Optimal energy management and control of microgrids in modern electrical power systems

Microgrids (MGs) are becoming more popular in modern electric power systems owing to their reliability, efficiency, and simplicity. The proportional-integral (PI) based droop control mechanism has been widely used in the MG control domain as the setpoint generator for the primary controller which has several drawbacks. In order to mitigate these issues, and to enhance the transient and steady-state operations in islanded MGs, advanced control and intelligent optimization methodologies are presented in this dissertation. First, to improve the existing PI-based droop relationship in DCMGs, a multi-objective optimization (MOO) based optimal droop coefficient computation method is proposed. Considering the system voltage regulation, system total loss minimization, and enhanced current sharing among the distributed generators (DGs), the Pareto optimal front is obtained using the Elitist non dominated sorting genetic algorithm (NSGA II). Then, a fuzzy membership function approach is introduced to extract the best compromise solution from the Pareto optimal front. The drawbacks of PI-based droop control cannot be entirely mitigated by tuning the droop gains. Hence, a droop free, approximate optimal feedback control strategy is proposed to optimally control DGs in islanded DCMGs. Further, to gain the fully optimal behavior, and to mitigate constant power load (CPL) instabilities, a decentralized optimal feedback control strategy is also introduced for the active loads (ALs) in the MG. In both algorithms, the approximate dynamic programming (ADP) method is employed to solve the constrained input infinite horizon optimal control problem by successive approximation of the value function via a linear in the parameter (LIP) neural network (NN). The NN weights are updated online by a concurrent reinforcement learning (RL) based tuning algorithm, and the convergence of the unknown weights to a neighborhood of the optimal weights is guaranteed without the persistence of excitation (PE). Finally, a local optimal control strategy is presented to path optimization of islanded ACMGs to enhance the transient operations while mitigating the voltage and frequency deviations caused by the traditional droop control. Optimal state and control transient trajectories in the d-q reference frame are obtained by Pontryagin's minimum principle which drives each DG from a given initial condition to their steady-state manifold. Both simulation and experimental results are presented to validate the concepts

Energy Management of Grid-Connected Microgrids, Incorporating Battery Energy Storage and CHP Systems Using Mixed Integer Linear Programming

In this thesis, an energy management system (EMS) is proposed for use with battery energy storage systems (BESS) in solar photovoltaic-based (PV-BESS) grid-connected microgrids and combined heat and power (CHP) applications. As a result, the battery's charge/discharge power is optimised so that the overall cost of energy consumed is minimised, considering the variation in grid tariff, renewable power generation and load demand. The system is modelled as an economic load dispatch optimisation problem over a 24-hour time horizon and solved using mixed integer linear programming (MILP) for the grid-connected Microgrid and the CHP application. However, this formulation requires information about the predicted renewable energy power generation and load demand over the next 24 hours. Therefore, a long short-term memory (LSTM) neural network is proposed to achieve this. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the energy management system (EMS) that benefits from using actual generation and demand data in real-time. At each time-step, the LSTM predicts the generation and load data for the next 24 h. The dispatch problem is then solved, and the real-time battery charging or discharging command for only the first hour is applied. Real data are then used to update the LSTM input, and the process is repeated. Simulation results using the Ushant Island as a case study show that the proposed online optimisation strategy outperforms the offline optimisation strategy (with no RH), reducing the operating cost by 6.12%. The analyses of the impact of different times of use (TOU) and standard tariff in the energy management of grid-connected microgrids as it relates to the charge/discharge cycle of the BESS and the optimal operating cost of the Microgrid using the LSTM-MILP-RH approach is evaluated. Four tariffs UK tariff schemes are considered: (1) Residential TOU tariff (RTOU), (2) Economy seven tariff (E7T), (3) Economy ten tariff (E10T), and (4) Standard tariff (STD). It was found that the RTOU tariff scheme gives the lowest operating cost, followed by the E10T tariff scheme with savings of 63.5% and 55.5%, respectively, compared to the grid-only operation. However, the RTOU and E10 tariff scheme is mainly used for residential applications with the duck curve load demand structure. For community grid-connected microgrid applications except for residential-only communities, the E7T and STD, with 54.2% and 39.9%, respectively, are the most likely options offered by energy suppliers. The use of combined heat and power (CHP) systems has recently increased due to their high combined efficiency and low emissions. Using CHP systems in behind-the-meter applications, however, can introduce some challenges. Firstly, the CHP system must operate in load-following mode to prevent power export to the grid. Secondly, if the load drops below a predefined threshold, the engine will operate at a lower temperature and hence lower efficiency, as the fuel is only half-burnt, creating significant emissions. The aforementioned issues may be solved by combining CHP with a battery energy storage system. However, the dispatch of CHP and BESS must be optimised. Offline optimisation methods based on load prediction will not prevent power export to the grid due to prediction errors. Therefore, a real-time EMS using a combination of LSTM neural networks, MILP, and RH control strategy is proposed. Simulation results show that the proposed method can prevent power export to the grid and reduce the operational cost by 8.75% compared to the offline method. The finding shows that the BESS is a valuable asset for sustainable energy transition. However, they must be operated safely to guarantee operational cost reduction and longer life for the BESS

