scholarly journals Optimal Operating Schedule for Energy Storage System: Focusing on Efficient Energy Management for Microgrid

Processes ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 80 ◽  
Author(s):  
Sooyoung Jung ◽  
Yong Tae Yoon
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Power Grid ◽  
Optimal Operation ◽  
Operating Conditions ◽  
Mixed Integer ◽  
Small Scale ◽  
Basic Operation ◽  
Electricity Use ◽  
Operation Schedule

A microgrid is a group of many small-scale distributed energy resources, such as solar/wind energy sources, diesel generators, energy storage units, and electric loads. As a small-scale power grid, it can be operated independently or within an existing power grid(s). The microgrid energy management system is a system that controls these components to achieve optimized operation in terms of price by reducing costs and maximizing efficiency in energy consumption. A post-Industry-4.0 consumer requires an optimal design and control of energy storage based on a demand forecast, using big data to stably supply clean, new, and renewable energy when necessary while maintaining a consistent level of quality. Thus, this study focused on software technology through which an optimized operation schedule for energy storage in a microgrid is derived. This energy storage operation schedule minimizes the costs involved in electricity use. For this, an optimization technique is used that sets an objective function representing the information and costs pertaining to electricity use, while minimizing its value by using Mixed Integer Linear Programming or a Genetic Algorithm. The main feature of the software is that an optimal operation schedule derivation function has been implemented with MATLAB for the following circumstances: when the basic operation rules are applied, when operating with another grid, when the external operating conditions are applied, and when the internal operating conditions are applied.

scholarly journals A Prosumer Model Based on Smart Home Energy Management and Forecasting Techniques

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1724
Author(s):  
Nikolaos Koltsaklis ◽  
Ioannis P. Panapakidis ◽  
David Pozo ◽  
Georgios C. Christoforidis
Keyword(s):  
Neural Network ◽  
Energy Storage ◽  
Electric Vehicle ◽  
Energy Management ◽  
Power Grid ◽  
Renewable Energy Sources ◽  
Energy System ◽  
Mixed Integer ◽  
Elman Neural Network ◽  
Home Energy Management

This work presents an optimization framework based on mixed-integer programming techniques for a smart home’s optimal energy management. In particular, through a cost-minimization objective function, the developed approach determines the optimal day-ahead energy scheduling of all load types that can be either inelastic or can take part in demand response programs and the charging/discharging programs of an electric vehicle and energy storage. The underlying energy system can also interact with the power grid, exchanging electricity through sales and purchases. The smart home’s energy system also incorporates renewable energy sources in the form of wind and solar power, which generate electrical energy that can be either directly consumed for the home’s requirements, directed to the batteries for charging needs (storage, electric vehicles), or sold back to the power grid for acquiring revenues. Three short-term forecasting processes are implemented for real-time prices, photovoltaics, and wind generation. The forecasting model is built on the hybrid combination of the K-medoids algorithm and Elman neural network. K-medoids performs clustering of the training set and is used for input selection. The forecasting is held via the neural network. The results indicate that different renewables’ availability highly influences the optimal demand allocation, renewables-based energy allocation, and the charging–discharging cycle of the energy storage and electric vehicle.

scholarly journals Optimal operation of a hybrid-energy microgrid with energy storage system

2019 ◽  
Vol 138 ◽  
pp. 01003 ◽  
Author(s):  
Mohamed Elgamal ◽  
Nikolay V. Korovkin ◽  
Ahmed Refaat ◽  
Akram Elmitwally
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Storage System ◽  
Bat Algorithm ◽  
Energy Storage System ◽  
Optimal Operation ◽  
Rule Base ◽  
Hybrid Energy ◽  
Management Scheme ◽  
Management Optimization

