energies

Article Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

Zi-Xuan Yu 1,†, Meng-Shi Li 1,*,†, Yi-Peng Xu 2,*,†, Sheraz Aslam 3,* and Yuan-Kang Li 2

1 School of Electric Power, South China University of Technology, Guangzhou 510641, China; [email protected] 2 School of Mathematical Sciences, Tiangong University, Tianjin 300387, China; [email protected] 3 Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol 3036, Cyprus * Correspondence: [email protected] (M.-S.L.); [email protected] (Y.-P.X.); [email protected] (S.A.) † These authors contributed equally to this paper.

Abstract: The optimal planning of grid-connected microgrids (MGs) has been extensively studied in recent years. While most of the previous studies have used fixed or time-of-use (TOU) prices for the optimal sizing of MGs, this work introduces real-time pricing (RTP) for implementing a demand response (DR) program according to the national grid prices of Iran. In addition to the long-term planning of MG, the day-ahead operation of MG is also analyzed to get a better understanding of the DR program for daily electricity dispatch. For this purpose, four different days corresponding



 to the four seasons are selected for further analysis. In addition, various impacts of the proposed DR program on the MG planning results, including sizing and best configuration, net present cost Citation: Yu, Z.-X.; Li, M.-S.; Xu, (NPC) and cost of energy (COE), and emission generation by the utility grid, are investigated. The Y.-P.; Aslam, S.; Li, Y.-K. optimization results show that the implementation of the DR program has a positive impact on the Techno-Economic Planning and technical, economic, and environmental aspects of MG. The NPC and COE are reduced by about Operation of the Microgrid USD 3700 and USD 0.0025/kWh, respectively. The component size is also reduced, resulting in a Considering Real-Time Pricing Demand Response Program. Energies reduction in the initial cost. Carbon emissions are also reduced by 185 kg/year. 2021, 14, 4597. https://doi.org/ 10.3390/en14154597 Keywords: optimal planning; renewable energy sources; demand response program; carbon emis- sions Academic Editors: José Matas and Luis Hernández-Callejo

Received: 23 June 2021 1. Introduction Accepted: 24 July 2021 1.1. Motivations Published: 29 July 2021 In recent years, Iran’s electricity consumption has encountered an accelerating trend of an average of 8% per year [1–10]. This has become a significant issue for the Iranian Publisher’s Note: MDPI stays neutral energy market [11–17]. Traditional fossil fuel-based power plants are not able to provide with regard to jurisdictional claims in the power demand [17–24], and blackouts are inevitable, especially in the hot months of published maps and institutional affil- the year [25–33]. Meanwhile, renewable energy sources (RESs) are one of the efficient iations. solutions for dealing with such an important issue [34–43]. In fact, Iran benefits from high-capacity potentials of solar energy with over 310 days with adequate sunlight and an average irradiance of 4.4–5.5 kWh/m2/day. Furthermore, wind power production potentials have been reported to up to 15 GW (about 35% of the nowadays electricity Copyright: © 2021 by the authors. generation). The governmental organization SATBA (Renewable Energy and Energy Effi- Licensee MDPI, Basel, Switzerland. ciency Organization) [44,45] has recently dedicated a few incentives to encourage localizing This article is an open access article clean energy generation plants, which make the concept of clean energy affordable for distributed under the terms and households [46–48]. conditions of the Creative Commons Nevertheless, there are several challenges in utilizing RESs in Iran, namely operation Attribution (CC BY) license (https:// and maintenance, control and shift of power between resources (energy management), creativecommons.org/licenses/by/ the short lifetime of system components, and sustainability, which have not been wholly 4.0/).

Energies 2021, 14, 4597. https://doi.org/10.3390/en14154597 https://www.mdpi.com/journal/energies Energies 2021, 14, 4597 2 of 28

investigated [49–51]. Utilizing microgrids (MGs) in different levels such as residential, commercial, and industrial could be an appropriate solution for these challenges. According to [52], MG is “a distribution system consisting of distributed generations (DGs), energy storage systems (ESSs) and responsive loads”. In fact, MGs are operated as an isolated network or interconnected networks. Upstream grid considers MG as a controllable system operating as a power source or controllable load. In an on-grid connection, an MG sends or receives power from the upstream grid and neighbor MGs in the area. Nevertheless, factors such as decreasing the power quality of the upstream grid based on the specific standards, significant disruption to the upstream grid, or maintenance program led to isolating the MG from the upstream grid [53–56].

1.2. Literature Review In order to utilize RESs in an economic and efficient manner, the optimal sizing of MG components is essential. According to the type of the optimization method, the full utilization of RESs and minimum investment along with the efficient MG performance can be assured [56]. For this purpose, various optimization methods have been employed in recent studies [57]. In Ref. [58], the optimal sizing of WT, photovoltaic (PV), and fuel cell (FC) units in a 31-bus distribution system, as well as the best location, are determined using the particle swarm optimization (PSO) technique. The aim of the study was to minimize the total cost of RESs, the total power losses, emissions, and total harmonic distortion (THD). The results showed that the utilization of RESs considerably decreases the emissions. In addition, it was revealed that RESs improve the voltage stability of the system. However, they neglected to consider an appropriate demand response (DR) program. The researchers in [59] proposed an optimal sizing ESS approach to minimize the total costs by optimiz- ing generation power, thermal units, load demand, and wind curtailment. The authors proposed a method to transform the chance constraints, such as the network constraints, into deterministic constraints. They also considered the correlations of the forecast errors among WT and load at different buses, which improve the ESS operational reliability. In another research study [60], the authors adopted the fuzzification process to find the best location of RESs for optimizing a tri-objective function, including the installation cost, emissions, and reliability. Furthermore, different techniques such as the Least Square tech- nique [61–63], the Loss of Power Supply Probability technique [64–66], and the Trade-Off technique [67,68] are represented to compute the optimal architecture of MG in terms of economic and technical analysis, which is known as techno-economic analysis. One of the efficient ways to obtain the optimal sizing and combination of MGs’ re- sources to electrify an MG is to employ HOMER Pro software. This software has extensively been used in the MG industry. In [69], the authors used HOMER Pro to compute the eco- nomic benefits of utilizing PV, WT, and ESSs in different climate zone of Iran. They also implemented an electricity pricing strategy based on the electricity tariffs considered by the Ministry of Energy of Iran. The optimization results indicate that a moderate and rainy climate zone such as Urmia city has the least net present cost (NPC) and levelized cost of energy (COE). However, a semi-moderate and rainy climate zone such as Golestan city has the highest NPC and COE. While the effect of different electricity pricing was investigated in this study, demand changes corresponding to the price changes were not investigated by the authors. The authors of [70] developed a decision-making method for the optimal sizing of RESs, diesel generators, and converters. In addition, a multi-criteria decision- making technique is proposed. According to this technique, a utility could concern various self-organize criteria for the best system configuration, each with a different weighting. The criteria were based on the costs of the system including, COE, COE, and investment costs. The optimal system considering the suggested technique consisted of a diesel generator, PV, WT, EES, and converter. However, DR programs have not been concerned in the proposed multi-criteria decision-making technique. In [71], the researchers used HOMER Pro for the techno-economic planning of islands in South Korea. In this study, three scenarios based on the three off-grid islands located in South Korea are optimized. The study prioritized Energies 2021, 14, 4597 3 of 28

the first scenario due to the competitiveness of economic results (lowest NPC and COE comparing other scenarios). The optimal system configuration for this island was obtained from a diesel generator, PV, WT, and ESS. Nevertheless, the pricing scheme was not clearly described in this study. In [72], an off-grid energy system was modeled by HOMER Pro for a local city in Kenya. The project was aimed to select a few feasible solutions obtained by HOMER, which provides the loads and then chooses the best available solution that could satisfy the objectives identified at the start of the study by analytic hierarchy process (AHP). The results indicated the importance of ESS utilization in the system architecture, which reduces the dumped energy to a satisfying value. A similar method was introduced in [73] for an educational institute. Therefore, they performed planning of the MG’s energy resources using multi-criteria decision-making based on the AHP approach. Both studies neglected price and load changes in the decision-making process. However, considerable impacts of load and electricity price values cannot be ignored. In [74], an RESs-based MG including PV/WT/ESSs is proposed for a grid-connected hybrid system in Iran. The authors used this software to specify the optimum MG structure regarding predicted load data. They also considered the load growth rate in their study to consider the impact of consumption growth in the systems configuration and economic costs. To this end, the previous years’ load data are used to forecast the future years’ load data, and the growth rate is extracted from the mean growth rate of the predicted values. Among various system configurations, the one that is the most economical was selected. The combination of the WT system and ESSs, with a Total Net Present Cost (NPC) of 452,454 USD, was the optimum configuration. While the annual load growth rate is considered in this study, a practical technique that improves the flexibility of the system was not suggested by the authors. In another study [75], a hybrid Organic Rankine Cycle (ORC) is proposed and optimized for a household in Rayen, Iran. The exergy and exergo-economic analyses of hybrid ORC are also performed. Then, HOMER software is employed to optimize a stand-alone MG. The results showed that due to the higher COE and NPC of the ORC-based system, RESs-based energy systems are proposed for the residential area. The software is employed to opti- mize a stand-alone MG. The results showed that the optimum configuration of the MG consists of PV/WT/Diesel/ESSs with a total NPC of 268,592 USD. In [76], a grid-connected PV system for agricultural applications was proposed in Tabriz, Iran. They also utilized PVSYST software to calculate the performance of the grid-connected PV system, which has not been considered in previous research. The simulation results indicated that the utility grid supplied 79.836% of the required electricity of the selected location and merely 20% provided by the PV system. The suggested microgrid reduced greenhouse gas emissions by about 508,713 kg per year. In [77], an MG including PV/WT/microturbine/BSS/FC is optimized using HOMER software to anticipate the size of power generation units in a grid-connected MG based in Nain, Iran. They used real data of Nain city for load demand, market prices, and weather data for the optimization. According to the results, the demand was provided by 23% of PV power, 43% of WT power, 32% of microturbine participation and 2% of fuel cell power provision. However, the ESS as a back-up resource had few participations over a year. In addition to traditional resources in MG such as DGs and RESs, demand response (DR) programs can also be introduced as the programmable resources in the MGs energy management. The DR programs are one of the effective resources to obtain the economic operation of MGs [51]. DR programs can enhance the performance of RESs in the MGs. They can decrease the peak load consumption and shave the daily load pattern so that the RESs supply the load demand efficiently. Therefore, the simultaneous utilization of the DR program and RESs can improve the operation and planning results of the MGs. In general, the DR programs are divided into two groups: incentive-based DR programs and price-based DR programs. The latest refer to users altering their consumptions based on the electricity price. Incentive-based DR programs refer to users decreasing or increasing their consumption based on financial incentives. The potential of DR and RESs integration has been investigated considerably [78]. Recent works in the literature discuss the merits of Energies 2021, 14, 4597 4 of 28

