Optimal Load Scheduling Research Articles

Home energy management system for residential fuel cell-based combined heat and power system

The goal of this paper is to propose an optimization scheme for enhancing power dispatch and load scheduling for residential fuel cell-based combined heat and power systems (FC-CHPS) using mixed integer linear programming (MILP), considering the lifetime degradation of both the fuel cell system (FCS) and battery energy storage systems (BESS). The scheme is applied in the home energy management system that oversees the electric and thermal power of residential FC-CHPS. First, the nonlinearity in the relationship between the natural gas consumption and output electric power, and between the residual thermal power and output electric power of the fuel cell system (FCS) in the FC-CHPS is approximated using piecewise linearization. Then, the piecewise linearization is integrated with MILP to derive optimal power dispatch and load scheduling solutions, while accounting for the nonlinearity of the FCS. Next, cost functions representing the lifetime degradation of FCS and BESS are formulated. These cost functions result in nonlinear optimization constraints. Therefore, innovative methods are proposed to linearize these constraints, allowing the continued use of MILP as the optimization method and factoring in the lifetime degradation of FCS and BESS. Compared to the mixed-integer non-linear programming (MINLP) method that does not linearize the non-linearity in the FCS or the lifetime degradation of the FCS and BESS, the proposed MILP scheme achieves the identical objective function value. Furthermore, the computation time for the proposed MILP scheme is 99.94 % lower than that required by the MINLP method. The proposed scheme provides accurate results in short computation times.

Multi-User Optimal Load Scheduling of Different Objectives Combined with Multi-Criteria Decision Making for Smart Grid

Load balancing between required power demand and the available generation capacity is the main task of demand response for a smart grid. Matching between the objectives of users and utilities is the main gap that should be addressed in the demand response context. In this paper, a multi-user optimal load scheduling is proposed to benefit both utility companies and users. Different objectives are considered to form a multi-objective artificial hummingbird algorithm (MAHA). The cost of energy consumption, peak of load, and user inconvenience are the main objectives considered in this work. A hybrid multi-criteria decision making method is considered to select the dominance solutions. This approach is based on the removal effects of criteria (MERECs) and is utilized for deriving appropriate weights of various criteria. Next, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method is used to find the best solution of load scheduling from a set of Pareto front solutions produced by MAHA. Multiple pricing schemes are applied in this work, namely the time of use (ToU) and adaptive consumption level pricing scheme (ACLPS), to test the proposed system with regards to different pricing rates. Furthermore, non-cooperative and cooperative users’ working schemes are considered to overcome the issue of making a new peak load time through shifting the user load from the peak to off-peak period to realize minimum energy cost. The results demonstrate 81% cost savings for the proposed method with the cooperative mode while using ACLPS and 40% savings regarding ToU. Furthermore, the peak saving for the same mode of operation provides about 68% and 64% for ACLPs and ToU, respectively. The finding of this work has been validated against other related contributions to examine the significance of the proposed technique. The analyses in this research have concluded that the presented approach has realized a remarkable saving for the peak power intervals and energy cost while maintaining an acceptable range of the customer inconvenience level.

Design and deployment of a novel decisive algorithm to enable real-time optimal load scheduling within an intelligent smart energy management system based on IoT

Consumers routinely use electrical devices, leading to a disparity between consumer demand and the supply side a significant concern for the energy sector. Implementing demand-side energy management can enhance energy efficiency and mitigate substantial supply-side shortages. Current energy management practices focus on reducing power consumption during peak hours, enabling a decrease in overall electricity costs without sacrificing usage. To tackle the mentioned challenges and maintain system equilibrium, it is essential to develop a flexible and portable system. Introducing an intelligent energy management system could pre-empt power outages by implementing controlled partial load shedding based on consumer preferences. During a demand response event, the system adapts by imposing a maximum demand limit, considering various scenarios and adjusting appliance priorities. Experimental work, incorporating user comfort levels, sensor data, and usage times, is conducted using Smart Energy Management Systems (SEMS) integrated with cost-optimization algorithms.

