Search

Wind power integration and carbon reduction cooperation, as low-carbon approaches, are introduced into the current power dispatching process under carbon reduction requirements. In order to measure the environmental performance of power dispatch influenced by the above approaches, this paper firstly presents a low-carbon power dispatch model for wind power integrated system imported with carbon reduction cooperation. Next, the methods related to wind speed prediction, optimization algorithm and efficiency evaluation are fully discussed. Finally, empirical analysis shows that: (1) the overall effect of ARIMA-NARX model is superior to that of ARIMA model for wind speed prediction, whose MSE is reduced by 79.73%, (2) compared with GA and PSO, GA-PSO has better effect of optimization, which can effectively jump out of the local optimal value, and its convergence speed keeps improving, (3) the two low-carbon approaches can facilitate the achievement of system carbon reduction targets with good environmental performance of power dispatch, and (4) low-carbon power dispatching strategies taking full account of these two approaches are developed, which proves to be of reliability, economy and environment. • A power dispatching model integrated with two low-carbon approaches is proposed. • ARIMA-NARX hybrid model with good effect of wind speed prediction is constructed. • GA-PSO with better optimization effect is designed. • Environmental performance of low-carbon power dispatch is measured. • Power dispatching strategies developed are of reliability, economy and environment. [ABSTRACT FROM AUTHOR]

With the development of low-carbon electricity, the scale of wind power is expanding continuously and carbon trading for thermal power is popularized gradually. In this context, the optimal combination of thermal power and wind power needs to be further promoted to build up the synergy of carbon reduction. To solve such low-carbon power dispatching problem in the wind power integrated system imported with carbon trading, this paper firstly presents a distributed robust optimization model. Next, the scenario-based characterization of wind power and allocation methods of initial carbon emission rights are discussed for model solution. Finally, empirical analysis shows that: (1) the proposed model proves to be rational and feasible, which can accomplish a good compromise between economy, environment and robustness of power system, (2) wind power integration dose help carbon reduction ratio to achieve up to 50% with lower operating costs and carbon emissions, while carbon trading is really an effective approach for tapping greater carbon reduction potential of thermal power, and (3) more reasonable proportions of wind power in coping with its inherent uncertainties, and more appropriate cooperation modes of thermal power for dealing with carbon trading unpredictability are determined under the different requirement of carbon reduction. [ABSTRACT FROM AUTHOR]

Abstract Low-carbon power dispatching with wind power has been attracting increasing attention recently, whereas it also encounters some difficulties under carbon trading mechanism, such as the resistance to carbon emission reduction, the fairness of carbon trading, and the disturbance of wind power uncertainty. For harmonizing these contradictions, this paper presents a stochastic dynamic economic dispatch model focusing on wind power uncertainty and carbon emission rights simultaneously, which tries to minimize total electrical energy costs under uncertainty, and to meet the specific requirement of carbon emission reductions. Moreover, several traditional allocation methods for initial carbon emission rights emphasizing the equality are introduced, and the uncertainty characterizations of wind power are discussed through the scenario-generation technique. The simulation results eventually demonstrate that the operating cost for non-cooperative mode cannot be optimized as the ratio of carbon emission reductions rises; carbon trading mechanism is beneficial for lower cost under the pressure of carbon emission reduction; carbon price ranges that suit both carbon trading parties could be deduced. Synthesizing wind power uncertainty and carbon emission rights, this study is helpful for allocating the load demand of wind power integrated system to each generating unit more scientifically and reasonably based on carbon trading mechanism. Highlights • A stochastic DED model is proposed for low-carbon power dispatching problem. • Carbon trading mechanism proved to be helpful for carbon emission reduction. • Carbon price ranges that suit both carbon trading parties are finally deduced. [ABSTRACT FROM AUTHOR]

With the introduction of carbon trading in wind power integrated system, decision-makers may face more contradictions, such as economy and environment, objectivity and subjectivity, certainty and uncertainty. In order to balance these contradictions, this paper presents an economic emission dispatch model focusing on carbon price and wind power uncertainty simultaneously. Specifically factors for eliminating adverse effects of wind power uncertainty in both economic and environmental respects are particularly considered, decision-makers’ subjective investment attitudes towards wind power development could be quantitatively depicted according to carbon price. The simulation results eventually demonstrate that increasing the penalty coefficient core reflects decision-makers’ more radical investment attitudes, while improving the reserve coefficient core embodies more conservative investment attitudes; increasing carbon price will generally encourage decision-makers to explore more wind power, and to restrict thermal power; the more radical attitudes decision-makers hold, scheduling outputs the more sensitive with carbon price, and the faster wind power advance. Taking into full account carbon price and wind power uncertainty, the proposed model is beneficial to exploring a more balanced low-carbon power dispatching strategy for wind power integrated system. [ABSTRACT FROM AUTHOR]

