Efficient Energy Management System Research Articles

Multi-level optimal energy management strategy for a grid tied microgrid considering uncertainty in weather conditions and load

Microgrids require efficient energy management systems to optimize the operation of microgrid sources and achieve economic efficiency. Bi-level energy management model is proposed in this paper to minimize the operational cost of a grid-tied microgrid under load variations and uncertainties in renewable sources while satisfying the various technical constraints. The first level is day ahead scheduling of generation units based on day ahead forecasting of renewable energy sources and load demand. In this paper, a recent meta-heuristic algorithm called Coronavirus Herd Immunity Optimizer (CHIO) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem, while the Lagrange multiplier method is used to determine the set-point of the Diesel Generator (DG). The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. The obtained results show that the CHIO-based bi-level EMS demonstrates an optimal economic operation for a grid-connected microgrid in real-time when there are uncertainties in weather, utility tariffs, and load forecasts.

Evaluation of Deep Learning-Based Non-Intrusive Thermal Load Monitoring

Non-Intrusive Load Monitoring (NILM), which provides sufficient load for the energy consumption of an entire building, has become crucial in improving the operation of energy systems. Although NILM can decompose overall energy consumption into individual electrical sub-loads, it struggles to estimate thermal-driven sub-loads such as occupants. Previous studies proposed Non-Intrusive Thermal Load Monitoring (NITLM), which disaggregates the overall thermal load into sub-loads; however, these studies evaluated only a single building. The results change for other buildings due to individual building factors, such as floor area, location, and occupancy patterns; thus, it is necessary to analyze how these factors affect the accuracy of disaggregation for accurate monitoring. In this paper, we conduct a fundamental evaluation of NITLM in various realistic office buildings to accurately disaggregate the overall thermal load into sub-loads, focusing on occupant thermal load. Through experiments, we introduce NITLM with deep learning models and evaluate these models using thermal load datasets. These thermal load datasets are generated by a building energy simulation, and its inputs for the simulation were derived from realistic data like HVAC on/off data. Such fundamental evaluation has not been done before, but insights obtained from the comparison of learning models are necessary and useful for improving learning models. Our experimental results shed light on the deep learning-based NITLM models for building-level efficient energy management systems.

The development of energy saving measures as a basis of ensuring energy efficiency in higher education institutions in healthcare of Ukraine

The implementation of efficient energy management systems (EnMS) to meet the requirements of ISO 50001 is an urgent problem, taking into account the aggravation of the issue of energy supply and energy saving in the country, especially in the conditions of martial law. The main issue facing higher education institutions (HEIs) of Ukraine when creating rational activities for the use of energy resources is the implementation of an energy management system to meet the requirements of DSTU ISO 50001:2020. The main problems faced by all HEIs now are revision of legislation, rising prices and cost of energy resources, high inflation, frequent exchange rate changes. EnMS will allow each institution to monitor energy consumption and provide an opportunity to develop and implement effective energy saving projects. Aim. To theoretically substantiate and propose practical energy saving measures for a higher education institution (HEI) in the healthcare sector of Ukraine within the framework of the implemented energy management system. Materials and methods. The research materials were data from scientific publications, statistical reports and the results of own research, in particular measures for the energy modernization of electric lighting in public areas of educational buildings of HEI. Such scientific methods as the method of content analysis, comparative analysis, the method of generalization and systematization of information, as well as the methods of mathematical and statistical calculations were applied. Results and discussion. The calculation of electricity costs and savings for lighting public areas of all educational buildings of HEI of healthcare of Ukraine has been carried out, and the need for lighting equipment has been formed. The total electricity savings during the energy modernization of lighting in educational buildings of HEI of healthcare of Ukraine and the total economic efficiency from the electricity savings have been calculated. It has been found that the energy efficiency during the energy modernization of lighting in the educational buildings of HEI is 25,126 kWh/year, and the total economic efficiency from electricity savings is 69,600 UAH/year. Conclusions. Energy saving measures have been proposed as part of the implemented energy management system, which will make it possible to significantly save electricity and reduce the financial costs for its purchase and distribution. This, in turn, will allow HEI to form its own general energy supply strategy aimed at improving both energy efficiency and overall operational efficiency.

