EV Charging Station Siting and Capacity in Active Distribution Networks
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Solution Overview
Problem
The complexity of power distribution networks is increased by distributed generation and electric vehicles, requiring a planning model that considers location and capacity determination of electric vehicle charging stations to balance energy demands and ensure network stability.
Innovation Solution
A method is developed to establish an active distribution network planning model that integrates traffic and power networks, using queuing models and time-series methods to determine EV charging station capacity and location, and couples traffic flow with power grids to optimize energy storage and distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If EV charging stations are added to meet increasing electric vehicle charging demands, then charging service coverage and convenience are improved, but construction costs and network complexity increase
Solution Approach 1:
The patent segments the distribution network into multiple planning zones and divides EV charging station planning into hierarchical levels (regional centers, community stations, roadside stations). This segmentation allows independent optimization of each zone while reducing overall system complexity and construction costs.
Solution Approach 2:
The patent implements location-specific charging station configurations based on local EV traffic patterns, road types, and power grid conditions. Different station types (fast charging, slow charging, battery swapping) are deployed according to local demands, optimizing service coverage while avoiding unnecessary construction costs in each specific location.
2Loss of energy
If distributed generation and energy storage devices are integrated to balance DG output and load demands, then energy complementation and cost reduction are achieved, but system complexity and planning difficulty increase
Solution Approach 1:
The patent merges distributed generation, energy storage devices, and EV charging stations into integrated local microgrids. This combining allows seamless energy complementation where DG supplies power during peak generation periods and energy storage devices balance fluctuations, achieving efficient energy utilization while managing complexity through unified control systems.
Solution Approach 2:
The patent designs multi-functional energy nodes that can simultaneously perform power generation, energy storage, and EV charging functions. These universal nodes reduce overall system complexity by consolidating multiple functions into single integrated units rather than separate independent systems.
3Loss of time
If charging station capacity is increased to reduce waiting time for EV users, then service quality and user satisfaction are improved, but construction costs and power grid load increase
Solution Approach 1:
The patent implements dynamic charging station capacity allocation based on real-time EV arrival rates, time of day, and grid conditions. During peak periods, additional charging resources are activated while during off-peak periods, capacity is scaled back. This dynamic adjustment reduces average waiting times without requiring permanently oversized infrastructure, thereby controlling construction costs.
4Reliability
If multiple planning objectives (economic cost, voltage quality, traffic satisfaction) are simultaneously optimized, then comprehensive planning quality is improved, but computational complexity and model difficulty increase
Solution Approach 1:
The patent transforms the multi-objective optimization problem into a multi-dimensional hierarchical evaluation framework. Economic cost, voltage quality, and traffic satisfaction are evaluated as separate dimensions with assigned weights, allowing comprehensive planning quality assessment without requiring complex simultaneous optimization calculations. This dimensional approach simplifies the computational model while maintaining comprehensive evaluation capability.
Data Source
AI summary
Disclosed is a method for establishing an active distribution network planning model considering location and capacity determination of an electric vehicle charging station. In terms of location and capacity determination of electric vehicle charging stations, a traffic flow of electric vehicles is converted into a charging demand flowing in a traffic network, and an electric vehicle traffic network model is established based on an M/M/s queuing model and a flow capturing location model in the traffic field. A distributed generation (including wind power and photovoltaic) and load model is established, based on a time series method. An energy storage element model in a power distribution network is established based on an idea of equivalent load. A nested planning model is established by taking economy and reliability of a power distribution network and a maximum traffic flow intercepted by the electric vehicle charging stations as objectives.