Issue 
Sci. Tech. Energ. Transition
Volume 79, 2024
Decarbonizing Energy Systems: Smart Grid and Renewable Technologies



Article Number  76  
Number of page(s)  10  
DOI  https://doi.org/10.2516/stet/2024079  
Published online  02 October 2024 
Regular Article
Energy management of the residential smart microgrid with optimal planning of the energy resources and demand side
^{1}
Prince Sattam Bin Abdulaziz University, College of Engineering, Department of Electrical Engineering, Alkharj, 11942, Saudi Arabia
^{2}
Laboratory LaTICE, Ecole Nationale, Supérieure D’ingénieurs de Tunis ENSIT, University of Tunis, Tunisia
^{3}
College of Engineering and Information Technology, University of Dubai, Academic City, 14143, Dubai, UAE
^{4}
Department of Chemistry, University College of Duba, University of Tabuk, Tabuk, Saudi Arabia
^{5}
Marwadi University Research Center, Department of Electrical Engineering, Faculty of Engineering & Technology, Marwadi University, Rajkot 360003, Gujarat, India
^{6}
NIMS School of Electrical and Electronics Engineering, NIMS University Rajasthan, Jaipur, Rajasthan, India
^{7}
Department of Nuclear and Renewable Energy, Ural Federal University Named after the First President of Russia Boris Yeltsin, Ekaterinburg 620002, Russia
^{8}
Head of the Department “Physics and Chemistry”, “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, Tashkent, Uzbekistan
^{9}
Scientific Researcher, University of Tashkent for Applied Sciences, Str Gavhar 1, Tashkent 100149, Uzbekistan
^{10}
Western Caspian University, Scientific Researcher, Baku, Azerbaijan
^{11}
Department of Medical Laboratories Technology, AlNisour University College, Nisour Seq. Karkh, Baghdad, Iraq
^{12}
College of Pharmacy, The Islamic University, Najaf, Iraq
^{13}
College of pharmacy, the Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq
^{14}
College of pharmacy, The Islamic University of Babylon, Babylon, Iraq
^{15}
The Department of energy, Madrid Institute for Advanced Studies in Energy, Madrid, Spain
^{*} Corresponding author: yersi.luis.ro@gmail.com
Received:
2
July
2024
Accepted:
2
September
2024
The study specifically aimed to model the optimal operation of these appliances based on their usage patterns, rather than relying on the capacity of demand flexibility in demand response (DR) and energy pricing. The modeling operation of the appliances is done using twolayer energy optimization. In this optimization, energy consumption by appliances is reshaped via DR and load shifting in firstlayer optimization. Then, minimizing the consumption costs and consumers’ discomfort in the second layer is formulated with consideration of the optimized consumption from the first layer. The lpmetric method is employed to solve the proposed optimization in the GAMS software. Finally, the efficiency of the twolayer optimization is confirmed using testing proposed case studies in the numerical simulation.
Key words: Optimal operation / Demand flexibility / Energy consumption / Load shifting / Consumers’ discomfort
© The Author(s), published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Nomenclature
t, T : Hour of operation, Hour
si : Solar irradiance, kW/m^{2}
S _{PV} : Total area of PV, m^{2}
P _{N,WT} : Total rated power of WT, kW
P _{WT}, P _{PV} : Power generated by WT and PV, kW
A, B, C : DGs’ cost factor, $/kW
ED, D _{NS}, ED ^{B} : Electrical demand, nonsupply demand, and electrical demand before DR, kW
η ^{ch}, η ^{dis} : ESS’s efficiency in charge and discharge states, %
${C}_{o}^{\mathrm{ess}}$ : ESS cost, $
C _{DG}, C _{MG} : DG cost and MG cost, $
c _{MG}, P _{MG} : Electrical price traffic of the MG and MG power, $/kW, kW
P _{GS} : Power generated by the generation side, kW
Ψ: Total demand with nonsupply demand, kW
Ψ_{DL} : Desired level of demand, kW
μ _{ESS} : Binary variable of the ESS
Γ: Amount of consumptionshifting strategy in DR, %
1 Introduction
1.1 Aims and context
A smart grid is a modernized electrical grid system that utilizes advanced technology to boost efficiency. Smart grids employ different digital communication and automation technologies to supervise and govern the electricity flow, enabling improved energy resource management [1]. One of the key components of a smart grid is the integration of smart homes, which are equipped with intelligent devices and appliances that can communicate with the grid [1, 2]. This integration enables homeowners to actively participate in energy management by optimizing their energy consumption and contributing to a more sustainable and resilient electrical grid system. Energy consumers in the residential, commercial, and industrial sectors now need electricity. Although the energy networks have worked separately, there is an increased focus on the integration of the electricity network, and other energies are being used to form energy systems [3]. An electrical grid consisting of wind power, natural gas power, solar power, thermal generation, and hydropower, can play various energy sources’ potential and advantages using complementarities of natural resources [3, 4]. In contrast to independent hydropower generation, wind power systems, and photovoltaic (PV) power systems, the electrical system has highreliability characteristics, flexibility, and stability that can be used to overcome the advantages of intermittency, randomness, seasonality, and volatility of renewable energy power systems [3, 4]. Energy management implementation in its entirety can improve economic and social outcomes by increasing electric energy efficiency.
