由于模型的目标是最大化系统的净现值(NPV)，因此模型将选择在不同的分布式能源资源(DER)中优先考虑成本最少的问题。通过使用蒙特卡罗模拟法，观察了载荷不确定度的影响。因此，确定性模型给出的解决方案可能过于乐观，在现实生活情境中可能无法实现。通过应用TORO，生成了解决方案的概要，以指导投资者在考虑不确定需求时做出决策。结果表明，悲观的投资者会降低净现值目标，因为他们会加大对存储设施的投资，从而导致电力库存成本增加。相反, 即使存在更多存储库存成本的风险，一个乐观的投资者也倾向于积极地购买发电设备以满足大部分需求。

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.

Key words： microgrid renewable resources robust optimization target-oriented robust optimization

收稿日期: 2017-12-20 出版日期: 2018-09-05

通讯作者: SY 查勒 E-mail: charlle.sy@dlsu.edu.ph

Corresponding Author(s): Charlle SY

引用本文:

UY 兰兹, UY 查尔茜, SIY 乔森, 周纯峰安东尼, SY 查勒. 微电网系统投资模型的目标导向鲁棒优化[J]. Frontiers in Energy, 2018, 12(3): 440-455.
Lanz UY, Patric UY, Jhoenson SIY, Anthony Shun Fung CHIU, Charlle SY. Target-oriented robust optimization of a microgrid system investment model. Front. Energy, 2018, 12(3): 440-455.

链接本文:

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

Fig.1

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

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

Fig.2

Fig.3

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

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

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