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



Article Number  88  
Number of page(s)  10  
DOI  https://doi.org/10.2516/stet/2024089  
Published online  30 October 2024 
Regular Article
Modelling smart energy consumption with hybrid demand management in offgrid electrical system considering technoeconomic indices
^{1}
Information Systems Engineering Department, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, Iraq
^{2}
Manipur International University, Imphal, Manipur, India
^{3}
Head of the Department “Physics and Chemistry”, “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers institute” National Research University, Tashkent, Uzbekistan
^{4}
Scientific Researcher, University of Tashkent for Applied Sciences, Str. Gavhar 1, Tashkent 100149, Uzbekistan
^{5}
Scientific Researcher, Western Caspian University, Baku, Azerbaijan
^{6}
Marwadi University Research Centre, Department of Pharmacy, Faculty of Health Science, Marwadi University, Rajkot, 360003, Gujarat, India
^{7}
Deparment of Chemistry, NIMS Institute of Engineering & Technology, NIMS University Rajasthan, Jaipur, India
^{8}
Department of Applied Sciences, Chandigarh Engineering College, Chandigarh Group of Colleges, Jhanjeri, Mohali, 140307, Punjab, India
^{9}
Centre for Research Imapct & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India
^{10}
Department of Computer Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq
^{11}
Department of Computer Techniques Engineering, College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq
^{12}
Department of Computer Techniques Engineering, College of Technical Engineering, The Islamic University of Babylon, Babylon, Iraq
^{13}
Department of Management, AlNisour University College, Nisour Seq. Karkh, Baghdad, Iraq
^{14}
Department of Electrical Engineering, Islamic Azad University, Branch of Central Tehran, Tehran, Iraq
^{*} Corresponding author: mahmud.en.ac@gmail.com
Received:
11
July
2024
Accepted:
26
September
2024
This study proposes dayahead power scheduling for electrical systems in offgrid mode, emphasizing consumer involvement. BiDemand Side Management (DSM) approaches like strategic conversion and demand shifting are proposed for consumer involvement. Multiple objectives are modelled to voltage profile improvement and reduce the operation energy cost. The nondominated solutions of the voltage of buses and operation energy cost are generated by enhanced epsilonconstraint technique, simultaneously. The General Algebraic Modeling System (GAMS) software is proposed for solving optimization problems. A combination of decisionmaking methods like weight sum and fuzzy procedures are implemented for finding optimal solution nondominated solutions. The proposed method’s effectiveness is confirmed through numerical simulations carried out on several case studies that utilize the 33bus electrical system. The findings illustrate the substantial effectiveness of demandside participation in improving power dispatch and the optimal rate of multiple objectives. By using DSM, operation cost is reduced by 21.58% and the voltage index is improved by 13.36% than the lack of implementing DSM.
Key words: Dayahead power scheduling / Offgrid mode / Consumer involvement / Bidemand side management approaches / Nondominated solutions
© 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
Indices
Parameters
A, B, C : Fuel cost factors of EGs
C_{SU}, C_{SD}: Cost sharing up and down for EGs
UP, DP : Ramp up and ramp down for EGs
UT, DT : Up and down time for EGs
Δ: Level of participation IN shifting demand
ξ_{pr}: Prices offered for demand reduction
D, QD : Active and reactive demand of consumers
Decision variables
C_{DR}: Demand reduction’s cost
λ : Status of the demand reduction (Binary variable)
k_{DR}, w_{DR}: Status of the starting and ending times for demand reduction (Binary variable)
α ^{Off}, α ^{On} : Status of the EG’s in off and on modes (Binary variable)
1 Introduction
1.1 Motivations and background
