Optimal Load Scheduling in Multi-Sourced Smart Home Energy Management System for Effective Prepaid Energy Management

Optimal Load Scheduling in Multi-Sourced Smart Home Energy Management System for Effective Prepaid Energy Management

Anthony Kaku¹,  John Kojo Annan²

¹Department of Electrical and Electronic Engineering, Cape Coast Technical University, Cape Coast-Ghana

²Department of Electrical and Electronic Engineering, University of Mines and Technology, Tarkwa-Ghana

DOI: https://doi.org/10.51584/IJRIAS.2025.1005000112

Received: 13 May 2025; Accepted: 16 May 2025; Published: 20 June 2025

ABSTRACT

The economic development of any nation relies on diverse forms of energy. The concept of energy mix to solve challenges in power generation and distribution of electric energy has been widely proposed and implemented by researchers and engineers. Renewable energy sources have recently been of great contribution to the energy mix for quick demand response. Despite the diverse efforts in generation and technological advancement in electric power generation, transmission and distribution, population growth and industrialization have caused a continuous increase in demand over supply. In recent times, electrical energy usage billing has changed from postpaid to the prepaid system where purchased power available on prepaid meters could get used up to plunge customers into darkness if not well managed. This paper therefore explores the need to optimise available power for adequate and efficient usage, especially in residential facilities that use prepaid meters instead of postpaid meters. The paper seeks to design a Smart Home Energy Management System (SHEMS) that can optimise domestic energy usage utilizing appliance consumption prioritization. This involved the development of a circuit and operational framework for the implementation of intelligent appliance control. The optimisation circuit for effective consumption of available electric power on prepaid meters was designed and simulated using Proteus Software Version 8.9. The design provides appropriate options and measures to help users choose the best-integrated power consumption plan to guide users in customising a befitting power utilisation option concerning available power and power demand. Power demand was well organised to improve the residential peak load by scheduling the use of shiftable and sheddable loads. The research realised the effectiveness of load management, energy consumption review patterns and power control options leading to a significant reduction in end-users’ electricity bills.

Keywords:  Power Consumption, Energy Optimisation, Load Scheduling, Peak Load,  Smart Home Energy Management System

INTRODUCTION

The expansion of residential and industrial infrastructure in recent times, coupled with population growth has caused a remarkable surge in electricity demand [1]. This has led to an imbalance in the demand and supply of electricity, necessitating engineers to explore alternative power generation methods, such as coal power plants, solar energy, wind energy, thermal sources, and nuclear power plants, in addition to traditional sources, to solve it. In Ghana, population growth and economic expansion have significantly elevated energy demand to high levels, putting immense pressure on utility companies. A report from [2] and [3] indicates that major drivers of electricity consumption include industrial growth, petroleum activities, mining, and ongoing electrification projects. The rapid expansion of key cities like Accra, Tema, Takoradi, and Kumasi has also fueled residential electricity demand [4].

Reports from [2], [3] and [5] indicate that despite substantial developments and expansions in the country’s energy sector, demand keeps escalating due to increased economic, industrial, and domestic activities, resulting in heightened electricity consumption.  According to [9] and [10] many households in Ghana employ inefficient electrical appliances with high energy consumption, ultimately leading to high electricity bills.  In recent times, smart grid systems, smart meters, and energy optimisation management techniques have been employed to enhance energy efficiency, reduce power consumption, and improve system reliability [8]–[11].

In addressing the surging energy demand, Generation-Side Management (GSM) and Demand-Side Management (DSM) [12]–[14] are usually considered. GSM involves enhancing the generation capacity, DSM focuses on managing users’ energy consumption, either through load management or demand response programs [15]. DSM programmes encompass planning, execution, and monitoring of activities by electric utilities aimed at encouraging consumers to modify their electricity usage levels and patterns  [16]–[18].

According to the Ghana Energy Commission 2022 report, the generation mix is 34.1%, 65.3% and 0.55% of hydro, thermal and other renewable sources respectively. The growth in the country’s economy and population has resulted in the increase of generation from 7,000 GWh in 2000 to 21,000 GWh in 2021 [5]. Figure 1 and Figure 2 show the electricity generation mix and the peak demand in Ghana respectively [5].

Figure 1 Electricity Generation Mix (2000 – 2021)

Figure 2 System and Ghana Peak Load

Despite the numerous advantages offered by SHEMS, particularly the integration of renewable energy sources, its implementation remains limited in many African countries, endowed with abundant solar and wind energy resources.

Motivated by research this paper endeavours to offer innovative solutions for enhancing energy efficiency in Smart Homes (SH) in the Ghanaian context. The main areas of focus include real-time pricing, demand response strategies, user comfort and the integration of renewable energy resources by incorporating the Artificial Bee Colony (ABC) optimization technique.

