Energy Management System for Stand-Alone Wind-Powered-Desalination Microgrid

A novel energy management method based on Deep Q Network algorithm for low operating cost of an integrated hybrid system

Microgrid with hybrid renewable energy sources is a promising solution where the distribution network expansion is unfeasible or not economical. Integration of renewable energy sources provides energy security, substantial cost savings and reduction in greenhouse gas emissions, enabling nation to meet emission targets. Microgrid energy management is a challenging task for microgrid operator (MGO) for optimal energy utilization in microgrid with penetration of renewable energy sources, energy storage devices and demand response. In this paper, optimal energy dispatch strategy is established for grid connected and standalone microgrids integrated with photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), diesel generator (DG) and battery energy storage system (ESS). Techno-economic benefits are demonstrated for the hybrid power system. So far, microgrid energy management problem has been addressed with the aim of minimizing operating cost only. However, the issues of power losses and environment i.e., emission-related objectives need to be addressed for effective energy management of microgrid system. In this paper, microgrid energy management (MGEM) is formulated as mixed-integer linear programming and a new multi-objective solution is proposed for MGEM along with demand response program. Demand response is included in the optimization problem to demonstrate it’s impact on optimal energy dispatch and techno-commercial benefits. Fuzzy interface has been developed for optimal scheduling of ESS. Simulation results are obtained for the optimal capacity of PV, WT, DG, MT, FC, converter, BES, charging/discharging scheduling, state of charge of battery, power exchange with grid, annual net present cost, cost of energy, initial cost, operational cost, fuel cost and penalty of greenhouse gases emissions. The results show that CO2 emissions in standalone hybrid microgrid system is reduced by 51.60% compared to traditional system with grid only. Simulation results obtained with the proposed method is compared with various evolutionary algorithms to verify it’s effectiveness.

Power curve (PC) monitoring can be applied to evaluate the wind turbine generator (WTG) power output and detect deviations between the expected and the measured value, often a precursor of unexpected faults. In this research, the instantaneous SCADA data is used to show the fault forecast ability of Artificial Intelligence (AI) based PC monitoring of a pitch regulated WTG. The measured PCs illustrate that the instantaneous data is better than averaged data, widely used in the literature, to present the dynamics of WTG operation. The influence of ambient temperature, generator speed and pitch angle on WTG power output is analyzed using measured data. The analysis illustrates that the generator speed and pitch angle have a significant effect on WTG power generation. The performance of the proposed model option is compared against previously published option using the same data sets collected from a 2 MW Pitch Regulated WTG. The comparison is based on the mean absolute error (MAE), the root mean squared error (RMSE) and the correlation coefficient (R2). The result shows that models considering generator speed and pitch angle performs better with lowest MAE and RMSE and highest R2 values. A case study illustrated that the AI models, using wind speed, generator speed and pitch angle inputs, would have successfully detected a pitch fault due to the slip ring malfunction nearly 5 hours earlier than the existing fault detection mechanisms.

With the increasing presence of intermittent energy resources in microgrids, it is difficult to precisely predict the output of renewable resources and their load demand. In order to realize the economical operations of the system, an energy management method based on a model predictive control (MPC) and dynamic programming (DP) algorithm is proposed. This method can reasonably distribute the energy of the battery, fuel cell, electrolyzer and external grid, and maximize the output of the distributed power supply while ensuring the power balance and cost optimization of the system. Based on an ultra-short-term forecast, the output power of the photovoltaic array and the demand power of the system load are predicted. The offline global optimization of traditional dynamic programming is replaced by the repeated rolling optimization in a limited period of time to obtain power values of each unit in the energy storage system. Compared with the traditional DP, MILP-MPC and the logic based real-time management method, the proposed energy management method is proved to be feasible and effective.

Microgrids are becoming a viable solution for satisfying energy demand of rural and remote areas. Indeed, energy demand of islands can be met by renewable energy sources, energy storage systems, and micro-conventional generation sources-based microgrid systems. The optimal scheduling of these energy sources requires an energy management system for microgrids. Bretagne region in France has huge potential in marine renewable energy sources. Therefore, islands in this region can use tidal turbines with other energy sources to meet their local energy consumption. A case study of stand-alone marine microgrid system for Ouessant island is proposed in this paper. The considered microgrid includes PV system, tidal turbine, diesel generator, and Li-ion battery. The architecture and optimal scheduling of the developed microgrid system is presented to reduce operating and maintenance costs. The developed energy management architecture can help microgrid systems planning for islands in the near future.