Smart grid management involves utilizing cuttingedge technologies and data analysis to enhance the efficiency of electricity distribution and usage [1]. This involves realtime monitoring of electricity demand patterns, integrating renewable energy sources, and employing predictive algorithms to forecast demand fluctuations [2, 3]. By leveraging artificial intelligence, grid operators can make informed decisions to balance supply and demand, reduce outages, and enhance the overall reliability of the electricity grid [4, 5]. This proactive strategy does not just enhance effectiveness but also promotes the shift towards a more sustainable energy system. Smart grid solutions encompass a range of technologies and systems designed to improve the efficiency of electricity consumption [6–8]. These options consist of smart meters, demand response initiatives, and automated energy management systems that give consumers the ability to oversee and regulate their energy consumption instantly. By providing users with detailed insights into their consumption patterns, smart grid technologies encourage energy conservation and enable dynamic pricing models that incentivize offpeak usage. Additionally, these solutions facilitate better integration of energy resources in the grid [9, 10]. The Demand Side Management (DSM) involves sophisticated strategies and tools to optimize electricity usage across the grid. This involves the utilization of demand response, enabling consumers to modify their energy usage in peak times for monetary rewards. Smart grids utilize realtime data analytics to identify peak demand times and communicate with consumers and businesses to reduce load [11–14]. Furthermore, DSM can incorporate automated systems that adjust energy usage based on grid conditions, ensuring a more balanced and efficient energy distribution while minimizing the need for additional generation capacity [15, 16]. Smart grid technology plays a crucial role in controlling electricity demand by enabling realtime communication between utilities and consumers. Through the use of smart meters and advanced metering infrastructure, utilities can monitor consumption patterns and implement demand response strategies effectively [17, 18]. This technology allows for the dynamic adjustment of electricity prices based on demand, encouraging consumers to shift their usage to offpeak times [19–22]. Additionally, smart grid systems can automatically manage loads by temporarily reducing power to nonessential devices during peak demand periods, thereby preventing grid overload and enhancing overall system stability [23–28].
In Figure 1, the proposed electrical grid in offmode is shown, in which electrical generators (EGs), operators and consumers are cooperated based on exchange data for optimal energy dispatch in the system.
Fig. 1 Proposed electrical grid in offmode. 
1.2 Previous researches and contributions
Some previous studies are examined in this section. Authors in [29] presented resources to design and sit in an electrical system by longterm planning for minimising costs in the system. In [30] modeling control system is proposed for voltage regulation in offgrid systems via an oscillations damping mechanism. In [31] installing storage systems in offgrid systems is proposed for voltage control and loadshedding management via optimal generation in peak demand. The power dispatch in electrical systems by electric vehicles in parking lots is proposed in [32] to improve reliability and reduce costs. The author in [33] power management of electrical systems considers economic and environmental issues studied via optimal siting and load dispatch strategy. In [34] energy management is implemented for efficiency improvement and cost reduction in the offsmart grid in homes via optimal operation of the generation side. The local power generation in the offgrid system by renewable energy resources is presented in [35] via optimal sizing and siting. The multiple modelling of energy systems by design approaches of resources with consideration of environmental, reliability and economic indices is done in [36]. The configuration modelling of electrical grids is proposed in [37] for improving reliability and reducing power losses. The uncertainty modelling of electrical systems considering load demand and power production by renewable energies is implemented in [38]. In [39] planning of multiple energy systems for improving the performance of electrical systems through optimal cooperation is proposed. In [40] scheduling model of electrical systems by HOMER software for optimal siting and design of resources is proposed. In [41] hydrogen storage systems and parking lots of electrical cars are proposed for the optimal operation of electrical systems in the critical status of system.
