No abstract

This paper introduces an optimal sizing algorithm for a hybrid renewable energy system using smart grid load management application based on the available generation. This algorithm aims to maximize the system energy production and meet the load demand with minimum cost and highest reliability. This system is formed by photovoltaic array, wind turbines, storage batteries, and diesel generator as a backup source of energy. Demand profile shaping as one of the smart grid applications is introduced in this paper using load shifting-based load priority. Particle swarm optimization is used in this algorithm to determine the optimum size of the system components. The results obtained from this algorithm are compared with those from the iterative optimization technique to assess the adequacy of the proposed algorithm. The study in this paper is performed in some of the remote areas in Saudi Arabia and can be expanded to any similar regions around the world. Numerous valuable results are extracted from this study that could help researchers and decision makers.

The sizing problem of the hybrid energy system (HES) is a crucial issue especially in rural communities because any wrong results can mislead the decision makers for building the new HES. Due to the intermittent nature of the renewable energy sources (RES) such as wind and PV, there will be a need for a high storage system and/or a standby diesel engine, which increase the investment, required, and increases the cost of energy (CoE). The use of smart grid concepts like the demand response (DR) using dynamic tariff can improve the system performance, enhance the stability, reduces the size and investments of HES components, reduces the customers' bills, and increases the energy providers' profits. The DR strategy will allow the customers to share the responsibility of the HES stability with the energy providers to maintain the stability of the HES. The DR strategies should be selected to ensure the balance between the available RES and the load requirements. In this article, a novel DR strategy is introduced to model the required change in the tariff with the battery state of charge and its charging/discharging power. The novel DR strategy is used in the sizing of the HES based on techno-economic objectives using three different soft computing optimization techniques. This article introduces modeling and simulation of the smart grid integrated with hybrid energy systems to supply a standalone load for a rural site in the north of Saudi Arabia. The sizing of the HES is built based on minimizing the CoE and the loss of load probability. The novel DR strategy introduced in this article reduced the size of the HES compared to the fixed load technique by 20.66%. The results obtained from this novel strategy proved its superiority in the sizing and operation stage of the HES.

Advanced knowledge of the minimum capacitor value required for self-excitation of an induction generator is of practical interest. To find this capacitor value two nonlinear equations have to be solved. Different numerical methods for solving these equations are known from previous literature. However, these solutions involve some guessing in a trial-and-error procedure. In the paper a new simple and direct method is developed to find the capacitance requirement under R L load. Exact values are derived for the minimum capacitance required for self-excitation and the output frequencies under no-load, inductive and resistive loads. These calculated values can be used to predict theoretically the minimum value of the terminal capacitance required for selfexcitation. For stable operation C must be chosen to be slightly greater than Crnin. Furthermore, it is found that there is a speed threshold, below which no excitation is possible no matter what the capacitor value. This threshold is called the cutoff speed. Expressions for this speed under no-load and inductive load are also given. .ist of symbols RS, R , , R= p.u. per phase stator, rotor (referred to stator) and load resistance, respectively X h , Xb, X, X , = p.u. per phase stator leakage, rotor leakage (referred to stator), load and magnetising reactances (at base frequency), respectively = p.u. maximum saturated magnetising reactance = per phase terminal-excitation capacitance = p.u. per phase capacitive reactance (at base frequency) of the terminalexcitation capacitor = p.u. frequency and speed, respectively = base speed in rev/min = per phase base impedance = per phase airgap and output voltages,

No abstract