Optimal load scheduling strategy for isolated building-integrated DC microgrid using binary PSO to maximize user comfort and minimize peak-to-average ratio

Due to the growth of electricity consumption in residential and commercial buildings, there is a growing burden on utilities in urban areas. Sustainable buildings are being developed by integrating renewable energy generation to make an eco-friendly environment. An isolated building-integrated (IBI) DC microgrid fed by a hybrid solar PV (photo voltaic) generator-thermo electric generator (TEG) is one viable option for obtaining self-sustainable energy for the building. The battery is an important component in such IBI DC microgrid fed by the hybrid solar PV generator-TEG for providing reliable power to the loads. However, the operation and planning of the IBI DC microgrid are challenging, and therefore it needs a suitable load scheduling strategy. This paper proposes the optimal load scheduling strategy (OLSS) under demand response to schedule the household loads in the building. The proposed OLSS aims to balance two conflictive objectives, user comfort and peak-to-average ratio (PAR), using a binary particle swarm optimization algorithm (BPSO). The proposed OLSS optimizes user comfort and PAR and reduces peak demand and battery usage. It also helps in the optimum utilization of power generated from hybrid solar PV generator-TEG by shifting the loads during sun hours. Reducing battery usage reduces the conversion loss and extends the battery life. To validate the proposed OLSS, the investigations are carried out using the data obtained across different seasons. The results show that the proposed OLSS schedules the household loads with the desired user comfort and reduces PAR by 22 % in future buildings.

Optimal scheduling and demand response implementation for home energy management

The optimal scheduling of the loads based on dynamic tariffs and implementation of a direct load control (DLC) based demand response program for the domestic consumer is proposed in this work. The load scheduling is carried out using binary particle swarm optimization and a newly prefaced nature-inspired discrete elephant herd optimization technique, and their effectiveness in minimization of cost and the peak-to-average ratio is analyzed. The discrete elephant herd optimization algorithm has acceptable characteristics compared to the conventional algorithms and has determined better exploring properties for multi-objective problems. A prototype hardware model for a home energy management system is developed to demonstrate and analyze the optimal load scheduling and DLC-based demand response program. The controller effectively schedules and implements DLC on consumer devices. The load scheduling optimization helps to improve PAR by a value of 2.504 and results in energy cost savings of ₹ 12.05 on the scheduled day. Implementation of DLC by 15% results in monthly savings of ₹ 204.18. The novelty of the work is the implementation of discrete elephant herd optimization for load scheduling and the development of the prototype hardware model to show effects of both optimal load scheduling and the DLC-based demand response program implementation.

Optimal household appliances scheduling for smart energy management considering inclining block rate tariff and net-metering system

Smart grids that integrate household renewable energy sources and share information with households can help create and maintain a smarter data-driven environment. Within this environment, flexible home energy management policies that minimize household energy costs can be adopted. This paper considers a smart home with a renewable energy source that favors satisfying its energy needs at minimum cost. This is achievable by smartly scheduling the use of its domestic appliances to match a given energy grid tariff. Focusing on the case of Egypt in which an inclining block rate (IBR) tariff is imposed, this paper fills a gap in the literature regarding the load scheduling models aiming to minimize energy cost at the household level whenever such a tariff exists. A new mixed integer quadratic programming (MIQP) model is formulated for this scheduling problem, considering the adopted net metering system with installed domestic photovoltaic (PV) systems in Egypt. The model generates the optimal household load schedule and the optimal amounts of energy to exchange with the grid while considering all the system and consumer utility constraints. To assess the applicability of the proposed model, a survey is conducted to identify the diversity and characteristics of using the electrical appliances by the Egyptian households. Based on the collected survey results, the effectiveness of the proposed MIQP model is investigated. Results confirm the effectiveness of the proposed model to minimize energy cost for different categories of the Egyptian households.

Heuristic control of power consumption by up to 1000 V electrical loads at mining enterprises

