This paper proposes a comprehensive scheme for day-ahead congestion management of distribution networks with high penetration of distributed energy resources (DERs). In the proposed scheme, the dynamic tariff (DT), network reconfiguration and re-profiling products are integrated, which combines the advantages of these methods. In addition, the previously proposed DT model is relaxed in order to handle possible infeasibility of the DT problem and set a limit for the DT. With the utilization of the flexibilities from various types of DERs and the advantages of the three congestion management methods, the proposed comprehensive scheme can solve the congestion more effectively and at the same time ensures that the congestion management prices are within an acceptable level. Three case studies were conducted with the modified Roy Billinton Test System (RBTS) to validate the effectiveness and advantages of the proposed comprehensive scheme.
Load restoration is an important issue for power system restoration after a blackout. A second order conic programming (SOCP) model is proposed based on the information gap decision theory (IGDT) to maximize load pickup considering the uncertainty of load increment. Because distribution functions of load increment are difficult to obtain, the optimization of load pickup is transformed to maximize the fluctuation range of load increment by the IGDT. The derived optimal fluctuation range can ensure that the reenergized system is secure, and the amount of load pickup is always better than the specified expectation. Moreover, because the optimization model of the fluctuation range is a mixed-integer nonlinear model which is challenging to solve accurately and efficiently, the nonlinear model is transformed into a SOCP model that can be efficiently solved using CPLEX. The efficiency of the IGDT-based SOCP model is validated using the New England (10-machine 39-bus) system. The simulation results show that the derived load pickup shows expected robustness with respect to the load increment uncertainty.
This paper focuses on economic dispatch (ED) in power systems with intermittent wind power, which is a very critical issue in future power systems. A stochastic ED problem is formed based on the recently proposed versatile probability dis-tribution (VPD) of wind power. The problem is then analyzed and proved to be strictly convex. Although such convex optimiza-tion is tractable in many cases, it may take a long time to solve due to its large scale. This paper proposes a dual decomposition method to decompose the large problem. Then two methods are employed to solve the decomposed problem, namely, the subgra-dient method and a faster method, limited-memory BFGS with box constraints (L-BFGS-B, a quasi-Newton method). Case stud-ies were conducted to verify the efficiency of the dual decomposi-tion and L-BFGS-B method for solving the stochastic ED problem.
Investment for renewables has been growing rapidly since the beginning of the new century, and the momentum is expected to sustain in order to mitigate the impact of anthropogenic climate change. Transition towards higher renewable penetration in the power industry will not only confront technical challenges, but also face socio-economic obstacles. The connected between environment and energy systems are also tightened under elevated penetration of renewables. This paper will provide an overview of some important challenges related to technical, environmental and socio-economic aspects at elevated renewable penetration. An integrated analytical framework for interlinked technical, environmental and socio-economic systems will be presented at the end.
In this paper, a two-stage optimal charging scheme based on transactive control is proposed for the aggregator to manage day-ahead electricity procurement and real-time EV charging management in order to minimize its total operating cost. The day-ahead electricity procurement considers both the day-ahead energy cost and expected real-time operation cost. In the real-time charging management, the cost of employing the charging flexibility from the EV owners is explicitly modelled. The aggregator uses a transactive market to manage the real-time charging demand to provide the regulating power. A model predictive control (MPC) based method is proposed for the aggregator to clear the transactive market. The real-time charging decisions of the EVs are determined by the clearing of the proposed transactive market according to the real-time requests and preferences of the EV owners. As such, the aggregatorb's decisions in the real-time EV charging management and regulating power markets can be optimized. At the same time, the charging requirements and response preferences of the EV owners are respected. Case studies using real world driving data from the Danish National Travel Surveys were conducted to verify the proposed framework.
The re-emergence of 2-phase bipolar DC distribution network, which utilizes the neutral wire for efficient distribution, has spurred research interest in recent years. In practice, system efficiency (power loss) and voltage unbalance are major concerns for the planning and design of the 2-phase DC bipolar network. While most of the existing methodologies are power electronics solutions, there are very few works on resolving the problem from the power system perspective. This paper proposes a model based optimization method by firstly formulating the power flow model for 2-phase DC bipolar network using the single line modeling technique and nodal analysis. Secondly, a binary integer load distribution model is proposed to consider the redistribution of unipolar loads across the two unipolar distribution poles. Together with the power flow model, the system power loss and system voltage unbalance indices are formulated as a binary integer quadratic model. Thirdly, a multi-objective optimization model is formulated and solved using the weighted sum approach. The proposed method is applied to a DC LED lighting system design which considers both voltage unbalance and power loss. Using a 15 bus single source and a 33 bus multi-source network as case studies, the developed power flow model is validated with very high accuracy. Compared to existing iterative methods, the proposed model-based approach is able to significantly improve the voltage balancing across the distribution system.