Abstract

Abstract

Abstract
This paper proposes a comprehensive framework for generation and transmission planning of renewable dominated power systems, which is formulated as a stochastic multi-objective problem. In this regard, a Normalized Normal Constraint (NNC) solution approach is proposed to solve the introduced stochastic multiobjective generation and transmission planning (GTP) problem. The NNC is utilized in this paper as a relation between different objective functions with different dimensions to find the optimal weighting factors of these objectives. The NNC is applied for solving the GTP problem with objective functions including the investment and operation costs along with the transmission losses, while considering the cost of unserved energy, as well as the uncertainty of load and Renewable Energy Resources (RERs). A fuzzy-based decision making framework is utilized to select the best solution among the optimal non-dominated solution points. A scenario-based approach is used to model the uncertainties. The Garver 6-bus and IEEE 118-bus test systems are utilized to perform the numerical analysis. The simulation results validate the performance and importance of the proposed model, as well as the effectiveness of the NNC to find the evenly distributed Pareto solutions of the multiobjective problems.

Abstract
With the increase in the number of electric vehicles (EVs), there might be substantial problems due to the charging transactions in the power system and the balancing between supply and demand sides can be provided in the modern power system by considering EVs as a flexible load. EVs cannot directly participate in buying and selling energy from/to the electricity market because of their relatively low energy and power capacities. In this manner, considering that EVs are generally parked during the day, an EV parking lot (EVPL) can offer economic charging opportunities to EV owners as multiple EVPLs can offer/bid for the buying/selling from/to the electricity market through an EVPL aggregator (EVPLA). In this study, a model in which the EVPLA offers/bids for the day-ahead (DA) and secondary reserve market in order to minimize the total cost is propounded. Furthermore, uncertainties related to the EV owners' behavior and market prices are handled by considering scenarios with real data in a stochastic manner. In addition, the EVPLA also takes into account the comfort of the EV owners when carrying out this operation. The comfort of EV owners as an essential issue similar to serving EV owners more economically is achieved by sustaining the minimum desired charge level by EV owners at the departure time. The results consist of a set of case studies to reveal the effectiveness of the proposed model considering the pricing conditions in Turkey, Finland, and USA-PJM DA and reserve markets. According to the results of the study, it is observed that an EV aggregator participating in DA and RE markets can make a significant profit for the three market conditions. An important result is also that the profit by the participation in reserve markets increases significantly compared to solely DA market participation.

Abstract
Modernization of electricity networks is currently being carried out using the concept of the smart grid; hence, the active participation of end-user consumers and distributed generators will be allowed in order to increase system efficiency and renewable power accommodation. In this context, this paper proposes a comprehensive methodology to optimally control lead-acid batteries operating under dynamic pricing schemes in both independent and aggregated ways, taking into account the effects of the charge controller operation, the variable efficiency of the power converter, and the maximum capacity of the electricity network. A genetic algorithm is used to solve the optimization problem in which the daily net cost is minimized. The effectiveness and computational efficiency of the proposed methodology is illustrated using real data from the Spanish electricity market during 2014 and 2015 in order to evaluate the effects of forecasting error of energy prices, observing an important reduction in the estimated benefit as a result of both factors: 1) forecasting error and 2) power system limitations.

Abstract
The major challenge in coordinating between fast-acting energy storage systems (FA-ESSs) and renewable energy sources (RESs) in the existing transmission grid is to determine the location and capacity of the FA-ESS in the power systems. The optimal allocation of FA-ESS with conventional hourly discrete time method (DTM) can result in the increased operation cost, non-optimal placements and larger storage capacity and therefore, having an opposite effect on the operation. Accordingly, in this paper, a continuous-time method (CTM) is proposed to coordinate FA-ESS and RESs to cover fast fluctuations of renewable generations (RGs). Besides, based on the CTM, an adaptive interval-based robust optimization framework, to deal with uncertainty of the RGs, has been proposed. The proposed optimal allocation of FA-ESS with CTM provides the best sitting and sizing for the installation of the FA-ESSs and the best possible continuous-time scheduling plan for FA-ESSs. Also, in other to have better implementations of their ramping capability to track the continuous-time changes and deviations of the RGs rather than hourly DTM. The proposed model has been implemented and evaluated on the IEEE Reliability Test System (IEEE-RTS).