A techno-economic assessment of energy efficiency in energy management of a micro grid considering green-virtual resources

https://doi.org/10.1016/j.scs.2020.102169Get rights and content

Highlights

  • The impacts of green virtual resources are investigated to manage energy scheduling.

  • A mathematical model of the energy efficiency programs is suggested.

  • Techno-economic indices for energy efficiency evaluation are presented.

  • Optimal status of GVRs aiming at economic efficiency maximization is determined.

Abstract

Nowadays, Energy Efficiency as a strategic energy policy has been extended to handle the power system either in demand-side or supply-side affecting the proficiency of the grid. On the other hand, the penetration rates of green resources as PhotoVoltaic (PV) system and Vehicle to Grid (V2G) technologies and; Demand Response (DR) resources as virtual resources have been extremely expanded in the smart power system. Accordingly, in this paper, a novel concept of energy efficiency form economic aspect in presence of Green Virtual Resources (GVRs) will be presented while an economic model for Energy Efficiency Programs (EEPs) is nominated. Therefore, an economic efficiency maximization model of energy management in a Micro Grid (MG) is suggested based upon the difference of revenues and financial burden; whereas the optimal sitting and sizing of GVRs are determined. Furthermore, three multifarious techno economic coefficients including Economic Efficiency Index (EEI), Technical Efficiency Index (TEI) and Total Technical Efficiency Index (TTEI) are nominated to evaluate the impact of GVRs in efficiency improvement of the MG. To evaluate the capability of GVRs, the standard 33-bus IEEE distribution system is conducted. Results indicate that by selecting proper location and size of GVRs, significant improvement is obtained.

Introduction

Nowadays, the smart power system is considered as an efficient strategy to overcome the problems of growing energy consumption including economic and environmental issues. Demand Side Management (DSM), as one of the aspects of smart environment, is contemplated as a crucial issue in power system studies over the different horizon times. In fact, DSM programs, including Demand Response programs (DRPs) and Energy Efficiency Programs (EEPs), can be named Negawatt since contemplating as an alleviation of energy consumption (Tabandeh, Abdollahi, & Rashidinejad, 2016). Furthermore, the renewable energy resources are also considered as another interesting advantages of smart grid’s implementation, increasing the energy efficiency as well as reducing the power losses (Dorahaki, Rashidinejad, Abdollahi, & Mollahassani-pour, 2018).

Several studies have been performed in last decades to scrutinize the impact of DRPs and/or green resources on energy scheduling of the smart Micro Grid (MG). The effects of critical peak pricing programs as well as tariff and hourly pricing programs have been evaluated on operation of a distribution system in (Gutiérrez-Alcaraz, Tovar-Hernández, & Lu, 2016). An elastic demand function based upon hourly demand and price elasticity is utilized to investigate the impact of DR on short-term electricity prices’ variations; while the generation scheduling is handled in presence of wind turbines (WTs) uncertainty as well as DR resources (De Jonghe, Hobbs, & Belmans, 2012). In Sheikhi, Rayati, Bahrami, and Ranjbar (2015), an integrated DSM in smart energy hubs associated with electricity and natural gas is provided, while concentrating on energy consumption expenditures. The uncertainties of wind speed and solar irradiance are scrutinized in (Mazidi, Zakariazadeh, Jadid, & Siano, 2014) while probability distribution function and Latin hypercube sampling are used to model the historical data; however, the impacts of DSM in generation management of the MG are not considered. In fact, the energy management focuses on optimal utilization of all energy resources to procure demands along the entire horizon time which will be more complicated in presence of unpredictable demands and generation uncertainty of the distributed energy resource. In Kanchev, Lu, Colas, Lazarov, and Francois (2011) and Salinas, Li, Li, and Fu (2013), aggregation and implementation of the determinist energy management methods for business customers in the MG are organized. DSM is one of the significant measures to enhance the energy scheduling of the grid due to its strategic role in consumer side (Palensky & Dietrich, 2011). In Nwulu and Xia (2017), an economic energy dispatch in a grid-connected MG is performed associated with incentive-based DRPs and Distributed Generations (DGs) including photovoltaic (PV), diesel and, WT. In Wen, Lan, Fu, David, and Zhang (2015), the optimal location and capacity of DGs from technical aspects are determined while concentrating on minimization of losses and system stability improvement. In Wang, Shi, Wang, Liu, and Wang (2018), the impacts of simultaneous optimal presence of static compensator (STATCOM) and PV systems are investigated in the grid; whereas concentrating on minimization of investment expenditures and operation cost. The distributed resources’ allocation have been handled in Bohre, Agnihotri, & Dubey (2016), Cetinay, Kuipers, & Guven (2017) and Prakash and Khatod (2016), while concentrating on power losses minimization, cost minimization and maximization of node voltage deviations in n presence of demand- and supply-side uncertainties. More completed model of sitting and sizing problem of distributed generations is proposed in Sultana and Roy (2016) while aiming at both technical targets including stability and voltage deviation, as well as economic aspects including operation cost and power losses expenditures. In Khan, Wang, Ma, Xiong, and Li (2019), a multi-agent-based technique is utilized to control and manage the distribution system from different aspects including configuration, generation capacity, power flow, and fault control in presence of renewable energy resources. In Li and Gao (2017), the MG has been controlled regarding the optimal management of renewable energy resources including WTs and PV systems as well as potential of electricity saving via increasing efficiency of appliances. It should be noted that, in recent researches (Gutiérrez-Alcaraz et al., 2016; Li & Gao, 2017), the impacts of EEPs on energy management of the MG have been neglected.

