Optimal Energy Management System Research Articles

An optimal home energy management system with integration of renewable energy and energy storage with home to grid capability

Residential building consumes a significant amount of energy. To address the issue, these structures have been supplied with renewable energy sources (RES), an energy storage system (ESS), and an electric vehicle (EV). In a home, a home energy management system (HEMS) has been implemented to schedule and regulate domestic appliances. Many studies in HEMS have been conducted in order to reduce the cost of power and the peak to average ratio (PAR). However, there is insufficient use of RES, ESS, EV, and excess domestic energy. As a result, this research presents a HEMS architecture that is integrated with RES and ESS and includes home-to-grid (H2G) power flow functionality. The RES electricity is initially utilized to power domestic appliances and charge ESS before being transferred back into the main grid to reap economic rewards. During the low-price period, the ESS and EV are charged and discharged during the high-price period. The paper built a multi-objective optimization problem using the provided approach that integrates electricity cost and system PAR. The grey wolf optimization algorithm is used to tackle the multi-objective optimization problem. The results clearly show that the proposed technique decreases expenditures by 45.80% and PAR by 28.44%, compared to the baseline timetable in 24 h. The H2G power flow feature allows the home to send back excess energy to the grid, cutting electricity expenditures by 70% and achieving additional economic benefits.

Energy management system for a hybrid PV-Wind-Tidal-Battery-based islanded DC microgrid: Modeling and experimental validation

DC microgrids are becoming integral part of a modern power system due to the growing penetration of DC distributed energy resources and loads, with additional advantages in terms of power quality improvement. They can be used for remote area electrification including villages, mountains, and islands. In this paper, an islanded DC microgrid is considered as a case study for islands with tidal energy potential, which consists of renewable energy resources including solar, wind and tidal, along with Li-ion battery storage. To ensure the efficient operation, an optimal energy management system is proposed for resource scheduling and utilization. In this regard, this paper proposes a two-stage supervisory energy management system for optimal operation of PV/Wind/Tidal microgrid with one-time communication to avoid excessive use of communication bandwidth in real-time. The primary stage schedules the optimal energy share from each energy source thereby enhancing the efficiency of the operation. The secondary stage updates the decision strategies by analyzing aggregated scheduled generation and demand profiles to ensure an effective utilization. The proposed energy management model has been experimentally validated to prove its effectiveness in achieving optimized operation for an islanded DC microgrid. Word Count: 4380

Interactive Multi-level planning for energy management in clustered microgrids considering flexible demands

This paper presents a novel interactive multi-level planning strategy for the energy management of distribution networks with clustered microgrids (CMGs). CMGs are a group of microgrids with multiple renewable energy resources that comprise various technologies, such as photovoltaic systems, wind turbines, micro turbines and electric vehicles. This study develops an innovative multi-level optimization framework for the energy management coordination between microgrids and CMGs in the lower level, between clusters and distribution systems, and finally between distribution systems and upstream networks in the upper level. Accordingly, an hourly optimal energy management (HOEM) system is applied to minimize the multi-objective objective function for each level. The lower level may be operated in islanded or grid-connected mode in some hours. This is decided by changing switches between MGs, clusters, and grids, while the upper level is only operated in the grid-connected mode. Moreover, a demand response program that has a great effect on the hourly planning of switches is modeled in the upper level. The proposed model is tested on CMGs and actual distribution systems. The results show the significance of this planning strategy in the techno-economic aspects and optimal power transaction in the distribution system operation.

Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets

A multi-energy microgrid (MEMG) consisting of different forms of distributed generation, e.g., combined heat and power (CHP) units and renewable distributed energy resources (RDERs), is considered as a key technology for accommodating RDERs and for the introduction of multiple forms of energy sources into the electricity market due to the multi-energy complementarity and flexible operation modes. However, the MEMG is subject to source and demand uncertainties which are the primary obstacles to its market participation. The source and demand uncertainties will pose serious challenges to the management of the MEMG and incur the penalty cost to participate in the real-time market. To minimize the operational cost, a multi-stage optimal energy management system (EMS) for participating in the deregulated electricity market considering the cost of market participation and the additional cost (e.g. the purchasing natural gas cost and the depreciation cost of energy storage system) is proposed in this paper. The proposed EMS consists of forecasting stage, day-ahead scheduling stage and real-time dispatch stage. The long short-term memory (LSTM) is adopted for day-ahead data forecasting during the forecasting stage. In the day-ahead scheduling stage, the cost for participating in the day-ahead market (DM) is minimized based on the forecasted data. In the real-time dispatch stage, the cost for participating in the intraday balancing market (IBM) and the additional cost are minimized based on the rolling model predictive control (MPC) method. The proposed method is verified and compared with benchmark solutions. The numerical results demonstrate that the proposed solution can outperform the benchmark solutions and reduce the peak-to-average ratio of the total net-load of multiple MEMGs.

Implementing an Optimal Energy Management System for a Set of Microgrids Using the Harmony Search Algorithm

A microgrid (MG) refers to a set of loads, generation resources and energy storage systems acting as a controllable load or a generator to supply power and heating to a local area. The MG-generated power management is a central topic for MG design and operation. The existence of dispersed generation (DG) resources has faced MG management with new issues. Depending on the level of exchanges between an MG and the main grid, the MG operation states can be divided into independent or grid-connected ones. Energy management in MGs aims to supply power at the lowest cost for optimal load response. This study examines MG energy management in two operational modes of islanded and grid-connected, and proposes a structure with two control layers (primary and secondary) for energy management. At the principal level of control, the energy management system is determined individually for all MG by taking into consideration the probability constraints and RES uncertainty by the Weibull the probability density function (PDF), generation resources’ power as well as the generation surplus and deficit of each MG. Then, the information of the power surplus and deficit of each MG must be sent to the central energy management system. To confirm the proposed structure, a case system with two MGs and a condensive load is simulated by using a multi-time harmony search algorithm. Several scenarios are applied to evaluate the performance of this algorithm. The findings clearly show the effectiveness of the proposed system in the energy management of several MGs, leading to the optimal performance of the resources per MG. Moreover, the proposed control scheme properly controls the MG and grid’s performance in their interactions and offers a high level of robustness, stable behavior under different conditions and high quality of power supply.

Optimizing energy consumption patterns of smart home based on Sine Cosine Algorithm

This paper presents a new application of the sine cosine algorithm (SCA) to obtain optimal home energy management systems (HEMS) that use the load shifting strategy of demand side management (DSM) to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to decrease electricity bills and peak to average ratio (PAR) while maintaining user comfort through coordination among home devices. In order to meet the load demand of electricity consumers, this study schedules the load on a day-ahead and real-time basis. The main objective of the paper is to help balance the load through ON-peak hours and OFF-peak hours. Three price signals, namely: real-time pricing (RTP), time of use (TOU), and critical peak pricing (CPP), are used to evaluate the proposed algorithm. The results show that the electricity bill and PAR are minimized by up to 40% and 50%, respectively. The scheduling using coordination between devices in real-time does not significantly impact the cost of electricity and the peak to average ratio.

Optimal energy management system for microgrids considering energy storage, demand response and renewable power generation

To ensure the autonomous power supply in microgrids (MGs) in stand-alone mode while also maintaining stability, energy storage systems (ESSs) and demand-side flexibility can be utilized together. Motivated by this fact, in this study, a scenario-based energy management system (EMS) modelled as a mixed-integer linear programming (MILP) problem is presented by taking the stochastic nature of wind and photovoltaic (PV) sources into account in order to analyze the operational behaviour of MGs and thereby to reduce the network energy losses. Direct load control (DLC) based demand response (DR) program is implemented to the system with the objective of exploiting the remarkable potential of thermostatically controllable appliances (TCAs) for energy reduction while satisfying comfort and operational constraints. Furthermore, a common ESS with a bi-directional power flow facility is incorporated in the proposed structure and electric vehicles (EVs) are employed as an additional flexible load in grid-to-vehicle (G2V) mode. To testify the effectiveness of the proposed optimization algorithm, different case studies are conducted considering diverse scenarios. Moreover, the performance is compared with a deterministic method from the perspective of achieving loss reduction and capturing the uncertainties.

