Performance characterization and optimization of microgrid-based energy generation and storage technologies

Renewable energy-powered microgrids have proven to be a valuable technology for self-contained (off-grid) energy systems. Characterizing microgrid system performance pre-deployment would allow the system to be appropriately sized to meet all required electrical loads at a given renewable source operational time frequency. A vanadium redox battery was empirically characterized to determine operating efficiency as a function of charging characteristics and parasitic load losses. A model was developed to iteratively determine system performance based on known weather conditions and load requirements. A case study was performed to compare modeled system performance to measurements taken during operation of the microgrid system. Another iterative model was developed to incrementally predict the microgrid operating performance as a function of diesel generator operating frequency. Calibration of the model was performed to determine accurate PV panel and inverter efficiencies. A case study was performed to estimate the constant loads the system could power at varying diesel generator operating frequencies. Typical Meteorological Year 3 (TMY3) data from 217 Class I locations throughout the United States was inserted into the model to determine the quantity of external AC and DC load the system could supply at intermittent diesel generator variable operational frequencies. Ordinary block Kriging analysis was performed using ArcGIS to interpolate AC and DC load power between TMY3 Class I locations for each diesel generator operating frequency. Figures representing projected AC and DC external load were then developed for each diesel generator operating frequency --Abstract, page iv

Development of a stochastic model for performance characterization of a PV/VRB microgrid

Photovoltaic (PV) Microgrids have been proven to be a useful technology in providing an environmentally friendly source of energy when compared to the use of fossil fuels. Accurately characterizing the performance of a microgrid system would ensure that the system is appropriately sized to meet electrical loads without a heavy reliance on diesel generators. A microgrid that is sized properly will reduce the cost of diesel fuel, while also reducing the risk of wasting money on an oversized system. A deterministic model which was created to characterize the performance of PV microgrids based on percent of time generator running was modified in order to perform a stochastic Monte Carlo analysis. The analysis used four random variables: global horizontal irradiance (GHI), ambient temperature, vanadium redox battery state of charge (VRB SOC), and energy load. Values for these variables in the model will be generated using PDFs derived from probability plots. Data for GHI and ambient temperature were taken from a TMY3 data set for the microgrid locations. Energy load data was collected over eight months and used to characterize the energy load for one year. The VRB SOC distribution was determined using engineering judgment. Three test methods will be performed on two microgrid systems to predict the performance of each system using stochastic and deterministic methods. --Abstract, page iv

Modeling and optimization of energy storage system for microgrid

The vanadium redox flow battery (VRB) is well suited for the applications of microgrid and renewable energy. This thesis will have a practical analysis of the battery itself and its application in microgrid systems. The first paper analyzes the VRB use in a microgrid system. The first part of the paper develops a reduced order circuit model of the VRB and analyzes its experimental performance efficiency during deployment. The statistical methods and neural network approximation are used to estimate the system parameters. The second part of the paper addresses the implementation issues of the VRB application in a photovoltaic-based microgrid system. A new dc-dc converter was proposed to provide improved charging performance. The paper was published on IEEE Transactions on Smart Grid, Vol. 5, No. 4, July 2014. The second paper studies VRB use within a microgrid system from a practical perspective. A reduced order circuit model of the VRB is introduced that includes the losses from the balance of plant including system and environmental controls. The proposed model includes the circulation pumps and the HVAC system that regulates the environment of the VRB enclosure. In this paper, the VRB model is extended to include the ESS environmental controls to provide a model that provides a more realistic efficiency profile. The paper was submitted to IEEE Transactions on Sustainable Energy. Third paper discussed the optimal control strategy when VRB works with other type of battery in a microgrid system. The work in first paper is extended. A high level control strategy is developed to coordinate a lead acid battery and a VRB with reinforcement learning. The paper is to be submitted to IEEE Transactions on Smart Grid --Abstract, page iv

Optimization in microgrid design and energy management

The dissertation is composed of three papers, which cover microgrid systems performance characterization, optimal sizing for energy storage system and stochastic optimization of microgrid operation. In the first paper, a complete Photovoltaic-Vanadium Redox Battery (VRB) microgrid is characterized holistically. The analysis is based on a prototype system installation deployed at Fort Leonard Wood, Missouri, USA. In the second paper, the optimal sizing of power and energy ratings for a VRB system in isolated and grid-connected microgrids is proposed. An analytical method is developed to solve the problem based on a per-day cost model in which the operating cost is obtained from optimal scheduling. The charge, discharge efficiencies, and operating characteristics of the VRB are considered in the problem. In the third paper, a novel battery operation cost model is proposed accounting for charge/discharge efficiencies as well as life cycles of the batteries. A probabilistic constrained approach is proposed to incorporate the uncertainties of renewable sources and load demands in microgrids into the UC and ED problems --Abstract, page iv

Prioritized experience replay based deep distributional reinforcement learning for battery operation in microgrids

This is the author accepted manuscript. The final version is available on open access from Elsevier via the DOI in this recordData availability: Data will be made available on request.Reinforcement Learning (RL) provides a pathway for efficiently utilizing the battery storage in a microgrid. However, traditional value-based RL algorithms used in battery management focus on formulating the policies based on the reward expectation rather than its probability distribution. Hence the scheduling strategy is solely based on the expectation of the rewards rather than the distribution. This paper focuses on scheduling strategy based on probability distribution of the rewards which optimally reflects the uncertainties in the incoming dataset. Furthermore, the prioritized experience replay samples of the training experience are used to enhance the quality of the learning by reducing bias. The results are obtained with different variants of distributional RL algorithms like C51, Quantile Regression Deep Q-Network (QR-DQN), Fully Quantizable Function (FQF), Implicit Quantile Networks (IQN) and rainbow. Moreover, the results are compared with the traditional deep Q-learning algorithm with prioritized experienced replay. The convergence results on the training dataset are further analyzed by varying the action spaces, using randomized experience replay and without including the tariff-based action while enforcing the penalties for violating battery SoC limits. The best trained Q-network is tested with different load and PV profiles to obtain the battery operation and costs. The performance of the distributional RL algorithms is analyzed under different schemes of Time of Use (ToU) tariff. QR-DQN with prioritized experience replay has been found to be the best performing algorithm in terms of convergence on the training dataset, with least fluctuation in validation dataset and battery operations during different tariff regimes during the day.European Regional Development Fun

Performance Prediction of a Vanadium Redox Battery for use in Portable, Scalable Microgrids

Vanadium redox batteries (VRBs) have proven to be a viable energy storage technology for portable microgrids due to their rechargeability and high energy density. VRBs exhibit parasitic load loss during operation due to pumping of electrolyte across the membrane during charging and discharging cycles, as well as required temperature control in the form of heating, ventilation and air conditioning. This paper focuses on empirically characterizing VRB efficiency based on known climatic operating conditions and load requirements. A model is created to determine system performance based on known climatic and load data collected and analyzed over an extended time period. A case study is performed using known data for a week time period to characterize system performance, which was compared to actual system performance observed during this same time period. This model allows for appropriate sizing of the PV array and discretionary loads based on required energy density of the system