A Distributed Smart Grid Control Model for Integration of Renewables.
详细信息
文摘
Future smart grids will likely support bi-directional flow of electricity and include power production from multiple,disparate,and uncontrollable sources due to a high penetration of distributed renewable energy resources. Some of the more challenging problems for the future grid include maximizing the use and efficiency of renewable resources,and realizing optimal demand and power production responses that can complement renewable intermittency. Integration of renewables together with energy storage systems has been motivated by the increasing attention to feature renewable energies not only from solar and wind power but also from the excess generation from many customers. Effective use of renewable resources using battery systems can be realized by balancing distributed energy resources DERs) with complementary demand and dispatchable generation responses. The spatial distribution,intermittency,and uncontrollability of most renewable resources,however,make stable and reliable electricity transmission and distribution difficult especially with high renewable market penetration in large-scale complex power networks. In order to use energy storage systems effectively for optimizing DERs and realizing a reliable and sustainable future grid with a lot of real-time end-use devices that anticipate demands automatically,we present an autonomous distributed control model that can realize optimum power flow control together with demand and power response. The proposed model,which integrates tie-set graph theory with an autonomous agent system,effectively divides the power network into a set of independent loops tie-sets). Autonomous agents constantly navigate the network to dynamically synchronize state information within tie-sets and completely automate the future power network. Due to the theoretical basis of a tie-set graph,the supply and load of electric power at every instant can be balanced even if the future load is uncertain and renewable generation is highly variable and unpredictable. In this thesis,we developed a distributed control model to deal with the following important problems: Optimal Real-time Distribution of renewable Energy Resources ORDER),Optimal Real-time Power Flow ORPF),and Power-flow Loss Minimization PLM). The objective of the ORDER problem is minimizing the power supply from a Centralized Generation Facility CGF),which is a power production station that uses fossil fuels or any other resources except renewables,with balanced distribution and allocation of DERs. To minimize the power supply from CGF,the net demands loads minus renewables) and power from CGF should be constantly balanced under the condition that the storage system at each node has at least certain amount of energy within the fixed range of the capacity. If all the net demands are balanced,power supply from CGF can be minimized and blackouts can be prevented. In ORPF problem,we expand the model by considering cost functions of using CGF and battery. The goal is to minimize the total cost,which consists of 1) the cost of power production by the CGF using fossil fuels and 2) the cost of using batteries across multiple time periods to balance the fluctuation of renewable power generation and loads. To solve the ORPF problem,we present a novel decentralized algorithm to autonomously allocate the CGF generations and battery charges/discharges. In PLM problem,power flow loss is taken into account on top of the model. A distributed algorithm is proposed to minimize the loss while distributing energy resources to consumers in a future grid that connects many real-time distributed generation systems. We employ the notion of tie-sets to create a linear vector space represented by a set of loops in the power network where the power loss function can be simply formulated and solved. As finding a solution for each tie-set enables global optimization,we propose parallel computing based on tie-sets by integrating autonomous agents； the power loss on every link can be minimized with iterative optimization within a system of independent tie-sets.