This paper presents a dayahead power scheduling strategy specifically designed for offgrid electrical systems, emphasizing the critical role of consumer participation in the energy management process. The study recognizes that active involvement from consumers can lead to more efficient energy usage and improved system performance. To facilitate this engagement, the research proposes innovative biDSM techniques, which include strategic conversion and demand shifting. These techniques are aimed at encouraging consumers to adjust their energy consumption patterns, thereby enhancing their participation. To systematically address the challenges associated with power scheduling, a multiobjective optimization model is developed. This model focuses on two primary objectives: improving voltage profiles within the electrical system and minimizing operational energy costs. By optimizing these two aspects, the research aims to create a more reliable and costeffective energy supply for offgrid systems. To solve the optimization problem, the enhanced epsilonconstraint method is employed. This method is particularly effective in generating nondominated solutions, allowing for the simultaneous consideration of both voltage profile improvements and operational energy cost reductions. By utilizing this approach, the research ensures that tradeoffs between the two objectives are effectively managed, leading to a more balanced and efficient power scheduling strategy. The General Algebraic Modeling System (GAMS) software is recommended as a powerful tool for addressing the optimization challenges presented in this research. GAMS provides a robust platform for formulating and solving complex mathematical models, making it an ideal choice for the proposed multiobjective optimization framework. In addition to the optimization techniques, the research incorporates a blend of decisionmaking approaches, including weight sum and fuzzy methods. These approaches are utilized to identify the optimal nondominated solutions from the set of potential outcomes generated by the optimization process. By integrating these decisionmaking strategies, the research enhances the robustness of the solution selection process, ensuring that the chosen solutions align with the preferences and priorities of stakeholders. To validate the effectiveness of the proposed approach, numerical simulations are conducted on various case studies, specifically focusing on the electrical system. These simulations provide empirical evidence of the impact of demandside participation on optimizing power dispatch. The results demonstrate that consumer engagement significantly contributes to achieving multiple objectives, such as improved voltage stability and reduced operational costs. Overall, this research underscores the importance of consumer participation in offgrid electrical systems and presents a wellstructured framework for dayahead power scheduling. By leveraging advanced optimization techniques and decisionmaking methods, the study offers valuable insights into how DSM can enhance the efficiency and reliability of offgrid energy systems.
2 BiDSM approaches modelling
The BiDSM approaches modelling as loadshifting and load reduction methods are formulated as follows [42–45]:
The loadshifting method is formulated by equations (1)–(2) as follows:$$\begin{array}{cc}D\left(t\right)=\sum _{{t}^{\text{'}}}^{}\sum _{m=1}^{M}D({t}^{\text{'}},t)\sum _{i}^{}\sum _{m=1}^{M}D(t,{t}^{\text{'}})& \forall m,t\end{array}$$(1) $$\begin{array}{cc}0\le \sum _{{t}^{\text{'}}}^{}\sum _{m=1}^{M}D(t,{t}^{\text{'}})\le \Delta \times \sum _{m=1}^{M}D\left(t\right)& \forall m,t\end{array}.$$(2)
And loadreduction method is formulated by equations (3)–(5) as follows:$$\begin{array}{cc}{C}_{\mathrm{DR}}=\sum _{m=1}^{M}\sum _{t=1}^{T}{\xi}_{\mathrm{pr}}\times D\left(t\right)\times \lambda \left(t\right)& \forall m\end{array}$$(3) $$\begin{array}{cc}{k}_{\mathrm{DR}}\left(t\right){w}_{\mathrm{DR}}\left(t\right)=\lambda \left(t\right)\lambda (t1)& \forall m\end{array},t$$(4) $$\begin{array}{cc}{k}_{\mathrm{DR}}\left(t\right)+{w}_{\mathrm{DR}}\left(t\right)\le 1& \forall m\end{array},t.$$(5)
3 Multiple objectives model
The multiple objectives are modelled considering minimizing operation costs and voltage profile improvement as follows:
3.1 Operation cost objective
The operation cost modelling is computed considering costs of demand reduction and EG units as follows:$$\mathrm{min}{f}_{\mathrm{EO}}=\sum _{t=1}^{T}\left[\sum _{n=1}^{N}{C}_{N}\left(t,N\right)+{C}_{\mathrm{DR}}\left(t\right)\right]$$(6)