In this paper, a day-ahead profit-maximizing energy management scheme for a grid-tied microgrid operation is proposed. The microgrid contains various types of distributed energy resources (DERs) and an inverter-interfaced battery-bank storage system. The average of day-ahead hourly forecasted data for loads, wind speed, and solar radiation are inputted into the framework of energy management (EMF). To optimize the microgrid performance, EMF determines the hourly dispatch of reactive and active power for each DER. Also, it specifies the discharging and charging times of the energy storage system and the onload tap changer position setting of the transformer connected to the main grid. The main aim is to maximize the revenue of microgrid meeting all technical limitations. The main grid can sell/buy reactive and active powers to/from the microgrid with a variable daily energy price of the market. A collective rule base-BAT algorithm is implemented as a solver of the energy management optimization problem for a grid-tided microgrid. Furthermore, the ability of the suggested EMF is proved in comparison with recent approaches.

scholarly journals Enhanced Intelligent Energy Management System for a Renewable Energy-Based AC Microgrid

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3268
Author(s):  
Mehdi Dhifli ◽  
Abderezak Lashab ◽  
Josep M. Guerrero ◽  
Abdullah Abusorrah ◽  
Yusuf A. Al-Turki ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Energy Management ◽  
Management System ◽  
Optimal Operation ◽  
Green Energy ◽  
Energy Management System ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Ac Microgrid

This paper proposes an enhanced energy management system (EEMS) for a residential AC microgrid. The renewable energy-based AC microgrid with hybrid energy storage is broken down into three distinct parts: a photovoltaic (PV) array as a green energy source, a battery (BT) and a supercapacitor (SC) as a hybrid energy storage system (HESS), and apartments and electric vehicles, given that the system is for residential areas. The developed EEMS ensures the optimal use of the PV arrays’ production, aiming to decrease electricity bills while reducing fast power changes in the battery, which increases the reliability of the system, since the battery undergoes fewer charging/discharging cycles. The proposed EEMS is a hybrid control strategy, which is composed of two stages: a state machine (SM) control to ensure the optimal operation of the battery, and an operating mode (OM) for the best operation of the SC. The obtained results show that the EEMS successfully involves SC during fast load and PV generation changes by decreasing the number of BT charging/discharging cycles, which significantly increases the system’s life span. Moreover, power loss is decreased during passing clouds phases by decreasing the power error between the extracted power by the sources and the required equivalent; the improvement in efficiency reaches 9.5%.

scholarly journals Analysis of Different Energy Storage Technologies for Microgrids Energy Management

2020 ◽  
Vol 173 ◽  
pp. 03004
Author(s):  
Darío Benavides ◽  
Paúl Arévalo ◽  
Luis G. Gonzalez ◽  
José A. Aguado
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Storage Systems ◽  
Storage System ◽  
Lithium Ion ◽  
Operating Conditions ◽  
Energy Storage Systems ◽  
Efficiency Performance ◽  
Storage Technologies ◽  
Different Levels

The importance of energy storage systems is increasing in microgrids energy management. In this study, an analysis is carried out for different types of energy storage technologies commonly used in the energy storage systems of a microgrid, such as: lead acid batteries, lithium ion batteries, redox vanadium flux batteries and supercapacitors. In this work, it is analyzed the process of charging and discharging (slow and fast) in these systems, the calculation of energy efficiency, performance and energy supplied under different load levels, in its normal operating conditions and installed power capacity is developed. The results allow us to choose the optimal conditions of charge and discharge at different levels of reference power, analyzing the strengths and weaknesses of the characteristics of each storage system within a microgrid.

scholarly journals Optimal Operation of Interprovincial Hydropower System Including Xiluodu and Local Plants in Multiple Recipient Regions

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 144 ◽  
Author(s):  
Jianjian Shen ◽  
Xiufei Zhang ◽  
Jian Wang ◽  
Rui Cao ◽  
Sen Wang ◽  
...  
Keyword(s):  
Power Grid ◽  
Optimal Operation ◽  
Dry Season ◽  
Power Grids ◽  
Mixed Integer ◽  
Multi Objective Optimization ◽  
Hydropower Generation ◽  
Multi Objective ◽  
Hydropower System ◽  
Local Plants