On the other side, utilizing energy storage system is a promising technology to facilitate the challenges of power system studies, since it can be participated in the power market either as a load in off-peak periods or as a generation resource in peak periods (Chen & Chang, 2016). In Ganguly and Samajpati (2017), both technical and economic targets in optimum sitting and sizing problem of fast charging stations along the highway has been assessed in presence of budget limitations. Optimal sizing of WTs, PV systems and batteries are handled in a hybrid power system while concentrating on reliability improvement, cost minimization, optimum charging and discharging status of batteries and, declining the injected power fluctuation (Xu, Ruan, Mao, Zhang, & Luo, 2013). The uncertainty of renewable resources including WT, PV, micro-turbine, fuel cell and energy storage in the MG scheduling are considered in Mohammadi, Soleymani, and Mozafari (2014) whereas a two-phase scenario-based stochastic model is utilized. In AREFIFAR, Ordonez, and Mohamed (2016), the energy management aiming on cost minimization is performed in presence of Vehicles to Grids (V2Gs), DR resources, WTs and, PV systems in a multi MG; while the uncertainties in generation of WTs and PV systems as well as load are also considered; the success of energy management scenarios is also measured via a probabilistic index. In aforementioned studies (AREFIFAR et al., 2016; Chen & Chang, 2016), although the impacts of energy storage systems as well as green and virtual resources are considered; however the EEPs have not been addressed in energy scheduling of a MG.

However, the importance of EEPs has been contemplated in few studies of energy management problem. In Alvarez and Rudnick (2010), the effects of EE on distribution companies is evaluated utilizing a mathematical model based upon efficiency boundaries; while multifarious regulatory mechanisms and financial incentives are performed to determine the implementation of EE policies on power distribution network. A two-stage model is presented to investigate the impacts of EEPs on unit commitment problem in Dorahaki et al. (2018); whereas the level of yearly EE investment is specified in the first stage and, in the second one, the unit commitment scheduling regarding the EE investment level is solved. In Simab and Haghifam (2010), distribution companies’ efficiency is handled regarding data envelopment analysis incorporating fuzzy C-Means clustering and principle component analysis. The building remodeling to enhance the energy efficiency of an office is evaluated in Basir and Siraj (2015); while renewable energy impacts on expenditures as well as demand are also investigated. In Maheswaran, Rangaraj, Kailas, and Kumar (2012), it is shown that generated emissions in a system has been reduced by utilizing EE offers such as efficient motor, soft starters with energy saver and variable speed drives. In Rafiei and Bakhshai (2012), a survey regarding the importance of EE utilization in the industry is performed due to high energy prices and increasing generated pollutants in past decades.

