Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach

In microgrids, forecasting solar power output is crucial for optimizing operation and reducing the impact of uncertainty. To forecast solar power output, it is essential to forecast solar irradiance, which typically requires historical solar irradiance data. These data are often unavailable for residential and commercial microgrids that incorporate solar photovoltaic. In this study, we propose an hourly day-ahead solar irradiance forecasting model that does not depend on the historical solar irradiance data; it uses only widely available weather data, namely, dry-bulb temperature, dew-point temperature, and relative humidity. The model was developed using a deep, long short-term memory recurrent neural network (LSTM-RNN). We compare this approach with a feedforward neural network (FFNN), which is a method with a proven record of accomplishment in solar irradiance forecasting. To provide a comprehensive evaluation of this approach, we performed six experiments using measurement data from weather stations in Germany, U.S.A, Switzerland, and South Korea, which all have distinct climate types. Experiment results show that the proposed approach is more accurate than FFNN, and achieves the accuracy of up to 60.31 W/m2 in terms of root-mean-square error (RMSE). Moreover, compared with the persistence model, the proposed model achieves average forecast skill of 50.90% and up to 68.89% in some datasets. In addition, to demonstrate the effect of using a particular forecasting model on the microgrid operation optimization, we simulate a one-year operation of a commercial building microgrid. Results show that the proposed approach is more accurate, and leads to a 2% rise in annual energy savings compared with FFNN

Optimal Control Strategy for Distributed Energy Resources in a DC Microgrid for Energy Cost Reduction and Voltage Regulation

Distributed energy resources (DERs), including renewable energy resources (RESs) and electric vehicles (EVs), have a significant impact on distribution systems because they can cause bi-directional power flow in the distribution lines. Thus, the voltage regulation and thermal limits of the distribution system to mitigate from the excessive power generation or consumption should be considered. The focus of this study is on a control strategy for DERs in low-voltage DC microgrids to minimize the operating costs and maintain the distribution voltage within the normal range based on intelligent scheduling of the charging and discharging of EVs, and to take advantage of RESs such as photovoltaic (PV) plants. By considering the time-of-use electricity rates, we also propose a 24-h sliding window to mitigate uncertainties in loads and PV plants in which the output is time-varied and the EV arrival cannot be predicted. After obtaining a request from the EV owner, the proposed optimal DER control method satisfies the state-of-charge level for their next journey. We applied the voltage sensitivity factor obtained from a load-flow analysis to effectively maintain voltage profiles for the overall DC distribution system. The performance of the proposed optimal DER control method was evaluated with case studies and by comparison with conventional methods

Tuning of the PI Controller Parameters of a PMSG Wind Turbine to Improve Control Performance under Various Wind Speeds

This paper presents a method to seek the PI controller parameters of a PMSG wind turbine to improve control performance. Since operating conditions vary with the wind speed, therefore the PI controller parameters should be determined as a function of the wind speed. Small-signal modeling of a PMSG WT is implemented to analyze the stability under various operating conditions and with eigenvalues obtained from the small-signal model of the PMSG WT, which are coordinated by adjusting the PI controller parameters. The parameters to be tuned are chosen by investigating participation factors of state variables, which simplifies the problem by reducing the number of parameters to be tuned. The process of adjusting these PI controller parameters is carried out using particle swarm optimization (PSO). To characterize the improvements in the control method due to the PSO method of tuning the PI controller parameters, the PMSG WT is modeled using the MATLAB/SimPowerSystems libraries with the obtained PI controller parameters

Voltage Control Method Using Distributed Generators Based on a Multi-Agent System

This paper presents a voltage control method using multiple distributed generators (DGs) based on a multi-agent system framework. The output controller of each DG is represented as a DG agent, and each voltage-monitoring device is represented as a monitoring agent. These agents cooperate to accomplish voltage regulation through a coordinating agent or moderator. The moderator uses the reactive power sensitivities and margins to determine the voltage control contributions of each DG. A fuzzy inference system (FIS) is employed by the moderator to manage the decision-making process. An FIS scheme is developed and optimized to enhance the efficiency of the proposed voltage control process using particle swarm optimization. A simple distribution system with four voltage-controllable DGs is modeled, and an FIS moderator is implemented to control the system. Simulated data show that the proposed voltage control process is able to maintain the system within the operating voltage limits. Furthermore, the results were similar to those obtained using optimal power flow calculations, even though little information on the power system was required and no power flow calculations were implemented

The Impact of Policy and Technology Parameters on the Economics of Microgrids for Rural Electrification: A Case Study of Remote Communities in Bolivia