where,$${C}_{N}\left(N,d\right)=\left\{{\mathrm{AP}}_{n}^{2}\left(t,N\right)+{\mathrm{BP}}_{n}\left(t,N\right)+C\right\}+\left\{{C}_{\mathrm{SU}}\times {\alpha}^{\mathrm{on}}\left(t,N\right)\right\}+\left\{{C}_{\mathrm{SD}}\times {\alpha}^{\mathrm{off}}\left(t,N\right)\right\}.$$(7)
3.2 Voltage profile model
The voltage profile improvement is computed as follows:$$\mathrm{min}{f}_{\mathrm{TO}}=\sum _{t=1}^{T}\left\sum _{i,j\in \mathrm{\Lambda}}^{N}{V}_{\mathrm{\Lambda}}\left(t,\mathrm{\Lambda}\right){V}_{\mathrm{ref}}\right.$$(8)
4 Constraints model
4.1 Power balance constraint
The power balance constraint is modelled for active and reactive powers as follows:$$\begin{array}{cc}\sum _{n=1}^{N}{P}_{n}\left(t,N\right)D(t,M)=\sum _{i,j\in \mathrm{\Lambda}}^{}{V}_{i}\left(t,i\right)\times {V}_{j}\left(t,j\right)\times {Y}_{i,j}\times \mathrm{cos}\left[{\theta}_{i,j}+{\delta}_{j}\left(t,j\right){\delta}_{i}(t,i)\right]& \forall t,\mathrm{\Lambda}\end{array}$$(9) $$\begin{array}{cc}\sum _{n=1}^{N}{Q}_{n}\left(t,N\right)\mathrm{QD}(t,M)=\sum _{i,j\in \mathrm{\Lambda}}^{}{V}_{i}\left(s,t,i\right)\times {V}_{j}\left(s,t,j\right)\times {Y}_{i,j}\times \mathrm{sin}\left[{\theta}_{i,j}+{\delta}_{j}\left(s,t,j\right){\delta}_{i}(s,t,i)\right]& \forall s,t,\mathrm{\Lambda}\end{array}.$$(10)
4.2 Voltage constraint
The constraint of the voltage profile is modelled by (11):$$\begin{array}{cc}{V}_{\mathrm{\Lambda}}^{\mathrm{min}}\le {V}_{\mathrm{\Lambda}}(s,t,\mathrm{\Lambda})\le {V}_{\mathrm{\Lambda}}^{\mathrm{max}}& \forall s,t,\mathrm{\Lambda}\end{array}.$$(11)
4.3 EG constraints
The EGs have various technical constraints, including power generation limits, rampup and down rates, and minimum up and down times. These constraints are as follows:$$\begin{array}{cc}{P}_{N}^{\mathrm{min}}\le {P}_{N}(t,N)\le {P}_{N}^{\mathrm{max}}& \forall t,\mathrm{\Lambda}\end{array}$$(12) $$\begin{array}{cc}{P}_{N}\left(t,N\right){P}_{N}(t1,N)\le \mathrm{UP}& \forall t,N\end{array}$$(13) $$\begin{array}{cc}{P}_{N}\left(t1,N\right){P}_{N}(t,N)\le \mathrm{DP}& \forall t,N\end{array}$$(14) $$\begin{array}{cc}{\alpha}^{\mathrm{On}}\left(t,N\right)+\sum _{\tau =T+1}^{\mathrm{min}(T,t1+\mathrm{UT})}{\alpha}^{\mathrm{Off}}(\tau ,N)\le 1& \forall t,N\end{array}$$(15) $$\begin{array}{cc}{\alpha}^{\mathrm{Off}}\left(t,N\right)+\sum _{\tau =T+1}^{\mathrm{min}(T,t1+\mathrm{DT})}{\alpha}^{\mathrm{On}}(\tau ,N)\le 1& \forall t,N\end{array}.$$(16)
5 Solution method
The solution method for handling and solving multiple objective problems is done by enhanced epsilonconstraint technique with the mathematical formulation as follows [46]:$$\begin{array}{cc}\mathrm{min}\left[{f}_{1}\left(x\right)\delta \sum _{n=1}^{N}\frac{{s}_{n}}{{r}_{n}}\right]& {10}^{6}\le \delta {\le 10}^{3}\end{array}.$$(17)
Subject to:$$\begin{array}{cc}{f}_{n}\left(x\right)+{s}_{n}+{\epsilon}_{n}^{z}& n=\mathrm{2,3},\dots ,N;{s}_{n}\in {R}^{+}\end{array}.$$
And:$$\begin{array}{cc}{\epsilon}_{n}^{z}={f}_{n}^{\mathrm{max}}\left[\frac{{f}_{n}^{\mathrm{max}}{f}_{n}^{\mathrm{min}}}{{q}_{n}1}\right]\times z& z=\mathrm{0,1},\dots ,{q}_{n}\end{array}.$$(18)
Where:
n = nth objective function.
δ = Slack variable.
x = Decision variable.
q_{ n } = Equal range.
${\epsilon}_{n}^{z}$ = zth interval of nth objective.
r_{ n }= Objectives range.
5.1 Decisionmaking modeling
In this section, the optimal solution of the voltage and operation cost in multiple objectives is obtained by weight sum and fuzzy methods as a decisionmaking approach. The generated nondominated solutions of voltage and operation cost are normalized by the fuzzy method in equation (19). Then, the optimal solution is achieved by the weight sum method in equation (20) [47]:$${\vartheta}_{n}^{m}=\{\begin{array}{cc}1& {f}_{n}^{m}\le {f}_{n}^{\mathrm{min}}\\ \frac{{f}_{n}^{\mathrm{max}}{f}_{n}^{\mathrm{m}}}{1}& {f}_{n}^{\mathrm{min}}\le {f}_{n}^{m}\le {f}_{n}^{\mathrm{max}}\\ 0& {f}_{n}^{m}\ge {f}_{n}^{\mathrm{max}}\end{array}$$(19) $$\begin{array}{ccc}{\vartheta}_{n}^{m}=\frac{\sum _{n=1}^{N}{\omega}_{n}{\vartheta}_{n}^{m}}{\sum _{m=1}^{M}\sum _{i=1}^{I}{\omega}_{n}{\vartheta}_{n}^{m}}& \sum _{n=1}^{N}{\omega}_{n}=1& {\omega}_{n}\ge 0\end{array}.$$(20)
Where:
${f}_{n}^{m}$ = Membership function of objectives.
${\vartheta}_{n}^{m}$ = Quantity of objectives.
6 Case studies
The proposed multiple objective optimisations are confirmed in this section via case studies considering the implementation and nonimplemention of DSM in an electrical grid in offgrid mode. The case studies are expressed in Table 1.