It is worth mentioning that, some researches such as (Maheswaran et al., 2012; Motevasel & Niknam, 2013; Rafiei & Bakhshai, 2012) handle the MG scheduling with consideration of environmental aspects and or reported the level of emitted pollutants as an important factor over the scheduling process. In Motevasel and Niknam (2013), the energy management scheduling is performed to determine the optimal performance of distributed energy resources, electrical and thermal storage systems whereas minimizing both operating cost and generated emission. In Barzegar, Rashidinejad, Abdollahi, Afzali, and Bakhshai (2020), a new reliability based index has been proposed to optimal scheduling of the MG in presence of renewable resources, demand side management programs and storage system; whereas the suggested model concentrates on all economic, environmental and technical targets.

In order to overcome the defects of the previous researches, a novel techno-economic concept for energy efficiency in a MG is introduced in present paper. Therefore, a combined model of energy management scheduling associated with Green Virtual Resources (GVRs) including PV systems, V2Gs, DRPs and EEPs has been proposed while concentrating on energy efficiency maximization from economic aspect. Furthermore, a novel model for EEPs is suggested which is depended on average energy price, level of incenetives as well as the avarage incured cost of efficieny imporvement. Regarding, the nominated model, the optimal locations as well as capacities of GVRs are determined in the MG; whereas multifarious techno-economic efficiency indices are applied. The suggested efficiency indices are i) Economic Efficiency Index (EEI): shows the profitability of GVRs in comparison with total expenses; ii) Technical Efficiency Index (TEI): represents the proficiency of a distribution line and; iii) Total Technical Efficiency Index (TTEI): expresses the ability of the MG to satisfy demand in an islanding mode or a connected mode. Furthermore, the Current Value of Cash Inflows (CVCI) diagram will be utilized to show the effectiveness of GVRs implementation in a MG.

The rest of this paper is organized as follows. The framework of energy management incorporating GVRs in a MG is elaborated in Section 2. The DRPs and EEPs models are also presented in Section 2. The proposed energy management model concentrating on economic efficiency maximization is formulated in Section 3. Detailed explanations about techno-economic efficiency coefficients as well as economic analysis are provided in Section 4. Section 5 presents the results and analysis of the proposed model using the 33-bus test radial distribution system. Conclusions are given in Section 6.

Section snippets

Proposed flowchart of the MG scheduling in presence of GVRs

In this section, first, the hierarchy of Energy scheduling in MG with EE maximization is provided in details. Then, the utilized models of DRPs and EEPs as GVRs are fully explained.

Formulation of of energy management of a MG associated with GVRs

The aim of the proposed scheduling model is to determine the optimal sitting and sizing of GVRs satisfying the demand; whereas the efficiency of the MG from economic aspect is maximized. Although utilizing GVRs impose an additional expenditures to the MG; however, the hidden income as well as opportunity cost can compensate the incurred costs. The economic Objective Function (OF) of the suggested model is provided in (3). Regarding to (3), the MG will be more efficient from economic aspect, if

Assessment of GVRs impacts on energy management of a MG

Here, regarding the obtained results in the previous section, the techno-economic analysis of the MG scheduling associated with GVRs is performed. First, three different coefficients are nominated to evaluate the techno-economic effects of the proper selection of GVRs on energy management. Then, the economic analysis based upon cash inflow diagram is implemented.

Numerical evaluation

The performance of the proposed model is demonstrated using the standard 33-bus IEEE distribution system with scheduling time horizon of 20 years. The system has one feeder with four different laterals, 32 distribution lines and total peak load of 3720 kW and 2300 kVAr. More required data was obtained from MATPOWER software. The active and reactive power cost are considered equal to 4 Cent/kWh and 3.3 Cent/kVArh, respectively. It is assumed that V2Gs’ parking lot is residential; henece average

Conclusion

In present paper, a new concept of energy efficiency from economic aspect is suggested; whereas concentrating on determining the proper locations and sizes of GVRs including PV systems, V2Gs parking lots, EEPs and DR resources. In presented model, the aim of GVRs’ implementation is improving the MG efficiency; while an economic model of voluntary EEPs and incentive based DRPs are utilized. First, the optimal sitting and sizing of GVRs are determined based upon the maximization of differences

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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