Optimal energy management for cooperative microgrids with renewable energy resources

2013 ◽  
Author(s):  
Duong Tung Nguyen ◽  
Long Bao Le
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Energy Resources ◽  
Renewable Energy Resources ◽  
Optimal Energy ◽  
Optimal Energy Management

scholarly journals Dynamic Programming for Optimal Energy Management of Hybrid Wind–PV–Diesel–Battery

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3039 ◽  
Author(s):  
Luu An ◽  
Tran Tuan
Keyword(s):  
Dynamic Programming ◽  
Energy Management ◽  
Hybrid System ◽  
Electricity Market ◽  
Environmental Benefits ◽  
Energy Resources ◽  
Renewable Energy Resources ◽  
Diesel Generator ◽  
Optimal Energy ◽  
Optimal Energy Management

With the dramatic development of renewable energy resources all over the world, Vietnam has started to apply them along with the conventional resources to produce the electrical power in recent years. Visually, the aim of this action is to improve the economic as well as the environmental benefits. Therefore, a vast of hybrid systems that combine Wind turbine, Photovoltaic (PV), Diesel generator and battery have been considered with different configurations. According to this topic, there are lots of research trends in the literature. However, we aim to the optimal energy management of this hybrid system. In particular, in this paper, we propose an optimization method to deal with it. The interesting point of the proposed method is the usage of the information of sources, loads, and electricity market as an embedded forecast step to enhance the effectiveness of the actual operation via minimizing the operation cost by scheduling distributed energy resources (DER) while regarding emission reduction in the hybrid system is considered as the objective function. In this optimization problem, the constraints are determined by two terms, namely: the balance of power between the supply and the load demand, and also the limitations of each DER. Thus, to solve this problem, we make use of the dynamic programming (DP) to transform a system into a multi-stage decision procedure with respect to the state of charge (SOC), resulting in the minimum system cost (CS). In order to highlight the pros of the proposed method, we implement the comparison to a rule-based method in the same context. The simulation results are examined in order to evaluate the effectiveness of the developed methodology, which is a so-called global optimization.

scholarly journals Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2700
Author(s):  
Grace Muriithi ◽  
Sunetra Chowdhury
Keyword(s):  
Renewable Energy ◽  
Reinforcement Learning ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Critical Role ◽  
Winter Season ◽  
Learning Approach ◽  
Solar Pv ◽  
Optimal Energy ◽  
Optimal Energy Management

In the near future, microgrids will become more prevalent as they play a critical role in integrating distributed renewable energy resources into the main grid. Nevertheless, renewable energy sources, such as solar and wind energy can be extremely volatile as they are weather dependent. These resources coupled with demand can lead to random variations on both the generation and load sides, thus complicating optimal energy management. In this article, a reinforcement learning approach has been proposed to deal with this non-stationary scenario, in which the energy management system (EMS) is modelled as a Markov decision process (MDP). A novel modification of the control problem has been presented that improves the use of energy stored in the battery such that the dynamic demand is not subjected to future high grid tariffs. A comprehensive reward function has also been developed which decreases infeasible action explorations thus improving the performance of the data-driven technique. A Q-learning algorithm is then proposed to minimize the operational cost of the microgrid under unknown future information. To assess the performance of the proposed EMS, a comparison study between a trading EMS model and a non-trading case is performed using a typical commercial load curve and PV profile over a 24-h horizon. Numerical simulation results indicate that the agent learns to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility and battery wear cost) in all the studied cases. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one was found to decrease costs by 4.033% in summer season and 2.199% in winter season.

scholarly journals Multi-Objective Energy Management of a Micro-Grid Considering Stochastic Nature of Load and Renewable Energy Resources

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 403
Author(s):  
Deyaa Ahmed ◽  
Mohamed Ebeed ◽  
Abdelfatah Ali ◽  
Ali S. Alghamdi ◽  
Salah Kamel
Keyword(s):  
Renewable Energy ◽  
Objective Function ◽  
Energy Management ◽  
Energy Resources ◽  
Management Problem ◽  
Renewable Energy Resources ◽  
Multi Objective ◽  
Stochastic Nature ◽  
Whale Optimization ◽  
Micro Grid

