Short-Term Bidding Strategy for a Price-Maker Virtual Power Plant Based on Interval Optimization

A virtual power plant is proposed to aggregate various distributed renewable resources with controllable resources to overcome the uncertainty and volatility of the renewables so as to improve market involvement. As the virtual power plant capacity becomes remarkable, it behaves as a strategic price maker rather than price taker in the market for higher profit. In this work, a two-stage bi-level bidding and scheduling model is proposed to study the virtual power plant strategic behaviors as a price maker. A mathematical problem with an equilibrium constraints-based method is applied to solve the problem by transforming the two level problem into a single level multi-integer linear problem. Considering the deficiency of computational burden and implausible assumptions of conventional stochastic optimization, we introduce interval numbers to represent the predicted output of uncertainty resources in a real-time stage. The pessimism degree-based method is utilized to order the preferences of profit intervals and tradeoff between expected profit and uncertainty. An imbalance cost mitigation mechanism is proposed in this pessimism degree-based interval optimization manner. Results show that the bidding price directly affects the cleared day ahead of the locational marginal price for higher profit. Interior conventional generators, energy storage and interruptible loads are comprehensively optimized to cover potential power shortage or profit from market. Moreover, controllable resources can decrease or even wipe out the uncertainty through the imbalance cost mitigation mechanism when the negative deviation charge is high. Finally, a sensitivity analysis reveals the effect of interval parameter setting upon optimization results. Moreover, a virtual power plant operator with a higher pessimism degree pursues higher profit with higher uncertainty.

Trend Prognosis and Online Diagnostics of Thick Walled Boiler Components for a Flexible Mode of Operation

In a scenario of an increasing use of renewable energy, conventional power plants will be more and more forced to compensate for the volatility of the natural resources. Even huge coal-fired units which have been designed for baseload operation will face an increased number of start-up/shutdown cycles and the requirement for faster load changes. For the power plant operator that means a challenge as well as a chance: a challenge because the plant experiences higher alternating stresses which may reduce the lifetime. A chance because usually there are incentives for contributions to the grid stability which may give him additional profits. Coping with the challenges and making the best of the chances will require a detailed and quantitative assessment of the lifetime consumption in various modes of operation. In this paper, an online software solution is presented that provides this kind of information right at the fingertips of the plant engineers. The innovative approach integrates the most recent European standards concerning the calculation of lifetime consumption from load cycling with state-of-the-art methods of predictive analytics and cutting-edge FEM technologies: the recent standards supported by FEM calculations allow an estimation of lifetime consumption which is unchained from unnecessary allowances. The predictive analytics easily correlate plant operation and lifetime consumption and allow for a reliable prediction of fatigue. This in turn gives the necessary information to make the best of the chances of load flexibilization while mitigating the risks of increased lifetime consumption. If the expected fatigue in a given period is exceeded due to the current mode of operation one can react with a more moderate mode of operation — or the other way round, if the expected fatigue is not reached. Examples from German coal-fired power plants which have been put under economic pressure by the ongoing “Energiewende” (energy turnaround) are presented to demonstrate this approach.

Nuclear Power Plant Operator Reliability Research Based on Fuzzy Math

This paper makes use of the concept and theory of fuzzy number in fuzzy mathematics, to research for the response time of operator in accident of Chinese nuclear power plant. Through the quantitative analysis for the performance shape factors (PSFs) which influence the response time of operators, the formula of the operator response time is obtained based on the possibilistic fuzzy linear regression model which is used for the first time in this kind of research. The research result shows that the correct research method can be achieved through the analysis of the information from a small sample. This method breaks through the traditional research method and can be used not only for the reference to the safe operation of nuclear power plant, but also in other areas.