Microgrid Energy Management System Research Articles

Aperiodic two-layer energy management system for community microgrids based on blockchain strategy

Regulatory changes in different countries regarding self-consumption and growing public concern about the environment are encouraging the establishment of community microgrids. These community microgrids integrate a large number of small-scale distributed energy resources and offers a solution to enhance power system reliability and resilience. This work proposes a geographically-based split of the community microgrids into clusters of members that tend to have similar consumption and generation profiles, mimicking the most typical layout of cities. Assuming a community microgrid divided into clusters, a two-layer architecture is developed to facilitate the greater penetration of distributed energy resources in an efficient way. The first layer, referred as the market layer, is responsible for creating local energy markets with the aim of maximising the economic benefits for community microgrid members. The second layer is responsible for the network reconfiguration, which is based on the energy balance within each cluster. This layer complies with the IEC 61850 communication standard, in order to control commercial sectionalizing and tie switches. This allows the community microgrid network to be reconfigured to minimise energy exchanges with the main grid, without requiring interaction with the distributed system operator. To implement this two-layer energy management strategy, an aperiodic market approach based on Blockchain technology, and the additional functionality offered by Smart Contracts is adopted. This embraces the concept of energy communities since it decentralizes the control and eliminates intermediaries. The use of aperiodic control techniques helps to overcome the challenges of using Blockchain technology in terms of storage, computational requirements and member privacy. The scalability and modularity of the Smart Contract-based system allow each cluster of members to be designed by tailoring the system to their specific needs. The implementation of this strategy is based on low-cost off-the-shelf devices, such as Raspberry Pi 4 Model B boards, which operate as Blockchain nodes of community microgrid members. Finally, the strategy has been validated by emulating two use cases based on the IEEE 123-node system network model highlighting the benefits of the proposal.

Scheduling and Sizing of Campus Microgrid Considering Demand Response and Economic Analysis.

Current energy systems face multiple problems related to inflation in energy prices, reduction of fossil fuels, and greenhouse gas emissions which are disturbing the comfort zone of energy consumers and the affordability of power for large commercial customers. These kinds of problems can be alleviated with the help of optimal planning of demand response policies and with distributed generators in the distribution system. The objective of this article is to give a strategic proposition of an energy management system for a campus microgrid (µG) to minimize the operating costs and to increase the self-consuming energy of the green distributed generators (DGs). To this end, a real-time based campus is considered that currently takes provision of its loads from the utility grid only. According to the proposed given scenario, it will contain solar panels and a wind turbine as non-dispatchable DGs while a diesel generator is considered as a dispatchable DG. It also incorporates an energy storage system with optimal sizing of BESS to tackle the multiple disturbances that arise from solar radiation. The resultant problem of linear mathematics was simulated and plotted in MATLAB with mixed-integer linear programming. Simulation results show that the proposed given model of energy management (EMS) minimizes the grid electricity costs by 668.8 CC/day ($) which is 36.6% of savings for the campus microgrid. The economic prognosis for the campus to give an optimum result for the UET Taxila, Campus was also analyzed. The general effect of a medium-sized solar PV installation on carbon emissions and energy consumption costs was also determined. The substantial environmental and economic benefits compared to the present situation have prompted the campus owners to invest in the DGs and to install large-scale energy storage.

Soft computing technique with maintenance and controlling for distributed energy using differential evolution based local power distribution system and fuzzy radial basis function neural network

Learning-based modelling methods are now being used to construct a precise prediction method for renewable energy sources. CI (Computational Intelligence) methods are proven to be effective in developing and optimising renewable instruments. Difficulty of this type of energy is determined by its coverage of enormous amounts of data as well as factors that must be properly analyzed. Soft computing approaches are widely recognized as critical instruments for improving performance of spinning electrical devices in both controls as well as design. The progress of gentle computing methods employed in rotating electrical machines is crucial for a wide range of energy conversion devices, including generators, high-performance electric engines and electric cars.This research proposes a novel technique for maintaining and controlling distributed energy systems. Here the maintenance and controlling data have been evaluated by differential evolution based local power distribution system (DE-LPDS) for control system as well as fuzzy radial basis function neural network (FRBFNN) is used in Micro-grid energy management system. The suggested energy management system is modelled to coordinate among many flexible sources by establishing priority resources, direct demand control signals, and power prices. Experimental results show various maintenance and controlling datasets in terms of QoS of 82%, energy efficiency of 95%, power consumption of 59 %, computational time of 34 ms and training accuracy of 95 % by proposed technique.