DR for RESs integration, particularly PV and WT systems, which are thoroughly analyzed in [79,80]. Furthermore, participating behavior, namely load shifting, load transfer, and load rebound is an essential factor for amending the reliability and feasibility of dispatching DR sources in MG operation. The potential and applicability of DR programs to MGs is yet another interesting research area, particularly in terms of peak consumed load demand minimization [81], pollution reduction [82], energy costs [83], and reliability improvement [84]. Nevertheless, few investigations have examined the participating behavior of DR programs in the long-term planning of residential MGs [85–88]. In [85], a multi-energy MG is optimized considering stochastic programming. The flexible demand control was also conducted by load shifting of controllable loads (on/off appliances scheduling). The model was applied for two days in the summer and winter seasons for a variety of studies, including the storage impact, energy sell-back to the network, charging and discharging of electric vehicles (EVs), and planning flexible equipment. According to the results, around 6% and 51% cost savings occurred for winter and summer days, respectively, when the power was offered to the power distribution system. In [86], an off-grid MG including RESs and ESSs system and a diesel generator is considered for addressing the issues of long-term optimal capacity determination, short-term operation, and planning for an off-grid MG. A RESs-based DR program dynamic pricing economic model to improve the flexibility of the system was proposed. The implemented model considers the intermittence of RESs as an instrument of effective demand-side flexibility improvement and system planning. The objective of this study was to minimize annual investments costs, operating costs, and demand-side management costs. According to the results, the mismatch between demand and generation was minimized through using the implemented DR-program, which considerably increased system flexibility. In [87], an integrated approach for optimal planning and operation of a multi-energy MG is proposed in which electrical loads are shifted based on TOU price signals. Uncertainties of various load and WT generation are considered using Monte Carlo simulation in this study. Then, the scenarios are reduced using the K-means clustering algorithm. Therefore, a two-stage optimization model repre- sented as a stochastic programming problem was proposed in this study, which addresses the impacts of the uncertain parameters. In addition, Benders decomposing approach is applied to solve the complex model of coordinated planning and operation problem. The authors of [88] examined and investigated the capability of DR programs in equipment size optimization of an off-grid MG. Responsive loads could be controlled in order to balance the demand and supply, in such way that the low production flexibility compensates for a significant extent [88].

1.3. Contributions Reviewing the above-mentioned studies, there still exists a research gap in proposing a DR program that is based on the electricity usage features of consumer loads. To the best of the author’s knowledge, there is no significant study in the size optimization of MGs using HOMER software that considers the DR program as a flexibility tool. Therefore, in this paper, a price-based DR program is implemented for long-term optimal capacity sizing and short-term operation of the grid-connected residential MG system located in Tehran, Iran. The MG has consisted of the PV unit, WT unit, bidirectional converter, and ESS. Then, the proposed DR program is compared with the TOU DR strategy currently employed in Iran’s electricity market. Environmental impacts of the proposed DR program are also analyzed, and other analyses are also performed. The essential contributions of the paper are as follows: • Proposing a price-based DR program according to the long-term electricity load consumptions. • Techno-economic analysis of an off-grid MG considering the price-based DR program to increase the flexibility of the system. Energies 2021, 14, 4597 5 of 28

Energies 2021, 14, 4597 5 of 28 • Techno-economic analysis of an off-grid MG considering the price-based DR pro- gram to increase the flexibility of the system. • • ConsideringConsidering coordinated coordinated planning planning and and operation operation of of the the MG MG under under different different pricing pricing schemes,schemes, as as well well as as concerning concerning the the environmental environmental impacts impacts of of each each scheme. scheme. •• PerformingPerforming aa sensitivity sensitivity analysis analysis onon the the inflation inflation rate rate and and discount discount rate rate to to evaluate evaluate thethe corresponding corresponding effects effects on on optimization optimization results. results.

2.2. Materials Materials and and Methods Methods ThisThis section section methodology methodology ofof the the study, study, including including system system architecture, architecture, optimization optimization method,method, system system components, components, energy energy management management strategy, strategy, and and the proposedthe proposed DR program, DR pro- isgram, discussed. is discussed.

2.1.2.1. System System Overview Overview TheThe understudy understudy MG MG structure structure is representedis represented in Figure in Figure1. As 1. illustrated, As illustrated, the understudy the under- MGstudy consists MG consists of RESs of including RESs including a PV, WTa PV, system, WT system, battery battery bidirectional bidirectional converter, converter and , ESS.and EnergyESS. Energy management management carries carrie outs an out important an important task suchtask assuch balancing as balancing demand demand and supplyand supply and cost and optimization cost optimization of the MG.of the The MG. DR The program DR program is also connectedis also connected to the energy to the managementenergy management system, whichsystem, can which enhance can enhance system performance. system performance. The energy The management energy man- systemagement is responsiblesystem is responsible for finding for the finding most economicalthe most economical system configuration system configuration and optimal and equipmentoptimal equipment sizing. While sizing. the While proposed the systemproposed includes system two includes renewable two componentsrenewable compo- and a batterynents and ESS, a thebattery optimum ESS, the design optimum may not design benefit may from not all benefit these from sources all accordingthese sources to the ac- economiccording to profits. the economic profits.

DR Program

Battery Energy Storage

Utility Grid

Wind Turbines Energy Management

Load Consumption

Photovoltaic Panels

FigureFigure 1. 1.Structure Structure of of the the MG MG with with energy energy management management module. module.

2.2.2.2. Optimization Optimization Method Method TheThe optimization optimization is is performed performed according according to to the the financial financial factors factors for for feasible feasible planning planning forfor investigations. investigations. The The system system configurations configurations are are ranked ranked according according to the to lowestthe lowest NPC. NPC. The NPCThe NPC of equipment of equipment is the is current the current values values of all equipmentof all equipment operating operating and installing and installing costs minuscosts minus the current the current values values of all theof all incomes the incomes the system the system obtains obtains during during the optimization the optimiza- period.tion period. The costs The consistcosts consist of initial of initial investment, investment, replacement, replacement, O&M, O&M, and buying and buying electricity elec- fromtricity the from utility. the utility. Incomes Incomes consists consists of grid of grid sales sales revenue revenue and and salvage salvage value. value. HOMER HOMER computescomputes thethe annualizedannualized costcost by dividing the the NPC NPC by by the the capital capital recovery recovery factor factor (CRF) (CRF) in inthe the project project time time horizon. horizon. The The following following Equations Equations (1) (1)–(4)–(4) are are used used for for calculating calculating the the an- annualizednualized cost cost of of the the system system [74 [74,88,88]. ].

Cnpc C − = (1) ann total CFR(R, i)

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Cnpc = Ccap + CO&M + Cemissions + Creplacement + Cp−grid − Cs−grid − Csalvage (2)

i(1 + i)R CRF(R, i) = (3) (1 + i)R − 1 i0 − f i = (4) 1 + f In this equation, CFR(R, i) is the capital recovery factor, R is the project lifetime (year), i is the real discount rate (%), i0 is the nominal discount rate, and f is the inflation rate. In addition, Cnpc is the NPC of the system, Ccap is the capital cost of the system, CO&M is the operating and maintenance cost, Cemissions is the emissions cost, Creplacement is the replacement cost, Cp−grid is the cost for purchasing power from the grid, Cs−grid is the revenue from selling power to the grid, Csalvage is the salvage value at the end of the project. The COE is computed as the mean cost per kWh of useful electricity generated by the MG, as follows [58,88]. Cann,total CCOE = (5) Es

In (2), CCOE is Levelized COE, Cann,total is the total annualized cost, and Es is the total load served

2.3. Renewability Renewable fraction (RF) is the other important term associated with the extent of using renewable generation in hybrid systems. The RF shows the ratio of the delivering energy to the load originated from RESs. Equations (6) and (7) are used for the definition of RF [44–46]. E RF = 1 − elec (6) Es P RP = Ren (7) Lserved

where Eelec is conventional electrical generation per kWh/year. Moreover, the renewable penetration (RP) in each time-step describes that PRen is the total renewable output power (kW) and Lserved is the total electrical load served (kW).

