Reliable, affordable access to electrical power is a requirement for almost all aspects of developed societies. Challenges associated with reducing carbon emissions has led to growing interest in nuclear-renewable hybrid energy systems (N-RHES). Much work has already been done in suggesting and analyzing various N-RHES using a variety of optimization techniques and assumptions. This work builds upon previous techniques for simultaneous combined design and dispatch optimization (CDDO) for hybrid energy systems (HES). The first contribution of this work is the development and application of sensitivity analysis tailored to the combined design and dispatch optimization problem. This sensitivity analysis cover uncertainty in design parameters, time series and dispatch horizon lengths. The result is a deeper insight into which sources of uncertainty are most important to account for and how the uncertainty around these sources can be quantified. The second contribution of this work is a novel multi-scale optimization algorithm for the combined HES design and dispatch optimization. This algorithm supports optimization of nonlinear models over very long-time horizons. This method is based on a multi-dimensional distribution of the optimal capacities for a system as determined by a large number of combined design and dispatch optimization problems each covering a subset of the complete time horizon. This method shows good agreement with the direct solution to multiple example systems and is then used to solve a problem with a dispatch horizon length 112.5 times longer than is solvable directly. The third contribution of this work is the application of the novel multi-scale method to three HES. Each of the application systems is used to demonstrate the strengths, validation and applicability of the developed algorithm to a wide range of possible HES/NHES designs.

Multi-objective optimization metaheuristics (MOMs) are powerful methods for solving complex optimization problems but can require a large number of function evaluations to find optimal solutions. Thus, an efficient multi-objective optimization method should generate accurate and diverse solutions in a timely manner. Improving MOMs convergence speed is an important and challenging research problem which is the scope of this thesis. This thesis conducted the most comprehensive comparative study ever in MOMs. Based on the results, multi-objective (MO) versions of particle swarm optimization (PSO) and differential evolution (DE) algorithms achieved the highest performances; therefore, these two MOMs have been selected as bases for further acceleration in this thesis. To accelerate the selected MOMs, this work focuses on the incorporation of leadership concept to MO variants of DE and PSO algorithms. Two complex case studies of MO design of renewable energy systems are proposed to demonstrate the efficiency of the proposed MOMs. This thesis proposes three new MOMs, namely, leader and speed constraint multi-objective PSO (LSMPSO), opposition-based third evolution step of generalized DE (OGDE3), and multi-objective DE with leadership enhancement (MODEL) which are compared with seven state-of-the-art MOMS using various benchmark problems. LSMPSO was found to be the fastest MOM for the problem undertaken. Further, LSMPSO achieved the highest solutions accuracy for optimal design of a photovoltaic farm in Toronto area. OGDE3 is the first successful application of OBL to a MOM with single population (no-coevolution) using leadership and self-adaptive concepts; the convergence speed of OGDE3 outperformed the other MOMs for the problems solved. MODEL embodies leadership concept into mutation operator of GDE3 algorithm. MODEL achieved the highest accuracy for the 30 studied benchmark problems. Furthermore, MODEL achieved the highest solution accuracy for a MO optimization problem of hydrogen infrastructures design across the province Ontario between 2008 and 2025 considering electricity infrastructure constraints.

In a energy scenario moving fast towards the deployment of renewable energy technologies and the need of reducing CO2 emissions, hybrid energy systems for rural electrification are a feasible alternative solution to the utilization of conventional Diesel generators. The project focuses on the design and optimization of an off-grid hybrid energy system for a village of around 250 inhabitants in Gujarat, India. The energy system is part of a bigger project, “G.I.V.E. Center of Excellence”, which has an innovative concept on a more sustainable rural lifestyle. The system, which has to depend mainly on locally available resources, intends to serve three main services: electrical demand, water purification and thermal energy for cooking. Two system configurations were designed and optimized to supply the estimated demand. The main outcome is a techno-economic analysis of the different system performances, which leads to a conclusion: dealing with the services individually has lower costs of implementation, less than half if compared to the implementation of a conventional Diesel generator. Furthermore, CO2 emissions are drastically reduced. A sensitivity analysis was performed to address the different uncertainties such as the cost of the fuel. The result shows that if enough biomass resource would be available, a system based only on renewable energy technologies is economically profitable. / G.I.V.E. Scandinavia

