IEEE Access

The rapid advancement of new resources of energy has been increasing in the world day by day with traditional resources of power generation. The goals of such developments are to increase both economic and environmental benefits. In the literature, there have been a lot of hybrid systems that have come into existence as a combination of different energy resources with different configurations. In this scenario, the combination of different energy resources like photovoltaic (PV), water turbine (hydro), diesel generator (D), and battery energy storage system (BESS), each with a different configuration, is taken into account, leading to a hybrid energy management system. Furthermore, the proposed system leads to a real-world optimization problem that is solved by the use of dynamic programming. For this purpose, we have taken into account the existing day-ahead data of load, electricity, and other sources for the optimal output of the proposed hybrid system. Moreover, the objective function is the minimization of cost by organizing distributed energy resources as well as the emission reduction of toxic gases. The objective function is constrained by balancing the power between the supply and load requirements and the restrictions of each distributed energy resource. A multistage decision procedure known as Dynamic programming is used in regard to state of charge (SOC), which yields the optimal cost of the system. For this purpose, we tried to make use of conventional as well as local turbines, which lead to small hydropower plants that contribute to the proposed system as a case study in the northern areas of Pakistan in order to produce low-cost electricity. The purpose of the proposed study is to develop an optimal hybrid energy management system that generates more power at a minimum cost. Moreover, the maintenance cost is reduced to a high extent as compared to the previous hybrid system in the literature. Due to the minimum use of diesel generator, the proposed hybrid system produces optimal results in terms of clean energy provision as compared to the existent techniques in the literature.

In this paper, the integration of an optimizer and a forecaster into the energy management system (EMS) of a hybrid renewable energy system is studied. The role of the optimal EMS is to select the best decision set for the operation of the system based on a 24-hour forecast, reducing power conversion losses and unnecessary battery charge discharge cycles. Different forecast methods have been chosen for the 24-hour forecast of load, wind speed, and solar irradiance. A Genetic Algorithm is used for the optimizer. The cost function for evaluating system performance accounts for the fuel consumed, battery degradation, the amount of load shed, and the startups of the diesel engine. The results of the simulation have shown about 50% reduction in the number of battery cycles while preserving the same level of diesel engine fuel consumption as compared to classical EMS.

Tanzania Journal of Engineering and Technology

A photovoltaic (PV)/wind/battery/Generator-Set hybrid power system's energy dispatch is discussed in this research, along with the ideal solution. The optimization model enables the hybrid power system's generating devices to share power appropriately. The main goals are to minimize fuel consumption and costs and maximize the usage of Renewable Energy Sources (RES). The proposed simulation plan succeeds in lowering fuel costs because the diesel generator only kicks on at night and in the early morning when the energy from RES is insufficient to supply the load. The load is supplied with power, and the battery is charged throughout the day, when the production of electricity from the wind and solar generators is highest. The use of diesel is minimal. However, based on their particular characteristics and the operational limitations of the system, the generating devices effectively share the power needed from the hybrid power system.

Renewable Energy, 2011

A well designed hybrid energy system can be cost effective, has a high reliability and can improve the quality of life in remote rural areas. The economic constraints can be met, if these systems are fundamentally well designed, use appropriate technology and make use effective dispatch control techniques. The first paper of this tri-series paper, presents the analysis and design of a mixed integer linear mathematical programming model (time series) to determine the optimal operation and cost optimization for a hybrid energy generation system consisting of a photovoltaic array, biomass (fuelwood), biogas, small/micro-hydro, a battery bank and a fossil fuel generator. The optimization is aimed at minimizing the cost function based on demand and potential constraints. Further, mathematical models of all other components of hybrid energy system are also developed. This is the generation mix of the remote rural of India; it may be applied to other rural areas also.

2014

Hybrid power systems for off-grid sites are commonly designed using simulation. Operating rules for the controller dispatch strategy are defined, and a simulation uses site-specific data to find the minimumcost system composition that satisfies the site’s electricity demand. The resulting design is strongly influenced by the a priori choice of strategy. We present a new approach to design isolated sites: off-grid electricity supply optimization (OGESO). In our approach, the dispatch rules are not fixed, and we simultaneously optimize the composition and the controller strategy. A mixed integer programming model finds the optimal system composition and the optimal dispatch based on one year of deterministic hourly data. Because the optimal dispatch is complex, it is analyzed using data mining to find a practical controller strategy. Computational results on diesel-wind-battery hybrid systems demonstrate the benefits of the proposed approach.