Purpose. To develop a method for synthesizing the structure and algorithm of the system for automated control of power consumption by up to 1000 V electrical receivers at mining enterprises with iron ore underground mining methods. This enables direct control of the load connection to the industrial power grid to ensure minimum power costs depending on its cost per day ahead. Methodology. The problem of controlling power consumption of electrical receivers at iron ore underground mines is formalized as a binary form of mixed integer programming. To solve it, a binary implementation of the heuristic genetic algorithm is used. The mathematical modeling method analyzes the impact of genetic algorithm settings, such as the number of phenotypes in the population, the number of elite phenotypes that pass unchanged to the next generation, and the method of phenotype crossover on its quality. Findings. As a result of the research, it is found that the most effective way to control the process of power consumption based on an evolutionary genetic algorithm is to use the Laplace crossover function and keep the percentage of elite phenotypes in the population at 10 %. Moreover, at the smallest population size, the best accuracy is observed when using the Laplace function, while at one- and two-point crossover functions, it worsens, but not significantly (no more than 0.2 %). However, as the number of elite phenotypes increases, the duration of the evolutionary search in the control process is reduced by almost a factor of two in the case of one- and two-point crossovers. Originality. For the first time, the structure of a heuristic system for automated control of power consumption by underground electrical receivers with a supply voltage of up to 1000 V at iron ore underground mines has been developed on the basis of an evolutionary genetic algorithm. Depending on the designed volumes of ore production and the daily power cost per day, this allows determining the optimal power load schedule of underground distribution substations in advance, which, subject to the accepted limits on hourly and daily power, minimizes the cost of purchasing power, and thus reduces the cost of the final product. Practical value. The architecture of a heuristic system for controlling power consumption by electrical receivers with a voltage of up to 1000 V based on an evolutionary genetic algorithm is developed and recommended when optimizing the power load schedule of transformer substations of mining and metallurgical enterprises, in particular, of iron ore underground mines operating in this voltage class.

Analytic hierarchy process-based optimal load scheduling framework in an islanded distribution network

This paper proposes an analytic hierarchy process (AHP) based optimal load scheduling (AOLS) framework in an islanded distribution network to schedule power among prioritized loads under limited generation conditions. The scheduling problem is solved in two stages: in the first stage, AHP is used to determine the priority ranking of loads by assigning weights that are calculated based on the relative importance of loads and their attributes; in the second stage, optimal load scheduling is implemented by minimizing the total cost of load curtailment, subjected to network constraints and security limits. The scheduling problem is solved in four different cases by varying the total power generation using General Algebraic Modeling System (GAMS) to supply power to the loads in the order of priority. Taking the modified IEEE 34 node distribution network as an example, a simulation study is performed to show that the total cost of load curtailment according to AOLS is 18.51%, 24.86%, 28.65%, and 31.54% less than that of optimal load scheduling framework with equal weightage to all loads (OLSEW) in cases 1, 2, 3 & 4, which verifies the effectiveness of the proposed AOLS framework.

Design, control, reliability, economic and energy management of microgrid: A review

Currently, fossil fuels still dominate as the primary energy source, accounting for around 80% of global output. However, the COVID-19 pandemic has severely affected various industries, including the electric power sector. Consequently, the energy industry has recognized the importance of renewable energy sources in addressing environmental concerns and establishing a sustainable future. Recently, microgrid systems based on renewable energy have emerged as crucial players in reducing pollution and promoting environmental sustainability. This paper offers a comprehensive assessment of microgrid systems, starting with the optimal design of the microgrid and extending to the evaluation of its control system. Through a detailed analysis of existing literature and case studies, the review identifies several key findings. Firstly, effective design and control strategies are crucial for optimizing the operation of microgrid's and maximizing their economic and energy management potential. Secondly, the integration of renewable energy sources and energy storage systems can significantly enhance the reliability and resilience of microgrid's. Thirdly, advanced control techniques and optimization algorithms play a vital role in achieving optimal energy management, cost reduction, and efficient load scheduling within microgrid systems. Furthermore, the paper explores energy management, reliability assessment, and economic analysis within the microgrid context. Lastly, the paper highlights the application of cutting-edge trends like artificial intelligence, data analysis, and blockchain in the field of microgrid systems. The numerical results showcase the enhanced reliability and resilience of microgrid's with the integration of energy storage systems, reducing the frequency and duration of power outages by 30% compared to traditional grids.

Optimal Comfortable Load Schedule for Home Energy Management Including Photovoltaic and Battery Systems

Although the main concern of consumers is to reduce the cost of energy consumption, zero-energy buildings are the main concern of governments, which reduce the carbon footprint of the residential sector. Therefore, homeowners are motivated to install distributed renewable energy resources such as solar energy, which includes photovoltaics (PVs), solar concentrators, and energy storage systems (ESSs); these installations are intended to maintain the homeowners’ energy consumption, and the excess energy can be sold to the grid. In light of the comfort consumption suggestions made by users, this paper presents an optimal home energy management (HEM) for zero-energy buildings and low energy consumption. Firstly, this paper proposes a new optimization algorithm called random integer search optimization (RISO). Afterwards, we propose a new objective function to enable zero energy consumption from the grid and lower costs. Therefore, in this study, the primary energy resources for homes are PVs and ESSs, while the grid is on standby during the intermittency of the primary resources. Then, the HEM applies the RISO algorithm for an optimal day-ahead load schedule based on the day-ahead weather forecast and consumers’ comfort time range schedule. The proposed HEM is investigated using a schedule of habits for residential customers living in Hong Kong, where the government subsidizes the excess clean energy from homes to the grid. Three scenarios were studied and compared in this work to verify the effectiveness of the proposed HEM. The results revealed that the load schedule within the comfort times decreased the cost of energy consumption by 25% of the cost without affecting the users’ comfort.

