Abstract:The implementation of demand response (DR) could contribute to significant economic benefits meanwhile simultaneously enhancing the security of the concerned power system. A well-designed carbon emission trading mechanism provides an efficient way to achieve emission reduction targets. Given this background, a virtual power plant (VPP) including demand response resources, gas turbines, wind power and photovoltaics with participation in carbon emission trading is examined in this work, and an optimal dispatching model of the VPP presented. First, the carbon emission trading mechanism is briefly described, and the framework of optimal dispatching in the VPP discussed. Then, probabilistic models are utilized to address the uncertainties in the predicted generation outputs of wind power and photovoltaics. Demand side management (DSM) is next implemented by modeling flexible loads such as the chilled water thermal storage air conditioning systems (CSACSs) and electric vehicles (EVs). On this basis, a mixed integer linear programming (MILP) model for the optimal dispatching problem in the VPP is established, with an objective of maximizing the total profit of the VPP considering the costs of power generation and carbon emission trading as well as charging/discharging of EVs. Finally, the developed dispatching model is solved by the commercial CPLEX solver based on the YALMIP/MATLAB (version 8.4) toolbox, and sample examples are served for demonstrating the essential features of the proposed method.

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In recent years, the ever-increasing charging demand of electric vehicles (EVs) imposes challenges on both power supply security and reliability in the distribution system. In this paper, an EV accommodation capability evaluation model of a distribution system, with high penetrations of flexible resources, is established. Firstly, according to the actual classifications of EVs and transportation rules, a Monte Carlo simulation is used to simulate the charging behaviors of EVs so as to obtain the relevant parameters of EV charging. Then, a coordinated charging optimization model for various types of EVs is proposed based on the charging characteristics of EVs. The presented model comprises a mixed-integer linear programming problem and a constrained optimization problem which are respectively solved by CPLEX (the Simplex method implemented in the • C programming language) and the particle swarm optimization (PSO) algorithm. Last of all, a real-life distribution system in the coastal areas of China is served for demonstrating the feasibility and efficiency of the proposed approach. Moreover, the impacts of flexible resources, distribution network zoning rules, and EV growth on the EV accommodation capability of a distribution system are also discussed.Energies 2019, 12, 3056 2 of 20 capability for EVs (ACE). At the same time, the optimized EV charging load (OECL) obtained by the coordinated charging strategies is also conducive to understanding the relationship between EV accommodation and charging load distribution.It is generally agreed that the problems of environmental pollution and energy security can be abated by increasing the penetration level of both EVs and flexible resources, such as a variety of distributed generations (DGs) and energy storage systems (ESSs). Flexible resources on the demand side will play a pivotal role in reducing load fluctuations in the distribution system [10]. Besides, better utilization of the flexible resources on both power supply and demand sides is one approach to increase the ACE. Joint optimal scheduling of flexible resources and EVs have been investigated by a number of researchers. Simultaneous provision of interruptible loads for flexible ramping products and demand relief are considered in Reference [11]. The impact of EVs and demand response (DR) on the flexibility of a microgrid are analyzed in Reference [12]. A multi-objective optimal scheduling method for a distribution network with the integration of numerous EVs is proposed in Reference [13].In terms of the ACE evaluation, there are mainly two types of methods. One method is to optimize the maximum ACE under the assumption of EV behaviors being guided by a smart charging strategy or a market mechanism. In Reference [14], a market mechanism that optimally allocates available charging capacity is proposed, considering the network stability and the EV owners' individual preferences. In Reference [15], a market-based multi-agent control mechanism that takes into account the distribution transformer and voltage constra...

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The large-scale development of grid-connected wind power, which is intermittent, random and uncontrollable, has introduced great challenges to power system planning and operation. Power system planning should fully consider the capability for accommodating wind power, as well as system’s regulation capability and spare capacity. Given this background, a bi-level transmission system planning model for this purpose is proposed in this paper. In the proposed model, demand-side response resources, such as incentive load and interruptible load, are used for peak-load shifting so as to optimize power flow distribution, reduce transmission investment and improve the utilizaiton level of wind power. The two levels are implemented interactively and iteratively, and finally converge to an optimized planning scheme considering both security and economy. The essential features of the developed model and adopted algorithms are demonstrated by a modified 18-bus test system.