2.4. System Components 2.4.1. Solar PV System The solar irradiance and temperature are used for calculating the PV system’s output power. Generally, there are two kinds of PV panel choices in Iran: concentric and generic flat plate. The latter is more profitable; hence, a flat plate is considered in this study for further analysis. For the proposed MG, a generic flat-plate PV is utilized with an efficiency of 16.25%, a rated capacity of 1 kW, and an operating temperature of 45 ◦C. This kind of PV is represented with a converter, and the converter efficiency curve is modulated based on the datasheet (95%) [74]. The PV system output power can be computed as follows [44,47]. ! GT   PPV = fPV XPV 1 + αp(Tc − Tc,STC) (8) GT,STC

In this equation, fPV is the derating factor of PV per percent, XPV represents the PV array rated capacity, which means that the output power of the PV array under standard test 2 condition is in kW, GT indicates hourly solar irradiance on the PV surface in kW/m , GT,STC denotes the solar irradiation under standard test conditions, the temperature coefficient of ◦ power is shown by αp (%/ C), Tc signifies hourly temperature of the PV cell, and Tc,STC shows the temperature of the PV cell under standard test conditions. Energies 2021, 14, 4597 7 of 28

2.4.2. WT System In order to model and take the number and cost of WTs to fit the load demands with other resources optimally, the initial step is to determine the type of the WT. Referring to the components’ library, there are possibilities for different WT size ranges from 1000 to 1500 kW. In this paper, a generic model with a rated 1 kW capacity WT is selected [74]. The overall WT output power is computed using Equation (9) [89].

1 P = × ρ × A × V3 × C × 10−3 (9) WT 2 p

In the above equation, ρ is the air density (0.1225 × 10−1 kg/m3), A is the cross- sectional area of wind, V is the wind speed, and Cp is the power efficiency of the WT system. The capital cost for the WT system is 725 USD, and the O&M cost is 70.0 USD/year.

2.4.3. Battery ESS In HOMER, batteries are used as the backup resources during power outages or insuf- ficiencies of RESs in demand provision [90]. However, these resources play an important role in the stand-alone operation of MGs because it is usually cost-effective to sell generated power by RESs to the utility grid than storing it into battery ESS [68]. As there is an existing variability in load demand and RESs generations, it is recommended to consider ESS in the recommended system structure. In this paper, we proposed a battery ESS with a capital cost of 124 USD and an O&M cost of 10 USD/year. The battery ESS output power is computed as (10) [91]. Pt,BSS = VBSSCBSS(SOCt − SOCt−1) (10)

In the above equation, VBSS and CBSS are the voltage and total capacity (A.h) of the battery. In addition, SOCt shows the state of charge of the battery during the operation of the MG.

2.4.4. Converter Systems containing both DC and AC components require a converter. In this paper, a generic converter is considered, and its inverter consists of a power converter unit, power control unit, power distribution unit, and bypass unit [88]. The converter has an initial cost of 137.5 USD and O&M cost of 10 USD/year, also, the converter efficiency is considered as 95% [74].

2.5. DR Program DR programs can respond to market price signals by increasing the demand-side flexibility via reducing or temporary shifting load peaks. DR programs are also applicably proposing incentive mechanisms that result in the avoidance of capacity investment and costly electricity purchasing [50]. One of the most important components for the successful DR performance is the RTP mechanism. In this regard, an RTP strategy based on the TOU pricing is suggested in this paper. The RTP model will be utilized for implementing the proposed DR program. More information regarding the proposed method can be found in [92]. In this paper, five types of loads are considered for MG. The loads are HVAC and fans HVAC InterLight OutLight MainEquip (Pt ), interior lights (Pt ), exterior lights (Pt ), main equipment (Pt ), MiscEquip and misc equipment (Pt ). The total load demand of the MG according to load types could be calculated as shown in (11).

8760  HVAC InterLight OutLight MainEquip MiscEquip TLoad = ∑ Pt + Pt + Pt + Pt + Pt (11) t=1 Energies 2021, 14, 4597 8 of 28 Energies 2021, 14, x FOR PEER REVIEW 8 of 28

The mean load is computed as shown in (12). TLoad 𝑇 =𝑃 +𝑃 P+𝑃mean = +𝑃 +𝑃 (11)(12) 8760 TheFurthermore, mean load real-time is computed prices as are shown determined in (12). based on (13) and (14). 𝑇 𝑃 =  (12) rtp 8760TLoad tou Kr = Kr (13) Furthermore, real-time prices are determinedPmean based on (13) and (14). 𝑇 (13) 𝐾rip=,min rip𝐾rip,max kr ≤ kr ≤ kr (14) 𝑃 rip,min rip,max where Pmean is the float factor,,k and k ,are respectively the upper and lower(14) 𝑘 r ≤𝑘 ≤𝑘r bounds of RTP. In the proposed method,, the price-based, DR program has been suggested where 𝑃 is the float factor, 𝑘 and 𝑘 are respectively the upper and to evaluate the effects of the DR on the MG planning and operation, as (15). lower bounds of RTP. In the proposed method, the price-based DR program has been suggested to evaluate the effects of the DR on the MGrtp planningtou and operation, as (15). Kr − Kr DRP = T + · T· Pt Pt e 𝐾Pt −𝐾rtp (15) 𝑃 =𝑃 +𝑒.𝑃 . Kr (15) 𝐾 T InIn the aboveabove equation,equation,P 𝑃t is is the the load load profile profile of theof the MG, MG,e represents e represents the demand-pricethe demand- tou priceelasticity elasticity coefficient coefficient that is that considered is considered as [−0.5], as [− and0.5],K andr is𝐾 TOU is prices TOU prices (see Figure (see 2Figure)[ 51]. 2) [51].

Start

Hourly base data for load consumption and Step 1 electricity price (TOU) are collected.

Step 2 The total load demand value is calculated.

Average load demand is calculated based on total Step 3 load demand value.

Real time price is computed based on the TOU Step 4 price and the hourly/average load demand data.

Modified load demand data based on real time Step 5 electricity price is calculated.

End

Figure 2. StepsSteps taking taking for for implementing implementing the RTP-based DR program [[92].92]. 2.6. Energy Management Strategy 2.6. Energy Management Strategy HOMER software benefits from two energy management systems, i.e., cycle charging HOMER software benefits from two energy management systems, i.e., cycle charging (CC) and load following (LF). In this paper, the LF dispatch strategy is used for energy (CC) and load following (LF). In this paper, the LF dispatch strategy is used for energy management of the MG. In this energy management strategy, HOMER dispatches the MG’s management of the MG. In this energy management strategy, HOMER dispatches the controllable power resources i.e., grid and ESS, to meet the hourly electrical load demand MG’s controllable power resources i.e., grid and ESS, to meet the hourly electrical load at the minimum NPC, providing the operating reserve requirements. The NPC consists demand at the minimum NPC, providing the operating reserve requirements. The NPC of capital cost, O&M cost, and replacement cost. For this purpose, HOMER computes the consists of capital cost, O&M cost, and replacement cost. For this purpose, HOMER com- marginal and fixed costs of each dispatchable resource [92]. putes the marginal and fixed costs of each dispatchable resource [92]. The fixed and marginal costs of the utilized battery ESS are equal to zero and the ESS The fixed and marginal costs of the utilized battery ESS are equal to zero and the ESS degradation cost, respectively. In addition, the grid fixed cost is zero, and the marginal cost degradationis equal to the cost, grid respectively. electricity price. In addition, The marginal the grid cost fixed of thecost grid is zero, rises and by consideringthe marginal a costcost is for equal carbon to the emissions. grid electricity price. The marginal cost of the grid rises by considering a cost for carbon emissions.

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AfterAfter characterizing characterizing each each dispatchable dispatchable resource, resource, HOMER HOMER explores explores to find to the find optimum the opti- configurationmum configuration of production of production resources resources that supply that supply electrical electrical load demand load demand and operating and oper- reserve at minimum cost. Figure3 shows a flowchart of the energy management system ating reserve at minimum cost. Figure 3 shows a flowchart of the energy management in HOMER. In general, the output power of RESs is designated for supplying primary system in HOMER. In general, the output power of RESs is designated for supplying pri- loads, and surplus power will be used for grid sales or storing in the ESS according to their mary loads, and surplus power will be used for grid sales or storing in the ESS according marginal costs. to their marginal costs.

Figure 3. Flowchart of the energy management system. Figure 3. Flowchart of the energy management system.

3.3. Case Case Study Study and and Results Results InIn this this paper, paper, a residentiala residential household household in in Tehran, Tehran, the the capital capital of of Iran, Iran, is is selected selected as as the the casecase study. study. The The reason reason for for studying studying this this city city is is the the availability availability of of the the infrastructure infrastructure for for implementingimplementing large-area large-area MGs MGs and and DR DR programs. programs. The The location location of the of understudy the understudy household house- ishold depicted is depicted in Figure in4 .Figure Tehran 4. is Tehran located is in located semi-arid in weathersemi-arid conditions weather [conditions69]. Weather [69]. parametersWeather parameters including airincluding temperature, air temperature, solar irradiation, solar irradiation, and wind and speed wind are speed represented are rep- inresented Figure5. in The Figure monthly 5. The values monthly are gatheredvalues are from gathered NASA’s from surface NASA’s meteorology surface meteorology that can bethat found canin be the found software’s in the software’s environment. environmen Figure5a,bt. Figure indicate 5a,b that indicate the understudy that the understudy location benefitslocation from benefits adequate from solaradequate energy solar over energy a year. over Nevertheless, a year. Nevertheless, a notable solar a notable potential solar ispotential available is during available the during summer the monthssummer of months the year. of the As year. can beAs seencan be in seen Figure in Figure5a,b, the 5a,b, ◦ maximumthe maximum daily daily temperature temperature is below is below 30 C, 30 and °C, solarand solar irradiation irradiation scarcely scarcely arrives arrives at its at its peak value at 0.95 kWh/m2/day. The location has a moderate wind potential over the year (Figure 5c) [74].

