Journal of Modern Power Systems and Clean Energy

Global warming and climate change are two key probing issues in the present context. The electricity sector and transportation sector are two principle entities propelling both these issues. Emissions from these two sectors can be offset by switching to greener ways of transportation through the electric vehicle (EV) and renewable energy technologies (RET). Thus, effective scheduling of both resources holds the key to sustainable practice. This paper presents a scheduling scenario-based approach in the smart grid. Problem formulation with dual objective function including both emissions and cost is developed for conventional unit commitment with EV and RET deployment. In this work, the scheduling and commitment problem is solved using the fireworks algorithm which mimics explosion of fireworks in the sky to define search space and the distance between associated sparks to evaluate global minimum. Further, binary coded fireworks algorithm is developed for the proposed scheduling problem in the smart grid. Thereafter, possible scenarios in conventional as well as smart grid are put forward. Following that, the proposed methodology is simulated using a test system with thermal generators.

Microgrids have presented themselves as an effective concept to guarantee a reliable, efficient and sustainable electricity delivery during the current transition era from passive to active distribution networks. Moreover, microgrids could offer effective ancillary services (AS) to the power utility, although this will not be possible before the traditional planning and operation methodologies are updated. Hence, a probabilistic multi-objective microgrid planning (POMMP) methodology is proposed in this paper to contemplate the large number of variables, multiple objectives, and different constraints and uncertainties involved in the microgrid planning. The planning methodology is based on the optimal size and location of energy distributed resources with the goal of minimizing the mismatch power in islanded mode, while the residual power for AS provision and the investment and operation costs of the microgrid in grid-connected mode are maximized and minimized, respectively. For that purpose, probabilistic models and a true multi-objective optimization problem are implemented in the methodology. The methodology is tested in an adapted PG&E 69-bus distribution system and the non-dominated sorting genetic algorithm II (NSGA-II) optimization method and an analytic hierarchy process for decision-making are used to solve the optimization problem.