• Energy Research

AbstractHybrid energy power plants are remarkable option for the electrification of isolated areas, which commonly fulfill their energy demand by means of diesel generators. An energy combination comprising also PV or wind systems would lead to a reduction of costs and is, therefore, being gradually esteemed. In this paper, an optimal sizing approach was established based on a long-term energy analysis, to study the techno-economic feasibility of different hybrid systems proposed to electrify an isolated area located in the north of Saudi Arabia under different fuel cost scenarios. For each fuel cost scenario, the hybrid system has been designed and optimized to get a maximum renewable penetration ratio at a low cost of energy. An optimization model based on genetic algorithm is developed to determine the optimum hybrid systems. Three different systems are studied, with different diesel price, to relatively analyze the different hybrid systems and the result reveals that PV/battery/diesel with zero LPSP is the most cost-effective system for the proposed remote area. Sensitivity analysis reveals that that the hybrid systems is the most economically choice even if the solar radiation decreases to half. It also found that irrespective of the wind speed, PV/battery/diesel system is the optimal choice if the wind speed is less than about 6.75 m/s. At solar radiation and wind speed less than 1500 W/m2, 5.6 m/s, respectively, diesel only system is cost effective. According to the present results, there is a good economic prospective to shift the diesel plants to hybrid systems, with cost reduction opportunities of around 41% of the cost of energy.

Under partial shading conditions (PSCs), solar photovoltaic (PV) energy systems generate multiple peaks; one global peak (GP) and several local peaks (LPs). Thus, tracking the GP of the PV systems under PSCs is necessary to enhance the system reliability and efficiency. Conventional maximum power point tracker (MPPT) algorithms are capable of tracking the unique peak under uniform conditions but they fail to track the GP under PSCs. To the best of our knowledge, this paper represents the first study that introduces a comprehensive comparison of three efficient maximum power point tracker (MPPT) algorithms that are used to extract the GP of the PV system under both uniform and PSCs. These MPPT techniques include two metaheuristic techniques, which are cuckoo search optimization (CSO) and particle swarm optimization (PSO) techniques in addition to one conventional MPPT; perturb and observe (P&O). Although the simulation and dSPACE-based experimental results demonstrated the superiority of CSO and PSO in tracking the GP, CSO requires less tracking time and thus provides a higher efficiency than the PSO. In addition, P&O can be used to follow the first peak, regardless if it is a local peak or global peak with notable oscillation.

Abstract This article presents the design of an Adaptive Neuro-Fuzzy Inference Controller (ANFIC) for a 160 MW power plant. The space of operating conditions of the plant is partitioned into five regions. For each of the regions, an optimal controller is designed to meet a set of design objectives. The resulting five linear controllers are used to train the ANFIC. To enhance the applicability of the control system, a new algorithm that reduces the fuzzy rules to the most essential ones is also presented. This algorithm offers substantial savings in computation time while maintaining the performance and robustness of the original controller.

Accurate prediction of future electrical power demands greatly facilitates the task of power generation reliably and economically. In this paper, the new soft computing technologies, namely neuro-fuzzy techniques are adopted to improve precision of medium to long-term load forecasting. Performance of the proposed methodology is verified using simulations of some data pertaining to the Riyadh power system. This approach is compared with time series and neural networks design methods. It is demonstrated that the proposed methodology surpasses other methods by producing very accurate peak load forecasts. Keywords: Fuzzy logic, Neural networks, Neuro-fuzzy, Load forecasting