Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty

Hybrid energy storage systems (HESS) involve synergies between multiple energy storage technologies with complementary operating features aimed at enhancing the reliability of intermittent renewable energy sources (RES). Nevertheless, coordinating HESS through optimized energy management strategies (EMS) introduces complexity. The latter has been previously addressed by the authors through a systems-level graphical EMS via Power Pinch Analysis (PoPA). Although of proven efficiency, accounting for uncertainty with PoPA has been an issue, due to the assumption of a perfect day ahead (DA) generation and load profiles forecast. This paper proposes three adaptive PoPA-based EMS, aimed at negating load demand and RES stochastic variability. Each method has its own merits such as; reduced computational complexity and improved accuracy depending on the probability density function of uncertainty. The first and simplest adaptive scheme is based on a receding horizon model predictive control framework. The second employs a Kalman filter, whereas the third is based on a machine learning algorithm. The three methods are assessed on a real isolated HESS microgrid built in Greece. In validating the proposed methods against the DA PoPA, the proposed methods all performed better with regards to violation of the energy storage operating constraints and plummeting carbon emission footprint

Probabilistic adaptive model predictive power pinch analysis (PoPA) energy management approach to uncertainty

This paper proposes a probabilistic power pinch analysis (PoPA) approach based on Monte–Carlo simulation (MCS) for energy management of hybrid energy systems uncertainty. The systems power grand composite curve is formulated with the chance constraint method to consider load stochasticity. In a predictive control horizon, the power grand composite curve is shaped based on the pinch analysis approach. The robust energy management strategy effected in a control horizon is inferred from the likelihood of a bounded predicted power grand composite curve, violating the pinch. Furthermore, the response of the system using the energy management strategies (EMS) of the proposed method is evaluated against the day-ahead (DA) and adaptive power pinch strategy

Reinforcement Learning based Adaptive Model Predictive Power Pinch Analysis Systems Level Energy Management Approach to Uncertainty in Isolated Hybrid Energy Storage Systems

Ph. D. ThesisHybrid energy storage systems (HESS) involves the integration of multiple energy storage technologies with different complementary characteristics which are significantly advantageous compared to a single energy storage system, and can greatly improve the reliability of intermittent renewable energy sources (RES). Aside from the advantages HESS offer, the control and coordination of the multiple energy storages and the vital elements of the system via an optimised energy management strategy (EMS) involves increased computational time. Nevertheless, a systems-level graphical EMS based on Power Pinch Analysis (PoPA) which is a low burden computational tool was recently proposed for HESS. In this respect, the EMS which effectively resolved deficit and excess energy objectives was effected via the graphical PoPA tool, the power grand composite curve (PGCC). PGCC is basically a plot of integrated energy demands and sources in the system as a function of time. Although of proven success, accounting for uncertainty with PoPA is a cogent research question due to the assumption of an ideal day ahead (DA) generation and load profiles forecast. Therefore, the proposition of several graphical and reinforcement learning based ‘adaptive’ PoPA EMSs in order to address the issue of uncertainty with PoPA, has been the major contribution of this thesis. Firstly, to counteract the combined effect of uncertainty with PoPA, an Adaptive PoPA EMS for a standalone HESS has been proposed. In the Adaptive PoPA, the PGCC was implemented within a receding horizon model predictive framework with the current output state of the energy storage (in this case the battery) used as control feedback to derive an updated sequence of EMS, inferred via PGCC shaping. Additionally, during the control and operation of the HESS, re-computation of the PGCC only occurs if a forecast uncertainty occurs such that the error between the real and estimated battery’s state of charge becomes greater than an arbitrarily chosen threshold value of 5%. Secondly a Kalman filter for the optimal estimation of uncertainty distributed as a normal Gaussian is integrated into the Adaptive PoPA in order to recursively predict the State of Charge of the battery based on the likelihood of uncertainty. Thus, the Kalman filter Adaptive PoPA by anticipating the effect of uncertainty offers an improved approach to the Adaptive PoPA particularly when the uncertainty is of a Gaussian distribution. The algorithm is therefore more sophisticated than the Adaptive PoPA but nevertheless computationally efficient and offers a preventive measure as an improvement. Furthermore, Tabular Dyna Q-learning algorithm, a subset of reinforcement learning which employs a learning agent to solve a discrete Markov Decision Process by maximising an expected reward in accordance with the Bellman optimality, is integrated within the Power Pinch Analysis. Thereafter, a deep neural network is used to approximate the Q-Learning Table. These aforementioned methods which have been highlighted in order of computational time can be deployed with only a minimal level of historical data requirements such as the average load profile or base load data and solar irradiance forecast to produce a deterministic solution. Nevertheless, this thesis proposed a probabilistic adaptive PoPA strategy based on a (recursive least square) Monte Carlo simulation chance constrained framework, in the event where there is sufficient amount of historical data such as the probability distribution of the uncertain model parameters. The probabilistic approach is no doubt more computationally intensive than the deterministic methods presented though it proffers a much more realistic solution to the problem of uncertainty. In order to enhance the probabilistic adaptive PoPA, an actor-critic deep neural network reinforcement learning agent is incorporated. The six methods are evaluated against the DA PoPA on an actual isolated HESS microgrid built in Greece with respect to the violation of the energy storage operating constraints and plummeting carbon emission footprint.Petroleum Technology Development Funds (PTDF

