To realize the carbon-neutral goal, China commits to building a new type of power system with renewable energy generation as the main part of its supply side and leading deep penetration distributed PV in its demand side, which aims to achieve the friendliness interaction of the source-grid-load-storage and the organic integration of various energies. However, the rapid growth of centralized and distributed renewable generations and energy storage devices will significantly change the load patterns, operation modes and operating characteristics of the new type of distribution network. The emerging and co-existing of diverse forms of the network including wide-area network, active distribution network, and micro-grids brings great challenges to the control and operation of new type power systems and may threaten their security and stability. The synergy optimization and dispatch control of “Source-Grid-Load-Storage” and realization of multi energy complementary are effective ways to help achieve the optimized regulation of the whole power system at different levels.
The research goal is to adopt state-of-art theories, technologies, and approaches to realize dispatch control and synergy optimization of new types of active distribution networks (ADN) with various new types of loads and distributed resources such as electric vehicles (EVs), EV charging stations, electric boiler heat reservoirs, distributed PVs and distributed energy storages. The better observable, measurable, controllable, and interactive capability of the ADN could be achieved through the demand response (DR), source-network-load-storage interaction and multi-energy complementarity. There are many challenges that require further research and development on the precise power forecasting of renewable generation and load under different scenarios, the intelligent perception, optimal control and operation of micro-grids or near-zero energy consumption district in ADN with distributed energy storage and BIPV, the analysis of trading strategy of demand response aggregators and virtual power plant in electricity market, etc. Advanced skills and methods including deep learning, reinforcement learning, digital empowerment, and so on could be enhanced and adopted. Please refer to, but not be limited to, the following research scopes.
1. Regional load forecasting with deep penetration of distributed PV systems
2. Forecasting model of distributed PV power generation and wind power clusters
3. Building integrated PV (BIPV) and near-zero energy consumption district technology
4. Optimize operation strategy of virtual power plant and DR aggregator in electricity market
5. Optimal operation of multi-energy complementary micro-grid and distributed energy storage
6. Multi-agent digital empowerment interactive and intelligent perception technology of new types of ADN.
To realize the carbon-neutral goal, China commits to building a new type of power system with renewable energy generation as the main part of its supply side and leading deep penetration distributed PV in its demand side, which aims to achieve the friendliness interaction of the source-grid-load-storage and the organic integration of various energies. However, the rapid growth of centralized and distributed renewable generations and energy storage devices will significantly change the load patterns, operation modes and operating characteristics of the new type of distribution network. The emerging and co-existing of diverse forms of the network including wide-area network, active distribution network, and micro-grids brings great challenges to the control and operation of new type power systems and may threaten their security and stability. The synergy optimization and dispatch control of “Source-Grid-Load-Storage” and realization of multi energy complementary are effective ways to help achieve the optimized regulation of the whole power system at different levels.
The research goal is to adopt state-of-art theories, technologies, and approaches to realize dispatch control and synergy optimization of new types of active distribution networks (ADN) with various new types of loads and distributed resources such as electric vehicles (EVs), EV charging stations, electric boiler heat reservoirs, distributed PVs and distributed energy storages. The better observable, measurable, controllable, and interactive capability of the ADN could be achieved through the demand response (DR), source-network-load-storage interaction and multi-energy complementarity. There are many challenges that require further research and development on the precise power forecasting of renewable generation and load under different scenarios, the intelligent perception, optimal control and operation of micro-grids or near-zero energy consumption district in ADN with distributed energy storage and BIPV, the analysis of trading strategy of demand response aggregators and virtual power plant in electricity market, etc. Advanced skills and methods including deep learning, reinforcement learning, digital empowerment, and so on could be enhanced and adopted. Please refer to, but not be limited to, the following research scopes.
1. Regional load forecasting with deep penetration of distributed PV systems
2. Forecasting model of distributed PV power generation and wind power clusters
3. Building integrated PV (BIPV) and near-zero energy consumption district technology
4. Optimize operation strategy of virtual power plant and DR aggregator in electricity market
5. Optimal operation of multi-energy complementary micro-grid and distributed energy storage
6. Multi-agent digital empowerment interactive and intelligent perception technology of new types of ADN.