Toward an intelligent community microgrid energy management system based on optimal control schemes

This article investigates a modelization of the energy management strategy of a grid-tied photovoltaic (PV), wind generator, and battery system. The model uses a smart metering system to collect data from various electrical components of a microgrid. The power optimization strategy is modeled under demand response, including time-of-use pricing to create a home's energy flow more efficiently. Two optimal control strategies are developed to coordinate energy expenses, expressed as the cost of minimizing electricity supplied by the utility grid and maximizing the opportunity for energy to sell to the grid. A nonlinear open-loop control and quadratic programming using model predictive control (MPC) are compared to assess the performance of the developed model. The design model takes into consideration the operational cost of batteries and degradation. The importance of the energy storage system is demonstrated in the context of energy conservation and gain on the demand side. MPC's capacity to specify the proper control and forecast future system reaction enables the proposed method to ensure significant daily energy and cost savings from 70.44 to 24.74 $ day−1 in the first scenario (without disturbances) and from 70.44 to 18.24 $ day−1 in the second case (with disturbances). In both methods, the net savings of greenhouse gas are approximately 288.10 (kgCO2-eq) and 338.87 (kgCO2-eq). These show better performance of the MPC compared to the linear model. Highlights Investigate the dynamic performance of an intelligent home energy management scheme. Implement a grid-tied photovoltaic, wind turbine, and energy storage system. Use intelligent metering to collect data from various electrical components of a smart home. Compare the energy saving from an open-loop algorithm and a model predictive control. Assess the topmost benefits of the optimal control method of grid-tied distributed energy resources.

Optimal Energy Management of Hybrid MicroGrid Using Storage System and Fuzzy-GA Method

Due to the increase of the demand for electrical power and the gaining of momentum of decentralized electrical production, the importance of microgrids is increasing constantly. Therefore, it is pertinent to understand how microgrids work and develop efficient systems to supervise and manage the produced electrical power. This paper deals with an optimal energy management system (EMS) for multi-sources hybrid microgrid. The investigated hybrid system incorporating Diesel Engine (DE), Photovoltaic generator (PV), Wind farm, Fuel Cells (FCs) and Electrolyzers. The proposed strategy was based on a combined optimal Fuzzy Logic and Genetic Algorithm (GA). The GA have been employed to optimize the scaling factors of fuzzy logic to give better results. The power demand variation was considered as disturbance, where, the optimal Fuzzy-GA was implemented to ensure microgrid balance. The effectiveness of the proposed strategy was demonstrated througth various scenarios, and the behavior of the microgrid was analyzed without and with storage system. The obtained results were compared and the proposed strategy proves the validity as a strong optimization tool that can cope with load variation.

Attention and masking embedded ensemble reinforcement learning for smart energy optimization and risk evaluation under uncertainties

Integrating residential-level photovoltaic energy generation and energy storage for the on-grid system is essential to reduce electricity use for residential consumption from the grid. However, reaching a reliable and optimal control policy is highly challenging due to the intrinsic uncertainties in the renewable energy sources and fluctuating demand profile. In this work, we proposed and designed an ensemble deep reinforcement learning (DRL) algorithm combined with risk evaluation to solve the energy optimization problem under uncertainties. Meanwhile, the attention and masking layer, the state-of-the-art natural language processing techniques, were incorporated into the algorithm to handle the issue of hard constraints, which are frequently encountered in the renewable energy optimization problem. To the best of our knowledge, this work is the first attempt to tackle the energy optimization problem under uncertainty using a scenario-based ensemble DRL approach with a risk evaluation. Through a well-designed single household microgrid energy management system, we found that the attention and masking layer played a crucial role in fulfilling the hard constraint. The ensemble DRL with the increased number of agents showed a significantly improved energy management policy leading to ∼75% of the cost reduction compared with those obtained by using conventional DRL with a single agent. The risk evaluation revealed that the current ensemble DRL approach possessed a high-risk/high-profit feature, which could be significantly improved by designing a risk-aware reward function in future investigations.

