Sicong Tan

Bio: Sicong Tan is an academic researcher from National University of Singapore. The author has contributed to research in topics: Economic dispatch & Microgrid. The author has an hindex of 5, co-authored 5 publications receiving 836 citations.

Consensus Based Approach for Economic Dispatch Problem in a Smart Grid

Abstract: Economic dispatch problem (EDP) is an important class of optimization problems in the smart grid, which aims at minimizing the total cost when generating certain amount of power. In this work, a novel consensus based algorithm is proposed to solve EDP in a distributed fashion. The quadratic convex cost functions are assumed in the problem formulation, and the strongly connected communication topology is sufficient for the information exchange. Unlike centralized approaches, the proposed algorithm enables generators to collectively learn the mismatch between demand and total amount of power generation. The estimated mismatch is then used as a feedback mechanism to adjust current power generation by each generator. With a tactical initial setup, eventually, all generators can automatically minimize the total cost in a collective sense.

Consensus based approach for economic dispatch problem in a smart grid

Abstract: Economic dispatch problem (EDP) is an important problem in the smart grid. Its aim is to minimize the total cost when generating certain amount of power. This paper proposes a novel consensus based algorithm to solve EDP in a distributed fashion. The quadratic cost functions are adopted in the problem formulation, and the strongly connected communication topology is used for the information exchange. Unlike the centralized approach, the proposed algorithm allows generators to learn the mismatch information between demand and total power generation through a distributed manner. The estimated mismatch information is used as a feedback to adjust current power generation by each generator. With a tactical initial setup, generators can automatically minimize the total cost in a collective sense while satisfying power balance equation.

Optimization of Distribution Network Incorporating Distributed Generators: An Integrated Approach

Abstract: Previous studies of distributed power and network focused only on the optimization of either the microgrid load dispatch or reconfiguration power loss. Micorgrid economic load dispatch approach normally does not support distribution network. Network reconfiguration usually does not take distributed generators into consideration. Thus, it is necessary to integrate these two sub-problems together in order to benefit the whole network. In this paper, an integrated solution that takes care of both microgrid load dispatch and network reconfiguration is proposed. The stochastic nature of wind, PV and load is taken into consideration. The forecasting of the wind, PV and load data are considered. The four bio-inspired optimization schemes are adopted to solve the problem. The results obtained have shown that the four optimization techniques are all capable of solving this problem. By using the integrated approach, microgrid can be incorporated into the network more effectively. The network can adjust itself more efficiently to allow utilization of the renewable energy resources.

Optimization of economic load dispatch for a microgrid using evolutionary computation

Abstract: Economic load dispatch of a microgrid system is a highly nonlinear and multi-objective problem. The two objectives are minimizing the emission of the thermal generators and minimizing the total operating cost. This microgrid system consists of thermal generators, wind turbines and polymer electrolyte membrane (PEM) fuel cells. Two state-of-the-art multi-objective methods, strength pareto evolutionary algorithm 2 (SPEA2) and non-dominated sorting genetic algorithm (NSGA-II), are adopted to perform the optimization. The results show that SPEA2 has a faster convergence speed and NSGA-II has a better convergence eventually for large number of generations. It is suggested that SPEA2 is recommended if time is the most important concern. However, if the accuracy of the results is top priority, NSGA-II is preferred.

Optimization of distribution network incorporating microgrid using vaccine-AIS

Abstract: Previous studies focused only on the optimization of either microgrid load dispatch or reconfiguration power loss. Micorgrid economic load dispatch approach normally assumed that it could draw unlimited power from the utility grid. Network reconfiguration usually did not take microgrid into consideration. Thus, it is necessary to integrate these two sub-problems together in order to benefit the whole network. In this paper, an integrated solution that takes care of both microgrid load dispatch and network reconfiguration is proposed. The stochastic nature of wind, PV and load is taken into consideration. The forecasting of the wind, PV and load data are considered. The bio-inspired optimization scheme Vaccine-AIS is adopted to solve the problem. The results obtained have shown that the optimization technique is capable for this problem. By using the integration approach, microgrid can be incorporated into the network more effectively. The network can adjust itself more efficiently to allow the utilization of the renewable energy resources.

State of the Art in Research on Microgrids: A Review

Abstract: The significant benefits associated with microgrids have led to vast efforts to expand their penetration in electric power systems. Although their deployment is rapidly growing, there are still many challenges to efficiently design, control, and operate microgrids when connected to the grid, and also when in islanded mode, where extensive research activities are underway to tackle these issues. It is necessary to have an across-the-board view of the microgrid integration in power systems. This paper presents a review of issues concerning microgrids and provides an account of research in areas related to microgrids, including distributed generation, microgrid value propositions, applications of power electronics, economic issues, microgrid operation and control, microgrid clusters, and protection and communications issues.

Recent Advances in Consensus of Multi-Agent Systems: A Brief Survey

Abstract: In this paper, we mainly review the topics in consensus and coordination of multi-agent systems, which have received a tremendous surge of interest and progressed rapidly in the past few years. Focusing on different kinds of constraints on the controller and the self-dynamics of each individual agent, as well as the coordination schemes, we categorize the recent results into the following directions: consensus with constraints, event-based consensus, consensus over signed networks, and consensus of heterogeneous agents. We also review some applications of the very well developed consensus algorithms to the topics such as economic dispatch problem in smart grid and k -means clustering algorithms.

Self-Healing Resilient Distribution Systems Based on Sectionalization Into Microgrids

Abstract: This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the revenues. A rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts. In the self-healing mode, the on-outage portion of the distribution system will be optimally sectionalized into networked self-supplied microgrids (MGs) so as to provide reliable power supply to the maximum loads continuously. The outputs of the dispatchable DGs will be rescheduled accordingly too. In order to take into account the uncertainties of DG outputs and load consumptions, we formulate the problems as a stochastic program. A scenario reduction method is applied to achieve a tradeoff between the accuracy of the solution and the computational burden. A modified IEEE 123-node distribution system is used as a test system. The results of case studies demonstrate the effectiveness of the proposed methodology.

Coordinated Energy Management of Networked Microgrids in Distribution Systems

Abstract: This paper proposes a novel control strategy for coordinated operation of networked microgrids (MGs) in a distribution system. The distribution network operator (DNO) and each MG are considered as distinct entities with individual objectives to minimize the operation costs. It is assumed that both the dispatchable and nondispatchable distributed generators (DGs) exist in the networked MGs. In order to achieve the equilibrium among all entities and take into account the uncertainties of DG outputs, we formulate the problem as a stochastic bi-level problem with the DNO in the upper level and MGs in the lower level. Each level consists of two stages. The first stage is to determine base generation setpoints based on the load and nondispatchable DG output forecasts and the second stage is to adjust the generation outputs based on the realized scenarios. A scenario reduction method is applied to enhance a tradeoff between the accuracy of the solution and the computational burden. Case studies of a distribution system with multiple MGs of different types demonstrate the effectiveness of the proposed methodology. The centralized control, deterministic formulation, and stochastic formulation are also compared.