Proposed Smart Home Energy Management System (Shems) Framework

The proposed SHEMS as shown in Figure 3 consists of a smart meter, an Energy Management Centre (EMC) which manages the available energy from the grid and the photovoltaic (PV) system, the electrical appliances,  and a battery bank that stores energy from the PV in a SH. The system manages residential energy usage efficiently in various household categories by load prioritization and load scheduling based on the availability of energy.

Figure 3 Proposed SHEMS

Household Categories and Energy Consumption Patterns

The patterns of energy demand in households are influenced by diverse factors including the type of dwelling, household size, income, and access to energy services. Understanding these demand patterns is crucial for designing efficient and equitable energy systems, especially in a developing country such as Ghana.

Households in Ghana are broadly divided into several categories based on geographical location, income levels, and access to energy resources. These categories exhibit distinct energy demand profiles which are shaped by the different technologies used, the quality of housing, and the socio-economic status of the residents.

Urban versus Rural Households

Urban households in Ghana tend to have higher energy demands than their rural counterparts. This can be attributed to greater access to the national grid, higher levels of industrialization, and greater ownership of electrical appliances such as air conditioners, refrigerators, and entertainment systems [19], [20]. In contrast, rural households rely more heavily on traditional biomass energy sources such as wood, charcoal, and kerosene for cooking and lighting. A study by [21], [22] indicated that rural households, which represent a significant portion of the population, exhibit lower electricity consumption due to limited access to grid infrastructure and a greater dependence on non-electric energy sources.

Income Levels and Energy Demand

Energy consumption in Ghana also correlates with income levels. High-income households, especially in urban areas, tend to have higher energy demands due to the widespread use of modern electrical appliances. According to [2], and [3], high-income and middle-income households are more likely to own energy-intensive appliances such as refrigerators and washing machines, whereas lower-income households may only be able to afford basic lighting and cooking appliances. Furthermore, [20] highlights the significant disparity in energy access between wealthy and poorer households, with the latter often resorting to inefficient and environmentally harmful energy sources.

Energy Demand by Household Categories

Low-Income Households

Low-income households in Ghana face significant challenges in energy demand, both in terms of availability and affordability. These households, particularly those in informal settlements or rural areas, often have limited access to reliable electricity, if any at all. When connected to the grid, their energy consumption tends to be minimal, primarily limited to basic lighting and the occasional use of small electrical devices. According to [23]–[25], the average energy consumption of low-income households is significantly lower than that of their higher-income counterparts, with many still relying on traditional cooking fuels like firewood, charcoal, and kerosene. These households typically have lower energy-intensive lifestyles, with the majority of energy demand focused on lighting and cooking. The average monthly energy consumption is estimated to be between 50 kWh – 100 kWh [2], [3]

Furthermore, low-income households are more likely to use inefficient cooking technologies, contributing to both higher per capita energy demand and serious health risks due to indoor air pollution. A study by [26], [27] found that households in low-income areas consumed up to 70% of their energy on cooking, with many resorting to biomass fuels that are not only inefficient but also expensive over time.

Middle-Income Households

Middle-income households in Ghana generally exhibit higher energy consumption than low-income households, reflecting greater access to energy-efficient appliances, improved living conditions, and higher disposable income. Middle-income households are more likely to use multiple appliances including electric cooking appliances (such as rice cookers and electric stoves), high energy-consuming appliances (including refrigerators and air conditioners),  as well as modern lighting and entertainment devices [19], [20]. Their electricity consumption can be significantly higher, often exceeding the needs for basic lighting and cooking. These households also tend to have higher energy demand in the range of  100 kWh – 600 kWh/month.

In terms of overall energy demand, middle-income households are typically at the forefront of adopting energy-efficient technologies. For example, many households in this category use CFL or LED lighting, and they are more likely to invest in energy-efficient refrigerators, fans, and other household appliances. However, while their overall energy use is higher than that of low-income households, the impact on the national grid is less severe due to the relatively more efficient energy practices employed.

High-Income Households

High-income households in Ghana, primarily in urban centres such as Accra, exhibit the highest energy demand. These households often use a wide range of electrical appliances, including high-power devices such as air conditioning units, washing machines, and electric cookers, which significantly contribute to their electricity consumption. As noted by [28], [29], high-income households also tend to live in larger homes with more rooms, which require more lighting and heating. These households are also more likely to have backup generators and inverters to ensure continuous power supply during periods of grid instability.