Optimal Scheduling of Campus Microgrid Considering the Electric Vehicle Integration in Smart Grid.

High energy consumption, rising environmental concerns and depleting fossil fuels demand an increase in clean energy production. The enhanced resiliency, efficiency and reliability offered by microgrids with distributed energy resources (DERs) have shown to be a promising alternative to the conventional grid system. Large-sized commercial customers like institutional complexes have put significant efforts to promote sustainability by establishing renewable energy systems at university campuses. This paper proposes the integration of a photovoltaic (PV) system, energy storage system (ESS) and electric vehicles (EV) at a University campus. An optimal energy management system (EMS) is proposed to optimally dispatch the energy from available energy resources. The problem is mapped in a Linear optimization problem and simulations are carried out in MATLAB. Simulation results showed that the proposed EMS ensures the continuous power supply and decreases the energy consumption cost by nearly 45%. The impact of EV as a storage tool is also observed. EVs acting as a source of energy reduced the energy cost by 45.58% and as a load by 19.33%. The impact on the cost for continuous power supply in case of a power outage is also analyzed.

Comparison of Two Energy Management System Strategies for Real-Time Operation of Isolated Hybrid Microgrids

The propagation of hybrid power systems (solar–diesel–battery) has led to the development of new energy management system (EMS) strategies for the effective management of all power generation technologies related to hybrid microgrids. This paper proposes two novel EMS strategies for isolated hybrid microgrids, highlighting their strengths and weaknesses using simulations. The proposed strategies are different from the EMS strategies reported thus far in the literature because the former enable the real-time operation of the hybrid microgrid, which always guarantees the correct operation of a microgrid. The priority EMS strategy works by assigning a priority order, while the optimal EMS strategy is based on an optimization criterion, which is set as the minimum marginal cost in this case. The results have been obtained using MATLAB/Simulink to verify and compare the effectiveness of the proposed strategies, through a dynamic microgrid model to simulate the conditions of a real-time operation. The differences in the EMS strategies as well as their individual strengths and weaknesses, are presented and discussed. The results show that the proposed EMS strategies can manage the system operation under different scenarios and help power system operator obtain the optimal operation schemes of the microgrid.

A sensor-less control and optimal energy management algorithm for a stand-alone photovoltaic system considering partial shading condition

One of the most important part of a stand-alone photovoltaic system is energy management. This part uses several sensors to measure solar radiations, load power and battery energy to determine operation of the system components. Reduction the number of sensors and optimization of energy management system are two key steps to improve the efficiency and reliability of the system without additional cost. Hence, in this paper, a comprehensive energy management algorithm along with a complementary algorithm to estimate radiation under uniform and partial shading conditions is proposed. According to the system state, the energy management algorithm commands to activate/ deactivate a part or entire of photovoltaic system which can increase the system lifetime and thus increase the system reliability. Another feature of the proposed algorithm is a new mode in the absence of radiation for parallelizing and sharing the battery’s current to the load by unidirectional and bidirectional converters. Furthermore, a sensor-less complementary algorithm to determine the amount of radiation and also the partial shading condition is proposed. To prove the feasibility of the proposed algorithms, the system was simulated and constructed. The simulation and experimental results are found closely comparable and confirm the effectiveness of the proposed methods.