Optimal inclusion of a photovoltaic system and wind energy resources in electrical grids is a strenuous task due to the continuous variation of their output powers and stochastic nature. Thus, it is mandatory to consider the variations of the Renewable energy resources (RERs) for efficient energy management in the electric system. The aim of the paper is to solve the energy management of a micro-grid (MG) connected to the main power system considering the variations of load demand, photovoltaic (PV), and wind turbine (WT) under deterministic and probabilistic conditions. The energy management problem is solved using an efficient algorithm, namely equilibrium optimizer (EO), for a multi-objective function which includes cost minimization, voltage profile improvement, and voltage stability improvement. The simulation results reveal that the optimal installation of a grid-connected PV unit and WT can considerably reduce the total cost and enhance system performance. In addition to that, EO is superior to both whale optimization algorithm (WOA) and sine cosine algorithm (SCA) in terms of the reported objective function.

Energy Optimization and Power Stack Shed Reduction of Oil Depots by using Renewable Energy Resources

2021 ◽  
Vol 4 (2) ◽  
pp. 125-130
Author(s):  
Muhammad Azhar Mahmood ◽  
Muhammad Kamran Liaqat Bhatti ◽  
S. Raza ◽  
M. Riaz
Keyword(s):  
Renewable Energy ◽  
Wind Energy ◽  
Energy Management ◽  
Unit Cost ◽  
Research Work ◽  
Energy Resource ◽  
Energy Resources ◽  
Renewable Energy Resources ◽  
Oil Sector ◽  
Continuous Power

Most of the industries including the oil sector are looking forward towards the renewable energy resources with proper energy management system (EMS) as it is the need of time. For this purpose, solar and wind energy are the renewable energy resources, which are obtained from natural resources and produce clean and environment -friendly electrical energy and can be used for oil depots. The proper utilization of solar and wind energy from natural resource may result in economical and cost-effective EMS. In the proposed research work, an effective energy management demonstration is delivered to ensure the ceaseless flexibility of power. Furthermore, reduction of production per unit cost to the oil sector industry by utilizing multiple objectives streamlining. In the proposed oil depot, connected loads are divided into Shiftable and Non-Shiftable loads and then apply Branch and Bound Algorithm (BnB) with binary integer linear programming (BILP). By using the BnB technique, selected shiftable loads are shifted to the low cost energy resource automatically and resultantly, we get the low price unit cost and continuous power supply. Simulation results for the above-mentioned research work are performed on MATLAB. The proposed technique helps to reduce the power stack shedding issue as well.

scholarly journals Optimal Energy Management of Railroad Electrical Systems with Renewable Energy and Energy Storage Systems

2019 ◽  
Vol 11 (22) ◽  
pp. 6293 ◽  
Author(s):  
Seunghyun Park ◽  
Surender Reddy Salkuti
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Energy Management ◽  
Storage Systems ◽  
Solar Pv ◽  
Energy Storage Systems ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Optimal Energy ◽  
Optimal Energy Management

The proposed optimal energy management system balances the energy flows among the energy consumption by accelerating trains, energy production from decelerating trains, energy from wind and solar photovoltaic (PV) energy systems, energy storage systems, and the energy exchange with a traditional electrical grid. In this paper, an AC optimal power flow (AC-OPF) problem is formulated by optimizing the total cost of operation of a railroad electrical system. The railroad system considered in this paper is composed of renewable energy resources such as wind and solar PV systems, regenerative braking capabilities, and hybrid energy storage systems. The hybrid energy storage systems include storage batteries and supercapacitors. The uncertainties associated with wind and solar PV powers are handled using probability distribution functions. The proposed optimization problem is solved using the differential evolution algorithm (DEA). The simulation results show the suitability and effectiveness of proposed approach.

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