Foresighted Demand Side Management

We consider a smart grid with an independent system operator (ISO), and distributed aggregators who have energy storage and purchase energy from the ISO to serve its customers. All the entities in the system are foresighted: each aggregator seeks to minimize its own long-term payments for energy purchase and operational costs of energy storage by deciding how much energy to buy from the ISO, and the ISO seeks to minimize the long-term total cost of the system (e.g. energy generation costs and the aggregators' costs) by dispatching the energy production among the generators. The decision making of the entities is complicated for two reasons. First, the information is decentralized: the ISO does not know the aggregators' states (i.e. their energy consumption requests from customers and the amount of energy in their storage), and each aggregator does not know the other aggregators' states or the ISO's state (i.e. the energy generation costs and the status of the transmission lines). Second, the coupling among the aggregators is unknown to them. Specifically, each aggregator's energy purchase affects the price, and hence the payments of the other aggregators. However, none of them knows how its decision influences the price because the price is determined by the ISO based on its state. We propose a design framework in which the ISO provides each aggregator with a conjectured future price, and each aggregator distributively minimizes its own long-term cost based on its conjectured price as well as its local information. The proposed framework can achieve the social optimum despite being decentralized and involving complex coupling among the various entities

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

Charging Scheduling of Electric Vehicles with Local Renewable Energy under Uncertain Electric Vehicle Arrival and Grid Power Price

In the paper, we consider delay-optimal charging scheduling of the electric vehicles (EVs) at a charging station with multiple charge points. The charging station is equipped with renewable energy generation devices and can also buy energy from power grid. The uncertainty of the EV arrival, the intermittence of the renewable energy, and the variation of the grid power price are taken into account and described as independent Markov processes. Meanwhile, the charging energy for each EV is random. The goal is to minimize the mean waiting time of EVs under the long term constraint on the cost. We propose queue mapping to convert the EV queue to the charge demand queue and prove the equivalence between the minimization of the two queues' average length. Then we focus on the minimization for the average length of the charge demand queue under long term cost constraint. We propose a framework of Markov decision process (MDP) to investigate this scheduling problem. The system state includes the charge demand queue length, the charge demand arrival, the energy level in the storage battery of the renewable energy, the renewable energy arrival, and the grid power price. Additionally the number of charging demands and the allocated energy from the storage battery compose the two-dimensional policy. We derive two necessary conditions of the optimal policy. Moreover, we discuss the reduction of the two-dimensional policy to be the number of charging demands only. We give the sets of system states for which charging no demand and charging as many demands as possible are optimal, respectively. Finally we investigate the proposed radical policy and conservative policy numerically

Leaky Bucket-Inspired Power Output Smoothing with Load-Adaptive Algorithm

The renewables will constitute an important part of the future smart grid. As a result, the growing portion of renewable generation in the power grid will bring challenges to the operations of the power grid because of the fluctuation and intermittency properties of renewables. In order to make the operations of power grid stable and reliable, the power outputs from renewable energy sources must be smoothed. In this paper, we propose a scheme inspired from the idea of the leaky bucket mechanism for smoothing the power output from a renewable energy system. In our proposed method, the settings of energy storage size and power output level have significant effects on the system performance and thus needs to be determined. An optimization framework is thus proposed for storage and power output planning of the renewable energy system. To operate our proposed scheme practically, a load-adaptive power smoothing algorithm is devised aiming to match the power output level with the actual load in the grid. Our simulation studies show that the proposed algorithm can reduce the operation cost comparing to other algorithms and maintain high renewable energy utilization.postprin

Optimal Smart Grid Management System in Campus Building

The utilization of well-managed electrical energy sources will result in high energy efficiency and reliability. Smart grid uses electricity management with 2-way communication that allows loads and sources to corporate each other. Campus is a place that requires priority in the availability of energy and it requires smart grid management. This research will contain smart grid management systems on campus that use multisource to fulfil dynamic loads conditions so as to produce optimal smart grid management. The method that use to analysis the system is conventional method. The optimal smart grid achieved by analysis the sources and loads energy needed and then create a management system that have substantial impact on campus electrical system. The results of this research that smart grid system ensures electrical conditions for the needs of these dynamic loads can be fulfilled which is without a smart grid there is lack of energy for 3 days, whereas with a smart grid there is no lack of energy in the campus building.Keywords : Smart Grid, Campus, Managemen