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Home All issues Volume 79 (2024) Sci. Tech. Energ. Transition, 79 (2024) 61 Full HTML
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Sci. Tech. Energ. Transition
Volume 79, 2024
Decarbonizing Energy Systems: Smart Grid and Renewable Technologies
Article Number 61
Number of page(s) 12
DOI https://doi.org/10.2516/stet/2024057
Published online 02 September 2024

© The Author(s), published by EDP Sciences, 2024

Licence Creative CommonsThis 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.

1 Introduction

1.1 Motivations and aims

The adoption of alternative energy sources has risen due to increasing worries about the amount, quality, cleanliness, and reliability of electrical power [1]. It is expected that distributed generation (DG) technologies will play a more significant role in the energy sector in the coming years [2]. The grid must be linked with unconventional energy sources to harness electricity from them. Conversely, an extensive amount of distributed generation poses numerous challenges that could potentially impact the power grid’s ability to operate securely and effectively [3, 4]. Microgrids, smaller versions of traditional grids, are implemented to help address some of these challenges [5, 6]. Distributed generation sources are interconnected within microgrids and linked to the low-voltage grid [7, 8]. These systems, which consist of unconventional and renewable energy sources like solar panels and generators, offer cost-effective and environmentally friendly solutions that have overcome previous limitations related to expenses and emissions [9–13]. It can be difficult to manage renewable energy sources like solar and wind power because of their unpredictable and fluctuating nature [14, 15]. Operators need to closely monitor and make adjustments to ensure these sources can meet power demand, even if they may not always perform as expected [16]. To secure a dependable and consistent electricity supply, grid managers need to uphold an energy reserve [17, 18]. This reserve can be established by either increasing the electricity sourced from the main grid or by enhancing the utilization of distributed energy storage devices, such as batteries. However, the substantial installation expenses of these reserve units might outweigh the financial advantages of renewable energy [19, 20].

1.2 Related researches

Energy management is crucial for the development of smart grids. Recently, there has been a significant increase in research initiatives focusing on this area. The concept of demand-side management (DSM) was initially introduced by researchers in [21] to ensure the efficient functioning of residential and rural microgrid systems. Furthermore, the researchers investigated the application of firefly hybridization combined with particle swarm optimization in their research on DSM [22]. In [23], a community microgrid scheduling system was introduced by the authors to operate in real time. In the event of a power outage, developers showcased a novel method for scheduling DGs to provide power to loads within a microgrid (MG) [24]. To tackle DSM concerns, innovators proposed a logical energy consumption distribution strategy within the grid [25]. A new optimized energy management system solution was presented by the authors, which included multi-MG systems in reference [26]. A new method was presented in reference [27] to enhance power generation for photovoltaic systems. Moreover, a demand management program based on an optimization algorithm was proposed for residential loads in reference [28]. Reference [29] discusses a control technique for inverters operating in parallel for eco-friendly purposes. Additionally, reference [30] highlights the availability of demand response for renewable-based MGs in power networks. In [31], a new algorithm was presented to address the issue of minimizing load change in demand management for smart devices. The smooth load transfer capability can help decrease consumer frustration. Meanwhile, the authors of [32] suggested a novel approach to tackle day-ahead scheduling obstacles by utilizing the Internet of Things. Additionally, [33] recommended employing a cloud-based multi-agent framework to promote the integration of smart grid technology in residential sections. They are concentrating on minimizing peak load and energy expenses in smart homes by employing microgrids and intelligent home agents. In contrast, a multi-objective scheduling strategy using arithmetic optimization techniques was presented for energy management in [34]. Furthermore, the authors in [35] designed a power generation model for constructing islanded microgrids with multiple systems. A study in [36] examined modeling energy consumption by implementing efficient demand management strategies. The researchers in [37] analyzed factors such as energy consumption patterns and technical indices to determine the most effective load management technique.