2016

This paper discussed the benefits of integrating multiple storage facilities into hybrid renewable energy system(HRES) configuration.A scheduling algorithm was developed for a hybrid renewable energy system that consists of wind and solar energy sources, pumped-hydro and battery storage with diesel generator as backup, for a selected site. The sequential quadratic programming (SQP) approach for solving convex non–linear optimization problems was used to determine an adequate and costeffective capacity for each of the incorporated energy systems. An algorithm for supervisory controller for optimal scheduling of the hybrid renewable energy system was developed and simulated on Matlab. The cost analysis of the hybrid renewable energy system was carried out using the cost per kW of each component for obtaining the total installation cost and the cost of consumed diesel for each of the considered scenarios. These costs were compared with the cost of grid extension and the cost of electri...

Journal of Elecrtical Systems, 2024

The high cost of delivering power to rural areas is a major concern, leading to many areas lacking electricity. To address this, some countries use diesel generators or small-scale renewable energy sources like photovoltaics and wind. However, these generators have drawbacks such as high fuel requirements and non-linear load demand profiles. To address these issues, hybrid power generation systems can be formed, combining photovoltaic and wind turbines with diesel generators. This system reduces fuel consumption, minimizes fuel costs, and reduces environmental pollution. The research aims to develop two control strategies to minimize daily operational costs of hybrid systems involving PV/WT/DG and batteries, using MATLAB functions to simulate the control strategies. The models will help optimize power flow and minimize operational costs.

Renewable Energy Focus, 2018

Electric power shortage is a serious problem in remote rural communities in Egypt. Over the past few years, electrification of remote communities including efficient on-site energy resources utilization has achieved high progress. Remote communities usually depend on diesel generator (DG) networks to supply the community with reliable energy and fresh water. The main objective of this paper is to design an optimal economic power supply from a hybrid standalone energy system (HSES). The system is intended to cover the energy required for a desalination unit (DU) installed in a farm in Noubarya, Egypt. The proposed system consists of PV panels, wind turbines (WT), batteries, and DG. DU load is about 105.6 kWh/day rated power with 6.6 kW peak load operating 16 h a day. The objective of system optimization is to reach the suitable size of each component and the control strategy that provide reliable, efficient, and cost effective system using net present cost (NPC) as a criterion. The harmonization of different energy sources, energy storage and load requirements is a challenging task. Thus, the performance of various possible configurations is investigated, using iHOGA software that is based on genetic algorithm (GA). In this study the achieved optimum configuration is further improved by adapting the daily load pattern to the periods of high renewable generated energy to increase direct energy utilization rather than charging batteries. This will result in effective minimization of battery bank size.

Recently, there has been a growing interest in harnessing renewable energy resources particularly for electricity generation. One of the main concerns in the design of an electric power system that utilizes renewable energy sources, is the accurate selection of system components that can economically satisfy the load demand. This depends on the load that ought to be met, the capacity of renewable resources, the available space for wind machines and solar panels, and the capital and running costs of system components. Once size optimization is achieved, the autonomous system must be controlled in order to correctly match load requirements with instantaneous variation of input energy. In this paper, a new formulation for optimizing the design of an autonomous wind-solar-diesel-battery energy system is developed. This formulation employs linear programming techniques to minimize the average production cost of electricity while meeting the load requirements in a realiable manner. The computer program developed reads the necessary input data, formulates the optimization problem by computing the coefficients of the objective function and the constraints and provides the optimum wind, solar, diesel, and battery ratings. In order to study the effect of parameters predefined by the designer on the optimum design, several sensitivity analysis studies are performed, and the effects of the expected energy not served, the load level, the maximum available wind area, the maximum available solar area, and the diesel engines' lifetime are investigated. A controller that monitors the operation of the autonomous system is designed. The operation of this controller is based on three major policies; in the first, batteries operate before diesel engines and hence the storage system acts as a fuel saver, while in the second diesel engines are operated first so that the unmet energy is lower but the fuel cost is high. According to the third policy, the supply is made through diesel engines only. This is done for the purpose of making a performance comparison between the isolated diesel system and the hybrid renewable energy system. The proposed optimization and control techniques are tested on Lebanese data. Although three different control policies have been adopted in this work, the software is able to accommodate other policies.

Electronics

Hybrid renewable energy systems are a promising technology for clean and sustainable development. In this paper, an intelligent algorithm, based on a genetic algorithm (GA), was developed and used to optimize the energy management and design of wind/PV/tidal/ storage battery model for a stand-alone hybrid system located in Brittany, France. This proposed optimization focuses on the economic analysis to reduce the total cost of hybrid system model. It suggests supplying the load demand under different climate condition during a 25-years interval, for different possible cases and solutions respecting many constraints. The proposed GA-based optimization approach achieved results clear highlight its practicality and applicability to any hybrid power system model, including optimal energy management, cost constraint, and high reliability.