This paper proposes a proficient hybrid approach based energy management system (EMS) of microgrid (MG) in grid-tied mode. The microgrid connected system like photovoltaic (PV), wind Turbine (WT), and microturbine (MT) battery. The major intention of the proposed approach is “to reduce the cost of electricity and enhancing the power flow (PF) among the source and load side.” The proposed method is the combination of Artificial Neural Network (ANN) and Hybrid Whale Optimization (HWO) approach, hence it is known as ANN-HWO. The necessary load requirement of grid connected microgrid scheme is constantly monitored by ANN. The HWOA develops the optimal combination of the microgrid based on the predicted load demand. Moreover, the two methods for microgrid energy management have been employed to minimize the impact of renewable energy predicting faults. The first method is focused on the various renewable energy sources (RESs) programming to reduction of electricity cost while the MG function. The second method is to balance the PF and minimize the forecasting errors impacts depending upon rule outlined from the scheduled power reference. The proposed technique is activated in MATLAB/Simulink site, and then the efficiency is classified with and without grid of MG structure. The effectiveness can be compared based on cost analysis and power generation of PV, MT, WT, and battery of existing methods. The power generation from the various sources based upon the ANN-HWO and existing methods is evaluated. Also, the total cost of the system also analyzed with different existing methods. The statistical evaluation, like mean, median, and standard deviation is also analyzed. The accuracy, specificity, recall and precision, RMSE, MAPE, MBE and consumption time of ANN-HWO, and existing techniques are also conducted. The mean, median, and standard deviation for 50 numbers of trails of the proposed technique is 1.3116, 1.2931, and 0.0354. The mean, median, and standard deviation for 100 numbers of trails of the proposed technique is 1.3232, 1.2786, and 0.0946. The RMSE, MAPE, MBE, and execution time for 50 numbers of trails of the proposed technique is 7.840, 0.748, 0.9971, and 1.011s. The RMSE, MAPE, MBE,and execution time for 100 numbers of trails of the proposed technique is 5.21, 0.93, 1.93, and 2.005 s. The proposed technique in 100, 200, 500, and 100 numbers of trails, the efficiency is 99.9673%, 99.7890%, 99.89402%, and 99.77879%.

Novel energy management method for suppressing fuel cell degradation in hydrogen and electric hybrid energy storage systems compensating renewable energy fluctuations

Forecasting plays a significant role in microgrid energy management. The energy management function of a typical Microgrid Energy Management System (MEMS) schedules the Distributed Energy Resources (DERs) such as solar PV systems, wind turbines, and energy storage systems using the renewable energy and load forecasts. These schedules must ensure the optimal operation of the microgrid. However, the forecasting errors are a cause of sub-optimal schedules that affect the objectives and goals of the microgrid. Model Predictive Control (MPC) is currently gaining much attention as a method for microgrid energy management. Therefore, this paper investigates the effect of renewable energy forecasting error on MPC-based microgrid energy management. It is shown that the system only slightly deviates from the optimal operation when there are forecasting errors. However, the importance of developing better formulations to quantify the deviation from the optimal operation is highlighted.

Microgrids usually employ distributed energy resources such as wind turbines, solar photovoltaic modules, etc. When multiple distributed generation resources with different features are used in microgrids, managing these resources becomes an important problem. The generated power of solar photovoltaic modules and wind turbines used in microgrids is constantly changing with solar irradiation and wind speed. Due to this impermanent and uncertain nature of renewable energy resources, generally, energy storage systems are employed in microgrid systems. To control the distributed energy resources and energy storage units and sustain the supply and demand balance within the microgrid and provide sustainable and reliable energy to the loads, energy management systems are used. Many methods are used to realize and optimize energy management in microgrids. This review article provides a comparative and critical analysis of the energy management systems used in microgrids. The energy management system can be tailored for different purposes, which are also discussed in detail. Additionally, various uncertainty measurement methods are summarized to manage the variability and intermittency of renewable energy sources and load demand. Finally, some thoughts about potential future directions and practical applications are given.