Optimal energy management system using biogeography based optimization for grid-connected MVDC microgrid with photovoltaic, hydrogen system, electric vehicles and Z-source converters

Currently, the technology associated with charging stations for electric vehicles (EV) needs to be studied and improved to further encourage its implementation. This paper presents a new energy management system (EMS) based on a Biogeography-Based Optimization (BBO) algorithm for a hybrid EV charging station with a configuration that integrates Z-source converters (ZSC) into medium voltage direct current (MVDC) grids. The EMS uses the evolutionary BBO algorithm to optimize a fitness function defining the equivalent hydrogen consumption/generation. The charging station consists of a photovoltaic (PV) system, a local grid connection, two fast charging units and two energy storage systems (ESS), a battery energy storage (BES) and a complete hydrogen system with fuel cell (FC), electrolyzer (LZ) and hydrogen tank. Through the use of the BBO algorithm, the EMS manages the energy flow among the components to keep the power balance in the system, reducing the equivalent hydrogen consumption and optimizing the equivalent hydrogen generation. The EMS and the configuration of the charging station based on ZSCs are the main contributions of the paper. The behaviour of the EMS is demonstrated with three EV connected to the charging station under different conditions of sun irradiance. In addition, the proposed EMS is compared with a simpler EMS for the optimal management of ESS in hybrid configurations. The simulation results show that the proposed EMS achieves a notable improvement in the equivalent hydrogen consumption/generation with respect to the simpler EMS. Thanks to the proposed configuration, the output voltage of the components can be upgraded to MVDC, while reducing the number of power converters compared with other configurations without ZSC.

Dynamic Real-Time Pricing Mechanism for Electric Vehicles Charging Considering Optimal Microgrids Energy Management System

Managing electric vehicles (EVs) charging activities when the power grid is congested is a challenging task. This article proposes a dynamic, real-time, demand-influenced energy pricing mechanism to accurately allocate fairness to the billing strategy of the EVs charging process. The proposed mechanism is based on the concept of the inverse-demand function to reflect instantaneous energy prices following the microgrids’ real-time energy supply. It comprises two pricing fractions: retail energy price representing the energy cost for non-EVs demand during peak hours, and congested energy price allocated for billing EVs charging events during congested timeslots. The proposed methodology is implemented in a hierarchal multiagent architecture with an optimal energy management system to provide a cost-efficient microgrid operation. The input to the optimization problem is stochastic models that represent day-ahead photovoltaic (PV) production forecast, EVs energy levels, and connectivity times’ prediction models based on a discrete-time Markov chain. Additionally, a predictive model of daily load is also proposed based on adaptive artificial neural networks. The aforementioned models utilized historical data for Miami-Dade County, South Florida. Through numerical simulations, we attest that the proposed pricing mechanism achieves significant energy price reduction for non-EVs consumers when compared with results from previously published pricing policies.

Design and optimization of off‐grid hybrid renewable power plant with storage system for rural area in Rwanda

In this paper, a system comprising a solar photovoltaic (PV)/micro-hydropower/battery bank/converter has been designed, modelled, simulated, and optimized for the rural area of Wimana village, Rwanda. The total load has been fairly estimated for the residential electric utility needs. To satisfy the load demand, solar photovoltaic (4 kW) and micro-hydro (15 kW) power capacity were considered as the main sources of energy to supply electricity. Either in peak load times or during low generation of primary sources, storage battery banks can also be discharged. The simulation and optimization were done based on the load demand, climatic data, the economics of integrated system components, and other parameters in which the total net present cost has to be minimized to select an economically feasible and technically capable hybrid power system. Based on the load profile of the data collected for the village, the daily energy consumption was found to be 180.99 kWh/day with a peak load demand of 18.56 kW. The net present cost and the cost of energy for the optimized power system amount to $78,763.26 and $0.0757/kWh respectively. HOMER Pro simulating software tool was used to design optimal off-grid and energy management systems.