Cases in this paper.
Figure 2, shows an electrical grid in offgrid mode as a 33bus system. The DICOPT solver and mixed integer nonlinear program (MINLP) are used for solving proposed optimisation in GAMS software.
Fig. 2 Electrical grid in offgrid mode. 
In Figure 3, the contract between the operator and consumers for the loadreduction method is shown. The information on systems like EGs, load demand and level of participation in load shifting are extracted from references [48–51]. The voltage reference is 1 pu and all consumers participate in DSM.
Fig. 3 Contract among operators and consumers for loadreduction method. 
6.1 Results
The results of optimisation considering case studies are analyzed in this section. The proposed dayahead power scheduling without and with considering DSM in cases 1 and 2 are examined, respectively.
In Figure 4, results and solutions of multiple objectives in case 1 for voltage and operation cost are shown. The solutions are generated by enhanced epsilonconstraint and by using weight sum and fuzzy methods; the optimal solution is determined in red colour in Figure 4. The voltage profile and operation cost are 8.8 pu and $3233.3 in the optimal solution, respectively.
Fig. 4 Determined optimal solution in Case 1. 
Also, the value of the voltage of buses in the 33bus system is depicted in Figure 5. The voltage index at bus 18 is experiencing a significant drop, attributed to peak consumption levels and the extended length of the power lines required to meet demand.
Fig 5 Voltage of buses in electrical grid in Case 1. 
Figure 6 illustrates the power produced by the EGs. It is evident that EG 1 generates less power than other EGs, primarily because of the high cost of fuel.
Fig 6 EGs’ power generated in Case 1. 
On the other side, DSM is implemented in case 2. In Figure 7, the implementation of DSM with load shifting and load reduction is shown. By using DSM, load demand with DSM in some hours is more than the original demand. This reaction is due to the implementation of loadshifting method. The solutions of multiple objectives in case 2 for voltage and operation cost are shown in Figure 8. The voltage profile and operation cost are equal to 7.6 pu and $2965.3 in the optimal solution, respectively.
Fig 7 Implememtion of DSM in Case 2. 
Fig 8 Determined optimal solution in Case 2. 
By using DSM, operation cost is reduced by 21.58% and voltage index is improved by 13.36% than case 1, respectively.
Figure 9 shows the voltage of buses for Case 2. Optimal consumption by DSM during peak hours enhances the improvement of voltage buses’ deviation from the reference voltage. Figure 10 shows how DSM helps optimize the production of EGs during peak demand, resulting in decreased costs for power flow and EGs.
Fig. 9 Voltage of buses in electrical grid in Case 2. 
Fig. 10 EGs’ power generated in Case 2. 
7 Conclusions
This research presented scheduling power for electrical systems in offgrid mode a day in advance, with a focus on engaging consumers. Strategic conversion and demand shifting are suggested for consumer engagement in DSM strategies. A model with multiple goals is created to enhance the voltage profile and lower the operational energy expenses. Enhanced epsilonconstraint technique generates nondominated solutions for voltage profile and operation energy cost simultaneously. The GAMS software is suggested for resolving optimization issues. Weight sum and fuzzy procedures are used together to discover the best nondominated solutions through decisionmaking techniques. The results demonstrate the significant impact of demandside involvement in enhancing power scheduling and achieving the best possible outcome for several goals.
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All Tables
All Figures
Fig. 1 Proposed electrical grid in offmode. 

In the text 
Fig. 2 Electrical grid in offgrid mode. 

In the text 
Fig. 3 Contract among operators and consumers for loadreduction method. 

In the text 
Fig. 4 Determined optimal solution in Case 1. 

In the text 
Fig 5 Voltage of buses in electrical grid in Case 1. 

In the text 
Fig 6 EGs’ power generated in Case 1. 

In the text 
Fig 7 Implememtion of DSM in Case 2. 

In the text 
Fig 8 Determined optimal solution in Case 2. 

In the text 
Fig. 9 Voltage of buses in electrical grid in Case 2. 

In the text 
Fig. 10 EGs’ power generated in Case 2. 

In the text 
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