The risk of accelerated electric vehicle battery degradation is commonly cited as a concern inhibiting the implementation of vehicle-to-grid (V2G) technology. However, little quantitative evidence exists in prior literature to refute or substantiate these concerns for different grid services that vehicles may offer. In this paper, a methodology is proposed to quantify electric vehicle (EV) battery degradation from driving only vs. driving and several vehicle-grid services, based on a semi-empirical lithium-ion battery capacity fade model. A detailed EV battery pack thermal model and EV powertrain model are utilized to capture the time-varying battery temperature and working parameters including current, internal resistance and state-of-charge (SOC), while an EV is driving and offering various grid services. We use the proposed method to simulate the battery degradation impacts from multiple vehicle-grid services including peak load shaving, frequency regulation and net load shaping. The degradation impact of these grid services is compared against baseline cases for driving and uncontrolled charging only, for several different cases of vehicle itineraries, driving distances, and climate conditions. Ten-year degradation simulation results show that frequency regulation and peak load shaving at power rates typical for vehicle charging and discharging will not significantly accelerate battery degradation in comparison to the degradation incurred from driving and calendar aging.
California has issued ambitious targets to decarbonize transportation through the deployment of electric vehicles (EVs), and to decarbonize the electricity grid through the expansion of both renewable generation and energy storage. These parallel efforts can provide an untapped synergistic opportunity for clean transportation to be an enabler for a clean electricity grid. To quantify this potential, we forecast the hourly system-wide balancing problems arising out to 2025 as more renewables are deployed and load continues to grow. We then quantify the system-wide balancing benefits from EVs modulating the charging or discharging of their batteries to mitigate renewable intermittency, without compromising the mobility needs of drivers. Our results show that with its EV deployment target and with only one-way charging control of EVs, California can achieve much of the same benefit of its Storage Mandate for mitigating renewable intermittency, but at a small fraction of the cost. Moreover, EVs provide many times these benefits if two-way charging control becomes widely available. Thus, EVs support the state's renewable integration targets while avoiding much of the tremendous capital investment of stationary storage that can instead be applied towards further deployment of clean vehicles.
The increased integration of Distributed Energy Resources (DERs) is bringing a number of challenges to the power grid. These include reverse power flows in distribution systems and potentially transmission systems, and grid stability. So far, specialized tools have been developed to capture some of the impact of DERs at the distribution level. However, Distribution System Operators (DSOs) lack visibility into the overall system conditions. Furthermore, the impact of increasing DERs is not limited to the distribution level but also influences the transmission grid. To support the planning and operation of the grid, we developed a co-simulation platform called CyDER (A Cyber Physical Co-simulation Platform for Distributed Energy Resources in Smart Grids) that integrates various domain-specific simulation tools. CyDER is based on the Functional Mock-up Interface (FMI) standard. This paper gives an overview of CyDER and demonstrates its use based on two applications.
A blockchain is designed to make consistent and reliable agreement in an unreliable and decentralized environment. It also permits processing transactions, making smart contracts, which allows end users to perform the contracts without any intermediate entities. However, there are some challenges in retrieving the state in a smart contract on the blockchain. For example, an external database or user-defined data structures can be used to retrieve the data from a smart contract in a range, which can increase the management overhead and decrease the overall performance of the blockchain system. In this paper, we propose a scheme that enables SQL query operations in a blockchain system. In our proposed scheme, the register and query managers provide fast retrieval of range data without any user-defined data structure, and management at low cost without any external database, respectively. We have implemented our scheme on quorum which is an Ethereum-based blockchain system and evaluated it using a synthetic benchmark. The experimental results show that our system can improve the search performance up to about 22x compared with the existing system with low memory usage.
The increased penetration of Distributed Energy Resources (DERs) on the distribution network creates local challenges in balancing consumption and generation. To coordinate the roll-out and the operation of DERs, distribution-level energy markets have been proposed, but there are currently few tools for simulating the operation of DERs in these proposed markets. We present a framework which utilizes a grid cosimulation platform (Mosaik) to simulation DER operation, while simulating market clearing operations with a blockchain network (Ethereum). The use of blockchains, an emerging technology for decentralized computing and data storage, allows us to model secure decentralized execution of market clearing functions and payment processes. By unifying simulation of market clearing rules and the physical grid, we are able to ensure that economic incentives are aligned with physical constraints, helping facilitate the development of more effective distributed energy markets. We demonstrate the use of this new simulation platform on a small feeder, for which a market mechanism to incentivize DER integration is explored.
Purpose This paper aims to consider both the greenhouse gas (GHG) emissions and behavioural response in the optimal sizing of solar photovoltaic systems (PV modules and batteries) for energy communities. The objective is to achieve a high self-sufficiency rate whilst taking into account the grid carbon intensity and the global warming potential of system components. Design/methodology/approach Operation and sizing of energy communities leads to optimization problems spanning across multiple timescales. To compute the optimisation in a reasonable time, the authors first apply a simulation periods reduction using a clustering approach, before solving a linear programming problem. Findings The results show that the minimum GHG emissions is achieved for self-sufficiency rates of 19% in France and 50% in Germany. Research limitations/implications The analysis is restricted to specific residential profiles: further work will focus on exploring different types of consumption profiles. Practical implications This paper provides relevant self-sufficiency orders of magnitude for energy communities. Originality/value This paper combines various approaches in a single use case: environmental considerations, behavioural response as well as multi-year energy system sizing.
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