Abstract

This paper presents a multi-objective energy scheduling for the daily operation of a Smart Grid (SG) considering maximization of the minimum available reserve in addition to the cost minimization, to take into account the reliability requirements of critical and vulnerable loads. A Virtual Power Player (VPP) manages the day-ahead energy resource scheduling in the smart grid, considering Distributed Generation (DG) and Vehicle-To-Grid (V2G), while maintaining a highly reliable power for the critical loads. This work considers high penetration of critical loads, e.g. industrial processes that require high power quality, high reliability and few interruptions. A mathematical formulation is described and a deterministic technique based on Mixed-Integer Linear Programming (MILP) is used to solve the multi-objective problem. The effect of some customers with DR in this context is analyzed to assess the benefits in the energy scheduling problem. A case study using a 180-bus Portuguese distribution network with 90 load points, several DG units and a large fleet of Electric Vehicles (EVs) with V2G is used to illustrate the performance of the proposed method. info:eu-repo/semantics/publishedVersion

Abstract

Energy supply through integrated renewable energy sources (RESs) and battery systems will be of higher importance for future residential sectors. Optimal energy management and sizing for the components of residential systems can enhance efficiency, self-suffiency, and meanwhile can be cost-effective by reducing investment as well as operating costs. Accordingly, this paper proposes an exhaustive optimization model for determining the capacity of RESs, namely: wind turbines and photovoltaic (PV) systems. In this study, batteries and electric vehicles (EVs) are utilized in line with other sources to capture fluctuations of RESs. To model the uncertainties of RESs, energy prices, and load demands a linearized stochastic programming framework is applied. The proposed framework involves long-term and efficient resource development alongside with short-term management and utilization of these resources for supplying the demand load. In our study, we utilize the roulette wheel mechanism (RWM) method as well as proper probability distribution functions (PDFs) to generate scenarios for all sources of uncertainties, including wind turbines, PV systems, demand, and electricity market price. The approach is verified in two different cases, including an individual home and a larger micro-grid (MG). The results of multiple numerical simulations demonstrate the effectiveness of the proposed stochastic model.