Design of Optimal Energy Management System in a Residential Microgrid Based on Smart Control

ABSTRACT Optimal use of microgrids for efficient and economic management of energy resources is of special importance. At the residential level, energy costs can be reduced by using the concepts of price-based intelligent control (response to demand). In this paper, an energy management problem that proposed to decrease the cost of operating a residential microgrid with Micro-CHP equipped by a smart meter. The problem objective function formulation in the form of short-term production planning is based on the total cost of Micro-CHP gas consumption and auxiliary burner and the cost of electricity purchased from the network or sell to it in 24 hours. The aim is to present the ideal energy management strategy of the resources available based on smart control, and due to the non-linear problem, GAMS software by Mixed Integer Non-Linear Programming (MINLP) was used to solve the problem. Finally, the proposed method is implemented on the sample microgrid and the results are compared with Heat-Led Control, the effect of battery on microgrid participation in load response programs was demonstrated. Also, the effect of different tariffs of the market price on the cost of microgrid operation and how to exchange with the grid in different cases are evaluated that results showed the proper performance of the proposed method.

An optimal stochastic energy management system for resilient microgrids

This paper presents a stochastic mixed-integer nonlinear programming model for the optimal energy management system of unbalanced three-phase of alternating current microgrids. The proposed model considers the following random variables: nodal demands, nodal renewable generation and voltage reference at the point of common coupling. Furthermore, the proposed model is aimed at providing resilient energy management system solutions via contingency constraints. The proposed mixed-integer nonlinear programming model is transformed into a mixed-integer linear programming model through a set of linearizations that can be solved via off-the-shelf convex programming solvers. The analyzed microgrid comprises photovoltaic generation, energy storage systems, electric vehicle chargers, direct load control, and non-renewable generation, which operates when the microgrid is in islanded mode. The stochastic nature of the problem is considered through a scenario-based approach. The solution to the model determines the day-ahead operation of the microgrid resources that minimizes the average operational cost. An unexpected islanded operation at any given time is considered via contingency constraints. Tests are performed using data of the real microgrid at the Laboratory of Intelligent Electrical Networks (LabREI), at University of Campinas. Results show that the proposed model produces resilient day-ahead energy management system solutions while minimizing the average operational costs and maximizing the use of local renewable energy sources.

Applying a Theta-Krill Herd Algorithm to Energy Management of a Microgrid Considering Renewable Energies and Varying Weather Conditions

Abstract Recently, researchers have shown an increased interest in using renewable-based distributed generations (DGs) in microgrids (MGs). Therefore, the economic operation of MGs plays a vital role in reducing total daily costs and greenhouse gas emissions in the modern power system. This study presents a day-ahead optimization management for a grid-tied MG supplied by small-scale renewable energy sources (RESs) like photovoltaic (PV) systems. The major aim of the suggested optimal energy management system is to minimize the cost of RERs and storage facilities in the MG for power generation while satisfying technical constraints. In addition, an improved mathematical model is suggested for the PV power generation using real data for four dissimilar days. To attain accurate results, uncertainties in the generations, load demand, and market price are probabilistically modeled. To handle the optimization problem, the θ-modified krill herd (θ-MKH) algorithm is used. The suggested algorithm for solving the optimization problem is investigated on numerical examples with different RESs and storages in the MG and compared with conventional approaches. The results attained illustrate that the recommended algorithm can utilize the cheapest sources while covering technical constraints.

An optimized energy management system for vehicle to vehicle power transfer using micro grid charging station integrated Gridable Electric Vehicles

World nations are promoting Gridable Electric Vehicles (GEVs’) in the context of increasing environmental pollution. GEVs’ include Plug-in Hybrid Electric vehicles (PHEVs’) and Battery Electric vehicles (EVs). The increased penetration of GEVs’ into the utility grid for charging, can lead to grid instability and regulation issues. The charging scenarios would be un-coordinated in countries like India, resulting in unstable load demand on the grid. The solution to this is the adoption of an aggregator regulated Micro Grid charging station (MGCS). The MGCS would facilitate energy flow to acceptor GEV clients from donor GEV clients through Vehicle to Vehicle (V2V) power transfer, thereby reducing the load on the utility grid. The MGCS would be supported by a photovoltaic system. This paper proposes the utilization of GEVs’ in supporting the MGCS through the regulation of an on-board Intelligent Energy Management System (IEMS). The IEMS performs communication and price negotiation with the MGCS aggregator; decides the energy level that the donor GEV clients can provide considering its future journey and criticality; and regulate the on board converter to facilitate slow, regular and fast charging of the acceptor GEV clients.