Microgrid Energy Management System Based on Fuzzy Logic and Monitoring Platform for Data Analysis

Energy management and monitoring systems are significant difficulties in applying microgrids to smart homes. Thus, further research is required to address the modeling and operational parts of the system’s future results for various applications. This paper proposes a new technique for energy management in a microgrid using a robust control approach and the development of a platform for real-time monitoring. The developed controller is based on a fuzzy logic method used in the energy Internet paradigm with connected distributed generators (DGs) in the microgrid. The developed method regulates the power flow of the microgrid, and frequency/voltage regulation improved the load-management performance and monitoring system using the ThingSpeak platform for real-time data analysis. The MATLAB. simulation results show the feasibility and effectiveness of the proposed strategy and the introduced approach in microgrid control under various operating conditions. Additionally, the results show that the proposed monitoring platform facilitates real-time data analysis.

Modeling and analysis of cost-effective energy management for integrated microgrids

A microgrid concept is an innovative approach for integrating hybrid and renewable energy sources into the utility grid. The uncertainties because of the intermittent nature of renewable energy resources, the load, and market price are significant challenges. In the traditional heuristic method, data is forecast but not known perfectly. Improving energy storage systems and energy management systems (EMS) development using optimization-based methods is a possible solution to improve the performance of microgrid operations. The EMS is an essential part of the distributed energy resources in the microgrid system, especially when power generation, transmission, distribution, utilization, and variable pricing are involved. This optimization process developed in this paper uses forecasted costs and loading conditions to store or sell the energy from an integrated grid battery system. Two approaches are introduced in this research work: the heuristic method using state flow (chart flow) and the optimization method based on linear programming (LP), which minimizes operation costs (savings of around 19% cost) subject to operational constraints. The LP optimization saves roughly 3.44–5.01% of excess grid energy. Several plausible outcomes of this research study simplify the comprehensive, integrated microgrid simulation for EMS optimization algorithm validation. The suggested integrated microgrid management system might be a testbed for smart grid technology research. • This paper uses linear programming optimization methods to develop a microgrid energy management system (EMS). • A constrained linear program (LP) optimization minimizes the total cost of electricity consumption. • The case studies are presented to show the performance of the proposed optimization and compare it with the heuristic approach. • The method predicts pricing and loading conditions and optimally stores/sells energy from a grid-scale battery system. • The cost of electricity consumption in the optimization method is around 19% less than in the heuristic method.

Analysis of crowdfunding platforms for microgrid project investors via a q-rung orthopair fuzzy hybrid decision-making approach

Effective crowdfunding platforms positively contribute toward improving microgrid energy management systems. Therefore, a comprehensive analysis is required to understand the key factors responsible for success in crowdfunding systems by considering various criteria. This study evaluates crowdfunding platforms for microgrid project investments. In this context, a novel fuzzy decision-making model that includes two different stages is proposed. First, the selected criteria for the crowdfunding platforms for microgrid project investments are evaluated. Second, alternatives, regarding the microgrid project investments, are ranked. In this process, a multi-stepwise weight assessment ratio analysis (M-SWARA) approach based on q-rung orthopair fuzzy sets (q-ROFSs) is considered. Intuitionistic and Pythagorean fuzzy sets are also used in the calculation process to make a comparative evaluation. Similarly, a sensitivity analysis of the ranking alternatives is also conducted with 12 different q values. All the results are rather similar; thus, the findings are reliable. Another model is also created for this purpose with the help of the decision-making trial and evaluation laboratory (DEMATEL) and the technique for order preference by similarity to ideal solution methodologies to check the performance of the proposed model. It is defined that by considering the q-ROF DEMATEL weights, the ranking results vary for different cases. The proposed model with a M-SWARA is more reliable than the model created via the DEMATEL method. This situation provides information regarding the superiority of the model proposed in this study. It is concluded that security is the most important factor in crowdfunding platforms for smart-grid project investors. Additionally, solar panels and energy storage systems/batteries are the most significant alternatives for microgrid project investors. Necessary measures should be taken to forestall the risk of fraud that may occur on this platform. Therefore, the website to be established must be secure against possible hacking attacks. Another important conclusion of this study is that solar panels should be preliminarily developed to increase the effectiveness of microgrid systems.