Wind power plays a significant role in economic and environmental operation of electric power system. Meanwhile, the variability and uncertainty characteristics of wind power generation bring technical and economical challenges for power system operation. In order to harmonize the relationship between environmental protection and risk management in power dispatching, this paper presents a stochastic dynamic economic emission dispatch model combining risk perception with environmental awareness of decision-makers by following the principle of chance-constrained programming. In this power dispatching model, the description of wind power uncertainty is derived from the probability statistic character of wind speed. Constraints-handling techniques as a heuristic strategy are embedded into non-dominated sorting genetic algorithm-II. In addition, more information is digested from the Pareto optimum solution set by cluster analysis and fuzzy set theory. The simulation results eventually demonstrate that the increase of the share of wind power output will bring higher risk, though it is beneficial for economic cost and environmental protection. Since different risk perception and environmental awareness can possibly lead to diverse non-dominated solutions, decision-makers may choose an appropriate dispatching strategy according to their specific risk perception and environmental awareness. [ABSTRACT FROM AUTHOR]

Economic environmental dispatch (EED) is a significant optimization problem in electric power system. With more wide spread use of wind power, it is necessary to include wind energy conversion system (WECS) in the EED problem. This paper presents a model to solve the EED problem incorporating wind power. In addition to the classic EED factors, the factors accounting for overestimation and underestimation of available wind power in both economic and environmental aspects are also considered. In order to obtain some quantitative results, the uncertain characteristic of available wind power and the performance of WECS are determined on the basis of the statistical characteristic of wind speed. The optimization problem is numerically solved by a scenario involving two conventional generators and two wind-powered generators. The results demonstrate that the allocation of system generation capacity may be influenced by multipliers related to the cost for overestimation and underestimation of available wind power, and by the multiplier related to the emissions for underestimation of available wind power. Nevertheless, the multiplier related to the emissions for overestimation of available wind power has little impact on the allocation. Taking account of economic factors, environmental factors and impacts of wind power penetration, the proposed EED model is beneficial to finding the right balance between radical and conservative strategy for wind power development. [ABSTRACT FROM AUTHOR]

A desirable dispatching strategy is essentially important for securely and economically operating of wind-thermal hybrid distribution systems. Existing dispatch strategies usually assume that wind power has priority of injection. For real-time control, such strategies are simple and easy to realize, but they lack flexibility and incur higher operation and maintenance (O&M) costs. This study analyzed the power dispatching process as a dynamic sequential control problem and established a Markov decision process model to explore the optimal coordinated dispatch strategy for coping with wind and demand disturbance. As a salient feature, the improved dispatch strategy minimizes the long-run expected operation and maintenance costs. To evaluate the model efficiently, a Monte Carlo method and the Q-learning algorithm were employed to the growing computational cost over the state space. Through a specified numerical case, we demonstrated the properties of the coordinated dispatch strategy and used it to address a 24-h real-time dispatching problem. The proposed algorithm shows high efficiency in solving real-time dispatching problems. ● An advanced real-time dispatching strategy was proposed for distribution systems. ● The strategy minimizes long-run expected cost instead of myopic immediate cost. ● The model considers the volatilities of wind speed and demand load. ● Monte Carlo and Q -learning algorithm are employed to solve the model efficiently. [ABSTRACT FROM AUTHOR]

The prediction and early warning of infectious diseases is an important work in the field of public health. This study constructed the grey self-memory system model to predict the incidence trend of infectious diseases affected by many uncertain factors. The design of this study is a combination of the prediction method and empirical analysis. By organically coupling the self-memory algorithm with the mean GM(1,1) model, the tuberculosis incidence statistics of China from 2004 to 2018 were selected for prediction analysis. Meanwhile, by comparing with the other traditional prediction methods, three representative accuracy check indexes (MSE, AME, MAPE) were conducting for error analysis. Owing to the multiple time-points initial fields, which replace the single time-points, the limitation of the traditional grey prediction model, which is sensitive to the initial value, is overcome in the self–memory equation. Consequently, compared with the mean GM model and other statistical methods, the grey self-memory model shows significant forecasting advantages, and its single-step rolling prediction accuracy is superior to other prediction methods. Therefore, the incidence of tuberculosis in China in the next year can be predicted as 55.30 (unit: 1/105). The grey self-memory system model can closely capture the individual random fluctuation in the whole evolution trend of the uncertain system. It is appropriate for predicting the future incidence trend of infectious diseases and is worth popularizing to other similar public health prediction problems. [ABSTRACT FROM AUTHOR]