The purpose of smart grids is to facilitate the widespread adoption of demand response (DR) in large energy areas, such as urban residential buildings [3, 4]. The improvement of demandside management programs is becoming more of a trend due to the advancement of smart grids [3, 4]. All actions taken to modify the load curve are included in the DR concept. Telecommunications infrastructure and sophisticated measurement sensors are needed for the DR implementation [4]. One could think of Advanced Metering Infrastructures (AMI) as a starting point of DR for improving the energy balance between generators and demand via smart energy management systems [4, 5]. Smart energy management systems need several tools for the optimal energy flow in energy infrastructure [4, 5]. One of the main challenges for smart energy management systems is the enhancement of the operating system based on costeffective indices via reliable communications [6, 7]. In such systems, multiagent tools develop hybrid optimization algorithms to perform optimal energy operation that meets a large range of constraints and objectives [8]. According to these explanations, employing the DR in smart energy management systems can provide costeffective indices by encouraging consumers [9, 10]. Informing consumers via telecommunications infrastructure for the management of energy consumption and energy price is a major agent in this system. On the other hand, costeffective indices are not only important objectives. Other indices, such as technical and social indices, are the main objectives of smart energy management systems [11, 12].
In Figure 1, a background of the proposed smart energy management systems in the residential buildings is demonstrated. In this system, smart energy management centers (SEMC), distribution energy generators (DEGs), main grids (MGs), and residential buildings are the main agents for energy interaction. The SEMC has a coordination role among other agents for optimal energy management using communication data [13]. The DEGs are photovoltaic (PV), electrical storage systems (ESS), diesel generators (DGs), and wind turbines (WT). The MG is an electrical distribution grid in an urban section with diverse traffic electricity prices in each hour of the day. The residential buildings are energy consumers in the SEMC, and they can participate in the DR program using controllable appliances such as dryers, washing machines, etc.
Fig. 1 Smart energy management system in this study. 
1.2 Related works
Much research on diverse energy operations in energy systems has been done in recent years. For example, in [14] the operation of smart hybrid energies considering local energy generation in buildings is proposed for the reduction of the consumers’ bills. Authors in [15] focused on managing emission and economic objectives in the electrical grids with optimal sizing of the resources. Energy storage devices are automatically utilized by the pricing algorithm in the reference [16], allowing them to save electricity when the cost is low and use it when the cost is high. In [17] energy saving with optimal operation and scheduling of the demand and joint modeling with energy price is presented. In addition to the encouragementbased response program, a hybrid modeling of the demand participation was proposed [18]. In their model, subscribers have received incentives to reduce their load during peak times. The energy operation in smart standalone buildings is reported in [19] with optimal participation of appliances and electric vehicles in energy consumption and generation. In [20], the mathematical modeling of load demand dispatch is proposed to improve reliability and reduce costs. This study has not established the ideal electricity price or incentives because the emphasis has been on boosting subscriber profits despite the cost of introducing loadtoproduction programs. In [21], the design of the energy resources in the electrical grids is studied to decrease the losses and increase the penetration of wind energy in residential buildings. Authors in [22] presented a novel scheme for designing energy system grids with microgrid formation and employing storage systems, renewable generation, fuel cells, and energy storage for the operation of the system under uncertainties. In [23], modeling hybrid energy distribution grids is proposed with consideration of the local energy markets and energy pricing. The energy resources sizing and siting are proposed in [24] for minimizing the emission, costs, and loess in electrical grids. The DR scheduling using reserve modeling and resources management is introduced in [25] for dropping cost and emission pollution. The new operation of the appliances is studied in [26] using continuous and discrete optimization modeling in the energy systems. Authors in [27] modeled economic objectives presented for appliance energy scheduling without consumers’ discomfort index.
1.3 Contributions
This article presents the optimal operation of energy consumption in smart buildings considering household appliances’ performance. The optimal performance of the appliances is done based on the DR approach via a consumptionshifting model. The operation approach of the energy consumption is modeled by a twolayer optimization problem. In the twolayer optimization problem, the consumption of household appliances is optimized via the consumption shifting model in the first layer. In the second layer optimization problem, two objectives, 1) energy consumption costs and 2) consumers’ discomfort, are minimized considering optimized consumption in the first layer. The solving operation problem is carried out by the lpmetric method in GAMS software. Hence, contributions and novelties can be synopsized as follows:

A twolayer operation problem is presented for optimal consumption of household appliances.

The DR modeling basedconsumption shifting model is considered in the first layer.

A twoobjective modeling for energy consumption costs and consumers’ discomfort in the second layer is formulated.

The lpmetric method is presented for solving problems by GAMS software.
2 System modeling
As shown in Figure 1, system modeling based on mathematical formulation is presented in the following subsection:
2.1 PV modeling
The solar irradiance basedPV modeling is formulated by (1) [28]:$${P}_{\mathrm{PV}}\left(\mathrm{si}\right)={\eta}_{\mathrm{PV}}\times {S}_{\mathrm{PV}}\times \mathrm{si}.$$(1)
2.2 WT modeling
The wind speed basedWT power generation is formulated using (2) [28, 29]:$${P}_{\mathrm{WT}}\left(v\right)=\{\begin{array}{cc}0& \mathrm{if}v\le {V}_{\mathrm{Ci}}\\ {P}_{\mathrm{N},\mathrm{wt}}\times \left\{\frac{v{V}_{\mathrm{Ci}}}{{V}_{R}{V}_{\mathrm{Ci}}}\right\}& \mathrm{if}{V}_{\mathrm{Ci}}\le v\le {V}_{R}\\ {P}_{\mathrm{N},\mathrm{wt}}& \mathrm{if}{V}_{R}\le v\le {V}_{\mathrm{Co}}\\ 0& \mathrm{if}{V}_{\mathrm{Co}}\le v\end{array}.$$(2)
2.3 DG modeling
The DG modeling is formulated considering fuel cost as follows:$${C}_{\mathrm{DG}}\left(t,d\right)=A{P}_{d}^{2}\left(t,d\right)+B{P}_{d}\left(t,d\right)+C.$$(3)
2.4 ESS modeling
The ESS modeling considering economic parameters and operation modes are formulated as follows [30]:$${C}_{\mathrm{ESS}}\left(t,\mathrm{ess}\right)=\left\{{C}_{o}^{\mathrm{ess}}\times {P}_{\mathrm{ESS}}\left(t,\mathrm{ess}\right)\right\},$$(4) $$\begin{array}{ccc}{P}_{\mathrm{ESS}}\left(t\right)/{\eta}_{\mathrm{dis}}\le {P}_{\mathrm{ESS}\mathrm{dis}}^{\mathrm{max}}\times {\mu}_{\mathrm{ESS}}& \mathrm{discharge}& {P}_{\mathrm{ESS}}\left(t\right)\ge 0,\\ {P}_{\mathrm{ESS}}\left(t\right)\times \left({\eta}_{\mathrm{ch}}\right)\le {P}_{\mathrm{ESS}ch}^{\mathrm{max}}\times (1{\mu}_{\mathrm{ESS}})& \mathrm{charge}& {P}_{\mathrm{ESS}}\left(t\right)\le 0.\end{array}$$(5)
By formulas (4) and (5), the cost of ESS and ESS modes in the discharge and charge status can be calculated, respectively.
2.5 MG modeling
The MG modeling is formulated considering price traffic as follows:$${C}_{\mathrm{MG}}\left(t\right)={\pi}_{\mathrm{MG}}\left(t\right)\times {P}_{\mathrm{MG}}\left(t\right).$$(6)
3 Twolayer optimization modeling
The modeling optimization is implemented as follows:
3.1 First layer modeling