An Optimal Energy-Saving Home Energy Management Supporting User Comfort and Electricity Selling With Different Prices

In this study, we investigate the operation of an optimal home energy management system (HEMS) with integrated renewable energy system (RES) and energy storage system (ESS) supporting electricity selling functions. A multi-objective mixed integer nonlinear programming model, including RES, ESS, home appliances and the main grid, is proposed to optimize different and conflicting objectives which are energy cost, user comfort and PAR. The effect of different selling prices on the objectives is also considered in detail. We further develop a formula for the lower bound of energy cost to help residents or engineers quickly choose best parameters of RES and ESS for their homes during the installation process. The performance of our system is verified through extensive simulations under three different scenarios of normal, economic, and smart with different selling prices using real data, and simulation results are compared in terms of daily energy cost, PAR, user's convenience and consecutive waiting time to use appliances. Numerical results clearly show that the economic scenario achieves 51.6% reduction of daily energy cost compared to the normal scenario while sacrificing the user's convenience, PAR, and consecutive waiting time by 49%, 132%, and 1 hour, respectively. On the other hand, the smart scenario shows only slight degradation of user's convenience and PAR by 2% and 18%, respectively while achieving 46.4% reduction of daily energy cost and the same level of consecutive waiting time. Furthermore, our simulation results show that a decrease of selling prices has tiny impacts on PAR and user comfort even though the daily energy cost increases.

Reinforcement Learning Based EV Charging Management Systems–A Review

To mitigate global warming and energy shortage, integration of renewable energy generation sources, energy storage systems, and plug-in electric vehicles (PEVs) have been introduced in recent years. The application of electric vehicles (EV) in the smart grid has shown a significant option to reduce carbon emission. However, due to the limited battery capacity, managing the charging and discharging process of EV as a distributed power supply is a challenging task. Moreover, the unpredictable nature of renewable energy generation, uncertainties of plug-in electric vehicles associated parameters, energy prices, and the time-varying load create new challenges for the researchers and industries to maintain a stable operation of the power system. The EV battery charging management system plays a main role in coordinating the charging and discharging mechanism to efficiently realize a secure, efficient, and reliable power system. More recently, there has been an increasing interest in data-driven approaches in EV charging modeling. Consequently, researchers are looking to deploy model-free approaches for solving the EV charging management with uncertainties. Among many existing model-free approaches, Reinforcement Learning (RL) has been widely used for EV charging management. Unlike other machine learning approaches, the RL technique is based on maximizing the cumulative reward. This article reviews the existing literature related to the RL-based framework, objectives, and architecture for the charging coordination strategies of electric vehicles in the power systems. In addition, the review paper presents a detailed comparative analysis of the techniques used for achieving different charging coordination objectives while satisfying multiple constraints. This article also focuses on the application of RL in EV coordination for research and development of the cutting-edge optimized energy management system (EMS), which are applicable for EV charging.

Optimal Schedule for Networked Microgrids Under Deregulated Power Market Environment Using Model Predictive Control

Microgrids are considered as a key technology for the introduction of renewable energy systems in the electrical market. Nevertheless, microgrids are subject to random conditions such as changes in the energy forecast or component failures which will force microgrids to incur in the penalty costs applied in real time markets. In order to minimize the aforementioned costs, an optimal Energy Management System (EMS) for the economic re-schedule of a network of interconnected microgrids with hybrid Energy Storage System (ESS) under failure conditions is developed and validated using Model Predictive Control (MPC). The algorithm is specifically designed to achieve lower economic losses under failure conditions through the establishment of a local energy market in the network of microgrids than participating in the intraday or real-time markets.