Application of Machine Learning Methods for Asset Management on Power Distribution Networks

This study aims to study the different kinds of Machine Learning (ML) models and their working principles for asset management in power networks. Also, it investigates the challenges behind asset management and its maintenance activities. In this review article, Machine Learning (ML) models are analyzed to improve the lifespan of the electrical components based on the maintenance management and assessment planning policies. The articles are categorized according to their purpose: 1) classification, 2) machine learning, and 3) artificial intelligence mechanisms. Moreover, the importance of using ML models for proper decision making based on the asset management plan is illustrated in a detailed manner. In addition to this, a comparative analysis between the ML models is performed, identifying the advantages and disadvantages of these techniques. Then, the challenges and managing operations of the asset management strategies are discussed based on the technical and economic factors. The proper functioning, maintenance and controlling operations of the electric components are key challenging and demanding tasks in the power distribution systems. Typically, asset management plays an essential role in determining the quality and profitability of the elements in the power network. Based on this investigation, the most suitable and optimal machine learning technique can be identified and used for future work. Doi: 10.28991/ESJ-2022-06-04-017 Full Text: PD

Addressing the Long-Term Management of High-level and Long-lived Nuclear Wastes as a Socio-Technical Problem:Insights from InSOTEC

This report summarises the lessons to be drawn from the three-year collaborative social sciences research project ‘International Socio-Technical Challenges for implementing geological disposal’ (InSOTEC). Adopting an approach that is relatively novel in this context, the project focused its investigations on the complex interplay between what are typically seen as distinct technical and social dimensions of radioactive waste management (RWM), in particular in the context of the design and implementation of geological disposal. The aim of the InSOTEC project was not to arrive at a prescription for facilitating the implementation of geological disposal, but to foster and deepen the growing awareness of the interaction between social and technical aspects of RWM that has been evident within the technical expert community by providing stakeholders and experts of all kinds with a better understanding of the processes that shape the challenges which confront them. The report brings together insights for RWM that have been generated within the different research strands of the project and offers observations on their implications for practice, addressing in particular the processes of research and development, public and stakeholder involvement in RWM, and long-term governance of geological disposal of higher activity radioactive wastes

Modelling, thermoeconomic analysis and optimization of hybrid solar-biomass organic Rankine cycle power plants