Energy Efficient Business Management System for Improving QoS in Network Model

Assuring the safe and effective operation of a company's technological infrastructure is an essential part of business management. Monitoring, administering, and troubleshooting the many components and systems of the network are all part of the process. Additionally, it is the responsibility of business administrators to find methods to enhance the existing system, as well as to make certain that all resources are distributed in such a manner that their use may be maximized. Management of the business is a key component of the technological infrastructure of any organization, and it is of the utmost importance that it be carried out effectively in order to guarantee a safe and effective operation. It is essential for network operators to discover strategies to improve the energy efficiency of their networks without adversely affecting the quality of service (QoS) for reasons related to both cost and sustainability. In this research, an in-depth analysis of business management techniques for effective resource utilization is presented. This analysis begins with the design of the network and continues all the way through to the delivery of accurate data. An Energy Efficient Network (EEN) must be able to give programmability and flexibility to network infrastructures, as well as the ability to operate networks in a rapid way and provide operators with more control. It is impossible to ignore energy throughout the process of meeting the needs and requirements of network services, especially when taking into consideration the consequences on the long-term viability of both the environment and businesses. Energy efficiency in both current and future networks is the topic of discussion in this research. The findings indicate that energy efficient networks are successful in overcoming the present issues that stand in the way of the application of energy efficiency techniques, also the discussed model are effective in addressing some of the obstacles that are encountered by small and medium-sized businesses.

Identification of energy management configuration concepts from a set of pareto-optimal solutions

Implementing resource efficient energy management systems in facilities and buildings becomes increasingly important in the transformation to a sustainable society. However, selecting a suitable configuration based on multiple, typically conflicting objectives, such as cost, robustness with respect to uncertainty of grid operation, or renewable energy utilization, is a difficult multi-criteria decision making problem. The recently developed concept identification technique can facilitate a decision maker by sorting configuration options into semantically meaningful groups (concepts).In this process, the partitioning of the objectives and design parameters into different sets (called description spaces) is a very important step. In this study we focus on utilizing the concept identification technique for finding relevant and viable energy management configurations from a very large data set of Pareto-optimal solutions. The data set consists of 20,000 realistic Pareto-optimal building energy management configurations generated by a many-objective evolutionary optimization of a high quality Digital Twin energy management simulator. We analyze how the choice of description spaces, i.e., the partitioning of the objectives and parameters, impacts the type of information that can be extracted. We show that the decision maker can introduce constraints and biases into that process to meet expectations and preferences. The iterative approach presented in this work allows for the generation of valuable insights into trade-offs between specific objectives, and constitutes a powerful and flexible tool to support the decision making process when designing large and complex energy management systems.

The Moderating Effect of Corporate Governance Mechanisms on the Nexus between Sustainability Reporting and the Value of Nigerian-Listed Manufacturing Companies

The study investigates the Moderating Effect of Corporate Governance on the Nexus between Sustainability Reporting (SR) on the Value of listed Manufacturing Companies in Nigeria, precisely the effect of Board Size (BS) and Board Independence (BI) on the nexus subsisting between SR and FV. Secondary data for 36 sampled companies was obtained from the Nigerian Exchange Group (NGX) database for eleven (11) years, 2012–2022, which was analysed using descriptive statistics and a two-step GMM system. Findings therefrom revealed that SR exhibits a positive and significant impact on EVA. At the same time, it was found not to influence Tobin's Q. Further revealed from the analysis was that none of the CG mechanisms introduced had a moderating effect on either of the FV proxies used. The study recommended that management provide clear and concise information on how sustainability initiatives align with the company's financial goals and contribute to value creation over time. These companies should invest in energy efficiency and resource management systems, develop sustainable products and services that meet market demand and align with sustainability goals to reduce their cost of capital. Also, the Financial Reporting Council of Nigeria (FRC) should make a provision requiring companies to appoint a significant number of the board with relevant knowledge in sustainability issues and that there is the need for them to review the Nigerian Code of Corporate Governance, specifically board composition, to include sustainability committees.
  