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EnergiesEnergies 2021 2021, 14, ,x 14 FOR, x FOR PEER PEER REVIEW REVIEW 2 10 of10 28 of 28 peak value at 0.95 kWh/m /day. The location has a moderate wind potential over the year (Figure5c) [74].

FigureFigure 4. The 4. The location location of the of theunderstudy understudy residential residential household, household, Tehran, Tehran, Iran. Iran. Figure 4. The location of the understudy residential household, Tehran, Iran. In order to have accurate techno-economic values for the grid-connected MG, it is InIn orderorder toto havehave accurate techno-economic techno-economic values values for for the the grid-connected grid-connected MG, MG, it itis necessary to consider realistic technical and economic data. In this paper, essential eco- isnecessary necessary to toconsider consider realistic realistic technical technical and and economic economic data. data. In this In thispaper, paper, essential essential eco- nomic parameters, such as inflation and discount rates, are assumed as 16.18% and 18%, economicnomic parameters, parameters, such such as as inflation inflation and and discount discount rates, rates, are are assumed assumed as 16.18% andand 18%,18%, respectively [69,93–97]. Furthermore, the simulation lifetime is considered to be 25 years. respectivelyrespectively [[69,93–97].69,93–97]. Furthermore,Furthermore, thethe simulationsimulation lifetime lifetime is is considered considered to to be be 25 25 years. years.

(a) (a) Figure 5. Cont.

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(b)

(c)

Figure 5. Weather data used forfor simulationssimulations ((aa)) temperature;temperature; ((bb)) solarsolar irradiance;irradiance; ((cc)) windwind speed.speed.

3.1. Utility Grid and DR Program Implementation TOU pricing is considered a DR strategy that is implemented in many countries, in- cluding Iran.Iran. AccordingAccording toto thisthis DR DR strategy, strategy, three three electricity electricity rates rates are are determined determined according accord- toing the to hourthe hour of the of day.the day. The ratesThe rates are defined are defined according according to the to off-peak, the off-peak, mid-peak, mid-peak, and peak and hourspeak hours of the of day. the The day. rates The could rates be could changed be changed during theduring hot andthe coldhot and months. cold Formonths. instance, For ininstance, peak months, in peak twomonths, peak two periods peak could periods be consideredcould be considered by the utility by the grid. utility It is assumedgrid. It is thatassumed the consumers that the consumers react to thereact price to the signals price signals and lower and theirlower consumption their consumption during during peak pricespeak prices and increase and increase their their usage usage in off-peak in off-pea hoursk hours of the of days. the days. This This strategy strategy ultimately ultimately led toled peak to peak load load shaving shaving and and flexibility flexibility of the of powerthe power system. system. In Iran, the Ministry of Energy (MOE) is responsible forfor definingdefining thethe TOUTOU rates.rates. There are generally two types of electricity tariffs in Iran. The firstfirst one is the constant electricity pricing, and the second is TOU electricityelectricity pricing.pricing. In order to implement TOU prices,prices, itit isis necessary to install smart meters.meters. Such meters are substitutingsubstituting withwith thethe mechanicalmechanical andand analog devices, which are merely used for recoding constant electricity prices. In contrast, smart meters can be used for registering three different rates over a day. According toto thethe legislation, thethe electricity priceprice at peak hours is four four times times higher higher than than the the mid-peak mid-peak prices. prices. Furthermore, the electricity price in off-peak times is a quarter of the mid-peak price. Furthermore, the electricity price in off-peak times is a quarter of the mid-peak price. Figure6 shows the TOU electricity prices for the residential sector over a year. As can be seen, there are four peak hours with the highest electricity prices. In addition to

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FigureFigure 6 shows 6 shows the the TOU TOU electricity electricity prices prices for forthe the residential residential sector sector over over a year. a year. As As can be seen, there are four peak hours with the highest electricity prices. In addition to canthis, be seen, a fixed there feed-in are tarifffour peak (FiT) hours is also with considered the highest in this electricity study [92 prices.]. It means In addition that surplus to this, a fixed feed-in tariff (FiT) is also considered in this study [92]. It means that surplus this,electricity a fixed feed-in generated tariff by (FiT) RESs is could also considered be sold to the in this utility study grid [92]. according It means to thisthat rate.surplus electricityelectricity generated generated by byRESs RESs could could be soldbe sold to theto the utility utility grid grid according according to thisto this rate. rate.

FigureFigureFigure 6. TOU 6.6. TOU pricing pricing implemented implementedimplemented on onIran’son Iran’sIran’s national nationalnational grid. grid.grid.

To ToToperform performperform long-term long-termlong-term planning planningplanning and andand implement implementimplement a DR aa DRDR program, program,program, it is itit essential isis essentialessential to tohaveto havehave hourlyhourlyhourly load loadload values valuesvalues for forforat least atat leastleast a year. aa year.year. In Inthis thisthis study, study,study, five fivefive types typestypes of ofloadof loadload consumption consumptionconsumption are areare consideredconsideredconsidered for for forthe the the consumer, consumer, consumer, including including including Fans Fans Fans & & HVAC, HVAC,& HVAC, interiorinterior interior andand and exterior exterior exterior lights, lights, lights, main main main and andmiscellaneousand miscellaneous miscellaneous interior interior interior equipment. equipment. equipment. Hourly Hourly Hourly load load consumptionload consumption consumption according according according to theto theto mentioned the men- men- tionedcategorytioned category category is depicted is depicted is depicted in Figurein Figure in Figure7[ 937 [93].]. 7 It[93]. It is is clearIt clear is clear that that that interior interior interior equipmentequipment equipment has has the the high- highest high- estconsumption estconsumption consumption rate rate rate among among among the the otherthe other other ones. ones. ones. In In addition, addition,In addition, Fans Fans Fans and and HVACand HVAC HVAC have have have minor minor minor electricity elec- elec- tricityconsumptionstricity consumptions consumptions especially especially especially compared compared compared to interior to interiorto interior equipment. equipment. equipment.

0.06 0.30 0.06 0.30 0.05 0.05 0.05 0.25 0.05 0.25 0.04 0.04 0.04 0.20 0.04 0.20 0.03 0.03 0.03 0.15 0.03 0.15

0.02 0.02 0.10 0.02 0.02 0.10

Fans& HVAC Consumption[kWh] 0.01 Exterior Lights consumptions [kWh] consumptions Lights Exterior

0.01 LightsConsumptions[kWh]Interior

Fans& HVAC Consumption[kWh] 0.05 0.01 Exterior Lights consumptions [kWh] consumptions Lights Exterior

0.01 LightsConsumptions[kWh]Interior 0.05

0.00 0.00 2000 4000 6000 8000 0 2000 4000 6000 8000 0 2000 4000 6000 8000 2000 4000 6000 8000 0 2000 4000 6000 8000 0 2000 4000 6000 8000 Hour Hour Hour Hour Hour Hour

(a) (a) (b) (b) (c) (c) ]

] 0.55 0.55 0.10 0.10 0.50 0.09 0.50 0.09 0.45 0.08 0.45 0.08 0.40 0.07 0.40 0.07 0.35 0.06 0.35 0.06 0.30 0.05 0.30 0.05 0.25 0.04 0.25 0.04Interior Equipment Consumptions [kWh] Interior Equipment Consumptions [kWh]

Interior Equipment(Misc)Interior Consumptions [kWh 0.20 0.03 Equipment(Misc)Interior Consumptions [kWh 0.20 0.03 0 2000 4000 6000 8000 2000 4000 6000 8000 0 2000 4000 6000 8000 2000 4000 6000 8000 Hour Hour Hour Hour

(d) (d) (e) (e) Figure 7. Electric load profiles based on the type of the consumer (a) fans and HVDC consumptions; (b) interior lights FigureFigure 7. Electric 7. Electric load loadprofiles profiles based based on the on type the of type the ofconsumer the consumer (a) fans ( aand) fans HVDC and consumptions; HVDC consumptions; (b) interior (b) lights interior consumptions;consumptions; (c) exterior(c) exterior lights lights consumptions; consumptions; (d) (interiord) interior equipment equipment consumptions; consumptions; (e) interior(e) interior equipment equipment (misc) (misc) con- con- lights consumptions; (c) exterior lights consumptions; (d) interior equipment consumptions; (e) interior equipment sumptions.sumptions. (misc) consumptions.