Privacy-aware optimal load scheduling for energy management system of smart home

The wide deployment of smart meters has brought the significant challenge to privacy protection of residential users. However, the existing rechargeable battery schemes cannot protect the privacy when the consumer power usage is either high or low for a long period. A new energy management scheme for smart home is proposed to enhance the privacy protection ability while ensuring the electricity cost and comfort in the article. This article enhances privacy while addressing the joint optimization problem for smart home. Firstly, an energy management architecture incorporated the rechargeable batteries and renewable resources is constructed for enhancing privacy, where the user’s privacy is enhanced by utilizing the rechargeable batteries as well as the home appliance shifting. Secondly, the incentive probability is defined according to user satisfaction and the participation degree of demand response, and a probabilistic incentive mechanism is introduced to encourage more users to participate in household load schedule. Finally, the power cost, privacy as well as user satisfaction are systematically characterized in the objective function of the load scheduling, which is further decomposed by load classification. Then a hierarchical optimization method is leveraged to solve the optimal problem, maximizing the utilization of both residential users and power provider. The simulation results based on Pecan Street dataset validate the performance of the proposed strategy, and compared with the rechargeable battery schemes, the proposed scheme can save 19.72% of the electricity cost while ensuring privacy.

Optimal active load scheduling in a day-ahead energy market with uncertainty in demand

The existing power loads are continuously increasing and leading to various challenges related to economics and systems constraints. Any uncontrolled fluctuations of the demand over consecutive hours would dramatically complicate the correct management of the power generation. Therefore, this paper provides an effective solution for managing the uncertainty in loads and providing optimal scheduling of the power generation based on active load optimization in the day-ahead energy market. The proposed optimization model relies on operating active loads to encounter any unexpected change in the load pattern with taken into consideration the characteristics of these loads. The objective of the optimization model is to procure the lowest energy bill by reducing operational costs by taking into account the compensation cost in case of operating the active loads. The optimized problem is solved using mixed-integer linear programming through two technical stages. The first stage handles the normal operation of generation and passive demand, while the second stage treats all the uncertainty in stochastic scenarios. The active loads are operated under specific constraints such as the instantaneous min/max amount and the min/max duration over 24-h period of time. Case studies are used to demonstrate the effectiveness of implementing active loads.

Classification of energy use patterns and multi-objective optimal scheduling of flexible loads in rural households

The significant stochastic volatility of renewable energy makes the regulation of flexible loads important in improving the matching degree of energy supply and demand. Under the background of urbanization, rural China is developing rapidly with an increasingly obvious trend of rural differentiation, resulting in different energy use patterns in different households. To accurately assess the flexibility potential, this study identified the use patterns of typical flexible load equipment based on the data from a field survey in the rural Guanzhong Plain, and classified rural households there accordingly. Giving full consideration to flexible loads, this study built a multi-objective optimal load scheduling model aimed at economic efficiency and user comfort. The non-dominated sorting genetic algorithm (NSGA-II) was applied to solve the proposed multi-objective optimization problem in MATLAB software. Then technique for order preference by similarity to an ideal solution (TOPSIS) method was to make the final decision from the Pareto frontier. According to the results of this study: 1) The usage patterns of washing machines were divided into morning usage pattern (Wm) and the late afternoon usage pattern (Wa). The charging patterns of scooters were classified into evening charging pattern (Ve) and night charging pattern (Vn). The usage patterns of air conditioners included midday usage pattern (ACm) and evening usage pattern (ACe). 2) The Wm-Vn-ACe households show greatest flexibility potential under the Scenario TOU (Time-of-use), the peak-valley difference of load curve is reduced by 22.9% compared with that before the optimal dispatching; The Wa-Vn-ACm households show greatest flexibility potential under the Scenario PVS (Photovoltaic system), the self-consumption increased by 10.7% compared with that before the optimal dispatching.3) Air conditioner usage patterns had a significant impact on the flexibility potential.

