Abstract
This work proposes a novel mixed-integer linear programming model for the medium-term multistage planning of active distribution systems and electric vehicle charging stations (EVCSs). Investment alternatives include the installation of conductors, capacitor banks, voltage regulators, dispatchable and nondispatchable distributed generation, energy storage units, and EVCSs. Hence, the model identifies the best size, location, and installation time for the candidate assets under the uncertainty associated with electricity demand, energy prices, renewable energy sources, and EVCSs’ load profiles. Unlike classical planning approaches, conventional load is modeled as voltage-dependent. Besides, EVCSs are planned by zones to optimize the coverage of the service provided to users of electric vehicles and to reduce the discrepancy between the geographical requirements and the optimal locations for the installation of EVCSs in the system. EVCSs’ load profiles are calculated using a travel simulation algorithm based on real travel patterns that consider fast, slow, and residential chargers. Moreover, as another salient feature, constraints for CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions are incorporated into the model. The resulting model is formulated as a stochastic scenario-based program, which is driven by the minimization of the total expected cost. Tests are conducted using a 69-node system to demonstrate the effectiveness of the proposed model.
Highlights
CONCERNS on global warming and the depletion of fossil fuels are causing the electricity and transportation industries, the largest consumers of fossil fuels, to change their modus operandi in order to reduce environmental pollution and dependence on oil [1]
The population census data can be used to estimate the number of electric vehicles (EVs) adopted in each zone. is allows the proposed optimization problem to determine the best size, location, and installation time of electric vehicle charging stations (EVCSs) for each zone. e proposed approach aims to reduce the existing discrepancy between the optimal location in the distribution system and the appropriate geographic location for the EVCSs installation. e aspects described above have not been explored as alternatives to solving the problem addressed in this work, and the results show that considering them has a significant impact on the total cost of the distribution system planning (DSP). e remainder of this paper is organized as follows: Section II shows the method used to estimate the EVs’ charging demand
A multistage mixed-integer linear programming model is proposed in this paper to address the problem of medium-term planning of active distribution systems and electric vehicle charging stations (EVCSs). e model considers several investment alternatives, such as the replacement of conductors, installation of capacitor banks, voltage regulators, dispatchable and renewable distributed generation, and energy storage units. e uncertainties, e.g., wind speed, solar irradiation, electricity demand, energy prices, and EVCSs load profiles, are characterized through a set of scenarios
Summary
CONCERNS on global warming and the depletion of fossil fuels are causing the electricity and transportation industries, the largest consumers of fossil fuels, to change their modus operandi in order to reduce environmental pollution and dependence on oil [1]. E system operation is represented using a linearized ac power flow formulation Uncertainties, such as electricity demand, nondispatchable renewable generation, energy prices at the substations, and EVCSs’ load profiles are addressed through a scenario-based stochastic programming model. Incorporate the scenarios’ data within the mixed-integer linear programming model to solve the multistage medium-term planning problem of active distribution networks and EVCSs considering the voltage-dependent load behavior. MATHEMATICAL MODELING OF THE PROBLEM e proposed model is based on the following premises: (i) the planning horizon is divided into |Ω | stages, (ii) representative scenarios are used to model the annual variation of the uncertain parameters under consideration; (iii) a linearized ac model is used to represent the steady-state operation of the network; (iv) the distribution network operates with a radial topology; (v) a ZIP polynomial representation is used to model the conventional load as voltage-dependent; (vi) there is an environmental policy that. It is considered that the infrastructure for EVCSs can be installed only at candidate nodes of the distribution system. ese candidate nodes are determined according to their proximity to the vehicular traffic network
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