A Regulation Policy of EV Discharging Price for Demand Scheduling

Mao, Tian; Lau, Wing Cheong; Shum, Chong; Chung, H.S.H.; Tsang, Kim-Fung; Tse, Norman C. F.

doi:10.1109/tpwrs.2017.2727323

Cited by 50 publications

(20 citation statements)

References 36 publications

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“…The real-time pricing policy for V2G is determined in accordance with the wholesale energy market or the real-time supply and demand condition by EVs charging and discharging [11]. Especially, there also exist some V2G pricing policies that provide discriminative incentives to EV users who permit the discharging in support of the grid operations [25]. Please note that no matter what policy the utility price and the V2G price follow, they are changing stochastically depending on the time of day, typically holding high values during the daytime and low values during the nighttime.…”

Section: Overall System Structurementioning

confidence: 99%

Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

2018

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A smart grid facilitates more effective energy management of an electrical grid system. Because both energy consumption and associated building operation costs are increasing rapidly around the world, the need for flexible and cost-effective management of the energy used by buildings in a smart grid environment is increasing. In this paper, we consider an energy management system for a smart energy building connected to an external grid (utility) as well as distributed energy resources including a renewable energy source, energy storage system, and vehicle-to-grid station. First, the energy management system is modeled using a Markov decision process that completely describes the state, action, transition probability, and rewards of the system. Subsequently, a reinforcement-learning-based energy management algorithm is proposed to reduce the operation energy costs of the target smart energy building under unknown future information. The results of numerical simulation based on the data measured in real environments show that the proposed energy management algorithm gradually reduces energy costs via learning processes compared to other random and non-learning-based algorithms.

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Section: Overall System Structurementioning

confidence: 99%

Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

2018

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“…(2) reactive power exchange is not considered at the current stage; (3) the price for EV charging and other loads is different from the V2G power price for incentive purposes [17], i.e., the power load price ϑL,t and the V2G power price ϑV2G,t are two different price signals; (4) the charging/discharging power is assumed to be the rated power of the EV battery.…”

Section: Modeling For V2a Charging Modementioning

confidence: 99%

“…Therefore, all the charging statuses, including charging, V2H/V2B/V2V, and V2G should be determined at each timeslot. Meanwhile, for the development of the proposed model, the following assumptions are used: (1) The EV vehicles studied are pure EVs; (2) reactive power exchange is not considered at the current stage; (3) the price for EV charging and other loads is different from the V2G power price for incentive purposes [17], i.e., the power load price ϑ L,t and the V2G power price ϑ V2G,t are two different price signals; (4) the charging/discharging power is assumed to be the rated power of the EV battery.…”

Section: Modeling For V2a Charging Modementioning

confidence: 99%

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Modeling and Solving Method for Supporting ‘Vehicle-to-Anything’ EV Charging Mode

2018

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Electric vehicles (EVs) are an attractive solution to make traditional transportation energy-efficient and environmentally friendly. EVs are also considered an important element to bring new opportunities for power grids. In addition to the role of a pure load, along with the development of discharging technology for batteries, EVs can deliver energy back to power grids or another power consumption entity or community, performing various discharging activities such as vehicle-to-home (V2H), vehicle-to-building (V2B), vehicle-to-vehicle (V2V), vehicle-to-grid (V2G), etc. These charging scenarios can be uniformly named the 'Vehicle-to-anything' (V2A) charging mode. The paradigm of this charging mode has already been given; however, the modeling and solving approaches are still insufficient. Therefore, the analysis and modeling of V2A are investigated herein. The aim is to propose a generic model suitable for different applications of V2A in different places. The characteristics of the V2A charging mode are given and analyzed first. Then a model that can adapt to the V2A scenario is illustrated. The optimization problem is transformed and solved as an INLP (integer nonlinear programming) problem. The effectiveness of the approach is verified with simulation results. The outcome also indicates that V2A applications have great potential for lowering power consumption costs; for instance, a reduction of 4.37% in energy expense can be achieved in a case study with EV discharging enabled for a household.

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“…In the meantime, the objective functions (e.g., cost minimization, load smoothing) can be also formed or reformulated as convex functions. Hence, the EV scheduling issue can be modeled as a convex optimization problem and solved through CVX [15], GAMS [16] or CPLEX [17]. However, these tools come with license concerns, i.e., one must pay for their license to legally use these tools.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Energy Management Algorithms for EV-charging Scheduling with Consideration of Multiple EV Charging Modes

2019

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Electric vehicles (EVs) are now attracting increasing interest from both industries and countries as an environmentally friendly and energy efficient mode of travel. Therefore, the EV charging and/or discharging issue has become an important challenge and research topic in power systems in recent years. An advanced and economic EV charging process, however, should employ smart scheduling, which depends on effective and robust algorithms. To that end, a comprehensive intelligent scatter search (ISS) algorithm within the frame of a basic scatter search has been designed with both unidirectional and bidirectional charging considered. The ISS structure also supports both a flexible and constant charging power rate by respectively employing filter-SQP (sequential quadratic programming) and mixed-integer SQP as local solvers with module control. The detailed design of ISS is presented and the objectives of smoothing the daily load profile and minimizing the charging cost have been tested. Compared with methods based on GS (global search), GA (genetic algorithm), and PSO (particle swarm optimization), the outcome-verified ISS can produce attractive results with a significantly short computational time. Moreover, to handle a large scale EV charging scenario, a hybrid method comprised of a GA and ISS approach has been further developed. Simulation results also verified its prominent performance, plus superbly low computational time.

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A Regulation Policy of EV Discharging Price for Demand Scheduling

Cited by 50 publications

References 36 publications

Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

Modeling and Solving Method for Supporting ‘Vehicle-to-Anything’ EV Charging Mode

Intelligent Energy Management Algorithms for EV-charging Scheduling with Consideration of Multiple EV Charging Modes

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