This paper focuses on the monthly operations of an interprovincial hydropower system (IHS) connected by ultrahigh voltage direct current lines. The IHS consists of the Xiluodu Hydropower Project, which ranks second in China, and local plants in multiple recipient regions. It simultaneously provides electricity for Zhejiang and Guangdong provinces and thus meets their complex operation requirements. This paper develops a multi-objective optimization model of maximizing the minimum of total hydropower generation for each provincial power grid while considering network security constraints, electricity contracts, and plant constraints. The purpose is to enhance the minimum power in dry season by using the differences in hydrology and regulating storage of multiple rivers. The TOPSIS method is utilized to handle this multi-objective optimization, where the complex minimax objective function is transformed into a group of easily solved linear formulations. Nonlinearities of the hydropower system are approximatively described as polynomial formulations. The model was used to solve the problem using mixed integer nonlinear programming that is based on the branch-and-bound technique. The proposed method was applied to the monthly generation scheduling of the IHS. Compared to the conventional method, both the total electricity for Guangdong Power Grid and Zhejiang Power Grid during dry season increased by 6% and 4%, respectively. The minimum monthly power also showed a significant increase of 40% and 31%. It was demonstrated that the hydrological differences between Xiluodu Plant and local hydropower plants in receiving power grids can be fully used to improve monthly hydropower generation.

scholarly journals Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2712 ◽  
Author(s):  
Mahmoud Elkazaz ◽  
Mark Sumner ◽  
David Thomas
Keyword(s):  
Energy Storage ◽  
Real Time ◽  
Energy Management ◽  
Storage System ◽  
Reference Value ◽  
Energy Storage System ◽  
Small Scale ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Battery Energy

A new energy management system (EMS) is presented for small scale microgrids (MGs). The proposed EMS focuses on minimizing the daily cost of the energy drawn by the MG from the main electrical grid and increasing the self-consumption of local renewable energy resources (RES). This is achieved by determining the appropriate reference value for the power drawn from the main grid and forcing the MG to accurately follow this value by controlling a battery energy storage system. A mixed integer linear programming algorithm determines this reference value considering a time-of-use tariff and short-term forecasting of generation and consumption. A real-time predictive controller is used to control the battery energy storage system to follow this reference value. The results obtained show the capability of the proposed EMS to lower the daily operating costs for the MG customers. Experimental studies on a laboratory-based MG have been implemented to demonstrate that the proposed EMS can be implemented in a realistic environment.

scholarly journals Optimal Operation of Soft Open Points-Based Energy Storage in Active Distribution Networks by Considering the Battery Lifetime

2021 ◽  
Vol 8 ◽  
Author(s):  
Jian Wang ◽  
Niancheng Zhou ◽  
Anqi Tao ◽  
Qianggang Wang
Keyword(s):  
Energy Storage ◽  
Reactive Power ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Operation Model ◽  
Battery Lifetime ◽  
Battery Degradation ◽  
Active Distribution Networks

Soft open point-based energy storage (SOP-based ES) can transfer power in time and space and also regulate reactive power. These characteristics help promote the integration of distributed generations (DGs) and reduce the operating cost of active distribution networks (ADNs). Therefore, this work proposed an optimal operation model for SOP-based ES in ADNs by considering the battery lifetime. First, the active and reactive power equations of SOP-based ES and battery degradation cost were modeled. Then, the optimal operation model that includes the operation cost of ADNs, loss cost, and battery degradation cost was established. The mixed integer nonlinear programming model was transformed to a mixed integer linear programming model derived through linearization treatment. Finally, the feasibility and effectiveness of the proposed optimization model are verified by the IEEE33 node system.

scholarly journals Water-Energy Management for Demand Charges and Energy Cost Optimization of a Pumping Stations System under a Renewable Virtual Power Plant Model

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2900
Author(s):  
Natalia Naval ◽  
Jose M. Yusta
Keyword(s):  
Power Plant ◽  
Energy Management ◽  
Energy Cost ◽  
Irrigation System ◽  
Market Price ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Virtual Power ◽  
Virtual Power Plant ◽  
Operating Profit