Bi-LSTM predictive control-based efficient energy management system for a fuel cell hybrid electric vehicle

Energy management systems (EMS) in hybrid electric vehicles play a vital role in achieving optimal energy utilization, improved fuel efficiency, lower emissions, and improved vehicle performance. Thus, this paper proposes a Bidirectional Long-Term Memory (Bi-LSTM) model based on an efficient EMS for a hybrid electric vehicle that manages the powertrain elements of the IC engine, electric motor(s), fuel cell, and energy storage system. The proposed EMS also continuously monitors various parameters, including vehicle speed, driver inputs, battery state of charge, engine load, and environmental conditions, to make real-time decisions on power distribution and operation modes. Further, the proposed system leverages the capabilities of the Bi-LSTM model to capture intricate temporal dependencies and bidirectional context in driving patterns using the real-time dataset. Empirical studies are conducted to substantiate the efficacy of the proposed energy management strategy vis-à-vis diverse controllers, encompassing Model Predictive Control and Sliding Mode Control. Real-world driving data is employed as the basis for these investigations, aiming to provide a robust assessment of the proposed energy management approach in practical scenarios. Results demonstrate the efficacy of the suggested methodology compared to traditional EMS approaches, showcasing improvements in energy utilization and reduced environmental impact. Finally, this research emphasizes a comparative analysis of the proposed topology with the existing approaches in a real-world fuel cell hybrid electric vehicle system. The findings provide insights into the feasibility of deploying intelligent EMS strategies for improved sustainability and efficiency in a modern energy-efficient environment.

Multi-agent-based decentralized residential energy management using Deep Reinforcement Learning

In smart grid, energy consumption has grown exponentially in residential houses, which necessitates the adoption of demand response management. To alleviate and handle the energy management in residential houses, an efficient residential energy management (REM) system can be employed to regulate the energy consumption of appliances for several energy loads such as non-shiftable, shiftable, and controllable loads. Many researchers have focused on the REM using machine learning and deep learning techniques which is not able to provide secure and optimal energy management procedure. Thus, in this paper, a multi-agent-based decentralized REM, i.e., MD-REM approach is proposed using Deep Reinforcement Learning (DRL) with the utilization of blockchain. Furthermore, The combinatorial model DQN, i.e., Q-learning and deep neural network (DNN) is employed, to gain the optimal price based on the reduced energy consumption by appliances associated with different energy loads utilizing the Markov Decision Process (MDP). Here, multiple agents are designed to handle different energy loads and consumption is controlled by the DQN agent, then reduced consumption data is securely shared among all stakeholders using blockchain-based smart contract. The performance evaluation of the proposed MD-REM approach seems to be efficient in terms of reduced energy consumption, optimal energy price, reward, and total profit analysis. Moreover, blockchain-based result is evaluated for the proposed MD-REM approach considering the performance metrics such as transaction efficiency, Interplanetary File System (IPFS) bandwidth utilization, and data storage cost comparison.

Modular Open Source Solar Photovoltaic-Powered DC Nanogrids with Efficient Energy Management System

Initially the concept of a DC nanogrid was focused on supplying power to individual homes. Techno-economic advances in photovoltaic (PV) technology have enabled solar PV stand-alone nanogrids to power individual devices using device-specific architectures. To reduce costs and increase accessibility for a wider range of people, a modular open-source system is needed to cover all applications at once. This article introduces a modular PV-powered nanogrid system, consisting of a do it yourself (DIY) PV system with batteries to allow for off-grid power. The resultant open-source modular DC nanogrid can deliver DC power to loads of different voltage levels, which is possible because of the efficient and parametric energy management system (EMS) that selects modes of operation for the grid based on DC bus voltage and state of charge of batteries. Simulation results verify the coordination between the EMS and the PV-battery system under varying PV power generation and load conditions. This EMS has potential to enable easy personalization of a vast area of applications and expand appropriate technology for isolated communities. A thorough stability analysis has been conducted, leading to the development of an LQR (Linear Quadratic Regulator) controller as a replacement for the conventional PI (Proportional - Integral) controllers for better transient stability of the system.