Demand Response Management of a Residential Microgrid Using Chaotic Aquila Optimization

In this paper, Chaotic Aquila Optimization has been proposed for the solution of the demand response program of a grid-connected residential microgrid (GCRMG) system. Here, the main objective is to optimize the scheduling pattern of connected appliances of the building such that overall user cost are minimized under the dynamic price rate of electricity. The GCRMG model considered for analysis is equipped with a fuel cell, combined heat and power (CHP), and a battery storage system. It has to control and schedule the thermostatically controlled deferrable and interruptible appliances of the building optimally. A multipowered residential microgrid system with distinct load demand for appliances and dynamic electricity price makes the objective function complex and highly constrained in nature, which is difficult to solve efficiently. For the solution of such a complex highly constrained optimization problem, both Chaotic Aquila Optimization (CAO) and Aquila optimization (AO) algorithms are implemented, and their performance is analyzed separately. Obtained simulation results in terms of optimal load scheduling and corresponding user cost reveal the better searching and constrained handling capability of AO. In addition, experimental results show that a sinusoidal map significantly improves the performances of AO. Comparison of results with other reported methods are also made, which supports the claim of superiority of the proposed approach.

A multi-agent system based coordinated multi-objective optimal load scheduling strategy using marginal emission factors for building cluster demand response

Building cluster load management to harness energy flexibility and reduce both electricity cost and carbon emissions is an important but inadequately addressed issue in the context of carbon neutrality. This study develops a multi-agent system (MAS) based coordinated optimal load scheduling strategy for building cluster load management in response to dynamic electricity price and marginal emission factor (MEF) simultaneously. The strategy effectively solves the multi-objective optimization problem of conflicts, i.e., minimizing the electricity cost, carbon emissions and peak load while maintaining a good level of users’ satisfaction with electricity use quantified by a utility function. Case study on a campus building cluster is carried out to test the strategy developed. Three demand response (DR) schemes are designed for the building cluster, i.e., price-based DR, MEF-based DR, and the price and MEF hybrid-based DR which implements the optimal scheduling strategy developed. In addition, two real scenarios with opposite correlations between dynamic electricity price and MEF, i.e., positively correlated (scenario 1) and negatively correlated (scenario 2), are extracted from German electricity market. The electricity costs, carbon emissions, peak loads, and utility of the three DR schemes in the two scenarios are critically compared. The results show that the price-based DR may result in the rise of carbon emissions, and the MEF-based DR may lead to higher electricity cost, depending on the correlation between dynamic electricity price and MEF. The optimal strategy developed can achieve a compromise between the conflicting objectives in both scenarios. Under the extremely disadvantageous situation like scenario 2, where the trends of the price and MEF are completely opposite, the price-based DR results in an increase of carbon emission of 2.78%, and the MEF-based DR leads to an increase of electricity cost of 2.63%. The hybrid-based DR can reduce the peak power by 5.54% without increasing electricity cost and carbon emissions in scenario 2. This research provides an effective optimal load scheduling strategy as well as the application guideline for building cluster DR management towards decarbonization and economic benefit.

Optimal Load Scheduling of Home Appliances Considering Operation Conditions

To reduce energy consumption arising from increasing energy efficiency in response to energy depletion around the world, energy price rises, climate change, and accidents of electric power are cooperating simultaneously. Recognizing the seriousness of the above-mentioned problems, feasible and effective policies for reducing greenhouse gas have been promoted in developed countries since the 2000s. Moreover, industry and academia are actively researching to develop energy-efficient and eco-friendly technologies, respectively. This study proposes an optimal model for scheduling home appliances that minimizes power costs by assuming a smart-home environment with smart metering and advanced metering infrastructure. In addition, a case-study was performed using actual data from South Korea, and sensitivity analysis was performed according to changes in parameters. The experiment considered possible real-life situations, such as an increase and decrease in power cost and a limitation in power usage, and proved that the proposed model was excellent to establish a power schedule for home appliances. This research result seems to serve as a guideline in relation to the control of home appliances to reduce power and smart homes.