The modeling first layer is implemented based on a consumptionshifting strategy of the DR approach. In this modeling, energy demand is optimized considering price traffic in the MG. The smart buildings can participate in the DR approach via controllable appliances operation in lowprice traffic. The modeling first layer is implemented as follows:$$\mathrm{min}{f}^{\mathrm{fl}}=\sum _{t=1}^{t}\{\mathrm{ED}\left(t\right)\times {c}_{\mathrm{MG}}\left(t\right)\}.$$(7)
Subject to:$$\sum _{t=1}^{t}\mathrm{ED}\left(t\right)=\sum _{t=1}^{t}{\mathrm{ED}}^{B}\left(t\right)$$(8) $$\mathrm{ED}\left(t\right)={\mathrm{ED}}^{B}\left(t\right)\times \mathrm{\Gamma}\left(t\right)$$(9) $$1\mathrm{\Gamma}\left(t\right)\le \mathrm{\Gamma}\left(t\right)\le 1+\mathrm{\Gamma}\left(t\right).$$(10)
Equation (8) indicates that the value of the total demand after DR is equal to before DR implementation. The rate of the consumption shift is modeled by equation (9). The participation value of the controllable appliances in DR is given by equation (10).
3.2 Second layer modeling
In this layer, twoobjective modeling includes energy costs and consumers’ discomfort are minimized simultaneously:
3.2.1 First objective modeling
The energy costs of the generation side, like MG, DGs, and ESS, are minimized as follows:$$\mathrm{min}{f}_{1}^{\mathrm{sl}}=\sum _{t=1}^{T}\left\{\sum _{d=1}^{\mathrm{D}}{C}_{\mathrm{DG}}\left(t,d\right)+\sum _{\mathrm{ess}=1}^{\mathrm{ESS}}{C}_{\mathrm{ESS}}\left(t,\mathrm{ess}\right)+{C}_{\mathrm{MG}}\left(t\right)\right\}.$$(11)
3.2.2 Second objective modeling
Minimizing consumers’ discomfort is considered the second objective in the second layer. This objective is proposed considering nonsupply demand rather than the optimal rate of consumption. The consumers’ discomfort modeling is as follows [31]:$${\mathrm{min}{f}^{\mathrm{sl}}}_{2}=\left[\frac{\sum _{t=1}^{T}\left\mathrm{\Psi}\left(t\right){\mathrm{\Psi}}^{\mathrm{DL}}\right}{\sum _{t=1}^{T}\mathrm{\Psi}\left(t\right)}\right].$$(12)
Where:$$\mathrm{\Psi}\left(t\right)=\mathrm{ED}\left(t\right)+{\mathrm{D}}_{\mathrm{NS}}\left(t\right)$$(13) $$\mathrm{E}{\mathrm{D}}_{\mathrm{NS}}\left(t\right)=\{\begin{array}{cc}\mathrm{E}{\mathrm{D}}_{\mathrm{NS}}\left(t\right)>0& \mathrm{ED}\left(t\right)>{P}_{\mathrm{GS}}\left(t\right)\\ \mathrm{E}{\mathrm{D}}_{\mathrm{NS}}\left(t\right)=0& \mathrm{ED}\left(t\right)<{P}_{\mathrm{GS}}\left(t\right)\end{array}$$(14) $${\mathrm{\Psi}}^{\mathrm{DL}}=\frac{\sum _{t=1}^{T}\mathrm{ED}\left(t\right)}{T}.$$(15)