AN ABSTRACT OF THE THESIS OFCarlos Eduardo Teran Rivero, for the Master of Science degree in Electrical Engineering, presented on October 15, 2020, at Southern Illinois University Carbondale. TITLE: HYBRID RENEWABLE ENERGY SYSTEMS ANALYSIS VIA HOMER PRO AND ETAP: A CASE STUDY IN VENEZUELA MAJOR PROFESSOR: Dr. Arash Asrari The main objective of this project is to design a realistic hybrid renewable energy system as a micro-grid in order to supply required power to the villages of Coche Island located in Venezuela. Due to the deterioration of Venezuela’s power system, the native people inhabiting in the island frequently find themselves without electricity when there exists a shortage in supply. Considering “Margarita” as an example, the priority of the supply is always considered for the larger communities where there is any relevant issue which will leave the small communities of the Coche Island without any power. The motivation of this thesis is to propose a hybrid micro-grid located in some of the larger villages in the Coche Island using renewable energy resources (RERs) such as photovoltaic and wind turbines such that the community of the corresponding villages can locally generate power in order to cover their basic needs in normal and emergency situations. Toward this end, this thesis presents a hybrid configuration integrated with RERs to address the aforementioned challenges. The suggested frameworks are developed via two different software including ETAP (electrical transient analyzer program) and HOMER (hybrid optimization model for electric renewables), and a comprehensive comparison between the obtained results is provided to validate the effectiveness of the both software in the field of designing hybrid energy systems.

With the growing capacity of renewable energy production sources, nuclear energy, once a mainstay of power generation, faces challenges due to its limited adaptability to fluctuating energy demands. This inherent rigidity makes it less desirable than the more flexible renewable sources. However, integrating thermal energy storage (TES) systems offers a promising avenue, enabling nuclear power plants (NPPs) to enhance their operational flexibility and remain competitive in an evolving renewable market. A comprehensive ranking methodology has been introduced, delineating the criteria and processes to determine the most synergistic TES/NPP design couplings. This methodology considers the unique characteristics of both current and prospective reactor fleets, ensuring broad applicability across various nuclear technologies. Economic analysis further supports the case for TES integration. Findings indicate that when equipped with TES systems, NPPs can remain price competitive, even with carbon-neutral alternatives like solar power generation. A lab-scale TES system was meticulously designed and constructed to validate these theoretical propositions. For its control, the Python GEKKO model predictive control (MPC) was employed, a decision influenced by the proven efficacy of GEKKO in managing complex systems. Tests conclusively demonstrated the feasibility and efficiency of using GEKKO for MPC of TES systems. A novel methodology for the MPC of a RELAP5-3D input deck has been proposed and elaborated upon. This methodology was rigorously tested at two distinct scales. The initial focus was on a thermal-hydraulic model of the lab-scale TES system. Subsequent efforts scaled up to control a more intricate thermal-hydraulic model, representing a small modular reactor (SMR) paired with an oil-based TES system. In both scenarios, GEKKO exhibited exemplary performance, controlling the RELAP5-3D models with precision and ensuring they met the stipulated demand parameters. The research underscores the potential of RELAP5-3D MPC in streamlining the licensing process for TES systems intended for NPP coupling. This approach could eliminate the need for expensive and time-consuming experiments, paving the way for more efficient and cost-effective nuclear energy solutions.

