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 aggregator's 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.

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.

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.

In the future smart grids, it is important for the prosumers to manage the uncertainties from the distributed renewable energy sources (RES) such as PV generation. As a type of distributed energy resources (DERs), electrical vehicles (EVs) are regarded as a promising solution of the problem. In this paper, a transactive real-time EV charging management scheme is proposed for the building energy management system (BEMS) of commercial buildings with PV on-site generation and EV charging services. Instead of direct EV charging control, the proposed EV charging management scheme applies a transactive energy concept based approach to address the real-time EV charging management. With the proposed scheme, the BEMS can schedule its net electricity exchange with the external grid under the uncertainties of PV generation and EV parking and maximize its profit in the real-time operation. Meanwhile, the EV owners need not provide the BEMS with any further private information (such as future driving plans) but only their real-time charging requirements and preference setting of the response to the BEMS’s pricing signal in the proposed scheme. As such, the BEMS as a charging service provider only requires the minimal necessary information from the EV owners. The EV owners’ charging requirements, preference setting of the response curves and their required reimbursements for the response are respected by the real-time charging management and their contributions to the demand response are reimbursed by the BEMS. Case studies with real world driving data from the Danish National Travel Survey were carried out to verify the proposed framework.

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.

This paper proposes an optimal active power dispatch algorithm for wind farms based on Wind Turbine (WT) load sensitivity. The control objectives include tracking power references from the system operator and minimizing fatigue loads experienced by WTs. The sensitivity of WT fatigue loads to power references is defined which simplifies the formulation of the optimal power dispatch problem. Since the sensitivity value is calculated at the local WT controller, the computation burden of the central wind farm controller is largely reduced. Moreover, explicit analytical equations of the fatigue load sensitivity are derived, which significantly improves the computation efficiency of the local WT controller. The proposed algorithm can be implemented in different active power control schemes. Case studies were conducted with a wind farm under balance control for both low and high wind conditions. By comparing the rainflow cycles and Damage Equivalent Load (DEL), the efficacy of the proposed algorithm is verified.