1.3 Contributions and research gaps

The description provided indicates that extensive research has been carried out to minimize the peak-to-average ratio (PAR) of the load curve, lower energy expenses, and enhance customer savings and utility benefits. Regrettably, current methods do not prioritize user satisfaction when implementing time of use (TOU), particularly for low-income individuals in economically challenged countries. Additionally, when it comes to an incline block tariff, it is crucial to accurately determine the block size to ensure financial advantages for both utility companies and customers. Some articles fail to utilize metaheuristic techniques like the grasshopper optimization algorithm (GOA) to cut costs in various systems. The researchers have yet to release a two-stage model utilized for the optimization of energy storage systems layered size planning and energy scheduling in grid-connected. This study aims to tackle these obstacles through the contributions outlined below:

  1. The discussed publications serve as valuable sources for pertinent research that tackles scheduling challenges to some extent. Each method comes with its own set of characteristics and limitations that enhance its effectiveness. For instance, issues involving stochastic and nonlinear effects, demands for scheduling during odd hours, and the risk of high dimensionality cannot be resolved through mathematical approaches. Even in cases where mathematical calculations yield accurate outcomes, they are inherently complex and time-intensive.

  2. Heuristic algorithms often face premature convergence, resulting in a decrease in population diversity, parameter adjustment, and termination criteria. Previous initiatives did not address the reduction of user discomfort along with energy costs. To overcome these challenges, a new model is required. A ToU system is implemented in which evolutionary algorithms are used to optimize the power rate and block size, thereby benefiting both residential consumers and the utility company financially. The recommended algorithm ensures that customers with low incomes will not have to worry about making any payments. The proposed approach’s results are compared to those of existing methods, along with anticipated cost savings on electricity expenses for various consumer groups.

  3. The authors of the study have introduced a novel two-stage stochastic model for nested capacity planning and energy scheduling co-optimization of energy storage systems in grid-connected mode. This model is specifically based on the two-stage algorithm. It is the first research of its kind to focus on energy optimization utilizing the advanced meta-heuristic algorithm two-stage algorithm, which aims to reduce the cost of purchasing storage capacity. Through the application of a two-stage algorithm, the study successfully identified the most efficient energy storage system design from a selection of more than forty cutting-edge evolutionary algorithms. The decision to use a two-stage algorithm was based on its proven statistical superiority in MG capacity planning applications.

2 System modeling

Every household needs to install a small-scale photovoltaic energy storage system due to the rapid growth of distributed energy. The system structure diagram for a residential house, depicted in Figure 1, is designed around a smart home energy management system (SHEMS). Each load under management is linked to a smart socket, enabling direct control by the SHEMS. This article focuses on optimizing controllable loads and scheduling energy storage devices to minimize carbon emissions and household power costs. Additionally, the model accounts for electricity exchange between homes and the grid, detailing decision variables, objectives, constraints, and how the energy storage system handles limitations.

thumbnail Fig. 1

SHEMS model.
thumbnail Fig. 2

Framework of optimization approach.
thumbnail Fig. 3

Energy optimization without demand management.
thumbnail Fig. 4

Energy optimization with demand management and GOA.
thumbnail Fig. 5

Energy optimization with demand management and AROA.
thumbnail Fig. 6

Cost of system without demand management.
thumbnail Fig. 7

Cost of system with demand management and GOA.
thumbnail Fig. 8

Cost of system with demand management and AROA.

2.1 Mathematical modeling

The six distinct characteristic categories of the residential house model have been chosen as the determining factors to optimize electricity consumption as follows:(1)

where

t = Time;

i = Number of home.

= Binary variable of power purchasing.

= Binary variable of power selling.

= Controllable load’s.

= Power purchasing.

= Power selling.

= Power of energy storage system.

2.2 Objective functions modeling

The optimization goals include electricity cost, demand reduction value, and carbon emissions, considering the residents’ electrical usage, demand involvement, and environmental factors to maximize the economic and social advantages of consumption.

2.2.1 Power generation cost

The electricity cost objective function encompasses the expenses associated with obtaining power for the household, as well as the operational costs of storage units and photovoltaic units.(2)

In a day with T total hours, the electricity cost is denoted as E_Cost. The total number of residential microgrids is represented by I, where and are the prices of power bought and sold. The operating expenses of photovoltaic and energy storage devices are expressed in terms of M_Cos t, calculated as follows:(3)where , , and are photovoltaic power, storage cost, photovoltaic cost, and storage power, respectively.

