• Energy Research

Duqm is located in the Al Wasta Governorate in Oman and is currently fed by 10 diesel generators with a total capacity of around 76 MW and other rental power sources with a size of 18 MW. To make the electric power supply come completely from renewables, one novel solution is to replace the diesel with hydrogen. The extra energy coming from the PV-wind system can be utilized to produce green hydrogen that will be utilized by the fuel cell. Measured data of solar insolation, hourly wind speeds, and hourly load consumption are used in the proposed system. Finding an ideal configuration that can match the load demand and be suitable from an economic and environmental point of view was the main objective of this research. The Hybrid Optimization Model for Multiple Energy Resources (HOMER Pro) microgrid software was used to evaluate the technical and financial performance. The findings demonstrated that the suggested hybrid system (PV-wind-fuel cell) will remove CO2 emissions at a cost of energy (COE) of USD 0.436/kWh and will reduce noise. With a total CO2 emission of 205,676,830 kg/year, the levelized cost of energy for the current system is USD 0.196/kWh. The levelized cost for the diesel system will rise to USD 0.243/kWh when taking 100 US dollars per ton of CO2 into account. Due to system advantages, the results showed that using solar, wind, and fuel cells is the most practical and cost-effective technique. The results of this research illustrated the feasibility and effectiveness of utilizing wind and solar resources for both hydrogen and energy production and also suggested that hydrogen is a more cost-effective long-term energy storage option than batteries.

This paper presents a dq power flow based energy storage control system for reliable and stable operation of a renewable power generation based microgrid system. The control objectives are storing the excess energy from the microgrid into the storage unit or supplying energy deficit from the storage unit to the microgrid to achieve power equity between the generation and load, and regulation of voltage and frequency during stand-alone microgrid operation. Whereas during grid-connected microgrid operation, the control objective is to ensure storing energy in the storage unit and exchange power between the microgrid and the utility grid. The proposed controller is developed for inverter interface energy storages using dq power flow. The dq power flow is formulated using bus voltage components and the bus admittance matrix in dq frame. The dq power flow in the developed controller generates command (reference) active and reactive powers for the inverter interfaced storage unit connected to the microgrid buses. In addition, the implemented current controller of the inverter assures such command powers exchange between the storage unit and the microgrid. The developed dq power flow based storage unit (DQPFSU) control system is tested under various operating conditions for both in grid-connected and stand-alone microgrid operation. The test results of the developed DQPFSU controller illustrates satisfactory performance in generating fast control actions to ensure reliable and stable microgrid operation under various changing conditions. Moreover, the validity of such control actions has examined from the frequency response and bus voltages of the case study microgrid under various tested operational conditions.

Appropriate renewable distributed generation (RDG) placement is one of the most significant issues for the efficient operation of current power systems. Since the inverter-interfaced RDG lacks rotating mass to sustain the system’s inertia, microgrids have low total system inertia, which impairs frequency stability and can yield significant frequency and voltage instability in severe disruptions. The virtual synchronous generator (VSG), which uses concepts that regulate the inverter to simulate a conventional synchronous generator, is one of the most promising solutions to address these challenges. Hence, this research proposes a unique technique of simultaneous optimal solution for RDG and VSG sizing and placement in distribution networks using a recent metaheuristic technique called the Multi-objective Salp Swarm Optimization Algorithm (MOSSA). The objective function was to minimize the frequency deviation and maximize the total annual energy savings and operational costs of the RDG and VSG units. This study assesses IEEE 33 bus, 69 bus distribution network, and practical Masirah network as the test systems. Moreover, the MOSSA Pareto fronts are superior to two recent metaheuristics employed in this research domain: Multi-objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The results demonstrate that the MOSSA Pareto fronts satisfied the frequency and energy-saving objectives. In addition, all Pareto fronts accurately prevented voltage limit infringements, and the overall energy losses were significantly reduced.

An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development. This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO). The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability. A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method. The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and a higher standard deviation. The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.12% can be achieved. The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions.

The world's demand for water and energy is continuously growing due to population increase. Traditional water systems are driven by energy produced using fossil fuels, which lead to global warming due to rise of greenhouse gas pollution. Global warming is an increasing motivation to integrate renewable energy resources in water systems for different purposes like water pumping, water supply, and water distribution systems. As a result, to have a smart, sustainable and low-cost water system, renewable resources, energy management, and monitoring should be simultaneously implemented. Therefore, this paper provides a comprehensive review of the research conducted on solutions and effects of integrating different types of renewable resources on water systems. As essential tools for secure integration of renewable resource in the water industry, the pivotal role of energy management and intelligent technologies within the water system have been explored. This review confirms the potential of achieving smart and sustainable water systems by simultaneously considering the use of renewable resources, conducting energy management strategies, and smartening the water system; however, further research is essential to improve water system performance and increase the efficiency of the system.

Power system stability is greatly affected by two types of stochastic or random disturbances: (1) topological and (2) parametric. The topological stochastic disturbances due to line faults caused by a series of lightning strikes (associated with circuit breaker, C.B., opening, and auto-reclosing) are modeled in this paper as continuous-time Markov jumps. Additionally, the stochastic parameter changes e.g., the line reactance, are influenced by the phase separation, which in turn depends on the stochastic wind speed. This is modeled as a stochastic disturbance. In this manuscript, the impact of the above stochastic disturbance on power system small-disturbance stability is studied based on stochastic differential equations (SDEs). The mean-square stabilization of such a system is conducted through a novel excitation control. The invariant ellipsoid and linear matrix inequality (LMI) optimization are used to construct the control system. The numerical simulations are presented on a multi-machine test system.