The effects of climate change seriously affect agriculture at different latitudes of the planet because periods of drought are intensifying and the availability of water for agricultural irrigation is reducing. In addition, the energy cost associated with pumping water has increased notably in recent years due to, among other reasons, the maximum demand charges that are applied annually according to the contracted demand in each facility. Therefore, very efficient management of both water resources and energy resources is required. This article proposes the integration of water-energy management in a virtual power plant (VPP) model for the optimization of energy costs and maximum demand charges. For the development of the model, a problem related to the optimal operation of electricity generation and demand resources arises, which is formulated as a nonlinear mixed-integer programming model (MINLP). The objective is to maximize the annual operating profit of the VPP. It is worth mentioning that the model is applied to a large irrigation system using real data on consumption and power generation, exclusively renewable. In addition, different scenarios are analyzed to evaluate the variability of the operating profit of the VPP with and without intraday demand management as well as the influence of the wholesale electricity market price on the model. In view of the results obtained, the model that integrates the management of the water-energy binomial increases the self-consumption of renewable energy and saves electricity supply costs.

An energy scheduling method for multiple users of residential community connected to the grid and wind energy source

2017 ◽  
Vol 39 (3) ◽  
pp. 295-309 ◽  
Author(s):  
Sandeep Kakran ◽  
Saurabh Chanana
Keyword(s):  
Energy Source ◽  
Wind Energy ◽  
Energy Management ◽  
Mixed Integer ◽  
Small Scale ◽  
Management Problem ◽  
Satisfaction Level ◽  
Cost Of Electricity ◽  
Multiple Users ◽  
Residential Consumers

Energy management of residential consumers plays a very important role in the achievement of aim of energy saving, lesser cost of electricity for the residential consumers and highest possible satisfaction of the consumers. In this paper, a common energy scheduler for the multiple users has been designed for energy scheduling of residential consumers having smart appliances. Mathematical modelling of different types of appliances has been done to achieve the required results. Time of use tariff and day ahead real time pricing tariff have been used for energy scheduling. A wind energy source has been considered to reduce the burden of energy demand on the grids and to analyse the effect of renewable energy sources in a small scale energy management problem. A delay factor has been also included in the main objective function of the problem to maintain the satisfaction level of the consumers by reducing cost of waiting. The final designed problem is solved by mixed integer linear programming technique using GAMS software. The time taken for the solution is very less. In this paper, to increase the energy efficiency and the accuracy of the required results, the selected time horizon of 24 h is divided into 96 small time slots, each of 15 min duration. Achievement of the aim of the paper, that is reduction in the cost of electricity and attainment of the highest possible satisfaction level of the users, has been verified by the simulation results. Practical application: Our work is completely applicable in the practical field. This work can be used for the energy scheduling of home appliances.

scholarly journals Optimal Dispatch Strategy of Cogeneration with Thermal Energy Storage for Building Energy Management System

2017 ◽  
Vol 10 (2) ◽  
pp. 156-166
Author(s):  
Kebsiri Manusilp ◽  
David Banjerdpongchai
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Thermal Energy ◽  
Building Energy ◽  
Test System ◽  
Optimal Operation ◽  
Energy Management System ◽  
Excessive Heat ◽  
Optimal Dispatch ◽  
Building Energy Management

This paper presents optimal dispatch strategy of cogeneration with thermal energy storage (TES) for building energy management system (BEMS). In previous research related to cogeneration as a supply system, it is observed that there is some excessive heat from cogeneration operation released to the atmosphere. In order to improve energy efficiency, we therefore incorporate TES to utilize the excessive heat from cogeneration into two objective functions, i.e., total operating cost (TOC) and total carbon dioxide emission (TCOE). In particular, we aim to minimize TOC which is referred to economic optimal operation and to minimize TCOE which is referred to environmental optimal operation. Both optimal operations are subjected to energy dispatch strategy which TES constraint is taken into account. We demonstrate the dispatch strategy with a load profile of a large shopping mall as a test system and compare the results to that of previous dispatch of cogeneration without TES. The proposed strategy of cogeneration with TES can reduce TOC of the test system up to 4.15% and 1.85% for economic and environmental optimal operations, respectively. Furthermore, TCOE can be reduced up to 5.25% and 6.25% for economic and environmental optimal operations, respectively.

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