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In order to actively respond to the "14th Five-Year Plan," the PGA algorithm is used to develop a new energy planning strategy in this paper. The project can make full use of my country's abundant renewable energy resources, encourage energy conservation and reduction of emissions, improve the energy structure's low-carbon level, support the development of smart green energy, and achieve ecological civilization construction. This solution can show users how much greenhouse gas emissions can be reduced through some environmental changes, as well as the basic issues of meeting the future energy needs. It can display the benefits, costs, and emissions data under different scenarios in the future and use the scenario demonstration method to show energy planning to make energy data more vivid. It allows people, technicians, and decision makers to understand what will happen to China's carbon emissions over time in the next 15 years. This paper innovatively combines a particle swarm optimization algorithm with a genetic algorithm and designs a PGA algorithm for path optimization. In terms of carbon emission reduction, comparative trials demonstrate that the PGA algorithm's path optimization is 58.06 percent greater than the genetic algorithm; In terms of cost, the PGA algorithm's path optimization is 15.72% less expensive than the genetic algorithm's. This article provides a reference path for selecting the best results for future energy planning schemes and provides a new strategy for the "14th Five-Year" energy plan. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) is a crucial issue to maintain the reliable operation of power systems. However, achieving this objective is not easy, especially when renewable energy sources (RESs) are penetrated into the power system due to their uncertainty nature. This paper provides an optimal solution for the power flow problem including two different types of RESs based on a marine predator algorithm (MPA). The OPF model used in this paper has three different types of energy resources (thermal, wind, and solar). The output power from wind or solar generator has two probabilities either underestimation or overestimation consequently. These two probabilities have been translated into the objective function by two extra costs, penalty cost, and reserve cost, respectively. To check the validity of the proposed algorithm, it is applied to a modified IEEE-30 and IEEE-57 bus systems. The obtained results are compared with some recent optimization methods. The results show the effectiveness of marine predator algorithm in providing the optimal solution for the power flow problem with maintaining the power system constraints inviolate. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to explore the performance of a residential grid-tied hybrid (GTH) system which relies on economic and environmental aspects. A photovoltaic- (PV-) wind turbine- (WT-) battery storage system with maximizing self-consumption and time-of-use (ToU) pricing is conducted to examine the system efficiency. In so doing, technical optimization criteria with taking into consideration renewable energy benefits including feed-in-tariff (FIT) and greenhouse gas emission (GHG) reduction are analyzed. As the battery has a substantial effect on the operational cost of the system, the energy management strategy (EMS) will incorporate the daily operating cost of the battery and the effect of the degradation. The model can give the opportunity to the network to sell or purchase energy from the system. The simulation results demonstrate the effectiveness of the proposed approach in which the new objective function achieves the maximum cost-saving (99.81%) and income (5.16 $/day) compared to other existing strategies as well as the lowest GHG emission. Furthermore, the battery enhances the best daily self-consumption and load cover ratio. Then, as the model is nonlinear, a comparison with other existing algorithms is performed to select the feasible, robust, and reliable model for the residential application. A hybrid algorithm (HGAFMINCON) is developed to demonstrate the superiority of the algorithm over FMINCON and GA shown in terms of cost savings and income. [ABSTRACT FROM AUTHOR]

Contemporary distribution networks can be seen with diverse dispatchable and nondispatchable energy resources. The coordinated scheduling of these dispatchable resources, together with nondispatchable resources, can provide several technoeconomic and social benefits. Since battery energy storage systems (BESSs) and microturbine units (MT units) are capital-intensive, a thorough investigation of their coordinated scheduling under the economic criterion will be a challenging task while considering dynamic electricity prices and uncertainties of renewable power generation and load demand. This paper proposes a comprehensive methodological framework for optimal coordinated scheduling of BESSs with MT unit considering existing renewable energy resources and dynamic electricity prices to maximize the daily profit function of the utility by employing a recently explored modified African buffalo optimization algorithm. The key attributes of the proposed methodology are comprised of mean price-based adaptive scheduling embedded within a decision mechanism system (DMS) to maximize arbitrage benefits. DMS keeps track of system states as a priori, thus resulting in an artificial intelligence-based solution technique for sequential optimization. Further, a novel concept of fictitious charges is also proposed to restrict the counterproductive operational management of BESSs. The proposed model and method are demonstrated on the 33-bus distribution system, and the obtained results verify the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Photovoltaic solar energy is one of the most important renewable energy sources. However, the production of this energy is nonlinear and varies depending on atmospheric parameters. Therefore, the operating point of the photovoltaic panel (PV) does not always coincide with the maximum power point (MPP). A mechanism that allows the research of the maximum power point known as maximum power point tracking (MPPT) algorithm is then needed to yield the maximum power permanently. This paper presents an intelligent control technique based on the ESC (Extremum Seeking Control) method for MPPT under varying environmental conditions. The proposed technique is an improvement of the classical ESC algorithm with an additional loop in order to increase the convergence speed. A detailed stability analysis is given not only to ensure a faster convergence of the system towards an adjustable neighborhood of the optimum point but also to confirm a better robustness of the proposed method. In addition, simulation results using Matlab/Simulink environment and experimental results using Arduino board are presented to demonstrate that the proposed modified ESC method performs better than the classical ESC under varying atmospheric conditions. [ABSTRACT FROM AUTHOR]

In order to mitigate the negative influence of geographical dispersity of modern power system and the penetration of renewable energy, a system compensation based model predictive AGC method is proposed in this paper. A compensation unit is introduced in AGC system to reduce the influence of PV power output fluctuation. Furthermore, because the distributed power system is geographically dispersed, which leads to some defects such as time delay and packet dropouts, a selection and optimization algorithm is presented to obtain the appropriate variable when delays exist. Finally, a two-area interconnected power grid with PV systems is shown as an example to testify the feasibility and effectiveness of the proposed method. The simulation results indicate that the system controlled by the proposed method has the expected dynamic performance. [ABSTRACT FROM AUTHOR]

Spectrum sensing is one of the key tasks in cognitive radio. This paper proposes a fast two-step energy detection (FED) algorithm for spectrum sensing via improving the sampling process of conventional energy detection (CED). The algorithm adaptively selects N-point or 2N-point sampling by comparing its observed energy with prefixed double thresholds, and thereby is superior in sampling time and detection speed. Moreover, under the constraint of constant false alarm, this paper optimizes the thresholds from maximizing detection probability point of view. Theoretical analyses and simulation results show that, compared with CED, the proposed FED can achieve significant gain in detection speed at the expense of slight accuracy loss. Specifically, within high signal-to-noise ratio regions, as much as 25% of samples can be reduced. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) energy is one of the most important energy sources since it is clean and inexhaustible. It is important to operate PV energy conversion systems in the maximum power point (MPP) to maximize the output energy of PV arrays. An MPPT control is necessary to extract maximum power from the PV arrays. In recent years, a large number of techniques have been proposed for tracking the maximum power point. This paper presents a comparison of different MPPT methods and proposes one which used a power estimator and also analyses their suitability for systems which experience a wide range of operating conditions. The classic analysed methods, the incremental conductance (IncCond), perturbation and observation (P&O), ripple correlation (RC) algorithms, are suitable and practical. Simulation results of a single phase NPC grid connected PV system operating with the aforementioned methods are presented to confirm effectiveness of the scheme and algorithms. Simulation results verify the correct operation of the different MPPT and the proposed algorithm. [ABSTRACT FROM AUTHOR]