Integrated Resource Planning And Decision-making Theories Of Active Distribution Networks Towards Low-carbon Economy

The low-carbon development of power sector is a core strategy to cope with global climate change and realize energy sustainability. As an advanced product of smart grid, active distribution network (ADN) integrates technologies of information communication, power electronics and intelligent control, which provides an important solution for large-scale and efficient utilization of renewable energies. With the notion of active use of renewable energies, this dissertation is put forward to focus on ADN formulation and implementations. Following the trinity principle of "one central task, two research areas and three plot lines", the work makes an indepth and systematic investigation on the planning and decision-making theories of ADN that supports the development towards low-carbon economy. The main contributions of this study are summarized as follows:Starting from the elements of ADN, the technical characteristics and steady-state operation models of micro turbine, wind power, solar photovoltaic, and various energy storage technologies, are analyzed. The emphases are made on demand response resources in both price-and incentive-based forms, and their impacts on renewable energy utilization. With joint consideration of resource complementarity, technology readiness, investment cost and reliability requirement, the integration modes of ADN resources, along with their applicability, are analyzed for the purpose of low-carbon development. For uncertainty of demand response mechanism, the improved FCM classification algorithm applicable under the big data environment is proposed, which provides an effective method for accurate identification of demand responsiveness.On the basis of integration mode, the framework of ADN planning for efficient utilization of renewable energy is put forward. Exploring the underlying coupling between planning decision and low-carbon benefits, the mechanism for blocking the large-scale utilization of renewable energies under ADN is examined. For effective consideration of uncertain factors, the concept of complicated scenario set is proposed and the Taguchi orthogonal array test is employed for reducing redundant information. On such basis, a bi-level scenario-based planning model is established for efficient utilization of renewable energy under ADN, wherein the potential of active network management has been focused. The economic costs and low-carbon benefits of the system under different planning paradigms are analyzed.An integrated resource planning methodology that considers demand response (DR) as an option for planning distribution systems in a transition towards the low-carbon ADN has been proposed. It is assumed that demand responsiveness is enabled by real-time pricing (RTP), and the customers’ behaviors compliance to RTP is described through a nodal-based DR model, in which the fading effect attended during the load recovery is highlighted. For the temporal discrepancy with respect to the existence domain of low-carbon factors, a two-stage modeling mechanism named "integral projection" is proposed. Taking into account of RDG and DR uncertainties, a two-stage chance-constraint model for describing above problem is developed. With the illustration of the case study, the impact of DR integration on the system costs and low-carbon benefits are revealed.Based on the non-cooperative game theory, ADS planning for promoting the utilization renewable energy under the electricity market environment is studied. Considering two different contexts i.e. deregulation and energy-saving services, the market structure for ADN planning and operation issues are analyzed respectively. On such basis, the framework of ADN planning in the electricity market is presented.With the analysis on the coupling between renewable energy usage and economic attributes, a bilevel coordinated ADN planning model is proposed, which balances the benefits of all the market participants. In view of the shortcomings of traditional methods, an intelligent optimization strategy combining artificial bee colony algorithm with L-M method is developed to solve the above problem efficiently. Numerical study based on a real distribution system in Hebei province verifies the effectiveness of the proposed method, and the influences of electricity price and carbon taxation on the low-carbon target of ADN are revealed.Finally, the decision-making theory for low-carbon ADN planning is examined. From the perspective of decarbonization capability, a system dynamics analysis is made to investigate the formation mechanism with respect to the low-carbon benefits in ADN. Then, a comprehensive index system is proposed for ADN planning evaluation under the low-carbon economy, which covers the contributions of techno-economic, environmental and other factors. In view of the market structure under the deregulated environment and logical hierarchy between different objectives, a hierarchal decision-make model for low-carbon ADN planning is formed and a situation algorithm is developed. Finally, the proposed methodology is tested on an ADN demonstration project in China, from which some suggestions are brought about concerning the scientific development of ADN.