Cooperative Distributed Demand Management for Community Charging of PHEV/PEVs Based on KKT Conditions and Consensus Networks

IEEE Trans. Sustain. Energy

Ding

2018

Abstract-This paper considers the optimal resource management problem for microgrids. Microgrids provide a promising approach to fulfil challenges of the integration of distributed renewable generations and energy storage systems. However, the resource management in a microgrid encounters the new difficulty, i.e. supply-demand imbalance, caused by the intermittence of renewable sources. Therefore, an optimal solution is proposed to the resource management by enhancing the communication and coordination under a multi-agent system framework. An agent is a participant, for instance, the distributed renewable generator/energy storage system of the microgrid. With this multi-agent system, the distributed optimal solution only utilizes the local information, and interacts with the neighbouring agents. Thus, single-node congestion is avoided since the requirement for a central control centre is eliminated, and it is robust against single-link/node failures. The analysis will show that the proposed solution can solve the resource management problem in an initialization-free manner. Additionally, the proposed strategy can maintain the supply-demand balance under a time-varying supply-demand deviation. The simulation studies are carried out for IEEE 14-bus and 162-bus power systems to validate the effectiveness of the proposed distributed solution.

Section: Simulation Results and Analysismentioning

confidence: 99%

Distributed Agent Consensus-Based Optimal Resource Management for Microgrids

IEEE Trans. Sustain. Energy

Ding

2018

“…2) Local Constraint of Each PEV: The allocated charging power of each PEV is locally bounded by different physical factors, such as the upper bound of outlet's power output, the charging current's tolerance and the charging level [21]. One local constraint is proposed to map the physical above constraints, i.e.,…”

Section: B Constraintsmentioning

confidence: 99%

“…In the case studies, the designed parameters are chosen as α = 12, β = 0.4, γ = 2, ε = 0.0085, which satisfy the condition in (21), and (25) specified in Theorem 4.1. In Case 5.1, we study the optimal charging problem with the constant total In this case, the total available charging power is assumed as 12kW.…”

Section: B Simulation Studiesmentioning

confidence: 99%

Distributed Initialization-Free Cost-Optimal Charging Control of Plug-In Electric Vehicles for Demand Management

Ding

2017

IEEE Trans. Ind. Inf.

Abstract-This paper considers the optimal charging problem of plug-in electric vehicles (PEVs) on demand side management. PEVs provide a promising alternative solution to reduction of environmental pollution and fuel emission. With a large number of PEVs connected to the grid, a well-designed charging coordination approach is needed to release the impacts on the power system. A distributed cooperative control strategy of PEVs is proposed to meet system interests while respecting each PEV's charging constraint. The proposed strategy is distributed, which only needs to be interacted with the neighbouring agents. Our analysis shows that the proposed strategy solves the optimal charging problem of PEVs in an initialization-free approach, which avoids any procedure for initialization during PEVs' charging process. Furthermore, the proposed strategy is robust to the time-varying available charging power and plug-and-play operations. The simulation studies validate the effectiveness of the proposed distributed strategy.

“…The Karush-Kuhn-Tucker (KKT) conditions are used here to solve the above nonlinear optimization problem theoretically [20], [21]. As long as the values of the weight coefficients are determined, the optimal solution can be updated at each control instant.…”

Section: Utility Function-based Controlmentioning

confidence: 99%

Analysis, control, and wireless charging of energy systems using ultracapacitors

2014 IEEE International Electric Vehicle Conference (IEVC)

Yin

et al. 2014

The battery-ultracapacitor hybrid energy storage system (HESS) and its management have been widely investigated in recent years. Meanwhile, there is a lack of general and quantitative analysis on the energy loss of the battery-ultracapacitor HESS that does not depend on any specific control algorithm or physical limitations. In addition, the battery-ultracapacitor HESS is actually a simple example of a networked energy system. An agent-based control approach is desirable in order to improve the synergy, and thus, the flexibility, scalability, faulttolerance and reliability of the hybrid energy systems, and also the computational efficiency. Finally, considering the requirement of fast and frequent wireless charging, ultracapacitors could be more suitable than batteries due to their excellent reliability and charge/discharge capability. In this paper, these three aspects for the energy systems using ultracapacitors, i.e., analysis, control, and wireless charging, are discussed.