Hydrogen as an energy vector to optimize the energy exploitation of a self-consumption solar photovoltaic facility in a dwelling house

Solar photovoltaic (PV) plants coupled with storage for domestic self-consumption purposes seem to be a promising technology in the next years, as PV costs have decreased significantly, and national regulations in many countries promote their installation in order to relax the energy requirements of power distribution grids. However, electrochemical storage systems are still unaffordable for many domestic users and, thus, the advantages of self-consumption PV systems are reduced. Thus, in this work the adoption of hydrogen systems as energy vectors between a PV plant and the energy user is proposed. As a preliminary study, in this work the design of a PV and hydrogen-production self-consumption plant for a single dwelling is described. Then, a technical and economic feasibility study conducted by modeling the facility within the Homer Energy Pro energy systems analysis tool is reported. The proposed system will be able to provide back not only electrical energy but also thermal energy through a fuel cell or refined water, covering the fundamental needs of the householders (electricity, heat or cooling and water). Results show that, although the proposed system effectively increases the energy local use of the PV production and reduces significantly the energy injections or demands into/from the power grid, avoiding power grid congestions and increasing the nano-grid resilience, operation and maintenance costs may reduce its economic attractiveness for a single dwelling. Keywords: Hydrogen, Solar photovoltaics, Energy vector, Power storage, Smart grids, Nano-grid

DYNAMICAL MODEL-BASED LOAD FREQUENCY CONTROL OF A MODERN POWER SYSTEM INTEGRATED WITH DELAYS, EV & RES

A modern power system demands an open communication channel to support the vast number of real-time data exchanges, which may introduce time delays and communication failures thus creates new challenges in power systems. To cope up with these issues, the paper proposed an Internal Model-Based Robust Controller (IMBRC) and IMBRC-PID controller designs for the decentralized LFC (Load Frequency Control) of the modern power system. Initially, a finite-ordered linear model of the power system integrated with RES (Renewable Energy Sources) and aggregated Electrical Vehicles (EV) has been developed. Later the full-order model was employed in the proposed design to achieve complete decentralized, robust, more reliable, and effortless control performances. The Internal Model Compensator (IMC) filter time constant is tuned using Artificial Bee Colony (ABC) optimization algorithm. The objective function considered was the scalarized integral of squared and absolute errors with various weighting factors. The Least-Square Model (LSM) approximation of the IMBRC transfer function determines the PID controller gains. The controller's robustness is verified for the power system components affected by structured and unstructured uncertainties. The error performance indices and simulation results convey that the suggested design keeps the system robustly stable even when subject to varying time delays and uncertainties

Optimal dispatch based on prediction of distributed electric heating storages in combined electricity and heat networks

The volatility of wind power generations could significantly challenge the economic and secure operation of combined electricity and heat networks. To tackle this challenge, this paper proposes a framework of optimal dispatch with distributed electric heating storage based on a correlation-based long short-term memory prediction model. The prediction model of distributed electric heating storage is developed to model its behavior characteristics which are obtained by the autocorrelation and correlation analysis with external factors including weather and time-of-use price. An optimal dispatch model of combined electricity and heat networks is then formulated and resolved by a constraint reduction technique with clustering and classification. Our method is verified through numerous simulations. The results show that, compared with the state-of-the-art techniques of support vector machine and recurrent neural networks, the mean absolute percentage error with the proposed correlation-based long short-term memory can be reduced by 1.009 and 0.481 respectively. Compared with conventional method, the peak wind power curtailment with dispatching distributed electric heating storage is reduced by nearly 30% and 50% in two cases respectively

Power electronics implementation of dynamic thermal storage as effective inertia in large energy systems

Modern large energy systems such as electricity grids and electrified transportation encounter increasing processed power in multi-physics domains, such as electrical, mechanical, thermal, and chemical. Although many systems are becoming predominantly electrical dependent, an integrated multi-physics energy approach creates additional avenues to higher power density, system efficiency, and reliability. Power electronics, serving as power conversion mechanisms, are key linking subsystems consisting of electronic devices, electro-mechanical units, energy storage, etc. This dissertation first studies the use of power electronic drives to implement dynamic thermal storage as effective inertia in solar-interfaced grid-connected low-energy buildings, as an example of a stationary large energy system. Dynamic management of energy components is used to offset variability of stochastic solar resources. Emphasis is on power electronic HVAC (heating, ventilation, and air-conditioning) drives, which can act as an effective electric swing bus to mitigate solar power variability. In doing so, grid power flows become substantially more constant, reducing the need for fast grid resources or dedicated energy storage such as batteries. The work defines a bandwidth over which such HVAC drives can operate. A practical band-pass filter is realized with a lower frequency bound such that the building maintains consistent temperature, and an upper frequency bound to ensure that commanded HVAC fan speeds do not update arbitrarily fast, avoid acoustic discomfort to occupants, and prevent undue hardware wear and tear. The dissertation then moves onto investigation of a mobile energy system, specifically more electric aircraft (MEA), with the purpose of evaluating thermal inertia’s efficacy in a microgrid-like inertia-lacking electrical system. Thermal energy inherent in the cabin air and aircraft fuel serves as a dynamic management solution to offset stochastic load power in the MEA power system. Power electronic controlled environmental control system (ECS) drives, emulating dynamic thermal inertia, showcase a more constant generator output power, allowing potential to downsize required generator ratings. An operating bandwidth is proposed similar to that of building HVAC systems, subject to additional degrees of constraints unique on MEA. A more sensitive virtual synchronous machine control boosts desirable inertia in sub-seconds scales in the MEA power system. To validate the thermal storage as effective inertia in both stationary and mobile energy systems, comprehensive simulation studies and experimental work are conducted at multiple levels. For the energy-efficient building research platform, building electrical and thermal energy systems modeling is addressed, including solar and HVAC systems as well as batteries and large-scale thermal storage. A lab-scale power system features various update rates of a variable frequency fan drive over stochastic solar data. A full-scale multiple-day case study provides insight on potential grid-side and storage-related benefits. The simulation and experimental studies are supported by 18 months of solar data collected on sub-millisecond time scales as a basis to evaluate efficacy, determine solar frequency-domain content, and analyze mitigation of variability. For the MEA research platform, steady-state and dynamic behaviors of electrical components in the Boeing 787 power systems, including electric machines, power converters, batteries, transformers, and loads, are modeled. In particular, in-depth discussions cover a multi-timescale parametric electrical battery model for use in dynamic electric transportation simulations. An integrated thermal model within electrical components and electrical systems captures temperature variations and ECS thermal dynamics. Simulation studies based on realistic load power demand over a 5-hour mission profile show mitigation of generator power transients while maintaining relatively comfortable cabin temperature bounds. Finally a scaled-down lab power system is implemented on a microcontroller-tied industrial drive to demonstrate feasibility in a potential commercial system

