Interactions Between China S National Emissions Trading Scheme And Electricity Market Practices And Policies
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Interactions Between China’s National Emissions Trading Scheme and Electricity Market: Practices and Policies
China’s national carbon market, the world’s largest emissions trading scheme (ETS), kicked off its first online trade recently. This can be called a milestone for the country towards the nation’s goals of having CO2 emissions peak before 2030 and achieving carbon neutrality by 2060. China’s national ETS initially covers the power sector, before being expanded to a much broader set of energy-intensive industries. On one hand, the electricity sector, the largest carbon-emitting industry, is responsible for about 40% of China’s emissions, and it has great significance to response to global climate change. On the other hand, the effectiveness of China’s ETS will rest on how well it is coordinated with power market regulations and policies. In this regard, the deepening of reform, as well as the advanced technology and its applications in the electricity market will add new challenges and opportunities to electricity trade, which, in turn, influences national ETS. Therefore, this brings urgency to accurately capture the dynamic interactions between national ETS and electricity market to transform carbon trading into a practical and effective way to decarbonize the power sector.
Proceedings of the 8th PURPLE MOUNTAIN FORUM on Smart Grid Protection and Control (PMF2023)
This book includes original, peer-reviewed research papers from the 8th PURPLE MOUNTAIN FORUM on Smart Grid Protection and Control(PMF2023), held in Nanjing, China, on August 11-13, 2023. The accepted papers cover the following topics: 1. Advanced power transmission technology2. AC/DC hybrid power grid technology3. Power Internet of Things Technology and Application4. Operation, control and protection of smart grid5. Active distribution network technology6. Power electronic technology and application7. New technology of substation automation8. Energy storage technology and application9. Application of new technologies such as artificial intelligence, blockchain, and big data10. Application of Information and Communication Technology11. Low-carbon energy planning and security12. Low-carbon operation of the power system13. Low-carbonenergy comprehensive utilization technology14. Carbon trading and power market15. Carbon emission stream and carbon capture technology16. Energy saving and smart energy technology17. Analysis and evaluation of low-carbon efficiency of power system18. Carbon flow modelling in power system operationThe papers included in this proceeding share the latest research results and practical application examples on the methodologies and algorithms in these areas, which makes the book a valuable reference for researchers, engineers, and university students.
Modern Optimization Methods for Decision Making Under Risk and Uncertainty
The book comprises original articles on topical issues of risk theory, rational decision making, statistical decisions, and control of stochastic systems. The articles are the outcome of a series international projects involving the leading scholars in the field of modern stochastic optimization and decision making. The structure of stochastic optimization solvers is described. The solvers in general implement stochastic quasi-gradient methods for optimization and identification of complex nonlinear models. These models constitute an important methodology for finding optimal decisions under risk and uncertainty. While a large part of current approaches towards optimization under uncertainty stems from linear programming (LP) and often results in large LPs of special structure, stochastic quasi-gradient methods confront nonlinearities directly without need of linearization. This makes them an appropriate tool for solving complex nonlinear problems, concurrent optimization and simulation models, and equilibrium situations of different types, for instance, Nash or Stackelberg equilibrium situations. The solver finds the equilibrium solution when the optimization model describes the system with several actors. The solver is parallelizable, performing several simulation threads in parallel. It is capable of solving stochastic optimization problems, finding stochastic Nash equilibria, and of composite stochastic bilevel problems where each level may require the solution of stochastic optimization problem or finding Nash equilibrium. Several complex examples with applications to water resources management, energy markets, pricing of services on social networks are provided. In the case of power system, regulator makes decision on the final expansion plan, considering the strategic behavior of regulated companies and coordinating the interests of different economic entities. Such a plan can be an equilibrium − a planned decision where a company cannot increase its expected gain unilaterally.