Mushfiqur R. Sarker

Optimal Operation and Services Scheduling for an Electric Vehicle Battery Swapping Station

For a successful rollout of electric vehicles (EVs), it is required to establish an adequate charging infrastructure. The adequate access to such infrastructure would help to mitigate concerns associated with limited EV range and long charging times. Battery swapping stations are poised as effective means of eliminating the long waiting times associated with charging the EV batteries. These stations are mediators between the power system and their customers. In order to successfully deploy this type of stations, a business and operating model is required, that will allow it to generate profits while offering a fast and reliable alternative to charging batteries. This paper proposes an optimization framework for the operating model of battery swapping stations. The proposed model considers the day-ahead scheduling process. Battery demand uncertainty is modeled using inventory robust optimization, while multi-band robust optimization is employed to model electricity price uncertainty. The results show the viability of the proposed model as a business case, as well as the effectiveness of the model to provide the required service.

Optimal Participation of an Electric Vehicle Aggregator in Day-Ahead Energy and Reserve Markets

An aggregator acts as a mediator between the system operator and residential customers, enabling mutually beneficial coordination for electric vehicle (EV) owners and the power system. The aggregator aims to maximize its profits from trading energy and regulation reserve in wholesale markets. Since the aggregator does not own EV batteries, the EV owners must be reimbursed for the degradation of their batteries due to the additional cycling beyond transportation needs. This paper proposes a bidding strategy for the aggregator to maximize its profits from participating in competitive energy and different regulating reserves markets, while compensating EV owners for degradation. The results show that depending on the battery cost, the aggregator splits its resources between the energy and reserves markets. The results also show the system operator attains cost savings, if an aggregator uses EVs to provide services.

Electric vehicle battery swapping station: Business case and optimization model

In order to increase the adoption rate of electric vehicles, they need to appeal to customers as much as their fossil fuel equivalents. However, major concerns include long battery charging times and range anxiety. These concerns can be mitigated if customers have access to battery swapping stations, where they can meet their motion energy requirements by swapping batteries for charged ones, in as much time as it takes to fill the gasoline reservoir of a conventional vehicle. Besides benefiting the customers, the battery swapping station is beneficial to the power system because it emulates an energy storage station capable of participating in electricity markets. In this station, the batteries can be scheduled to charge in grid-to-battery mode, inject power to the grid in battery-to-grid mode, and transfer energy between batteries in battery-to-battery mode, if there are economic advantages in doing so. This paper discusses how these various modes can be optimized and the results translated into a business case for battery swapping stations.

Decentralized Coordination of a Building Manager and an Electric Vehicle Aggregator

The ability to control commercial buildings and electric vehicles (EVs) is a promising source of demand flexibility. In some cases, buildings and EVs share common infrastructure (e.g., a transformer) or interact with each other to accomplish a goal (e.g., reduce local peak demand). In such cases, the building and EV demand scheduling problems are effectively a single demand scheduling problem. Ideally, it would be solved as a single optimization problem. However, doing so might not be possible due to a number of concerns (e.g., data privacy). This paper proposes the use of a mixed-integer adaptation of the Dantzig–Wolfe decomposition to solve the building-EV demand scheduling problem in a decentralized fashion. The effectiveness of the proposed methodology is demonstrated in three case studies, where the building and EV problems are coupled by either: 1) demand limits; 2) a peak demand charge; or 3) an itemized billing tariff. Results show that the optimal solution can be reached while sharing a minimal amount of information. Furthermore, we show that the proposed methodology is scalable.

Statistical characterization of electric vehicle charging in different locations of the grid

The advent of electric vehicles (EVs) will bring forth large increases to the pre-existing demand in the power grid. Adverse impacts to the system will arise if the charging of these EVs is uncontrolled. In order to mitigate this challenge, as a first step the estimation of the additional power due to EV charging is crucial. The estimation is dependent upon the temporal (i.e. time) and spatial (i.e. location) characteristics of the EV charging process. A tool is developed in this work, which estimates the additional demand using Monte Carlo simulations performed on a large fleet of EVs over several days. The simulations include EV travel data within predefined residential, workplace, and commercial zones that are determined using traffic flow information. This tool can be used by system operators and other entities to determine the opportunities and challenges posed by additional EV demand. The results show the power consumptions at each hour of the day can be modelled by a normal distribution, thus simplifying the estimation procedure.