2.2.2 DSM modeling

The demand curtailment value, defined as a measure of resident participation in the demand program, is utilized by reducing loads.(4)

In this instance, DR represents the demand curtailment value, signifies the demand curtailment rate, and L denotes the total number of controlled loads. The power of the load at time t equals the reduced power of the lth controlled load. The weight factor, , is set up to decrease partial loads.

2.2.3 Emission modeling

It is crucial to prioritize the adoption of low-carbon electricity among residents as carbon dioxide emissions greatly contribute to the greenhouse effect. The emission function is as follows:(5)where λ is the emission factor of the grid.

2.3 Constraints’ modeling

The schedule optimization model must adhere to the following constraints to ensure consistent communication between households and the grid:

2.3.1 Power balance modeling

The balance between the demand and generation sides is modeled as follows:(6)

2.3.2 Constraints of energy storage device

The charging and discharging powers of the energy storage device are denoted as by and , respectively. indicates the electricity demand at time t.(7) (8)

2.3.3 Inequality constraints

The inequality constraints like controlled loads, power of grid, and power transaction among grid and MGs are modeled by (9)–(12), respectively.(9) (10) (11) (12)

2.4 Appliances’ modeling

Residential homes are equipped with various loads. Users can categorize air conditioners, water heaters, and dishwashers based on their lifestyle preferences. Appliances in the hybrid category have a programmable work cycle, while non-interruptible appliances must be used immediately. This article discusses the operational constraints and mathematical modeling of key household appliances, determining the required runtime for each appliance [38–40].(13)

3 Optimization algorithm

Grasshopper swarms in natural environments can be simulated using the GOA. Below is a mathematical representation of the behavior exhibited by these swarms [41–43]:(14)where P_ i represents the grasshoppers’ position, SO_ i represents their social interaction force, GRE_ i represents their gravitational force, and W_ i represents their wind advection. It is essential for all metaheuristic algorithms to have a random distribution of search agents across the search space. The GOA random nature is demonstrated by modifying equation (14) in the following manner:(15)

The random integers r1, r2, and r3 within the range [0, 1] are denoted. The GOA recognized social connection as the primary search determinant.(16)

where

N = Grasshoppers number.

d ij = Euclidean distance among Grasshoppers.

Also(17)

Furthermore, the procedure described here can be utilized to acquire , which denotes a solitary vector from the ith to the jth grasshoppers.(18)

Below, you can observe that “s” denotes a function that describes the intensity of social forces.(19)

The symbols l and f represent the length scales of attraction and lustfulness. Grasshoppers utilize two distinct types of forces, namely attraction and repulsion, in their social interactions. The GRE_ i (gravity force) of the ith grasshopper is defined as:(20)

The gravitational constant, denoted as (), indicates the center of the earth, along with the unity vector pointing in the direction of g. The wind advection of the ith grasshoppers, W i , can be calculated in the following manner:(21)

The drift constant (u) and unity vector () represent the wind direction and direction, respectively. Adjusting the values of the components mentioned earlier in equation (14) can be expressed in the following manner:(22)

It is important to note that due to the grasshopper swarm not converging to a single site, equation (21) cannot directly address the optimization problem. To tackle the challenges posed by the equation, a modified form of equation (23) is being considered.(23)

where

c = Swarm closer to target.

Td = Dimension goal.

ubd = Upper bounds inside the dth dimension.

lbd = Lower bounds inside the dth dimension.

The reduction coefficient, which is the initial adjusting parameter of the GOA algorithm, should be decreased in line with the number of iterations. The update is carried out using the formula below.(24)

In this study, the variables cmax and cmin represent the maximum and minimum coefficient values of c. The symbols t and tmax stand for the current and maximum iterations, respectively. Algorithm 1 displays the pseudocode for GOA [41–43].

