Optimal Energy Management of Railroad Electrical Systems with Renewable Energy and Energy Storage Systems

Abstract

The proposed optimal energy management system balances the energy flows among the energy consumption by accelerating trains, energy production from decelerating trains, energy from wind and solar photovoltaic (PV) energy systems, energy storage systems, and the energy exchange with a traditional electrical grid. In this paper, an AC optimal power flow (AC-OPF) problem is formulated by optimizing the total cost of operation of a railroad electrical system. The railroad system considered in this paper is composed of renewable energy resources such as wind and solar PV systems, regenerative braking capabilities, and hybrid energy storage systems. The hybrid energy storage systems include storage batteries and supercapacitors. The uncertainties associated with wind and solar PV powers are handled using probability distribution functions. The proposed optimization problem is solved using the differential evolution algorithm (DEA). The simulation results show the suitability and effectiveness of proposed approach.

Optimal Operation of Electrified Railways with Renewable Sources and Storage

“…In this paper, minimization of total operating cost (TOC) of railway system is considered as the objective function, and it is solved subjected to various equality and inequality constraints. The TOC of electrified railway system consisting of cost of power generation from the external power system (i.e., from/to main utility grid), cost of power obtained from RERs such as wind and solar PV sources, cost of power from storage systems such as battery storage and supercapacitors, and the income obtained by selling excess power back to the main electrical grid [17]. This TOC minimization objective function can be formulated as, minimize, total operating cost (TOC), i.e.,…”

“…Last term in equation is the income obtained by selling excess power back to power network. This is calculated by using [17],…”

Method for Building Information Modeling Supported Project Control of Nearly Zero-Energy Building Delivery

“…Appendix I from [1,2] defines the framework for European Union member states to define the performance criteria that define NZEB. Outside the EU, NZEB energy criteria usually follow energy scheduling techniques (e.g., demand side management, [3,4]) that assess the efficiency of NZEB as well as the energy efficiency of a neighborhood. NZEB in EU standards must be achieved in both new buildings and renovated ones, as it is assumed that from all the buildings in 2050 the majority of them (75-90%) exist already today [5,6].…”

On the Implementation of the Nearly Zero Energy Building Concept for Jointly Acting Renewables Self-Consumers in Mediterranean Climate Conditions

“…Considering the energy consumption and production (where applicable) profiles of residential buildings, it is generally accepted that demand side management techniques and energy storage systems installation seem to be the most suitable smart-grid services in such applications. As far as demand side management is concerned, demand and response techniques could provide flexibility on electrical system operation either by reducing residential consumers' electricity consumption during electrical load peak periods, or by motivating buildings owners to modify their electrical consumption profile according to RES availability [6][7][8][9]. Demand side management may be more efficient when implemented at the level of final consumers' aggregators, which have direct control on air conditioning, heat pump units and water heaters during periods of peak demand or peak RES production.…”