Optimal Generation Control of Shipboard Power Systems

Traditionally, power system scheduling and control are separately implemented. To bridge the gap between these two activities, online adjustment of the optimal schedule is necessary. Because such adjustment degrades energy efficiency and dynamic response, it is desirable to integrate the two functions seamlessly. One possible solution is to optimize the control references directly. In this paper, a fully-distributed, multi-agent based control solution is presented to reduce the fuel consumption of shipboard power systems. Every generator has an associated agent that only communicates with its neighboring agents. With a properly-designed communication network, the solution can guarantee convergence, even during losses of the communication channel. This fully-distributed design can significantly improve the reliability and survivability of the system, especially during battle conditions. The improved subgradient based optimization solution can address both equality and inequality constraints and can provide performance comparable to that of centralized solutions. Simulation studies demonstrate the effectiveness of the proposed solution. INTRODUCTION Optimal generation scheduling is a much-studied problem in power system research. It aims at allocating the power generation to meet the power demand in an economic or profitable way, while continuously respecting the physical constraints of the power system [1-3]. The optimal generation schedule cannot be mapped to the control reference automatically, so online adjustment is necessary. Considering that the adjustment is not optimal, both energy efficiency and dynamic performance will degrade. To improve performance, it is desirable to bridge the gap between power system operation and control seamlessly. One possible solution is to optimize the control reference for generation directly, though doing so places demanding requirements on the speed of the supporting algorithm. Most existing generation scheduling solutions are centralized [4-5]. As centralized solutions require the communication and processing of large amounts of global data, they experience difficulty providing a fast response. The delays inherent in centralized decision-making render it unsuitable for online optimization [6], especially when considering the small inertia of a shipboard power system (SPS) and the severe changes in operating conditions. In addition, centralized solutions are inflexible and susceptible to single-point failures [7-8]. Improving the efficiency and survivability of high-performance naval SPSs requires more reliable alternatives. Because distributed solutions can overcome the previouslymentioned disadvantages of centralized solutions, they have attracted much attention in recent years. To address the needs of SPSs, a fully-distributed, multiagent system (MAS)-based solution is proposed to optimize the control references of distributed generators online. As one of the most popular distributed control solutions, MAS can provide good reliability and efficiency if properly designed. In the past years, MAS has been applied widely to various SPS problems [9-12]. Even though MAS has tended to be oversold, its potential has not been fully explored. Recent advancements in consensus and cooperative control make advanced MASbased design possible. The proposed solution is fully distributed in the sense that each distributed generator has an associate agent that communicates with its neighboring agents only. No centralized or specialized agent is used to coordinate the operation of the autonomous agents. The topology of the communication network for the agents, which is independent of the topology of the power network, is designed based on the N-1 rule. According to the design, any two agents are always connected directly under the loss of one communication channel. Thus, the proposed solution is less susceptible to single-point failures. Based on the designed communication network, the autonomous agents can realize an improved distributed sub-gradient algorithm. This algorithm is used to optimize the control references directly. Unlike existing distributed sub-gradient algorithms, the improved algorithm can address both equality and inequality constraints. The equality constraints are satisfied by adjusting local generations based on a properly-designed updating rule. The inequality constraints are addressed by constructing a Report Documentation Page Form Approved OMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE 2012 2. REPORT TYPE 3. DATES COVERED 00-00-2012 to 00-00-2012 4. TITLE AND SUBTITLE Distributed Optimal Generation Control of Shipboard Power Systems 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Sea Systems Command ,1333 Isaac Hull Avenue, SE ,Washington Navy Yard ,DC,20376 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR’S ACRONYM(S) 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 12. DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release; distribution unlimited 13. SUPPLEMENTARY NOTES Presented at the Electric Machines Technology Symposium (EMTS) 2012, MAY 23-24 2012, Philadelphia, PA