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| 1 |
ID:
166942
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| Summary/Abstract |
An assessment model is built to investigate the degree of order in the organisational structure of the electricity regulatory institution (ERI) in China. The model is based on Shannon entropy and is constructed from the perspective of timeliness and accuracy of the flow of information. The model is then used to evaluate the degree of order in the organisational structures of the ERI during three stages of reform that occurred during 2002–13. The results indicate that the reforms and improvements made in the organisational structure of China's ERI have resulted in a stepwise increase in their degree of order (corresponding to 0.3156, 0.3277, and 0.3324 in the three stages, respectively). On this basis, a scheme is put forward to optimise the degree of order in the structure of the energy regulatory institution in the current stage. The results show that downsizing the internal and subordinate departments appropriately and creating more governmental agencies to regulate energy are conducive to further improving the degree of order of the energy regulatory institution. Finally, we use principal component analysis to propose a priority scheme for adding more regulatory governmental agencies based on sorted energy production and consumption data.
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| 2 |
ID:
125415
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| Publication |
2013.
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| Summary/Abstract |
Least cost options are presented for supplying the Australian National Electricity Market (NEM) with 100% renewable electricity using wind, photovoltaics, concentrating solar thermal (CST) with storage, hydroelectricity and biofuelled gas turbines. We use a genetic algorithm and an existing simulation tool to identify the lowest cost (investment and operating) scenarios of renewable technologies and locations for NEM regional hourly demand and observed weather in 2010 using projected technology costs for 2030. These scenarios maintain the NEM reliability standard, limit hydroelectricity generation to available rainfall, and limit bioenergy consumption. The lowest cost scenarios are dominated by wind power, with smaller contributions from photovoltaics and dispatchable generation: CST, hydro and gas turbines. The annual cost of a simplified transmission network to balance supply and demand across NEM regions is a small proportion of the annual cost of the generating system. Annual costs are compared with a scenario where fossil fuelled power stations in the NEM today are replaced with modern fossil substitutes at projected 2030 costs, and a carbon price is paid on all emissions. At moderate carbon prices, which appear required to address climate change, 100% renewable electricity would be cheaper on an annual basis than the replacement scenario.
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| 3 |
ID:
094258
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2010.
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| Summary/Abstract |
A multi-objective optimization method was developed for the design of trigeneration plants. The optimization is carried out on technical, economical, energetic and environmental performance indicators in a multi-objective optimization framework. Both construction (equipment sizes) and discrete operational (pricing tariff schemes and operational strategy) variables were optimized based on realistic conditions. The problem is solved using a multi-objective evolutionary algorithm. An example of a trigeneration system in a 300 bed hospital was studied in detail in order to demonstrate the design procedure, the economic and energetic performance of the plant, as well as the effectiveness of the proposed approach even under fluctuating energy prices.
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| 4 |
ID:
174960
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| Summary/Abstract |
Due to the liberalization of the electricity market, evaluation of competitor behaviors, as an uncertainty factor, is a critical information for a Generation Company (GenCo) to maximize its profit by optimizing bidding strategies. In this paper, a new bidding strategy model has been presented based on the Genetic Algorithm and a refined Monte Carlo simulation model. This process is done through the similarity function and consideration of the seasonality trend as the main characteristic of the electricity spot price. The main contributions of this paper include: (a): Consideration of the similarity value for all days in historical dates in the database, (b): Consideration of the seasonality trend of market clearing price by applying K-Means algorithm for clustering historical data based on demand, (c): Application of the proposed model for each cluster's data, (d): Performance evaluation of the fitness function of each generated strategy by a simulation model based on historical data. The proposed model has been tested for the 10 subsets of Iran's electricity market 2016. The obtained results show that the proposed model is statistically efficient, and the prediction accuracy of MCP by the proposed model can be improved by more than 25% and 11% compared with a simple simulation model and the hybrid of simulation and Q-learning model.
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| 5 |
ID:
137693
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| Summary/Abstract |
This study aims to estimate carbon intensity abatement potential in China at the regional level by proposing a particle swarm optimization–genetic algorithm (PSO–GA) multivariate environmental learning curve estimation method. The model uses two independent variables, namely, per capita gross domestic product (GDP) and the proportion of the tertiary industry in GDP, to construct carbon intensity learning curves (CILCs), i.e., CO2 emissions per unit of GDP, of 30 provinces in China. Instead of the traditional ordinary least squares (OLS) method, a PSO–GA intelligent optimization algorithm is used to optimize the coefficients of a learning curve. The carbon intensity abatement potentials of the 30 Chinese provinces are estimated via PSO–GA under the business-as-usual scenario. The estimation reveals the following results. (1) For most provinces, the abatement potentials from improving a unit of the proportion of the tertiary industry in GDP are higher than the potentials from raising a unit of per capita GDP. (2) The average potential of the 30 provinces in 2020 will be 37.6% based on the emission's level of 2005. The potentials of Jiangsu, Tianjin, Shandong, Beijing, and Heilongjiang are over 60%. Ningxia is the only province without intensity abatement potential. (3) The total carbon intensity in China weighted by the GDP shares of the 30 provinces will decline by 39.4% in 2020 compared with that in 2005. This intensity cannot achieve the 40%–45% carbon intensity reduction target set by the Chinese government. Additional mitigation policies should be developed to uncover the potentials of Ningxia and Inner Mongolia. In addition, the simulation accuracy of the CILCs optimized by PSO–GA is higher than that of the CILCs optimized by the traditional OLS method.
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| 6 |
ID:
139084
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| Summary/Abstract |
Addressing the need for exploration of benthic zones utilising autonomous underwater vehicles, this paper presents a simulation for an optimised path planning from the source node to the destination node of the autonomous underwater vehicle SLOCUM Glider in near-bottom ocean environment. Near-bottom ocean current data from the Bedford Institute of Oceanography, Canada, have been used for this simulation. A cost function is formulated to describe the dynamics of the autonomous underwater vehicle in near-bottom ocean currents. This cost function is then optimised using various biologically-inspired algorithms such as genetic algorithm, Ant Colony optimisation algorithm and particle swarm optimisation algorithm. The simulation of path planning is also performed using Q-learning technique and the results are compared with the biologically-inspired algorithms. The results clearly show that the Q-learning algorithm is better in computational complexity than the biologically-inspired algorithms. The ease of simulating the environment is also more in the case of Q-learning techniques. Hence this paper presents an effective path planning technique, which has been tested for the SLOCUM glider and it may be extended for use in any standard autonomous underwater vehicle.
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