The total demand with nonsupply demand in the system is modeled by (13). The modeling of nonsupply demand considering the generation side unable in supplydemand is presented by (14). The desired level of demand is given by (15).
3.2.3 Constraints of energy system
The proposed energy system has some constraints like energy balance and energy generation limit as follows:$${P}_{\mathrm{MG}}\left(t\right)+{P}_{\mathrm{PV}}\left(t\right)+{P}_{\mathrm{WT}}\left(t\right)+\sum _{d=1}^{\mathrm{D}}{P}_{d}\left(t,d\right)\pm \sum _{\mathrm{ess}=1}^{\mathrm{ESS}}{P}_{\mathrm{ESS}}\left(t,\mathrm{ess}\right)=\mathrm{E}{\mathrm{D}}_{\mathrm{E}}\left(t\right)\mathrm{E}{\mathrm{D}}_{\mathrm{NS}}\left(t\right)$$(16) $$0\le {P}_{d}(t,d)\le {P}_{d}^{\mathrm{max}}$$(17) $$0\le {P}_{\mathrm{MG}}\left(t\right)\le {P}_{\mathrm{MG}}^{\mathrm{max}}.$$(18)
The energy balance is modeled by constraints (16). Constraints (17) and (18) indicate the energy generation limit of the DGs and MG, respectively.
4 Solving method
In this section, the lpmetric is introduced as a solution approach for solving two objectives in secondlayer modeling. Hence, Pareto solutions for energy costs and customers’ discomfort are extracted by the lpmetric method. The lpmetric method for modeling is presented in equation (19) [32]:$$\mathrm{min}\mathrm{lp}=\left[\left\{w\times \frac{{f}_{1}^{\mathrm{sl}}{f}_{1}^{\mathrm{sl},\mathrm{ol}}}{{f}_{1}^{\mathrm{sl},\mathrm{ol}}}\right\}\left\{(1w)\times \frac{{f}_{2}^{\mathrm{sl}}{f}_{2}^{\mathrm{sl},\mathrm{ol}}}{{f}_{2}^{\mathrm{sl},\mathrm{ol}}}\right\}\right].$$(19)
Subject to:$$0\le w\le 1.$$(20)
Where f _{ 1 } ^{ ss,ol }, f _{ 2 } ^{ ss,ol,} and w are the optimal level of energy cost, the optimal level of the consumers’ discomfort, and the weight rate for objectives, respectively. By changing the weight rate, Pareto solutions are obtained. In equation (19), the minimum rate is selected as the best solution while considering the changing weight. The advantage of the lpmetric method is explained in Reference [32].
5 Case studies and simulation
In this section, numerical results of the smart energy management system are generated through the modeling of case studies and simulations using GAMS optimization software. The case studies specifically focus on the participation of DR in smart buildings, employing a consumptionshifting strategy.