Robust generation expansion planning in power grids under renewable energy penetration via honey badger algorithm

Robust reliability Generation Expansion Planning (GEP) turns out to be a crucial step for an efficient energy management system in a modern power grid, especially under renewable energy employment. The integration of all such components in a GEP model makes it a large-scale, nonlinear, and mixed-variable mathematical modeling problem. In this paper, the presence of wind energy uncertainty is analyzed. Both long and short-term uncertainties are incorporated into the proposed GEP model. The first step concerns the impact of long-term wind uncertainties through the annual variations of the capacity credit of two real sites in Egypt at Zafaranh and Shark El-ouinate. The second step deals with the short-term uncertainties of each wind site. The wind speed uncertainty of each wind site is modeled by probability distribution function. Then, wind power is estimated from the wind power curve for each wind site and Monte-Carlo Simulation is performed. Fast Gas Turbine and/or Pump Hydro Storage are incorporated to cope with short-term uncertainties. Sensitivity analysis is implemented for 3, 6, and 12 stages as short and long planning horizons to minimize the total costs with wind energy penetration and emission reduction over planning horizons. Also, a novel Honey Badger Algorithm (HBA) with model modifications such as Virtual Mapping Procedure, Penalty Factor Approach, and the Modified of Intelligent Initial Population Generation is utilized for solving the proposed GEP problem. The obtained results are compared with other algorithms to ensure the superior performance of the proposed HBA. According to the results of the applicable test systems, the proposed HBA performs better than the others, with percentage reductions over CSA, AO, BES, and PSO ranging up to 4.2, 2.72, 2.7, and 3.4%, respectively.

An indoor airflow distribution predictor using machine learning for a real-time healthy building monitoring system in the tropics

Indoor air quality is the foundation of a good indoor environment. The COVID-19 pandemic further highlighted the importance of providing real-time airflow distribution information within the Building Environmental Monitoring System (BEMS) to minimize the risk of infectious airborne transmission. This paper discusses the process of developing a predictive model for indoor airflow distribution prediction with indoor and outdoor input parameters using machine learning and its implementation in healthy BEMS for a classroom in the tropical climate region of Yogyakarta, Indonesia. This paper encompassed field measurement and simulation involving outdoor climate conditions and the operational status of the classroom’s windows, Air Conditioning units, and fans. Three machine learning models were constructed using OLS, LASSO, and Ridge methods. Datasets for the modeling were generated from CFD model simulations in IES VE and were assessed for correlation. The mean temperature and velocity differences between the CFD model simulation and measurement results are 0.21°C and 0.083 m/s, respectively. Outdoor climate conditions and the operational status of the classroom’s utilities significantly influence the indoor airflow distribution characteristics. The three models indicate a relatively poor performance, where the classroom had a relatively low sensitivity to input changes. However, the best model performance was achieved using the LASSO method, with average values from post-normalization of [Formula: see text] and Root Mean Square Error (RMSE) of 0.336 and 0.077, respectively. The model was implemented in healthy BEMS on the “Platform for Healthy and Energy Efficient Building Management System.” Practical Application: This research proposed a machine learning model of indoor airflow characteristics of a classroom in Yogyakarta. The proposed model can be adapted to produce monitoring systems that best represent the related conditions. The method can be adopted to develop a relatively simple, low-cost sensor or model to monitor an indoor environment. Future studies may explore the results of the real-world implementation in a case study.

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Many isolated locations, including hilly areas, remote sites, and military camps, lack feasible access to the main power grid. In these conditions, locally established Microgrids can provide the necessary power supply. However, to meet the demands of these isolated areas, numerous individual Microgrids are required. Although some of these Microgrids might be integratable, geographical constraints may preclude full integration. Under these circumstances, the establishment of a smart grid—integrating multiple Microgrids with a battery management system—can potentially solve many power supply issues. A smart grid control system paired with a centralized battery management system is proposed in this paper. The system considers the use of multiple renewable energy sources at various locations for stable and reliable power generation. The proposed method incorporates a long short-term memory (LSTM)-based artificial neural network (ANN) to ensure a stable, high-quality power supply at different load buses. Additionally, this work introduces an artificial intelligence-based operating system designed to maintain energy management under various conditions. To enhance voltage quality, a 7-level aligned multilevel inverter is incorporated into the system. As compared with PI and Fuzzy controllers, the proposed method with LSTM-ANN controller is improved the power quality under sudden changes in the system which are also presented in results, Voltage variation of PI, Fuzzy, and LSTM-ANN is 370V,180V,and 70V . The effectiveness of the proposed energy management system (EMS) is verified by using a hardware-in the-loop approach with OPAL-RT modules, yielding realistic results.