Energy management strategies for fuel cell vehicles: A comprehensive review of the latest progress in modeling, strategies, and future prospects

Fuel cell vehicles (FCVs) are considered a promising solution for reducing emissions caused by the transportation sector. An energy management strategy (EMS) is undeniably essential in increasing hydrogen economy, component lifetime, and driving range. While the existing EMSs provide a range of performance levels, they suffer from significant shortcomings in robustness, durability, and adaptability, which prohibit the FCV from reaching its full potential in the vehicle industry. After introducing the fundamental EMS problem, this review article provides a detailed description of the FCV powertrain system modeling, including typical modeling, degradation modeling, and thermal modeling, for designing an EMS. Subsequently, an in-depth analysis of various EMS evolutions, including rule-based and optimization-based, is carried out, along with a thorough review of the recent advances. Unlike similar studies, this paper mainly highlights the significance of the latest contributions, such as advanced control theories, optimization algorithms, artificial intelligence (AI), and multi-stack fuel cell systems (MFCSs). Afterward, the verification methods of EMSs are classified and summarized. Ultimately, this work illuminates future research directions and prospects from multi-disciplinary standpoints for the first time. The overarching goal of this work is to stimulate more innovative thoughts and solutions for improving the operational performance, efficiency, and safety of FCV powertrains

Optimal cost minimization strategy for fuel cell hybrid electric vehicles based on decision making framework

The low economy of fuel cell hybrid electric vehicles is a big challenge to their wide usage. A road, health, and price-conscious optimal cost minimization strategy based on decision making framework was developed to decrease their overall cost. First, an online applicable cost minimization strategy was developed to minimize the overall operating costs of vehicles including the hydrogen cost and degradation costs of fuel cell and battery. Second, a decision making framework composed of the driving pattern recognition-enabled, prognostics-enabled, and price prediction-enabled decision makings, for the first time, was built to recognize the driving pattern, estimate health states of power sources and project future prices of hydrogen and power sources. Based on these estimations, optimal equivalent cost factors were updated to reach optimal results on the overall cost and charge sustaining of battery. The effects of driving cycles, degradation states, and pricing scenarios were analyzed

Development of a control framework for hybrid renewable energy system in microgrid

Electrical energy has an essential role in society as it ensures high quality of life and steady economic development. Demand for the electric energy has been steadily growing throughout the recent history and this demand is expected to grow further in the future. Most of electrical energy nowadays is generated by burning fossil fuels and there are serious concerns about the resulting emission. Renewable energy sources appeared as a viable alternative for environmentally hazardous sources. However, sources of renewable energy have considerably unpredictable and environmental conditions dependent power output and as such can’t be directly incorporated into existing electrical grid. These sources are usually integrated to the electrical grid as part of microgrid or hybrid energy source that consists of two or more energy sources, converters and/or storage devices. In hybrid energy sources, generation and storage elements complement each other to provide high quality and more reliable power delivery. This area of research is its infant stage and requires a lot of research and development effort to be done. Main objective of this thesis is to develop a framework for analysis and control of power electronics interfaces in microgrid connected hybrid energy source. The framework offers the generalized approach in treatment of control problem for hybrid energy sources. Development of the framework is done for the generalized hybrid source comprised of energy source(s), storage element(s), power electronic interfaces and control system. The main contributions of this thesis are, generalization of control problem for power electronics interfaces in hybrid energy source, the development of switching algorithm for three phase switching converters based on the closed loop behavior of the converters and the development of a maximum power point tracking algorithm for the renewable energy sources

Managing electric vehicles with renewable generation through energy storage and smart grid principles