Incorporating storage as a flexible transmission asset in power system operation procedure

Managing uncertainty caused by the large-scale integration of wind power is a challenge in both the day-ahead planning and real-time operation of a power system. Increasing system flexibility is the key factor in preserving operational reliability. While distributed energy storage is a promising way to increase system flexibility, its benefits have to be optimally exploited to justify its high installation cost. Optimally operating distributed energy storage in an uncertain environment requires decisions on multiple time scales. Additionally, storage operation needs to be coordinated with the scheduling and dispatching of conventional generators. This paper proposes and demonstrates a three-level framework for coordinating day-ahead, near real-time and minute-by-minute control actions of conventional generating units and distributed energy storage. A case study illustrates the interactions between the three levels and the effectiveness of this approach both in terms of economics and operational reliability.

Optimal participation of an electric vehicle aggregator in day-ahead energy and reserve markets

An aggregator acts as a mediator between the system operator and residential customers, enabling mutually beneficial coordination for electric vehicle (EV) owners and the power system. The aggregator aims to maximize its profits from trading energy and regulation reserve in wholesale markets. Since the aggregator does not own EV batteries, the EV owners must be reimbursed for the degradation of their batteries due to the additional cycling beyond transportation needs. This paper proposes a bidding strategy for the aggregator to maximize its profits from participating in competitive energy and different regulating reserves markets, while compensating EV owners for degradation. The results show that depending on the battery cost, the aggregator splits its resources between the energy and reserves markets. The results also show the system operator attains cost savings, if an aggregator uses EVs to provide services.

Optimal Penetration of Home Energy Management Systems in Distribution Networks Considering Transformer Aging

In the near future, home energy management systems (HEMSs) will become common in reaction to time-varying pricing, and residential electricity customers’ striving to minimize their energy costs. However, if many residences connected to a common pole-top transformer are all optimizing their electricity consumption based on the same electricity tariff, their aggregated demand may result in large, short-lived peaks. Traditional demand peaks would be reduced since the electricity prices are high, but early morning (low price periods) demand peaks would be increased. Since high demand increases transformer temperatures and degrades their insulation, managing these peaks extends transformer life and reduces network upkeep costs. This paper presents an approach for managing the rollout of HEMSs on a distribution feeder. Several heuristic strategies for incentivizing customer adoption of HEMSs are investigated and compared to the optimal adoption strategy. A case study using representative data is conducted and the results show that transformer aging costs can be reduced by an order of magnitude by managing adoption rates and strategies. When incentivization costs are included, the optimal HEMS penetration and net benefit are reduced, but a value proposition still exists for targeted HEMS adoption.

Co-Optimization of Distribution Transformer Aging and Energy Arbitrage Using Electric Vehicles

The advent of electric vehicles (EVs) will bring forth increases in power transmitted over longer periods of time through the distribution power grid. Such an effect will result in accelerated loss-of-life of distribution grid assets including pole-top transformers. As preventive and corrective measures, the charging of the set of EVs connected to a particular pole-top transformer can be centrally managed (e.g., by a distribution system operator or independent aggregator). This paper proposes a centralized model to co-optimize the transformer loss-of-life with the benefits for EVs’ owners on charging/discharging management. The proposed model is compared against a decentralized optimization model in which EVs’ owners optimize their benefits, while ignoring the effect on the transformer. Results show the benefit of the centralized strategy in maintaining the grid assets, while modestly reducing consumers’ arbitrage benefits.

Optimal investment strategy in photovoltaics and energy storage for commercial buildings

In order to attain higher degrees of energy efficiency and lower energy consumption costs, buildings stakeholders are installing local photovoltaic (PV) renewable generation and energy storage (ES). The stakeholders, however, need to determine the sizing capacity of these resources in order to economically invest. The sizing of the resources result in to investment costs, which must be economically justified for them in the long-run. This only occurs if the savings obtained from the resources surpass the investment costs. An optimization model can incorporate these criteria along with the consideration of the time value of money and various battery techno-economic parameters (e.g. life expectancy, efficiencies, and costs) to determine the optimal capacities. The results show the value of the proposed model in assisting the stakeholder investment process.