Smart centralized energy management system for autonomous microgrid using FPGA

Smart energy management system (SEMS) has become an effective energy-saving tool. In this paper, an efficient energy management system is used for a hybrid system consists of PV, Fuel Cells (FCs), and wind energy systems. The hybrid system is accompanied by a battery energy storage system to act as a backup source in case that the loads exceed the power produced from the three sources. The SEMS is implemented on Altera Cyclone IV EP4CE6 field-programmable gate arrays (FPGA) board using QUARTUS II software and LABVIEW. FPGAs are highly flexible and reprogrammable logic devices. Analog to digital converter (ADC) is used to interface the current and voltage of the three sources with FPGA. As a result of the weather fluctuations, the voltage of the three sources is not stable. Therefore, the voltage of the three sources is adjusted using the Buck-Boost converter technology, which is set at 24 V until the merging process takes place. The results prove the high efficiency of the FPGA and its high response to the load changes.

A community energy management system for smart microgrids

Community micro-grid energy projects are needed to drive de-carbonisation and increase equity of energy systems among displaced communities. However, micro-grid solutions are often inflexible and lack functionality to respond to displaced community energy needs and ensure the long-term sustainability of interventions. This paper explores the use of fog-computing retrofit architectures deployed on community micro-grid infrastructures to enable flexible demand management to improve service delivery and longevity. A micro-services solution is proposed that decouples components increasing resilience and testability while allowing hybrid edge-cloud deployments. The architecture is outlined and demonstrated for a micro-grid providing energy to two nurseries and a playground in Kigeme refugee camp, Rwanda. To enact the community priorities within the demand management system, modified Genetic Algorithm (GA) methods are outlined and tested for different use-case scenarios. The performance of the GA methods are then compared with a pre-existing battery protect controller and an alternative deterministic (space-shared) energy manager model. To achieve a high utility function in almost every use-case, a search space GA method was required for GA to outperform both the existing battery protect controller and proposed deterministic method. The results show how simple community priorities can be set and used to enact control on the system in 24h timeframes that are in line with the local decision-making context.

Microgrid energy management system for smart home using multi-agent system

This paper proposes a multi-agent system for energy management in a microgrid for smart home applications, the microgrid comprises a photovoltaic source, battery energy storage, electrical loads, and an energy management system (EMS) based on smart agents. The microgrid can be connected to the grid or operating in island mode. All distributed sources are implemented using MATLAB/Simulink to simulate a dynamic model of each electrical component. The agent proposed can interact with each other to find the best strategy for energy management using the java agent development framework (JADE) simulator. Furthermore, the proposed agent framework is also validated through a different case study, the efficiency of the proposed approach to schedule local resources and energy management for microgrid is analyzed. The simulation results verify the efficacy of the proposed approach using Simulink/JADE co-simulation.

A System Management Method for Photovoltaic Microgrid Based on Energy Circuit

Microgrid is an important part of smart grid, and the system management method of microgrid has been widely concerned. In this paper, a system modeling method based on energy circuit is proposed to model the microgrid system structure. The branches are represented by logic variables and state variables. The model of microgrid can be easily generated on the computer by inputting the parameters of nodes and branches. Then, a method of demand side response (DSR) based on probability is proposed to solve the problem of uncertain load estimation. On this basis, the DSR method is applied to energy management strategy (EMS), in which the return coefficients of loads are changed. The simulation results show that the modeling method is effective and feasible for the energy management system of microgrid. The EMS including demand side response can significantly reduce the start‐up time of diesel generator (DSL) and the operation cost of microgrid. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Energy management system for a small-scale microgrid

In recent years, the power system has been evolved into microgrids, which are little pockets of self-contained entities. Different distributed, interconnected generation units, loads, and energy storage units make up a typical microgrid system. The increased energy efficiency of these units on microgrids is gaining popularity day by day. Because of their stochastic behavior, renewable generation causes an imbalance in the power system, which needs microgrid energy management. To solve these issues, a variety of novel approaches have been explored in the literature. For the stand-alone microgrid in this research, efficient energy management and control mechanism is adopted. A photovoltaic system, a wind turbine, and a battery energy storage device make up this stand-alone microgrid. The power stability of the hybrid system is ensured by a sophisticated controller. The main purpose of this study is to regulate the DC/DC bidirectional converter (DBC), which connects the Li-ion battery to the DC bus of the stand-alone microgrid. This paper describes the development of a wind/photovoltaic power generation system to the load, as well as MPPT techniques like perturb and observe (P&O). The system is simulated and the results are presented using MPPT techniques. There is no requirement for a specific power model in the suggested method. Only power and voltage system data are used by DBC. A stand-alone microgrid system was simulated using MATLAB.

