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A multi-area power system requires coordination to enhance reliability and reduce operating costs. Economic dispatch in such systems is crucial because of the uncertainties associated with variable loads and the increasing penetration of renewable energy sources. This paper presents a hierarchical consensus algorithm designed to determine the economic dispatch in a multi-area power system, accounting for the uncertainties in load and renewable generation. The proposed algorithm, which utilizes distributed agents, operates across three levels. Level 1 coordinates all areas, while levels 2 and 3 form a leader–follower consensus algorithm for overall economic dispatch. Breadth-first search is employed to identify the leader agent within each area. To address the uncertainties in loads and renewable generation, Monte Carlo simulations are performed. The efficacy of the proposed method is validated using the IEEE 39-bus and 118-bus systems, as well as a realistic 1968-bus power system in Taiwan. The traditional equal lambda method is employed to verify that the proposed approach is suitable for multi-area power systems using distributed computation. [ABSTRACT FROM AUTHOR]

Wind is an essential, clean and sustainable renewable source of energy; however, wind speed is stochastic and intermittent. Accurate wind power generation forecasts are required to ensure that power generation can be scheduled economically and securely. This work proposes a hybrid deep learning technique that incorporates a quantum-inspired neural network to predict wind speeds 24 h in advance. An innovative neural network technique is implemented by cascading parallel convolutional neural networks (CNNs) with a long short-term memory (LSTM) and a quantum-inspired neural network (QINN). The proposed hybrid model is optimized using two iterative loops: the outer loop is implemented by using quantum particle swarm optimization (QPSO) to tune the structure of model and other hyperparameters/parameters. The inner loop uses an Adam optimizer to tune the weights and biases of the proposed model. Spatiotemporal wind speeds at various locations provide the 2D input data. Simulation results reveal that the proposed method outperforms other methods for 24 h-ahead wind speed forecasting. [ABSTRACT FROM AUTHOR]

Recently, Taiwanese Government has announced an ambitious energy policy: up to 20% of total power will be generated from renewable energy resources by 2025. Under such high-level penetration level of renewable energy resources, the typical net load curve will be definitely reshaped as the so-called duck curve. Operation modes of pumped hydro storage units in Taiwan power system will be totally changed. To tackle out this difficulty, the optimal scheduling of Taiwan power system is developed in this article for examining the coordination of highly penetrated renewable energy resources and pumped hydro storage plants under the required spinning reserve. The associated cost function and various constraints used in this optimal scheduling problem are described first. Physical characteristics of pumped hydro storage units in Taiwan will also be studied. By following the data format in the open source MATPOWER optimal scheduling tool, platform developments of this optimal scheduling program are reported. To validate the effectiveness of the proposed scheme, intensive simulations of both current scenarios and future scenarios are conducted. Simulation results indicate that existing pumped hydro storage units in Taiwan can indeed mitigate the impact of the duck-shaped net load curve in the generator scheduling of future Taiwan power system even under the worst scenario. [ABSTRACT FROM AUTHOR]

Due to the global concern on the increasing amount of fossil energy consumed by traditional ships, the application of renewable energy into a ship power system provides a new solution to improve the energy efficiency and to reduce the greenhouse gas emissions. This study proposes a stand-alone power system on a large oil tanker including wind generation system, photovoltaic generation system, the diesel generator, and the energy storage system (ESS). Unlike on land, the wind generation on the shipboard not only relies on the natural wind speed but also the ship's course and speed. The installation of the wind turbines on the board is optimally designed, which takes the relative speed into account. In order to mitigate the intermittence of the renewable energy generation, a lead–acid battery serves as the ESS to enhance the stability of the ship power system, and the size is optimized by the multi-objective particle swarm optimization to minimize the whole system cost and CO2emissions. Additionally, variations of the ship loads are considered with respect to the different operational conditions. Various cases are compared in detail to demonstrate the applicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: A stand-alone power system is an independent system that comprises micro-turbines, diesel generators, a wind park, solar photovoltaic (PV) modules and/or energy storages, etc. This paper presents a new method for dealing with the short-term active power scheduling of a stand-alone system. The fuel cost of diesel units and CO2 emission are minimized and all operation constraints are satisfied. The maximum wind and solar PV powers with uncertainties are modeled using fuzzy sets. Adaptive chaos clonal evolutionary programming (ACCEP) is employed to solve this problem, which is formulated as an interactive multi-objective problem. Different degrees of fuzziness, preferred reference levels, and priority list for diesel generators are discussed. Simulation results show the applicability of the proposed method. [Copyright &y& Elsevier]

Owing to a shortage of fossil fuels and environmental pollution, renewable energy is gradually replacing fossil fuels in the power systems of hybrid ships. To exploit fully solar energy by the successful day-ahead scheduling of ships, this work proposes a new day-ahead spatio-temporal forecasting method. Ensemble empirical mode decomposition (EEMD) is used to extract data features and decompose original historical data into several frequency bands. After the original data are processed, data from the four land weather stations that are closest to the ship and self-organizing map-back propagation (SOM-BP) hybrid neural networks are used to forecast the solar radiation received by the ship in the next 24 h. Multiple comparative experiments are implemented. The results show that the EEMD-SOM-BP spatio-temporal forecasting method can accurately forecast the solar radiation on a ship that is sailing along a navigation route. [ABSTRACT FROM AUTHOR]

Owing to the strict restrictions imposed by the Marine Pollution Protocol and the rapid development of renewable energy, the use of solar generation and energy storage systems in ship power systems has been increasingly attracting attention. However, the improper sizing of a hybrid power generation system in a ship power system will result in a high investment cost and increased greenhouse gas emission. This paper proposes a method for determining the optimal size of the photovoltaic (PV) generation system, the diesel generator and the energy storage system in a stand-alone ship power system that minimizes the investment cost, fuel cost and the CO 2 emissions. The power generation from PV modules on a ship relies on the date, local time, time zone, longitude and latitude along a navigation route and is different from the conditions of power systems on land. Thus, a method, which takes the seasonal and geographical variation of solar irradiations and temperatures along the route from Dalian in China to Aden in Yemen into account, for correcting the output of PV modules is developed in this paper. The proposed method considers five conditions along the navigation route to model the total ship load. Four cases are studied in details to demonstrate the applicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Owing to the increasing concerns about the release of pollution by traditional ships, the use of the renewable energy in ships' power systems is attracting much attention. However, an improperly designed renewable generation system and energy storage system (ESS) will increase costs and greenhouse gas emissions. This paper proposes a mathematical model of a photovoltaic (PV) power generation system for a ship, taking into account the effects of ship rolling. A PV system on the shipboard, unlike one on land, has to confront dramatic power fluctuations that are caused by the motions of the ship and bad weather, so hybrid ESSs play a significant role in a ship's power system. In this work, the discrete Fourier transform (DFT) is employed to decompose the required balancing power into various time-varying periodic components, which are utilized to calculate the maximum required power of the hybrid energy storage systems. A cost analysis is performed using particle swarm optimization (PSO) algorithm to optimize the size and capacity of various types of energy storage systems. Simulation results reveal the efficiency of the optimal allocation of the ESSs. [ABSTRACT FROM AUTHOR]