The energy management will play an important role in the future smart grid by managing loads in an intelligent way. Energy management programs, realized via House Energy Management systems (HEMS) for smart cities, provide many benefits; consumers enjoy electricity price savings, and utility operates at reduced peak demand. This paper proposed an adaptive energy management system for islanded mode and grid-connected mode. In this paper, a hybrid system that includes distribution electric grid, photovoltaics, and batteries are employed as energy sources in the residential of the consumer in order to meet the demand. The proposed system permits coordinated operation of distributed energy resources to concede necessary active power and additional service whenever required. This paper uses home energy management system which switches between the distributed energy and the grid power sources. The home energy management system incorporates controllers for maximum power point tracking, battery charge and discharge and inverter for effective control between different sources depending upon load requirement and availability of sources at maximum powerpoint. Also, in this paper, the Maximum Power Point Tracking (MPPT) technique is applied to the photovoltaic station to extract the maximum power from hybrid power system during variation of the environmental conditions. The operation strategy of energy storage systems is proposed to solve the power changes from photovoltaics and houses loads fluctuations locally, instead of reflecting those disturbances to the utility grid. Furthermore, the energy storage systems energy management scheme will help to achieve the peak reduction of the houses daily electrical load demand. The simulation results have verified the effectiveness and feasibility of the introduced strategy and the capability of the proposed controller for a hybrid microgrid operating in different modes.
Smart Microgrid (MG) effectively contributes to supporting the electrical power systems as a whole and reducing the burden on the utility grid by the use of unconventional energy generation resources, in addition to backup Diesel Generators (DGs) for reliability increasing. In this paper, potential had been done on day-ahead scheduling of diesel generators and reducing the energy cost reached to the consumers side to side with renewable energy resources, where economical energy and cost-effective MG has been used based on optimization agent called Energy Management System (EMS). Improved Particle Swarm Optimization (IPSO) technique has been used as an optimization method to reduce fuel consumption and obtain the lowest energy cost as well as achieving the best performance to the energy system. Three scenarios are adopted to prove the efficiency of the proposed method. The first scenario uses a 24 hour time horizon to investigate the performance of the model, the second scenario uses two DGs and the third scenario depends on a 48-hour time horizon to validating the performance. The superiority of the proposed method is illustrated by comparing it with PSO and simulation results show using the proposed method can reducing the fuel demand and the energy cost by satisfying the user’s preference.
Hybrid electric vehicles have received considerable attention because of their ability to improve fuel consumption compared to conventional vehicles. In this paper, a series-parallel hybrid electric vehicle is used because they combine the advantages of the other two configurations. In this paper, the control unit for a series-parallel hybrid electric vehicle is implemented using a Nonlinear Model Predictive Control (NMPC) strategy. The NMPC strategy needs to create a vehicle energy management optimization problem, which consists of the cost function and its constraints. The cost function describes the required control objectives, which are to improve fuel consumption and obtain a good dynamic response to the required speed while maintaining a stable value of the state of charge (SOC) for batteries. While the cost function is subject to the physical constraints and the mathematical prediction model that evaluate vehicle's behavior based on the current vehicle measurements. The optimization problem is solved at each sampling step using the (SQP) algorithm to obtain the optimum operating points of the vehicle's energy converters, which are represented by the torque of the vehicle components.
In this paper, a control strategy for a combination PV-BESS-SC hybrid system in islanded microgrid with a DC load is designed and analyzed using a new topology. Although Battery Energy Storage System (BESS) is employed to keep the DC bus voltage stable; however, it has a high energy density and a low power density. On the other hand, the Supercapacitor (SC) has a low energy density but a high-power density. As a result, combining a BESS and an SC is more efficient for power density and high energy. Integrating the many sources is more complicated. In order to integrate the SC and BESS and deliver continuous power to the load, a control strategy is required. A novel method for controlling the bus voltage and energy management will be proposed in this paper. The main advantage of the proposed system is that throughout the operation, the State of Charging (SOC), BESS current, and SC voltage and current are all kept within predetermined ranges. Additionally, SC balances fast- changing power surges, while BESS balances slow-changing power surges. Therefore, it enhances the life span and minimizes the current strains on BESS. To track the Maximum Power Point (MPP) or restrict power from the PV panel to the load, a unidirectional boost converter is utilized. Two buck converters coupled in parallel with a boost converter are proposed to charge the hybrid BESS-SC. Another two boost converters are used to manage the discharge operation of the BESS-SC storage in order to reduce losses. The simulation results show that the proposed control technique for rapid changes in load demand and PV generation is effective. In addition, the proposed technique control strategy is compared with a traditional control strategy.
Wind energy and its conversion is part of renewable energy resources as cheaper and cleaner energy today even though the initial cost varies from place to place. Most of the government sector always promotes renewable energy with a provision of subsidies as observed worldwide. Wind energy is an actual solution over costlier conventional energy sources. If it is not properly placed and the selection of turbine design is not up to the mark, then investments may require more time to acquire Net Profit Value called as NPV. This research work is focused on the development of mathematical models to optimize the turbine size and locations considering all constraints such as the distance between the turbines, hub height, and investment in internal road and substation cost. Particle-Swarm-Optimization is an intelligent tool to optimize turbine place and size. The database management system is selected as the appropriate data storage platform for before and after optimization simulation. Various plots and excel outputs of .net programming are addressed for the success of optimization algorithms for the purpose of wind turbine placement and WTG design is suggested to manage wind energy such that power system reliability has been improved and the same is monitored through the reliability indices.
In this paper, enhancing dynamic performance in power systems through load frequency control (LFC) is explored across diverse operating scenarios. A new Neural Network Model Predictive Controller (NN-MPC) specifically tailored for two-zone load frequency power systems is presented. ” Make your paper more scientific. The NN-MPC marries the predictive accuracy of neural networks with the robust capabilities of model predictive control, employing the nonlinear Levenberg-Marquardt method for optimization. Utilizing local area error deviation as feedback, the proposed controller’s efficacy is tested against a spectrum of operational conditions and systemic variations. Comparative simulations with a Fuzzy Logic Controller (FLC) reveal the proposed NN-MPC’s superior performance, underscoring its potential as a formidable solution in power system regulation.