Improved and Practical Energy Management Systems for Isolated Microgrids

There are many remote communities around the world which do not have interconnection with the power grid because of technical and/or economic constraints, and thus have to manage their energy requirements independently, mainly from fossil-fuel-based and in some cases renewable-based generation, operating as isolated microgrids. The reliable and economic operation of a microgrid is handled by an Energy Management System (EMS), which includes scheduling and dispatching Distributed Energy Resources (DERs) such as Distributed Generators (DG), Energy Storage Systems (ESS), with controllable loads and demand response (DR), while maintaining appropriate reserve levels, and considering uncertainty in the forecast of renewables. Thus, this thesis focuses on developing comprehensive EMSs that consider Unit Commitment (UC), and Optimal Power Flow (OPF) constraints, smart load models for DR, and possible deviations in the forecast of renewable-based DGs. First, a mathematical model of smart loads in DR schemes is developed, based on a centralized and integrated UC and OPF EMS for isolated microgrids, to optimally dispatch generation and smart loads. These smart loads are modeled by a neural network (NN) load estimator as a function of the ambient temperature, time of day, Time of Use (TOU) price, and a peak demand constraint that the microgrid operator may set. A novel Microgrid EMS (MEMS) approach based on a Model Predictive Control (MPC) technique to manage forecast uncertainties is formulated; this tool yields optimal dispatch decisions of DGs, and ESS, and obtains optimal peak demand constraints for smart loads, considering power flow and UC constraints simultaneously. The impact of DR on the microgrid operation with the developed MEMS is studied using a CIGRE benchmark system that includes DERs and renewable-based generation, demonstrating its feasibility and advantages over existing EMS approaches, and showing the benefits of controllable loads in microgrids. In isolated microgrids, the network losses and voltage drops across feeders are relatively small. This feature is utilized through a novel linearization approach applied to the unbalanced power flow equations to propose practical EMSs. The proposed EMS models are Mixed Integer Quadratic Programming (MIQP) problems, requiring less computation time and thus suitable for online applications. The proposed practical EMS models are compared with a typical decoupled UC-OPF based EMS with and without consideration of system unbalancing. These EMS models, along with ``standard" EMS models, are tested and validated, using an MPC approach to account for forecast deviations, on the CIGRE medium voltage benchmark system and the real isolated microgrid of Kasabonika Lake First Nation (KLFN) in Northern Ontario, Canada. The presented results demonstrate the effectiveness, and practicability of the proposed models. In the third stage of the thesis, the impact of Electric Thermal Storage (ETS) systems on the operation of Northern Communities' microgrids is analyzed. A mathematical model of the ETS system is developed, in collaboration with a colleague from Karlsruhe Institute of Technology, and integrated into an EMS for isolated microgrids, in which the problem is divided into UC and OPF subproblems, to dispatch fossil-fuel-based generators, ESS, and ETS charging. To account for the deviations in the forecast of renewables and demand, an MPC technique is used. The proposed ETS-EMS framework is tested and studied on a modified CIGRE medium voltage benchmark system, which comprises various kinds of DERs, and on the real KLFN isolated microgrid system. It is shown that the ETS significantly reduces operating costs, and allows for better integration of intermittent wind and solar sources. Finally, equivalent CO2 emission models for fossil-fuel-based DG units are developed considering their individual emission characteristic and fuel consumption. These models are then integrated within a microgrid EMS model, together with constant energy, and demand shifting load models, to examine the possible impact of DR on the total system emissions and economics of a microgrid, using again an MPC approach to manage forecast uncertainties. The impact of including the developed emission models on the operation of an isolated microgrid, equivalent CO2 emissions, and costs are examined considering five different operating strategies. The proposed operating strategies are validated on a modified CIGRE medium voltage benchmark system, with the obtained results highlighting the effectiveness of the proposed EMS and also demonstrate the impact of DR on emissions and costs