A two-stage deep convolutional model for demand response energy management system in IoT-enabled smart grid

Home Energy Management System (HEMS) plays an integral role in SG that optimally schedules the appliances to achieve energy savings, cost reduction, Peak–Average Ratio (PAR) minimization via the Demand Side Management (DSM) system. Previous studies are developed to perform electricity demand scheduling for the efficient management of electricity in all the sectors. However, effective monitoring and energy management solutions are essential for the residential loads to control the operation of the appliances depending upon the consumption of power during high peak hours. For this purpose, we proposes an optimal load scheduling technique named Two Stage Deep Dilated Multi-Kernel Convolutional network (DDMKC)-Modified Elephant Herd optimization algorithm (MEHOA) approach to manages, shifts the load of the consumer and thereby lowers the electricity bill. In this model, the forecaster scheme utilizes the DR pricing information that accurately predict the future pricing signal to make optimal decision and achieve minimum degree of discomfort. Based on the forecasted future prices, the MEHOA algorithm schedule the power consumption pattern to the appliances to solve the problem residential load management issues of the consumers and thereby enhances the user comfort (UC), alleviate PAR, and lowers the payment of electricity bill. Experimental analysis is conducted in terms of several metrics like energy consumption, user comfort, and PAR measures and compared with other scheduling based optimization models. The proposed model achieves the percentage greater of 60%, 62%, and 65% for the DAP, RTP, and ToU DR pricing schemes respectively. The result shows that the proposed model effectively and efficiently.

A novel approach for multi-objective cost-peak optimization for demand response of a residential area in smart grids

In this paper, an optimal load scheduling model of household appliances for smart home energy management considering demand response for different pricing schemes is proposed. Henry gas solubility optimization method is used to obtain optimal load scheduling solutions, and then these solutions are sorted using VIKOR multi-criteria decision making method. Multiple pricing schemes have been used such adaptive consumption level pricing scheme and time of use. The use of multiple pricing scheme is to compare the results of customers cost saving obtained. The results show, the proposed method a saves more than 80% and 76% of end-user energy cost for time of use and adaptive consumption level pricing scheme respectively. Meanwhile, the peak load of end-user is reduced by 55% for the baseline operation. The outcomes of this paper are also compared with other works in the literature in order to validate the proposed method. It is concluded in this research that the proposed method provides essential saving for energy cost and demand's peak power as well as keeps acceptable range of customer inconvenience level.

Improved genetic algorithm for optimal demand response in smart grid

In Smart Grid system, the consumers are provided with the opportunity to schedule their home appliances in response to variations in electricity price over time. This paper presents the optimal scheduling of resident appliances. This optimal scheduling is formulated as an optimization problem and is solved by applying improved Genetic Algorithm (GA). In this improved GA, the selection of chromosomes is carried out using entropy method, blended crossover and mutation is performed using correlation coefficient. The simulation is performed for a single resident load profile for a set of appliances using Python programming. The result shows the reduction in the electricity cost to that of the original. The number of iterations taken by the improved Genetic Algorithm (GA) is comparatively lesser than the standard GA and the execution time is reduced by 2.76 s. Thus the result proves the effectiveness of the proposed improvisation in Genetic Algorithm for optimal load scheduling.

Smart Energy Management in Virtual Power Plant Paradigm With a New Improved Multilevel Optimization Based Approach

A virtual power plant (VPP) is a cloud based distributed power plant that aggregates the capacities of diverse distributed energy resources (DERs) for the purpose of enhancing power generation as well as trading or selling power on the electricity market. The main issue faced while working on VPPs is energy management. Smart energy management of a VPP is a complex problem due to the coordinated operation of diverse energy resources and their associated uncertainties. This research paper proposes a real-time (RT) smart energy management model for a VPP using a multi-objective, multi-level optimization-based approach. The VPP consists of a solar, wind and thermal power unit, along with an energy storage unit and some flexible demands. The term multi-level refers to three different energy levels depicted as three homes comprising of different amounts of loads. RT operation of a VPP is enabled by exploiting the bidirectional communication infrastructure. Multi-objective RT smart energy management is implemented on a community-based dwelling system using three alternative algorithms i.e., hybrid optimal stopping rule (H-OSR), hybrid particle swarm optimization (H-PSO) and advanced multi-objective grey wolf optimization (AMO-GWO). The proposed technique focuses on achieving the objectives of optimal load scheduling, real-time pricing, efficient energy consumption, emission reduction, cost minimization and maximization of customer comfort altogether. A comparative analysis is performed among the three algorithms in which the calculated real-time prices are compared with each other. It is observed that on average H-PSO performs 7.86 % better than H-OSR whereas AMO-GWO performs 10.49% better than H-OSR and 5.7% better than H-P-SO. This paper concludes that AMO-GWO is the briskest, most economical, and efficient optimization algorithm for RT smart energy management of a VPP.