Case I) Operation of the appliances without DR participation.

Case II) Operation of the appliances with DR participation.
The framework of smart energy management in Figure 2 is indicated. The parameters and data of the DEG are listed in Table 1. It should be mentioned that we used two DGs with the same data. The electrical price, wind speed, solar irradiance, and electrical demand are presented in Table 2. The participation value of the controllable appliances in DR is considered by 25%.
Fig. 2 Framework of smart energy management. 
DEGs data.
Value of electrical price, wind speed, solar irradiance, and electrical demand.
6 Results and discussion
This section presents the analysis of the discussion and results of Case Studies I and II. The utilization of the DR approach to optimize consumption, specifically through the implementation of a consumptionshifting strategy in the first layer, is illustrated in Figure 3. The figure demonstrates the use of controllable appliances at times when the price of electricity in the MG is low. Consequently, the obtained results of the case studies are discussed about the participation of DR in Case I and Case II.
Fig. 3 Electrical demand with DR. 
As mentioned before, customers’ discomfort and energy costs were modeled by two objectives in the second layer. Using the lpmetric method, the Pareto frontier for the mentioned objectives is extracted. In Figure 4, the Pareto frontier and the best solution for Cases I and II are shown. The weight step for extracting the Pareto frontier using the lpmetric method is changed by 0.1 for objectives. The best solution in Figure 4 for Case studies I and II have amounts of 0.56 and 0.53 as minimum values in the lpmetric method, respectively. In Figure 4a, consumers’ discomfort and energy cost in Case Study I are equal to 24.8% and $691,993.3, respectively. On the other side, with the participation of the DR approach in Case Study II, consumers’ discomfort and energy cost in the best solution in Figure 4b have quantities of 14.88% and $559,022.2, respectively.
Fig. 4 Pareto frontier. a) Case I and b) Case II. 
Regarding the results of the case studies, consumers’ discomfort in Case II is reduced by 9.92% compared to Case I. As well, energy cost with the participation of the DR approach in Case II is minimized by 19.2% in comparison to Case I. Due to DR implementation in Case II, the energy cost of the MG and DGs is reduced by 10.1% and 9.1% compared to Case I.
The energy operation of the DEG and MG in the smart energy management system for Cases I and II is indicated by Figure 5. In Figure 5a, the MG operates during peak consumption and highprice traffic, resulting in increased energy costs. On the other hand, energy demand in peak hours 12, 13, 15–17 is not met by DEGs and MG. In Case I, 271.2 kW of the energy demand is not fed. The operation of the ESS in charging mode in Figure 5a is done at hours 1 and 4 at low price traffic of MG, whereas power discharge is operated at high price.
Fig. 5 Operation of the energy generation. a) Case I and b) Case II. 
Figure 5b presents the energy operation of the DEG and MG with the DR approach in Case II. In this case, the total amount of the nonsupply demand is equal to 123.3 kW, which is done at hours 12–14. By comparing Case II with Case I, 54.535% of the nonsupply demand is reduced, which leads to minimizing consumers’ discomfort. Implementation of the DR in Case II also leads to decreased energy generation of the MG in the highprice traffic. In Case II, the participation of the ESS with lowcost factors than MG and DGs in supplydemand is increased than in Case I. The discharge energy of the ESS in Figure 5b at peak demand and high price is operated for minimizing energy cost and nonsupply demand.
7 Conclusion
The implementation of a twolayer optimization approach is carried out in this study to achieve the optimal operation of the appliancesbased DR strategy in smart buildings. The DR modeling is proposed in the first layer optimization, considering the consumptionshifting strategy of the appliances rather than energy price traffic. Thus, the second layer incorporates optimized consumption by demand response (DR) to reduce both the customers’ discomfort and energy costs. The optimization of the customers’ discomfort and energy costs is done by the lpmetric method. Finally, numerical simulation considering without DR (Case I) and with DR (Case II) is done for confirmation of the optimal operation of the appliances. The obtained results of the simulation represent the optimal level of the customers’ discomfort and energy costs with the DR approach in Case II.
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All Tables
All Figures
Fig. 1 Smart energy management system in this study. 

In the text 
Fig. 2 Framework of smart energy management. 

In the text 
Fig. 3 Electrical demand with DR. 

In the text 
Fig. 4 Pareto frontier. a) Case I and b) Case II. 

In the text 
Fig. 5 Operation of the energy generation. a) Case I and b) Case II. 

In the text 
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