Zhaoxi Liu

Distribution Locational Marginal Pricing Through Quadratic Programming for Congestion Management in Distribution Networks

This paper presents the distribution locational marginal pricing (DLMP) method through quadratic programming (QP) designed to alleviate the congestion that might occur in a distribution network with high penetration of flexible demands. In the DLMP method, the distribution system operator (DSO) calculates dynamic tariffs and publishes them to the aggregators, who make the optimal energy plans for the flexible demands. The DLMP through QP instead of linear programing as studied in previous literatures solves the multiple solution issue of the aggregator optimization which may cause the decentralized congestion management by DLMP to fail. It is proven in this paper, using convex optimization theory, the aggregators optimization problem through QP is strictly convex and has a unique solution. The Karush-Kuhn-Tucker (KKT) conditions and the unique solution of the aggregator optimization ensure that the centralized DSO optimization and the decentralized aggregator optimization converge. Case studies using a distribution network with high penetration of electric vehicles (EVs) and heat pumps (HPs) validate the equivalence of the two optimization setups, and the efficacy of the proposed DLMP through QP for congestion management.

Combined Active and Reactive Power Control of Wind Farms Based on Model Predictive Control

This paper proposes a combined wind farm controller based on Model Predictive Control (MPC). Compared with the conventional decoupled active and reactive power controls, the proposed control scheme considers the significant impact of active power on voltage variations due to the low

Optimal Reconfiguration-Based Dynamic Tariff for Congestion Management and Line Loss Reduction in Distribution Networks

X/R

Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging Through Chance Constrained Mixed-Integer Programming

ratio of wind farm collector systems. The voltage control is improved. Besides, by coordination of active and reactive powers, the Var capacity is optimized to prevent potential failures due to Var shortage, especially when the wind farm operates close to its full load. An analytical method is used to calculate the sensitivity coefficients to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both normal and emergency conditions. A wind farm with 20 wind turbines was used to verify the proposed combined control scheme.

Uncertainty Management of Dynamic Tariff Method for Congestion Management in Distribution Networks

This paper presents an optimal reconfiguration-based dynamic tariff (DT) method for congestion management and line loss reduction in distribution networks with high penetration of electric vehicles. In the proposed DT concept, feeder reconfiguration (FR) is employed through mixed integer programming when calculating the DT, leading to minimized energy cost and reduced DT as compared with the DT concept without FR. This paper further demonstrates that the line losses can be taken into account during the calculation of DT. As a result, the line loss reduction can be realized in a decentralized manner through the DT framework. Three case studies were conducted to validate the optimal reconfiguration-based DT method for congestion management and line loss reduction in distribution networks.

Optimal Day-Ahead Charging Scheduling of Electric Vehicles Through an Aggregative Game Model

This paper presents a distribution locational marginal pricing (DLMP) method through chance constrained mixed-integer programming (MIP) designed to alleviate the possible congestion in the future distribution network with high penetration of electric vehicles (EVs). In order to represent the stochastic characteristics of the EV driving patterns, a chance constrained optimization of the EV charging is proposed and formulated through MIP. With the chance constraints in the optimization formulations, it guarantees that the failure probability of the EV charging plan fulfilling the driving requirement is below the predetermined confidence parameter. The efficacy of the proposed approach was demonstrated by case studies using a 33-bus distribution system of the Bornholm power system and the Danish driving data. The case study results show that the DLMP method through chance constrained MIP can successfully alleviate the congestion in the distribution network due to the EV charging while keeping the failure probability of EV charging not meeting driving needs below the predefined confidence.

Transactive energy: A review of state of the art and implementation

The dynamic tariff (DT) method is designed for the distribution system operator (DSO) to alleviate congestions that might occur in a distribution network with high penetration of distributed energy resources (DERs). Uncertainty management is required for the decentralized DT method because the DT is determined based on optimal day-ahead energy planning with forecasted parameters such as day-ahead energy prices and energy needs which might be different from the parameters used by aggregators. The uncertainty management is to quantify and mitigate the risk of the congestion when employing the DT method, which is achieved by firstly formulating the problem as a chance constrained two-level optimization and then solving the problem through an iterative procedure. Two case studies were conducted to demonstrate the efficacy of the uncertainty management of DT method.

Study of demand as frequency controlled reserve in Nordic power system

The electric vehicle (EV) market has been growing rapidly around the world. With large scale deployment of EVs in power systems, both the grid and EV owners will benefit if the flexible demand of EV charging is properly managed through the electricity market. When EV charging demand is considerable in a grid, it will impact spot prices in the electricity market and consequently influence the charging scheduling itself. The interaction between the spot prices and the EV demand needs to be considered in the EV charging scheduling, otherwise it will lead to a higher charging cost. A day-ahead EV charging scheduling based on an aggregative game model is proposed in this paper. The impacts of the EV demand on the electricity prices are formulated with the game model in the scheduling considering possible actions of other EVs. The existence and uniqueness of the pure strategy Nash equilibrium are proved for the game. An optimization method is developed to calculate the equilibrium of the game model through quadratic programming. The optimal scheduling of the individual EV controller considering the actions of other EVs in the game is developed with the EV driving pattern distribution. Case studies with the proposed game model were carried out using real world driving data from the Danish National Travel Surveys. The impacts of the EV driving patterns and price forecasts on the EV demand with the proposed game model were also analysed.

Sensitivity analysis of dynamic tariff method for congestion management in distribution networks

In future smart grids, large-scale deployment of distributed energy resources (DERs) and renewable energy sources (RES) is expected. In order to integrate a high penetration level of DERs and RES in the grid while operating the system safely and efficiently, new control methods for power system operations are in demand so that the flexibility of the responsive assets in the grid can be further explored. Transactive control, considered as one of the most novel distributed control approaches for power system operations, has been extensively discussed and studied around the world in recent years. This paper provides a bibliographical review on the researches and implementation of the transactive energy concepts and transactive control techniques in power systems. The ideas of transactive control are introduced mainly according to the transactive energy framework proposed by the GridWise Architecture Council. The implementation pilots and research studies on transactive control applications in power systems are reviewed subsequently.

Two-Stage Optimal Scheduling of Electric Vehicle Charging based on Transactive Control

This paper investigates the efficacy of Demand Frequency Reserve (DFR) in Nordic power system. Heat pump, due to its switching flexibility, less disturbing impacts to customers and promising future in application, is used to represent DFR in the study. Thermodynamics of the heat pump unit is modelled to investigate the dynamic behaviour of DFR. Two DFR control logics, designed according to different appliance features, are implemented into the heat pump model. In this study, DFR acts as both disturbance and normal operation reserves to fulfill the requirement of frequency reserve by Danish Transmission System Operator (TSO). Accordingly, two case scenarios are designed for the contingency and normal operation, respectively. The simulation models are implemented in RTDS, by means of which the Hardware In the Loop (HIL) test of the developed frequency response device (SmartBox) is carried out.