The domestic sector will play an important role in the decarbonisation and decentralisation of the energy sector in the future. Installation numbers of building-integrated small-scale energy systems such as photovoltaics (PV), wind turbines and micro-combined heat and power (CHP) have significantly increased. However, the power output of PV and wind turbines is inherently linked to weather conditions; thus, the injected power into the public grid can be highly intermittent. With the increasing share of renewable energy at all voltage levels challenges arise in terms of power stability and quality. To overcome the volatility of such energy sources, storage technologies can be applied to temporarily decouple power generation from power consumption. Two emerging storage technologies which can be applied at residential level are hydrogen systems and vanadium-redox-flow-batteries (VRFB). In addition, the building-integrated energy sources and storage system can be combined to form a hybrid renewable energy system (HRES) to manage the energy flow more efficiently. The main focus of this thesis is to investigate the dynamic performance of two emerging energy storage technologies, a hydrogen loop composed of alkaline electrolyser, gas storage and proton exchange membrane (PEM) fuel cell, and a VRFB. In addition, the application of building-integrated HRES at customer level to increase the self-consumption of the onsite generated electricity and to lower the grid interaction of the building has been analysed. The first part deals with the development of a research test-bed known as the Hybrid Renewable Energy Park (HREP). The HREP is a residential-scale distributed energy system that comprises photovoltaic, wind turbine, CHP, lead acid batteries, PEM fuel cell, alkaline electrolyser and VRFB. In addition, it is equipped with programmable electronic loads to emulate different energy consumption patterns and a charging point for electric vehicles. Because of its modular structure different combinations of energy systems can be investigated and it can be easily extended. A unified communication channel based on the local operating network (LON) has been established to coordinate and control the HREP. Information from the energy systems is gathered with a temporal resolution of one second. Integration issues encountered during the integration process have been addressed. The second part presents an experimental methodology to assess the steady state and dynamic performance of the electrolyser, the fuel cell and the VRFB. Operational constrains such as minimum input/output power or start-up times were extracted from the experiments. The response of the energy systems to single and multiple dynamic events was analysed, too. The results show that there are temporal limits for each energy system, which affect its response to a sudden load change or the ability to follow a load profile. Obstacles arise in terms of temporal delays mainly caused by the distributed communication system and should be considered when operating or simulating a HRES at system level. The third part shows how improved system models of each component can be developed using the findings from the experiments. System models presented in the literature have the shortcoming that operational aspects are not adequately addressed. For example, it is commonly assumed that energy systems at system level can respond to load variations almost instantaneously. Thus, component models were developed in an integrated manner to combine theoretical and operational aspects. A generic model layout was defined containing several subsystems, which enables an easy implementation into an overall simulation model in MATLAB®/Simulink®. Experimental methods were explained to extract the new parameters of the semi-empirical models and discrete operational aspects were modelled using Stateflow®, a graphical tool to formulate statechart diagrams. All system models were validated using measured data from the experimental analysis. The results show a low mean-absolute-percentage-error (<3%). Furthermore, an advanced energy management strategy has been developed to coordinate and to control the energy systems by combining three mechanisms; statechart diagrams, double exponential smoothing and frequency decoupling. The last part deals with the evaluation, operation and control of HRES in the light of the improved system models and the energy management strategy. Various simulated case studies were defined to assess a building-integrated HRES on an annual basis. Results show that the overall performance of the hydrogen loop can be improved by limiting the operational window and by reducing the dynamic operation. The capability to capture the waste heat from the electrolyser to supply hot water to the residence as a means of increasing the overall system efficiency was also determined. Finally, the energy management strategy was demonstrated by real-time experiments with the HREP and the dynamic performance of the combined operation has been evaluated. The presented results of the detailed experimental study to characterise the hydrogen loop and the VRFB as well as the developed system models revealed valuable information about their dynamic operation at system level. These findings have relevance to the future application and for simulation studies of building-integrated HRES. There are still integration aspects which need to be addressed in the future to overcome the proprietary problem of the control systems. The innovations in the HREP provide an advanced platform for future investigations such as electric-vehicles as decentralised mobile storage and the development of more advanced control approaches.

Backup diesel generator (DG) systems continue to be a heavily polluting and costly solution for institutions with unreliable grid connections. These systems slow economic growth and accelerate climate change. Photovoltaic (PV), energy storage (ES), grid connected, DG – Hybrid Energy Systems (HESs) or, PV-HESs, can alleviate overwhelming costs and harmful emissions incurred from traditional back-up DG systems and improve the reliability of power supply. However, from project conception to end of lifetime, PV-HESs face significant barriers of uncertainty and variable operating conditions. The fit-and-forget solution previously applied to backup DG systems should not be adopted for PV-HESs. To maximize cost and emission reductions, PV-HESs must be adapted to their boundary conditions for example, irradiance, temperature, and demand. These conditions can be defined and monitored using measurement equipment. From this, an opportunity for performance optimization can be established. The method demonstrated in this study is a techno-economic and environmental approach to improving the performance of PV-HESs. The method has been applied to a case study of an existing PV-HES in Kenya. A combination of both analytical and numerical analyses has been conducted. The analytical analysis has been carried out in Microsoft Excel with the intent of being easily repeatable and practical in a business environment. Simulation analysis has been conducted in improved Hybrid Optimization by Genetic Algorithms (iHOGA), which is a commercially available software for simulating HESs. Using six months of measurement data, the method presented identifies performance inefficiencies and explores corrective interventions. The proposed interventions are evaluated, by simulation analyses, using a set of techno-economic and environment key performance indicators, namely: Net Present Cost (NPC), generator runtime, fuel consumption, total system emissions, and renewable fraction. Five corrective interventions are proposed, and predictions indicate that if these are implemented fuel consumption can be reduced by 70 % and battery lifetime can be extended by 28 %, net present cost can be reduced by 30 % and emissions fall by 42 %. This method has only been applied to a single PV-HES; however, the impact this method could have on sub-Saharan Africa as well as similar regions with unreliable grid connections is found to be significant. In the future, in sub-Saharan Africa alone, over $500 million dollars (USD) and 1.7 billion kgCO2 emissions could be saved annually if only 25 % of the fuel savings identified in this study were realized. The method proposed here could be improved with additional measurement data and refined simulation models. Furthermore, this method could potentially be fully automated, which could allow it to be implemented more frequently and at lower cost.