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After collecting five load categories, it is time to calculate the total hourly load values After collecting five load categories, it is time to calculate the total hourly load values to optimizeAfter collecting and implement five load the categories, proposed it DRis time program. to calculate As mentioned the total hourly in (13), load TOU values prices to optimize and implement the proposed DR program. As mentioned in (13), TOU prices andto optimize total hourly and implement load values the are proposed used for DR calculating program. As RTP. mentioned Figure in8 shows(13), TOU RTP prices values and total hourly load values are used for calculating RTP. Figure8 shows RTP values based basedand total on thehourly load load consumptions. values are used In fact, for calculatingreal-time electricity RTP. Figure values 8 shows are inRTP response values to on the load consumptions. In fact, real-time electricity values are in response to electricity electricitybased on thedemand. load consumptions. It can be seen Inthat fact, in real-timethe hours electricity with higher values load are consumption, in response toelec- demand. It can be seen that in the hours with higher load consumption, electricity prices electricity demand. It can be seen that in the hours with higher load consumption, elec- aretricity greater prices than are those greater hours than with those lower hours load with consumption. lower load So,consumption. instead of havingSo, instead a fixed of tricity prices are greater than those hours with lower load consumption. So, instead of patternhaving a for fixed electricity pattern usage, for electricity which mayusage, be which ineffective may becausebe ineffective of variability because inof variability consumer having a fixed pattern for electricity usage, which may be ineffective because of variability powerin consumer usage, power an efficient usage, RTP an efficient is considered RTP is in considered response toin loadresponse consumption. to load consumption. in consumer power usage, an efficient RTP is considered in response to load consumption. 0.1 0.1 0.09 0.09 0.08 0.08 0.07 0.07 0.06 0.06 0.05 0.05

0.040.04

0.030.03

0.020.02

0.010.01

00 00 1000 2000 3000 3000 4000 4000 5000 5000 6000 6000 7000 7000 8000 8000 HourHour Figure 8. RTP based on the TOU prices and load consumption. Figure 8. RTP based on thethe TOUTOU pricesprices andand loadload consumption.consumption.

TheThe proposedproposed DR program program is isis implemente implementedimplemented dby by RTP RTPRTP and andand TOU TOUTOU pricing pricingpricing according accordingaccording to toto (14).(14).(14). FigureFigure9 9 shows shows load loadload demand demanddemand data data with with with and and and without without without implementing implementing implementing the the the proposed proposed proposed DR DR DR program.program.program. AsAs cancan bebe seen,seen, a a significantsignificantsignificant reduction reductionreduction in inin load loadload demand demanddemand consumption consumptionconsumption occurred occurredoccurred overover thethe year.year. TheThe aimaim of of the the DR DRDR program programprogram is isis to toto shave shaveshave the thethe load loadload profile profileprofile over overover the thethe year yearyear so soasso asas tototo achieveachieveachieve aa flatterflatter patternpattern for forfor load loadload demand. demand.demand. A AA flatter flatterflatter load loadload profile profileprofile not notnot only onlyonly has hashas economic economiceconomic meritsmerits forfor thethe consumerconsumer but but also also for forfor power powerpower systems. systems.systems. For ForFor example, example,example, improvement improvementimprovement in inthein thethe stabilitystability ofof powerpower systemssystems on onon a aa large largelarge scale. scale.scale. Table Table 11 1 showsshows shows gr grid gridid emission emission emission production production production in in in grams per kilogram [74]. These criteria will be used for evaluating the environmental im- grams per kilogram kilogram [74]. [74]. These These criteria criteria will will be be used used for for evaluating evaluating the theenvironmental environmental im- pacts of implementing the DR program in the MG structure. impactspacts of ofimplementing implementing the the DR DR program program in inthe the MG MG structure. structure.

0.9 0.50 0.9 0.50 0.48 0.8 0.48 0.8 0.46 0.46 0.7 0.44 0.7 0.44 0.42 0.6 0.42 0.6 0.40 0.5 0.40 0.38 0.5 0.4 0.360.38 Load Demand After DR Program [kWh] DR Program After Load Demand LoadDRDemand [kWh] Program Before 0.4 0.340.36 0.3 Load Demand After DR Program [kWh] DR Program After Load Demand LoadDRDemand [kWh] Program Before 0.320.34 0.3 2000 4000 6000 8000 2000 4000 6000 8000 0.32 Hour 2000 4000 6000 8000 2000H 4000our 6000 8000 (a)Hou r (b) Hour (a) Figure 9. Load demand (a) before implementing the RTP-based DR program;(b )( b) after implement- ing the RTP-based DR program. Figure 9. Load demand ((aa)) beforebefore implementingimplementing the the RTP-based RTP-based DR DR program; program; (b ()b after) after implementing implement- ing the RTP-based DR program. the RTP-based DR program.

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Table 1. Grid greenhouse gases based on Iran’s national grid factors [74].

Emission Type Value Unit Carbon Dioxide 6.32 g/kWh Sulfur Dioxide 2.74 g/kWh Nitrogen Oxides 1.34 g/kWh

3.2. Long-Term Planning Results In order to conduct better evaluations of the proposed DR method, two scenarios are defined based on the type of the DR program, as shown below. • Scenario 1: Optimal planning of the MG using TOU pricing mechanism. • Scenario 2: Optimal planning of the MG using RTP mechanism.

3.2.1. Scenario 1 This scenario is designated for considering standard TOU pricing in the planning of the MG. Therefore, the proposed MG is optimized using the RESs and other specifications of the system. Table2 shows techno-economic results of the proposed MG, including component sizing and NPC. As can be seen, solar PV with a capacity of 11 kW in addition to 6 kW of converter were found to be economical for supplying the MG’s load demand. In contrast, the utilization of the WT system and BSS were not found to be economical. For the WT system, the higher initial cost and potential of the environment are two important reasons. BSS is a backup resource, and it is equipped when there is a lack of power in the system. Since the utility grid is assumed to be reliable, typically, there would not be any need for backup resources in the system structure. The NPC (−19,687 USD) and COE (−0.0635 USD/kWh) values show the system’s high profits, which is particularly due to the electricity sell-back to the utility grid. Other configurations show that the increase in system equipment, including the WT system and BSS, raises the NPC and COE values. However, utilizing both WT and PV systems such as configuration 3, the RF of the system improves from 87.2% to 89.5%.

Table 2. Techno-economic planning results for Scenario 1.

Technical Results Economic Results SPV WT BSS Conv. RF COE NPC Initial (kW) (kW) (kW) (kW) (%) (USD/kWh) (USD) Cost (USD) 11.00 - - 6.00 87.2 −0.0635 −19,687 4675 11.00 - 1.00 6.00 87.2 −0.0595 −19,272 4799 11.00 1.00 - 6.00 89.5 −0.0619 −19,202 5400 11.00 1.00 1.00 6.00 85.5 −0.0580 −18,788 5524

Figure 10 shows the electricity purchased/sold to the utility grid over the months. It is clear that a significant amount of produced electrical energy by the PV unit is sold to the utility grid. In addition, the purchasing power from the utility grid is lower than the electricity sold every month. In cold months, we observe that the need for electricity is increased; therefore, the amount of sell-back electricity decreases. Variability in PV output power can be observed in Figure 11. It indicates that the output power of the PV unit is highly dependent on weather conditions. Table3 also indicates the number of produced emissions through the grid connection. As mentioned before, the utilization of grid connections causes the production of greenhouse gas emissions. In this scenario, 1397 kg/year of carbon dioxide is produced, which is significant compared to sulfur dioxide (6.08 kg/year) and nitrogen oxides (2.96 kg/year). Energies 2021, 14, 4597 15 of 28 Energies 2021, 14, x FOR PEER REVIEW 15 of 28 Energies 2021, 14, x FOR PEER REVIEW 15 of 28

1200 Electricity Purchased 1200 Electricity Electricity Sold Purchased Electricity Sold 1000 1000

800 800

600 600 Power [kW]

Power [kW] 400 400

200 200

0 0 July May June April July May March June August April October January March August February December October November January September

February December November September Figure 10. Average monthly purchased/sold power from/to utility grid. FigureFigure 10. 10.Average Average monthly monthly purchased/sold purchased/sold power power from/to from/to utilityutility grid.grid.

) 12

) 12 kW ( kW 10( 10

8 8 utput Power O utput Power 6 O 6

4 4

2 2

verage Monthly PV 0 A verage Monthly PV 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov

A Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Month Figure 11. Average monthly PV system output power. FigureFigure 11. 11.Average Average monthly monthly PV PV system system output output power. power. Table 3. Grid emission productions over the optimization year. TableTable 3. 3.Grid Grid emission emission productions productions over over the the optimization optimization year. year. Emissions Value EmissionsEmissions Value Value Carbon Dioxide 1397 kg/yr Carbon Dioxide 1397 kg/yr Carbon DioxideSulfur Dioxide 1397 kg/year 6.08 kg/yr Sulfur DioxideSulfur Dioxide 6.08 kg/year 6.08 kg/yr Nitrogen Oxides 2.96 kg/yr Nitrogen OxidesNitrogen Oxides 2.96 kg/year 2.96 kg/yr 3.2.2. Scenario 2 3.2.2.3.2.2. Scenario Scenario 2 2 In this scenario, simulations are performed using the proposed DR program. The op- InIn this this scenario, scenario, simulationssimulations are performe performedd using using the the proposed proposed DR DR program. program. The The op- timal sizing results of this scenario are indicated in Table 4. As can be observed, the opti- optimaltimal sizing sizing results results of of this this scenario scenario are are indica indicatedted in inTable Table 4. 4As. As can can be beobserved, observed, the theopti- mal capacity for a solar PV system is reduced from 11 kW in Scenario 1 to 9 kW in Scenario optimalmal capacity capacity for a for solar a solar PV system PV system is reduced is reduced from 11 from kW in 11 Scenario kW in Scenario 1 to 9 kW 1 in to Scenario 9 kW 2. In addition to this, the NPC and COE are obtained as −23,461 USD and −0.0660 in2. Scenario In addition 2. In to addition this, the to this,NPC theand NPC COE and are COE obtained are obtained as −23,461 as − USD23,461 and USD −0.0660 and USD/kWh. In comparison to the previous scenario, improvements in both NPC and COE −USD/kWh.0.0660 USD/kWh. In comparison In comparison to the previous to the previous scenario, scenario, improvements improvements in both NPC in both and NPC COE can be observed. The main reason is the modifications of the consumer’s load profile. As andcan COE be observed. can be observed. The main The reason main is reasonthe modifi is thecations modifications of the consumer of the consumer’s’s load profile. load As indicated, the proposed DR program modified the consumer’s peak load consumption, profile.indicated, As the indicated, proposed the DR proposed program DR modified program the modified consumer’s the peak consumer’s load consumption, peak load and the total demand for electricity was reduced. Therefore, the consumer can sell more consumption,and the total anddemand the total for electricity demand for was electricity reduced. was Therefore, reduced. the Therefore, consumer the can consumer sell more electricity to the grid and obtain economic profits. canelectricity sell more to electricitythe grid and to theobtain grid economic and obtain profits. economic profits.

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TableTable 4. 4.Techno-economic Techno-economic planning planning results results for for Scenario Scenario 2. 2.

TechnicalTechnical Results Results Economic Economic Results Results SPVSPV WTWT BSSBSS Conv.Conv. RFRF COECOE NPCNPC InitialInitial Cost Cost (kW)(kW) (kW)(kW) (kW)(kW) (kW)(kW) (%)(%) (USD/kWh)(USD/kWh) (USD)(USD) (US$)(US$) 9.009.00 -- - - 6.006.00 83.383.3 −0.0660−0.0660 −23,461−23,461 39753975 9.009.00 1.001.00 - - 6.00 6.00 90.090.0 −0.0647−0.0647 22,97722,977 4700 4700 9.009.00 1.001.00 1.001.00 6.006.00 87.387.3 −0.0618−0.0618 22,66322,663 4099 4099 9.00 1.00 1.00 6.00 90.0 −0.0605 22,178 4824 9.00 1.00 1.00 6.00 90.0 −0.0605 22,178 4824

FigureFigure 12 12 shows shows the the purchased/sold purchased/sold electricity electricity from/to from/to the the utility utility grid. grid. A A result result that that claimsclaims attention attention is is the the lower lower level level of of monthly monthly load load consumption consumption in in this this scenario, scenario, which which showsshows the the significance significance of of the the DR DR program program on on the the consumption consumption rate. rate. In In addition addition to to this, this, electricityelectricity sell-back sell-back is is increased increased as as a result a result of aof decrease a decrease in total in total load load consumption. consumption. However, How- theever, total the generated total generated electricity electricity by the by PV the system PV system is a bit is lower a bit lower than the than previous the previous scenario sce- becausenario because of the lower of the designatedlower designated capacity capacity (Figure (Figure13). Table 13).5 alsoTable shows 5 also the shows amount the amount of the emittedof the emitted greenhouse greenhouse gases in gases Scenario in Scenario 2. Comparing 2. Comparing the values the with values previous with previous scenario, sce- it cannario, be seenit can that be seen the proposedthat the proposed DR program DR program had a positive had a positive impact onimpact the environmental on the environ- issues of MGs. The value of produced CO , as well as other emissions, is reduced over mental issues of MGs. The value of produced2 CO2, as well as other emissions, is reduced aover year. a year. Therefore, Therefore, it can it becan concluded be concluded that th implementationat implementation of of an an efficient efficient DR DR program program suchsuch as as the the proposed proposed one one can can significantly significantly improve improve the the system’s system’s technical, technical, economic, economic, and and environmentalenvironmental performances. performances.

1200 Electricity Purchased Electricity Sold 1000

800

600 Power [kW] 400

200

0 July May June April March August October January February December November September

FigureFigure 12. 12.Average Average monthly monthly purchased/sold purchased/sold power power from/to from/to utilityutility grid.grid.

Table10 5. Grid emission productions over the optimization year. 9 Emissions Value 8

7 Carbon Dioxide 1212 kg/year 6 Sulfur Dioxide 5.26 kg/year Nitrogen Oxides 2.57 kg/year 5 4 3 2

1 0 Average Monthly PV Output Power (kW) Power Output PV Monthly Average Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 13. Average monthly PV system output power.

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Table 4. Techno-economic planning results for Scenario 2.

Technical Results Economic Results SPV WT BSS Conv. RF COE NPC Initial Cost (kW) (kW) (kW) (kW) (%) (USD/kWh) (USD) (US$) 9.00 - - 6.00 83.3 −0.0660 −23,461 3975 9.00 1.00 - 6.00 90.0 −0.0647 22,977 4700 9.00 1.00 1.00 6.00 87.3 −0.0618 22,663 4099 9.00 1.00 1.00 6.00 90.0 −0.0605 22,178 4824

Figure 12 shows the purchased/sold electricity from/to the utility grid. A result that claims attention is the lower level of monthly load consumption in this scenario, which shows the significance of the DR program on the consumption rate. In addition to this, electricity sell-back is increased as a result of a decrease in total load consumption. How- ever, the total generated electricity by the PV system is a bit lower than the previous sce- nario because of the lower designated capacity (Figure 13). Table 5 also shows the amount of the emitted greenhouse gases in Scenario 2. Comparing the values with previous sce- nario, it can be seen that the proposed DR program had a positive impact on the environ- mental issues of MGs. The value of produced CO2, as well as other emissions, is reduced over a year. Therefore, it can be concluded that implementation of an efficient DR program such as the proposed one can significantly improve the system’s technical, economic, and environmental performances.

1200 Electricity Purchased Electricity Sold 1000

800

600 Power [kW] 400

200

0 July May June April March August October Energies 2021, 14, 4597 January 17 of 28 February December November September Figure 12. Average monthly purchased/sold power from/to utility grid.

10 9 8

7 6 5 4 3 2

1 0 Average Monthly PV Output Power (kW) Power Output PV Monthly Average Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month FigureFigure 13. 13.Average Average monthly monthly PV PV system system output output power. power. 3.3. Day-Ahead Operation Results This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn. To better understand the DR program of the operation of the MG, two scenarios are considered based on the utilization of the proposed DR program, as shown below. • Scenario 1: Optimal operation of the MG using TOU pricing mechanism. • Scenario 2: Optimal operation of the MG using RTP mechanism. Most of the previous papers do not consider the operation of the optimal system and focus mainly on the planning and techno-economic results. However, it is of great importance to analyze the daily operation of the MG to get more insights into the sys- tem’s behavior. Therefore, in the following, we will analyze the results and draw some important conclusions.

3.3.1. Scenario 1 In this scenario, the day-ahead operation results of the MG are presented using TOU pricing. To have a more accurate understanding, a sample day from each season is selected for further analysis. Hence, four days are considered as representative of the four seasons selected in this study. The reason for selecting four days is the variability of load consumption, renewables output power, and grid sales/purchases in each season. In this way, a better understating of the MG over the optimization period could be achieved. The daily standard deviation (STD) values for the load demand corresponding to four seasons are indicated in Table6. Figure 14a–d shows the power balance of the MG, including the generated PV system, electricity consumption, grid sales, and purchases during four days. Figure 14a shows the operating results for a winter day. It is clear that PV output power could not fully supply the load demand at most hours; hence, the MG is forced to buy electricity from the grid, especially in peak hours. Peak generation has occurred at 10:00, and peak load consumption has occurred at 20:00. At three distinct hours, we can see that the MG is injecting extra generated electricity back into the grid. Figure14b indicates the operation of the MG over a spring day. PV production is relatively significant at this time of the year, and the PV system is able to provide load demand for twelve hours of the day. In addition to this, a significant amount of surplus generation is sold to the grid, which obtains economic profits for the MG. Similar to the winter day, there are two peaks on this day. However, the overall consumption on the winter day is more remarkable than on the spring day. Figure 14c also shows the optimal operation of the MG on the summer day. Power generation by the PV system is considerable even from a spring day. PV generation started at 6:00 and lasted for 14 h. Furthermore, the PV is the primary source of power supply in the MG system. On the summer day, most PV generation occurred between 12:00 Energies 2021, 14, x FOR PEER REVIEW 18 of 28