Algorithm 1GOA pseudocode

          Start:input, Grasshopper Optimization Algorithm parameters
                Initialize: the swarm of grasshoppers randomly
          Initialize: Cmin, Cmax, and maximum number of ittrations tmax
                  Evaluate: the fitness of each grasshopper f(Pt)
                                T = the best solution
                                while (t < tmax) Do
                                     update:C
                                for i = 1,2 …, n Do
                                 for j = 1:1: N Do
                  Normalize: the distance among grasshoppers
                  update the present position of the grasshopper
                fetch the present grasshopper if it drives out the limits
                                      end for
        update T if the present solution is better than the previus optimum solution
                                  t ← t + 1
                                   end while
                      output: return T = optimum soluion
                                      end

5 Optimal sizing of storage system capacity

Calculating the economic value of an energy storage system investment involves creating an objective function that incorporates the concepts of net present cost (NPC) and net present value (NPV). The NPC, which is associated with each newly installed component like the inverter and battery, can be obtained as [44]:(27) (28) (29) (30) (31)

where

Nc = Ideal capacity.

CC = Capital cost.

RC = Replacement cost.

O&M = Operation and maintenance cost.

CRF = Recovery factor.

SPPW = Single-payment present-worth factor.

PL = Project lifespan.

CL = Component lifetime.

ir = interest rate.

The lifespan of the storage components can be readily transformed into calendar life by utilizing the subsequent equation:(32)

where

Ns = Capacity of energy storage system.

Qthr = Annual Stored energy.

Qlife = Lifetime of storage system.

Furthermore, the total power exchanged with the grid over the project’s lifetime can be used to calculate the NPV by utilizing the formula provided.(33)where is as follows:(34)

Consequently, the objective function for the storage sizing problem is defined as:(35)where NPVexch is NPV of power exchanges, and NPCB, NPCSC, and NPCI stand for battery based on NPVexch for inverter, respectively.

6 Two-stage optimization modeling

The diagram in Figure 2 illustrates the two-stage solution approach developed for the hybrid storage capacity optimization model using meta-heuristics. The problem is split into two parts, as depicted in Figure 2: the nested optimal energy scheduling problem and the outer loop storage sizing problem. The hybrid storage capacity optimization model’s two-stage solution approach using meta-heuristics is a sophisticated method that allows for the efficient and effective optimization of storage capacity in a hybrid energy system. By breaking down the problem into two stages, the model can tackle the complex issues of energy scheduling and storage sizing separately, allowing for a more focused and targeted approach to finding the optimal solution. In the first stage, the nested optimal energy scheduling problem is addressed. This involves determining the best energy scheduling options for the system based on various decision variables. The outer loop then uses this information to make choices for the inner loop, which generates wait-and-see options for further evaluation. The second stage focuses on the outer loop storage sizing problem. This stage determines the optimal storage sizes for the system based on the choices made in the first stage. By iteratively solving the optimal scheduling problem every 24 h during the baseline year, the model can continuously evaluate the design fitness and make adjustments as needed. The MATLAB two-stage operational planning model takes this approach a step further by formulating the long-term investment planning problem as a linear programming problem. This allows for the creation of an investment portfolio that includes optimal storage sizes and dispatch schedules at each iteration, ensuring that the system is constantly optimized for maximum efficiency and effectiveness.