Predictive Control of Adaptive Micro-Grid Energy Management System Considering Electric Vehicles Integration

This paper addresses the problems of control and energy management in micro-grid with the incorporation of renewable energy generation, hybrid storage technologies, and the integration of the electric vehicles (EVs) with vehicle to grid (V2G) technology. The adaptive model predictive control (AMPC) technique is used to optimize the charge/discharge of the EVs in a receding horizon manner in order to reduce operational cost in a renewable energy-based micro-grid. V2G systems integration can be a crucial element in the assurance of network reliability against variability in loads. In this context, the paper presents an AMPC algorithm for the optimization of a micro-grid coupled with a V2G system consisting of six electric vehicle charging stations. The proposed algorithm effectively manages the use of renewable energy sources, vehicles charge, energy storage units, and the purchase and sale of electric power to the external network. Two scenarios are investigated in this paper to examine the performance of the proposed controller to manage the renewable energy sources in the micro-grid system. The first case uses a load shifting mechanism to solve the charge management problem during a known interval of parking time. The second case introduces the EVs with V2G capabilities when connected with the micro-grid. In this case, the vehicle battery collaborates with the ESS of the micro-grid to maximize costs benefits and mitigate the intermittency of renewable generation. Furthermore, other benefits of V2G concepts, such as voltage and frequency control for the micro-grid stability, are investigated. Therefore, it is evident from the obtained results that the proposed control algorithm was able to effectively manage the renewable energy sources, energy storage units, vehicles charge, and the purchase and sale of electric power with the grid. Keywords: Adaptive model predictive control, Energy management system, Electric vehicles, Vehicle to grid technology, Grid reliability, Load shifting, Optimization problem and MATLAB/Simulink.

A nonlinear double‐integral sliding mode controller design for hybrid energy storage systems and solar photovoltaic units to enhance the power management in DC microgrids

In this paper, a nonlinear decentralized double-integral sliding mode controller (DI-SMC) is designed along with an energy management system (EMS) for the DC microgrid (DCMG). This DCMG includes having a hybrid energy storage system (HESS) that incorporates a battery energy storage system (BESS) and supercapacitor energy storage system (SCESS) while the load demand is met through the power generated from solar photovoltaic (SPV) units. First, dynamical models of each subsystem of DCMGs such as the SPV system, BESS, and SCESS are developed to capture highly nonlinear behaviors of DCMGs under various operating conditions. The proposed nonlinear DI-SMC is then designed for each power unit in DCMGs to ensure the desired voltage level at the common DC-bus and appropriate power dispatch of different components to fulfill the load requirement of the DCMG. On the other hand, an energy management system (EMS) is designed to determine the set point for the controller with an aim of ensuring the power balance within DCMGs under various operating conditions where the overall stability is assessed using the Lyapunov theory. Simulation studies along with the processor-in-loop validation, including a comparative study with a proportional-integral (PI) controller, verify the applicability and effectiveness of the EMS-based DI-SMC under different operating conditions of the DCMG.

A Novel Deep Reinforcement Approach for IIoT Microgrid Energy Management Systems

Introducing Deep Learning in the Industrial Internet of Things (IIoT) brings many benefits, such as network resilience and bandwidth usage reduction. In this work, we propose an innovative reinforcement learning architecture to implement distributed energy management systems for microgrids. The architecture is based on novel reinforcement learning and on time series prediction. The designed reinforcement learning uses classical recurrent neural networks instead of the habitual SAR (State Action Reward) method that most of the recent bibliography considers. We applied various techniques (Exact resolution, Rule-Based, Q-Learning, and our designed reinforcement learning) on a distributed IIoT energy control architecture. The proposed method has shown better results compared to the exact resolution and the Q-Learning algorithm. It results in fast learning systems with a small number of training samples. We identified and tested several management strategies. Integer Linear Programming (ILP) optimal expressions and strategy-based implementations are derived. We utilize the obtained results to train the recurrent neural network. Comparative results are very encouraging and prone to a generalization of our approach instead of the classical methods.