Energies 2021, 14, 4597 18 of 28

Table 6. Standard deviation of load demand for different seasons before DR program implementa- andtion. 4:00, while this has taken place from 8:00 to 13:00 on the spring day. Finally, Figure 14d indicates the power balance results of theStandard optimal Deviation system on Values the fall day. The operation is moreHour similar to the winter day. However, the main difference is in the PV production, Winter Spring Summer Fall which more considerable than the winter day. 1 0.1445 0.1298 0.10951 0.17311 Table 6. Standard2 deviation of0.14837 load demand for different0.13355 seasons before DR0.11306 program implementation.0.17687 3 0.15048 0.13441 0.11422 0.18068 Standard Deviation Values Hour4 0.15054 0.13399 0.11399 0.18336 5 Winter0.14973 Spring0.13345 Summer0.11345 Fall0.18475 16 0.14450.14938 0.12980.1331 0.109510.11343 0.173110.18446 27 0.148370.14986 0.133550.13316 0.113060.11386 0.176870.18396 38 0.150480.15054 0.134410.1336 0.114220.11435 0.180680.18431 49 0.150540.15129 0.133990.13408 0.113990.11489 0.183360.18511 5 0.14973 0.13345 0.11345 0.18475 610 0.149380.15175 0.13310.1343 0.113430.11489 0.184460.18608 711 0.149860.15185 0.133160.13394 0.113860.11458 0.183960.18665 812 0.150540.15148 0.13360.13355 0.114350.11423 0.184310.18721 913 0.151290.15106 0.134080.13309 0.114890.11389 0.185110.1874 10 0.15175 0.1343 0.11489 0.18608 1114 0.151850.15063 0.133940.13298 0.114580.11389 0.186650.1869 1215 0.151480.15066 0.133550.13301 0.114230.114 0.187210.18632 1316 0.151060.15088 0.133090.13314 0.113890.1142 0.18740.18588 1417 0.150630.15133 0.132980.13341 0.113890.1146 0.18690.18618 15 0.15066 0.13301 0.114 0.18632 18 0.15142 0.13386 0.11472 0.18681 16 0.15088 0.13314 0.1142 0.18588 1719 0.151330.15021 0.133410.13394 0.11460.11431 0.186180.18576 1820 0.151420.147 0.133860.13335 0.114720.11319 0.186810.18253 1921 0.150210.14272 0.133940.13144 0.114310.11053 0.185760.17854 20 0.147 0.13335 0.11319 0.18253 22 0.13954 0.12775 0.10676 0.17501 21 0.14272 0.13144 0.11053 0.17854 2223 0.139540.13902 0.127750.12455 0.106760.10566 0.175010.17236 2324 0.139020.14083 0.124550.12515 0.105660.1067 0.172360.17142 24 0.14083 0.12515 0.1067 0.17142

1.0 PV Output Power [kWh] 0.9 Load Consumption [kWh] Grid Purchased Power [kWh] 0.8 Grid Sell-back Power [kWh] 0.7

0.6 [kWh] 0.5

ower 0.4 P 0.3

0.2

0.1

0.0 5101520 Hour (a)

Figure 14. Cont.

Energies 2021, 14, 4597 19 of 28 Energies 2021, 14, x FOR PEER REVIEW 19 of 28

6 PV Output Power [kWh] Load Consumption [kWh] 5 Grid Purchased Power [kWh] Grid Sell-back Power [kWh]

4

3 Power Power [kWh] 2

1

0 5 101520 Hour (b) 7 PV Output Power [kWh] Load Consumption [kWh] 6 Grid Purchased Power [kWh] Grid Sell-back Power [kWh] 5

4

3 Power [kWh]

2

1

0 5 101520 Hour (c)

PV Output Power [kWh] Load Consumption [kWh] 3 Grid Purchased Power [kWh] Grid Sell-back Power [kWh]

2 Power [kWh] Power

1

0 5101520 Hour (d)

Figure 14. TOU-based operation resultsresults ofof thethe MG MG for for ( a()a) winter winter day; day; (b ()b spring) spring day; day; (c )(c summer) summer day; day; (d) fall day. (d) fall day.

Energies 2021, 14, 4597 20 of 28

3.3.2. Scenario 2 In this scenario, the day-ahead operation results of the system are represented using the RTP mechanism. The operation results for the same days as Scenario 1 are indicated in Figure 15a–d. The daily standard deviation (STD) values for the load demand correspond- ing to four seasons are indicated in Table7. An important result that claims attention is the impact of the suggested DR program on the load consumption profile. Applying the DR program could successfully shave the two peaks, as shown in Figure 15a. This has resulted in a decrease in the purchasing power from the grid during peak hours and, ultimately, a reduction in the NPC of the system. Considering the proposed DR program, the MG is more responsive to the electricity prices. Similar to BSS, the DR program is a flexible resource for the MGs. While the optimum system was not equipped with BSS, the DR program played a similar role as BSS for the MG. Typically, BSS are charged during off-peak times and discharged in peak times when the electricity price is higher than average. The proposed DR program also plays relatively the same role as BSS in the MG operation. The same conclusions are also valid for the other operation days, as illustrated in Figure 15b–d.

Table 7. Standard deviation of load demand for different seasons after DR program implementation.

Standard Deviation Values Hour Winter Spring Summer Fall 1 0.034416 0.047639 0.055011 0.05175 2 0.034409 0.048225 0.055883 0.05198 3 0.03439 0.048599 0.05661 0.052198 4 0.034516 0.048866 0.057055 0.052301 5 0.034909 0.048697 0.056891 0.05189 6 0.034949 0.048192 0.056684 0.051631 7 0.034916 0.048154 0.056872 0.051924 8 0.035133 0.048423 0.057149 0.052184 9 0.035191 0.048554 0.057514 0.052633 10 0.03551 0.048833 0.057764 0.053087 11 0.036092 0.048742 0.057743 0.053169 12 0.036252 0.048524 0.057517 0.052986 13 0.036187 0.048451 0.057321 0.052985 14 0.03636 0.048177 0.057111 0.052849 15 0.036365 0.048123 0.057178 0.052868 16 0.03641 0.048225 0.057346 0.053017 17 0.03654 0.048426 0.057593 0.053172 18 0.036347 0.048709 0.057584 0.052925 19 0.036119 0.04861 0.057272 0.052391 20 0.036209 0.048216 0.056662 0.051917 21 0.036243 0.047592 0.055711 0.051463 22 0.036214 0.047027 0.054623 0.050832 23 0.036315 0.046783 0.054019 0.050678 24 0.036226 0.046692 0.054231 0.051171

3.4. Sensitivity Analysis In this subsection, two sensitivity analyses on the inflation rate and discount rate are performed in order to evaluate the optimal configuration and economic results of the system under pessimistic economic conditions. Similar to previous subsections, two cases as sensitivity analyses are suggested based on the proposed RTP-based DR program. Energies 2021, 14, x FOR PEER REVIEW 21 of 28

Energies 2021, 14, 4597 21 of 28

(a) 5 PV Output Power [kWh] Load Consumption [kWh] Grid Purchased Power [kWh] 4 Grid Sell-back Power [kWh]

3

2 Power [kWh] Power

1

0 5 10 15 20 Hour (b)

6 PV Output Power [kWh] Load Consumption [kWh] Grid Purchased Power [kWh] 5 Grid Sell-back Power [kWh]

4

3 Power [kWh] Power 2

1

0 5 10 15 20 Hour (c)

Figure 15. Cont.

Energies 2021, 14, 4597 22 of 28

3 PV Output Power [kWh] Load Consumption [kWh] Grid Purchased Power [kWh] Energies 2021, 14, 4597 22 of 28 Energies 2021, 14, x FOR PEER REVIEW Grid Sell-back Power [kWh]22 of 28

2

3 PV Output Power [kWh] Load Consumption [kWh] Power Power [kWh] 1 Grid Purchased Power [kWh] Grid Sell-back Power [kWh]

2

0 5101520 Power [kWh] Power 1 Hour (d) Figure 15. RTP-based operation results of the MG for (a) winter day; (b) spring day; (c) summer day; (d) fall day.

3.4. Sensitivity0 Analysis 5 10 15 20 In this subsection, two sensitivity analysesHour on the inflation rate and discount rate are performed in order to evaluate the optimal (configurationd) and economic results of the sys- temFigure under 15. RTPpessimistic-based operation economic results conditions. of the MG Similar for (a to) winter previous day; subsections,(b) spring day; two (c )cases summer as Figure 15. RTP-based operation results of the MG for (a) winter day; (b) spring day; (c) summer day; sensitivityday; (d) fall analyses day. are suggested based on the proposed RTP-based DR program. (d) fall day.

