12 research outputs found
Numerical Analysis of National Travel Data to Assess the Impact of UK Fleet Electrification
Accurately predicting the future power demand of electric vehicles is
important for developing policy and industrial strategy. Here we propose a
method to create a representative set of electricity demand profiles using
survey data from conventional vehicles. This is achieved by developing a model
which maps journey and vehicle parameters to an energy consumption, and
applying it individually to the entire data set. As a case study the National
Travel Survey was used to create a set of profiles representing an entirely
electric UK fleet of vehicles. This allowed prediction of the required
electricity demand and sizing of the necessary vehicle batteries. Also, by
inferring location information from the data, the effectiveness of various
charging strategies was assessed. These results will be useful in both National
planning, and as the inputs to further research on the impact of electric
vehicles
Improving the Scalability of a Prosumer Cooperative Game with K-Means Clustering
Among the various market structures under peer-to-peer energy sharing, one
model based on cooperative game theory provides clear incentives for prosumers
to collaboratively schedule their energy resources. The computational
complexity of this model, however, increases exponentially with the number of
participants. To address this issue, this paper proposes the application of
K-means clustering to the energy profiles following the grand coalition
optimization. The cooperative model is run with the "clustered players" to
compute their payoff allocations, which are then further distributed among the
prosumers within each cluster. Case studies show that the proposed method can
significantly improve the scalability of the cooperative scheme while
maintaining a high level of financial incentives for the prosumers.Comment: 6 pages, 4 figures, 2 tables. Accepted to the 13th IEEE PES PowerTech
Conference, 23-27 June 2019, Milano, Ital
Numerical Comparisons of Linear Power Flow Approximations: Optimality, Feasibility, and Computation Time
Linear approximations of the AC power flow equations are of great
significance for the computational efficiency of large-scale optimal power flow
(OPF) problems. Put differently, the feasibility of the obtained solution is
essential for practical use cases of OPF. However, most studies focus on
approximation error and come short of comprehensively studying the AC
feasibility of different linear approximations of power flow. This paper
discusses the merits of widely-used linear approximations of active power in
OPF problems. The advantages and disadvantages of the linearized models are
discussed with respect to four criteria; accuracy of the linear approximation,
optimality, feasibility, and computation time. Each method is tested on five
different systems
Modelling of the Ability of a Mixed Renewable Generation Electricity System with Storage to Meet Consumer Demand
In this paper, we explore how effectively renewable generation can be used to meet a country’s electricity demands. We consider a range of different generation mixes and capacities, as well as the use of energy storage. First, we introduce a new open-source model that uses hourly wind speed and solar irradiance data to estimate the output of a renewable electricity generator at a specific location. Then, we construct a case study of the Great Britain (GB) electricity system as an example using historic hourly demand and weather data. Three specific sources of renewable generation are considered: offshore wind, onshore wind, and solar PV. Li-ion batteries are considered as the form of electricity storage. We demonstrate that the ability of a renewables-based electricity system to meet expected demand profiles can be increased by optimising the ratio of onshore wind, offshore wind and solar PV. Additionally, we show how including Li-ion battery storage can reduce overall generation needs, therefore lowering system costs. For the GB system, we explore how the residual load that would need to be met with other forms of flexibility, such as dispatchable generation sources or demand-side response, varies for different ratios of renewable generation and storage
The opportunity for smart charging to mitigate the impact of electric vehicles on transmission and distribution systems
A rapid increase in the number of electric vehicles is expected in coming years, driven by government incentives and falling battery prices. Charging these vehicles will add significant load to the electricity network, and it is important to understand the impact this will have on both the transmission and distribution level systems, and how smart charging can alleviate it. Here we analyse the effects that charging a large electric vehicle fleet would have on the power network, taking into account the spatial heterogeneity of vehicle use, electricity demand, and network structure. A conditional probability based method is used to model uncontrolled charging demand, and convex optimisation is used to model smart charging. Stochasticity is captured using Monte Carlo simulations. It is shown that for Great Britain’s power system, smart charging can simultaneously eliminate the need for additional generation infrastructure required with 100% electric vehicle adoption, while also reducing the percentage of distribution networks which would require reinforcement from 28% to 9%. Discussion is included as to how far these results can be extended to other power systems
Modelling of the Ability of a Mixed Renewable Generation Electricity System with Storage to Meet Consumer Demand
In this paper, we explore how effectively renewable generation can be used to meet a country’s electricity demands. We consider a range of different generation mixes and capacities, as well as the use of energy storage. First, we introduce a new open-source model that uses hourly wind speed and solar irradiance data to estimate the output of a renewable electricity generator at a specific location. Then, we construct a case study of the Great Britain (GB) electricity system as an example using historic hourly demand and weather data. Three specific sources of renewable generation are considered: offshore wind, onshore wind, and solar PV. Li-ion batteries are considered as the form of electricity storage. We demonstrate that the ability of a renewables-based electricity system to meet expected demand profiles can be increased by optimising the ratio of onshore wind, offshore wind and solar PV. Additionally, we show how including Li-ion battery storage can reduce overall generation needs, therefore lowering system costs. For the GB system, we explore how the residual load that would need to be met with other forms of flexibility, such as dispatchable generation sources or demand-side response, varies for different ratios of renewable generation and storage