Retracted: The Construction of a Smart City Energy Efficiency Management System Oriented to the Mobile Data Aggregation of the Internet of Things

Retracted: The Construction of a Smart City Energy Efficiency Management System Oriented to the Mobile Data Aggregation of the Internet of Things

Rational Management of Energy Efficiency in a Power Complex

The problem of energy efficiency management in the energy complex under stochastic conditions and taking into account the human factor is analysed. Undesirable activities of the Executive of complex subdivision associated with the presence of his own goals have been identified. Such unwanted activity can lead to a decrease in the energy efficiency of the complex. To improve this efficiency, a control system is proposed that uses a digital learning procedure for the Manager of the complex with the help of the Counsellor, who, in turn, learns to evaluate actual energy efficiency. In addition, this system includes digital procedures for planning and classifying the Executive, depending on the energy efficiency of the subdivision he heads. The task is to determine a control system in which the actual energy efficiency of the subdivision will be equal to the maximum possible under stochastic conditions. This problem is solved by synthesizing a control system that includes a set of decision-making procedures by the Manager (planning and classification) together with a set of advisory procedures used by the Counsellor (evaluation and judgment). The problems of forming such an energy efficiency management system in the energy complex are discussed.

Implementation Of Green Business Ecoprint Industry In Purworejo Regency

The ecoprint industry in Purworejo Regency is one of the industries committed to doing business in an environmentally friendly manner. Green business is the main focus in the operation of the ecoprint industry. In this context, green business refers to business practices that aim to improve sustainability and reduce negative environmental impacts.  This descriptive qualitative research aims to explain how green business is applied in the ecoprint industry in Purworejo Regency. To find out how green business is implemented in the ecoprint industry in Purworejo Regency, the author interviewed the owner and the production department. Miles and Huberman techniques were used to analyse the data. The results of this study are that the application of green business in the ecoprint industry is reflected in the use of natural materials from plants and natural fibres that have been proven to reduce water and air pollution produced, there are efforts to educate consumers about the importance of environmentally friendly products and their benefits to the environment, and have used environmentally friendly packaging. In addition, the ecoprint industry in Purworejo Regency is implementing an efficient energy management system. Green business in the ecoprint industry is the right step to reduce negative impact on the environment. Overall, the ecoprint industry in Purworejo Regency has successfully operated a business with green business principles. In an era that is increasingly aware of the importance of sustainability, these steps not only provide benefits to the environment, but also enhance the good image of SMEs.

An Innovative Power Management Strategy for Hybrid Battery–Supercapacitor Systems in Electric Vehicle

Currently, batteries and supercapacitors play a vital role as energy storage systems in industrial applications, particularly in electric vehicles. Electric vehicles benefit from the high energy density of lithium batteries as well as the high power density of supercapacitors. Hence, a robust and efficient energy management system is required to coordinate energy flows between these two storage systems, ensuring road safety. In this study, we develop a novel rule-based strategy called “Continuous Regulation with Dynamic Battery Power Limiting” to establish robust control between the lithium-ion battery and the supercapacitor. A comparative analysis is conducted to evaluate the performance of this proposed approach in comparison to conventional methods. The results show that this approach significantly enhances driving comfort and prevents depletion of the main energy source, resulting in a gain of nearly 30% compared to a lithium-ion battery electric vehicle. Additionally, this new rules-based strategy ensures that the supercapacitor is charged at the end of each drive cycle.

Model-free reinforcement learning-based energy management for plug-in electric vehicles in a cooperative multi-agent home microgrid with consideration of travel behavior

The rise in popularity of plug-in electric vehicles (PEVs) and the increasing use of renewable energy sources (RESs) have paved the way for advanced energy management systems (EMSs) that optimize energy usage and distribution in home microgrids (H-MGs). This study focuses on the integration of PEVs in H-MGs using an EMS and analyzes its impact on electrical power grids (EPGs). We examine the effect of PEV charging and discharging patterns on H-MG energy flow, considering various scenarios such as high renewable energy generation, different levels of PEV penetration, and EPG connection status. Our findings indicate that an efficient EMS can significantly enhance H-MGs’ overall efficiency by intelligently scheduling PEV charging and discharging, maximizing the use of locally generated renewable energy, and reducing peak load on the EPG. We demonstrate that a cooperative multi-agent system EMS, driven by a robust continuous and real-time fuzzy Q-learning (FQL) method, can reduce electricity market prices by 15% by increasing the use of renewable energy generation by 25%. To fully realize the benefits of EMS, we address challenges such as reducing dependence on EPG by 30%, improving battery state by 12%, and ensuring EPG stability in the face of uncertainties.

Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning

Review Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning Min Hua 1, Bin Shuai 1,2, ∗ , Quan Zhou 1, Jinhai Wang 1, Yinglong He 3, and Hongming Xu 1 1 School of Engineering, University of Birmingham, Birmingham B152TT, UK 2 School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China 3 School of Mechanical Engineering Sciences, University of Surrey, Guildford, GU27XH, UK * Correspondence: shuaib@mail.tsinghua.edu.cn     Received: 29 August 2023 Accepted: 11 December 2023 Published: 19 December 2023   Abstract: The growing adoption of hybrid electric vehicles (HEVs) presents a transformative opportunity for revolutionizing transportation energy systems. The shift towards electrifying transportation aims to curb environmental concerns related to fossil fuel consumption. This necessitates efficient energy management systems (EMS) to optimize energy efficiency. The evolution of EMS from HEVs to connected hybrid electric vehicles (CHEVs) represent a pivotal shift. For HEVs, EMS now confronts the intricate energy cooperation requirements of CHEVs, necessitating advanced algorithms for route optimization, charging coordination, and load distribution. Challenges persist in both domains, including optimal energy utilization for HEVs, and cooperative eco-driving control (CED) for CHEVs across diverse vehicle types. Reinforcement learning (RL) stands out as a promising tool for addressing these challenges. Specifically, within the realm of CHEVs, the application of multi-agent reinforcement learning (MARL) emerges as a powerful approach for effectively tackling the intricacies of CED control. Despite extensive research, few reviews span from individual vehicles to multi-vehicle scenarios. This review bridges the gap, highlighting challenges, advancements, and potential contributions of RL-based solutions for future sustainable transportation systems.

Unknown Appliances Detection for Non-Intrusive Load Monitoring Based on Conditional Generative Adversarial Networks

Non-intrusive load monitoring (NILM) provides fine-grained consumption information at the appliance level by analyzing the terminal voltage and total current measured. It shows prospective applications in demand side management, such as demand response, energy efficiency, and home energy management system. However, most cutting-edge NILM models have a critical assumption that switching events are triggered by known appliances in the training set, which may be unrealistic. In reality, new appliances are constantly being added, reducing the effectiveness of current methods for load monitoring. In this paper, we propose a conditional generative adversarial network (CGAN) to correctly classify all the known appliances while simultaneously detecting unknown ones using the V-I trajectory features. We integrate variational autoencoder and capsule networks in the generator network, the capsule features of the same known appliance class are forced to match a pre-defined Gaussian and a group of Gaussian priors (one for each appliance class) as the posterior distribution approximately. Furthermore, using an additional encoder network maps the generated V-I trajectory to its latent representation, minimizing the distance between the latent representations and the feature vectors of the generator aids in learning the data distribution for known appliance samples during the training process. In these ways, we can improve the ability of the model to detect unknown appliances. Experimental results on two public datasets demonstrate the effectiveness and superiority of our method.

An effective energy management system for intensified grid-connected microgrids

The utility's utilization of communication technology and renewable energy sources has paved the path for self-sustaining microgrids (MGs). However, the intermittency of solar and wind energies raises concerns about meeting demand effectively. To ensure optimal performance of distributed MGs, an efficient energy management system (EMS) is crucial to tackle this uncertainty. Historically, MGs have primarily achieved operational cost reduction through optimal functioning. Integrating demand response (DR) into the EMS could further enhance operational efficiency and peak reduction. This research work addresses this challenge by incorporating DR programs into grid-connected MGs' energy management. Stochastic programming is employed to account for the unpredictable solar and wind behaviours. Flexible price elasticity is used to calculate price elasticity coefficients, portraying customer responses effectively. The implemented research work compares the Dragon Fly Algorithm with other heuristic approaches, resulting in a 12.42 % reduction in overall operating costs and the efficacy of the proposed algorithm is shown.. Using the Analytic Hierarchy Process (AHP), the User Satisfaction Index is assessed, revealing that the CPP demand response initiative tops the satisfaction scale with a score of 0.92881.. Moreover, this research offers an exhaustive evaluation of techno-economic markers for each scenario, systematically ranked using the proposed AHP methodology..