3.4.1.3.4.3.4.1. Sensitivity Sensitivity Sensitivity Analysis Analysis Analysis 1 1 InInIn this this this part, subsection, part, TOU-based TOU-based two sensitivityprices prices are are utilized analyses utilized to toon perform performthe inflation sensitivity sensitivity rate analysis.and analysis. discount To To this rate this end, end,are theperformedthe implemented implemented in order inflation inflation to evaluate and and discountthe discount optimal rates rates configuration are are increased increased and from from economic 0% 0% to to 100%. results 100%. Simulation Simulationof the sys- results,temresults, under including including pessimistic optimal optimal economic configuration configuration conditions. type type Similar and and the theto valuesprevious values for for subsections, the the NPC NPC and and two COE, COE,cases are areas indicatedsensitivityindicated as analyses as shown shown inare in Figure s Figureuggested 16. 16 It. based Itcan can be on be s een seenthe proposedfrom from Figure Figure RTP 16a 16-based athat that DRthe the program.optimal optimal system system configurationconfiguration under under different different values values of inflation of inflation and and discount discount rates rates is not is affected not affected by uti- by lizing3.4.1.utilizing Sensitivitythe WT the units. WT Analysis units. However, However,1 under under higher higher inflation inflation rates and rates moderate and moderate discount discount rates, werates, canIn weobservethis can part, observe the TOU integration-based the integration prices of the are WT utilized of the system WT to perform systemto the MG to sensitivity the structure MG structure analysis. (Figure (FigureTo 16a). this Fur- end, 16a). thermore,theFurthermore, implemented the increase the inflation increase of the and ofinflationthe discount inflation rate rates reduces rate are reduces theincreased NPC the and from NPC COE 0% and of to the COE 100%. system of Simulat the (Figure systemion 16b).results,(Figure This including 16 isb). primarily This optimal is primarily due configurationto the due considerable to the type considerable andimpact the ofvalues impact the inflation for of the the NPC inflationrate on and therate COE, annual on are the costindicatedannual of the cost assystem, ofshown the as system, in shown Figure as in shown16. Equation It can in Equationbe (1). seen In some from (1). Inconditions, Figure some conditions,16a higherthat the renewable higher optimal renewable system pene- trationconfigurationpenetration and grid and under revenues grid different revenues cannot values cannotbe ignored. of inflation be ignored. However, and discount However, the discount rates the is discountrate not hasaffected an rate adverse by has uti- an impactlizingadverse theon impact WTthe systemunits. on theHowever, costs, system as illustratedunder costs, higher as illustratedin Figureinflation 16c. in rates Figure It can and 16be moderatec. seen It can that bediscount the seen discount that rates, the ratewediscount canincreases observe rate the increases the NPC integration and the NPCCOE of andof the the COE WT system. of system the The system. to discount the The MG discount ratestructure also rate is(Figure correlated also is 16a). correlated with Fur- thethermore,with annual theannual thecost increase of costthe system. of of the thesystem. inflation rate reduces the NPC and COE of the system (Figure 16b). This is primarily due to the considerable impact of the inflation rate on the annual cost of the system, as shown in Equation (1). In some conditions, higher renewable pene- tration and grid revenues cannot be ignored. However, the discount rate has an adverse impact on the system costs, as illustrated in Figure 16c. It can be seen that the discount rate increases the NPC and COE of the system. The discount rate also is correlated with the annual cost of the system.

(a)

Figure 16. Cont.

(a)

Optimal System Type 32.36 PV/WT/Grid

PV/Grid 30.742

29.124

27.506

25.888

24.27

22.652

21.03

19.416 Inflation Rate [%] InflationRate 17.798

16.18 Energies 2021, 14, 4597 18 19.8 21.6 23.4 25.2 27 28 30.6 32.4 34.2 2336 of 28 Discount Rate [%] (a)

0 -0.055 NPC

COE

-50,000 -0.06

-100,000 -0.065

-150,000

-0.07 NPC [US$] NPC -200,000

-0.075 COE[US$/kW] -250,000

-300,000 -0.08 16.18 17.798 19.416 21.03 22.652 24.27 25.888 27.506 29.124 30.742 32.36 Inflation Rate [%] (b)

0 0 NPC

COE -0.01 -50,000 -0.02

-100,000 -0.03

-0.04 -150,000

NPC [US$] NPC -0.05 -200,000 -0.06

-250,000 -0.07 18 19.8 21.6 23.4 25.2 27 28 30.6 32.4 34.2 36 Discount Rate [%] (c) Figure 16. The results of sensitivity analysis regarding (a) optimal system type; (b) effect of different valuesFigure of 16.inflationThe results rate on of NPC sensitivity and COE analysis of the regarding MG; (c) effect (a) optimal of different system values type; of (b )discount effect of rate different on NPCvalues and ofCOE inflation of the rateMG. on NPC and COE of the MG; (c) effect of different values of discount rate on NPC and COE of the MG. 3.4.2. Sensitivity Analysis 2 3.4.2. Sensitivity Analysis 2 In this part, simulation results using the proposed RTP mechanism are presented. In this part, simulation results using the proposed RTP mechanism are presented. Sensitivity analyses results are indicated in Figure 17. The results show that the increase Sensitivity analyses results are indicated in Figure 17. The results show that the increase ofof the the inflation inflation rate rate (in (inlower lower discount discount rates) rates) will willlead lead to the to integration the integration of WT of units WT unitsto the to MGthe architecture, MG architecture, which which could coulddecrease decrease NPC and NPC COE and of COE the of system. the system. This is This mainly is mainly be- causebecause of renewable of renewable penetration penetration increase increase and and financial financial incomes incomes from from the the sell sell-back-back energy energy toto the the grid. grid. However, However, the the major major impact impact of of the the inflation inflation rate rate (as (as well well as the discount rate)rate) is is onon thethe annualannual costcost ofof the the system system (Figure (Figure 17 17b).b). Similar Similar to to previous previous sensitivity sensitivity analysis, analysis, the Energies 2021, 14, x FOR PEER REVIEWthediscount discount rate rate has has a negative a negative impact impact on theon systemthe system costs, costs, as demonstrated as demonstrated in Figure in Figure 172c. of As 2

17c.illustrated, As illustrated, the increase the increase of discount of discount rates rates will raisewill raise the NPC the NPC and COEand COE of the of system.the system.

Optimal System Type PV/WT/Grid 32.36 PV/Grid 30.742

29.124

27.506

25.888

24.27

Energies 2021, 14, x. https://doi.org/10.3390/xxxxx22.652 www.mdpi.com/journal/energies

21.03

19.416 Inflation Rate [%] InflationRate 17.798

16.18 18 19.8 21.6 23.4 25.2 27 28 30.6 32.4 34.2 36 Discount Rate [%] (a)

Figure0 17. Cont. 0 NPC COE -0.01 -50,000 -0.02 -100,000 -0.03

-150,000 -0.04

NPC [US$] NPC -0.05 -200,000

-0.06 COE[US$/kW] -250,000 -0.07

-300,000 -0.08 16.18 17.798 19.416 21.03 22.652 24.27 25.888 27.506 29.124 30.742 32.36

(b) 0 0 NPC

COE -0.01 -50,000 -0.02

-10,000 -0.03

-0.04 -150,000

NPC [US$] NPC -0.05 -200,000 -0.06

-250,000 -0.07 18 19.8 21.6 23.4 25.2 27 28 30.6 32.4 34.2 36 Discount Rate [%] (c) Figure 17. The results of sensitivity analysis regarding (a) optimal system type; (b) effect of differ- ent values of inflation rate on NPC and COE of the MG; (c) effect of different values of discount

Energies 2021, 14, x FOR PEER REVIEW 2 of 2

Optimal System Type PV/WT/Grid 32.36 PV/Grid 30.742

29.124

27.506

25.888

24.27

22.652

21.03

19.416 Inflation Rate [%] InflationRate 17.798

16.18 Energies 2021, 14, 4597 18 19.8 21.6 23.4 25.2 27 28 30.6 32.4 34.2 3624 of 28 Discount Rate [%] (a)

0 0 NPC COE -0.01 -50,000 -0.02 -100,000 -0.03

-150,000 -0.04

NPC [US$] NPC -0.05 -200,000

-0.06 COE[US$/kW] -250,000 -0.07

-300,000 -0.08 16.18 17.798 19.416 21.03 22.652 24.27 25.888 27.506 29.124 30.742 32.36

(b) 0 0 NPC

COE -0.01 -50,000 -0.02

-10,000 -0.03

-0.04 -150,000

NPC [US$] NPC -0.05 -200,000 -0.06

-250,000 -0.07 18 19.8 21.6 23.4 25.2 27 28 30.6 32.4 34.2 36 Discount Rate [%] (c)

FigureFigure 17. 17. TheThe results results of of sensitivity sensitivity analysis analysis regarding regarding ( (aa)) optimal optimal system system type; (b) effect of differentdiffer- entvalues values of inflationof inflation rate rate on on NPC NPC and and COE COE of theof the MG; MG; (c) ( effectc) effect of differentof different values values of discountof discount rate on NPC and COE of the MG.

4. Conclusions In this paper, a renewable-based MG is proposed for optimal planning and day-ahead operation. The MG is a residential household located in Tehran, Iran. The location has significant potentials for solar radiation and a moderate temperature. An RTP-based DR program is also implemented in HOMER software to improve the techno-economic perfor- mance. The HOMER Optimizer is also used for finding optimal sizing of the components. Two electricity pricing mechanisms, TOU pricing and RTP, are compared in two scenarios. Among the proposed system components (PV/WT/ESS/converter), the combi- nation of PV and converter found to be economical in both scenarios. The DR program significantly shaved the peak load values. Moreover, the MG planning results indicated that the proposed DR program reduces NPC and COE from −19,687 USD to −23,461 USD and −0.0635 USD to −0.0660 USD/kWh, respectively. The obtained results for NPC and COE indicate an improvement in the financial results of the study. While purchasing power from the utility grid decreased, the power sales to the grid increased. The capacity of the PV system was also reduced from 9 to 12 kW. In addition, carbon emissions are reduced from 1397 to 1212 kg/year by implementing the proposed DR program. Daily operation results indicated that the proposed DR program modifies load profiles for economic purposes. Since the battery ESS is considered a backup resource, it was not found to be economical in the optimum configuration. However, under an unreliable electricity grid, it could be essential for a constant power supply. The results of sensitivity analysis showed the negative impact of discount rate on the NPC and COE of the MG. In addition, higher inflation rates reduce the NPC and COE of the MG due to the renewable penetration and grid revenues. Uncertainties of RESs may affect the planning results; hence, for future work, stochastic planning of the proposed method will be conducted by the authors. Energies 2021, 14, 4597 25 of 28

Author Contributions: Investigation, writing, review, Z.-X.Y., M.-S.L. and Y.-P.X.; edit, S.A. and Y.-K.L.; supervision, M.-S.L. and Y.-P.X. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.

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