Special issue on “Optimal design and operation of energy systems”

Nowadays the number of distributed generation (DG) units especially renewable energy resources (RES) and energy storage systems (ESS) in distribution networks are increasing because of cost saving and depleting fossil fuels. There are a lot of problems faced with controlling such a high amount of information and small supplies of DGs, when these small RESs are connected to power system and are participated in energy market directly. Virtual power plant (VPP), as an aggregation of some DGs with their own local energy management system and some fix and variable loads, is used to solve these problems in association with DGs and networks. All energy managements in virtual power plants have been done without RESs including wind turbine, fuel cell, photo voltaic, and geothermal and ESSs, so, in this publication, the electrical energy management for a VPP considering some RESs and ESSs will be done via three different scenarios, and MATLAB tools will be applied to evaluate the method. In the final scenario, the superiority of applying the proposed energy management method considering RESs in comparison with usual energy management methods in VPP will be shown.

Energy management system for stand-alone diesel-wind-biomass microgrid with energy storage system

This article addresses a voltage control and energy management strategy of active distribution systems with a grid-connected dc microgrid as well as for an islanded dc microgrid with hybrid energy resources. In the islanded mode, a control and management strategy using a backup diesel generator (DG), a renewable energy source (RES), and an energy storage system plays a vital role in maintaining the microgrid bus voltages within the limits. However, operating backup diesel generator (DG) has its own challenges including startup delay, frequent switching, and uneven loading when operated along with a RES. Additionally, fuel efficiency and emission characteristics vary with loading since most of DGs are driven by constant-speed diesel engines. Hence, an exhaustive power management scheme (PMS) is proposed by utilizing the hybrid energy storage system. Real-time simulation and experimental validation of the proposed scheme are provided using a real-time digital simulator (RTDS) and a laboratory scale prototype, respectively. Extreme scenarios including DG failure/scheduled maintenance, low power generation, and battery charge are analyzed in the islanded mode. Furthermore, a dc microgrid is connected to an IEEE active distribution system feeder to analyze control and management challenges for the grid connected mode with contribution from a microgrid and with no contribution from a microgrid. These scenarios resemble more realistic unbalanced utility grid conditions. A centralized optimization problem is formulated at an advanced distribution management system level to maintain all the node voltages within limits in the IEEE test system. RTDS is used to simulate dc microgrid connected with the IEEE test system and an optimization algorithm is implemented in MATLAB. Superior performance of the developed algorithms are demonstrated and validated for coordination between centralized optimization at ADMS and the microgrid energy management system.

This paper presents a study carried out on maximizing energy harvesting of wind turbines. One way of improving the output power of the wind turbines is by optimizing the power conversion coefficient. The power conversion coefficient factor is expressed as a function of the wind turbine blade tip speed ratio and the turbine blade pitch angle. Optimization of the wind turbine generator output power is done by considering the effects of variations of wind speed, blade tip speed ratio, and pitch angle. An intelligent soft computing technique known as an adaptive neuro-fuzzy inference system (ANFIS) with a fuzzy logic controller for blade pitch actuator was applied to optimize the generator output power. The simulation result showed that the power conversion coefficient of 0.513 is achieved. The study was verified by using real-time wind speed data of Adama II wind farm in Ethiopia and specifications of the Gamesa G80 horizontal axis wind turbine generator unit by MATLAB software. Accordingly, a promising and satisfying improvement in power harvesting capacity is obtained. The output power of this generator is improved by 9.47% which is by far better result as compared to the existing literature.

This study proposes an Energy Management System (EMS) for allocation of DG source in a grid connected hybrid power system. Modeling and simulation for EMS is implemented using MATLAB/SIMULINK package. The objective of proposed EMS for micro grid is to optimize the fuel cost, improving the energy utilization efficiency and to manage the peak load demand by scheduling the generation according to the availability of the fuel. The proposed intelligent energy management system is designed to optimize the availability of energy to the load according to the level of priority and to manage the power flow. The developed management system performance was assessed using a hybrid system having PV panels, Wind Turbine (WT), battery and biomass gasifier. Real time field test has been conducted and the parameters i.e., solar irradiance, temperature, wind speed are gathered from 4.05 KW off grid and 2.0 KW On grid Solar Photovoltaic systems (SPV) system and wind turbine. The dynamic behavior of the proposed model is examined under different operating conditions. The simulation results of proposed EMS using fuzzy logic expert system shows the minimization on the operating cost and emission level of micro grid by optimal scheduling of power generation and maintains the State of Charge (SOC) of batteries in desired value which improves the battery life. The proposed multi objective intelligent energy management system aims to minimize the operational cost and the environmental impact of a micro grid.