The need for modern energy systems to embrace the requirements of energy security, sustainability and affordability in their designs has placed emphatic importance on exploitation of renewable resources, such as solar and wind energy, etc. However, these resources often lead to reduced reliability and dispatchability of energy systems; less-efficient conversion processes; high cost of power production; etc. One promising way to ameliorate these challenges is through hybridization of renewable energy resources, and by using organic Rankine cycle (ORC) for power generation. Thus, this PhD research project is aimed at conceptual design and techno-economic optimization of hybrid solar-biomass ORC power plants. The methodologies adopted are in four distinct phases: - First, novel hybrid concentrated solar power (CSP)-biomass scheme was conceived that could function as retrofit to existing CSP-ORC plants as well as in new hybrid plant designs. Thermodynamic models were developed for each plant sub-unit, and yearly techno-economic performance was assessed for the entire system. Specifically, the ORC was modelled based on characteristics of an existing CSP-ORC plant, which currently operates at Ottana, Italy. Off-design models of ORC components were integrated, and their performance was validated using experimental data obtained from the aforementioned real plant. - Second, detailed exergy and exergoeconomic analyses were performed on the proposed hybrid plant, in order to examine the system components with remarkable optimization potentials. The evaluation on optimization potentials considered intrinsic irreversibilities in the respective components, which are imposed by assumptions of systemic and economic constraints. This has been termed enhanced exergy and enhanced exergoeconomic analyses here. - Third, the techno-economic implications of using siloxane mixtures as ORC working fluid were investigated, with the aim of improving heat transfer processes in the ORC plant. The studied fluid pairs were actively selected to satisfy classical thermodynamic requirements, based on established criteria. - Fourth, the biomass retrofit system was optimized multi-objectively, to minimize biomass consumption rate (maximize exergetic efficiency) and to minimize exergy cost rate. Non-dominated Sorting Genetic Algorithm (NSGA-II) was adopted for multi-objective optimization. The conceptual scheme involves parallel hybridization of CSP and biomass systems, such that each is capable of feeding the ORC directly. Results showed that the proposed biomass hybridization concept would increase both thermodynamic efficiency and economic performance of CSP-ORC plants, thereby improving their market competitiveness. Total exergy destroyed and exergy efficiency were quantified for each component, and for the whole system. Overall system exergetic efficiency of about 7 % was obtained. Similarly, exergoeconomic factor was obtained for each system component, and their implications were analysed to identify system components with high potentials for optimization. Furthermore, it was observed that thermodynamic performance of the hybrid plant would be optimized by using siloxane mixtures as ORC working fluid. However, this would result in larger heat exchange surface area, with its attendant cost implications. Lastly, biomass combustion and furnace parameters were obtained, which would simultaneously optimize exergetic efficiency and exergy cost rate for the hybrid plant. In sum, a novel scheme has been developed for hybridizing solar and biomass energy for ORC plants, with huge potentials to improve techno-economic competitiveness of solar-ORC systems

Optimal sizing and techno-economic analysis of grid-connected nanogrid for tropical climates of the savannah

Reliability and costs are mainly considered in performance analysis of renewable energy-based distributed grids. Hybrid Optimization of Multiple Energy Renewables was used in techno-economic analysis of renewable energy systems involving photovoltaics, wind, diesel and storage in tropical regions of Amazon, Central Asia and Mediterranean. In a study for a Guinea Savannah region, 70% of renewable energy fraction was achieved. However, levelized cost of energy of 0.689 $/kWh was higher than tariff rate of 0.6$/kWh. This paper considers Hybrid Optimization of Multiple Energy Renewables to achieve lower levelized cost of energy and net present costs of a nanogrid for increased reliability and low per capita energy consumption of 150 kWh in a Sudan Savannah region of Nigeria. The proposed grid connected nanogrid aims to serve daily residential demand of 355 kWh. A range of 0.0110 $/kWh to 0.0095$/kWh and $366,210 to$288,680 as negative values of levelized cost of energy and net present cost respectively were realized, implying potentials for a large grid export. The renewable energy fraction of up to 98% was also achieved in addition to low greenhouse gas emission of 2,328 tons/year. The results may further be consolidated with strategies for power dispatch and load scheduling

Optimal control of a flywheel-based automotive kinetic energy recovery system

This thesis addresses the control issues surrounding flywheel-based Kinetic Energy Recovery Systems (KERS) for use in automotive vehicle applications. Particular emphasis is placed on optimal control of a KERS using a Continuously Variable Transmission (CVT) for volume car production, and a wholly simulation-based approach is adopted. Following consideration of the general control issues surrounding KERS operation, a simplified system model is adopted, and the scope for use of optimal control theory is explored. Both Pontryagin’s Maximum Principle, and Dynamic Programming methods are examined, and the need for numerical implementation established. With Dynamic Programming seen as the most likely route to practical implementation for realistic nonlinear models, the thesis explores several new strategies for numerical implementation of Dynamic Programming, capable of being applied to KERS control of varying degrees of complexity. The best form of numerical implementation identified (in terms of accuracy and efficiency) is then used to establish via simulation, the benefits of optimal KERS control in comparison with a more conventional non-optimal strategy, showing clear benefits of using optimal control