The energy demand in high-income households reflects not only the use of modern appliances but also the higher levels of comfort and technological integration in daily life. Studies suggest that the total electricity consumption of high-income households can be several times that of low-income households. According to [2], high-income households in urban areas can consume up to 2.5 times more electricity than middle-income households, primarily due to the larger size of homes and the increased ownership of energy-intensive appliances. These households are likely to own multiple high-end appliances, including refrigerators, air conditioners, and home entertainment systems [19], [20].

Load Categorisation

The electrical appliances in the proposed SHEMS are group into three categories namely; Non-Sheddable Loads, Sheddable Loads, and Shiftable Loads.

Non-Sheddable Loads

Non-sheddable loads refer to appliances that are essential to the consumer and are utilized periodically throughout the day. The consumer uses these loads periodically within a day. The periodic usage of these loads does not change unless emergencies or critical states. Household appliances such as refrigerators, coolers, cell phones, computers, television sets, blenders, lighting systems, and extractor hoods are examples of Non-sheddable loads.

Sheddable Loads

Sheddable loads refer to appliances that customers can voluntarily decrease or forfeit usage during specific times of the day. These adjustments can be initiated by the consumer or even electricity supply companies and do not affect the comfort and quality of life of the consumer. These loads when forfeited do not result in occurrences that are irreparable in the home. This is of high benefit to the customer and the power providers.

Shiftable Loads

Shiftable loads are appliances that during 24 hours period of a day, a customer can use but can defer its time to use within the day. The shiftable appliances are also of interest in the study in addition to the sheddable loads.

MATERIALS AND METHODS USED

The research employs the use of Proteus Software Version 8.9  for schematic circuit design, coding and simulation after a thorough consideration of typical variations in household setups and related electrical equipment usage in Ghana. Household setups and electrical equipment usage were analysed and categorised into low, average and high-priority electric power consumers.

Optimization of the Proposed Shems

Optimization involves the determination of the minimum or maximum value of a function within specified limitations, ensuring adherence to the solution (Ampimah et al., 2018a). This study centres on the enhancement of comfort levels for consumers while reducing electricity costs. The SHEMS is achieved through the utilization of the Artificial Bee Colony Optimization (ABC) Technique, as illustrated in Figure 4. The proposed system employs ABC to optimize the energy utilization of household appliances. The proposed approach computes the available energy by evaluating both grid energy and alternative energy sources, subsequently storing this data for subsequent examination. A comparative evaluation is then conducted to determine an appropriate course of action based on the prioritization of energy usage, which is subjected to the availability of energy and the end user preferences.

Figure 4 Proposed SHEMS Simulation Diagram 

Objective function

The aim is to enhance consumers’ satisfaction and convenience through an objective function comprising two parts: the utility part and the actual payment associated with load shedding. The utility section measures the overall energy consumption of a customer and is unrelated to any cost-cutting strategy.

The second part represents the payment a consumer would have made to participate in a program promoting load shedding during peak hours and load increase during off-peak periods. The disparity between these two components indicates the advantage to the consumer of either shedding or retaining load. A substantial difference signifies a more compelling motivation for the customer, resulting in heightened satisfaction and comfort. Consequently, customers are not disadvantaged by completely cutting off loads; on the contrary, they are encouraged to shift the load to more favourable periods with lower tariffs. The objective function is expressed as;

The optimal power consumption of a customer is represented by Pc(t) indicating load cut down at peak. U(Pc(t) is the utility cost to a customer for consuming a load of Pc(t) which is the optimized consumption whereas Uc(t) is the new price attained by a customer for taking part in the scheme. This discount attained by a customer is used to generate a reduced tariff rate for the customer. Non-sheddable, sheddable, and shiftable loads are represented by n, s, and d respectively. For a customer with an integrated PV participating in the load scheduling scheme, the objective function is stated as;

 is the weight assigned to the right part of the objective function to alter the participation level in the program when a customer uses an integrated renewable energy source.

 Constraints

The constraints for this study are outlined as follows;

E^l and E^u are the daily lower and upper limit energy consumption respectively for a customer. The lower limit is to ensure that the energy producers and suppliers break even on their investment when there is minimal consumption by customers while the upper limit is to ensure consumers do not demand energy above the limit that is likely to cause damage to the power plant.

RESULTS AND DISCUSSION

The electricity consumption of the appliances was apportioned into three parts namely non-sheddable load, sheddable load and shiftable load. The non-sheddable load is a fixed consumption and therefore needs no adjustment because it is assumed to be the basic need for essentials in life.

Results

The optimisation is focused on sheddable load and shiftable load which can be adjusted by consumers to help them pay less for the electricity used. Table 1 shows the hourly energy consumption of the various categories of loads under consideration.