Throughout the developing world, most remote and isolated communities are still without reliable electricity in the twenty-first century, and this is primarily due to the high cost of grid extensions. In communities that do have electricity, they usually rely on diesel generators, though these have high operating and maintenance costs, while also polluting the environment. A more sustainable approach is to deploy microgrids, however, microgrids have a high upfront cost, which is a major obstacle, especially in rural areas of developing countries. This study aims to investigate the parameters that can be influenced to make microgrids more economical for rural electrification. Through sensitivity analyses, five key policy and technology parameters were identified. They include real discount rates, diesel prices, grants, battery chemistry, and operating strategies. The system was then redesigned using scenarios formulated by varying these parameters. Results show that the parameters affect the configuration, levelized cost of energy (LCOE), renewable energy penetration (REP), and pollutant emissions. The study uses three remote communities in the Beni Department of Bolivia as case studies. MDSTool was used as a modeling framework to design the microgrids. The unique insights and lessons learned during the design process are discussed at length because these may be valuable for future microgrid designs for remote communities

Optimal PEV Charging and Discharging Algorithms to Reduce Operational Cost of Microgrid Using Adaptive Rolling Horizon Framework

This paper presents an optimal operation algorithm for a grid-connected microgrid that incorporates renewable energy sources (RESs), plug-in electric vehicle (PEV) charging/discharging stations, and local loads. The aim of this work is to not only propose a microgrid operating algorithm but also implement it within the microgrid energy management system. The primary objective of the proposed algorithm is to reduce the overall operational cost of the microgrid while minimizing the PEV charging bills simultaneously. To this end, we propose an adaptive PEV power charging/discharging and power exchange (grid exporting/importing) scheduling strategy that accounts for the uncertainty of RES power generation, loads, and electric pricing. To address the dynamic arrival of PEVs, we propose an online optimization scheme using an adaptive rolling horizon framework. The size of the rolling window is adjusted in each time step to adapt to the dynamic nature of the PEV charging period. Additionally, we propose the design of a dynamic pricing model for PEV charging and discharging to achieve power system balance within the microgrid, thereby optimizing operating costs and minimizing PEV charging bills further. Through simulations, we demonstrate the effectiveness of the proposed strategies, which are expected to benefit both the microgrid operators and PEV owners

An Analysis of Variable-Speed Wind Turbine Power-Control Methods with Fluctuating Wind Speed

Variable-speed wind turbines (VSWTs) typically use a maximum power-point tracking (MPPT) method to optimize wind-energy acquisition. MPPT can be implemented by regulating the rotor speed or by adjusting the active power. The former, termed speed-control mode (SCM), employs a speed controller to regulate the rotor, while the latter, termed power-control mode (PCM), uses an active power controller to optimize the power. They are fundamentally equivalent; however, since they use a different controller at the outer control loop of the machine-side converter (MSC) controller, the time dependence of the control system differs depending on whether SCM or PCM is used. We have compared and analyzed the power quality and the power coefficient when these two different control modes were used in fluctuating wind speeds through computer simulations. The contrast between the two methods was larger when the wind-speed fluctuations were greater. Furthermore, we found that SCM was preferable to PCM in terms of the power coefficient, but PCM was superior in terms of power quality and system stability

Day-Ahead Solar Irradiance Forecasting Using Hybrid Recurrent Neural Network with Weather Classification for Power System Scheduling

At the present time, power-system planning and management is facing the major challenge of integrating renewable energy resources (RESs) due to their intermittent nature. To address this problem, a highly accurate renewable energy generation forecasting system is needed for day-ahead power generation scheduling. Day-ahead solar irradiance (SI) forecasting has various applications for system operators and market agents such as unit commitment, reserve management, and biding in the day-ahead market. To this end, a hybrid recurrent neural network is presented herein that uses the long short-term memory recurrent neural network (LSTM-RNN) approach to forecast day-ahead SI. In this approach, k-means clustering is first used to classify each day as either sunny or cloudy. Then, LSTM-RNN is used to learn the uncertainty and variability for each type of cluster separately to predict the SI with better accuracy. The exogenous features such as the dry-bulb temperature, dew point temperature, and relative humidity are used to train the models. Results show that the proposed hybrid model has performed better than a feed-forward neural network (FFNN), a support vector machine (SVM), a conventional LSTM-RNN, and a persistence model

Intelligent Control of Battery Energy Storage for Multi-Agent Based Microgrid Energy Management

Microgrids can be considered as controllable units from the utility point of view because the entities of microgrids such as distributed energy resources and controllable loads can effectively control the amount of power consumption or generation. Therefore, microgrids can make various contracts with utility companies such as demand response program or ancillary services. Another advantage of microgrids is to integrate renewable energy resources to low-voltage distribution networks. Battery energy storage systems (BESSs) can effectively compensate the intermittent output of renewable energy resources. This paper presents intelligent control schemes for BESSs and autonomous energy management schemes of microgrids based on the concept of multi-agent systems. The proposed control scheme consists of two layers of decision-making procedures. In the bottom layer, intelligent agents decide the optimal operation strategies of individual microgrid entities such as BESSs, backup generators and loads. In the upper layer, the central microgrid coordinator (MGCC) coordinates multiple agents so that the overall microgrid can match the load reduction requested by the grid operator. The proposed control scheme is applied to Korea Power Exchange’s Intelligent Demand Response Program