Victor Isaac Herrera

Optimal energy management of a battery-supercapacitor based light rail vehicle using genetic algorithms

In this paper an optimal energy management strategy (EMS) for a light rail vehicle with an onboard energy storage system combining battery (BT) and supercapacitor (SC) is presented. The optimal targets for the proposed EMS are obtained by an optimization process with multi-objective genetic algorithms (GA). The fitness functions are expressed in economic terms, and correspond to the costs related to the energy absorbed from the catenary as well as the BT and SC cycling cost. The case study selected is the tramway of Sevilla. The aim was to minimize the daily operating cost of the tramway taking into account the BT and SC degradation approach and fulfilling the performance of the tramway in the catenary-less zone. A sizing analysis is done taking as optimization variables the BT and SC sizing to evaluate the impact on the daily operating cost. A comparison between the optimal solutions and a base scenario is presented.

Optimal energy management of a hybrid electric bus with a battery-supercapacitor storage system using genetic algorithm

This paper is focused on a series hybrid electric bus (SHEB). A rule-based energy management strategy is proposed by controlling the state of charge (SOC) of the battery (BT) and a variable output control for the auxiliary power unit (APU). Furthermore, a power splitter control is developed to split the power among BT and supercapacitor (SC). The optimization to obtain the values for the control levels is carried out with multi-objective genetic algorithm (GA). The aim of the optimization is to minimize the daily operating cost of the bus. The objective functions are the costs related to fuel and energy (BT and SC degradation by cycling cost, recharge from the grid cost) consumption. The results are given in a Pareto front with a set of optimal solutions, the optimal one will depend of an analysis on which objective has priority to be minimized and what are the consequences of this decision on the other one.

Optimal Energy Management and Sizing of a Battery--Supercapacitor-Based Light Rail Vehicle With a Multiobjective Approach

In this paper, an optimal energy management strategy (EMS) for a light rail vehicle with an onboard energy storage system (ESS) combining batteries (BT) and supercapacitors (SC) is presented. The optimal operating targets for the proposed EMS and ESS sizing (BT+SC) are obtained by multiobjective (MO) optimization with genetic algorithms. The MO fitness function is expressed in economic terms and includes the cost of the energy absorbed from the catenary, as well as the operating cost of the BT and SC pack (initial investment and cycling cost). The case study selected is the tramway of Seville. The aim is to minimize the daily operating cost of the tramway, taking into account the fulfillment of the tramway performance in the catenary-less zone and the BT-SC degradation by cycling during operation. Furthermore, a sizing analysis is done by comparing the current scenario (SC-based) with the proposed approach (optimal EMS and sizing) in terms of daily operating cost reduction.

Management Strategy for Market Participation of Photovoltaic Power Plants Including Storage Systems

Energy storage systems (ESS) integration with large-scale photovoltaic (PV) power plants, named intelligent PV (IPV) power plant, could contribute to improving the viability of these plants and to provide ancillary services to the main grid. The number and extension of the provided services depend not only on the size of the ESS, but also on the manner in which this storage system is managed. In this paper, the market participation based on different firming control strategies of an IPV power plant is proposed to optimize the economic exploitation based on the storage system management considering PV generation predictions. The most appropriate firming control strategy is selected to participate on the daily market, which is strengthened with an online model predictive control (MPC) to compensate the PV prediction errors participating in the intraday market. The real operation of the Iberian Peninsula market integration is also explained and analyzed. The development of this management strategy considers as a case study a real IPV plant located in Tudela (Navarre, Spain) and owned by Acciona Energía, which included a Lithium-ion based ESS from 2012 to 2013 in the framework of a European project. The results show that considering the MPC approach, an increase around 20% in the benefits is obtained by comparing with the daily market participation.

Optimal Operation Mode Control and Sizing of a Battery-Supercapacitor Based Tramway

In this paper an optimal operation mode control for a tramway with a hybrid energy storage system (battery+supercapacitor) is presented. The optimization process to obtain the setpoints for the control strategy is done by mean of multi-objective genetic algorithm (GA). The objective fitness functions are based on a cost model considering: the energy absorbed from the catenary, the battery+supercapacitor cycling cost and the cost of the lost energy in the crowbar. The aim of the proposed approach is to minimize the daily operating cost of the tramway taking into account the degradation of the energy storage systems and fulfilling the performance of the tramway in the catenary-less zone. Furthermore, a sizing analysis is done by comparing the proposed solution with a base scenario (supercapacitor based) to evaluate the impact on the daily operating cost under several unscheduled-stop events during the trip.

Intelligent photovoltaic power plants management strategy for market participation

Energy storage systems integration with large-scale PV power plants, named Intelligent PV power plant (IPV), could contribute to improve the viability of these plants and to provide ancillary services to the main grid. The number and extension of the provided services depend not only on the size of the storage system, but also on the manner how this storage system is managed. In this paper the market participation based on different firming control strategies of an IPV power plant is proposed to optimize the economic exploitation based on the storage system management considering PV generation predictions. The most appropriate firming control strategy is selected to participate on the daily market which is strengthened with an online model predictive control (MPC) to compensate the PV prediction errors participating in the intraday market. The real operation of the Iberian Peninsula market integration is also explained and developed. The development of this management strategy considers as a case study a real IPV plant located in Tudela (Navarre, Spain) and owned by Acciona Energía, which included a Lithium-ion based energy storage system from 2012 to 2013 in the framework of a European project.

Multi-Objective Optimization of Energy Management and Sizing for a Hybrid Bus with Dual Energy Storage System

In this paper an optimal energy management strategy (EMS) for a hybrid electric bus (HEB) with a dual energy storage systems (ESS) combining batteries (BT) and supercapacitors (SC) is presented. The scenario considers the hybrid operation (engine+ESS), as well as the full electric operation (only ESS). Optimal targets for the proposed EMS are obtained by an optimization process with a multi-objective genetic algorithm (GA). The fitness functions are expressed in economic terms, and the aim is to minimize the daily operating cost of the HEB taking into account the BT and SC degradation approach and the performance fulfillment during bus operation in the zero-emission zone (full electric).