The increasing demand for renewable energy sources to meet climate targets and reduce carbon emissions poses challenges to the power grid due to their intermittent nature. One potential solution to maintain grid stability is by implementing Hybrid Energy Systems (HESs) that incorporate a Battery Energy Storage System (BESS). To achieve the most favorable outcome in terms of both technical feasibility and profitability of a BESS, it is essential to employ models for simulating and optimizing the control of system components. This thesis focuses on the analysis of energy and revenue streams in a HES consisting of a BESS, photovoltaics (PVs), and an energy load including a fast charging station for electric vehicles (EVs). The objective is to optimize the system based on revenue generation by comparing the control techniques of peak shaving, energy arbitrage, and the integration of ancillary services within the Swedish energy market. The research questions explore the optimal utilization of the BESS and assess the impact of the different control techniques. A model is created in Python with the package CasADi where data from an ongoing installation of a HES in southern Sweden is combined with data from literature research. The model includes an objective function that minimizes the total cost of power from the grid based on the day-ahead price, battery degradation, and monthly peak power.  To answer the research questions, four different scenarios are simulated. The first scenario is a base for comparison, the second one focuses on peak shaving and energy arbitrage, the third on participation in the ancillary service FCR-D upwards regulation, and the last one is a combination of peak shaving, energy arbitrage, and the ancillary service FCR-D. The results show that the remuneration from the ancillary service FCR-D is comparably much higher than the revenues generated from peak shaving and energy arbitrage, providing more than 500% of revenue compared to the same system but without a BESS. The scenario with peak shaving and energy arbitrage shows an increase in revenue of 29% but with more cycling of the battery which could cause losses in performance in the long term. To validate the results, sensitivity analyses are conducted by evaluating weighting in the objective function, implementing Model Predictive Control (MPC), and reviewing price variations.  In conclusion, efficient control techniques can enhance system performance, minimize losses, and ensure optimal utilization of different energy sources, leading to improved feasibility and profitability. The optimal usage of a BESS involves finding a balance between maximizing revenue generation and minimizing battery degradation. This can be achieved through control strategies that optimize the charging and discharging patterns of the BESS based on electricity price signals, demand patterns, and battery health considerations.

Thermal energy storage (TES), the storage of heat or cooling, is a cost-effective energy storage technology that can greatly enhance the performance of the energy systems with which it interacts. TES acts as a buffer between transient supply and demand of energy. In solar thermal systems, TES enables the power output of the plant to be effectively regulated, despite fluctuating solar irradiance. In district energy systems, TES can be used to shift loads, allowing the system to avoid or take advantage of peak energy prices. The benefit of TES, however, can be significantly enhanced by dynamically optimizing the complete energy system. The ability of TES to shift loads gives the system newfound degrees of freedom which can be exploited to yield optimal performance. In the hybrid solar thermal/fossil fuel system explored in this work, the use of TES enables the system to extract nearly 50% more solar energy when the system is optimized. This requires relaxing some constraints, such as fixed temperature and power control, and dynamically optimizing the over a one-day time horizon. In a district cooling system, TES can help equipment to run more efficiently, by shifting cooling loads, not only between chillers, but temporally, allowing the system to take advantage of the most efficient times for running this equipment. This work also highlights the use of TES in a district energy system, where heat, cooling and electrical power are generated from central locations. Shifting the cooling load frees up electrical generation capacity, which is used to sell power to the grid at peak prices. The combination of optimization, TES, and participation in the electricity market yields a 16% cost savings. The problems encountered in this work require modeling a diverse range of systems including the TES, the solar power plant, boilers, gas and steam turbines, heat recovery equipment, chillers, and pumps. These problems also require novel solution methods that are efficient and effective at obtaining workable solutions. A simultaneous solution method is used for optimizing the solar power plant, while a static/dynamic decoupling method is used for the district energy system. / text