Implementation of Hour-by-Hour Restoration Plan for Electrical Distribution Networks to improve Network Resiliency

After extreme natural events like earthquakes, storms, and floods, the electrical distribution network may be isolated from the upstream grid. In such a situation, the electrical power of the upstream grid is lost and the available microgrids (MGs) are the only power sources. Since the power outputs of MGs are restricted, restoring all de-energized loads is impossible and restoring critical loads (CLs) becomes the most important concern of the distribution network operators. Therefore, in this paper, a new hour-by-hour restoration strategy for CLs restoration after a blackout is proposed. The desired objective functions include restored energy and switching operations. The proposed restoration strategy presents a restoration plan for each hour of the outage period. The efficiency of the proposed strategy is carried out on an IEEE 123-bus distribution network and the simulation results confirm the superiority of the proposed hour-by-hour restoration strategy over the other conventional restoration methods

Bi-level model for operational scheduling of a distribution company that supplies electric vehicle parking lots

Nowadays, the presence of renewable energy resources (RERs), electric vehicle (EV) penetration, and the implementation of demand response (DR) programs are the main affecting factors in the operational scheduling of a distribution company (DISCO). By the new market participants such as parking lot (PL) owners in the DISCO, a bi-level framework can be created for modeling the distribution network. Therefore, in this paper, a new bi-level model is suggested for DISCO’s operational scheduling that involves technical and environmental terms in the objective function. The maximization of the profit of the DISCO owner and the PL owner are the objective functions in each level. These purposes depend on the customers’ load, the power purchased from the upstream network, the power exchanged with the PL owner (for the upper-level) and the power exchanged with the DISCO owner, as well as the EV owners (for the lower-level). Linearization of the model is carried out by applying the Karush–Kuhn–Tucker (KKT) condition and Fortuny-Amat and McCarl linearization approach. Furthermore, EVs’ and RERs’ uncertainties, as well as DR programs are modeled. Also, three types of risk are described including risk-seeker, risk-neutral, and risk-averse (with conditional value-at-risk (CVaR) index). For evaluation of the proposed model, it is applied to the IEEE 15-bus test system. Results show that by charging/discharging schedule of EVs and critical peak pricing program, the DISCO owner gains more profit. Also, the sensitivity analysis allows determining that the EV penetration, nominal power of RERs and customer involvement in the DR program directly affect the DISCO owner’s profit.© 2019 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Optimal charge scheduling of electric vehicles in solar energy integrated power systems considering the uncertainties

Nowadays, vehicle to grid (V2G) capability of the electric vehicle (EV) is used in the smart distribution network (SDN). The main reasons for using the EVs, are improving air quality by reducing greenhouse gas emissions, peak demand shaving and applying ancillary service, and etc. So, in this chapter, a non-linear bi-level model for optimal operation of the SDN is proposed where one or more solar based-electric vehicle parking lots (PLs) with private owners exist. The SDN operator (SDNO) and the PL owners are the decision-makers of the upper-level and lower-level of this model, respectively. The objective functions at two levels are the SDNO’s profit maximization and the PL owners’ cost minimization. For transforming this model into the single-level model that is named mathematical program with equilibrium constraints (MPEC), firstly, Karush–Kuhn–Tucker (KKT) conditions are used. Furthermore, due to the complementary constraints and non-linear term in the upper-level objective function, this model is linearized by the dual theory and Fortuny-Amat and McCarl linearization method. In the following, it is assumed that the SDNO is the owner of the solar-based EV PLs. In this case, the proposed model is a single-level model. The uncertainty of the EVs and the solar system, as well as two programs, are considered for the EVs, i.e., controlled charging (CC) and charging/discharging schedule (CDS). Because of the uncertainties, a risk-based model is defined by introducing a Conditional Value-at-Risk (CVaR) index. Finally, the bi-level model and the single-level model are tested on an IEEE 33-bus distribution system in three modes; i.e., without the EVs and the solar system, with the EVs by controlled charging and with/ without the solar system, and with the EVs by charging/discharging schedule and with/without the solar system. The main results are reported and discussed.fi=vertaisarvioitu|en=peerReviewed

Risk-Based Bi-Level Model for Simultaneous Profit Maximization of a Smart Distribution Company and Electric Vehicle Parking Lot Owner

In this paper, the effect of renewable energy resources (RERs), demand response (DR) programs and electric vehicles (EVs) is evaluated on the optimal operation of a smart distribution company (SDISCO) in the form of a new bi-level model. According to the existence of private electric vehicle parking lots (PLs) in the network, the aim of both levels is to maximize the profits of SDISCO and the PL owners. Furthermore, due to the uncertainty of RERs and EVs, the conditional value-at-risk (CVaR) method is applied in order to limit the risk of expected profit. The model is transformed into a linear single-level model by the Karush–Kuhn–Tucker (KKT) conditions and tested on the IEEE 33-bus distribution system over a 24-h period. The results show that by using a proper charging/discharging schedule, as well as a time of use program, SDISCO gains more profit. Furthermore, by increasing the risk aversion parameter, this profit is reduced