Electric vehicles (EVs) are the most comprehensive method of sustainable transportation because they are environmentally friendly, quiet and low maintenance. However, they suffer from low usability because of the limited distance that can be covered on a single charge, which limits the freedom of transportation. Further, the charging process to restore the initial driving range is relatively long compared with conventional solutions. The only proposed way to improve the distance on a charge is to install a large energy storage system (ESS), which takes up more space, thereby limiting the usability of space by passengers and increasing the weight of the EV. The increased weight and size of the EV also negatively affect the distance range. In addition, the larger size of the battery, which is the main component of the ESS, requires a longer charging time. The current solution for fast charging requires more time than traditional refuelling techniques. This study aims to design, develop and analyse a novel approach for improving the energy consumption of EVs using optimisation techniques. In the first phase of the study, detailed analysis is conducted of the existing systems of EVs to determine which areas can be improved. The outcome of this investigation is used to determine presented loading profile of the various loads in EVs and determine the way to characterise them. These results are applied to design the new architecture for the loads to improve the connectivity of the various components of EVs and introduce interaction between loads. The developed architecture has centralised topology with separated control bus for the safety systems to satisfy the ISO 26262 safety standard. The newly developed system considers various loading requests at the same time to supply the load. The control algorithm schedules the power supply to the selected loads or, in some cases, clips the load request to decrease the momentary energy consumption. To achieve better optimisation, the thermal energy generation is analysed because it has a significant effect on the electric energy consumption in the heating elements. The second part of the developed approach is deep integration of loads with the overall energy flow in EVs. As a result, the recuperated energy in the propulsion system can be transferred to the components of the ESS and to supply the auxiliary loads on demand. The xviii generation units are combined with a photovoltaic system to improve the generation capability of the architecture. One of the key aims of this research is the simulation and experimental study of the developed architecture to identify weak spots in the solution and compare its performance with existing solutions under various solutions that go beyond traditional driving cycles

Energy management in electric vehicles: Development and validation of an optimal driving strategy

Electric vehicles (EVs) are a promising alternative energy mode of transportation for the future. However, due to the limited range and relatively long charging time, it is important to use the stored battery energy in the most optimal manner possible. Existing research has focused on improvements to the hardware or improvements to the energy management strategy (EMS). However, EV drivers may adopt a driving strategy that causes the EMS to operate the EV hardware in inefficient regimes just to fulfil the driver demand. The present study develops an optimal driving strategy to help an EV driver choose a driving strategy that uses the stored battery energy in the most optimal manner. First, a strategy to inform the driver about his/her current driving situation is developed. Then, two separate multi-objective strategies, one to choose an optimal trip speed and another to choose an optimal acceleration strategy, are presented. Finally, validation of the optimal driving strategy is presented for a fleet-style electric bus. The results indicated that adopting the proposed approach could reduce the electric bus’ energy consumption from about 1 kWh/mile to 0.6-0.7 kWh/mile. Optimization results for a fixed route around the Missouri S&T campus indicated that the energy consumption of the electric bus could be reduced by about 5.6% for a 13.9% increase in the trip time. The main advantage of the proposed strategy is that it reduces the energy consumption while minimally increasing the trip time. Other advantages are that it allows the driver flexibility in choosing trip parameters and it is fairly easy to implement without significant changes to existing EV designs. --Abstract, page iii

Optimized Energy Management Schemes for Electric Vehicle Applications: A Bibliometric Analysis towards Future Trends

Concerns over growing greenhouse gas (GHG) emissions and fuel prices have prompted researchers to look into alternative energy sources, notably in the transportation sector, accounting for more than 70% of carbon emissions. An increasing amount of research on electric vehicles (EVs) and their energy management schemes (EMSs) has been undertaken extensively in recent years to address these concerns. This article aims to offer a bibliometric analysis and investigation of optimized EMSs for EV applications. Hundreds (100) of the most relevant and highly influential manuscripts on EMSs for EV applications are explored and examined utilizing the Scopus database under predetermined parameters to identify the most impacting articles in this specific field of research. This bibliometric analysis provides a survey on EMSs related to EV applications focusing on the different battery storages, models, algorithms, frameworks, optimizations, converters, controllers, and power transmission systems. According to the findings, more articles were published in 2020, with a total of 22, as compared to other years. The authors with the highest number of manuscripts come from four nations, including China, the United States, France, and the United Kingdom, and five research institutions, with these nations and institutions accounting for the publication of 72 papers. According to the comprehensive review, the current technologies are more or less capable of performing effectively; nevertheless, dependability and intelligent systems are still lacking. Therefore, this study highlights the existing difficulties and challenges related to EMSs for EV applications and some brief ideas, discussions, and potential suggestions for future research. This bibliometric research could be helpful to EV engineers and to automobile industries in terms of the development of cost-effective, longer-lasting, hydrogen-compatible electrical interfaces and well-performing EMSs for sustainable EV operations