Shaojun Huang

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.

A Sufficient Condition on Convex Relaxation of AC Optimal Power Flow in Distribution Networks

This paper proposes a sufficient condition for the convex relaxation of ac optimal power flow (OPF) in radial distribution networks as a second order cone program (SOCP) to be exact. The condition requires that the allowed reverse power flow is only reactive or active, or none. Under the proposed sufficient condition, the feasible sub-injection region (power injections of nodes excluding the root node) of the ac OPF is convex. The exactness of the convex relaxation under the proposed cond

Coordinated Voltage Control of a Wind Farm Based on Model Predictive Control

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

ition is proved through constructing a group of monotonic series with limits, which ensures that the optimal solution of the SOCP can be converted to an optimal solution of the original ac OPF. The efficacy of the convex relaxation to solve the ac OPF is demonstrated by case studies of an optimal multi-period planning problem of electric vehicles in distribution networks.

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

This paper presents an autonomous wind farm voltage controller based on model predictive control. The reactive power compensation and voltage regulation devices of the wind farm include static Var compensators, static Var generators, wind turbine generators and on-load tap changing transformer, and they are coordinated to keep the voltages of all the buses within the feasible range. Moreover, the reactive power distribution is optimized throughout the wind farm in order to maximize the dynamic reactive power reserve. The sensitivity coefficients are calculated based on an analytical method to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both voltage violated and normal operation conditions. A wind farm with 20 wind turbines was used to conduct case studies to verify the proposed coordinated voltage control scheme under both normal and disturbance conditions.

Optimal siting and sizing of Energy Storage System for power systems with large-scale wind power integration

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.

Dynamic Subsidy Method for Congestion Management in Distribution Networks

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.

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

This paper proposes algorithms for optimal siting and sizing of Energy Storage System (ESS) for the operation planning of power systems with large scale wind power integration. The ESS in this study aims to mitigate the wind power fluctuations during the interval between two rolling Economic Dispatches (EDs) in order to maintain generation-load balance. The charging and discharging of ESS is optimized considering operation cost of conventional generators, capital cost of ESS and transmission losses. The statistics from simulated system operations are then coupled to the planning process to determine the optimal siting and sizing of storage units throughout the network. These questions are investigated using an IEEE benchmark system.

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

Dynamic subsidy (DS) is a locational price paid by the distribution system operator (DSO) to its customers in order to shift energy consumption to designated hours and nodes. It is promising for demand side management and congestion management. This paper proposes a new DS method for congestion management in distribution networks, including the market mechanism, the mathematical formulation through a two-level optimization, and the method solving the optimization by tightening the constraints and linearization. Case studies were conducted with a one node system and the Bus 4 distribution network of the Roy Billinton test system with high penetration of electric vehicles and heat pumps. The case studies demonstrate the efficacy of the DS method for congestion management in distribution networks. Studies in this paper show that the DS method offers the customers a fair opportunity to cheap energy prices and has no rebound effect.

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.