Table 1 Hourly Load Energy Consumption and Utility Bills

Four cases were established, with three cases assigned weights (σ) to indicate the percentage of renewable energy sources used by a customer when taking part in the scheme. The values of the assigned weights are 0.2, 0.4 and 0.6 indicating case 2, case 3, and case 4 respectively. The simulation results obtained are shown in Figures 5, 6a, 6b and 7.  From the optimal solution of the objective function, sheddable and shiftable loads could be optimised to enable consumers to reduce loads at peak hours and increase loads at off-peak hours to enjoy incentives that translate to a reduction in their utility bills.

Figure 5   Comparison of Non-Sheddable, Sheddable and Shiftable Loads Energy

Consumption

A comparison of the hourly energy consumption of non-sheddable load, sheddable load and shiftable load is shown in Figure 5. It is deduced from the figure that sheddable loads have a constant energy consumption pattern while shiftable loads are scheduled to utilized energy when there is low energy consumption of non-sheddable loads. This achieves the objective of reducing the peak demand.

Figure 6a Case Distribution of Utility Charges

Figure 6b Case Distribution of Hourly Utility Charges

From Figures 6a and 6b, it is realized that a consumer without integrated renewable energy (case 1)  has a higher utility bill compared to a consumer with integrated renewable energy (cases 2, 3 and 4). Case 2 shows a marginal decrease in the bill as compared to case 1.  Cases 3 and 4 indicate a 24.5% and 36.8% reduction in utility bills respectively.

Figure 7  Case Distribution of Hourly Potential Savings

Figure 7 demonstrates the financial savings achieved for participating in the proposed load scheduling scheme. Case 4 has a higher savings potential compared to cases 2 and 3. The savings are achieved as a result of a customer taking part in the load scheduling scheme with an integrated renewable energy source.

DISCUSSIONS

From the optimal patterns in Figure 5, a consumer could increase the load in all the periods before the 6^th hour of the day and after the 20^th hour of the day to obtain reduced traffic since those periods are conducive for load increase on the grid which will not endanger its stability. The simulation results suggest that consumers should have household appliances which operate with little or no human interaction and are in the category of shiftable loads to be used overnight. This will help consumers enjoy credit incentives by shifting the load from peaks to off-peaks. The 4 cases considered in the research are all important and can be adopted depending on the policy being implemented. For the purpose and objective of this study, case 4 is the best option since end-users are encouraged to integrate renewable energy resources while reducing load at peak hours and increase load at off-peaks to achieve low electricity bills.

CONCLUSION

This paper underscores the role of Smart Home Energy Management Systems (SHEMS) in addressing the growing energy demand, particularly from the Ghanaian perspective. As economic and urbanization expansion continuously drive electricity consumption, there is a high demand for efficient energy management solutions. The study emphasizes the potential of SHEMS, particularly through the categorization of non-sheddable, sheddable, and shiftable loads, to reduce peak demand and improve energy utilization in residential settings.

The application of the ABC optimization technique enhances the system’s efficiency, enabling load scheduling that benefits both consumers and energy providers. By optimizing sheddable and shiftable loads, consumers are incentivized to shift energy usage to off-peak periods, thereby reducing utility bills. The introduction of weight factors (σ) to signify the percentage of renewable energy integration further illustrates the economic benefits, with significant cost reductions observed in Case 3 (σ = 0.4) and Case 4 (σ = 0.6), achieving utility bill reductions of 24.5% and 36.8%, respectively.

The results from the simulation reveal that optimal load scheduling through SHEMS can lead to considerable energy cost savings and increased energy efficiency. The comparative analysis of four distinct cases demonstrates the substantial financial benefits of integrating renewable energy sources into SHEMS. Case 4, with the highest renewable energy contribution, provides the most significant reduction in utility bills, affirming the value of renewable energy integration in residential energy management.

This research contributes to the growing body of knowledge on demand-side energy management and highlights the potential for SHEMS to support the transition toward more sustainable energy systems. The findings are particularly relevant to Ghana and other sub sharan African countries with abundant renewable energy resources but limited SHEMS adoption. Policymakers, energy providers, and consumers stand to benefit from the proposed framework, which not only enhances grid reliability but also improves consumer comfort and reduces energy costs.

Future work could explore the integration of advanced machine learning algorithms into SHEMS to further optimize load scheduling and improve predictive capabilities. Additionally, the adoption of real-time pricing models and consumer behaviour analysis could provide deeper insights into user preferences and enhance system responsiveness. By continuing to advance SHEMS technology and policy support, there is significant potential to achieve more sustainable and efficient energy use in residential buildings across Ghana and beyond.

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