Rule-Based Inferential System for Microgrid Energy Management System

The installation of grid-connected microgrids ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu {\rm{Gs}}$</tex-math></inline-formula> ) is considered a suitable solution to enhance the modernization of distributed generation systems into smart grids. This realization has raised the need for the development of an energy management system <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$( {{\rm{EMS}}} )$</tex-math></inline-formula> for achieving efficient monitoring, control, and management of the energy flows in the system. In this article, the fuzzy inference system (FIS) based EMS synthesis is proposed to efficiently control the distribution of energy flows of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu {\rm{G}}$</tex-math></inline-formula> in real time. The FIS is further optimized using a genetic algorithm approach to achieve faster evaluation and robust EMS development. In the development process of the EMS, the graph theory-based representation of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu {\rm{G}}$</tex-math></inline-formula> s is proposed for efficient representation of the energy demand and energy generation with the energy systems connected in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu {\rm{G}}$</tex-math></inline-formula> . To assess the performance of the proposed EMS, a photovoltaic generation of 19.95 kWp and an aggregated load of 8 kWp are characterized along with a battery energy storage system to form a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu {\rm{G}}$</tex-math></inline-formula> . The results identified the benefits of the proposed approach regarding profit generated and battery usage during the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu {\rm{G}}$</tex-math></inline-formula> operation.

Energy management and control system for microgrid based wind-PV-battery using multi-agent systems

Energy generation is currently evolving into a smart distribution system that incorporates several green energy resources at a distributed level, ensuring that clean energy is generated without releasing harmful gases, that operational procedures are consistent, and that energy management and supervision arrangements are improved. This paper proposes a multi-agent system-based microgrid energy management and proper control in distributed systems. For the complexity of energy management in distributed systems, a multi-agent system-based decentralized control architecture was developed. The proposed technique is based on several smart agents, each agent is based on the microgrid data for energy management and frequency control. The proposed energy management system based on the multi-agent system was tested by simulation under renewable resource fluctuations and seasonal load demand. The simulation results show that the proposed energy management system proved to be more resilient and high-performance controls than conventional centralized energy control systems.

Analysis of a linear programming‐based decision‐making model for microgrid energy management systems with renewable sources

With the increasing share of renewable energy sources in microgrids, systems enhancing the power flexibility at the demand side have become mandatory in microgrid architecture. Thus, the electrical energy storage system has been mostly integrated into microgrids although its capital and operating costs are very high. Hence, diversifying microgrid energy storage based on the end-energy usage can improve the reliability, operating cost, and power demand flexibility of the microgrid. The hereby study integrates an absorption chiller and electrical heat pump coupled to a heat storage into a renewable energy resources-based microgrid and develops its centralized energy management model for both off-grid and on-grid operation modes. The model considers the current energy level of energy storages in the microgrid, current, and forecasted information state to compute the vector-valued decision minimizing a realistic microgrid operating cost. The operating cost includes the energy purchasing/generation, heating/electrical storage, and penalty cost due to load shedding. Hence, such an objective function leads to maximizing the consumption of the generated renewable power, minimizing the energy storage and load shedding cost. The load shedding has been allowed to increase the flexibility in microgrid energy management. However, a high penalty has been imposed on each electrical, heating, or cooling load shedding decision. The decision vector components are thermal and electrical power flow between each energy source to loads and energy storage systems. The problem has been modeled as a Linear Programming with forecasted multi-parametric inputs at different prediction horizons. The effect of the charging/discharging rate and capacity of energy storages, coefficient of performance of absorption chiller and heat pump, prediction horizon, and energy level of storage devices provided to the myopic energy management model has been evaluated for better integration of renewable sources and thermal storage systems into microgrids. Suggestions have been made to improve the microgrid self-consumption, energy efficiency, and myopic decision-making model using the energy level of storage devices obtained from the full look-ahead optimization model. Highlights The optimization problem combining electricity, heating, and cooling is formulated The electrical and thermal storage charging/discharging rate significantly affect the power supplied by the renewable power source decision of the microgrid energy management model. Accurate energy storage level information into the myopic energy management model improves the model decisions. The absorption chiller coefficient of performance greatly impacts the cooling and electrical loads shedding.