7 Numerical results

The results of the recommended SHEMS simulation are presented in this part. The primary goals of this software include decreasing PAR, cutting down on electricity costs, and enhancing User Comfort by reducing wait times. According to this study, a 24-hour schedule is believed to achieve a satisfactory balance among these objectives. The data of simulation for SHEMS and resources are extracted from references [45–48]. A comparison is made between the results of the artificial rabbits optimization algorithm (AROA) and the GOA algorithms to validate the system’s precision. Figure 3 demonstrates the success of the suggested modeling without demand-side management in households. Figure 4 showcases the efficiency of the proposed home demand-side control using the GOA technique. Figure 5 displays the effectiveness of the proposed home demand-side regulation through the AROA method. The cost of the recommended home demand-side management without the corrective action is displayed in Figure 6. The cost of implementing the proposed GOA demand-side control strategy at home is shown in Figure 7. Meanwhile, the expense of the suggested home demand-side management system utilizing the AROA method is presented in Figure 8. Optimal systems with random patterns exhibit a higher maximum periodic energy consumption compared to the recommended technique. The findings also indicate that, in all simulated scenarios, the top systems attempt to schedule loads outside of peak hours to minimize the user’s expenses during peak hours. Figures 6 and 7, demonstrate the total daily electricity costs for planned patterns using electrical price signals as the electricity rates, amounting to 6820.690 cents. Overall, the cost of implementing the proposed demand-side control strategies at home is significant, but the potential savings in daily electricity expenses are substantial. The AROA method shows a 17.75% reduction in costs, while the GOA strategy results in a 20.31% decrease in daily electricity expenses. These findings highlight the potential for significant cost savings through the implementation of demand-side management systems and the importance of considering different strategies to optimize energy consumption and reduce expenses. Additionally, the comparison of optimal systems with random patterns emphasizes the effectiveness of the recommended techniques in scheduling loads outside of peak hours to minimize costs for the user. These results provide valuable insights for homeowners and policymakers looking to implement demand-side control strategies to manage electricity expenses. Thus, based on a proposed method optimum scheduling scheme, the simulations show that SHEMS finds the best trade-off between the target functions and performs well in doing so. Without considering demand management, the expense is 6820.6 (cents). However, after applying the AROA, the cost is found to be 5792.45, and after applying the GOA, it is 5668.9. When comparing the suggested approach to the traditional way, the GOA saved 20.31% daily and the AROA saved 17.75% daily. Table 1 shows the electricity consumption and cost of purchase/hour of MG without demand management. Table 2 shows the electricity consumption and cost of purchasing of microgrid using AROA, Table 3 shows the electricity consumption and cost of purchasing of microgrid using GOA. In Table 4, value of emission, PAR, and discomfort index by proposed algorithms considering demand management is listed.

Table 1

Electrical consumption and cost of MG without demand management.

Table 2

Electrical consumption and cost of MG with demand management by AROA.

Table 3

Electrical consumption and cost of MG with demand management by GOA.

Table 4

Values of emission, PAR, and discomfort index by proposed algorithms.

8 Conclusion and future aspects

The research paper presented an approach to energy management in smart homes, with a focus on customer satisfaction and cost reduction. The study advocates for the integration of a home energy management system to effectively manage energy consumption and electricity expenses for users. By implementing demand-side management, the research aims to optimize energy usage and improve user satisfaction through the use of smart devices and energy management technology. The study utilizes the GOA to enhance battery and appliance scheduling flexibility, ultimately reducing microgrid system costs. The comparison of the proposed method with other algorithms demonstrates its effectiveness in optimizing energy consumption and reducing carbon emissions across various scenarios. The simulation results highlight the potential of the proposed method to significantly enhance energy efficiency and environmental sustainability in practical applications, while also minimizing discomfort for occupants and achieving substantial energy savings and emissions reductions. Overall, the study offers a sustainable solution for energy management in buildings and validates the efficiency and feasibility of the proposed method in addressing energy management challenges considering minimizing emission, cost, discomfort index, and PAR values.

This paper can be expanded into more topics such as optimal energy flow in commercial and industrial sections with new optimization approaches and objective functions.

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All Tables

Table 1

Electrical consumption and cost of MG without demand management.

In the text
Table 2

Electrical consumption and cost of MG with demand management by AROA.

In the text
Table 3

Electrical consumption and cost of MG with demand management by GOA.

In the text
Table 4

Values of emission, PAR, and discomfort index by proposed algorithms.

In the text

All Figures

thumbnail Fig. 1

SHEMS model.
In the text
thumbnail Fig. 2

Framework of optimization approach.
In the text
thumbnail Fig. 3

Energy optimization without demand management.
In the text
thumbnail Fig. 4

Energy optimization with demand management and GOA.
In the text
thumbnail Fig. 5

Energy optimization with demand management and AROA.
In the text
thumbnail Fig. 6

Cost of system without demand management.
In the text
thumbnail Fig. 7

Cost of system with demand management and GOA.
In the text
thumbnail Fig. 8

Cost of system with demand management and AROA.
In the text

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